Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach

Abstract

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher’s exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications.

Communicated by Ramaswamy H. Sarma

Keywords:

• COVID-19
• post-COVID-19 syndrome
• protein-protein interaction network
• MCODE
• cancers
• neurological disorders
• pathway analysis
• Fisher’s exact test

Acknowledgement

Navami Krishna gratefully acknowledges the Kerala State Council for Science,Technology, and Environment (KSCSTE), Government of Kerala, for her PhD research fellowship. Sijina KP gratefully acknowledges the UGC and Ministry of Social Justice and Empowerment, Government of India (NF-OBC) for her PhD research fellowship.

Disclosure statement

All of the authors declare that they have no competing interests in this work.