BACKGROUND:Lung cancer leptomeningeal metastasis (LC-LM) severely impacts patient survival and quality of life, yet current diagnostic methods lack sufficient sensitivity and specificity, particularly for early detection. Cerebrospinal fluid (CSF) metabolomics may reveal specific biomarkers reflecting brain metastasis. METHODS:We performed untargeted metabolomic profiling of CSF samples by high-resolution mass spectrometry in a cohort of 218 participants, including 99 samples from LC-LM (with cancer cells detected in the CSF), 12 samples from the lung cancer parenchymal brain metastases (with no cancer cells detected in the CSF), 27 samples from the control group, 21 samples from the breast cancer LM, 15 samples from patients with LM from other tumors such as melanoma and gastric cancer, and 36 samples from other diseases. Significant metabolites were identified and validated. Subsequently, targeted metabolomics was conducted on serum samples from an independent cohort (n = 233), including 50 LC-LM patients, 150 patients with primary lung cancer (stages I-III), and 33 benign pulmonary nodules. RESULTS:Untargeted CSF metabolomics revealed a distinct metabolic signature in LC-LM patients. Differential analysis identified metabolites significantly altered in LC-LM, notably elevated lactic acid, N1, N12-diacetylspermine, and altered amino acid metabolites (eg, l-proline, l-glutamic acid), each demonstrating strong diagnostic accuracy individually, with area under the receiver operating characteristic curve (AUC) > 0.90. Machine learning classification models based on CSF metabolite panels achieved perfect diagnostic performance (AUC = 1.00) in distinguishing LC-LM from controls and other groups. Targeted validation of 5 top metabolites in serum samples confirmed their diagnostic utility, with N1, N12-diacetylspermine achieving an AUC of 0.882, superior to traditional protein biomarkers. CONCLUSION:CSF-based metabolomic profiling combined with machine learning offers a highly accurate and minimally invasive diagnostic tool for LC-LM. Serum validation further supports its translational potential, emphasizing its significance in clinical practice for improving early detection and potentially enhancing patient management and outcomes.

Bile acids (BAs) are important molecules in the carcinogenesis of pancreatic cancer (PC). The BA profile in pancreatic diseases and its role in PC remain unclear. The concentrations of 63 BAs in serum were measured in a total of 421 participants from three medical centers using targeted metabolomics. The concentrations of seven common types of BAs in serum were determined and compared. The least absolute shrinkage and selection operator algorithm analysis was used to confirm the independent predictors and build a metabolite panel for prediction and discrimination. A panel of two BA metabolites DCA and ω-MCA showed high diagnostic performance for PC versus healthy controls with an AUC of 0.914 (95

To identify novel diagnostic biomarkers for acute pancreatitis (AP) and facilitate the early prediction of severe AP (SAP), this investigation characterized the serum metabolomic profiles of patients across distinct disease phases and integrated metabolomics with artificial intelligence to construct bile acid-based predictive models. The observational protocol was registered with the Chinese Clinical Trial Registry (ChiCTR2000034117) on June 24, 2020. Comparative metabolomic analysis revealed significant alterations in 303 metabolites and 461 lipid species in AP. Subsequent weighted gene coexpression network analysis demonstrated robust correlations between clinical parameters and specific metabolic clusters, particularly bile acids (BAs) and lipid species. Targeted quantification of 63 BAs was subsequently performed within a multicentre validation cohort (n = 948). Machine learning algorithms applied to these data facilitated the derivation of two distinct BA panels. The first panel, comprising nine BAs, demonstrated high diagnostic accuracy for AP, including among individuals with negative conventional enzymatic biomarkers, and effectively discriminated AP from acute cholangitis, as reflected by elevated area under the curve (AUC) values. A second panel, consisting of 13 BAs, reliably identified patients at elevated risk for SAP progression. Collectively, these results validate the translational potential of machine learning-driven metabolic biomarkers for the precision management of acute abdominal conditions, underscore the clinical utility of BAs as promising diagnostic and prognostic biomarkers in acute pancreatitis, and provide a new paradigm for the development of dynamic risk early-warning systems (Clinical Trial Registration Our study is an observational study registered in ChiCTR (ChiCTR2000034117) on 2020/06/24, not a prospective interventional clinical trial, and therefore does not fall under the ICMJE definition of a clinical trial requiring CONSORT compliance).

