Gene Co-expression Network analysis of Lung Squamous Cell Carcinoma data

TL;DR

This study used gene co-expression network analysis on lung squamous cell carcinoma data to identify gene modules associated with tumor growth and survival, highlighting key genes like RFC4 and ECT2 across datasets.

Contribution

It introduces a comprehensive co-expression analysis approach linking gene modules to prognosis in lung squamous cell carcinoma, validated across RNA-seq and microarray data.

Findings

Identified gene modules significantly associated with tumor growth.

Found RFC4 and ECT2 as key survival-related genes.

Validated key genes across different datasets.

Abstract

We performed a gene co-expression analysis on Lung Squamous Cell Carcinoma data to find modules (groups) of genes that may highly impact the growth of these type of tumors. Additionally, we used cancer survival data to relate modules to prognostic significance in terms of survival time. Analysis on RNA-seq data revealed modules which are significant in gene enrichment analysis. Specifically, two genes - RFC4 and ECT2 - have been found to be significant in terms of survival time. We also performed a second gene co-expression analysis on a second dataset of microarray data, and many significant genes found in this analysis could also be found in the RNA-seq data implying that these genes might indeed play a crucial role in Lung Squamous Cancer. All the R code for the analysis can be found at: \url{https://github.com/nafizh/Gene_coexpression_analysis_lung_cancer}