Human Drug Targets Identification in Breast Cancer by Computationally Based DNA Microarray Analysis
Syed Aun Muhammad *
Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University (BZU), Multan, Pakistan.
Nighat Fatima
Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan.
Hasan Afzaal
Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan.
Ziaullah Shah
Department of Biotechnology and Microbiology, Sarhad University of Science and Information Technology, Peshawar, Pakistan.
Amjad Ali
Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of Science and Technology, Islamabad, 44000, Pakistan.
*Author to whom correspondence should be addressed.
Abstract
Aims: The availability of large cDNA datasets make it feasible to find new genetic variants. In this study, we focused to perform micro array differential analysis of breast cancer dataset to reveal genetic mutants of this disease.
Methodology: Human drug targets of breast cancer (BC) was found by comparing normal breast tissue samples and breast invasive cancer samples using GSE31138 DNA microarray dataset. The dataset was accessed from Gene Expression Omnibus (GEO) NCBI. The differential analysis was performed using “R” software and Bioconductor packages.
Results: In differential expressed genes (DEGs), LIFR and PSMD10 were significant BC-linked genes. Matrix analysis of these DEGs showed interdependencies between the probe levels of two groups. Gene ontology and interactomic analysis explored the functional enriched map. These critical protein targets are involved in ubiquitin-dependent protein degradation, cell morphogenesis, anti-apoptosis, and positive regulation of cell proliferation and their dysregulation are responsible for tumorigenesis.
Conclusion: These protein targets not only reveal the understandings about BC but can also progress into biological markers for diagnosis or treatment.
Keywords: Breast cancer, microarray, DEGs, functional analysis, PPI network.