Identification of Hub Genes and Enriched Pathways in Lung Cancer using Single-Cell RNA Sequencing

This research analyzes RNA sequencing data collected from individual cell data from the GSE127471 and GSE245170 datasets to identify potential biomarkers and treatment targets in non-small cell lung carcinoma (NSCLC). The analysis pipeline, implemented in Google Colab using Python libraries like Scanpy and Scvelo, involves data preprocessing, reducing dimensionality, grouping of cells, and examining differential gene expression. We identified key genes utilizing the study of networks that involve interactions among proteins and executed pathway enrichment analysis using gseapy. Key hub genes, such as RPS27A, RPL13, and EEF1A1, and enriched pathways, including ribosome biogenesis and translation, were identified. These findings highlight potential biomarkers and therapeutic targets for NSCLC. Further validation through literature search using pymed revealed existing evidence supporting the role of these genes and pathways in lung cancer. Our study demonstrates the utility of bioinformatics tools for the recognition of possible treatment targets and biomarkers for complex diseases like NSCLC.