Construction autophagy-related prognostic risk signature combined with clinicopathological validation analysis for survival prediction of kidney renal papillary cell carcinoma patients

• Published in: BMC cancer Vol. 21; no. 1; pp. 411 - 12
• Main Authors: Fei, Hongjun, Chen, Songchang, Xu, Chenming
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.04.2021; BioMed Central; BMC
• Subjects: Autophagy; Autophagy (Cytology); Autophagy - genetics; Autophagy-related genes; Autophagy-Related Proteins - genetics; Biological markers; Biomarkers; Biomarkers, Tumor; Cancer; Carcinoma, Renal cell; Carcinoma, Renal Cell - diagnosis; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - mortality; Care and treatment; Gene expression; Gene Expression Profiling; Genetic aspects; Genomes; Genomics; Health aspects; Humans; Kidney cancer; Kidney Neoplasms - diagnosis; Kidney Neoplasms - genetics; Kidney Neoplasms - mortality; Kidney renal papillary cell carcinoma; Kidneys; Medical prognosis; Metastasis; mRNA; Oncology, Experimental; Phagocytosis; Prognosis; Prognostic risk signature; Protein Interaction Mapping; Protein Interaction Maps; Proteins; Regression analysis; Renal cell carcinoma; Reproducibility of Results; ROC Curve; Survival prediction; Targeted therapy; Transcriptome; China; Survival prediction; Autophagy-related genes; Targeted therapy; Kidney renal papillary cell carcinoma; Prognostic risk signature
• DOI: 10.1186/s12885-021-08139-2
• ISSN: 1471-2407

Little data is available on prognostic biomarkers and effective treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP) patients, to find potential prognostic biomarkers and new targets was an urgent mission for KIRP therapy. The differentially expressed autophagy-related genes (DEARGs) were screened out according to the RNA sequencing data in The Cancer Genome Atlas database, then identified survival-related DEARGs to establish a prognostic model for survival predicting of KIRP patients. Then we verified the robustness and validity of the prognostic risk model through clinicopathological data. At last, we evaluate the prognostic value of genes that formed the prognostic risk model individually. We analyzed the expression of 232 autophagy-related genes (ARGs) in 289 KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as DEARGs. The functional and pathway enrichment analysis was done and protein-protein interaction network was constructed for all DEARGs. To sift candidate DEARGs associated with KIRP patients' survival and create an autophagy-related risk prognostic model, univariate and multivariate Cox regression analysis were did separately. Eventually 3 desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were picked out and used for construct the autophagy-related risk model. The accuracy of the prognostic risk model for survival prediction was assessed by Kaplan-Meier plotter, receiver-operator characteristic curve, and clinicopathological correlational analyses. The prognostic value of above 3 genes was verified individually by survival analysis and expression analysis on mRNA and protein level. The autophagy-related prognostic model is accurate and applicable, it can predict OS independently for KIRP patients. Three independent prognostic DEARGs can benefit for facilitate personalized target treatment too.