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A zinc finger protein gene signature enables bladder cancer treatment stratification.

ABSTRACT: Bladder cancer (BC) is a commonly occurring malignant tumor affecting the urinary tract. Zinc finger proteins (ZNFs) constitute the largest transcription factor family in the human genome and are therefore attractive biomarker candidates for BC prognosis. In this study, we profiled the expression of ZNFs in The Cancer Genome Atlas (TCGA) BC cohort and developed a novel prognostic signature based on 7 ZNF-coding genes. After external validation of the model in the GSE48276 dataset, we integrated the 7-ZNF-gene signature with patient clinicopathological data to construct a nomogram that forecasted 1-, 2-, and 3-year OS with good predictive accuracy. We then accessed The Genomics of Drug Sensitivity in Cancer database to predict the therapeutic drug responses of signature-defined high- and low-risk BC patients in the TCGA cohort. Greater sensitivity to chemotherapy was revealed in the low-risk group. Finally, we conducted gene set enrichment analysis of the signature genes and established, by applying the ESTIMATE algorithm, distinct correlations between the two risk groups and the presence of stromal and immune cell types in the tumor microenvironment. By allowing effective risk stratification of BC patients, our novel ZNF gene signature may enable tailoring more intensive treatment for high-risk patients.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8148496 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBreast invasive carcinoma (BRCA) is a commonly occurring malignant tumor. Zinc finger proteins (ZNFs) constitute the largest transcription factor family in the human genome and play a mechanistic role in many cancers' development. The prognostic value of ZNFs has yet to be approached systematically for BRCA.MethodsWe analyzed the data of a training set from The Cancer Genome Atlas (TCGA) database and two validation cohort from GSE20685 and METABRIC datasets, composed of 3,231 BRCA patients. After screening the differentially expressed ZNFs, univariate Cox regression, LASSO, and multiple Cox regression analysis were performed to construct a risk-based predictive model. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and gene set enrichment analyses (GSEA) were utilized to assess the potential relations among the tumor immune microenvironment and ZNFs in BRCA.ResultsIn this study, we profiled ZNF expression in TCGA based BRCA cohort and developed a novel prognostic model based on 14 genes with ZNF relations. This model was composed of high and low-score groups for BRCA classification. Based upon Kaplan-Meier survival curves, risk-status-based prognosis illustrated significant differences. We integrated the 14 ZNF-gene signature with patient clinicopathological data for nomogram construction with accurate 1-, 3-, and 5-overall survival predictive capabilities. We then accessed the Genomics of Drug Sensitivity in Cancer database for therapeutic drug response prediction of signature-defined BRCA patient groupings for our selected TCGA population. The signature also predicts sensitivity to chemotherapeutic and molecular-targeted agents in high- and low-risk patients afflicted with BRCA. Functional analysis suggested JAK STAT, VEGF, MAPK, NOTCH TOLL-like receptor, NOD-like receptor signaling pathways, apoptosis, and cancer-based pathways could be key for ZNF-related BRCA development. Interestingly, based on the results of ESTIMATE, ssGSEA, and GSEA analysis, we elucidated that our ZNF-gene signature had pivotal regulatory effects on the tumor immune microenvironment for BRCA.ConclusionOur findings shed light on the potential contribution of ZNFs to the pathogenesis of BRCA and may inform clinical practice to guide individualized treatment.

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A zinc finger protein gene signature enables bladder cancer treatment stratification.

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