The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratification. Traditional lipidomics, which primarily rely on linear abundance, often fail to distinguish the HLAP-specific lipidome from the metabolic background of hypertriglyceridemia (HTG). To overcome this limitation, DeepLipiDecipher was developed as a knowledge-guided graph neural network framework that integrates lipid chemical structures with metabolic topology to identify latent lipotoxic features. In a retrospective cohort of 433 subjects, DeepLipiDecipher demonstrated robust classification performance (AUC = 0.810), effectively distinguishing the HLAP phenotype and outperforming conventional machine learning models. Interpretability analysis revealed that HLAP susceptibility correlated with a distinct structural lipid profile, marked by the synergistic enrichment of polyunsaturated and ether-linked phospholipids, rather than total lipid mass. Moreover, computational causal inference implicated a pathogenic mechanism wherein SMPD3-mediated ceramide accumulation induced basal cytotoxicity, and PTGS2 hyperactivation promoted the peroxidation of these vulnerable lipids, triggering systemic inflammation. These results highlight the value of incorporating network topology into lipidomic analysis and suggest the network-inferred SMPD3-Ceramide-PTGS2 immunometabolic axis as a potential therapeutic target for preventing the progression from metabolic dysfunction to acute organ injury.

Wuyi Mingcong (WYMC) is a distinctive tea germplasm resource from Wuyi Mountain, known for its unique aroma and quality characteristics. However, the aroma quality of WYMC has been insufficiently studied. In this study, the aroma profiles of seven characteristic tea plant resources WYMC tea samples were characterized using sensory evaluation combined with headspace solid-phase microextraction and gas chromatography–mass spectrometry (HS-SPME-GC-MS). The results revealed that “floral,” “fruity,” “clean and refreshing,” “woody,” and “sweet” were the main aroma characteristics. A total of 37 volatile compounds were found to contribute significantly to the aroma profiles of the seven WYMC tea samples, with dihydrolinalool and (E)-β-ionone likely being the key contributors to their floral and fruity notes. Ten key volatile markers were identified as responsible for aroma differences between the Fujian Shuixian (SX) and seven WYMC tea samples. Phenylethyl alcohol, cis-3-hexenyl benzoate, δ-cadinene, nerol, and β-myrcene may be critical for the formation of WYMC’s characteristic aroma. cis-3-hexenyl benzoate and nerol may act as “broad-spectrum” aroma contributors, enhancing the overall intensity or layered nature of WYMC’s scent. The results of this study enrich the understanding of the aroma characteristics of WYMC and provide a theoretical foundation for the development and utilization of tea germplasm resources in the Wuyi Mountain.

The profiling of metabolites provides an immediate snapshot that depicts crucial physiological information, holding immense potential for the early diagnosis and prognosis of diseases, including diabetes. Herein, we proposed an optimized and in-depth target-based metabolome platform through an integration of six distinct conditions, including a normal phase, a pre-column chemical derivatization and four reversed phase separation methods for the quantification of a total of 1609 small molecules (32 sub-classes) in serum after normalization using isotope-labeled internal standards. After undergoing rigorous methodological validation and comprehensive comparison with untargeted strategies, we present a new dataset of metabolomic profile encompassing a cohort of 200 healthy individuals and 100 newly diagnosed Type 2 diabetes mellitus (T2DM) patients from the northern region of China. The overall differential analysis results indicated obvious metabolic disturbance of amino acid, fatty acids, lysophosphatidyl-choline and triacylglycerol in T2DM. We hereby make these technical validation results and the profiling dataset publicly available to the scientific community, showcasing its exceptional sensitivity and robustness as an invaluable tool for the comprehensive targeted metabolome analysis.

