CDK1 and HSP90AA1 appears as novel regulatory gene in Non-Small Cell Lung Cancer: A Bioinformatics Approach
Nirjhar BhattacharyyaiD, Samriddhi Gupta, Shubham Mohan SharmaiD, Aman Soni, Sali Abubaker BagabiriD, Malini BhattacharyyaiD, Atreyee MukherjeeiD, Atiah H. AlmalkiiD, Mustfa AlkhananiiD, Shafiul HaqueiD, Ashwini Kumar RayiD, Md. Zubbair MalikiD
Abstract
Lung cancer is one of the most invasive cancer affecting over a million of population. Non-small cell lung cancer constitutes up to 85% of all lung cancer cases. Therefore, it is important to identify prognostic biomarkers of NSCLC for therapeutic purpose. The complex behaviour of the NSCLC gene-regulatory network interaction is investigated using a network theoretical approach. We used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, GSE31210 and meta analyse them to find differentially expressed genes (DEGs), construct protein-protein interaction (PPI) network, analysed its topological properties, significant modules using network analyser with MCODE, construct a PPI-MCODE network using the genes of the significant modules. We used topological properties such as Maximal Clique Centrality (MCC) and bottleneck from the PPI-MCODE network. We compare them with hub genes (those with highest degrees) to find key regulator (KR) gene. This result is also validated by finding of common genes among top twenty hub genes, genes with highest betweenness, closeness centrality and eigenvector values. It was found that two genes, CDK1 and HSP90AA1 were common in PPI-MCODE combined analysis, and it was also found that CDK1, HSP90AA1 and HSPA8 were common among hub and bottle neck properties and suggesting significant regulatory role of CDK1 in non-small cell lung cancer. After validation, the common genes among top twenty hubs and centrality values like Betweenness Centrality, Closeness Centrality and eigen vector properties, CDK1 again appeared as the common gene. Our study as a summary suggested CDK1 as key regulator gene in complex NSCLC network interaction using network theoretical approach and described the complex topological properties of the network.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 32
- FWCI
- 2.29
- Field percentile
- top 10%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Prognostic Significance of Potential Target Networks and P16INK4a In Early Diagnosis of Non-Small Cell Lung Cancer
Varnika Kundadka, Radha Vatsa, Navrinder Kaur·bioRxiv·Jun 28, 2024
- Identification of common key genes and pathways between Covid-19 and lung cancer by using protein-protein interaction network analysis
Kang Soon Nan, Kalimuthu Karuppanan, Suresh Kumar·bioRxiv·Feb 16, 2021
- Circ_0005962 functions as an oncogene to aggravate non-small cell lung cancer progression via circ_0005962/miR-382-5p/PDK4 regulatory network
- IDENTIFICATION OF MIRNA-MRNA-HUB GENE NETWORK PREDICTION IN SQUAMOUS CELL LUNG CARCINOMA VIA STARBASE SERVER AND INSILICO ANALYSIS OF HUB GENES
Ashwini Kalaiselven, Venkataramanan Swaminathan·bioRxiv·Aug 22, 2021
- Identification of Biomarker Genes Based on Multi-Omics Analysis in Non-Small Cell Lung Cancer
Ji Xia, Hai-bin He, … Mingyue Ma·bioRxiv·Sep 5, 2022
- A Novel Approach to Topological Network Analysis for the Identification of Metrics and Signatures in Non-Small Cell Lung Cancer
Isabella Wu, Xin Wang·Scientific ReportsPeer-reviewed·May 22, 2023