AUTHOR=He Minxin , Li Mingrui , Guan Yibing , Wan Ziyan , Tian Juanhua , Xu Fangshi , Zhou Haibin , Gao Mei , Bi Hang , Chong Tie 

TITLE=A New Prognostic Risk Score: Based on the Analysis of Autophagy-Related Genes and Renal Cell Carcinoma

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.820154

DOI=10.3389/fgene.2021.820154

ISSN=1664-8021

ABSTRACT=Abstract: 
Introduction: Clear cell renal cell carcinoma (ccRCC) patients suffers from its high recurrence and metastasis rate, a new prognosis to predict individuals with high possibility of recurrence or metastasis is in urgent need. Autophagy has been found having a dual influence on tumorigenesis. In this study we aim to analyze autophagy related genes(ATGs) and ccRCC patients and find a new prognosis risk score. 
Method: Analyzing differential expression genes (DEGs) in TCGA-KIRC dataset, and took intersection with ATGs. Through lasso, univariate, and multivariate cox regression, DEGs were choosen, the coefficients and expression levels of them were components constructing formula of risk score. Analyzing mRNA expression in tumor and normal tissue in ONCOMINE database and TCGA-KIRC dataset. The Human Protein Atlas (HPA) was used to analyze protein levels of DEGs. Protein-protein interaction(PPI) network was examined in STRING, and visualized in cytoscape. Functional enrichment analysis was performed in RStudio. To prove its ability and practicibility, we analyzed univariate and multivariate cox regression, Kaplan-Meier curve (K-M curve), risk factor association diagram, receiver operating characteristic curve (ROC curve) of survival and nomogram, the performance of nomogram was evalued by calibration curve. Then we further explored functional enrichment related to risk groups through Gene Set Enrichment Analysis (GSEA), weighted gene co-expression network analysis (WGCNA) and Metascape database. At last, we investigated immune cell infiltration of DEGs and two risk groups through TIMER database and “Cibersort” algorithm. 
Result: We identified 7 DEGs (BIRC5, CAPS, CLDN7, CLVS1, GMIP, IFI16 and TCIRG1) as components of construction of risk score. All the 7 DEGs were differently expressed in ccRCC and normal tissue according to ONCOMINE database and TCGA-KIRC dataset. Functional enrichment analysis indicated DEGs and their most associated genes were shown to be abundant in autophagy-related pathways and played roles in tumorigenesis and progression processes. A serious analyses proved that this risk score as independent risk signature of ccRCC patients.
Conclusion: The risk score constructed by 7 DEGs had the ability of predicting prognosis of ccRCC patients and conducive to the identification of novel prognostic molecular markers. However, further experiment is still needed to varify its ability and practicibility.