Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

Zhong, Xiaocui; Ran, Ruiqiong; Gao, Shanhu; Shi, Manlin; Shi, Xian; Long, Fei; Zhou, Yanqiu; Yang, Yang; Tang, Xianglan; Lin, Anping; He, Wuyang; Yu, Tinghe; Han, Ting‐Li

doi:10.3389/fonc.2022.916375

Cited by 10 publications

(11 citation statements)

References 82 publications

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“…In a prospective nested case-control study, Wang S conducted an untargeted metabolomics study in subjects with incident pancreatic cancer (n = 68) and matched controls (n = 136) and the results showed that alanine was signi cantly associated with an increased risk of pancreatic cancer (Wang 2022). Moreover, in another study, the concentration of alanine in plasma and urine of the control group was lower than that of ovarian cancer (Zhong 2022). In a case-control study, statistically signi cant differences in serum alanine levels were found in patients with prostate cancer compared to the control group (Dereziński 2017).…”

Section: Discussionmentioning

confidence: 88%

Association between circulating alanine and cancer susceptibility using Mendelian randomization

Cai

Liu

Lin

et al. 2023

Preprint

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Background Circulating alanine concentrations are associated with several cancers, but little is known about the causal direction of the associations. This study aims to explore whether there is a relationship between circulating alanine and ten common cancers. Methods We conducted two-sample Mendelian randomization (MR) analysis to assess the causal effects of circulating alanine on ten common cancers. According to published genome-wide association studies (GWASs), we obtained 36 alanine-related single nucleotide polymorphisms used as instrumental variables. For exposure data, genetic association data of lung, breast, pancreatic, liver, colorectal, esophageal, stomach, thyroid, prostate and ovarian cancer from GWAS Consortia were used, including up to 1,213,351 participants of European origin and 196,187 participants of East Asian. The inverse variance weighting (IVW) method was used for MR analysis, and MR-Egger and the weighted median method further evaluated the pleiotropic effect. Results Specific to cancer GWAS, we found that circulating alanine was significantly associated with increased squamous cell lung cancer (odds ratio [OR]: 1.37, 95% CI = 1.00-1.87; P = 0.048), pancreatic cancer (OR 3.02, 95% CI = 1.35 to 6.76; P = 0.007), low grade serous ovarian cancer (OR 1.81, 95% CI = 1.01 to 3.25; P = 0.047). We have no evidence of a convincing causal effect of circulating alanine concentrations predicted by genetics on other cancer risks. Conclusion We observed the possible causal relationship between circulating alanine and lung squamous cell carcinoma, pancreatic cancer, low-grade serial ovarian cancer. Further research is needed to verify this causal relationship.

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Section: Discussionmentioning

confidence: 88%

Association between circulating alanine and cancer susceptibility using Mendelian randomization

Cai

Liu

Lin

et al. 2023

Preprint

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“…Alterations in cellular metabolism have been reported in numerous human cancers and are thought to re ect the metabolic demands related to cancer development (Zhong et al, 2022). In this study we performed comprehensive metabolic pro ling of plasma exosome samples collected from 105 women diagnosed either with OC, BE, or controls (CON).…”

Section: Overall Changes In Metabolite Pro Lesmentioning

confidence: 99%

“…Other studies have found similar metabolic alterations in OC. Speci cally, Zhong et al observed an upregulation of glutathione metabolism in women diagnosed with this malignancy (Zhong et al, 2022). Elevated glutathione metabolism has been shown to accompany tumor growth, presumably to counteract the increased oxidative stress arising from accelerated metabolic rates(Huang et al; Gamcsik et al, 2012;Zhong et al, 2022).…”

Section: Metabolic Pathway Enrichment Analysismentioning

confidence: 99%

A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms

Long,

Pu,

Wang

et al. 2024

Preprint

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Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. Exosomes, nanosized extracellular vesicles found in the blood, have been proposed as promising biomarkers for liquid biopsies. In this study we recruited 37 OC patients, 22 benign ovarian tumor (BE) patients, and 46 control (CON) patients. Plasma exosomes were purified from blood samples and sensitive thermal separation probe-based mass spectrometry analysis using a global untargeted metabolic profiling strategy was employed to characterize the metabolite fingerprints. Uniform manifold approximation and projection (UMAP) analysis demonstrated a distinct separation of exosomes among the three groups. We screened for diagnostic biomarkers from plasma exosome metabolites using seven machine learning algorithms, including artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM). DT (0.98), RF (0.92), and ANN (0.89) were given the highest AUC values for the OC-CON comparison. RF (0.93), NB (0.88), and SVM (0.81) were given the highest AUCs for the OC-BE comparison. A total of 18, 138, and 157 metabolic features exhibited significant differences (FC= 1.5, p < 0.01, q < 0.01) in the OC vs BE, BE vs CON, and OC vs CON, comparisons, respectively. Notably, the quantities of 9-octadecenamide and 1,4-methanobenzocyclodecene were significantly elevated, while maltol showed a significant reduction in the OC group compared to the BE group. When comparing the OC group to the CON group, the concentrations of 4-amino-furazan-3-carboxylic acid 2-hydroxy-4-methoxybenzaldehyde, N-phenylethyl, and 4-morpholineethanamine were significantly elevated, while the remaining metabolites, including hydrazine and pyridine sulfonamide, were reduced, in the OC group. The metabolites showing different abundancies are associated with cancer-related mutations, immune responses, and metabolic reprogramming. We demonstrate that the RF algorithm, combined with sensitive thermal separation probe-based mass spectrometry analysis of plasma exosomes, can effectively identify OC patients with good accuracy. Thus, our study has shortlisted a set of potential biomarkers in plasma exosomes, and the proposed approach could serve as a routine prescreening tool for ovarian cancer.

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“…The noninvasive nature of hair sample collection reduces patient discomfort and allows for easier and more frequent sampling. Indeed, hair has been recently applied in discovering disease biomarkers in clinical research. , Additionally, the distribution of chemical compound concentrations along the hair shaft reflects changes in the corresponding composition during the measurement period. ,, …”

Section: Introductionmentioning

confidence: 99%

“…Indeed, hair has been recently applied in discovering disease biomarkers in clinical research. 9,18 Additionally, the distribution of chemical compound concentrations along the hair shaft reflects changes in the corresponding composition during the measurement period. 10,19,20 In this study, we aimed to perform a high-resolution mass spectrometry (HRMS)-based untargeted and targeted metabolomic approach on hair samples from 6-month-old 5xFAD mice, characterized by accumulation of amyloid plaques in the brain.…”

Section: ■ Introductionmentioning

confidence: 99%

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer’s Disease Using Mass Spectrometry-Based Metabolomics

Chang,

Hsu,

et al. 2024

ACS Chem. Neurosci.

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Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of L-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring.

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Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

Cited by 10 publications

References 82 publications

Association between circulating alanine and cancer susceptibility using Mendelian randomization

Association between circulating alanine and cancer susceptibility using Mendelian randomization

A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer’s Disease Using Mass Spectrometry-Based Metabolomics

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