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

Abstract

Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Technical and biological variations in the purification of extrachromosomal circular DNA (eccDNA) and the finding of more eccDNA in the plasma of lung adenocarcinoma patients compared with healthy donors

“…For instance, gene CTNND2 is located on Chr5, for which we demonstrated a greater eccDNA count per chromosome after length normalization in cancer samples (Figures 5C). Our assessments did not uncover any of the previously reported established biomarkers of lung cancer in our plasma eccDNA populations (5,29,30,(46)(47)(48)(49)(50).…”

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

“…To the best of our knowledge, this is the first study that comprehensively evaluates all 12 topological network scoring metrics and their effectiveness in identifying cancer-related biomarkers in biological networks. Compared to previous studies that solely relied on popular metrics like Degree 15 – 17 , selected metrics without any validation or reasoning provided, or simply used all 12 metrics together 6 , 25 , which was shown to be inefficient in this study, our study thoroughly evaluates all 12 metrics using our proposed Integrated AUC. Moreover, many previous studies only used one metric or lacked effective metric composition, which is not enough to accurately quantify and characterize the disease network.…”

“…Studies have revealed that genes do not function in isolation, and instead work together. As such, network-based approaches have emerged as powerful tools for investigating gene interactions and understanding their complex relationships 6 . In the past few years, topological data analysis (TDA) has revolutionized the oncology field, becoming one of the most powerful and widespread tools to extract useful information from high-dimensional biomedical data 7 , 8 .…”

Bioinformatics approaches for Biomedical Research

“…This includes gene expression, protein expression, and protein activity, the modulation of desired cellular phenotypes. 28 The protein/gene-protein/gene interaction networking, hub gene identification, gene enrichment, and functional gene annotation analyses are powerful tools for the identify potential diagnosis and treatment biomarkers in diseases such as cancer 1,2,[29][30][31] , bipolar disorder 2 , depression 32 , diabetes 33 , arteriosclerosis 34 , and others. For instance, Wan et al in 2020 applied bioinformatics analysis to identify eight candidate genes that could be a potential prognostic marker of thyroid carcinoma based on expression analysis profiles from the GEO database.…”