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. 2020 Aug 10;11(8):914.
doi: 10.3390/genes11080914.

Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis

Reyhaneh Naderi  1 , Homa Saadati Mollaei  2 , Arne Elofsson  3 , Saman Hosseini Ashtiani  3
Affiliations

Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis

Reyhaneh Naderi et al. Genes (Basel). .

Abstract

(1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis' etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein-protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein-protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic interactions. (4) Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed workflow could be of interest to a broader range of future biological network analysis studies.

Keywords: combination therapy; microarray gene expression analysis; modularity; network analysis; psoriasis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow of the study design of the network analysis of psoriasis vulgaris patients dataset (GSE30999).
Figure 2
Figure 2
PCA of gene expression values of all samples after normalization. PCA analysis conveys an overall certain discrimination associated with gene expression levels between the two sample types.
Figure 3
Figure 3
The statistics of 1481-gene PPI network after analysis by Cytoscape.
Figure 4
Figure 4
Network analysis results. (a) Main component of PPI network of DEGs among 18 components. Selected yellow nodes indicate primary communicative genes based on micro-level network analysis. (b) Modularity detection on primary communicative genes PPI network: colors red and blue indicate module 1 and 2 (related to cell cycle and immune system genes), respectively. (c) Top communicative genes subnetworks for each module. (d) Inter-module top communicative genes subnetwork.
See this image and copyright information in PMC

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