Global Cancer Etiology: Molecular Signatures, Therapeutic Disparities, and the role of Socio-economic Determinants.

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Published in final edited form as: Chem Biol Lett. 2026 May 7;13(2):1764. doi: 10.62110/sciencein.cbl.2026.v13.1764 Global Cancer Etiology: Molecular Signatures, Therapeutic Disparities, and the role of Socio-economic Determinants Param Patel 1 , Michael Oh 1 , Keykavous Parang 1, * Author information Article notes Copyright and License information 1 Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, 9401 Jeronimo Rd, Irvine, CA 92618, USA. * Corresponding Author: Prof. Keykavous Parang, Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, CA 92618, USA, parang@chapman.edu Issue date 2026 Apr 16. PMC Copyright notice PMCID: PMC13238271  NIHMSID: NIHMS2180410  PMID: 42254937 The publisher's version of this article is available at Chem Biol Lett Abstract The global burden of cancer is increasingly shaped by socio-economic determinants that extend beyond geographic incidence patterns. While historically considered a disease of affluence, cancer now reflects a divergence between high-income countries (HICs) and low- and middle-income countries (LMICs), driven by distinct environmental, occupational, and structural exposures. This narrative review examines how socio-economic conditions act as primary determinants of cancer etiology by linking epidemiological trends to molecular evidence. A comprehensive literature search was conducted (2006–2026), focusing on cancer etiology, environmental carcinogenesis, mutational signatures, and health disparities. Three domains of socio-economic influence emerged from search. First, harmful exposures, including asbestos, aflatoxin B1, arsenic, heavy metals, and air pollution, are disproportionately concentrated in under-regulated populations. Second, these exposures produce distinct mutational signatures; aflatoxin-related hepatocellular carcinoma is associated with TP53 R249S and COSMIC Signature 24, while cancers in HICs often involve PI3K/AKT/mTOR pathway alterations. Third, disparities in screening infrastructure create “screening deserts,” increasing mortality gaps. Socio-economic context fundamentally shapes cancer biology, incidence, and outcomes. Reducing disparities requires expanded genomic capacity in LMICs, targeted prevention strategies, and equitable access to precision oncology. Keywords: cancer etiology, environmental carcinogenesis, global health disparities, precision oncology, socio-economic determinants Graphical Abstract Introduction The global landscape of oncology is increasingly characterized by a profound etiological divergence, in which a nation’s economic status shapes not only the incidence of malignancy but also the fundamental mechanisms underlying its development. Historically, cancer was framed as a “disease of affluence,” largely associated with increased life expectancy and the metabolic consequences of Westernized lifestyles. 1 – 5 However, contemporary epidemiological data reveal a marked shift in this paradigm. 6 According to the GLOBOCAN 2020 estimates, an estimated 19.3 million new cancer cases and approximately 10.0 million cancer-related deaths occurred worldwide in 2020. 6 The regional distribution of this burden is profoundly unequal ( Figure 1 ). Asia alone accounted for 49.3% of all new cancer cases (~9.5 million) and a disproportionate 58.3% of all cancer deaths (~5.8 million), despite representing 59.5% of the global population. Europe contributed 22.8% of the incidence (~4.4 million cases) and 19.6% of the mortality (~1.96 million deaths), while comprising only 9.7% of the world’s population. The Americas accounted for 20.9% of incident cases (~4.0 million) and 14.2% of mortality (~1.42 million deaths). Notably, Africa’s share of cancer deaths (7.2%; ~0.72 million) exceeded its share of incidence (5.7%; ~1.1 million), reflecting higher case fatality rates driven by late-stage diagnosis and limited treatment access 6 ( Table 1 , Figure 1 ). This disproportionate mortality-to-incidence (MIR) ratio in Asia and Africa, compared with Europe and the Americas, underscores a fundamental transformation in the global burden of disease. Figure 1. Open in a new tab Regional Distribution of Global Cancer Burden, GLOBOCAN 2020. Panel A: Percentage share of global cancer incidence, mortality, and world population by region. Asia accounts for 49.3% of incidence but a disproportionate 58.3% of mortality; Africa similarly shows mortality (7.2%) exceeding its incidence share (5.7%), reflecting higher case fatality rates. Europe bears a disproportionately high cancer burden relative to its population share (9.7%). Panel B: Estimated absolute numbers of new cancer cases and deaths by region, with mortality-to-incidence ratios (MIR). Higher MIR values in Asia (0.610) and Africa (0.655) indicate poorer outcomes compared to the Americas (0.355) and Europe (0.455). 6 Table 1. Regional Distribution of Cancer Incidence and Mortality, GLOBOCAN 2020. 6 Region New Cases (millions) % of Global Incidence Deaths (millions) % of Global Mortality % of World Popul. Asia ~9.5 49.3% ~5.8 58.3% 59.5% Europe ~4.4 22.8% ~ 1.96 19.6% 9.7% Americas ~4.0 20.9% ~1.42 14.2% 13.3% Africa ~1.1 5.7% ~0.72 7.2% 17.2% Oceania ~0.25 1.3% ~0.07 0.7% 0.5% GLOBAL TOTAL 19.3 100% ~10.0 100% 100% Open in a new tab Note: Asia and Africa exhibit mortality shares exceeding their incidence shares, reflecting higher case fatality rates and limited treatment infrastructure in these regions. In 2023, there were approximately 18.5 million new cancer cases and 10.4 million cancer deaths globally. Notably, 57.9% of incident cases and 65.8% of deaths occurred in low- to middle-income regions, highlighting a disproportionate burden outside of high-income areas such as North America and Western Europe. 7 In contrast, high-income regions exhibit higher incidence rates but lower mortality rates, suggesting better detection and treatment infrastructure, whereas lower-income regions (including much of Africa and parts of South America) experience higher mortality relative to incidence. 7 For example, age-standardized incidence rates were highest in high-income regions (~303 per 100,000), while lower-middle-income regions had much lower incidence (~126 per 100,000) 7 , yet still faced substantial mortality burdens. These patterns indicate a global “epidemiologic divide,” in which regions such as North America and Europe tend to have higher detection rates, while regions in Africa and other lower-resource areas experience worse outcomes due to limited access to early diagnosis and treatment. This shift reflects a broader “epidemiological transition,” in which low- and middle-income countries (LMICs) face a dual burden of disease that is both persistent and emerging. 1 , 8 On one hand, these regions continue to experience high rates of infection-associated malignancies driven by pathogens such as Human Papillomavirus (HPV), 9 Hepatitis B virus (HBV), 10 and Helicobacter pylori 11 . On the other hand, they are increasingly exposed to the carcinogenic effects of industrialization, including occupational hazards, environmental pollutants, and lifestyle changes associated with urbanization. As a result, LMICs are not simply inheriting the disease patterns of high-income countries (HICs), but rather accumulating overlapping and often synergistic risk factors. 8 Importantly, disparities in cancer incidence and outcomes are not reducible to geography alone but are deeply embedded within the socio-economic determinants of health. 1 , 12 , 13 Factors such as the strength of industrial regulation, the structure of agricultural systems, and the accessibility of healthcare infrastructure collectively shape both exposure risk and disease progression. A critical and often overlooked dimension of this disparity is the global redistribution of hazardous exposures. As HICs adopt stricter environmental protections and transition toward cleaner energy sources, many high-risk industrial practices are increasingly relocated to less-regulated settings in developing regions. 8 This dynamic effectively externalizes carcinogenic risk, amplifying exposure among populations that are least equipped to mitigate its effects. 14 , 15 While existing reviews 16 – 20 have examined individual dimensions of cancer disparities, such as environmental carcinogenesis, infection-related cancers, or precision oncology barriers in isolation, this review is unique in its integrative approach. We synthesize evidence across five interconnected domains: (1) the global etiological divergence between HICs and LMICs; (2) the occupational, environmental, and structural determinants of exposure; (3) the epidemiological manifestation of socio-economic transitions; (4) the molecular bridge linking socio-economic context to distinct mutational signatures and genomic architectures; and (5) the implications for pharmacogenomics and precision oncology equity. By tracing the pathway from macro-level socio-economic forces to micro-level genomic consequences, this review advances the argument that socio-economic context is not merely a confounding variable but a central determinant that actively selects for the molecular subtypes of cancer that define populations worldwide. This integrated framework distinguishes our work from prior analyses and provides a foundation for understanding cancer as fundamentally a socio-economic disease with molecular consequences. Figure 2 is a schematic representation of how socio-economic factors act as upstream drivers that shape environmental, occupational, and lifestyle exposures. These exposures induce distinct molecular mechanisms, including direct DNA damage, oxidative stress, and aberrant signaling activation, leading to tumor initiation and progression. Divergent clinical outcomes arise across high-income and low- and middle-income settings due to differences in screening infrastructure, access to precision oncology, and treatment availability. A feedback loop highlights how poor outcomes reinforce underlying socio-economic disparities. Figure 2. Open in a new tab Socio-Economic Drivers of Cancer Etiology: A Multi-Level Framework Linking Exposure Pathways to Molecular Mechanisms and Clinical Disparities. In light of these complexities, this review moves beyond conventional geographic comparisons to examine how specific socio-economic forces, ranging from the relative “opulence” of developed nations to the structural “constraints” of developing economies, function as primary drivers of cancer etiology. By integrating epidemiological patterns with molecular evidence, particularly the emergence of distinct mutational signatures in key genes such as tumor suppressor gene TP53, this article emphasizes that socio-economic context is not simply a peripheral background factor, but a critical determinant in shaping the biological pathways of carcinogenesis globally. Results and Discussion 1. The Global Landscape: A Divergence of Etiological Profiles This section presents an overview of the key findings regarding the etiological divergence between HICs and LMICs, as identified through the reviewed literature (see Figure 3 and Table 2 for a comparative summary). The fundamental differences in cancer incidence between developed and developing nations can be understood through the “Crossover Effect,” a framework describing how carcinogenic risk evolves alongside socio-economic development. 