Open AccessApoptosis-Associated Gene Expression Profiling Is One New Prognosis Risk Predictor of Human Rectal Cancer
Author(s) -
Qiansan Zhu,
Xian Wu,
Lili Ma,
Haibo Xue
Publication year - 2022
Publication title -
disease markers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.912
H-Index - 66
eISSN - 1875-8630
pISSN - 0278-0240
DOI - 10.1155/2022/4596810
Subject(s) - kegg , proportional hazards model , receiver operating characteristic , colorectal cancer , oncology , gene , survival analysis , gene expression profiling , computational biology , biology , multivariate statistics , bioinformatics , gene expression , cancer , medicine , genetics , machine learning , computer science , transcriptome
Background. Prior research has revealed the predictive significance of a series of genetic markers in the prognosis of rectal cancer (RC), but the roles of apoptosis-associated genes in RC are rarely studied. Methods. The RNA-seq data as well as clinical data about patients with rectum adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Additionally, 87 apoptosis-associated genes were downloaded and acquired from Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Comprehensive bioinformatics analysis was carried out for deep exploration of the expression and prognostic significance of these genes. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis was performed for the establishment of a risk scoring equation for the prognosis model and construction of a survival prognosis model. ROC curves were drawn for evaluating the accuracy of the model. A real-time quantitative PCR assay was conducted for quantification of apoptosis-associated proteins related to prognosis. Results. Eight genes were identified as hub genes associated with the prognosis of PFS. A risk model of prognosis prediction based on four gene signatures (CYCS, IKBKB, NFKB1, and TRADD) was constructed. According to further analysis of this model, the high-risk group experienced worse overall survival than the other. The prognosis model demonstrated a favorable predictive ability, with areas under the receiver operating characteristic curves (AUC) of 0.720, 0.641, and 0.677 in forecasting the 1-, 2-, and 3-year prognosis, respectively. In addition, CYCS and NFKB1 presented low expression, while IKBKB and TRADD presented high expression in TCGA and clinical tumor samples. Conclusions. A four-gene signature risk model for prognosis forecasting of RC has been constructed, which possesses favorable predictive ability, which offers ideas and breakthrough points to the apoptosis-associated development of RC.