ArticleCell cycle (Georgetown, Tex.)2021
A nine-gene signature to improve prognosis prediction of colon carcinoma.
Article in Cell cycle (Georgetown, Tex.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma.Discover oncology · 2025Article
- Predicting colorectal cancer risk: a novel approach using anemia and blood test markers.Frontiers in oncology · 2024Article
- Subcellular Expression Patterns of FKBP Prolyl Isomerase 10 (FKBP10) in Colorectal Cancer and Its Clinical Significance.International journal of molecular sciences · 2023Article
- High VSX1 expression promotes the aggressiveness of clear cell renal cell carcinoma by transcriptionally regulating FKBP10.Journal of translational medicine · 2022Article
- Article
- Prognostic significance and immune landscape of a fatty acid metabolism-related gene signature in colon adenocarcinoma.Frontiers in genetics · 2022Article
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7 authors.
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Abstract
This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially expressed genes (DEGs) were obtained through "edgeR" analysis. Finally, a risk predication model was established through a series of regression analyses, and then prognostic performance of the model was comprehensively evaluated though Kaplan-Meier and receiver operating characteristic (ROC) analysis. Gene set enrichment analysis (GSEA) was further performed. Totally, 846 DEGs were obtained by analyzing the gene expression data in GSE39582 dataset. A 9-gene signature-based risk predication model was established via regression analyses, and the model-based risk score was formulated as: Riskscore = (-0.1214) * TNFRSF11A + (-0.2617) * TMEM97 + (-0.1041) * LGR5 + 0.0973 * KLK10 + 0.1655 * HOXB8 + 0.227 * FKBP10 + (-0.1312) * CXCL13 + (-0.1316) * CXCL10 + 0.2593 * CD36. Kaplan-Meier curve showed that colon carcinoma patients in the high-risk group had a lower survival rate. GSEA showed that high-risk group and low-risk group displayed significant difference in biological pathways including ECM RECEPTOR INTERACTION. Besides, correlation analysis between the riskscore of the model and clinical features of patients revealed that the model could effectively predict the prognosis of patients in different ages (age>65, age<65) and stages (tumor_stage I/II, tumor_stage III/IV, T3&T4, N0&N1, N2&N3, M0). This study provides a robust model for the prognosis prediction of colon carcinoma, and lays a basis for researching the molecular mechanism underlying the development of colon carcinoma.
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