2 , 3 , 21 , 22 As societies modernize, the dominant drivers of malignancy shift from infection-related causes to those associated with industrialization, lifestyle, and longevity. This transition does not occur uniformly, 3 , 21 – 23 but rather creates a clear divergence in etiological profiles between HICs and LMICs. Figure 3. Open in a new tab Comparative cancer etiology profiles in high-income countries (HICs) versus low- and middle-income countries (LMICs). (A) Schematic representation of the relative contribution of four major etiological categories. Categories are presented on a qualitative ordinal scale (Low/ Moderate/High) reflecting documented predominance patterns in the literature; the panel illustrates directional contrasts rather than precise quantitative proportions. 1 , 2 , 8 (B) Quantitative evidence for the infection-associated category, showing population attributable fractions (PAFs) of cancer cases caused by carcinogenic infectious agents in 2008, calculated using GLOBOCAN incidence data and IARC-classified Group 1 biological carcinogens (predominantly Helicobacter pylori , hepatitis B and C viruses, and human papillomaviruses Data. 35 Updated GLOBOCAN 2018 estimates yielded a similar geographic disparity, with infection-attributable age-standardized incidence rates of 37.9 and 33.1 per 100,000 person-years in eastern Asia and sub-Saharan Africa, respectively, compared with 13.6 in northern Europe. 36 Table 2. Comparative Cancer Etiology: High-Income Countries vs. Low- and Middle-Income Countries. Feature High-Income Countries (HICs) Low- and Middle-Income Countries (LMICs) Predominant Cancer Types Colorectal, breast, prostate, lung (tobacco) 23 , 27 Liver (HCC), cervical, gastric, esophageal 2 , 8 , 39 Primary Etiological Drivers Metabolic: obesity, sedentary behavior, caloric excess, aging 27 , 28 Infectious: HBV, HPV, H. pylori; Environmental: aflatoxin, arsenic, asbestos 2 , 8 , 40 Infection-Attributable Cancers <7.4% of total cancer burden 8 , 35 , 36 ~23% of total cancer burden 8 , 35 , 36 Dominant Molecular Pathways PI3K/AKT/mTOR, Wnt/β-catenin, metabolic reprogramming 28 , 30 – 34 , 41 TP53 R249S, COSMIC Sig. 24, oxidative DNA damage 40 , 42 Key Environmental Exposures Air pollution (outdoor), processed food, alcohol, tobacco 14 , 27 Indoor air pollution, arsenic, heavy metals, aflatoxin B1 8 , 9 , 40 Screening Infrastructure Widespread low-dose computed tomography (LDCT), mammography, colonoscopy 43 , 44 Limited/absent; “screening deserts” 9 , 45 , 46 Precision Oncology Access Routine genomic profiling, biomarker-driven therapy 41 , 47 Limited next-generation sequencing (NGS) access; conventional chemotherapy predominates 48 , 49 HCC Etiology NAFLD/ALD predominant 50 HBV/HCV predominant 2 , 10 , 39 5-Year Breast Cancer Survival 80–85% (Japan, S. Korea) 51 ~60% (India) 51 Open in a new tab In highly industrialized regions, the oncogenic landscape is largely dominated by lifestyle risk factors linked to caloric excess and sedentary behavior. Malignancies such as colorectal, breast, and prostate cancers are particularly prevalent, 23 – 27 reflecting both the metabolic consequences of a “Western lifestyle” and the increased life expectancy that allows for the accumulation of age-related genetic mutations. 27 – 29 At the molecular level, these cancers are increasingly associated with metabolic dysregulation. Chronic caloric surplus promotes insulin resistance and hyperinsulinemia, 30 – 32 which elevates circulating insulin-like growth factor-1 (IGF-1) 30 , 33 . This signaling axis activates proliferative pathways such as PI3K/AKT and mTOR 33 , 34 , while simultaneously suppressing apoptosis 30 . In this way, the “Western lifestyle” not only alters patterns of cancer incidence but actively drives tumorigenesis through defined metabolic and signaling mechanisms. Conversely, in the developing world, the etiological profile remains markedly different, characterized by a substantially higher burden of infection-related cancers. Infectious agents account for approximately 23% of total cancer cases in these regions, compared to less than 7.4% in developed nations. 35 , 36 This disparity reflects not only differences in pathogen prevalence but also systemic limitations in public health infrastructure, vaccination coverage, and early detection programs. 37 , 38 This divide is particularly evident in the global distribution of hepatocellular carcinoma (HCC), which serves as a paradigmatic example of how socio-economic conditions shape cancer etiology. Approximately 80% of HCC cases occur in developing regions of the Far East and South Asia, 39 where chronic infection with HBV and hepatitis C virus (HCV) remains the predominant driver 2 . In these settings, economic constraints hinder the implementation of essential preventive measures, including routine blood screening and access to sterile medical equipment, allowing iatrogenic and vertical transmission to persist. For example, vertical HBV transmission rates in Sub-Saharan Africa reach approximately 8%, whereas in Western Europe they range from 0.1% to 4.4% 10 , illustrating the profound impact of early-life exposure on long-term cancer risk. At the same time, this pattern is not static. As developed nations have reduced the burden of virus-associated HCC through vaccination and improved medical practices, they are witnessing a parallel rise in liver cancers driven by “opulence-related” conditions, particularly non-alcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD). This shift underscores a broader principle: economic advancement does not eliminate cancer risk but rather transforms its underlying drivers. Interestingly, the epidemiology of these ‘opulence-related’ liver diseases reveals distinct demographic vulnerabilities, such as significant sex disparities in the incidence and progression of alcohol-associated liver disease and alcohol-attributable cancers within the United States. 50 A comprehensive understanding of this global landscape must also account for regional heterogeneity within countries undergoing rapid economic transition. West Asia provides a compelling example of this internal divergence. Over the latter half of the 20th century, countries such as Turkey, Iran, and Qatar have experienced significant economic growth, accompanied by a rising incidence of “Western” cancers such as breast and colorectal malignancies. 23 However, these trends coexist with persistently high rates of upper gastrointestinal cancers linked to traditional environmental and lifestyle exposures. In northern Iran, for instance, gastric cancer incidence is approximately six times higher than in southern provinces. 23 This disparity has been attributed in part to the continued use of biomass fuels for heating in poorly ventilated homes, resulting in chronic exposure to polycyclic aromatic hydrocarbons (PAHs), a known class of carcinogens. 52 – 56 Importantly, this form of socio-economic stratification is not limited to comparisons between nations, but is also evident within developed countries themselves, where it manifests as a persistent “rich–poor gap” in cancer outcomes. In the United States, lung cancer mortality is approximately 20% higher in rural counties than in urban areas 45 and is significantly elevated in regions of “persistent poverty,” defined as counties in which at least 20% of the population has lived below the poverty line for more than 30 years. 45 These populations experience a convergence of risk factors, including higher smoking prevalence, reduced access to healthcare, and greater environmental exposures, that closely resemble those observed in developing regions. 45 , 57 , 58 2. Determinants of Exposure: Occupational, Environmental, and Structural Risk Vectors The following section synthesizes findings from occupational, environmental, and health systems research. Key carcinogens and their socio-economic contexts are summarized in Table 3 . Transitioning from a global etiological overview to a more granular analysis of risk exposure reveals that the unequal distribution of cancer is fundamentally driven by disparities in regulatory protection and infrastructural integrity. In this context, carcinogenic risk is not randomly distributed but systematically structured by socio-economic conditions that determine who is exposed, to what extent, and with what degree of protection. 13 , 14 These determinants operate across three primary domains: occupational, environmental, and structural factors. 