Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective

Abstract

Background Worldwide, more than 80% of identified lung cancer cases are associated to the non-small cell lung cancer (NSCLC). We used microarray gene expression dataset GSE10245 to identify key biomarkers and associated pathways in NSCLC. Results To collect Differentially Expressed Genes (DEGs) from the dataset GSE10245, we applied the R statistical language. Functional analysis was completed using the Database for Annotation Visualization and Integrated Discovery (DAVID) online repository. The DifferentialNet database was used to construct Protein–protein interaction (PPI) network and visualized it with the Cytoscape software. Using the Molecular Complex Detection (MCODE) method, we identify clusters from the constructed PPI network. Finally, survival analysis was performed to acquire the overall survival (OS) values of the key genes. One thousand eighty two DEGs were unveiled after applying statistical criterion. Functional analysis showed that overexpressed DEGs were greatly involved with epidermis development and keratinocyte differentiation; the under-expressed DEGs were principally associated with the positive regulation of nitric oxide biosynthetic process and signal transduction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway investigation explored that the overexpressed DEGs were highly involved with the cell cycle; the under-expressed DEGs were involved with cell adhesion molecules. The PPI network was constructed with 474 nodes and 2233 connections. Conclusions Using the connectivity method, 12 genes were considered as hub genes. Survival analysis showed worse OS value for SFN, DSP, and PHGDH. Outcomes indicate that Stratifin may play a crucial role in the development of NSCLC.

A Novel Approach to Topological Network Analysis for the Identification of Metrics and Signatures in Non-Small Cell Lung Cancer

“…The global metrics include Betweenness, Bottleneck, Eccentricity, Closeness, Radiality, Stress, and Edge Percolated Component (EPC). Often, literature studies utilize only one scoring method 14–16 . To ensure that no essential genes are missed and all possibilities are considered, we created a complete and comprehensive list of candidate biomarkers using all twelve scoring methods.…”

“…They often utilize popular and conventional geometric-based scoring metrics (e.g. Degree) 14–16 without reasoning, and use only a single scoring metric.…”

“…They often utilize popular and conventional geometric-based scoring metrics (e.g. Degree) [14][15][16] without reasoning, and use only a single scoring metric. Thus, the central goal of this study is to identify the top-performing topological scoring metric (or method) that best captures functional significance in protein networks.…”

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

“…Moreover, this gene was reported as an oncogene in esophageal squamous cell carcinoma cells 112 . Rakibul Islam et al 107 , 113 have done a survival analysis, and their results show a worse overall survival value for SFN , and Outcomes show that SFN may play a crucial role in the development of NSCLC. "Tandem of pore domain in a weak inwardly rectifying K + channels (TWIK)" pathway involves two genes of the magenta module, including KCNK7 and KCNK1 .…”

“…They introduced these 5 genes for NSCLC prognosis in patients. A computational approach based on protein–protein interaction (PPI) network analysis was used in a similar study, and Stratifin had an important role in NSCLC 6 development. Furthermore, Yun-Qiang Zhang et al 7 proposed HIST1H2BH and PLK1 as prognostic biomarkers for NSCLC patients.…”