Lung cancer is the leading cause of cancer-related deaths worldwide and is divided into two broad histological types, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Network module-based approach is applied to lung cancer subtypes in order to analyze and compare the results with previous literature and thus discover new genetic biomarkers and/or confirm previously discovered ones. Data were extracted and analyzed in GEO2R, later protein-protein interaction (PPI)networks were generated through STRING. Functional modules and genesoverlapping between modules were identified using Cytoscape plugins MCODE and ModuLand, which were compared subsequently. The tools complement each other as MCODE can help visualize the neighbors of nodes identified by ModuLand while ModuLand can help identify significant genes as MCODE identifies all genes equally. Venny was used to analyze the overlapping genes between the subtypes and FunRichfor functional enrichment. The results were consistent with findings of previous literature. ModuLand highlighted nodes previously reported to have a role in various types of cancer including lung cancer, which involved two common proteins: CDK1and HIGD1B. The two functional networks showed clusters belonging to the mitoticsister chromatid segregation. Perhaps the main defective part in the cell cycle of lungcancer is chromatin-related. In conclusion by establishing functional modules and highlighting common genes between the modules for each subtype can shed light on potential mechanisms and further support previous discoveries. Several important genes have been identified at the centre of highly interconnected biological complexes that could serve as candidate biomarkers and hallmarks for future studies.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-22423 |
| Date | January 2023 |
| Creators | Jamal, Noor Haval |
| Publisher | Högskolan i Skövde, Institutionen för biovetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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