The role of circadian disruption, sleep dysregulation, lifestyle factors, and metabolic reprogramming in the pathogenesis and progression of ovarian cancer.

The role of circadian disruption, sleep dysregulation, lifestyle factors, and metabolic reprogramming in the pathogenesis and progression of ovarian cancer - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice J Natl Cancer Cent . 2026 Feb 5;6(2):189–203. doi: 10.1016/j.jncc.2026.01.009 Search in PMC Search in PubMed View in NLM Catalog Add to search The role of circadian disruption, sleep dysregulation, lifestyle factors, and metabolic reprogramming in the pathogenesis and progression of ovarian cancer Malvi Surti Malvi Surti a Research and Development Cell (RDC), Parul University, Waghodia, Vadodara, Gujarat, India b Department of Microbiology, Parul Institute of Applied Sciences, Parul University, Waghodia, Vadodara, Gujarat, India Find articles by Malvi Surti a, b , Anjali Gupta Anjali Gupta a Research and Development Cell (RDC), Parul University, Waghodia, Vadodara, Gujarat, India b Department of Microbiology, Parul Institute of Applied Sciences, Parul University, Waghodia, Vadodara, Gujarat, India Find articles by Anjali Gupta a, b , Komal Janiyani Komal Janiyani a Research and Development Cell (RDC), Parul University, Waghodia, Vadodara, Gujarat, India b Department of Microbiology, Parul Institute of Applied Sciences, Parul University, Waghodia, Vadodara, Gujarat, India Find articles by Komal Janiyani a, b , Mohd Adnan Mohd Adnan c Department of Biology, College of Science, University of Ha’il, Ha’il, Saudi Arabia Find articles by Mohd Adnan c , Mitesh Patel Mitesh Patel d Department of Computer Science and Bioscience, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat, India Find articles by Mitesh Patel d, ⁎ Author information Article notes Copyright and License information a Research and Development Cell (RDC), Parul University, Waghodia, Vadodara, Gujarat, India b Department of Microbiology, Parul Institute of Applied Sciences, Parul University, Waghodia, Vadodara, Gujarat, India c Department of Biology, College of Science, University of Ha’il, Ha’il, Saudi Arabia d Department of Computer Science and Bioscience, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat, India ⁎ Corresponding author. [email protected] [email protected] Received 2025 Feb 21; Revised 2026 Jan 21; Accepted 2026 Jan 22; Collection date 2026 Apr. © 2026 Chinese National Cancer Center. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13083629  PMID: 42007213 Abstract Ovarian cancer remains one of the most lethal gynecologic malignancies, with high mortality due to late-stage diagnosis and limited effective treatments. Recent evidence highlights the importance of circadian rhythms, metabolic reprogramming, and lifestyle factors in shaping the initiation, progression, and treatment response of ovarian tumors. Disruption of the circadian clock, via genetic dysregulation or lifestyle behaviors such as night-shift work and poor sleep, has been associated with tumorigenesis, metabolic imbalance, and chemoresistance. Additionally, ovarian cancer cells demonstrate distinct metabolic adaptations, including enhanced glycolysis and glutaminolysis, supporting rapid proliferation and survival. Lifestyle factors such as diet, physical inactivity, obesity, and psychosocial stress further modulate cancer risk and therapeutic outcomes. This review synthesizes evidence on the molecular underpinnings of circadian and metabolic disruption in ovarian cancer and their interaction with modifiable behavioral factors. Furthermore, it explores emerging strategies such as chronotherapy and metabolic targeting to improve treatment efficacy and survivorship. The goal is to encourage a multidimensional perspective that integrates molecular biology with lifestyle medicine in the management of ovarian cancer. Keywords: Ovarian cancer, Circadian disruption, Insomnia, Psychological well-being, Lifestyle, Metabolic dysregulation 1. Introduction Ovarian carcinoma represents a significant global health burden, accounting for approximately 5 % of all cancer-related deaths among women worldwide. 1 , 2 The disease is predominantly diagnosed at an advanced stage, with nearly 60 % of cases detected in later stages, which correlates with a poor prognosis and limited therapeutic outcomes. For patients with metastatic ovarian cancer, the five-year overall survival rate remains dismal, estimated at only 30-40 %, highlighting the critical need to enhance our understanding of the disease's etiology and progression. 1 , 3 The pathogenesis of ovarian cancer is multifactorial, involving a complex interplay of genetic predispositions, environmental exposures, and modifiable lifestyle behaviors. 4 , 5 These factors necessitate a comprehensive research approach integrating molecular mechanisms with lifestyle influences to enhance prevention and treatment strategies. The staging of ovarian cancer, as defined by the International Federation of Gynecology and Obstetrics (FIGO), ranges from Stage I (confined to the ovaries) to Stage IV (distant metastases), with most cases diagnosed at Stage III or IV, when the malignancy has already disseminated beyond the ovaries, typically to the peritoneum or other organs, rendering treatment more challenging and adversely affecting prognosis. 6 , 7 The asymptomatic nature of early disease and frequent delays in diagnosis further exacerbate the clinical management and overall patient outcomes. 8 Over the past decade, global incidence and mortality rates of ovarian cancer have shown a concerning upward trajectory. For instance, in 2015, there were 239,000 new cases and 152,000 deaths, which decreased slightly in 2016 to 225,500 cases and 140,200 deaths. 9 , 10 However, the numbers surged again in subsequent years, with 286,100 cases and 176,000 deaths reported in 2017, 295,414 cases and 184,799 deaths in 2018, and 294,422 cases with 198,412 deaths in 2019. 11 , 12 , 13 The increasing burden continued into 2020, 2021, and 2022, with 314,000, 239,682, and 324,603 incident cases, and 207,000, 171,246, and 206,956 deaths, respectively. 14 , 15 , 16 Notably, regions such as South Asia, East Asia, and Western Europe exhibit the highest disease burden, with China and India contributing disproportionately to global mortality statistics. 17 , 18 Projections by the Global Cancer Observatory (GCO) indicate a significant global rise in ovarian cancer burden among females aged 0-85+ years over the coming decades. The estimated number of new cases is expected to increase from 324,603 in 2022 to approximately 369,041 in 2025, eventually reaching 503,790 by 2050. Similarly, mortality is projected to rise from 206,956 deaths in 2022 to 231,066 in 2025, and further to 351,164 by 2050. These trends reflect a growing global health concern and highlight the urgent need for enhanced early diagnostic tools, increased public health outreach, and development of effective therapeutic interventions to address the escalating disease burden. Cytoreductive surgery followed by a combination of platinum- and taxane-based chemotherapy, along with targeted therapies such as anti-angiogenic agents and poly (ADP-ribose) polymerase (PARP) inhibitors, represents the standard treatment approach for advanced epithelial ovarian cancer (EOC). 19 In recent years, clinical studies have highlighted the evolving role of PARP inhibitors as a promising therapeutic strategy, particularly in EOC patients exhibiting homologous recombination deficiency. 20 Nonetheless, the emergence of resistance to chemotherapeutic agents remains a major clinical challenge. 19 A subset of tumor cells possessing stem cell-like characteristics, referred to as cancer stem cells (CSCs), has been implicated in chemoresistance. Ovarian CSCs display intrinsic resistance to chemotherapy, 21 which may arise from various mechanisms, including enhanced drug efflux, metabolic inactivation of drugs, detoxification pathways, epigenetic alterations of critical genes, and tumor heterogeneity driven by modifications in the tumor microenvironment (TME). 21 An emerging dimension in ovarian cancer research involves the role of circadian rhythms, sleep patterns, and broader lifestyle behaviors in cancer risk and progression. Disruptions in circadian regulation, frequently caused by night-shift work or irregular sleep schedules, have been associated with increased cancer susceptibility due to impaired regulation of apoptosis, DNA repair, and cell cycle control. 22 , 23 , 24 Such misalignments can promote genomic instability and potentially facilitate tumorigenesis. Notably, circadian rhythms also influence chemotherapy efficacy; timing treatment under these rhythms may enhance therapeutic outcomes and mitigate resistance. 