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INTERNATIONAL CONGRESS OF

CancerGenetics

• Discovery of Lung Cancer Biomarkers via Bioinformatics: Focus on the CAV1 Gene

• Effat Seyedhashemi,¹ Javad Mohammadi,² Nikrooz Heidari,³ Shahla Mohammad Ganji,^4,*
  1. Department of Molecular Medicine, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
  2. Department of Medical Technology and Tissue Engineering, School of Life Science Engineering, College of Interdisciplinary Science and Technologies, University of Tehran, Tehran, 1439957131, Iran
  3. Department of Mathematics and Applications, Faculty of Mathematical Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
  4. Department of Molecular Medicine, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
  
  
  
• Introduction: Lung cancer is currently the leading cause of cancer-related death worldwide (1). It is estimated that the number of deaths from this type of cancer will increase to three million by 2035 (2). Because the symptoms of lung cancer are often minimal and nonspecific, most patients are diagnosed at an advanced stage, resulting in poor survival rates (3). Identifying biomarkers in patients could save their lives. The use of bioinformatics analysis provides a space for a comprehensive study and access to an effective marker. Since the level of gene expression changes, especially tumor suppressor genes and oncogenes, is considered one of the first common processes in cancer formation (4), we decided to use bioinformatics studies to investigate genes that have undergone expression changes to find effective biomarkers
• Methods: For this purpose, expression array studies with accession codes GSE85841, GSE18842, GSE74706, GSE103888, and GSE136043 were collected from the GEO database. Statistical analysis was performed between tumor and healthy samples with statistical criteria |logFC|>2, P-value <0.05, and Adjusted P-value (FDR)<0.02. Common genes of all 5 expression array studies were identified from statistical analyses. The set of common genes obtained was analyzed by Cytoscape software. Genes obtained from Cytoscape analysis were analyzed by the TCGA database in terms of survival rate (Kaplan-Meier plot). Finally, the expression level of genes among lung cancer patients and healthy individuals was studied in the UALCAN database.
• Results: A total of 36 genes were found to be significantly different in expression between normal and tumor samples in all 5 studies. Analysis in Cytoscape software showed that five genes (ACADL-LPL-CAV1-CD36-CFD) were more significant than the others. Examination of genes in the UALCAN database showed that all five genes were less expressed in lung cancer patients than in healthy subjects. Study of survival curves in the TCGA database revealed that only the CAV1 gene had a P-value <0.05. CAV1= Expression level in a cancer patient compared to a normal person downregulate Pvalue survival analysis =0.027
• Conclusion: The results of this bioinformatics study highlight the potential role of CAV1 as a biomarker for lung cancer. The downregulation of CAV1 observed in lung cancer patients compared to healthy individuals aligns with previous reports suggesting its involvement in tumor suppression and cancer progression. Its significant association with patient survival further supports its clinical relevance. However, while bioinformatics analyses provide powerful insights by integrating large datasets, validation through experimental and clinical studies is essential to confirm the diagnostic and prognostic value of CAV1. Future work should focus on laboratory experiments, including protein expression analysis and functional assays, to elucidate the mechanistic role of CAV1 in lung cancer development and progression. Additionally, exploring the interaction of CAV1 with other identified genes such as ACADL, LPL, CD36, and CFD might reveal novel pathways and targets for therapeutic intervention. The integration of such multi-gene biomarkers could improve the sensitivity and specificity of lung cancer detection. In conclusion, this study demonstrates the strength of bioinformatics in uncovering candidate biomarkers and lays the foundation for further translational research that could eventually contribute to earlier diagnosis and better management of lung cancer patients.
• Keywords: Lung cancer -bioinformatics analysis -gene expression-biomarker