An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes

Atif Noorul Hasan; Mohammad Wakil Ahmad; Inamul Hasan Madar; B Leena Grace; Tarique Noorul Hasan

doi:10.6026/97320630011229

An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes

Bioinformation. 2015 May 28;11(5):229-35. doi: 10.6026/97320630011229. eCollection 2015.

Authors

Atif Noorul Hasan¹, Mohammad Wakil Ahmad², Inamul Hasan Madar³, B Leena Grace⁴, Tarique Noorul Hasan⁵

Affiliations

¹ Dept. of Bioinformatics, Jamia Millia Islamia, New Delhi, India ; Division of Bioinformatics, Noor-Amna Foundation for Research and Education, Bettiah, Bihar, India.
² Dept. of Software Engg, College of Computer Science, King Saud University, Riyadh, Saudi Arabia.
³ Dept. of Biotechnology and Bioinformatics, Bishop Heber College, Tiruchirappalli, TN, India.
⁴ Dept of Biotechnology, Vinayaka Missions University, Salem, TN, India.
⁵ Division of Bioinformatics, Noor-Amna Foundation for Research and Education, Bettiah, Bihar, India ; R & D Center, Bharathiar University, Coimbatore-641046, TN,India.

Abstract

Smoking is the leading cause of lung cancer development and several genes have been identified as potential biomarker for lungs cancer. Contributing to the present scientific knowledge of biomarkers for lung cancer two different data sets, i.e. GDS3257 and GDS3054 were downloaded from NCBI׳s GEO database and normalized by RMA and GRMA packages (Bioconductor). Diffrentially expressed genes were extracted by using and were R (3.1.2); DAVID online tool was used for gene annotation and GENE MANIA tool was used for construction of gene regulatory network. Nine smoking independent gene were found whereas average expressions of those genes were almost similar in both the datasets. Five genes among them were found to be associated with cancer subtypes. Thirty smoking specific genes were identified; among those genes eight were associated with cancer sub types. GPR110, IL1RN and HSP90AA1 were found directly associated with lung cancer. SEMA6A differentially expresses in only non-smoking lung cancer samples. FLG is differentially expressed smoking specific gene and is related to onset of various cancer subtypes. Functional annotation and network analysis revealed that FLG participates in various epidermal tissue developmental processes and is co-expressed with other genes. Lung tissues are epidermal tissues and thus it suggests that alteration in FLG may cause lung cancer. We conclude that smoking alters expression of several genes and associated biological pathways during development of lung cancers.