25 Furthermore, modifiable lifestyle factors such as diet, physical activity, and sleep quality are increasingly recognized for their role in shaping cancer risk and survival. A composite healthy lifestyle score, incorporating metrics such as body mass index (BMI), exercise frequency, dietary patterns, and alcohol intake, has been inversely associated with ovarian cancer risk. 26 Epidemiological evidence suggests that individuals who maintain a healthy body weight and engage in regular physical activity may exhibit both a reduced risk of developing ovarian cancer and improved clinical outcomes. 27 , 28 Sleep quality and psychological well-being are also integral to comprehensive cancer care, with research indicating that circadian alignment and adequate rest may positively influence treatment response and quality of life (QOL). This manuscript presents a comprehensive review of current scientific literature on ovarian cancer, emphasizing the integration of circadian rhythm regulation, sleep behavior, metabolic reprogramming, and modifiable lifestyle factors in disease risk, progression, and therapeutic responsiveness. By critically examining both molecular mechanisms and behavioral influences, this review aims to bridge gaps between biological and environmental determinants of ovarian cancer. It proposes a multidisciplinary approach to understanding pathogenesis, highlights novel metabolic and circadian targets for intervention, and explores the potential of chronotherapeutics and lifestyle modifications as complementary strategies for improved patient outcomes. 2. Circadian rhythms and ovarian cancer Circadian clocks are innate oscillatory mechanisms in cells and living systems, facilitating biochemical mechanisms in alignment with a nearly 24-hour cycle. These clocks are governed by interconnected clock genes (CGs) that function through self-regulating transcriptional and translational feedback mechanisms. These feedback mechanisms produce and coordinate circadian rhythms, predominantly controlled by external light-dark cycles. 29 Important physiological processes, such as energy consumption, immune response, wakefulness, and sleep are regulated by the circadian rhythm. 30 , 31 Additionally, it enables organisms to respond to environmental changes, ensuring homeostasis at both systemic and tissue-specific levels. 31 , 32 The intricate regulation of these rhythms is orchestrated by a central master clock. 2.1. The suprachiasmatic nucleus and central regulation of circadian rhythms The master regulator of circadian timing in mammals is the suprachiasmatic nucleus (SCN), a region located in the hypothalamus. The SCN coordinates peripheral clocks found in other tissues and organs. It responds to external light signals received by retinal ganglion cells and uses these inputs to adjust internal timing mechanisms. 33 Through this signaling, the SCN ensures that internal rhythms remain synchronized with the external light-dark cycle. 34 Other environmental elements, including feeding schedules, furthermore, play a significant part in synchronizing peripheral oscillators. 35 Maintaining synchronization between the central clock in the SCN and peripheral tissue clocks is crucial for health ( Fig. 1 ). However, disturbances like irregular sleep, eating at odd times, night-shift work, or exposure to artificial light at night can disrupt this alignment. Such misalignment often leads to changes in gene expression through epigenetic modifications, which in turn may cause various health problems. 36 , 37 Fig. 1. Open in a new tab Mammalian circadian system and its regulation. The solar cycle regulates organismal feeding/fasting and wake/sleep cycles, synchronized with the CNS rhythm in the suprachiasmatic nucleus through light stimulation of the retina, allowing the central clock to coordinate almost every tissue, which has peripheral clocks. CNS, central nervous system; SCN, suprachiasmatic nucleus. Chronic circadian disruption is increasingly recognized as a contributing factor in various diseases, including cardiovascular disorders, metabolic syndrome, weakened immunity, and certain cancers. 38 These associations underscore the essential role of circadian balance in human health. Recent studies suggest that targeting circadian rhythms could provide therapeutic benefits for neuropsychiatric and cardiometabolic conditions. Furthermore, research into the role of circadian rhythms in cancer progression has illuminated the capability of chronotherapy as an innovative approach in oncology, offering a promising avenue for future treatments. 39 2.2. Circadian clock genes and their dysregulation in ovarian cancer Ovarian cancer is a severe and often fatal gynecological cancer, with studies suggesting that disturbances in circadian rhythms, including irregular sleep patterns or shift work, may elevate its risk. 40 Evidence shows that ovarian tumors exhibit altered expression of circadian clock genes compared to healthy tissue. Studies have reported reduced circadian locomotor output cycles kaput ( CLOCK ), cryptochrome 2 ( CRY2 ), period 1 ( PER1 ), and period 2 ( PER2 ) expression in ovarian cancerous tissues as opposed to those in healthy tissues. At the same time, cryptochrome 1 ( CRY1 ), period 3 ( PER3 ), and brain and muscle ARNT-like 1 ( BMAL1 ) display elevated expression, indicating preserved antiphase regulation between CRY1 and BMAL1 in ovarian tumors. 41 Notably, reduced expression of CRY1 and BMAL1 is strongly linked to grade 3 and mucinous ovarian tumors, as opposed to subclasses of serous and endometrioid. Low levels of BMAL1 and CRY1 together act as a separate prognostic indicator, alongside tumor stage and histological subtype. 41 Furthermore, it has been discovered that BMAL1 may limit the growth of ovarian cancer tumors. Overexpression of BMAL1 suppresses cell growth and enhances cisplatin sensitivity, suggesting its therapeutic potential. However, BMAL1 is often epigenetically silenced in ovarian cancer through histone alterations and promoter methylation, contributing to tumor progression and reduced chemosensitivity. 42 These findings underscore the central role of circadian genes in both tumor biology and response to therapy. The connection between ovarian cancer and circadian gene regulation involves complex interactions among metabolism, hormonal rhythms, and gene expression. Table 1 outlines key research findings highlighting the significance of circadian rhythms in ovarian cancer development and treatment response. Table 1. Key aspects of circadian rhythms and their implications for ovarian cancer. Aspect Key findings Implications References Genetic variation and cancer susceptibility Strong correlations were observed between ovarian cancer and circadian rhythm gene variants (e.g., BMAL1, PER3 ). Suggests that genetic predisposition related to circadian rhythms may influence ovarian cancer susceptibility. 43 Chemotherapy resistance PER2 inhibits ovarian cancer cell growth and metastasis; circadian disruption affects chemotherapy efficacy Highlights the potential for circadian rhythm modulation to enhance chemotherapy outcomes in ovarian cancer 25 Circadian genes and proliferation Identified relationships between circadian genes and ovarian cancer risk, particularly KLF10 ′s role in inhibiting cell proliferation Indicates that ovarian cancer risk indicators and potential treatment targets could be found in circadian rhythm genes. 44 Melatonin levels Reduced circulating melatonin levels may contribute to ovarian cancer development Suggests that melatonin's role in circadian regulation could act as a target for treatment in ovarian cancer 45 CLOCK gene and drug resistance CLOCK gene affects cisplatin sensitivity, with circadian disruption linked to resistance Implicates the importance of circadian genes in determining chemotherapy responses in ovarian cancer treatment 46 ARNTL silencing ARNTL ( BMAL1 ) downregulation was observed in ovarian cancer, indicating a potential role as a tumor suppressor Indicates that restoring ARNTL expression could offer a new therapeutic approach for ovarian cancer 42 PER2 overexpression PER2 overexpression inhibited tumor development and spread, potentially through the PI3K/PKB pathway Indicates that increasing PER2 expression could be an effective approach to inhibit ovarian cancer progression 47 PIWIL1 and tumorigenesis PIWIL1 suppresses circadian rhythms by degrading CLOCK and BMAL1 , linking circadian disruption to tumorigenesis Highlights the potential for targeting PIWIL1 to restore circadian function in ovarian cancer therapy 48 Shift work and cancer risk Shift work disrupts hormonal rhythms, affecting ovarian function and increasing cancer risk. Emphasizes the need for considering circadian factors in managing ovarian cancer in women with disrupted schedules 49 Open in a new tab Abbreviations: PI3K, phosphatidylinositol 3-kinase; PKB, protein kinase B. 2.3. Circadian clock genes and chemoresistance in ovarian cancer Circadian clock genes like PER2, CLOCK , and BMAL1 are increasingly recognized not only for their roles in maintaining physiological balance but also for influencing drug resistance mechanisms in ovarian cancer. PER2 is linked to cisplatin sensitivity; its downregulation activates the PI3K/Akt signaling pathway, increasing chemoresistance in ovarian cancer cells. Overexpression of PER2 in SKOV3 cells has been shown to restore chemosensitivity by promoting apoptosis and reducing tumor cell growth. 