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Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study

BMC Genomics volume 25, Article number: 97 (2024) Cite this article

Abstract

Background

Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.

Methods

In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database (http://gwas.mrcieu.ac.uk/datasets/). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger’s upstream test was used to determine the causal direction between the exposure and outcome variables.

Results

The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger’s upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.

Conclusions

The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

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Background

Ovarian cancer (OC) is the sixth most common cancer in women of developed countries [1]. It is important to note that OC is the most fatal of all gynecologic cancers and accounts for more than two thirds of all deaths due to gynecologic cancers [2], with a 5-year relative survival rate of 49% [3]. This is partly due to the fact that 70% of OC patients are diagnosed in advanced stage when they receive treatment for the first time, as there are no specific symptoms of OC in primary stages [4]. At present, early diagnosis of OC is dependent on B-ultrasound and serum cancer antigen 125 (CA125) detection. However, sensitivity of CA125 drops significantly in early-stage disease and in premenopausal women [5]. One of the most fundamental differences between cancer and non-malignant cells is their metabolism [6]. A large number of cancer-promoting components such as metabolites in ascites were reported to promote OC invasion and resistance to chemotherapy through surface-specific receptors on tumor cells [7]. Therefore, numerous studies have aimed to improve diagnosis and treatment of OC using metabolomics.

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes [8]. Recently, it has been successfully utilized to find new biomarkers and investigate pathogenesis in OC. Metabolomics analysis revealed association of some biomarkers, including lysophosphatide and adrenaline glycosamide with epithelial ovarian cancer (EOC) [9]. Chen et al. identified and validated 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide as a diagnostic marker that is complementary to CA125 [10]. Sphingomyelins 3–23 years before diagnosis were associated with increased risk of OC, regardless of histotype, with stronger associations among postmenopausal women [11]. To date, metabolic profiling for OC has been performed with relatively small sample sizes that limit the robustness and statistical significance of the results. In addition, most studies have had relatively limited follow-up with participants, which may be insufficient for detecting risk factors on cancer outcomes that take more than 10 years to develop.

An approach to overcome the potential limitations of observational epidemiology and strengthen the evidence for a potential causal role of metabolite on OC risk is MR. In a MR study, genetic variants associated with an exposure of interest are identified from GWAS and subsequently applied to an independent data set to derive an unbiased estimate of the exposure-outcome association [12]. The MR study design offers several advantages over traditional epidemiological studies. First, MR can avoid the bias caused by reverse causality to a certain extent [13]. Second, MR studies are independent to common behavioral, physiological, and socioeconomic confounders owing to random assignment of alleles at meiosis. Third, MR design resembling randomized controlled trial (RCT) significantly reduced concerns in terms of ethical, applicability, and financial issues [14].

In this study, we employed MR analysis to conduct a summary statistical analysis of the GWAS data in European populations, aiming to determine the causal effects of 637 human metabolites on OC.

Materials and methods

Data source and software preparation

The dataset corresponding to human metabolites is derived from the GWAS database (http://gwas.mrcieu.ac.uk/datasets/), which includes a total of 637 metabolites. The dataset associated with OC is encoded as ieu-b-4963 in the GWAS database. The MR analysis in this study primarily relied on the TwoSampleMR package in R software (version 4.1.3). Moreover, for the MR analysis and the graphical representation of the outcomes, a suite of R packages were utilized, encompassing “ieugwasr, plinkbinr, ComplexHeatmap, circlize, dendextend, dendsort, tidyverse, and ggforestplot”.

Instrument variable selection

The selection criteria for single nucleotide polymorphisms (SNPs) associated with the 637 metabolites are based on the following criterion: P < 5*10− 6. To ensure the independence among SNPs, i.e., adherence to the Mendelian second law, this study set the parameters for linkage disequilibrium as follows: r2 < 0.001 and kb > 10,000. SNPs selected based on these criteria are considered instrumental variables (IVs) and are included in subsequent analyses.

Exclusion of weak IVs

To ensure the accuracy of the results, this research employs the F-statistic as a measure to reflect the degree of association between instrumental variables and the exposure. The formula for calculating the F-statistic is as follows: F = (β/SE) 2. In this study, an evaluation criterion for weak IVs is set as F < 10. Given the first assumption of MR analysis, which is the assumption of instrument relevance, it is necessary to ensure a significant association between IVs and the exposure. Therefore, weak IVs selected based on the aforementioned method should be excluded.

