Comprehensive analysis of ZNF family genes in prognosis, immunity, and treatment of esophageal cancer

Author:

Hong Kunqiao,Yang Qian,Yin Haisen,Wei Na,Wang Wei,Yu Baoping

Abstract

Abstract Background As a common malignant tumor, esophageal carcinoma (ESCA) has a low early diagnosis rate and poor prognosis. This study aimed to construct the prognostic features composed of ZNF family genes to effectively predict the prognosis of ESCA patients. Methods The mRNA expression matrix and clinical data were downloaded from TCGA and GEO database. Using univariate Cox analysis, lasso regression and multivariate Cox analysis, we screened six prognosis-related ZNF family genes to construct the prognostic model. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, and nomogram to evaluate the prognostic value within and across sets, separately and combined. We also validated the prognostic value of the six-gene signature using GSE53624 dataset. The different immune status was observed in the single sample Gene Set Enrichment Analysis (ssGSEA). Finally, real-time quantitative PCR was used to detect the expression of six prognostic ZNF genes in twelve pairs of ESCA and adjacent normal tissues. Results A six prognosis-related ZNF family genes model consisted of ZNF91, ZNF586, ZNF502, ZNF865, ZNF106 and ZNF225 was identified. Multivariable Cox regression analysis revealed that six prognosis-related ZNF family genes were independent prognostic factors for overall survival of ESCA patients in TCGA and GSE53624. Further, a prognostic nomogram including the riskScore, age, gender, T, stage was constructed, and TCGA/GSE53624-based calibration plots indicated its excellent predictive performance. Drug Sensitivity and ssGSEA analysis showed that the six genes model was closely related to immune cells infiltration and could be used as a potential predictor of chemotherapy sensitivity. Conclusion We identified six prognosis-related ZNF family genes model of ESCA, which provide evidence for individualized prevention and treatment.

Funder

Science and Technology Fund of the Guizhou Provincial Health Commission

Guizhou Provincial Science and Technology Projects

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Genetics,Oncology

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