Using micro- and macro-level network metrics unveils top communicative gene modules in psoriasis

TL;DR

This study integrates micro- and macro-level network metrics to identify key gene modules involved in psoriasis, revealing potential targets for therapy and diagnostics through systemic network analysis of gene expression and protein interactions.

Contribution

It introduces a novel pipeline combining network metrics at multiple levels to uncover top communicative gene modules in psoriasis, advancing molecular understanding.

Findings

Identified 17 top communicative genes linked to psoriasis.

Genes clustered in cell cycle and immune system modules.

Proposed network analysis pipeline applicable to other biological studies.

Abstract

Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin with significant morbidity, characterized by hyper proliferation of the epidermis. Even though psoriasis etiology is not fully understood, it is believed to be multifactorial with numerous key components. Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein-protein interactions and transcriptomics levels, we analyzed a set of microarray gene expression analysis consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on protein-protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. Results: We found 17 top communicative genes, all of which experimentally proven to be pivotal in psoriasis were identified in two modules, namely, cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic mechanisms. Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed pipeline could be of interest to a broader range of biological network analysis studies.