ArticleFrontiers in oncology2022
Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- RNA 5-methylcytosine writer NSUN5 promotes hepatocellular carcinoma cell proliferation via a ZBED3-dependent mechanism.Oncogene · 2024Article
- Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine.Cancers · 2024Review
- Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer.iScience · 2024Article
- Development and Validation of a 15-gene Expression Signature with Superior Prognostic Ability in Stage II Colorectal Cancer.Cancer research communications · 2023Article
- Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.Translational cancer research · 2023Article
- Unveiling mitophagy-mediated molecular heterogeneity and development of a risk signature model for colorectal cancer by integrated scRNA-seq and bulk RNA-seq analysis.Gastroenterology report · 2023Article
- Smoking-mediated nicotinic acetylcholine receptors (nAChRs) for predicting outcomes for head and neck squamous cell carcinomas.BMC cancer · 2022Article
- Identification and validation of a 17-gene signature to improve the survival prediction of gliomas.Frontiers in immunology · 2022Article
- A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients. Methods: Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened Results: A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues. Conclusion: A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.