ArticleBioMed research international2020
A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of a metabolic-immune signature associated with prognosis in colon cancer and exploration of potential predictive efficacy of immunotherapy response.Clinical and experimental medicine · 2025Article
- Mechanistic insights into zearalenone-accelerated colorectal cancer in mice using integrative multi-omics approaches.Computational and structural biotechnology journal · 2023Article
- Construction and validation of four-metabolism related-long non-coding RNAs as potential signature in prognosis of colon cancer.Translational cancer research · 2022Article
- Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy.Frontiers in endocrinology · 2022Review
- Targeting cancer-specific metabolic pathways for developing novel cancer therapeutics.Frontiers in immunology · 2022Review
- Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases.Metabolites · 2021Article
- Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer.Frontiers in oncology · 2021Article
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6 authors.
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Abstract
A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.
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