Background Metabolic reprogramming, an essential hallmark of pancreatic cancer (PC), affects the treatment and prognosis of patients with this malignancy. For patients with nonresectable PC, chemotherapy is the primary treatment option, with gemcitabine recognized as a firstu2010line chemotherapeutic drug. However, the effectiveness of gemcitabine is often compromised by the rapid development of drug resistance. This study investigated metabolic targets in PC under gemcitabine treatment and evaluated the potential of matrine, a Chinese herb extract, as an adjuvant therapy for PC. Methods We used metabolomics, lipidomics, and transcriptomics to investigate metabolic alterations in PC. Bioinformatics analysis was performed to identify key metabolic pathways and potential biomarkers. Various techniques, including flow cytometry, western blot analysis, immunostaining, immunohistochemistry, and TUNEL assays, were used to assess cell apoptosis and molecular changes. Results The combined treatment of gemcitabine and matrine significantly enhanced cell apoptosis and inhibited invasion compared with individual treatments in Pancu20101 and H6C7 cell lines. We identified lipid metabolism as a target of matrine/gemcitabine in PC cells. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that matrine and gemcitabine modulated sphingolipid signaling pathway and canceru2010related pathways in PC. Compared with gemcitabine alone, the combination of matrine and gemcitabine resulted in significantly enhanced cell apoptosis and reduced tumor growth in a PC xenograft mouse model. Mechanistic analysis further discovered that the enhanced effect was mediated by upregulating NGFR expression and lipid metabolism. Conclusions Our findings suggest that matrine may serve as a promising adjuvant therapy for gemcitabine in PC, functioning through modulation of lipid metabolism. This study provides a foundation for the further exploration of lipid biomarkers to improve the efficacy of chemotherapy in patients with PC.

Background and objectives:Sex steroid hormones have been hypothesized to be associated with the risk of gastric cancer (GC); however, it has not been widely validated in prospective studies. We aimed to investigate the associations between sex steroid hormone metabolites and the risk of gastric cancer and precancerous lesions in a prospective cohort of Chinese men. Methods:Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and electrochemical luminescence immunoassay, we examined 20 sex steroid hormone metabolites and sex hormone-binding globulin (SHBG) in serum from 470 eligible men, including high-grade lesions or GC (n = 32), intestinal metaplasia (IM, n = 146), and 1:2 matched normal participants (n = 292) from 2007 to 2012. IM and normal participants were further followed up until December 2021, during which 32 new GC cases were identified with a median follow-up of 11.8 years. Associations between baseline sex steroid hormone metabolites and IM, high-grade lesions and gastric cancer were assessed using logistic regression, and associations between sex steroid hormone metabolites and incident GC risk were assessed using Cox proportional hazards regression in the prospective analysis. Results:In the cross-sectional analysis, androstenedione levels were potentially associated with IM risk and significantly associated with high-grade lesions or GC risk (ORcontinuous = 2.45, 95% CI: 1.01-5.95). Higher concentrations of 17α-hydroxypregnenolone (ORcontinuous = 2.35, 95% CI: 1.13-4.88), progesterone (ORcontinuous = 2.68, 95% CI: 1.09-6.61), and estrone (ORcontinuous = 5.36, 95% CI: 1.28-22.52) were also associated with an increased risk of high-grade lesions or GC. Furthermore, significant positive associations between GC risk and serum levels of SHBG (HRcontinuous = 2.57, 95% CI: 1.04-6.36), epitestosterone (HRcontinuous = 2.10, 95% CI: 1.07-4.15) and pregnenolone (HRcontinuous = 1.30, 95% CI: 1.03-1.63) were identified in the follow-up study focusing on participants diagnosed as normal or IM. Notably, the subgroup analyses stratified by H. pylori status revealed similar associations between androstenedione, 17α-hydroxypregnenolone, progesterone, estrone, SHBG, pregnenolone, and GC risk. Conclusions:Several sex hormone metabolites were significantly associated with gastric cancer and its precancerous lesions, indicating a role for sex hormones in gastric carcinogenesis and potentially providing novel biomarkers for the identification of high-risk populations and risk prediction for GC.

