Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Abstract

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.

GRAP2: A Novel Immune-Related Prognosis Biomarker in Cervical Cancer

“…Specifically, some TME-related genes were potentially able to predict prognosis. The same conclusions could be drawn from other studies [10,21]. Immediately thereafter, a total of 749 upregulated and 42 downregulated genes were detected between groups with high and low ImmuneScores and StromalScores, respectively.…”

The pathological risk score: A new deep learning‐based signature for predicting survival in cervical cancer

“…Yang et al 28 constructed a signature based on the immune‐related genes to predict survival. Wang et al 29 constructed a cervical cancer prognostic model based on 10 immune‐related genes (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR), while AUC of 0.738 in the TCGA public database. Tian et al 30 identified that the cervical cancer patients with five notable nonsynonymous mutant genes (PIK3CA, BRAF, GNA11, FBXW7, and CDH1) metastatic relapse significantly mutated mutations had significantly poor DFS and OS.…”

Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer

“…Therefrom, we confirmed immunotherapy treatment effective and survival outcome discrepancy between the two METAscore groups, which was a compelling clue that METAscore could evaluate the sensitivity to antitumor immunotherapy. Incumbent data on the scoring system and the prognostic scores of CESC mainly concentrated on the perspectives of immunogenomics and genetic alteration ( Cai et al, 2020 ; Li et al, 2019 ; Wang et al, 2021 ; Zhang et al, 2021 ) . Comparatively, the METAscore developed in our study was a promising breakthrough on the immunometabolism, offering novel insights into CESC immune diversity from the metabolic landscape and highlighting that METAscore could predict sensitivity to immunotherapy.…”