8 Each of which contributes to the formation of a highly unequal global “exposome.” Table 3. Key Environmental and Occupational Carcinogens by Socio-Economic Context. Carcinogen Primary Populations Affected Cancer Types Molecular Mechanism Key Finding Reference(s) Asbestos Workers in Russia, China, Brazil, Kazakhstan Mesothelioma, lung cancer Chronic inflammation, genomic instability 5.3x lung cancer mortality after 20yr exposure 8 , 59 Aflatoxin B1 SSA, SE Asia, China (Qidong) HCC Bulky DNA adducts (AFB1-N7-dG); TP53 R249S COSMIC Sig. 24 in 16% Hong Kong HCC vs 0.7% N. America 40 , 42 Arsenic (inorganic) Bangladesh, Ghana, Chile, India Skin, lung, bladder, liver ROS generation, 8-OHdG lesions, epigenetic silencing SMR 7.1 for lung cancer in Chilean cohort exposed in utero 68 , 70 , 71 , 74 Chromium (Cr VI) Tannery workers, Sudan, India Lung, skin Epigenetic silencing (p16, RASSF1A), oxidative damage Cr at tannery sites exceeds pediatric thresholds 110-fold 15 , 66 Iron particulates Welders, steelworkers (Korea, SSA) Breast, lung Fenton chemistry, hydroxyl radicals, hepcidin dysregulation 3.46x cancer risk in furnace workers vs office workers 63 – 65 , 67 PM2.5 (heavy metal-enriched) Dhaka, Chinese megacities Lung NF-κB/STAT3 activation, pro-inflammatory cytokines Hg exceeds USEPA guidelines >50-fold in Dhaka PM2.5 73 , 75 Biomass fuel combustion (PAHs) Northern Iran, rural LMICs Gastric, lung PAH-DNA adducts, chronic respiratory inflammation 6x gastric cancer in northern vs southern Iran 8 , 23 , 52 – 54 HBV/HCV SSA, SE Asia HCC HBx inhibits NER/p53; chronic inflammation (IL-6, TGF-β) Vertical HBV 8% in SSA vs 0.1–4.4% in W. Europe 10 , 76 – 78 Open in a new tab Abbreviations: HCC, hepatocellular carcinoma; NER, nucleotide excision repair; ROS, reactive oxygen species; SSA, Sub-Saharan Africa; SMR, standardized mortality ratio; PAH, polycyclic aromatic hydrocarbon. 2.1. Occupational Risk Transfer Within the occupational sphere, one of the most significant developments of the past several decades has been the global “transfer of risk.” As high-income nations have enacted increasingly stringent regulations on known carcinogens, hazardous industries have been progressively relocated to transitional economies, where labor costs are lower and environmental oversight is often limited. 8 This redistribution does not eliminate carcinogenic exposure, but rather displaces it onto more vulnerable populations, effectively externalizing industrial risk. 8 The trajectory of asbestos-related malignancies provides a paradigmatic example of this phenomenon. Although the International Agency for Research on Cancer (IARC) classified all forms of asbestos as carcinogenic as early as 1973, and 55 countries have since implemented comprehensive bans, 59 global production remains heavily concentrated in a small number of transitioning economies. The Russian Federation, China, Brazil, Kazakhstan, and Canada together account for approximately 99% of global asbestos output. 8 , 60 – 62 In these regions, the economic imperatives of industrial growth and urban development frequently supersede concerns for occupational safety, 8 resulting in the continued exposure of an estimated 125 million workers to chrysotile fibers annually. 59 The biological consequences of this exposure are well established: prolonged inhalation of asbestos fibers induces chronic inflammation and genomic instability, leading to a delayed but inevitable rise in mesothelioma and lung cancer incidence. Notably, studies of Chinese chrysotile miners demonstrate a 5.3-fold increase in lung cancer mortality after 20 years of exposure, alongside significant dose-dependent increases in digestive cancers. 8 Yet, in many of these regions, the full burden remains underrecognized due to limited diagnostic infrastructure and incomplete cancer registries. 8 Beyond traditional industrial carcinogens, emerging evidence highlights the role of occupational exposure to potentially toxic elements (PTEs) as an additional socio-economic driver of malignancy. In sectors such as welding, mining, and steel manufacturing, industries central to economic expansion in Sub-Saharan Africa and parts of Asia, workers are chronically exposed to metal-rich particulates through inhalation. For example, iron-abundant fumes can disrupt systemic iron homeostasis, 63 generating a pro-oxidative cellular environment through iron-catalyzed Fenton reactions. 64 This process promotes DNA damage and has been implicated in the pathogenesis of breast cancer. 65 Similarly, environmental assessments of Sudanese soils have identified extreme carcinogenic risks associated with chromium (Cr) and nickel (Ni), with chromium concentrations at tannery sites exceeding pediatric safety thresholds by up to 110-fold. 66 Supporting this association, a comparative occupational study in Korea found that steelworkers exposed to high-temperature furnaces were 3.46 times more likely to develop cancer than administrative employees within the same company. 67 These findings demonstrate how industrial expansion, in the absence of adequate regulation, translates directly into molecular-level carcinogenic processes. 15 , 68 Beyond direct occupational exposure, contamination of agricultural systems serves as a widespread vector for ingestion. Recent studies from Iran, for instance, demonstrate how toxic heavy metals accumulate in widely consumed cereal crops, posing severe, unchecked human health risks in developing economies. 69 2.2. Environmental Exposures and Geogenic Contamination Environmental exposures further amplify these disparities, particularly through the pathway of geogenic contamination. In many low-income regions, inadequate municipal infrastructure necessitates reliance on hand-dug wells that access groundwater naturally enriched with inorganic arsenic. As a result, more than 200 million individuals worldwide are exposed to arsenic concentrations exceeding the World Health Organization (WHO) safety threshold of 10 μg/L. 68 The socio-economic dimension of this crisis is evident in the adoption of more permissive national standards, such as Bangladesh’s 50 μg/L limit, 70 which reflect economic constraints rather than toxicological safety. In regions like Sanso, Ghana, this baseline exposure is compounded by illegal gold mining (galamsey), 71 which introduces additional arsenic into groundwater systems. The cumulative effect is a markedly elevated risk of multiple malignancies, including cancers of the skin, lung, bladder, liver, and colon. 2.3. Air Pollution and the Compact City Paradox Air pollution represents another domain in which socio-economic disparities shape exposure profiles in distinct ways. In many developing regions, the primary source of respiratory carcinogens is within the households. Approximately 3 billion people worldwide rely on biomass fuels and coal for cooking and heating, a necessity driven by persistent energy poverty. Consequently, indoor air pollution (IAP) accounts for approximately 3.7% of total disability-adjusted life year (DALY) losses in high-mortality developing countries. 8 However, the relationship between urbanization and exposure is not straightforward. In rapidly developing urban centers such as those in China, a “Compact City Paradox” has emerged. 72 While high-density urban design can reduce overall emissions, it may simultaneously increase individual-level exposure by trapping pollutants within densely populated “urban canyons.” This effect is further exacerbated in megacities like Dhaka, Bangladesh, where indoor particulate matter (PM2.5) is enriched with heavy metals, including mercury, lead, and cadmium. 73 In some cases, mercury concentrations exceed U.S. Environmental Protection Agency (USEPA) safety guidelines by more than 50-fold, 73 reflecting localized practices such as informal waste incineration and small-scale battery recycling. These patterns illustrate how both underdevelopment and rapid urbanization can independently generate hazardous exposure environments. 14 , 69 , 72 Broad panel data analyses across LMICs and HICs confirm that degrading air quality acts as an independent driver of lung cancer rates globally. 14 If current trends hold, projections suggest massive increases in the MIR by 2050. 79 Ultimately, mitigating these systemic risks cannot rely on individual behavioral modifications, which are frequently unfeasible for vulnerable populations. Instead, it demands macro-level structural interventions. As highlighted by Sadiq et al. (2025), public health protection in these transitional economies requires a clear shift toward eco-friendly industrial technologies, aggressive regulatory enforcement, and sustainable infrastructural development. 15 2.4. Structural Disparities in Screening and Healthcare Access Structural disparities in healthcare access and screening infrastructure represent a critical determinant of how exposure translates into clinical outcomes. In many cases, cancer mortality is less a function of tumor biology than of delayed detection. In developing regions, limited access to secondary prevention measures, such as Papanicolaou (Pap) smear screening for cervical cancer, results in disproportionately high rates of advanced HPV-related malignancies. In South Africa, for example, Pap smear coverage has reached approximately 52%, yet remains well below the national target of 70%, 9 with rural populations experiencing the lowest screening rates. Similarly, breast cancer survival across Asia demonstrates a strong correlation with national income, with five-year survival rates of approximately 60.4% in India compared to 84.7% in Japan. 51 Importantly, these structural inequities are not confined to low-income settings but persist within high-income countries as internal socio-economic gradients. In the United States, access to lung cancer screening via low-dose computed tomography (LDCT) is significantly reduced in counties characterized by persistent poverty and high smoking prevalence. 45 These regions, often referred to as “screening deserts”, are marked by a convergence of elevated risk and limited access to early detection. State-level analyses further demonstrate that Medicaid expansion is positively associated with increased rates of breast and cervical cancer screening in the American South. 80 In contrast, residents of non-expansion states, particularly those in rural and federally designated Health Professional Shortage Areas (HPSAs), exhibit the lowest screening rates across multiple cancer types. 