AUTHOR=Pudova Elena A. , Krasnov George S. , Kobelyatskaya Anastasiya A. , Savvateeva Maria V. , Fedorova Maria S. , Pavlov Vladislav S. , Nyushko Kirill M. , Kaprin Andrey D. , Alekseev Boris Y. , Trofimov Dmitry Y. , Sukhikh Gennady T. , Snezhkina Anastasiya V. , Kudryavtseva Anna V. 

TITLE=Gene Expression Changes and Associated Pathways Involved in the Progression of Prostate Cancer Advanced Stages

JOURNAL=Frontiers in Genetics

VOLUME=11

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.613162

DOI=10.3389/fgene.2020.613162

ISSN=1664-8021

ABSTRACT=<p>Prostate cancer (PC) is one of the most common cancers among men worldwide, and advanced PCs, such as locally advanced PC (LAPC) and castration-resistant PC (CRPC), present the greatest challenges in clinical management. Current indicators have limited capacity to predict the disease course; therefore, better prognostic markers are greatly needed. In this study, we performed a bioinformatic analysis of The Cancer Genome Atlas (TCGA) datasets, including RNA-Seq data from the prostate adenocarcinoma (PRAD; <italic>n</italic> = 55) and West Coast Dream Team – metastatic CRPC (WCDT-MCRPC; <italic>n</italic> = 84) projects, to evaluate the transcriptome changes associated with progression-free survival (PFS) for LAPC and CRPC, respectively. We identified the genes whose expression was positively/negatively correlated with PFS. In LAPC, the genes with the most significant negative correlations were <italic>ZC2HC1A</italic>, <italic>SQLE</italic>, and <italic>KIF11</italic>, and the genes with the most significant positive correlations were <italic>SOD3</italic>, <italic>LRRC26</italic>, <italic>MIR22HG</italic>, <italic>MEG3</italic>, and <italic>MIR29B2CHG</italic>. In CRPC, the most significant positive correlations were found for <italic>BET1</italic>, <italic>CTAGE5</italic>, <italic>IFNGR1</italic>, and <italic>GIMAP6</italic>, and the most significant negative correlations were found for <italic>CLPB</italic>, <italic>PRPF19</italic>, <italic>ZNF610</italic>, <italic>MPST</italic>, and <italic>LINC02001</italic>. In addition, we performed a gene network interaction analysis using STRINGdb, which revealed a significant relationship between genes predominantly involved in the cell cycle and characterized by upregulated expression in early recurrence. Based on the results, we propose several genes that can be used as potential prognostic markers.</p>