SPP1, a potential therapeutic target and biomarker for lung cancer: functional insights through computational studies
gene expression analysis; gene network analysis; Lung cancer; molecular docking; molecular dynamics simulation
NIH reported 128 different types of cancer of which lung cancer is the leading cause of mortality. Globally, it is estimated that on average one in every seventeen hospitalized patients was deceased. There are plenty of studies that have been reported on lung cancer draggability and therapeutics, but yet a protein that plays a central specific to cure the disease remains unclear. So, this study is designed to identify the possible therapeutic targets and biomarkers that can be used for the potential treatment of lung cancers. In order to identify differentially expressed genes, 39 microarray datasets of lung cancer patients were obtained from various demographic regions of the GEO database available at NCBI. After annotating statistically, 6229 up-regulated genes and 10324 down-regulated genes were found. Out of 17 up-regulated genes and significant genes, we selected SPP1 (osteopontin) through virtual screening studies. We found functional interactions with the other cancer-associated genes such as VEGF, FGA, JUN, EGFR, and TGFB1. For the virtual screening studies,198 biological compounds were retrieved from the ACNPD database and docked with SPP1 protein (PDBID: 3DSF). In the results, two highly potential compounds secoisolariciresinol diglucoside (-12.9 kcal/mol), and Hesperidin (-12.0 kcal/mol) showed the highest binding affinity. The stability of the complex was accessed by 100 ns simulation in an SPC water model. From the functional insights obtained through these computational studies, we report that SPP1 could be a potential biomarker and successive therapeutic protein target for lung cancer treatment. 2023 Informa UK Limited, trading as Taylor & Francis Group.
Annadurai Y.; Easwaran M.; Sundar S.; Thangamani L.; Meyyazhagan A.; Malaisamy A.; Natarajan J.; Piramanayagam S.
Journal of Biomolecular Structure and Dynamics, Vol-42, No. 3, pp. 1336-1351.
ISSN: 7391102; PubMed ID: 37096999; CODEN: JBSDD
Annadurai Y., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Easwaran M., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Sundar S., Department of Biotechnology, PSG College of Technology, Tamil Nadu, Coimbatore, India; Thangamani L., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Meyyazhagan A., Dipartimento di Medicina e Chirurgia, Universitdi Perugia, Perugia, Italy, Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India, Department of Translation Medicine and Surgery, Perugia University, Perugia, Italy; Malaisamy A., Transcription Regulation Group, International centre for Genetic Engineering and Biotechnology, New Delhi, India; Natarajan J., Text Mining Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Piramanayagam S., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India