Correlation of Zinc Finger Proteins with Immune Infiltration in Gastric Cancer Patients: A Prognostic Signature Model of Five Genes

Abstract

Background: Zinc finger (ZNF) proteins play pivotal roles in the initiation, progression, and metastasis of various cancer types. Nevertheless, the precise mechanism of ZNF genes (ZNFGs) in the prognosis and treatment of gastric cancer (GC) patients remains unclear. Methods: Transcriptomic data and clinical information related to GC, as well as ZNFG-related data, were retrieved from publicly available databases. Initially, differentially expressed ZNFGs (DE-ZNFGs) were identified through comparative analysis between GC and normal tissue samples. Subsequently, univariate and multivariate regression analyses, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm were utilized to identify potential biomarkers and formulate a risk assessment model. Furthermore, Kaplan-Meier survival curve analysis was conducted to analyze the correlation between the risk score and overall survival of GC patients, while the receiver operating characteristic (ROC) curve analysis was performed to evaluate the reliability of the model. Moreover, Gene Set Enrichment Analysis (GSEA) was performed to elucidate Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, comprehensive investigations were conducted to assess immune infiltration, immune checkpoints, and the immunophenoscore of distinct risk groups. Results: A total of 165 DE-ZNFGs were identified, from which, five genes (zinc finger protein 36 (ZFP36), zinc finger protein 121 (ZNF121), ZNF131, ZNF22, and Replication initiator 1 (REPIN1) were selected as biomarkers to construct the risk model. This model demonstrated high predictive accuracy for the prognosis of GC patients, with an area under the curve (AUC) exceeding 0.6 for 1-, 3- and 5-year survival rates. Both the risk score and patient age were observed to independently predict prognosis in GC. Moreover, GSEA results showed that high risk group exhibited enrichment in pathways related to mitogen-activated protein kinase (MAPK), calcium signaling, neuroregulation, cellular connections, and cytoskeletal regulation, while low risk group was characterized by pathways associated with metabolic processes, transcription of genetic information, and stringent regulation of genetic stability. Immune analysis revealed significantly elevated stromal, immune, and Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) composite scores in high-risk patients. Additionally, there was a notable difference in the expression levels of 19 immune cells and 13 immune checkpoints between the two groups, suggesting significant immunological differences. Conclusions: Our ZNFG-related risk model can be used to predict the survival of GC patients and may have potential guiding implications for GC treatment.