Background
Ovarian cancer (OC) is the most challenging and daunting disease among all gynecological malignancies [
1]. Due to its lack of typical early clinical symptoms and specific detection methods [
2], patients often miss the optimal opportunity for chemotherapy and molecular targeted therapy. Furthermore, because of the gaps in the identification of prognostic biomarkers and targeted drugs for OC, the high recurrence rate and the emergence of drug resistance lead to a poor prognosis for OC patients [
3].
Ovarian carcinogenesis is a complex multifactorial process, the possible causes include abnormal ovulatory cycles [
4], chronic inflammation of the fallopian tubes [
5], and gene mutations like Breast Cancer Gene 1 (BRCA1) [
6]. Among these, metabolic dysregulation is considered one of the significant contributors [
1,
7]. For instance, it is posited that local metabolic changes in the adipose tissue of obese individuals lead to various systemic metabolic alterations, such as insulin resistance, hyperglycemia, and chronic inflammation. These conditions more readily shape the tumor microenvironment, facilitating tumor initiation and progression [
8]. In addition, cancer is fundamentally a disorder of cell growth and proliferation. During tumor initiation and development, cellular metabolism undergoes changes [
9,
10], leading to meet the unrestrained proliferation energy needs of cancer cells and the synthesis of nucleic acids, proteins, and lipids. These metabolites act as cofactors or substrates, participating in enzymatic reactions involved in cancer cell epigenetic modifications and transcriptional regulation. Aberrant epigenetic regulatory modifications can further induce tumor development through metabolic reprogramming in cancer cells [
11].
The molecular interaction network based on metabolomics offers fresh perspectives for elucidating the molecular mechanisms of OC treatment, discovering new therapeutic targets, and identifying reliable and effective biomarkers. Numerous metabolic groups and classes are associated with OC risk, including organic acids and their derivatives [
12]. For example, studies have shown that circulating levels of pseudouridine in plasma are associated with a higher risk of developing OC 3-23 years prior to diagnosis [
13]. Additionally, some scholars believe that the spectra of amino acids and organic acids can serve as potential screening tools for epithelial ovarian cancer (EOC) [
14]. Currently, due to the following factors, these studies in OC remain less than satisfactory: (i) Intermediate metabolites have not been comprehensively studied. (ii) Most of the existing databases only contain distinct information about high-grade serous ovarian cancer (HGSOC) and lack histological types of other ovarian cancers. (iii) The absence of large-sample studies makes it difficult to explore the relationship between metabolites and OC in clinical practice [
15].
Mendelian randomization (MR) serves as a powerful epidemiological tool that can effectively eliminate confounders and reveal potential causal relationships. Studies indicate that genetic polymorphisms affect biochemical levels in serum, suggesting that genetic variations might play a role in racial differences in the gender and/or age-related variations of circulating metabolite levels [
16,
17]. A recent robust study on the GWAS of metabolites has pinpointed loci associated with the disease [
16]. Moreover, developments by So-Youn Shin [
17] on the database of genotype-dependent metabolic phenotypes, also known as genetically determined metabolites (GDM), have matched hundreds of metabolites and pathways with genetic data. This paves the way for further research into the potential relationship between serum metabolites in humans and associated genetic variations in the biological mechanisms of OC initiation and progression.
Our study aims to comprehensively investigate the causal relationship between various subtypes of OC and serum metabolic factors. Further, it provides reverse validation to ensure the directional accuracy of the results. By identifying metabolic pathways that may shed light on the mechanisms underlying the initiation of OC, this research offers practical and targeted guidance for the early detection, treatment, and prevention of high-risk OC patients and those with different OC subtypes.
Discussion
In this study, we identified 8 genetically determined metabolites as potential risk factors, and 6 as potential cancer risk reducers. Additionally, pathway enrichment analysis pinpointed four crucial metabolic pathways. To our knowledge, this is the first MR study that assesses the causal relationship between genetically determined metabolites and different subtypes of OC. Furthermore, we have conducted reverse validation of our results, which revealed no causal relationship, eliminating biases related to reverse causation and reinforcing the robustness of our primary MR findings.