46 For uninsured populations in these areas, financial barriers often preclude access to mobile screening units or distant healthcare facilities, resulting in a higher incidence of late-stage diagnoses. Studies focusing on rural and Indigenous communities reinforce this pattern, highlighting insurance status as a key determinant of whether potentially life-saving services are accessible. 46 , 80 3. Incidence Patterns: The Epidemiological Manifestation of Socio-Economic Transitions The translation of multifaceted exposure vectors into measurable disease incidence follows a trajectory that closely mirrors a nation’s stage of economic development. As populations in LMICs experience increased longevity due to improved control of infectious diseases, they simultaneously enter a period of heightened vulnerability to the delayed effects of industrial carcinogens and “Westernized” lifestyle changes. This convergence produces a distinct epidemiological crossover, 2 in which traditional and emerging risk factors interact to reshape national cancer profiles. 3.1. Urbanization and Lung Cancer in Transitioning Economies This transition is particularly evident in the rising incidence of lung cancer in regions undergoing rapid and often unregulated urbanization. In China, large-scale analyses across 180 cities demonstrate that both the speed of urban expansion and the degree of land-use mixing between industrial and agricultural zones are primary determinants of lung cancer incidence, 72 with air pollution serving as a critical mediating factor. In contrast to the declining lung cancer rates observed in many HICs 44 , these transitioning regions are experiencing sustained upward pressure on incidence. 65 Notably, county-level cities exhibit greater sensitivity to the pace of urban expansion than established metropolitan centers, indicating that early-stage industrial growth, often characterized by weaker regulation and concentrated emissions, may represent the most oncogenically potent phase of development. 72 As heavy industry disperses from Tier-1 urban hubs into lower-tier regions, localized particulate matter ≤ 2.5 micrometers in aerodynamic diameter (PM2.5) accumulation and “pollution trapping” effects intensify, disproportionately elevating lung cancer risk in these emerging urban environments. 3.2. Metabolic Transitions and the Double Burden A similar pattern of rising incidence is observed in West Asian populations, where economic growth has driven rapid shifts in diet, physical activity, and tobacco consumption. These changes have contributed to marked increases in colorectal and breast cancer incidence, 23 reflecting the growing influence of metabolic risk factors. In the United States, for example, colorectal cancer incidence has been strongly associated with obesity and metabolic syndrome, 27 particularly among younger populations. 81 Individuals with elevated body mass index (BMI) exhibit a significantly increased risk 27 , 81 , driven in part by insulin resistance 30 , 82 – 86 and chronic inflammatory signaling pathways 76 , 82 – 86 . These findings highlight that lifestyle-mediated exposures in high-income settings can produce carcinogenic effects comparable in magnitude to environmental toxins in developing regions, 27 , 31 , 87 – 89 albeit through distinct biological mechanisms. However, this transition is rarely uniform. In many rapidly developing regions, the emergence of “Western” cancers occurs alongside the persistence of environmentally and infection-driven malignancies, creating a complex and overlapping disease burden. In countries such as Qatar, Kuwait, and Bahrain, breast cancer has become the most prevalent malignancy among women, with incidence rates closely linked to rising obesity and shifts away from traditional diets. 23 Yet within these same regions, localized disparities persist. In northern Iran, for instance, gastric cancer incidence remains approximately six times higher than in southern provinces, 23 , 52 a pattern attributed to continued reliance on biomass fuels in low-income, cold-climate households. This coexistence of divergent risk profiles illustrates the “double burden” of cancer, 23 in which modernization introduces new etiological drivers without eliminating pre-existing ones. In Sub-Saharan Africa (SSA) ( Table 3 ), cancer epidemiology is strongly shaped by infection-related and environmental exposures, reflecting limited screening infrastructure and delayed diagnosis. High prevalence of viruses such as HBV and HCV contributes to elevated rates of liver cancers, while aflatoxin exposure further amplifies HCC risk. These shifting incidence patterns suggest a compounding crisis, with models projecting a rapid escalation in the global burden of breast cancer through 2040. 90 However, this risk is profoundly stratified by environmental and socio-economic geography. 12 For instance, cohort data demonstrate that neighborhood-level disparities, 12 coupled with chronic exposures like PM2.5, 91 act as independent drivers of elevated breast cancer incidence. Ultimately, when these environmental burdens intersect with structural barriers to care, they manifest as stark racial and socio-economic disparities in breast cancer mortality. 13 These regional patterns, summarized in Table 3 , highlight the critical role of socio-economic context in shaping distinct carcinogenic pathways, reinforcing the need for region-specific prevention and intervention strategies. 3.3. Internal Socio-Economic Gradients and Screening Deserts More importantly, this form of epidemiological stratification is not confined to global comparisons but is also evident within high-income countries as an internal socio-economic gradient. In the United States, lung cancer incidence and mortality remain significantly elevated in rural counties characterized by persistent poverty. 45 While urban and more affluent regions have benefited from widespread smoking cessation efforts and access to early detection, rural populations continue to experience disproportionately high exposure and delayed diagnosis. Smoking prevalence in these areas is approximately 25.4%, compared to 18.8% in urban counties, yet the disparity in outcomes cannot be attributed to behavior alone. 45 Rather, the interaction between rurality and poverty produces a synergistic effect, 45 limiting access to screening and treatment while amplifying cumulative risk. The consequences of this disparity are particularly evident at the diagnostic stage. Across the United States, approximately 70% of lung cancers are detected at a late stage, with 40% diagnosed at Stage IV and 30% at Stage III, and only ~15% identified prior to spread. 92 This pattern is especially pronounced in underserved regions such as Appalachian Kentucky, which has the highest lung cancer incidence and mortality rates in the country. 92 National-level analyses indicate that, despite federal coverage of LDCT screening since 2015, fewer than 10% of eligible individuals were screened in early implementation years, with marked geographic, racial, and socioeconomic disparities in uptake. 43 By contrast, late-stage presentation in regions like Appalachia limits curative-intent options, including surgery, given that 5-year survival falls from 54% in localized disease to approximately 4% in metastatic disease. 92 Even where screening infrastructure exists, eligibility and uptake are further complicated by comorbidities such as chronic obstructive pulmonary disease and asthma, 93 , 94 and reported screening rates may vary substantially across surveillance systems due to differences in case ascertainment methods. 94 , 95 As Yang et al. (2020) demonstrate, this disparity underscores a fundamental reality: the establishment of robust, standardized screening infrastructures is what definitively separates high-income survivability from the fragmented, opportunistic, or entirely non-existent early-detection systems prevalent in lower-resource settings 44 ( Figure 4 ). This contrast highlights how incidence patterns are inseparable from healthcare infrastructure, 46 with early detection serving as a key determinant of both measured incidence and survival. Figure 4. Open in a new tab Global Disparities in Cancer Screening and Outcomes. Panel A: Breast cancer five-year survival rates (2005–2009) across eight Asian countries demonstrate a strong correlation with national income and healthcare infrastructure, ranging from 56.5% in Mongolia to 86.7% in Israel. 51 Panel B: Cancer screening coverage varies widely both between and within nations. In the United States, despite federal coverage of LDCT lung cancer screening since 2015, fewer than 10% of eligible individuals were screened in early implementation years, with marked geographic, racial, and socioeconomic disparities in uptake, 43 In South Africa, lifetime Pap smear coverage among women aged 30 years and older is 52.0%, below the national target of 70%. 9 HP2030 = Healthy people 2030. 3.4. Temporal Latency of Socio-Economic Risk Beyond immediate exposure-response relationships, certain incidence patterns reveal the long temporal latency of socioeconomic risk. Malignancies associated with geogenic arsenic exposure provide a striking example of this delayed effect. In regions such as Bangladesh and northern Chile, historical exposure to high arsenic concentrations in drinking water has produced a persistent “cancer tail” that extends decades beyond the initial exposure period. In Antofagasta, Chile, individuals exposed in utero and during early childhood exhibit a lung cancer SMR of 7.1 in young adulthood, 74 demonstrating that early-life environmental conditions can shape cancer risk across the lifespan. In contemporary settings such as Sanso, Ghana, ongoing illegal mining activity has driven arsenic concentrations as high as 440 μg/L, 71 creating localized hotspots of elevated incidence for skin, bladder, and internal organ cancers that exceed the capacity of rural healthcare systems. At a mechanistic level, these incidence patterns are further shaped by the specific types of DNA damage induced by different carcinogenic exposures. Distinct environmental and occupational agents generate characteristic forms of genomic injury that are processed through different DNA repair pathways. For instance, oxidative stressors such as arsenic promote the accumulation of reactive oxygen species (ROS), 68 leading to diffuse DNA damage, whereas agents like aflatoxin form bulky DNA adducts 40 that induce more targeted mutational events. Over time, these differences give rise to distinct mutational signatures across populations, even within the same cancer type. Thus, the epidemiological manifestation of cancer is not merely a reflection of exposure frequency, but of exposure type, timing, and biological processing. 