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Updated: Jun 26, 2025
Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection
Published on: June 8, 2020
An innovative prognostic auxiliary for colon adenocarcinoma based on zinc finger protein genes
Fan Xu1, Jiahui Sun1, Xinyue Gu2
1Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
Background:
The carcinogenesis and progression of colon adenocarcinoma (COAD) are intensively related to the abnormal expression of the zinc finger (ZNF) protein genes. We aimed to employ these genes to provide a reliable prognosis and treatment stratification tool for COAD patients.
Methods:
Cox and the least absolute shrinkage and selection operator (LASSO) regression analysis were applied, utilizing The Cancer Genome Atlas (TCGA) metadata, to build a ZNF protein gene-based prognostic model. Using this model, patients in the training cohort and testing cohort (GSE17537) were labelled as either high or low risk. Kaplan-Meier (KM) survival analysis and time-dependent receiver operating characteristic (ROC) curve analysis were performed in the patients with opposite risk status to assess the predictive ability in each cohort. The potentiality of the mechanism was explored by the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the degrees of expression of model genes were validated by immunohistochemistry (IHC).
Results:
The prognostic model consisting of INSM1, PHF21B, RNF138, SYTL4, WRNIP1, ZNF585B, and ZNF514, classified patients into opposite risk statuses. Patients in the high-risk subset had a considerably lower chance of surviving compared to those in the low-risk subset. There is a high probability that these model genes were attached to immune-related biological processes, which can be confirmed by the results of the above mechanistic methods. Moreover, patients in the low-risk subset also significantly outperformed the patients in the high-risk subset when calculating immune cells and function scores. Drug sensitivity and tumor immune dysfunction and exclusion (TIDE) analyses showed a clear difference in the immunological and chemotherapeutic efficacy predictions within the two risk groups. Additionally, the degrees of expression of model genes in high-risk and low-risk subsets presented great discrepancies.
Conclusions:
The signature may be applied as a predictive classifier to shepherd special medication for COAD patients.
