Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures

Background: Lung squamous cell carcinoma (LUSC) patients are often diagnosed at an advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC is of utmost importance. Methods: Multiple datasets from the NCBI-GEO repository were obtained and merged to construct the complete dataset. We also constructed a subset from this complete dataset with only known cancer driver genes. Further, machine learning classifiers were employed to obtain the best features from both datasets. Simultaneously, we perform differential gene expression analysis. Furthermore, survival and enrichment analyses were performed. Results: The kNN classifier performed comparatively better on the complete and driver datasets' top 40 and 50 gene features, respectively. Out of these 90 gene features, 35 were found to be differentially regulated. Lassopenalized Cox regression further reduced the number of genes to eight. The median risk score of these eight genes significantly stratified the patients, and low-risk patients have significantly better overall survival. We validated the robust performance of these eight genes on the TCGA dataset. Pathway enrichment analysis identified that these genes are associated with cell cycle, cell proliferation, and migration. Conclusion: This study demonstrates that an integrated approach involving machine learning and system biology may effectively identify novel biomarkers for LUSC.

Automated summary

This study demonstrates the potential importance of an integrated approach involving machine learning and system biology in identifying novel biomarkers for lung squamous cell carcinoma (LUSC).