DE-Meta: revealing tumor gene expression by meta-analysis of RNA-Seq and proteomics datasets

Introduction Workflow Summary Detecting differentially expressed genes under different biological conditions is crucial to characterize mechanisms of cancer development and identifying determinants of patient outcome. High throughput technologies such as RNA sequencing and mass spectrometry-based proteomics have been widely used to identify differentially expressed genes (DEG), on a transcript and on a peptide basis, respectively. However, both transcription and translation of genes provide information about gene expression. Thus, leveraging both RNA and protein expression data could potentially produce more accurate results. Various statistical tools have been developed to tackle the differential expression problem for a single platform, such as edgeR, DESeq2, etc. However, a tool that integrates both transcriptome and proteomics data for differential expression analysis has not yet been developed. Meta-analysis can potentially increase statistical power and identify new cancer genes in large sample cohorts and potentially also in small sample cohorts. Here we present DE-Meta, a new tool developed using R, which performs combined meta-analysis of RNA-seq and MS-based proteomics on matched tumor specimens. Lung adenocarcinoma (LUAD) is a primary cause of cancer-related deaths worldwide, despite advances in somatically targeted therapeutics and immune checkpoint therapies. The diagnosis and treatment of LUAD patients pose significant challenges due to the vast morphological and molecular heterogeneity observed both within and between tumors. Two published lung adenocarcinoma datasets were used in this study: APOLLO11,7 and CPTAC2. APOLLO7 is a proteogenomic study seeking to describe the major genome, transcriptome, proteome and phosphoproteome alterations, subtypes, and molecular predictors of patient outcomes. Stratifying LUAD patients using expression subtypes can enhance prediction of clinical outcomes. Expression subtype status of these two cohorts were predicted
RITM0035818
Detecting differentially expressed genes under different biological conditions is crucial to characterize mechanisms of cancer development and identifying determinants of patient outcome. High throughput technologies such as RNA sequencing and mass spectrometrybased proteomics have been widely used to identify differentially expressed genes (DEG), on a transcript and on a peptide basis, respectively. However, both transcription and translation of genes provide information about gene expression. Thus, leveraging both RNA and protein expression data could potentially produce more accurate results. Various statistical tools have been developed to tackle the differential expression problem for a single platform, such as edgeR, DESeq2, etc. However, a tool that integrates both transcriptome and proteomics data for differential expression analysis has not yet been developed. Meta-analysis can potentially increase statistical power and identify new cancer genes in large sample cohorts and potentially also in small sample cohorts. Here we present DE-Meta, a new tool developed using R, which performs combined meta-analysis of RNA-seq and MS-based proteomics on matched tumor specimens.