Discovering potential key biomarkers and molecular mechanisms in Chronic Obstructive Pulmonary Disease and rheumatoid arthritis using integrated bioinformatics and machine learning approaches

Background:

Chronic Obstructive Pulmonary Disease (COPD) and Rheumatoid Arthritis (RA) are among the leading causes of illness and mortality globally. Many studies claim that these diseases are interconnected. However, the underlying molecular processes and shared biomarkers between RA and COPD are still undiscovered.

Methods:

This research is aimed at discovering the key potential biomarkers and mechanisms in RA and COPD, incorporating several Bioinformatics and Machine Learning (ML) approaches via R programming. Several validation methods are also applied to validate the key potential biomarkers.

Results:

By performing the differential expression analysis, we have discovered 90 common Differentially Expressed Genes (DEGs) between RA and COPD. From the Gene Set Enrichment analysis, we get the top molecular mechanisms related to the common DEGs. From the Protein Protein Interaction analysis, we get the candidate hub genes among the common DEGs. Among all the ML approaches, ElasticNet outperforms with the highest accuracy of 91.52% with a set of important genes. By intersecting the candidate hub genes and the ElasticNet important genes, we get four candidate genes, ETS1, YY1, CREBP5, and GTF2H1. Receiver Operating Characteristic (ROC) curve and Expression analysis show robust discriminatory efficacy for all four genes, while nomogram modeling further validates their predictive correlations with RA and COPD. Finally, the candidate transcription factors, microRNAs and drugs related to the potential biomarkers are also suggested.

Conclusion:

In conclusion, this research identifies the key potential molecular mechanisms and four potential biomarkers between RA and COPD. These findings could potentially improve our understanding of the molecular mechanisms behind COPD and RA by providing a preliminary basis for future experimental and clinical studies.

Loại tài liệu:

Article - Bài báo

Tác giả:

Wahia Tasnim

Đề mục:

Journal of Genetic Engineering and Biotechnology

Nhà xuất bản:

Elsevier

Ngày xuất bản:

September 2026

Số trang/ tờ:

Định dạng:

pdf

Định danh tư liệu:

DOI: https://doi.org/10.1016/j.jgeb.2026.100766 | ISSN 1687-157X

Nguồn gốc:

Journal of Genetic Engineering and Biotechnology, Volume 24, Issue 3, September 2026, 100766

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