Prognostic targets recognition of rectal adenocarcinoma based on transcriptomics

Abstract

Colorectal cancer is currently the third most common cancer around the world. In this study, we chose a bioinformatics analysis method based on network analysis to dig out the pathological mechanism and key prognostic targets of rectal adenocarcinoma (READ). In this study, we downloaded the clinical information data and transcriptome data from the Cancer Genome Atlas database. Differentially expressed genes analysis was used to identify the differential expressed genes in READ. Community discovery algorithm analysis and Correlation analysis between gene modules and clinical data were performed to mine the key modules related to tumor proliferation, metastasis, and invasion. Genetic significance (GS) analysis and PageRank algorithm analysis were applied for find key genes in the key module. Finally, the importance of these genes was confirmed by survival analysis. Transcriptome datasets of 165 cancer tissue samples and 9 paracancerous tissue samples were selected. Gene coexpression networks were constructed, multilevel algorithm was used to divide the gene coexpression network into 11 modules. From GO enrichment analysis, module 11 significantly associated with clinical characteristic N, T, and event, mainly involved in 2 types of biological processes which were highly related to tumor metastasis, invasion, and tumor microenvironment regulation: cell development and differentiation; the development of vascular and nervous systems. Based on the results of survival analysis, 7 key genes were found negatively correlated to the survival rate of READ, such as MMP14, SDC2, LAMC1, ELN, ACTA2, ZNF532, and CYBRD1. Our study found that these key genes were predicted playing an important role in tumor invasion and metastasis, and being associated with the prognosis of READ. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.

Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

“…Grove et al (4) quantitatively analyzed the computed tomography(CT) features from two independent cohorts, demonstrating that convexity score and entropy ratio were important prognostic factors for survival analysis, and were negatively correlated with overall survival in non-small cell lung cancer patients. Meanwhile, prognostic prediction based on biomarkers, such as transcriptome data and DNA methylation data, also plays an important role in helping physicians to identify patients with high mortality risk accurately (2,5,6). For example, by using the transcriptome data, Yi et al (5) identified 7 key genes negatively correlated with the survival rate of rectal adenocarcinoma (READ).…”

“…For example, by using the transcriptome data, Yi et al. ( 5 ) identified 7 key genes negatively correlated with the survival rate of rectal adenocarcinoma (READ). Yu et al.…”