In the present study, we identified suggestive causal associations for 4-acetamidobutanoate, alpha-hydroxyisovalerate, 3-(3-hydroxyphenyl)propionate, X-13183-stearamide, 1,5-anhydroglucitol (1,5-AG), ADpSGEGDFXAEGGGVR, estrone 3-sulfate, and 1-linoleoylglycerophosphoethanolamine associated with a high risk of developing OC. To our knowledge, previous research related to these 8 metabolites in association with OC has been limited. Among the 3 amino acids, 4-acetamidobutanoate is a derivative of γ-aminobutyric acid (GABA) [
33]. In recent years, GABA has been shown to be associated with promoting the proliferation of pancreatic cancer [
34]. Adding GABA to cell culture media promoted the proliferation of pancreatic cancer cells expressing GABRP [
35], which is somewhat consistent with our study. Notably, in a study on unique metabolomic characteristics related to cirrhosis mortality [
36], 4-acetamidobutanoate significantly predicted mortality. It's reported that in patients with acute kidney injury (AKI), 4-acetamidobutanoate increased 12-fold [
37], and its levels significantly increased in patients with morbid hypertension [
38]. Similarly, alpha-hydroxyisovalerate, an organic acid related to branched-chain amino acid metabolism, has been linked with liver injury [
39], diabetic nephropathy [
40], and Maple Syrup Urine Disease [
41]. These findings might help in predicting the prognostic features of OC patients.
X-13183-stearamide, estrone 3-sulfate, and 1-linoleoylglycerophosphoethanolamine are all lipid metabolic factors. Among them, estrone 3-sulfate (E1S) is a naturally occurring endogenous steroidal compound, classified under estrogen esters and estrogen conjugates [
42]. E1S has associations with multiple transport proteins and plays a pivotal role in the uptake and release of drugs and endogenous substances [
43]. It can be taken up by tumor cells through transport protein mediation, and upon cleavage by steroid sulfatase, eventually activating ERs and promoting tumor growth [
44]. This aligns with our research findings. 1-linoleoylglycerophosphoethanolamine is a vital member of the phosphatidylethanolamine (PE) family [
45], and might serve as an intermediary in the primary synthesis route of PE — the CDP-Ethanolamine Pathway [
46]. Studies have shown that this substance plays a part in the development of preeclampsia during pregnancy [
45] and colorectal cancer [
47]. PE family are critical determinants of protein structure and function [
46]. Aberrant levels of 1-linoleoylglycerophosphoethanolamine might lead to disruptions in the PE synthesis pathway, subsequently resulting in pathological conditions.
We identified suggestive causal associations for 6 metabolic products that inhibit OC development. Among them, asparagine is an essential natural amino acid that healthy cells utilize to maintain function and proliferation [
48]. Its role as a targeted anticancer amino acid aligns with our findings [
48]. Betaine, another vital amino acid, has been shown to have chronic disease prevention potential [
49]. Research indicates that the content of betaine is higher in gluten-free cereals and products, suggesting that this result might provide evidence for dietary guidance for patients.
Pathway enrichment analysis revealed four significant metabolic pathways, with three linked to MOC onset and two to OCED onset. The potential impact of caffeine metabolism on the risk of MOC and OCED may be attributed to how caffeine and its metabolic pathways affect the levels of sex hormones[
50,
51]. Coffee intake, as shown in a large retrospective study, reduces susceptibility to colon cancer[
52], possibly due to metabolites formed via liver cytochrome P450 enzyme system metabolism [
53]. These studies align with our findings, suggesting that intervening in caffeine metabolism could potentially reduce the risk of cancer onset.
Arginine synthesis and metabolic pathways maintain nitrogen balance and protein synthesis processes, providing cells with necessary substances and energy, supporting rapid proliferation and survival of cancer cells [
54]. Arginine can be degraded by enzymes in macrophages to produce urea and L-ornithine, which might inhibit the function of T cells [
55]. This mechanism might help cancer cells evade immune clearance, increasing the risk of tumor onset. It's worth mentioning that our results are consistent with the above, suggesting it is a potential MOC risk factor.
