Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Bioinformatics in Early Cancer Detection

Bioinformatics is a pretty recent branch of biology that encompasses the use of algebraic, analytic, and computing approaches to the processing and interpretation of biological information. A wide term, "bioinformatics" refers to the use of digital technology to study biological processes using high-dimensional data collected from many resources. The design and testing of the software tools required to evaluate the information are the core of bioinformatics research, which is conducted in great portions in silico and typically involves the synthesis of new learning from available data. Early diagnosis of cancer results in improved prognosis, but at the same time, it is difficult to conform to diagnosis at a very early stage. The use of DNA microarrays and proteomics studies for large-scale gene expression research has advanced technology, thus elevating the significance of bioinformatics tools. In today's research, wet experimentation and the application of bioinformatics analytics go side by side. Molecular profiling of tumor biopsies is becoming more and more crucial to both cancer research and the treatment of cancer.

Potential biomarkers uncovered by bioinformatics analysis in sotorasib resistant-pancreatic ductal adenocarcinoma

Background

Mutant KRAS-induced tumorigenesis is prevalent in lung, colon, and pancreatic ductal adenocarcinomas. For the past 3 decades, KRAS mutants seem undruggable due to their high-affinity GTP-binding pocket and smooth surface. Structure-based drug design helped in the design and development of first-in-class KRAS G12C inhibitor sotorasib (AMG 510) which was then approved by the FDA. Recent reports state that AMG 510 is becoming resistant in non-small-cell lung cancer (NSCLC), pancreatic ductal adenocarcinoma (PDAC), and lung adenocarcinoma patients, and the crucial drivers involved in this resistance mechanism are unknown.

Methods

In recent years, RNA-sequencing (RNA-seq) data analysis has become a functional tool for profiling gene expression. The present study was designed to find the crucial biomarkers involved in the sotorasib (AMG 510) resistance in KRAS G12C-mutant MIA-PaCa2 cell pancreatic ductal adenocarcinoma cells. Initially, the GSE dataset was retrieved from NCBI GEO, pre-processed, and then subjected to differentially expressed gene (DEG) analysis using the limma package. Then the identified DEGs were subjected to protein-protein interaction (PPI) using the STRING database, followed by cluster analysis and hub gene analysis, which resulted in the identification of probable markers.

Results

Furthermore, the enrichment and survival analysis revealed that the small unit ribosomal protein (RP) RPS3 is the crucial biomarker of the AMG 510 resistance in KRAS G12C-mutant MIA-PaCa2 cell pancreatic ductal adenocarcinoma cells.

Conclusion

Finally, we conclude that RPS3 is a crucial biomarker in sotorasib resistance which evades apoptosis by MDM2/4 interaction. We also suggest that the combinatorial treatment of sotorasib and RNA polymerase I machinery inhibitors could be a possible strategy to overcome resistance and should be studied in in vitro and in vivo settings in near future.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

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

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.

Current trend and development in bioinformatics research

This is an editorial report of the supplements to BMC Bioinformatics that includes 6 papers selected from the BIOCOMP'19-The 2019 International Conference on Bioinformatics and Computational Biology. These articles reflect current trend and development in bioinformatics research.