50 Beyond affecting tumor cell behavior, PER2 also modulates the TME by regulating inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which play roles in resistance to platinum-based therapies. 51 Disruptions in the circadian rhythm caused by genetic changes or environmental factors like night shift work further worsen drug resistance by altering melatonin production and increasing oxidative stress. 52 , 53 Recent research highlights that circadian rhythms govern the timing of critical cellular functions such as apoptosis and DNA repair, both essential in determining the efficacy of cisplatin treatment. Specifically, CLOCK and BMAL1 , transcriptional activators of circadian rhythms, regulate apoptotic gene expression, influencing chemotherapy sensitivity. PER2 enhances cisplatin-induced apoptosis by modulating mitochondrial pathways and upregulating pro-apoptotic markers such as caspases, facilitating programmed cell death, while its suppression correlates with diminished apoptosis in resistant cells. 54 , 55 , 56 Furthermore, the CLOCK - BMAL1 complex contributes to chemosensitivity by regulating genes involved in DNA repair, particularly through the nucleotide excision repair (NER) pathway. 57 Enhanced circadian rhythm stability is associated with increased DNA repair efficiency and better cisplatin response in preclinical ovarian cancer models. 58 Additionally, the interplay between circadian genes and other transcription factors, such as Snail and Slug, which are known for their roles in epithelial-mesenchymal transition (EMT), also significantly contributes to the discussion of drug resistance. Increased expression of Snail and Slug has been connected to enhanced resistance to various anticancer drugs, including cisplatin and paclitaxel. This indicates that the circadian regulation of these EMT factors could be a key mechanism allowing ovarian cancer cells to evade treatment. 59 The suppression of melatonin by artificial light at night or irregular sleep patterns exacerbates the dysregulation of these clock genes, leading to enhanced tumor progression and resistance. 60 These mechanistic insights have led to the emergence of chrono-chemotherapy, a strategy that aligns chemotherapy administration with circadian cycles to optimize efficacy. Evidence from animal models and clinical trials suggests that administering cisplatin when tumor cells are most vulnerable to apoptosis and least capable of DNA repair may significantly enhance outcomes. As such, targeting circadian clock genes and mitigating chronodisruptive exposures hold promise for reversing chemoresistance and enhancing treatment precision in ovarian cancer patients. 3. Sleep dysregulation in ovarian cancer Sleep is an essential part of lifestyle and is closely regulated by circadian rhythms. Sleep disturbances are frequently observed in cancer patients and can significantly affect their overall QOL. Compared to the general population, cancer patients are more likely to have sleep issues, including trouble falling asleep, staying asleep, waking up feeling fatigued, and excessive daytime fatigue. 61 , 62 , 63 , 64 Chemotherapy has been recognized as a factor contributing to lower sleep quality and increased daytime sleepiness. 64 , 65 Poor sleep negatively influences a cancer patient's ability to carry out daily activities and maintain productivity, although some studies have found inconsistent results in this area. 66 , 67 The interaction between tumors and the overall health of the body contributes to these sleep disturbances. Tumors can alter the behavior of nearby cells, like fibroblasts, T-cells, and macrophages, along with distant organs like the liver and brain, enabling them to evade the immune system and meet their energy requirements. 68 , 69 This dynamic interaction poses a complex challenge to the body, leading to symptoms such as fatigue, sleep disturbances, circadian rhythm disruptions, energy imbalances, inflammation, loss of appetite, and muscle wasting (cachexia). 70 , 71 Poor sleep has been linked to reduced QOL and increased mortality, even when other factors such as disease progression, age, hormonal levels, and depression are considered. 72 , 73 Ovarian cancer patients often face higher levels of psychological distress during diagnosis and treatment because of the dismal prognosis of the disease and intensive therapies, which make them more vulnerable to sleep problems. However, the progression of sleep disturbances after diagnosis and the factors contributing to worsening sleep over time are not well understood. 74 Depression, frequently observed in ovarian cancer patients, has been linked to decreased sleep duration, increased daytime fatigue, and frequent nighttime awakenings. 75 , 76 Anxiety, another common issue, is often linked to insomnia in this population. 77 , 78 It is hypothesized that worsening depression and anxiety exacerbate sleep problems, which in turn further diminish QOL ( Fig. 2 ). Understanding the relationship between sleep disturbances and cancer is complex due to differences in cancer types, patient characteristics, treatment approaches, and lifestyle factors. Fig. 2. Open in a new tab Potential mechanisms underlying the pathogenesis of cancer-related insomnia. OSA, obstructive sleep apnea; PLMD, periodic limb movement disorder; RLS, restless leg syndrome. Several studies have helped to clarify the extent and effects of sleep disturbances in ovarian cancer patients. One such study by Tanya L. Ross and colleagues, part of the OPAL (Ovarian Cancer, Prognosis, and Lifestyle) cohort, explored the prevalence of insomnia and its impact on QOL. Their findings showed that 36 % of women reported insomnia symptoms within three years of diagnosis, with 22 % experiencing new insomnia after their diagnosis. At three months post-diagnosis, 14 % of participants had clinical insomnia, while 28 % reported subclinical symptoms. Women suffering from clinical insomnia reported notably lower QOL scores, an 8.4-point reduction at the same time point (95 % CI [Confidence Interval]: 7.2–9.5) and a 5.5-point reduction three months later (95 % CI: 3.4–7.6). These results highlight the high prevalence and persistent impact of insomnia among ovarian cancer patients, along with its strong association with reduced QOL. 79 Liang et al. examined the relationship between sleep patterns and the incidence of ovarian cancer in 109,024 postmenopausal women who were included in the Women's Health Initiative (WHI) between 1993 and 2018. The proportional hazards model developed by Cox revealed no discernible relationship between the general risk of cancer of the ovary and sleep duration, quality, or insomnia. But a decreased risk of advanced serous ovarian cancer was linked to restful sleep (HR: 0.73), whereas a higher risk was linked to insomnia (HR: 1.36). Vital and non-serous, as well as Type I and Type II subtypes, had quite different associations between risk and insomnia. These findings suggest that while sleep habits may not influence overall ovarian cancer risk, sleep quality and insomnia may affect the risk of serous ovarian cancer that is intrusive, with variations built on cancer subtypes. 80 To better understand the potential cause-and-effect link between insomnia and ovarian cancer, a two-sample Mendelian randomization (MR) study was conducted. This analysis used genetic data from 23andMe and the UK Biobank, along with ovarian cancer risk and survival data from the Genome-Wide Association Study (GWAS I) of the Ovarian Cancer Association Consortium (OCAC), which included 66,450 women. The study found that insomnia was associated with a higher risk of endometrioid ovarian cancer (OR: 1.60). In comparison, the risk for high-grade serous ovarian cancer (HGSOC) and clear cell ovarian cancer was lower (OR: 0.79 and 0.48, respectively). Additionally, insomnia was linked to shorter survival in women with invasive ovarian cancer (OR: 1.45) and HGSOC (OR: 1.40). These associations weakened when factors such as body mass index and age at childbirth were considered. Among HGSOC patients in The Cancer Genome Atlas (TCGA) who received chemotherapy, insomnia was associated with poorer survival (OR: 2.48), but this connection was lessened after adjusting for clinical factors. These findings suggest that insomnia may influence both the development and progression of ovarian cancer and emphasize its potential role in cancer prevention and treatment. 81 4. Metabolic and hormonal dysregulation in ovarian cancer 4.1. Metabolic reprogramming in ovarian cancer cells Recent studies have revealed that changes in metabolic reprogramming are involved in increased stemness activity and chemoresistance. 