The relationship between the citrate cycle (TCA cycle) and MOC was also observed. The citrate cycle, a primary cellular energy production pathway, is implicated in cancer biology by regulating glycolysis [
56], immune responses [
57], and affecting tumor cell activity [
58]. Citrate synthase (CS) is one of the key enzymes in the TCA cycle. Silencing CS leads to proliferation defects in SKOV3 cells, inhibits invasion and migration, and increases chemosensitivity, indicating the citrate cycle pathway might affect the progression and drug resistance in OC [
59].
Moreover, Our research results also suggest that the metabolism of α-linolenic acid may be one of the protective pathways against the onset of OC. Numerous studies have confirmed α-linolenic acid, an essential polyunsaturated fatty acid, may regulate tumor proliferation, migration, and invasion by controlling inflammation-related cytokine secretion and cellular signal pathways [
60]. Eicosapentaenoic Acid (EPA) and Docosahexaenoic acid (DHA) are both metabolites of α-linolenic acid and have shown significant anti-ovarian cancer effects [
61]. However, the impact of this pathway on OC and its mechanisms warrant further study.
Regrettably, we must acknowledge that our findings do not pass the multiple testing correction. The reasons for these outcomes might include the following factors. Firstly, OC is a complex disease likely influenced by multiple factors. Metabolic disorders are just one aspect and are not specific to the pathogenesis of OC. They might manifest as abnormalities in the internal environment during the onset of OC. MR studies are primarily utilized to deduce causal relationships between exposures and outcomes. Therefore, abnormalities in serum metabolic factors, may indicative of aberrant metabolic environment during OC rather than merely representing a simple causal relationship.
Secondly, while individual intermediate metabolic products may exert only minor or indirect effects on the onset of OC, their combined impact could be significantly more substantial, resembling the effect of polygenic risk scores in complex traits.
The third potential factor may be attributed to individual variations in metabolic factors. While genetic elements significantly shape distinct metabolite profiles across various populations, it is imperative to recognize the substantial variability of serum metabolic factors among individuals. Influences such as sex, lifestyle, and dietary habits contribute to these disparities. For instance, sphingolipid depletion, known to impede vitamin absorption, is closely associated with vegetable intake [
62]. A Study highlights disparities in blood sphingolipid levels between traditional and non-traditional lifestyles in Swedish populations [
63]. Moreover, the metabolic environment in OC fluctuates across different disease stages [
64], and singular sampling and measurement may not accurately capture the patient's dynamic metabolic changes. Due to the limitations imposed by the original data, we were unable to categorize patients more precisely, pointing to the need for more nuanced research in this area.
Lastly, the research methodology may have also influenced these findings. Although MR is designed to mitigate the effects of confounding variables, potential uncontrolled confounders, including undetected genetic variations, might still exist.
Although we did not demonstrate a definitive causal effect of GDMs on OC and other subtypes, we believe these indicative results do not repudiate the role of blood metabolites in the pathogenesis of OC. An increasing number of observational studies indicate metabolic abnormalities in cancer patients compared to healthy controls, suggesting potential guidance for targeted treatment in OC patients [
65‐
67] For instance, beyond the indicative results we have already explained, our research discovered the potential protective role of 1,5-Anhydroglucitol (1,5-AG) as a potential protective factor against MOC and OCED. The reduced levels of 1,5-AG typically reflect increased blood glucose levels [
68], a known risk factor for OC [
69]. Furthermore, we observed that treatment alters measurable circulating metabolites and lipoprotein subfractions, potentially serving as biomarkers for recurrence risk [
70]. A metabolomic analysis involving 35 patients with EOC demonstrated that changes in serum metabolic factors could help predict EOC recurrence [
71]. Thus, while a precise causal relationship of individual metabolic products was not detected, they may still represent risk factors and key intermediaries in the development of OC.
Additionally, we observed that the study by Feng et al. also examined the relationship between GDMs and OC [
72]. Interestingly, our study found different associations, likely due to the different thresholds used for selecting IVs. These varying thresholds led to the inclusion of different genetic variants in our analysis. This discrepancy highlights the need to further explore the impact of diverse IV selection criteria, as they may uncover distinct biological relationships. Future research could beneficially focus on how these criteria affect MR analyses, thereby enriching our understanding of the genetic influences on metabolites and their role in the etiology of OC.