Data analysis

The analysis in this study was conducted using the TwoSampleMR package in R software (version 4.1.3). The IVW results was utilized to determine whether there is a significant causal effect between the exposure and the outcome. Building upon this, exposures identified as positive (P < 0.05) were further subjected to False Discovery Rate (FDR) testing to minimize the risk of Type I errors, or false positives. The FDR analysis was also conducted using the R software (version 4.1.3). Concurrently, with the aim of assessing the frequency of Type II errors, this study made use of the mRnd online resource (https://shiny.cnsgenomics.com/mRnd/) to perform calculations of statistical power [15]. During the calculation of statistical power, the parameter for the Type I error rate (α) was set to 0.05.

To mitigate potential biases introduced by individual models, the weighted median and MR-Egger methods were employed as supplementary and reference approaches to the IVW model. If the causal effect directions are consistent across these three models, it suggests a relatively stable causal effect between the exposure and the outcome. P < 0.05 was considered statistically significant.

Heterogeneity, horizontal pleiotropy, Steiger upstream test and PhenoScanner

The Cochran Q test and MR-Egger intercept test were employed in this study to assess heterogeneity and horizontal pleiotropy, respectively. Heterogeneity represents the variability in the causal effect of SNPs associated with the exposure on the outcome. If heterogeneity is significant, it suggests an unstable causal effect. Horizontal pleiotropy indicates the possibility that SNPs directly affect the outcome through factors other than the exposure. If horizontal pleiotropy is significant, it implies that the causal effect is not valid. The Steiger upstream test, also known as the Steiger filtering method, is used to assess the directionality of the exposure on the outcome. If the Steiger test yields a significant result, it indicates that the exposure is upstream of the outcome, confirming the correct directionality. In all three test methods mentioned, a significance level of P < 0.05 is considered statistically significant. Additionally, the results obtained from the PhenoScanner database [16] (http://www.phenoscanner.medschl.cam.ac.uk/) serve as an important reference for ruling out horizontal pleiotropy of this study.

Results

Data and detailed information

The metabolite-related GWAS dataset used in this study is primarily based on the genetic locus study conducted by Shin et al. [17] in 2014, which investigated the impact of genetic variations on human metabolism, as well as the genome-wide study on human circulating metabolites conducted by Kettunen et al. [18] in 2016. The dataset comprising 637 metabolites was presented in detail in Supplementary Material 1 (Suppl 1. Detailed Information of 637 Human Metabolite Data). The GWAS dataset corresponding to OC, encoded as ieu-b-4963, was derived from the sequencing results of 199,741 samples conducted by Burrows et al. in 2021. All the metabolite datasets and the OC related dataset were based on sequencing performed on individuals of European ancestry. Figure 1 illustrates the workflow for metabolite selection in this study.

Fig. 1
figure 1

Flow chart for filtering metabolites

Full size image

IVs selection and exclusion of weak IVs

The GWAS dataset for all metabolites was screened for SNPs based on the criterion of P < 5*10− 6. The parameters for linkage disequilibrium were set as r2 < 0.001 and kb > 10,000. If a metabolite had an insufficient number of SNPs for MR analysis, it was excluded from the study. The SNPs selected based on the criterion of F statistic ≥ 10 are presented in detail in Supplementary Material 2 (Suppl 2. List of Filtered SNPs). The F statistics for all SNPs ranged from 19.1702 to 508.7844.

MR analysis and test results

As shown in Fig. 2, the MR analysis revealed a potential causal effect between 31 metabolites and OC (P-value of IVW < 0.05). Following FDR analysis, no exposures were excluded due to adjusted P-values ≥ 0.05. The analysis process of the FDR-adjusted P-values is displayed in Supplementary Material 3 (Suppl 3. FDR-adjusted P-values of 31 Metabolites). Supplementary Material 4 (Suppl 4. IVW Analysis Results and Statistical Power Values for 31 Metabolites) presents the results of the statistical power calculations. It is noteworthy that the statistical power for most metabolites is not optimal, indicating a higher probability of committing Type II errors. In the context of this study, we prioritize a lower probability of Type I errors, which inevitably compromises some degree of statistical power. Faced with the trade-off between Type I and Type II error rates, our focus is predominantly on minimizing the occurrence of Type I errors.

Figure 3 presents a circular heatmap illustrating the P values and β values of IVW, MR-Egger and weighted median methods corresponding to the aforementioned metabolites (Fig. 3A and B). Among them, 26 metabolites showed consistent causal effects with the IVW direction based on the weighted median and MR-Egger. Furthermore, 9 metabolites successfully passed tests for heterogeneity, horizontal pleiotropy, and exhibited the correct causal effect direction in the Steiger test. Table 1 displays the 9 metabolites that passed the tests along with their respective P-values. Supplementary Material 5 (Suppl 5. Results of PhenoScanner) presents the results retrieved from the PhenoScanner database, which do not show significant horizontal pleiotropy. It is noteworthy that currently, there is no method that can completely eliminate the influence of horizontal pleiotropy, and the interpretation of the results from PhenoScanner also carries a certain degree of subjectivity.