82 Furthermore, ovarian cancer cells and CSCs acquire the energy necessary for their survival through the regulation of several metabolic pathways (e.g., glucose metabolism). Metabolic reprogramming in the tumor or its microenvironment (i.e., cancer-host crosstalk) may affect cancer cell survival. The original hypothesis demonstrated that cancer cells are primarily dependent on glycolysis, known as the Warburg effect, while recent evidence suggests that some cancer cells use mitochondrial oxidative phosphorylation (OXPHOS) over glycolysis. 83 , 84 These findings indicate that OXPHOS is also the energy source for EOC growth and survival. Metabolic preferences vary due to intertumoral and intratumoral heterogeneity, such as histological types and the hypoxic and acidic niches, or study design ( in vitro, in vivo , or animal model). 82 To date, there are no reports on adaptive strategies for energy metabolism comparing ovarian cancer cells and CSCs. In addition, malignant niches are formed by cancer cells and their adjacent host cells in the TME. However, the metabolic reprogramming of these cells has not yet been fully elucidated. Therefore, the development of therapeutic strategies based on metabolic reprogramming that affects cell survival and growth is a significant challenge. 4.1.1. Integration of glycolysis and mitochondrial oxidative phosphorylation in glucose metabolism In human cells, catabolic processes such as glycolysis are functionally interconnected with anabolic pathways like mitochondrial OXPHOS to ensure efficient production of cellular energy in the form of adenosine triphosphate (ATP). 85 Glycolysis is a rapid ATP production pathway, less efficient than OXPHOS. 85 The key enzymes are Hexokinase II (HKII), Glucose-6-phosphate dehydrogenase (G6PD), lactate dehydrogenase A (LDHA), pyruvate dehydrogenase kinase (PDK), and pyruvate dehydrogenase (PDH). 86 The PDH-PDK regulatory axis involves PDH, which converts pyruvate to acetyl-CoA, linking glycolysis with the TCA (Tricarboxylic acid) cycle. 87 PDK inhibits PDH activity, and reduced PDK expression shifts metabolism toward OXPHOS. Mitochondrial OXPHOS produces ATP via the electron transport chain and ATP synthase and provides precursors for nucleotides, lipids, proteins, and antioxidants. 88 The OXPHOS system operates through intricate biochemical networks involving the electron transport chain and ATP synthesis machinery to uphold cellular energy homeostasis. 4.1.2. Metabolic reprogramming and energetic adaptations in cancer cells Cancer cells demonstrate metabolic plasticity, allowing adaptation to hostile microenvironments such as hypoxia and nutrient deprivation through genetic and epigenetic modifications in key metabolic enzymes. 89 A defining feature of cancer is reprogrammed energy metabolism and a distinct metabolic phenotype. 90 Unlike normal cells that primarily rely on mitochondrial OXPHOS for ATP production, rapidly proliferating cancer cells show a shift toward glycolytic metabolism, utilizing glucose via aerobic glycolysis even in the presence of oxygen. 91 This shift is known as the “Warburg effect”, a hallmark of many cancer types, wherein glycolysis dominates over OXPHOS under normoxic conditions. 87 , 92 Some cancer cell types, however, exhibit elevated mitochondrial respiratory activity and increased OXPHOS to meet energy demands. Various studies present differing views, with some confirming that glycolysis is the major source of ATP. 87 , 92 , 93 and others emphasizing OXPHOS's prominence in fulfilling the energetic requirements of specific cancer cells. 93 To adapt to a dynamic TME, cancer cells may undergo metabolic transitions favoring OXPHOS, facilitated by mitochondrial dynamics such as fission and fusion events. This metabolic flexibility ensures cell survival and sustained proliferation under fluctuating conditions. Evidence indicates that metabolic reprogramming plays a critical role in oncogenic processes, including tumor initiation and progression across malignancies, exemplified in ovarian cancer. 94 4.2. Metabolism-related target molecules in ovarian cancer Cancer cells possess a high degree of metabolic plasticity, enabling them to thrive under conditions of hypoxia and nutrient scarcity. This adaptability is facilitated by genetic and epigenetic reprogramming of metabolism-related genes, favoring glycolysis over OXPHOS to meet the heightened bioenergetic and biosynthetic demands. 89 A central player in this adaptation is glucose transporter 1 (GLUT1), which is frequently upregulated to enhance glucose uptake. Elevated GLUT1 expression has been observed in high-grade serous carcinoma (HGSC), 94 and clear cell carcinoma (CCC). 95 It is strongly regulated by hypoxia-inducible factor-1α (HIF-1α), a master transcription factor promoting the Warburg effect. 94 Key glycolytic enzymes also contribute significantly to tumor metabolism. HKII, a rate-limiting enzyme and HIF-1α target, catalyzes the phosphorylation of glucose to glucose-6-phosphate and is implicated in tumor growth, chemoresistance, and poor prognosis. 87 , 96 HKII deletion has been shown to reduce tumor burden in preclinical studies. 97 Similarly, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) regulates glycolytic flux through fructose-2,6-bisphosphate and is elevated in ovarian malignancies, where it serves as a marker of recurrence and prognosis. 98 Pyruvate kinase M2 (PKM2), responsible for converting phosphoenolpyruvate to pyruvate, is associated with poor clinical outcomes due to its tumor-promoting functions. 99 The PDK/PDH regulatory axis, particularly pyruvate dehydrogenase kinase 1 (PDK1), inhibits mitochondrial pyruvate utilization and reinforces glycolysis. Elevated PDK1 levels are associated with larger tumor size, advanced stage, and reduced survival, 100 , 101 , 102 whereas PDK2 and PDK4 contribute to cisplatin resistance in CCC by repressing mitochondrial function. 103 , 104 However, some studies suggest context-dependent roles for PDK1, indicating its expression may correlate with improved prognosis in certain cases. 105 Lactate dehydrogenase isoforms, particularly LDHA, further accentuate the glycolytic phenotype by converting pyruvate to lactate. LDHA is frequently overexpressed in ovarian cancer and correlates with poor prognosis due to the resultant acidic TME. 106 , 107 , 108 This metabolic reprogramming is intensified by transcription factors such as cellular Myelocytomatosis oncogene (c-Myc) and HIF-1, which upregulate LDHA and other glycolytic enzymes, including GLUT1, HKII, Phosphofructokinase-1 (PFK1), PDK, phosphoglycerate kinase 1 (PGK1), aldolase A (ALDOA), and PKM2 under hypoxic conditions. 109 In contrast, tumor suppressor p53 inhibits glycolysis by downregulating HKII and supporting mitochondrial respiration. Consequently, loss of p53 function favors a shift toward glycolysis, a hallmark of many tumors. 109 Beyond glycolytic reprogramming, mitochondrial dysfunction also contributes to altered cancer metabolism. Mutations or downregulation of TCA cycle enzymes such as succinate dehydrogenase (SDH) and fumarate hydratase (FH) lead to the accumulation of oncometabolites like succinate and fumarate. These metabolites stabilize HIF-1α and promote glycolysis. 88 , 110 , 111 , 112 However, such mutations appear to be relatively rare in human ovarian and peritoneal tumors, 113 and emerging evidence suggests that some tumors retain active mitochondrial function with intact OXPHOS. 114 , 115 Furthermore, changes in mitochondrial dynamics, especially enhanced fission, have been linked to increased glycolytic reliance and acquisition of stem-like properties in cancer cells. 116 , 117 These mitochondrial adaptations underscore their role in tumor progression, metabolic flexibility, and resistance to therapy. 4.3. Circadian rhythm dysregulation and tumor metabolism Recent studies indicate that the biological clock is a key regulator of cellular metabolism, largely through mechanisms involving chromatin remodeling. 118 , 119 , 120 Mice with mutations in core clock genes often develop features of metabolic syndrome, such as increased lipid levels, hyperglycemia, and fatty liver (hepatic steatosis), highlighting the susceptibility of clock-disrupted models to obesity. 121 These observations reinforce the strong link between circadian regulation and metabolic homeostasis. Although the precise molecular pathways by which circadian proteins control metabolism are still being elucidated, current evidence points to chromatin remodeling as a central mechanism. As a result, certain circadian regulators may serve as critical nodes connecting epigenetic control and metabolic regulation ( Fig. 3 ). 119 , 122 Fig. 3. Open in a new tab Key regulators of glycolytic metabolism in ovarian cancer. This schematic illustrates the glycolysis and mitochondrial metabolic pathways, highlighting major enzymes (purple boxes) and inhibitors (green boxes). Yellow boxes represent intermediates and products. Blue and green arrows denote glycolytic and lactate-generating flux, while red arrows indicate mitochondrial entry via acetyl-CoA. Inhibitors like 2-DG, DCA, and antidiabetic agents block key glycolytic enzymes, whereas LDHA and PDK promote glycolytic dominance over OXPHOS. IDH2 and UCP2 contribute to ROS regulation in the TCA cycle. 