Identification of potential regulatory mechanisms and therapeutic targets for lung cancer

Abstract

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.

Communicated by Ramaswamy H. Sarma

Keywords:

• Lung cancer
• network theory
• protein-protein interaction network (PPI)
• key regulator
• therapeutical biomarker

Acknowledgement

AKR is supported by GIA Scheme of Department of Health Research (DHR), Indian Council of Medical Research (ICMR), Govt. of India (F. No. R.11013/12/2023-GIA/HR), National Heart, Lung, and Blood Institute (NHLBI) and Fogarty International Centre (FIC), NIH, USA grant D43TW009345 and IoE, 2022, FRP, University of Delhi. AP acknowledge Department of Health Research (DHR), Indian Council of Medical Research (ICMR), Govt. of India (F. No. R.11013/12/2023-GIA/HR) for financial support as JRF. AKR and VS gratefully acknowledge the computational facility provided by Bioinformatics Resources and Applications Facility (BRAF) of the Center for Development of Advanced Computing (CDAC, Pune) and Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio), IIT Delhi, India. VS acknowledge the Delhi School of Public Health, Institution of Eminence (DSPH-IoE), University of Delhi, Delhi for the financial support (IoE/2021/MKPDF/DSPH/143).

Disclosure statement

The authors declare that no conflicts of interest exist.

Author contributions

Conceptualized and designed the study: MZM and AKR. Performed the analysis: AP and MZM. Curated the data: AP, MZM and AKR. Wrote the original draft: AP and MZM. Interpreted the data: AP, MD, TAT, MI, MZM and AKR. Revised the manuscript: AP, VS,MD, TAT, MI, VS, RT, RM, AKS, PM, MZM and AKR. All authors have read and agreed to the published version of the manuscript.

Institutional review board statement

Not Applicable.

Informed consent statement

Not Applicable.

Data availability statement

Publicly available datasets were analysed in this study. This data can be found on BioXpress, http://hive.biochemistry.gwu.edu/tools/bioxpress

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.