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Integrative genomic expression analysis reveals stable differences between lung cancer and systemic sclerosis
Heng Li1,2, Liping Ding1, Xiaoping Hong1
1Department of Rheumatology and Immunology, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
BMC Cancer
|March 11, 2021
Summary
Systemic sclerosis patients have a higher lung cancer risk due to shared immune and cell cycle dysregulation. Counter-regulated genes may serve as novel pulmonary disease biomarkers.
Area of Science:
- Genomics
- Immunology
- Oncology
Background:
- Lung cancer has the highest incidence and mortality globally.
- Systemic sclerosis (SSc) patients exhibit a four-fold increased risk of lung cancer.
- The biological mechanisms linking SSc and lung cancer remain unclear.
Purpose of the Study:
- To investigate the molecular mechanisms underlying the increased lung cancer risk in patients with systemic sclerosis.
- To identify common and distinct gene expression profiles between SSc and lung cancer.
- To explore potential biomarkers for pulmonary diseases.
Main Methods:
- Integrated analysis of gene expression profiles from 355 peripheral blood samples (70 lung cancer, 61 SSc, 224 controls).
- Bioinformatic analysis to identify differentially expressed genes (DEGs) between disease and control groups.
- Gene Ontology (GO) and KEGG pathway enrichment analysis, and Protein-Protein Interaction (PPI) network construction.
Main Results:
- Identified 299 DEGs in SSc and 1644 DEGs in lung cancer, with 64 overlapping DEGs.
- Common changes include increased innate immune response and response to virus genes, and decreased negative regulation of cell cycle genes.
- Distinct trends observed in mitophagy regulation, chromatin binding, and fatty acid metabolism-related genes.
Conclusions:
- Enhanced innate immunity and weakened cell cycle regulation are potential shared mechanisms linking SSc and lung cancer.
- Counter-regulated DEGs present potential novel biomarkers for pulmonary diseases.
- Fatty acid metabolism-related DEGs may correlate with clinical blood lipid data.

