A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Early-stage non-small cell lung cancer (NSCLC) requires better methods for predicting recurrence and treatment response.
• A previous study developed a seven-gene qRT-PCR assay for recurrence risk and chemotherapy benefits.

Purpose of the Study:

• To validate and expand upon a seven-gene panel for NSCLC recurrence and therapeutic response prediction using RNA sequencing data.
• To investigate the regulatory network and clinical relevance of the seven-gene signature in NSCLC.

Main Methods:

• Application of a seven-gene panel to The Cancer Genome Atlas (TCGA) NSCLC RNA sequencing data (n=923).
• Construction of DNA copy number variation-mediated transcriptional regulatory networks using Boolean implication.
• Analysis of gene expression correlations with immune infiltration, drug response, and protein markers (Western blots).
• Utilized CRISPR-Cas9 and RNA interference (RNAi) for functional studies involving ZNF71 and PI3K pathway.

Main Results:

• The seven-gene panel was feasible in TCGA NSCLC RNA-seq data, showing significant predictive value (p < 0.05).
• A multi-omics network revealed genes, including PD-L1, correlated with immune infiltration and response to 10 NSCLC drugs.
• ZNF71 protein expression correlated with epithelial-mesenchymal transition markers, and PI3K was identified as a key proliferation pathway.

Conclusions:

• The validated seven-gene signature aids in predicting NSCLC recurrence and chemotherapy response.
• The multi-omics network provides insights into immune interactions and drug sensitivities, identifying PD-L1 as a relevant gene.
• Drug repositioning opportunities for NSCLC were identified based on the multi-omics network gene expression.