8 Socio-economic conditions shape each of these dimensions, ultimately determining not only who develops cancer, but how that cancer evolves at the molecular level. In this way, incidence patterns serve as a critical bridge between external risk environments and the genomic architecture of disease, reinforcing the central argument that socio-economic transitions are deeply embedded in both the epidemiology and biology of global cancer. 4. Biological Implications: The Molecular Bridge Between Context and Mutation This section examines the molecular evidence linking socio-economic exposures to specific genomic alterations. Key molecular pathways are summarized in Table 4 and illustrated in Figure 5 . Beyond the epidemiological patterns of disease, the socio-economic context leaves a measurable and lasting imprint on the human genome. 40 This “molecular bridge” links external exposures to internal biological consequences, translating environmental and structural inequalities into distinct patterns of genomic alteration. At its core, this process is captured through the study of mutational signatures, which function as a molecular archive of a population’s exposome. 40 Table 4. Molecular Pathways and Mutational Signatures by Socio-Economic Exposure Context. Exposure Context Molecular Pathway Key Genomic Alteration DNA Repair Pathway Affected Ref. Aflatoxin B1 (LMICs) Direct genotoxic: bulky DNA adducts TP53 R249S; COSMIC Signature 24 (G→T) Nucleotide excision repair (NER) 40 , 42 HBV infection (LMICs) HBx-mediated NER/p53 disruption Enhanced AFB1 adduct persistence; codon 249 TP53 NER impairment via DDB1 interaction 77 – 78 Arsenic (LMICs) Oxidative stress: ROS/Fenton 8-OHdG lesions; p16/RASSF1 A methylation Base excision repair (BER) 68 , 70 Iron particulates (occupational) Fenton chemistry; catechol-estrogen redox Hydroxyl radical DNA damage; hepcidin dysregulation Oxidative damage repair 63 – 65 PM2.5/heavy metals NF-κB/STAT3 inflammatory signaling Pro-inflammatory cytokine cascade (IL-6, TNF-α) Chronic inflammation-induced instability 73 , 75 Obesity/metabolic syndrome (HICs) PI3K/AKT/mTOR; insulin/IGF-1 axis Epigenetic reprogramming; sustained proliferative signaling Indirect: impaired apoptosis surveillance 27 , 28 , 82 Tobacco smoke (HICs/global) Direct mutagenesis KRAS mutations; high tumor mutational burden Multiple pathways overwhelmed 42 , 98 Indoor air pollution/PAHs (LMICs) PAH-DNA adduct formation EGFR mutations predominant in LCINS NER pathway engagement 96 – 98 Open in a new tab Abbreviations: LCINS, lung cancer in never-smokers; NER, nucleotide excision repair; BER, base excision repair; ROS, reactive oxygen species; 8-OHdG, 8-hydroxydeoxyguanosine. Figure 5. Open in a new tab Socio-Economic Context and Divergent Molecular Pathways of Carcinogenesis. Conceptual diagram illustrating how LMIC structural constraints and HIC affluence-related factors drive distinct molecular pathways of carcinogenesis. In LMICs, infectious agents and environmental toxins induce direct DNA damage (bulky adducts, ROS) leading to characteristic mutations (TP53 R249S, COSMIC Sig. 24). In HICs, metabolic dysregulation activates proliferative signaling (PI3K/AKT/mTOR). Both pathways converge on shared inflammatory mediators (NF-κB, STAT3), yet produce divergent therapeutic susceptibilities. 27 , 28 , 40 – 42 , 47 – 49 , 75 – 78 , 81 , 82 , 96 – 98 4.1. Metabolic Carcinogenesis in HICs In high-income populations, the biological consequences of socio-economic context are often mediated not by direct genotoxic exposure, but through metabolic dysregulation. Obesity and sedentary behavior promote insulin resistance, resulting in chronic hyperinsulinemia and elevated insulin-like growth factor-1 (IGF-1) signaling. 28 , 30 , 33 This, in turn, activates the PI3K/AKT/mTOR pathway, 28 , 33 , 34 a central regulator of cellular growth and survival, while simultaneously suppressing apoptosis. 28 , 30 , 82 Unlike exogenous carcinogens that induce discrete DNA lesions, metabolic syndrome drives tumorigenesis through sustained proliferative signaling, epigenetic reprogramming, and chronic low-grade inflammation. 28 , 82 These mechanisms underlie the development of malignancies such as colorectal and breast cancer, illustrating how affluence-associated exposures reshape cancer biology through signaling networks rather than direct DNA damage. 27 , 28 4.2. Aflatoxin-Driven Carcinogenesis in LMICs Many LMICs experience carcinogenesis driven by direct environmental mutagens, producing highly characteristic genomic alterations. A definitive example is the relationship between aflatoxin B1 (AFB1) exposure and HCC. In regions such as Qidong County, China, where inadequate food storage infrastructure leads to high levels of AFB1 contamination, whole-genome sequencing of liver tumors reveals a dominant presence of COSMIC Signature 24, characterized by a high frequency of G→T transversions. 40 This signature is virtually absent in HCC cases from North America (0.7%) and Japan (1%), yet is observed in up to 16% of cases in Hong Kong, 40 demonstrating a clear geographic and socio-economic gradient in mutational processes. At the mechanistic level, AFB1 exerts its carcinogenic effect by forming bulky DNA adducts, most notably the AFB1-N7-dG lesion. 40 These adducts preferentially target the TP53 tumor suppressor gene, 42 leading to the well-characterized R249S mutation found in approximately half of aflatoxin-associated HCC cases. 40 This provides strong evidence for a mechanistic relationship between a socio-economic exposure and a specific genomic driver of malignancy. 40 , 42 4.3. HBV-Aflatoxin Synergy and DNA Repair Impairment AFB1 carcinogenic effect is further amplified by coexisting infectious diseases. Chronic HBV infection, prevalent in many developing regions, creates an inflammatory and immunosuppressive microenvironment characterized by persistent cytokine signaling (e.g., IL-6, TGF-β), increased regulatory T-cell activity, and impaired immune surveillance. 76 In general, HBx-mediated reduction in nucleotide excision repair (NER), increases AFB1 bioactivation, and regenerative proliferation that favors fixation of AFB1-induced mutations. 99 At the molecular level, this synergy is partially mediated by the HBV X protein (HBx), which disrupts genomic maintenance pathways. HBx has been shown to bind components of the p53/TFIIH repair axis and to impair p53-dependent functions, providing direct mechanistic evidence that HBx functionally inhibits p53. 100 Following AFB1 exposure, hepatic enzymes convert the toxin into a reactive epoxide that forms DNA adducts 101 typically repaired by the NER pathway. Under normal conditions, damage recognition proteins such as UV-DDB and repair complexes including TFIIH (XPB/XPD) are recruited in a p53-dependent manner to excise these lesions. However, HBx interferes with this process by functionally inhibiting p53 78 , 99 and disrupting NER signaling 77 . Rather than completely abolishing repair, HBx reduces its fidelity and efficiency, increasing the likelihood that AFB1-induced lesions persist during replication. This promotes fixation of G→T transversion mutations, particularly at codon 249 of TP53, 42 , 78 , 99 , 102 , 103 thereby accelerating tumorigenesis. This interaction exemplifies how coexisting socio-economic conditions converge at the molecular level to amplify carcinogenic risk. 76 4.4. Arsenic, Oxidative Stress, and Micronutrient Deficiency Arsenic exposure provides a complementary model of how environmental conditions shape mutational pathways through oxidative mechanisms. Chronic ingestion of inorganic arsenic leads to the generation of ROS, which induce oxidative DNA lesions such as 8-hydroxydeoxyguanosine (8-OHdG). 68 Unlike bulky adducts, these lesions are repaired through the base excision repair (BER) pathway. However, in populations with prevalent micronutrient deficiencies, the cellular capacity to neutralize oxidative stress is significantly impaired. Deficiencies in zinc, vitamin E, and selenium, 68 common in resource-limited settings, compromise antioxidant defenses and DNA repair processes, increasing the likelihood of permanent DNA damage that can activate oncogenes and inactivate tumor suppressor genes. At a metabolic level, arsenate can substitute for phosphate in biochemical reactions, disrupting ATP production and mitochondrial function, thereby exacerbating oxidative stress and cellular dysfunction. This systemic oxidative stress not only facilitates carcinogenesis but is also associated with broader physiological fatigue and reduced physical capacity, 70 reinforcing a cycle of economic and biological vulnerability. 4.5. Occupational Iron Exposure and Fenton Chemistry Emerging evidence further suggests that occupational exposure to iron-rich particulates may contribute to carcinogenesis through Fenton chemistry. Chronic inhalation of iron-abundant fumes, as seen in industrial settings such as welding and steel manufacturing, leads to systemic iron overload and the generation of highly reactive hydroxyl radicals. 63 In breast tissue, this pro-oxidative environment may interact with estrogen metabolism via the redox cycling of catechol estrogens, producing quinones that can form DNA adducts. While direct epidemiological evidence remains limited, 64 dysregulation of iron homeostasis, particularly altered expression of hepcidin and ferroportin, 64 has been consistently observed in tumor biology. Tumors may exploit this dysregulation by upregulating local hepcidin expression, 64 promoting ferroprotein degradation and intracellular iron retention to support rapid proliferation. This provides a plausible mechanistic link between occupational exposure and tumor progression, 64 particularly in populations lacking adequate workplace protections. 