This study investigates the metabolic changes induced by endurance exercise, specifically race walking, in a cohort of 19 athletes. Blood samples were collected at four time points: pre-exercise (REST), immediately post-exercise (STAT), 3 hours into recovery (REC3), and 22 hours post-exercise (REC22). A total of 859 metabolites were identified through the untargeted method, and 465 metabolites and 411 lipids were identified through the targeted methods. Rigorous quality control measures were implemented throughout the study to ensure data reliability. The comprehensive dataset, which is publicly available on the Metabolomics Workbench website, offers valuable insights into the systemic metabolic shifts triggered by endurance exercise. This resource may prove instrumental in uncovering biomarkers associated with athletic performance, providing a foundation for future research in exercise physiology and metabolic health.

Microplastic-induced gut microbial enrichment was dominated by bacteria within Eubacteriales, correlated with the virome, and accompanied by colitis. The polyamine synthetic pathway was activated to maintain glutathionylspermidine homeostasis, concurrent with decreases in pathways involved in the production of energy and reactive oxygen species under microplastic exposure. Tryptophan-serotonin, phenylalanine-phenylethylamine, and tyrosine-thyroxine pathways increased, whereas tryptophan-kynurenine, tryptophan-indole, and tyrosine-tyramine pathways decreased under microplastic exposure. Enterolactone synthesis and cholesterol-derived hormone synthesis were increased under microplastic exposure. Bacteria within Eubacteriales (e.g., Oscillospiraceae bacterium and Clostridiales bacterium) contributed most to metabolic disturbances under microplastic exposure.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. The in-depth study of genes and metabolites related to nucleotide metabolism will provide new ideas for predicting the prognosis of HCC patients. This study integrated the transcriptome data of different cancer types to explore the characteristics and significance of nucleotide metabolism-related genes (NMGRs) in different cancer types. Then, we constructed a new HCC classifier and prognosis model based on HCC samples from TCGA and GEO, and detected the gene expression level in the model through molecular biology experiments. Finally, nucleotide metabolism-related products in serum of HCC patients were examined using untargeted metabolomics. A total of 97 NMRGs were obtained based on bioinformatics techniques. In addition, a clinical model that could accurately predict the prognostic outcome of HCC was constructed, which contained 11 NMRGs. The results of PCR experiments showed that the expression levels of these genes were basically consistent with the predicted trends. Meanwhile, the results of untargeted metabolomics also proved that there was a significant nucleotide metabolism disorder in the development of HCC. Our results provide a promising insight into nucleotide metabolism in HCC, as well as a tailored prognostic and chemotherapy sensitivity prediction tool for patients.

BackgroundMore than half of the cases of fetal structural anomalies have no known cause with standard investigations like karyotype testing and chromosomal microarray. The differential metabolic profiles of amniotic fluid (AF) and maternal blood may reveal valuable information about the physiological processes of fetal development, which may provide valuable biomarkers for fetal health diagnostics.MethodsThis cohort study of singleton-pregnant women had indications for amniocentesis, including structural anomalies and a positive result from maternal serum screening or non-invasive prenatal testing, but did not have any positive abnormal karyotype or chromosomal microarray analysis results. A total of 1580 participants were enrolled between June 2021 and March 2022. Of the 1580 pregnant women who underwent amniocentesis, 294 were included in the analysis. There were 137 pregnant women in the discovery cohort and 157 in the validation cohort.ResultsHigh-coverage untargeted metabolomic analysis of AF revealed distinct metabolic signatures with 321 of the 602 metabolites measured (53%) (false discovery rate, q < 0.005), among which amino acids predominantly changed in structural anomalies. Targeted metabolomics identified glutamate and glutamine as novel predictive markers for structural anomalies, their vital role was also confirmed in the validation cohort with great predictive ability, and the area under the receiver operating characteristic curves (AUCs) were 0.862 and 0.894 respectively. And AUCs for glutamine/glutamate were 0.913 and 0.903 among the two cohorts.ConclusionsOur results suggested that the aberrant glutamine/glutamate metabolism in AF is associated with nonchromosomal modificantions fetal structural anomalies. Based on our findings, a novel screening method could be established for the nonchromosomal modificantions fetal structural anomalies. And the results also indicate that monitoring fetal metabolic conditions (especially glutamine and glutamine metabolism) may be helpful for antenatal diagnosis and therapy.

