Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes.
Anushka Pravin ChawhanNorine DsouzaPublished in: Molecular genetics and genomics : MGG (2024)
Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.
Keyphrases
- bioinformatics analysis
- genome wide
- genome wide identification
- squamous cell carcinoma
- free survival
- genome wide analysis
- network analysis
- end stage renal disease
- dna methylation
- chronic kidney disease
- transcription factor
- machine learning
- radiation therapy
- stem cells
- cell proliferation
- ejection fraction
- newly diagnosed
- small molecule
- copy number
- mesenchymal stem cells
- climate change
- depressive symptoms
- pi k akt
- long non coding rna
- patient reported outcomes
- human health