Fig. 2
figure 2

Forest plot for MR results of 31 metabolites

Full size image
Fig. 3
figure 3

The circular heatmap shows the P values and β values of algorithms corresponding to 31 metabolites. (A) Heatmap of P values corresponding to 31 metabolites. (B) Heatmap of β values corresponding to 31 metabolites

Note:The clustering algorithm used in the circular heatmap is an unsupervised clustering method, which simply represents the similarity among the data points

Full size image
Table 1 Details of 9 metabolites that passed the test
Full size table

Scatter plots of MR analysis

Figures 4 and 5 present the regression results of the SNPs corresponding to the aforementioned 9 metabolites using the IVW, weighted median and MR-Egger algorithms. Figure 4 indicates that androsterone sulfate (Fig. 4A), propionylcarnitine (Fig. 4B), 5alpha-androstan-3beta,17beta-diol disulfate (Fig. 4C), total lipids in medium VLDL (Fig. 4D), and concentration of medium VLDL particles (Fig. 4E) exhibit significant positive causal effects on OC. Conversely, X-12,093 (Fig. 5A), octanoylcarnitine (Fig. 5B), N2,N2-dimethylguanosine (Fig. 5C), and cis-4-decenoyl carnitine (Fig. 5D) show significant negative causal associations with OC.

Fig. 4
figure 4

Scatter plots of 5 metabolites with forward direction

Full size image
Fig. 5
figure 5

Scatter plots of 4 metabolites with negative direction

Full size image

Discussion

OC is a silent cancer which rate survival mainly relays in early stage detection. The discovery of reliable OC biomarkers plays a crucial role in the disease management and strongly impact in patient’s prognosis and survival. In this study, we used MR method to determine the casual relationship between 637 metabolites and OC. Specifically, androsterone sulfate, propionylcarnitine, 5alpha-androstan-3beta-17beta-diol disulfate, and medium VLDL may promotes OC oncogenesis. While X-12,093, octanoylcarnitine, N2,N2-dimethylguanosine, and cis-4-decenoyl carnitine may suppress OC.

Accumulating evidence has demonstrated that lipid metabolism is substantially reprogrammed in cancers [19]. Lipid uptake and storage are also elevated in malignant tumors [20]. These mechanisms further affect tumor biology, such as immune escape and cellular invasion [21]. Cholesterol and triglycerides are insoluble in water and therefore these lipids must be transported in association with lipoproteins. Plasma lipoproteins can be divided into seven classes, including chylomicrons, chylomicron remnants, VLDL, IDL, LDL, HDL, and Lp (a). VLDL is responsible for carrying triacylglycerol synthesized in the liver to peripheral tissues for utilization. The surface proteins of VLDL include apolipoprotein (Apo) B-100, Apo C-I, Apo C-II, Apo C-III, and Apo E. Among these, Apo B-100 is the core structural protein and is produced by the liver [22]. A research revealed that VLDL levels were elevated in lung cancer patients compared to non-cancer subjects [23]. In addition, VLDL promote breast cancer progression and metastasis through Akt-induced (epithelial–mesenchymal transition) EMT and angiogenesis [24]. To the best of our knowledge, the effect of VLDL on OC has not been studied. And we found that medium VLDL may promotes OC oncogenesis. This could be due to higher concentrations of VLDLs in the blood are often translated into higher levels of atherogenic particles and LDLs. VLDL uptake brings lipids and offers a sustainable source of energy for cancer cells [25].

Androsterone sulfate was the most abundant 5 alpha-reduced androgen metabolite in serum. Androsterone sulfate was identified as independent variables associated with the incidence of hepatocellular carcinoma (HCC) and non-alcoholic fatty liver disease (NAFLD) advanced fibrosis [26, 27]. Androsterone sulfate seem robust to predict the abortion rate of the polycystic ovary syndrome (PCOS) group, with an AUC of 0.941 [28]. Our research showed androsterone sulfate may be the risk factor for OC. The underlying mechanism leading to the adverse outcome remain unclear.

Acylcarnitines are esters arising from the conjugation of fatty acids with L-carnitine. They are widely used and produced in cellular energy metabolism pathways. The well established biologic function of acylcarnitine is to transport acyl groups from the cytosol into the mitochondrial matrix for β-oxidation, leading to the production of energy to sustain cell activity [29]. Acylcarnitine had being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers [30]. In a previous study, propionylcarnitine was significantly associated with lung cancer incidence [31]. Chen J et al. suggested that elevated propionylcarnitine was considered as potential biomarker candidates to diagnosed EOC [10]. Our results were consistent with the above.