4.6. Air Pollution, Inflammatory Signaling, and Epigenetic Modification The biological consequences of air pollution similarly converge on oxidative stress and inflammatory signaling pathways. Exposure to fine PM2.5, especially when enriched with heavy metals such as chromium and nickel, induces the release of pro-inflammatory cytokines, including IL-6 and TNF-α. 75 These mediators activate key intracellular pathways such as NF-κB and STAT3, 75 , 76 which function as central regulators of cell proliferation, survival, and angiogenesis. NF-κB promotes the transcription of genes involved in tumor growth and resistance to apoptosis, while STAT3 further amplifies these effects and suppresses anti-tumor immune responses. Notably, this inflammatory signaling is not unique to air pollution but represents a shared downstream consequence of diverse exposures, including chronic infection, environmental toxins, and metabolic dysfunction. 75 , 76 In addition to direct DNA damage, many environmental carcinogens exert their effects through epigenetic modification. Heavy metals such as arsenic and hexavalent chromium have been shown to induce hypermethylation of tumor suppressor genes, including p16 and RASSF1A, 15 , 70 effectively silencing their regulatory functions. This epigenetic repression may allow for unchecked cellular proliferation even in the absence of overt genetic mutations, further expanding the spectrum of mechanisms through which socio-economic exposures influence carcinogenesis. Across these diverse pathways, a unifying biological theme emerges: the convergence of distinct exposures on shared molecular networks of inflammation and genomic instability. Chronic activation of signaling pathways such as NF-κB and STAT3 75 , 76 , 104 , 105 integrates inputs from infection, oxidative stress, and metabolic imbalance, promoting a tumor-permissive environment characterized by sustained proliferation, immune evasion, and DNA damage. Thus, while socio-economic conditions determine the initiating insult, the downstream pathways of carcinogenesis often converge on common biological mechanisms. 4.7. Implications for Lung Cancer in Never-Smokers The heterogeneity of these mutational processes carries significant implications for therapeutic strategy. This divergence in mutational processes is particularly evident in the rising global incidence of lung cancer in never-smokers (LCINS). 96 While tobacco consumption remains the primary cause of lung malignancies, LCINS now accounts for a substantial proportion of cases, particularly among women in East Asia. 96 Unlike smoking-related cancers, which frequently harbor KRAS mutations, 98 LCINS is characterized by a high prevalence of distinct driver mutations, particularly in the epidermal growth factor receptor (EGFR) gene. 98 This underscores how environmental exposures produce distinct molecular subsets of disease that require unique therapeutic interventions. 4.8. Socio-Economic Selection of Driver Mutations and Implications for Precision Therapy Because socio-economic conditions shape the dominant exposures within a population, they also influence the spectrum of driver mutations that arise during tumorigenesis. For instance, aflatoxin-associated HCC is frequently defined by the TP53 R249S mutation, 40 , 42 whereas metabolically driven cancers in high-income settings more commonly involve alterations in pathways such as PI3K/AKT or Wnt/β-catenin signaling. 27 , 28 , 34 , 35 , 41 These distinctions are not merely descriptive, but clinically consequential, 41 , 98 as the efficacy of targeted therapies and immunotherapies is often contingent upon the tumor’s genomic architecture. Consequently, disparities in environmental and socio-economic exposure may translate directly into disparities in treatment responsiveness. 48 Integrating molecular data with socio-economic context is therefore essential to advancing precision oncology. In this way, the “molecular bridge” extends beyond disease causation to influence therapeutic outcomes, reinforcing the need for globally inclusive cancer research frameworks that account for both biological and structural determinants of health. At their core, these mechanisms underscore a profound reality of global oncology: socio-economic conditions do not merely dictate the frequency of toxic exposure. Rather, they actively select for specific mutations that come to define entire molecular subtypes of cancer. Recognizing this is essential for understanding why tumors from different geographic and economic backgrounds respond differently to identical therapies. 5. Pharmacogenomics and Precision Oncology: Socio-Economic Barriers to Personalized Cancer Therapy Recent advances in pharmacogenomics and precision oncology have fundamentally transformed cancer therapy 41 , 106 – 110 by enabling biomarker-guided treatments tailored to the molecular profile of individual tumors. However, the global implementation of these innovations remains uneven, 48 , 49 raising critical questions about whether the benefits of personalized medicine will be equitably distributed. Just as socio-economic conditions shape the mutational landscape of cancer development, they also determine how these molecular alterations are detected, interpreted, and ultimately treated within clinical systems. At the core of precision oncology is pharmacogenomics, the study of how genetic variation influences drug response, which allows clinicians to move beyond traditional, anatomy-based treatment strategies toward targeted therapeutic interventions. In high-income countries, tumor genomic profiling is increasingly integrated into routine care, enabling the identification of actionable mutations and the selection of therapies that inhibit specific oncogenic pathways. 41 , 48 This shift represents a paradigm shift in oncology, 41 where treatment decisions are guided not only by tumor type but also by the underlying genomic architecture of the disease. Central to this approach is the identification of predictive biomarkers, molecular indicators that determine the likelihood of response to a given therapy. Several biomarkers now serve as cornerstones of modern oncology. 47 , 111 , 112 For example, mutations in the EGFR gene predict responsiveness to EGFR-targeted tyrosine kinase inhibitors in non-small cell lung cancer. 47 , 111 , 112 Amplification of the HER2/ERBB2 oncogene similarly guides the use of HER2-targeted therapies in breast and gastric cancers. 47 , 111 , 113 In colorectal cancer, mutations in KRAS or NRAS predict resistance to anti-EGFR monoclonal antibodies, 47 necessitating genetic screening prior to treatment. Additionally, biomarkers such as Programmed Death-Ligand 1 (PD-L1) expression and tumor mutational burden (TMB) have emerged as key determinants of response to immune checkpoint inhibitors, 49 , 114 – 118 reflecting the growing integration of genomic data into immunotherapy strategies. Importantly, these biomarkers do not arise in isolation, but are shaped by the same socio-economic and environmental forces that influence cancer incidence and mutation patterns. Large-scale genomic studies have demonstrated that tumors developing in different socio-economic contexts often exhibit distinct mutational profiles. HCC in regions with high aflatoxin exposure, for instance, are frequently characterized by the TP53 R249S mutation, 40 , 42 while lung cancers in highly polluted urban environments display mutational signatures associated with PAHs. 55 , 96 – 98 These patterns suggest that environmental carcinogenesis not only determines disease burden but may also influence therapeutic susceptibility, 97 , 98 linking exposure history directly to treatment response. Despite the establishment of highly standardized, evidence-based protocols, such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, translating these standards into global practice faces immense structural barriers. 49 The implementation gap is stark: while high-income healthcare systems increasingly integrate CPIC guidelines into routine clinical workflows to optimize drug efficacy and limit toxicity, resource-limited settings largely lack the advanced laboratory infrastructure, financial capital, and specialized expertise required for comprehensive genomic sequencing and biomarker testing. 49 Consequently, many LMICs lack routine access to next generation sequencing (NGS) and other molecular diagnostic platforms, effectively locking vulnerable populations out of the precision oncology revolution. In these settings, patients are often treated with conventional chemotherapy regimens rather than biomarker-driven therapies, even when their tumors harbor actionable mutations. This disparity reflects a broader structural pattern 48 : populations that bear the highest burden of carcinogenic exposure are frequently those with the least access to the tools required for precision treatment. This inequity extends beyond national boundaries and is also evident within high-income countries, where access to genomic testing and targeted therapies may be limited by insurance status, geographic location, and healthcare infrastructure. As a result, pharmacogenomics introduces a new dimension of disparity, 49 in which differences in molecular diagnostics and treatment access compound existing inequalities in exposure and incidence ( Figure 6 ). Figure 6. Open in a new tab The Precision Oncology Gap: Inverse Relationship Between Carcinogenic Exposure Burden and Access to Biomarker-Driven Therapy. In HIC, comparatively low ambient exposure to direct genotoxic carcinogens is paired with widespread access to NGS, implementation of CPIC guidelines, biomarker-driven targeted therapies, immune checkpoint inhibitors guided by PD-L1 and TMB, and established molecular tumor boards. 41 , 47 , 48 , 111 , 112 In LMICs, high cumulative exposure to infectious, environmental, and occupational carcinogens coexists with limited NGS infrastructure, scarce implementation of CPIC guidelines, predominant reliance on conventional chemotherapy, paucity of population-specific pharmacogenomic data, and frequent underdetection of actionable mutations. 