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Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis.

ABSTRACT: Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

SUBMITTER: Liu W

PROVIDER: S-EPMC11018850 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis.

Liu Wanshan W Ma Jinglan J Zhang Juxiang J Cao Jing J Hu Xiaoxiao X Huang Yida Y Wang Ruimin R Wu Jiao J Di Wen W Qian Kun K Yin Xia X

EMBO molecular medicine 20240214 4

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We f ...[more]

PMID: 38355748

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Project description:BackgroundThe pressing need for reliable biomarkers in colorectal cancer (CRC) diagnosis and prognosis is a major global health concern. Current diagnostic methods rely heavily on invasive procedures like colonoscopy, and existing biomarkers such as Carbohydrate Antigen 19-9 (CA19-9) and Carcinoembryonic Antigen (CEA) exhibit limitations in accuracy and specificity.AimsThis study aims to identify and validate novel biomarkers that can enhance the early detection and diagnostic precision of CRC while overcoming the shortcomings of conventional biomarkers.Materials and methodsLeveraging advancements in genomic and proteomic technologies, gene expression datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified differentially expressed genes (DEGs) and conducted further analyses, including Gene Ontology (GO) enrichment and Protein-Protein Interaction (PPI) network construction. Five promising biomarkers-INHBA, MMP7, PSAT1, SLC7A5, and TGFBI-were selected based on their robust performance in Receiver Operating Characteristic (ROC) curve analysis. Serum concentrations of these biomarkers were measured in 200 CRC patients and 100 healthy controls.ResultsThe study revealed significantly elevated expression levels of the selected biomarkers in CRC tissues compared to normal tissues. Additionally, serum concentrations of INHBA, MMP7, PSAT1, SLC7A5, and TGFBI were notably higher in CRC patients than in healthy individuals, with Area Under the Curve (AUC) values ranging from 0.8361 to 0.9869 indicating high diagnostic accuracy. Optimal cutoff values for diagnosis ranged from 38.9 pg/mL to 280.7 pg/mL, yielding sensitivity and specificity values between 74.5% and 92.9%.DiscussionThe findings underscore the potential of INHBA, MMP7, PSAT1, SLC7A5, and TGFBI as effective non-invasive biomarkers for CRC detection. Their elevated serum concentrations and robust discriminatory abilities highlight their promise in improving diagnostic accuracy and patient outcomes compared to traditional biomarkers.ConclusionThe identification and validation of these novel biomarkers represent a significant advancement in CRC diagnosis and management. Further studies are required to validate their clinical applicability in larger cohorts and to elucidate their functional roles in CRC pathogenesis, ultimately enhancing diagnostic strategies and personalized treatment approaches.

Project description:BackgroundEndometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC.MethodsIn this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry.ResultsThe concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression.ConclusionsOur data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.

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Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis.

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Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis.

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