2-DG, 2-deoxy-D-glucose; DCA, dichloroacetate; IDH2, isocitrate dehydrogenase 2; LDHA, lactate dehydrogenase A; OXPHOS, oxidative phosphorylation; PDK, pyruvate dehydrogenase kinase; ROS, reactive oxygen species; TCA, tricarboxylic acid (cycle); UCP2, uncoupling protein 2. 4.3.1. Circadian regulation of metabolism and its disruption in cancer Cancer cells exhibit distinctive metabolic features, such as aerobic glycolysis (Warburg effect), increased glutamine oxidation, and enhanced biosynthesis of lipids and nucleotides. 123 , 124 A similar metabolic profile has been observed in Bmal1⁻/⁻ mouse embryonic fibroblasts, which show elevated glycolysis, lactic acid production, and reduced lipid oxidation and ATP levels. 125 This suggests that circadian disruption may mimic tumor-like metabolic reprogramming, partly by affecting clock-controlled genes such as PDK1 and LDHA, both of which are involved in glycolysis. 125 Additionally, genes regulating gluconeogenesis, glycolysis, glycogen storage, and cholesterol metabolism (e.g., glucose-6-phosphatase, PCK2, pyruvate kinase, glucokinase, glucose transporter 2, and HMG-CoA reductase) have been shown to follow circadian rhythms. 126 Recent studies demonstrate that cells with a high circadian rhythm disruption (CRD) score exhibit increased activation of metabolic pathways like glycolysis and epithelial-mesenchymal transition. 127 This indicates a potential link between circadian misalignment and aggressive tumor behavior. Circadian biomarkers are also emerging as tools to predict disease outcomes and response to therapies, especially immunotherapy. 128 Moreover, loss of BMAL1 not only affects circadian synchronization but also promotes fibrotic changes and activation of cancer-associated fibroblasts (CAFs), which are central to tumor progression via metabolic rewiring and immune modulation. 123 , 129 Disruption of circadian rhythms is closely associated with the development of obesity and related metabolic disorders. 130 , 131 , 132 , 133 These disturbances may further contribute to malignancy risk and progression. 134 Nuclear receptors involved in metabolic control, such as peroxisome proliferator-activated receptors (PPARs α, γ, δ) and estrogen-related receptors (ESRRs α, β, γ), show tissue-specific circadian expression patterns. 135 Among these, peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) plays a critical role, with its rhythmic expression in metabolic tissues regulating energy balance, thermogenesis, and physical activity. Mice lacking PGC-1α display impaired energy homeostasis. 136 Another important regulatory node is the SIRT1-NAD⁺ axis, linking circadian rhythms with metabolism and cell survival. Sirtuin 1 (SIRT1) deacetylates various transcription factors (e.g., Liver X Receptor (LXR), Forkhead Box Protein O1 (FOXO1)), influencing lipid and glucose metabolism. 137 , 138 , 139 Its activity is dependent on intracellular NAD⁺, which shows circadian oscillations governed by nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD⁺ salvage pathway. 140 , 141 , 142 PARP enzymes, which also consume NAD⁺, interact with SIRT1 and further connect DNA repair with circadian and metabolic regulation. 143 Disruptions in NAMPT have been implicated in metabolic diseases and cancer. 144 Inhibition of NAMPT using FK866 can block NAD⁺ rhythmicity and SIRT1 function, triggering apoptosis in cancer cells. 142 , 145 These findings underscore the critical role of circadian-regulated NAD⁺/SIRT1 signaling in metabolic integrity and disease progression ( Fig. 4 ). Fig. 4. Open in a new tab Disruption of circadian rhythm promotes metabolic reprogramming in cancer cells. This schematic depicts how circadian disruption facilitates cancer progression by reprogramming central carbon metabolism. Core transcriptional regulators (PPAR, ESRR, SIR, and MYC) enhance glucose uptake and glutamine utilization, increasing NAD + consumption. Glucose is redirected toward nucleotide biosynthesis and lactate production, while pyruvate-derived acetyl-CoA enters the TCA cycle, generating citrate for lipid synthesis. Glutamine supports the TCA cycle via α-KG. Circadian disruption (symbolized by the blocked clock) contributes to the altered metabolic phenotype in tumor cells. α-KG, alpha-ketoglutarate; ESRR, estrogen-related receptor; MYC, MYC proto-oncogene; NAD + , nicotinamide adenine dinucleotide positive; PPAR, peroxisome proliferator-activated receptor; SIR, sirtuin; TCA, tricarboxylic acid (cycle). 4.4. Circadian regulation of hormones and their disruption in cancer The circadian clock plays a pivotal role in regulating the daily production and release of hormones that are crucial for metabolic homeostasis and cancer development. Key metabolic enzymes, including glucokinase, glucose-6-phosphatase, pyruvate kinase, glucose transporter 2, and 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, are under circadian control, and their rhythmic expression ensures the balance of glucose and lipid metabolism. The SCN, the central circadian pacemaker, orchestrates the rhythmic secretion of melatonin and cortisol. 146 Melatonin secretion is suppressed by light exposure and modulated through polysynaptic signaling pathways originating from the SCN. 147 Beyond its role in circadian regulation, melatonin influences glucose metabolism and exhibits anti-tumor properties. 148 , 149 , 150 Reduced melatonin levels, often caused by prolonged exposure to light and circadian disruption, have been linked to enhanced tumor growth in hepatic and mammary tissues. 151 , 152 Epidemiological studies have consistently shown an inverse relationship between melatonin levels and cancer risk. 153 , 154 In hormone-sensitive malignancies such as breast and potentially ovarian cancer, melatonin has been reported to lower estrogen levels and inhibit DNA synthesis in hormone-responsive tissues. 155 Disruption of glucocorticoid circadian rhythms, regulated by the hypothalamic-pituitary-adrenal (HPA) axis and further modulated by adrenal and sympathetic nervous system clocks, may also promote tumorigenesis. 156 , 157 , 158 , 159 CRD can impair glucocorticoid oscillations, leading to diminished circadian signaling and enhanced tumor progression. 160 Chronic stress-induced elevations in glucocorticoids have been associated with immune suppression and increased cancer progression. 161 Mechanistically, the transcription factor TSC22D3 has been identified as a key player in mediating immune suppression under elevated glucocorticoid conditions, thereby facilitating tumor immune evasion. 162 Altered glucocorticoid levels also influence lymphocyte function, further compromising anti-tumor immunity. 163 Additionally, cryptochromes (CRYs), which evolved from photoreceptive DNA repair enzymes, not only maintain circadian rhythm but also perform ancillary cellular functions that may modulate cancer cell behavior. 164 5. Lifestyle and environmental risk factors in ovarian cancer 5.1. Dietary patterns and nutritional influence in ovarian cancer Recent research has increasingly examined how diet may influence the development and progression of ovarian cancer. While genetics, hormones, and reproductive factors are well-known contributors, emerging evidence indicates that diet might also affect both the risk of developing ovarian cancer and survival rates after diagnosis. Nonetheless, the findings concerning the connection between dietary factors and ovarian cancer remain inconsistent, and further research is needed to clarify this relationship. Studies suggest that diet can impact biological pathways such as inflammation, oxidative stress, hormone regulation, and metabolism, all of which might contribute to ovarian cancer development. 165 , 166 , 167 For example, high consumption of red meat has been linked to an increased risk of ovarian cancer, with an odds ratio of 1.53 when comparing the highest to the lowest intake levels. 168 Historical data from post-World War II Japan reported a fourfold rise in ovarian cancer mortality, which was believed to be connected to a shift toward a Western-style diet rich in meat, eggs, and dairy. 169 Similarly, foods high in fat and starch have been associated with higher cancer risk, while fiber, carotenoids, and vitamins A, C, and E seem to provide some protective effects. 170 , 171 , 172 , 173 , 174 Green and black tea also show potential protective effects; each additional daily cup correlates with an 18 % reduction in ovarian cancer risk, possibly due to antioxidant properties, hormone modulation, or pro-apoptotic activity. 175 , 176 Recent studies have examined how diet influences survival after an ovarian cancer diagnosis. In a large Australian cohort of 811 women with invasive epithelial ovarian cancer, better survival was observed in those with higher pre-diagnosis consumption of dietary fiber hazard ratio (HR: 0.69), green leafy vegetables, fruits, and fish. 177 These associations were statistically significant, particularly for fiber intake, which remained protective even after adjusting for multiple comparisons. Fiber may help by lowering estrogen levels through increased bile excretion and reducing inflammation. 