Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics...

Area of Science:

• Oncology
• Bioinformatics
• Molecular Biology

Background:

• Non-small cell lung cancer (NSCLC) is the most common lung malignancy, with poorly understood molecular drivers.
• Identifying novel biomarkers is crucial for developing new diagnostic and therapeutic strategies for NSCLC.

Purpose of the Study:

• To identify novel biomarkers associated with NSCLC pathogenesis using bioinformatics analysis.
• To provide new diagnostic and therapeutic approaches for NSCLC.

Main Methods:

• Utilized Gene Expression Omnibus (GEO) datasets (GSE118370, GSE10072) to identify differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples.
• Constructed a protein-protein interaction (PPI) network and performed module and enrichment analyses.
• Validated the expression and prognostic values of 14 hub genes using GEPIA, UALCAN, cBioPortal, String, Cytoscape, and Timer tools.

Main Results:

• Identified three key genes (SPP1, PECAM1, PIK3R1) correlated with overall survival (OS) in lung adenocarcinoma.
• High SPP1 expression correlated with poor prognosis, while low PECAM1 and PIK3R1 expression indicated poor prognosis.
• Found associations between SPP1 and miR-146a-5p, PIK3R1 and miR-21-5p, and immune checkpoint genes (CD274, PDCD1LG2) with SPP1 in lung adenocarcinoma.

Conclusions:

• SPP1 acts as an oncogene, promoting lung adenocarcinoma, while PECAM1 and PIK3R1 function as tumor suppressors.
• These identified genes are involved in regulating biological activities in lung adenocarcinoma.
• The findings provide a basis for improving detection methods and identifying immunotherapeutic targets for lung adenocarcinoma.