48 , 49 Consequently, precision oncology represents both a major scientific advancement and a critical challenge for global health equity. 48 While pharmacogenomics offers the potential to dramatically improve clinical outcomes, its benefits remain unevenly distributed across populations shaped by vastly different socio-economic realities. Addressing this imbalance will require not only expanding genomic infrastructure and workforce capacity but also developing cost-effective, scalable biomarker testing strategies that can be implemented in resource-limited settings. Ultimately, integrating pharmacogenomics into a global framework of cancer care demands a shift in perspective: from viewing precision medicine as a purely technological innovation to recognizing it as a socio-economic issue. The same structural forces that shape carcinogenic exposure and mutational landscapes must be addressed to ensure equitable access to personalized therapies. 48 In this way, the promise of precision oncology can only be fully realized when it is aligned with broader efforts to reduce global disparities in cancer risk, detection, and treatment. 6. Critical Analysis and Research Gaps The findings synthesized in this review converge on a central thesis: socio-economic conditions are not merely contextual modifiers of cancer risk but function as primary determinants that shape the biology, epidemiology, and treatability of malignancy at every level, from the molecular to the population scale. This discussion integrates the key themes emerging from our analysis and considers their implications for global oncology. First, the evidence demonstrates that the global cancer burden is increasingly defined by a socio-economic gradient in etiological mechanisms. In LMICs, the convergence of infection-driven carcinogenesis (HBV, HPV, H. pylori ), environmental toxin exposure (aflatoxin, arsenic, heavy metals), and occupational hazards (asbestos, iron-rich particulates) creates a disease profile dominated by direct genotoxic insult. 8 In contrast, HICs experience a cancer landscape shaped predominantly by metabolic dysregulation, with obesity, insulin resistance, and chronic low-grade inflammation driving tumorigenesis through proliferative signaling pathways such as PI3K/AKT/mTOR. 27 , 28 , 34 Critically, these are not merely different expressions of the same disease. They represent fundamentally distinct molecular pathologies arising from divergent socio-economic contexts. Second, the concept of the “molecular bridge” emerges as a unifying framework for understanding how socio-economic context translates into genomic architecture. The demonstration that aflatoxin exposure produces a characteristic COSMIC Signature 24 and the TP53 R249S mutation in LMIC populations, 40 while metabolically driven cancers in HICs exhibit distinct pathway alterations, 27 , 28 , 34 , 35 provides direct evidence that economic conditions actively select for specific molecular subtypes of cancer. This has profound implications for precision oncology, as it suggests that therapeutic strategies developed in one socio-economic context may not be directly transferable to another. Third, the review identifies a compounding inequity in which the populations bearing the greatest carcinogenic exposure burden are simultaneously those with the least access to precision diagnostic and therapeutic tools. 48 , 49 The absence of NGS, CPIC guideline implementation, and biomarker-driven therapy in most LMICs means that even when actionable mutations are present, they remain undetected and untreated. This creates a “precision oncology gap” that mirrors and amplifies existing disparities in cancer incidence and mortality. Fourth, the identification of “screening deserts” both between and within nations 9 , 45 , 46 demonstrates that socio-economic determinants operate at multiple geographic scales. The parallels between late-stage lung cancer diagnosis in rural Appalachia 92 and similar patterns in sub-Saharan Africa suggest that structural barriers to early detection are not unique to resource-poor nations but are fundamentally driven by socio-economic disadvantage wherever it occurs. Finally, the phenomenon of risk externalization, whereby hazardous industries relocate from regulated HICs to less-regulated LMICs, 8 raises important ethical questions about global responsibility for cancer prevention. The current international framework permits the displacement of carcinogenic risk to populations least equipped to manage it, creating a form of “toxicological colonialism” that demands critical examination and policy intervention. While the evidence reviewed here provides a compelling case for socio-economic determinants as central drivers of cancer etiology, several important limitations and knowledge gaps must be acknowledged: 6.1. Data Limitations and Underrepresentation. A fundamental challenge in global cancer research is the marked underrepresentation of LMICs in large-scale genomic studies. The majority of cancer genome sequencing data has been generated from tumors arising in HIC populations, introducing a significant ascertainment bias. 48 , 49 This limits our understanding of the full spectrum of mutational signatures and driver mutations present in LMIC populations, where environmental exposures differ markedly. Additionally, incomplete cancer registries in many developing regions 8 lead to systematic underestimation of incidence and mortality, particularly for cancers associated with occupational and environmental exposures. 6.2. Causality and Confounding. Much of the evidence linking socio-economic factors to cancer etiology is derived from observational, ecological, or cross-sectional study designs, which limit causal inference. The complex interplay among concurrent exposures, infection, environmental toxins, nutritional deficiencies, and occupational hazards makes it difficult to isolate the independent contribution of any single factor. For example, the synergy between HBV infection and aflatoxin exposure in HCC development 100 – 103 is well-documented, but quantifying the relative contribution of each factor in the context of concurrent micronutrient deficiencies remains challenging. 6.3. Temporal Dynamics and Longitudinal Data. The epidemiological transitions described in this review are inherently dynamic, yet much of the available evidence is cross-sectional rather than longitudinal. There is a critical need for prospective cohort studies that track cancer incidence, exposure patterns, and molecular outcomes across decades of economic development, particularly in rapidly transitioning economies such as those in West Asia and sub-Saharan Africa. 6.4. Pharmacogenomic Data Gaps. The pharmacogenomic evidence base remains heavily skewed toward populations of European descent. 49 Given that drug-metabolizing enzyme polymorphisms, biomarker prevalence, and tumor genomic profiles vary substantially across populations, extrapolating HIC-derived pharmacogenomic data to LMIC populations is problematic. There is an urgent need for population-specific pharmacogenomic studies that can inform locally relevant clinical guidelines. 7. Future Directions Based on the gaps and themes identified in this review, we propose the following priority areas for future research and policy development: 7.1. Global Cancer Genomics Initiatives. There is an urgent need to expand cancer genome sequencing programs to include diverse LMIC populations. Initiatives analogous to The Cancer Genome Atlas (TCGA) but focused on tumors arising in high-exposure environments, such as aflatoxin-endemic regions, arsenic-contaminated zones, and areas with heavy industrial activity, would substantially advance our understanding of exposure-specific mutational processes and inform the development of region-appropriate therapeutic strategies. 7.2. Scalable Diagnostic Technologies. The development and validation of low-cost, point-of-care molecular diagnostic tools is essential for bridging the precision oncology gap. Emerging technologies such as CRISPR-based diagnostics, liquid biopsy platforms, and portable sequencing devices hold promise for enabling biomarker testing in resource-limited settings, potentially transforming the feasibility of precision oncology in LMICs. 7.3. Longitudinal Epidemiological Studies. Prospective cohort studies tracking the cancer consequences of economic transitions in rapidly developing nations are critically needed. Such studies should integrate molecular endpoints (mutational signatures, epigenetic changes) with epidemiological and socio-economic data to provide a comprehensive understanding of how development trajectories shape cancer risk over time. 7.4. Regulatory Harmonization and Environmental Justice. International policy frameworks are needed to prevent the externalization of carcinogenic risk through the relocation of hazardous industries. Harmonizing occupational exposure standards across economic contexts, strengthening environmental monitoring in transitioning economies, and establishing accountability mechanisms for transnational corporations would address the structural drivers of exposure inequality. 7.5. Integration of Socio-Economic Data into Precision Oncology. Future precision oncology frameworks should incorporate socio-economic and environmental exposure data alongside genomic profiling. Understanding a patient’s “exposomic context”, their lifetime history of environmental, occupational, and structural exposures, may improve therapeutic prediction and enable more equitable treatment stratification. 7.6. Artificial Intelligence and Machine Learning in Socio-Economic Cancer Research. A rapidly emerging area at the intersection of computational science, molecular oncology, and health equity is the application of artificial intelligence (AI) and machine learning (ML) to understand how socio-economic factors shape cancer biology, disparities, and outcomes. Recent developments in this field hold transformative potential for several dimensions of the research agenda outlined in this review. AI-driven tools are rapidly improving the accuracy, efficiency, and personalization of cancer diagnosis and treatment, particularly through automated image analysis and predictive modeling. 