178 , 179 Conversely, a higher dietary glycemic index (GI) was linked to poorer survival, possibly due to elevated levels of insulin-like growth factors that can promote tumor growth. 180 Saturated fat showed a trend toward adverse survival outcomes, supporting theories that high-fat diets could raise estrogen and progesterone levels, encouraging tumor growth. 181 Furthermore, a higher ratio of polyunsaturated to monounsaturated fat (PUFA: MUFA) was associated with lower mortality (HR: 0.76), indicating a beneficial role for anti-inflammatory fats found in foods like fish, seeds, and leafy greens. 182 Interestingly, vitamin C supplementation, but not dietary vitamin C, was linked to higher mortality within the first five years post-diagnosis (HR: 1.36), likely due to potential pro-oxidant effects or increased iron absorption, both of which could contribute to cancer progression. 183 These findings underscore that not only the type of nutrient but also its source may impact outcomes, and caution should be exercised when using dietary supplements during cancer treatment. Additional evidence comes from a case-control study in Zhejiang, China (1999–2000), which found that higher intake of vegetables and fruits was significantly linked to a reduced risk of ovarian cancer, while increased consumption of animal fat and salted (preserved) vegetables significantly elevated the risk. The odds ratios comparing the highest to lowest quartiles were 0.24 for vegetables, 0.36 for fruits, 4.6 for animal fat, and 3.4 for salted vegetables. A preference for fatty, fried, smoked, and cured foods was also associated with a higher risk, indicating a clear dietary pattern connected to ovarian cancer etiology. 184 Similarly, a prospective study within the Women’s Health Initiative cohort (636 cases among 161,808 women) evaluated prediagnosis diet quality using the Healthy Eating Index (HEI-2005). Higher diet quality was linked to lower all-cause mortality after ovarian cancer diagnosis, especially among women with lower waist circumference and no diabetes. The hazard ratio for the highest versus the lowest tertile of HEI was 0.73 (95 % CI: 0.55–0.97), supporting the protective role of overall diet quality on survival. 177 Despite these promising findings, significant gaps in research remain. Many studies rely on dietary data collected before diagnosis using food frequency questionnaires, which may be prone to recall bias and not reflect post-diagnosis eating habits. Moreover, only a few studies have examined the post-diagnosis diet to ovarian cancer survival, and little is known about how dietary changes after diagnosis might impact long-term outcomes. Differences in patient populations, such as age at diagnosis, cancer stage, and tumor type, also limit the comparability of findings across studies. 185 , 186 Furthermore, most studies examine individual foods or nutrients rather than overall dietary patterns. While one analysis found that a high HEI-2005 score was associated with better survival, it could not identify specific food components responsible for this effect. 177 This suggests that the overall pattern of dietary intake may be more relevant than isolated nutrients when assessing cancer outcomes. Other studies support these observations. For example, a Japanese cohort study of 64,327 women found that salted fish and pickled cabbage were associated with increased ovarian cancer mortality, though no other dietary components showed significant effects. 187 In the U.S., a case–control study (1994–1998) of 341 women reported better survival with higher intake of total vegetables and fruits, especially cruciferous and yellow vegetables, and poorer survival with higher consumption of red/cured meats and milk. 186 An earlier Australian study of 609 women also found that higher pre-diagnosis intake of vegetables and vitamin E improved survival, whereas dairy and meat products were linked to worse outcomes. 185 Although not all these findings were replicated in later studies, variations in age, dietary assessment timeframes, and changes in dietary habits over the years may explain the differences. In contrast, a prospective cohort study using data from the California Teachers Study found that plant-based dietary patterns were unexpectedly associated with a higher risk of ovarian cancer (relative risk (RR): 1.65 for highest vs. lowest quintile; 95% CI: 1.07–2.54). This result may reflect confounding or differences in plant-based food quality or preparation, suggesting that not all healthy-seeming dietary patterns confer protection. 188 Likewise, an Australian case-control study by Kolahdooz et al. identified three main dietary patterns: “snacks and alcohol,” “fruit and vegetables,” and “meat and fat.” Interestingly, while the “snacks” pattern initially showed a protective association odds ratio (OR: 0.59), this was attenuated after adjusting for wine intake. The “fruit and vegetable” pattern had no significant association, whereas the “meat and fat” pattern showed a strong positive correlation with ovarian cancer risk (OR: 2.49), reinforcing concerns about high-fat, meat-heavy diets in cancer risk. 189 Specific foods such as spinach and lettuce, rich in beta-carotene, folate, and flavonoids, have shown a protective effect, although this is not consistent across all studies. 190 , 191 , 192 Fish intake also appeared beneficial, possibly due to its omega-3 fatty acid content, which has anti-inflammatory and anti-cancer properties. 193 , 194 A meta-analysis found fish consumption protective in European and Australian populations, but not in Asian or North American groups. 195 Notably, dietary fiber showed a protective effect only at particularly high intake levels (43 g/day), which was higher than earlier study medians (35 g/day), possibly explaining why earlier studies found no association. 196 , 197 This body of research has several strengths, including large sample sizes, detailed tumor and lifestyle data, and long-term follow-up. However, limitations include reliance on pre-diagnosis dietary data, potential recall bias, and limited information on dietary changes after diagnosis. Although observational, these studies adjusted for major confounders, and dietary intake was found to correlate with later eating habits. In summary, higher intake of fiber, green leafy vegetables, fish, and a high PUFA: MUFA ratio appears to be associated with improved survival, while high GI and saturated fat intake may worsen outcomes. Notably, fiber showed a statistically significant protective association even after adjusting for multiple comparisons. These findings highlight the potential for diet to influence ovarian cancer progression and survival. Further large-scale, prospective, and interventional studies, especially focusing on diet after diagnosis, are essential to confirm these observations and inform clinical dietary guidelines for ovarian cancer patients. 5.2. Smoking, alcohol, caffeine, and their effect on ovarian cancer The role of modifiable lifestyle factors such as smoking, alcohol consumption, and caffeine intake in ovarian cancer risk has garnered increasing scientific attention, particularly to circadian rhythms and chronotype. While reproductive factors such as parity and oral contraceptive use have shown consistent inverse associations with ovarian cancer risk, these are not easily modifiable. In contrast, evidence regarding lifestyle behaviors, including caffeine and alcohol intake, remains mixed and context-dependent. 198 Coffee is a globally consumed beverage containing several bioactive compounds with potential antitumor effects. 199 , 200 Epidemiological investigations into coffee intake and ovarian cancer risk have produced conflicting results. While some cohort studies found no significant association between coffee consumption and ovarian cancer, 201 others reported a potential increase in risk among women consuming five or more cups of caffeinated coffee daily. 202 Similarly, other studies yielded statistically non-significant associations. 203 , 204 A 2007 meta-analysis initially found no robust association between coffee intake and ovarian cancer. 205 An updated dose–response meta-analysis incorporating 14 cohort studies likewise reported no statistically significant association (RR: 1.08; 95 % CI: 0.89–1.33), even when analyzing by incremental cup intake (RR: 1.02; 95 % CI: 0.99–1.05; P = 0.21). 206 Furthermore, no association was observed between caffeine, caffeinated coffee, or decaffeinated coffee and ovarian cancer risk. 207 Interestingly, one large retrospective case-control study reported an inverse association between caffeine and ovarian cancer, showing an approximate 40 % risk reduction in the highest versus lowest caffeine intake quartiles. This effect was more pronounced among postmenopausal women and those who had never used oral contraceptives. 208 Conversely, other retrospective studies reported a positive association, particularly among premenopausal women. 