Prognostic targets recognition of rectal adenocarcinoma based on transcriptomics

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Colorectal cancer is a global health concern, ranking as the third most common cancer worldwide.
• Rectal adenocarcinoma (READ) presents complex pathological mechanisms and requires identification of key prognostic targets.

Purpose of the Study:

• To elucidate the pathological mechanisms of READ using network-based bioinformatics analysis.
• To identify key prognostic targets for rectal adenocarcinoma.

Main Methods:

• Downloaded clinical and transcriptome data from The Cancer Genome Atlas (TCGA) for 165 READ samples and 9 normal samples.
• Applied differential gene expression analysis, network construction, community discovery, and correlation analysis to identify key gene modules and genes.
• Utilized Gene Ontology (GO) enrichment, Genetic Significance (GS), PageRank algorithm, and survival analysis to validate key genes.

Main Results:

• Constructed gene coexpression networks, identifying 11 modules; Module 11 significantly associated with clinical characteristics (N, T, event) and involved in tumor metastasis, invasion, and microenvironment regulation.
• Identified 7 key genes (MMP14, SDC2, LAMC1, ELN, ACTA2, ZNF532, CYBRD1) negatively correlated with READ survival rates.
• These genes are implicated in cell development, differentiation, vascular/nervous system development, tumor invasion, and metastasis.

Conclusions:

• The identified key genes play crucial roles in tumor invasion and metastasis, significantly impacting READ prognosis.
• This research provides novel potential therapeutic targets and insights for improving the management and prognosis of rectal adenocarcinoma.