In contrast to propionylcarnitine, octanoylcarnitine was gradually diminished in patients with HCC, and combining AFP with the metabolic biomarkers included octanoylcarnitine slightly increased the AUC value in the test set to 0.97 [32]. Our results also showed that octanoylcarnitine was negatively associated with OC. To date, the expression of octanoylcarnitine in different tumors remain conclusive [33], the expression in OC needs to be further verified. We also found that another acylcarnitine metabolite (cis-4-decenyl carnitine) was negatively correlated with OC. Studies of cis-4-decenyl carnitine in tumors are still lacking.

In addition to medium VLDL and acylcarnitine, we also found another two potential metabolites biomarker. The study of 5alpha-androstan-3beta-17beta-diol disulfate was limited to the field of Alzheimer’s disease (AD) [34]. And elevated serum N2, N2-dimethylguanosine was used for chronic kidney disease (CKD) diagnosis [35]. Urinary excretion of N2,N2-dimethylguanosine by adults was used for acute leukemia follow-up [36].

There are still some limitations in this study. Firstly, this study utilized GWAS data of the same ethnicity (European population) as both the exposure and outcome groups in the Mendelian randomization analysis, aiming to minimize potential confounding effects of ethnicity on the outcome. However, this approach introduces limitations to the generalizability of the findings, as the conclusions may be subject to bias when extrapolated to the entire human population due to racial factors. Secondly, this study has yet to provide evidence regarding the mechanisms and molecular pathways through which metabolites influence the development of OC. Ultimately, the statistical power associated with the metabolites in this study is generally low, suggesting an increased likelihood of incurring Type II errors. Upon meticulous review, we infer that this is attributed to the disproportionately low number of cases in the outcome (ovarian cancer). The reason for the low proportion of cases in the dataset should be attributed to the relatively low incidence rate of ovarian cancer. These limitations will guide our future research directions and drive continuous improvement.

Conclusion

To summarize, our study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AD:

Alzheimer’s disease

CA125:

Serum cancer antigen 125

CKD:

Chronic kidney disease

EOC:

Epithelial ovarian cancer

EMT:

Epithelial-mesenchymal transition

GWAS:

Genome-wide association study

HCC:

Hepatocellular carcinoma

IVs:

Instrumental variables

IVW:

Inverse variance-weighted method

MR:

Mendelian randomization

NAFLD:

Non-alcoholic fatty liver disease

OC:

Ovarian cancer

PCOS:

Polycystic ovary syndrome

RCT:

Randomized controlled trial

SNP:

Single nucleotide polymorphisms

TSMR:

Two-Sample Mendelian Randomization

VLDL:

Very-low-density lipoprotein

WM:

Weighted median

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Acknowledgements

The authors acknowledge the efforts of the Genome-Wide Association Study (GWAS) database and the PhenoScanner database in providing high quality open resources for researchers.

Funding

Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare; Joint Funds for the Innovation of Science and Technology, Fujian Province (Grant number: 2021Y9169, 2021Y9180); Special Health Subidy of Fujian Provincial Finance Department (Grant number: 210020650502302); Fujian Natural Sciences Foundation (Grant number: 2023J011223); Youth and Middle-aged Training of Fujian Provincial Health Technology Project (Grant number: 2021GGB014).

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Author notes

  1. Yedong Huang and Wenyu Lin contributed equally to this work.

Authors and Affiliations

  1. College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China

    Yedong Huang, Wenyu Lin & Xiangqin Zheng

  2. Department of Gynecology Oncology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China

    Yedong Huang & Xiangqin Zheng

  3. Laboratory of Gynecologic Oncology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China

    Wenyu Lin

  4. Fujian Province Key Clinical Specialty for Gynecology, Fujian Key Laboratory of Women and Children’s Critical Disease Research, National Key Gynecology Clinical Specialty Construction Insititution of China, Fuzhou, China

    Yedong Huang & Xiangqin Zheng

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Contributions

Y.H. and W.L. contributed to the acquisition, analysis, and interpretation of data; and to drafting the article. X.Z. and H.Y. contributed to the critically revised the article for important intellectual content, and supervised the study. All authors have given their final approval of the version to be submitted.

Corresponding author

Correspondence to Xiangqin Zheng.

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Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

. Detailed Information of 637 Human Metabolite Data

Supplementary Material 2

. List of Filtered SNPs

Supplementary Material 3

. FDR-adjusted P-values of 31 Metabolites

Supplementary Material 4

. IVW Analysis Results and Statistical Power Values for 31 Metabolites

Supplementary Material 5

. Results of PhenoScanner

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Huang, Y., Lin, W. & Zheng, X. Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study. BMC Genomics 25, 97 (2024). https://doi.org/10.1186/s12864-024-09997-3

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BMC Genomics

ISSN: 1471-2164

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