209 , 210 A prospective study supported these subgroup differences, finding an inverse association for coffee intake only among postmenopausal women who had never used postmenopausal hormones (PMH). In contrast, premenopausal women showed a suggestive positive association. 208 The biological basis for this interaction may lie in caffeine’s impact on hormone metabolism. In premenopausal women, caffeine may elevate estrogen levels and shorten menstrual cycles, 211 , 212 , 213 whereas in postmenopausal women, it may increase sex hormone-binding globulin (SHBG) and reduce free estrogen concentrations. 214 , 215 This hormonal modulation may explain differential cancer risks by age and hormone use. It has also been suggested that estradiol and other exogenous hormones may interfere with caffeine metabolism, thus masking its potential protective effects. 216 , 217 Additional findings indicate that genetic variations involved in caffeine metabolism may also influence the risk of ovarian cancer, as demonstrated by Kotsopoulos et al., further highlighting the need for personalized evaluation. 218 Findings on tea consumption and alcohol intake are similarly inconsistent. One prospective study observed a significant inverse association with tea consumption, 175 though this was not replicated in a second, smaller cohort. 219 The type of tea may influence outcomes; most participants consumed black tea, but it is unclear whether the observed effects stem from caffeine content or other bioactive compounds. Retrospective studies showing a positive link may be biased, as women with early abdominal symptoms could have increased tea intake. 208 , 220 , 221 , 222 The relationship between alcohol intake and ovarian cancer has been evaluated in multiple studies with generally null or modestly inverse findings. 210 , 221 , 223 A population-based case-control study by Goodman and Tung involving 558 ovarian cancer patients and 607 controls found no overall association between alcohol consumption and ovarian cancer risk. However, current alcohol drinkers, particularly red wine consumers, showed a significantly reduced risk of invasive ovarian cancer, especially the endometrioid subtype. Conversely, former wine drinkers and current spirit users had an increased risk of borderline mucinous and serous tumors, respectively. The study suggests that the relationship between alcohol and ovarian cancer may vary by tumor type and drinking patterns. 209 A case-control study involving 696 ovarian cancer patients and 786 controls in Australia found no evidence that alcohol increases ovarian cancer risk. In fact, women who consumed ≥2 standard drinks per day had a lower risk (OR: 0.49; 95 % CI: 0.30–0.81) compared to nondrinkers. This protective effect was specific to wine (OR: 0.56), with no significant effect from beer or spirits. A pooled analysis with six prior studies yielded a similar inverse association (OR: 0.72). The authors suggest the reduced risk may be due to antioxidants or phytoestrogens in wine, rather than alcohol itself. 224 A pooled analysis of 10 cohort studies also found no consistent association with total alcohol consumption, 225 , 226 , 227 , 228 , 229 and results stratified by type of alcohol remain inconclusive. Evidence connecting smoking to ovarian cancer risk is similarly inconsistent. While most studies report no significant overall association, 176 , 177 smoking seems to be a specific risk factor for mucinous ovarian tumors. A recent meta-analysis found that current smokers have a twofold increased risk of mucinous tumors, with a significant trend for increasing pack-years of smoking. This could be due to the histological similarity between mucinous ovarian tumors and colonic epithelium, tissues especially vulnerable to tobacco-induced carcinogenesis. 176 A Canadian population-based case-control study (1994–1997) involving 442 ovarian cancer cases and 2,135 controls showed that smoking was linked to increased ovarian cancer risk (OR: 1.22; 95 % CI: 0.98–1.53), especially among ex-smokers (OR: 1.30; 95 % CI: 1.01–1.67). The strongest association was with mucinous tumors (OR: 1.77; 95 % CI: 1.06–2.96), and the risk increased with smoking duration (OR: 4.89 for >40 years; P-trend = 0.002), cigarettes per day (OR: 2.28 for >20/day; P-trend = 0.014), and pack-years (OR: 2.39 for >30 pack-years; P-trend = 0.004). 178 These tumors may also be less affected by protective hormonal factors such as oral contraceptives. 230 , 231 Supporting this, a large prospective study found that although total ovarian cancer risk was not significantly linked to smoking, current smokers had a notably higher risk of mucinous tumors. 232 , 233 Similarly, smoking status, duration, and pack-years emerged as significant predictors of mucinous tumor incidence. Conversely, some cohort analyses, including Kuper et al., indicated an inverse or no association between smoking and overall ovarian cancer risk, further highlighting the importance of histological classification. 210 , 234 5.3. Obesity, sedentary lifestyle in ovarian cancer Obesity has been consistently identified as a significant risk factor for the development and progression of ovarian cancer. A meta-analysis conducted by Olsen et al. revealed that increased BMI is associated with an elevated risk of several ovarian cancer subtypes, particularly serous ovarian cancer. This association is biologically underpinned by the endocrine alterations resulting from obesity, such as elevated estrogen levels produced by adipose tissue. In postmenopausal women, excess fat leads to increased circulating estrogens, which may promote tumorigenesis. Furthermore, in premenopausal women, lower levels of SHBG may increase bioavailable estrogen, thereby complicating the obesity-ovarian cancer relationship. 235 In support of this, Liu et al. elucidated that obesity enhances ovarian cancer metastasis through mechanisms involving increased lipogenesis and tumor vascularity, creating a microenvironment favorable for tumor growth and dissemination. 236 The impact of obesity on clinical outcomes in ovarian cancer is equally significant. Protani et al. reported that obesity is associated with poorer overall survival, possibly due to metabolic dysregulation, inflammation, and reduced chemotherapy efficacy. 237 Altered pharmacokinetics in obese individuals can compromise treatment response. 238 Interestingly, some findings support the “obesity paradox,” where obese patients show improved chemotherapy tolerance compared to their normal-weight counterparts, complicating interpretations of obesity's impact on prognosis. 239 Region-specific data reinforce these findings. A case-control study conducted in Indonesia by Widiasih et al. reported that obesity carried an OR of 6.04 for developing ovarian cancer. Other significant factors included advanced age (OR: 19.76), low education (OR: 225.00), prior surgery (OR: 51.06), and poor sleep quality (OR: 15.75), collectively underlining obesity’s role as part of a broader risk constellation. 240 Sedentary behavior, often leading to obesity, further exacerbates cancer risk by contributing to chronic metabolic stress and inflammatory imbalance. A systematic review and meta-analysis by Biller et al. found a strong association between a sedentary lifestyle and increased ovarian cancer risk. Extended sedentary time may replace physical activity, thus promoting weight gain, metabolic disturbances, and systemic inflammation. 241 Bae et al. emphasized that sedentary individuals often experience insulin resistance and elevated inflammatory cytokines, both of which are linked to tumorigenesis. 238 Cho et al. demonstrated that obesity alters immune responses within the TME by reducing M1 macrophage presence, immune cells typically associated with anti-tumor activity, thereby enhancing metastatic potential in ovarian cancer patients. 242 5.4. Psychosocial stress, sleep, and circadian misalignment Psychosocial stress is increasingly recognized as a critical factor in cancer progression and patient outcomes. Elevated levels of norepinephrine, a catecholamine integral to the physiological stress response, have been documented in ovarian tumor tissues. Davis et al. found that greater social isolation was significantly associated with increased intratumoral norepinephrine levels, highlighting a direct biological link between psychosocial stressors and TME alterations. 243 Similarly, Lutgendorf et al. reported that reduced social support correlated with elevated norepinephrine concentrations in tumors, reinforcing the role of emotional and social contexts in modulating neuroendocrine signaling pathways that may influence tumor growth and metastasis. 244 Beyond biological changes, psychological distress, including anxiety and depression, is frequently reported among ovarian cancer patients and can substantially affect QOL and treatment outcomes. Clevenger et al. observed that psychological distress within the first-year post-diagnosis was closely associated with persistent sleep disturbances and overall reduced well-being. These findings emphasize the necessity of psychosocial interventions to mitigate emotional burden and promote better clinical trajectories. 