Computational systems biology analysis of biomarkers in lung cancer; unravelling genomic regions which frequently encode biomarkers, enriched pathways, and new candidates

Ibrahim O Alanazi; Sami A AlYahya; Esmaeil Ebrahimie; Manijeh Mohammadi-Dehcheshmeh

doi:10.1016/j.gene.2018.03.038

Computational systems biology analysis of biomarkers in lung cancer; unravelling genomic regions which frequently encode biomarkers, enriched pathways, and new candidates

Gene. 2018 Jun 15:659:29-36. doi: 10.1016/j.gene.2018.03.038. Epub 2018 Mar 17.

Authors

Ibrahim O Alanazi¹, Sami A AlYahya², Esmaeil Ebrahimie³, Manijeh Mohammadi-Dehcheshmeh⁴

Affiliations

¹ National Center for Biotechnology, Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia. Electronic address: ialenazi@kacst.edu.sa.
² National Center for Biotechnology, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia. Electronic address: salyahya@kacst.edu.sa.
³ Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia; School of Information Technology and Mathematical Sciences, Division of Information Technology, Engineering and the Environment, The University of South Australia, Adelaide, SA, Australia; Institute of Biotechnology, Shiraz University, Shiraz, Iran; School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, SA, Australia. Electronic address: esmaeil.ebrahimie@adelaide.edu.au.
⁴ School of Animal and Veterinary Sciences, The University of Adelaide, South Australia, Australia. Electronic address: manijeh.mohammadidehcheshmeh@adelaide.edu.au.

PMID: 29555200
DOI: 10.1016/j.gene.2018.03.038

Abstract

Exponentially growing scientific knowledge in scientific publications has resulted in the emergence of a new interdisciplinary science of literature mining. In text mining, the machine reads the published literature and transfers the discovered knowledge to mathematical-like formulas. In an integrative approach in this study, we used text mining in combination with network discovery, pathway analysis, and enrichment analysis of genomic regions for better understanding of biomarkers in lung cancer. Particular attention was paid to non-coding biomarkers. In total, 60 MicroRNA biomarkers were reported for lung cancer, including some prognostic biomarkers. MIR21, MIR155, MALAT1, and MIR31 were the top non-coding RNA biomarkers of lung cancer. Text mining identified 447 proteins which have been studied as biomarkers in lung cancer. EGFR (receptor), TP53 (transcription factor), KRAS, CDKN2A, ENO2, KRT19, RASSF1, GRP (ligand), SHOX2 (transcription factor), and ERBB2 (receptor) were the most studied proteins. Within small molecules, thymosin-a1, oestrogen, and 8-OHdG have received more attention. We found some chromosomal bands, such as 7q32.2, 18q12.1, 6p12, 11p15.5, and 3p21.3 that are highly involved in deriving lung cancer biomarkers.

Keywords: Biomarker; Lung Cancer; MicroRNA; Systems biology.

MeSH terms

Biomarkers, Tumor / genetics*
Cell Line, Tumor
Data Mining
Gene Regulatory Networks
Humans
Lung Neoplasms / genetics*
RNA, Untranslated / genetics
Systems Biology / methods*

Substances

Biomarkers, Tumor
RNA, Untranslated