In a recent study published in Cell Metabolism, Thomas A. Rando and colleagues reported a critical role of dysregulated glutathione (GSH) metabolism in driving the aging process of skeletal muscle stem cells (MuSCs), uncovering a novel mechanism underlying the divergent responses of quiescent stem cells to environmental stressors with age, thus providing a potentially accessible target to alleviate age-associated skeletal muscle degeneration.

Objective: Considering that there are no effective biomarkers for the screening of cardia gastric cancer (CGC), we developed a noninvasive diagnostic approach, employing data-independent acquisition (DIA) proteomics to identify candidate protein markers. Methods: Plasma samples were obtained from 40 subjects, 10 each for CGC, cardia high-grade dysplasia (CHGD), cardia low-grade dysplasia (CLGD), and healthy controls. Proteomic profiles were obtained through liquid chromatography-mass spectrometry (LC-MS/MS-based DIA proteomics. Candidate plasma proteins were identified by weighted gene co-expression network analysis (WGCNA) combined with machine learning and further validated by the Human Protein Atlas (HPA) database. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the biomarker panel. Results: There was a clear distinction in proteomic features among CGC, CHGD, CLGD, and the healthy controls. According to the WGCNA, we found 42 positively associated and 164 inversely associated proteins related to CGC progression and demonstrated several canonical cancer-associated pathways. Combined with the results from random forests, LASSO regression, and immunohistochemical results from the HPA database, we identified three candidate proteins (GSTP1, CSRP1, and LY6G6F) that could together distinguish CLGD (AUC = 0.91), CHGD (AUC = 0.99) and CGC (AUC = 0.98) from healthy controls with excellent accuracy. Conclusions: The panel of protein biomarkers showed promising diagnostic potential for CGC and precancerous lesions. Further validation and a larger-scale study are warranted to assess its potential clinical applications, suggesting a potential avenue for CGC prevention in the future.

Background:Currently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine.Methods:The integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis.Results:In this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that alanine, aspartate and glutamate (AAG) metabolism was significantly associated with the prevalence and progression of GC. Weighted metabolite correlation network analysis revealed that glycolysis/gluconeogenesis (GG) and AAG metabolism served as HER2-coexpressed metabolic pathways. Consensus clustering was used to stratify patients with GC into four subtypes with different metabolic characteristics (i.e. quiescent, GG, AAG and mixed subtypes). The GG subtype was characterised by a lower level of ERBB2 expression, a higher proportion of the inflammatory phenotype and the worst prognosis. However, contradictory features were found in the mixed subtype with the best prognosis. The GG and mixed subtypes were found to be highly sensitive to chemotherapy, whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.Conclusions:Transcriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC. Metabolomics analysis highlighted the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC. The novel integrated multi-omics strategy used in this study may facilitate the development of a more tailored approach to GC therapy.

Dear Editor, N6-methyladenosine(m6A)is an abundant epitranscriptomic modification that regulates messenger RNA(mRNA)biology.The m6A modification regulates mRNA splicing,transport,stability,and translation through coordinated activities by methyltransferases(writers),binding proteins(readers),and demethylases(erasers)(Huang et al.,2020;Wu et al.,2020).Among m6A regulators,fat mass of obesity-associ-ated protein(FTO),is the first discovered eraser with RNA m6A demethylation activity(Jia et al.,2011).Since then,FTO has been reported to play m6A-dependent roles in a variety of physiological processes including adipogenesis,neuro-genesis and tumorigenesis(Fischer et al.,2009;Li et al.,2017;Huang et al.,2020).Consequently,FTO deficiency in mice leads to dramatic phenotypes,such as decreased fat mass and impaired brain development(Fischer et al.,2009;Li et al.,2017).Similarly,inhibition of FTO reduces tumori-genesis in multiple types of cancer models,while FTO is highly expressed in many cancers(Huang et al.,2020).