245 Adding further biological insight, Cuneo et al. identified oxytocin, a hormone associated with social bonding within the ovarian TME, suggesting that positive psychosocial interactions might exert antitumor effects through neuroendocrine and immune-modulating mechanisms. 246 The impact of emotional coping strategies on survival outcomes has also been explored. Price et al. demonstrated that feelings of helplessness and hopelessness significantly predicted poorer survival in women with invasive ovarian cancer. These findings underscore the importance of fostering adaptive coping mechanisms to enhance psychological resilience and, potentially, to improve survival outcomes. Concurrently, sleep disturbances remain a persistent concern in this population. 247 According to Clevenger et al., many patients experienced long-term sleep disruptions regardless of chemotherapy treatment. 245 Inflammatory cytokines have been proposed as mediators linking cancer-related inflammation and disrupted sleep architecture, 248 suggesting physiological mechanisms underpinning these disturbances. Disruptions in circadian rhythms, which regulate hormonal, metabolic, and immune functions, further compound the psychosocial burden in ovarian cancer. Lutgendorf et al. proposed that stress-induced circadian misalignment may exacerbate norepinephrine elevation in tumors, thereby influencing tumor progression through dysregulated neuroendocrine signaling. 244 Supporting this, Lutgendorf et al. found that chronic stress can alter circadian rhythms via catecholaminergic pathways, enhancing oncogenic signaling. 249 Circadian disruption has also been associated with greater psychological distress and impaired sleep quality, 245 , 250 suggesting a reinforcing cycle between physiological dysregulation and mental health impairment. On a molecular level, the influence of psychosocial and circadian disruptions is increasingly evident. Dong et al. reported that overexpression of insulin-like growth factor II (IGF2) was linked to poor outcomes in ovarian cancer, with IGF2 implicated in stress-responsive pathways and chemotherapy resistance. 251 , 252 Additionally, Shu-fang et al. identified elevated levels of CCAAT/enhancer-binding protein alpha (CEBPA), a transcription factor associated with poor prognosis, reinforcing the role of stress-related molecular changes in disease progression. 253 As Kim et al. highlighted, symptom clusters such as fatigue and sleep disruption are key determinants of QOL, supporting the need for integrative therapies that address psychological stress, sleep health, and circadian alignment. 254 In this context, Davis et al. suggested that interventions to reduce psychosocial stress may lower intratumoral norepinephrine and potentially slow disease progression. Moreover, aligning treatment protocols with patients' circadian rhythms could enhance both mental health and therapeutic efficacy. 243 However, Tergas et al. emphasized persistent systemic barriers to mental health care in oncology, advocating for tailored psychosocial interventions to ensure equitable, holistic care for ovarian cancer patients. 255 6. Chronotherapeutics and emerging interventions The circadian rhythm significantly influences the biological processes underlying cancer treatment responses. Recent research has indicated that targeting circadian pathways can enhance the efficacy of chemotherapy in ovarian cancer. For instance, Wang et al. revealed that the core circadian gene PER2 regulates the effectiveness of cisplatin through the PI3K signaling pathway, suggesting that synchronizing chemotherapy with circadian rhythms may reduce drug resistance and improve treatment outcomes. 256 Additionally, Tesfay et al. highlighted that the inhibition of sideroflexin 4 (SFXN4) sensitizes ovarian cancer cells to platinum-based chemotherapeutics, indicating that targeting specific pathways related to circadian biology can enhance the effectiveness of existing therapies. 257 These results highlight the potential of circadian rhythm modulation as a treatment approach to improve treatment responses in ovarian cancer. Research indicates that alterations in circadian rhythms can contribute to the development of drug resistance, particularly in chemotherapy. 258 For instance, increased expression of the epidermal growth factor receptor (EGFR) has been associated with cisplatin resistance in ovarian cancer cells, and targeted delivery of EGFR small interfering RNA (siRNA) has been demonstrated to improve drug sensitivity. 259 Furthermore, studies have proposed that circadian disruption is associated with elevated metastatic potential, as evidenced by the correlation between altered cortisol rhythms and cancer progression. 260 By restoring circadian rhythms, it may be possible to mitigate these adverse effects and improve patient outcomes. Moreover, the FTY720 and cisplatin combined display antagonistic effects in ovarian cancer cells, emphasizing the need for innovative combination therapies that can overcome drug resistance. 261 Melatonin, a hormone that regulates circadian rhythms, is considered a potential adjunctive therapy for ovarian cancer. Studies indicate that melatonin can exert oncostatic effects, including apoptosis induction and modulation of inflammatory responses in cancer cells. 262 Additionally, lifestyle modifications that promote healthy circadian rhythms, such as regular sleep patterns and exposure to natural light, may enhance the effectiveness of melatonin supplementation and other treatments. 263 These lifestyle interventions can help stabilize circadian rhythms, potentially leading to improved treatment responses and QOL for ovarian cancer patients. Furthermore, research has shown that the downregulation of specific genes, such as hepatocyte nuclear factor 1 beta ( HNF1B) , is linked to drug resistance in ovarian cancer, highlighting the importance of understanding genetic factors and lifestyle modifications. 264 Future studies should prioritize the development of circadian rhythm-based interventions tailored to ovarian cancer treatment. This includes exploring the potential of chronotherapy, which involves timing drug administration to align with the patient's circadian rhythms. 265 Additionally, integrating multi-omics approaches could provide insights into the predictive value of circadian clock genes in ovarian cancer, facilitating the development of personalized treatment strategies. 266 Investigating the interaction between circadian rhythms and psychosocial factors may also yield valuable insights, as disruptions in circadian patterns have been linked to emotional distress in cancer patients. 260 Moreover, the function of long non-coding RNA (lncRNAs) in controlling drug resistance through circadian mechanisms presents a novel area for exploration, as evidenced by studies showing that nuclear enriched abundant transcript 1 ( NEAT1 ) knockdown can suppress cisplatin resistance in ovarian cancer. 267 Overall, a comprehensive understanding of circadian biology in ovarian cancer could lead to innovative therapeutic strategies that improve patient outcomes. 7. Conclusions Disruption of circadian rhythms, often driven by lifestyle factors such as insomnia, high-fat diets, and obesity, significantly contributes to ovarian cancer pathogenesis by promoting metabolic reprogramming and oncogenic signaling. Central circadian regulators like CLOCK, BMAL1 , and PER2 , along with nuclear receptors such as ESRRα and metabolic sensors like SIRT1, form an intricate regulatory network that governs cellular homeostasis, redox balance, and energy metabolism. Aberrations in this clock–metabolism axis facilitate tumor cell proliferation, immune evasion, and chemoresistance in ovarian cancer. Understanding the crosstalk between circadian transcription factors and oncogenic metabolic pathways offers novel opportunities for chronotherapy and targeted interventions aimed at restoring circadian-metabolic synchrony to improve therapeutic outcomes in ovarian carcinoma. CRediT authorship contribution statement Malvi Surti, Mitesh Patel, Mohd Adnan: Conceptualization, Data curation, Project administration and Writing – original draft. Anjali Gupta and Komal Janiyani: Data curation, Investigation, Visualization, Writing – review & editing; Mitesh Patel and Mohd Adnan: Validation, Writing – review & editing and Supervision. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References 1. Natarajan S., Foreman K.M., Soriano M.I., et al. Collagen remodeling in the hypoxic tumor- mesothelial niche promotes ovarian cancer metastasis. Cancer Res. 2019;79(9):2271–2284. doi: 10.1158/0008-5472.CAN-18-2616. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Cai Y., Hu Y., Yu F., et al. AHNAK suppresses ovarian cancer progression through the wnt/β-catenin signaling pathway. 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