119 First, AI-driven tools are advancing the identification of exposure-specific mutational signatures. The SigProfilerExtractor algorithm, a machine learning platform, has been used to analyze over 23,000 sequenced human cancers and has identified novel mutational signatures linking specific carcinogens to distinct cancer types. 120 This tool discovered a previously unknown tobacco-specific signature in bladder cancer (SBS92), demonstrating that ML can deconvolve complex mutational data to reveal exposure-cancer relationships that traditional methods miss. These AI-powered signature extraction tools are directly relevant to the “molecular bridge” concept developed in this review: they provide the computational infrastructure needed to decode how socio-economic exposures, aflatoxin, arsenic, industrial pollutants, and metabolic conditions leave characteristic imprints on the cancer genome across diverse populations. Second, ML is increasingly being applied to integrate social determinants of health (SDOH) into cancer risk prediction and outcomes modeling. Recent studies further demonstrate the clinical potential of machine learning for early cancer detection. For example, Barwade et al. (2025) 121 developed machine learning-based diagnostic approaches for blood cancers, highlighting how AI-driven models can enhance early detection and risk stratification, particularly in settings where traditional diagnostic infrastructure is limited. Deep learning–based diagnostic systems have demonstrated substantial improvements in cancer detection accuracy, particularly in imaging and pathology-based classification tasks. 122 Dankwa-Mullan and Weeraratne 123 have argued that AI/ML research must incorporate the complete etiologic context of a patient’s health, including socioeconomic status, environmental exposures, and occupational factors, to advance equitable cancer care. Practical applications of this principle are emerging: random forest models with geospatial data have been used to identify how social determinants and accessibility of mammography services explain variability in breast cancer screening rates across U.S. census tracts 124 ; AI-powered environmental risk assessment models can analyze satellite imagery, climate data, and pollution exposure metrics to predict geospatial cancer risk patterns 125 ; and ML-based MIR ratio models analyzing data from 185 countries have used SHAP (Shapley Additive Explanations) values to identify which health system factors, including access to radiotherapy, universal health coverage, and economic strength, are most closely linked to better survival in each nation, generating country-specific policy roadmaps. 125 However, a critical caveat accompanies these advances. Variability in model quality, reporting standards, and bias assessment remains a major limitation in the clinical translation of machine learning approaches in oncology. 126 AI/ML models are typically built on historical data, and populations that have been historically underrepresented in cancer research, including those from LMICs, racial and ethnic minorities, and socioeconomically disadvantaged communities, may be further marginalized by algorithmic biases. 123 Ensuring that AI tools are trained on diverse, representative datasets and that their deployment is accompanied by robust bias-mitigation frameworks is essential to realizing the equitable potential of these technologies. Future research should prioritize the development of AI platforms that can simultaneously analyze genomic, exposomic, and socio-economic data to generate integrated risk profiles and treatment recommendations that are sensitive to the full spectrum of determinants described in this review. Methods This narrative review was conducted through a comprehensive search of peer-reviewed literature across PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases. The search strategy employed combinations of the following key terms: “cancer etiology,” “socio-economic determinants,” “environmental carcinogenesis,” “mutational signatures,” “occupational cancer risk,” “cancer disparities,” “precision oncology,” “pharmacogenomics,” “cancer screening,” “aflatoxin,” “arsenic carcinogenesis,” “air pollution cancer,” “TP53 mutations,” and “global cancer burden.” Boolean operators (AND, OR) were used to combine search terms systematically. Inclusion criteria encompassed: (1) original research articles, systematic reviews, and meta-analyses published between 2006 and 2026; (2) studies examining the relationship between socio-economic factors and cancer etiology, incidence, molecular mechanisms, or treatment outcomes; (3) epidemiological studies with quantitative data on cancer burden across economic strata; (4) molecular and genomic studies linking environmental exposures to specific mutational processes; and (5) health systems research on screening infrastructure and precision oncology implementation. Exclusion criteria included: (1) case reports and editorials without original data; (2) studies published in languages other than English without available translations; and (3) conference abstracts without full-text availability. A total of 56 references were identified as meeting the inclusion criteria and were synthesized using a narrative review methodology. Given the breadth and interdisciplinary nature of the topic, spanning epidemiology, molecular biology, occupational health, environmental science, and health economics, a narrative rather than systematic approach was selected to enable integration across these diverse domains. The review was organized thematically into five major sections, reflecting the pathway from macro-level socio-economic determinants to molecular-level consequences and therapeutic implications. Reference quality was assessed based on study design, sample size, and consistency of findings across independent studies. Priority was given to large-scale epidemiological analyses, genome-wide studies, and reports from established international organizations (WHO, IARC). Conclusion This narrative review demonstrates that socio-economic conditions represent a central, rather than peripheral, determinant of cancer etiology worldwide. The evidence presented here reveals a systematic relationship between economic context and the fundamental biology of malignancy, operating through five interconnected mechanisms: (1) the divergence of etiological profiles between HICs and LMICs, driven by infection-predominant versus metabolic-predominant cancer landscapes; (2) the socio-economically structured distribution of occupational, environmental, and structural carcinogenic exposures; (3) the epidemiological manifestation of these exposures as distinct and evolving incidence patterns that mirror stages of economic development; (4) the molecular translation of socio-economic context into characteristic mutational signatures and genomic architectures that differ fundamentally between populations; and (5) the compounding inequity in precision oncology access, whereby populations bearing the greatest exposure burden have the least access to biomarker-driven therapeutic interventions. The concept of the “molecular bridge” between the socio-economic context and genomic alteration is a central contribution of this analysis. The demonstration that aflatoxin exposure in LMICs produces characteristic TP53 R249S mutations and COSMIC Signature 24, while affluence-associated metabolic conditions in HICs drive carcinogenesis through PI3K/AKT/mTOR pathway activation, establishes that economic conditions do not merely influence cancer frequency but actively select for the molecular subtypes that define populations. This insight carries direct implications for therapeutic strategy, as it indicates that precision oncology approaches must be calibrated not only to individual tumor genomics but to the broader socio-economic and exposomic context in which those tumors arise. Addressing the global cancer burden will require an integrated approach that transcends disciplinary boundaries, combining molecular oncology with environmental science, health economics, and social policy. The expansion of genomic infrastructure in LMICs, the development of cost-effective diagnostic technologies, the harmonization of international regulatory standards, and the integration of socio-economic data into precision medicine frameworks represent essential components of this effort. Only through such a comprehensive strategy can the promise of modern oncology be realized equitably across the full spectrum of human socio-economic experience. Acknowledgments The authors used Notebook LLM to assist with manuscript editing and formatting to the journal style. All content was reviewed, edited, and approved by the authors, who take full responsibility for the final manuscript. Funding The authors acknowledge the funding support from NIH grant no. 1R15CA301437-01 to K. Parang. Biographies Keykavous Parang Pharm.D., Ph.D. is a Full Professor of Medicinal Chemistry and Pharmacology at Chapman University School of Pharmacy, California. He previously served as Associate Dean of Research. His research focuses on medicinal chemistry, nanomedicine, and drug delivery, with an emphasis on the design of cyclic and hybrid cyclic-linear cell-penetrating peptides for intracellular and nuclear delivery of therapeutic cargo. Param Patel is a first-year Doctor of Pharmacy (Pharm.D.) student with a strong interest in oncology, public health, and the role of environmental and socioeconomic factors in disease outcomes. His academic work focuses on integrating scientific literature and data-driven analysis to better understand global health disparities and improve patient care. He is particularly interested in the application of emerging technologies, including artificial intelligence, in research and clinical decision-making. Michael Oh is currently pursuing his Doctor of Pharmacy (PharmD) degree at Chapman University School of Pharmacy. His academic interests focus on oncology, epidemiology, and the relationship between medicine and emerging technologies. 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