Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

Area of Science:

• Bioinformatics
• Cancer Genomics
• Transcriptomics

Background:

• Tumoral diseases involve complex genetic and regulatory alterations.
• Identifying common and specific deregulated genes and transcription factors (TFs) is crucial for understanding cancer progression.
• Lung cancer shares some molecular mechanisms with other cancers but also possesses unique features.

Purpose of the Study:

• To identify general and specific deregulated tumor genes and TFs in lung cancer compared to two other cancer types.
• To uncover common and unique molecular patterns and regulatory networks across different cancers.
• To identify potential biomarkers for general tumors and specifically for lung cancer.

Main Methods:

• Analysis of twenty microarray datasets using a bioinformatic pipeline.
• Identification of differentially expressed genes (DEGs) and hub genes.
• Construction of coexpression networks and enrichment analysis (DAVID, oPOSSUM).
• Kaplan-Meier survival analysis for identified TFs.

Main Results:

• Identified numerous deregulated TFs common to multiple cancers and specific to lung cancer.
• Revealed common connectivity patterns in coexpression networks between lung and other cancers.
• Discovered thirteen TFs significantly associated with lung cancer patient survival.
• Identified two TF networks regulating gene expression in lung and breast cancer.

Conclusions:

• A coregulatory network of TFs is implicated in the hallmarks of cancer.
• Lung cancer exhibits unique genes and TFs coexpressed during tumorigenesis.
• The study identified a regulatory 'metafirm' for cancer, both general and lung-specific, highlighting the heterogeneity of cancer.