Pancreatic cancer (PC) is burdened with a low 5-year survival rate and high mortality due to a severe lack of early diagnosis methods and slow progress in treatment options. To improve clinical diagnosis and enhance the treatment effects, we applied metabolomics using ultra-high-performance liquid chromatography with a high-resolution mass spectrometer (UHPLC-HRMS) to identify and validate metabolite biomarkers from paired tissue samples of PC patients. Results showed that the metabolic reprogramming of PC mainly featured enhanced amino acid metabolism and inhibited sphingolipid metabolism, which satisfied the energy and biomass requirements for tumorigenesis and progression. The altered metabolism results were confirmed by the significantly changed gene expressions in PC tissues from an online database. A metabolites biomarker panel (six metabolites) was identified for the differential diagnosis between PC tumors and normal pancreatic tissues. The panel biomarker distinguished tumors from normal pancreatic tissues in the discovery group with an area under the curve (AUC) of 1.0 (95%CI, 1.000-1.000). The biomarker panel cutoff was 0.776. In the validation group, an AUC of 0.9000 (95%CI = 0.782-1.000) using the same cutoff, successfully validated the biomarker signature. Moreover, this metabolites panel biomarker had a great capability to predict the overall survival (OS) of PC. Taken together, this metabolomics method identifies and validates metabolite biomarkers that can diagnose the onsite progression and prognosis of PC precisely and sensitively in a clinical setting. It may also help clinicians choose proper therapeutic interventions for different PC patients and improve the survival of PC patients.

To investigate the exposure of infants to phenylpyrazole and diamide insecticides during lactation, we collected 3467 breast milk samples of lactating women from 100 cities of 24 provinces in China and prepared 100 pooled samples together city-by-city. Among phenylpyrazole insecticides, fipronil and its metabolites (63-100%) were widely detected in breast milk, with total detection concentrations ranging from 178 to 2947 ng/L (median: 921 ng/L). Among diamide insecticides, chlorantraniliprole and flubendiamide were detected in breast milk, but their detection frequencies (20-85%) and concentration levels (nondetected to 89.9 ng/L) were far lower than those of total fipronils. The average estimated daily intake of infants exposed to total fipronils through breast milk is 209 ng/kg-bw/day by upper-bound scenario evaluation, which is higher than the acceptable daily intake (200 ng/kg-bw/day). This study indicates that infants have far higher exposure levels to fipronil than adults, while exposure levels to other types of phenylpyrazoles and diamide insecticides are low.

Gastric cancer (GC) is a common lethal malignancy worldwide. Gastroscopy is an effective screening technique for decreasing mortality. However, there are still limited useful non-invasive markers for early detection of GC. Bile acids are important molecules for the modulation of energy metabolism. With an in-depth targeted method for accurate quantitation of 80 bile acids (BAs), we aimed to find potential biomarkers for the early screening of GC. A cohort with 280 participants was enrolled, including 113 GC, 22 benign gastric lesions (BGL) and 145 healthy controls. Potential markers were identified using a random forest machine algorithm in the discovery cohort (n=180), then validated in an internal validation cohort (n=78) and a group with 22 BGL. The results represented significant alterations in the circulating BA pool between GC and the controls. BAs also exhibited significant correlations with various clinical traits. Then, we developed a diagnostic panel that comprised six BAs or ratios for GC detection. The panel showed high accuracy for the diagnosis of GC with AUC of 1 (95%CI: 1.00-1.00) and 0.98 (95%CI: 0.93-1.00) in the discovery and validation cohort, respectively. This 6-BAs panel was also able to identify early GC with AUC of 1 (95%CI: 0.999-1.00) and 0.94 (95%CI: 0.83-1.00) in the discovery and validation cohort, respectively. Meanwhile, this panel achieved a good differential diagnosis between GC and BGL and the AUC was 0.873 (95%CI: 0.812-0.934). The alternations of serum bile acids are characteristic metabolic features of GC. Bile acids could be promising biomarkers for the early diagnosis of GC.