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Bioinformatics in Human Disease Network Analysis

A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 502

Special Issue Editor

Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA
Interests: bioinformatics; computational biology

Special Issue Information

Dear Colleagues,

Disease diagnosis and treatment are at the center of human health research. A lot of effort has gone into study of individual diseases, which has had great success in many cases, tracing back down to certain genes or even certain SNPs as the culprit at the molecular biology level. Yet, genes are not isolated entities in the cell; rather, genes—via their product proteins—interact with one another to fulfill cellular functions in many biochemical processes, and these interactions form a network with genes and proteins as nodes and interactions as edges; malfunction in one node has the potential to affect other nodes, manifesting as multiple symptoms. At the clinical level, comorbidity, which is the co-occurrence of two or more diseases, is observed and presents special challenges to disease diagnosis and treatment.

This Special Issue focuses on bioinformatics solutions to disease in the context of network analysis at both the genotype and phenotype levels, and anything in between, to shed light on the diagnosis and treatment of comorbid diseases. While original research is the main focus of this issue, technical notes on tools and software and review articles surveying state-of-the-art solutions, techniques, and resources, such as databases, are also welcome.

Dr. Li Liao
Guest Editor

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Keywords

  • human interactome
  • comorbidity
  • disease network
  • disease interaction
  • genotype
  • gene–gene interaction
  • graph analysis

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Published Papers (1 paper)

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Research

19 pages, 2981 KB  
Article
A Comparative Bioinformatics Analysis of the Transcriptomic Profiles of Peri-Implantitis and Periodontitis and Their Common Signaling Pathways with Atherosclerosis
by Aleksandr V. Guskov, Anatoliy S. Utyuzh, Aleksandr A. Oleynikov, Aleksandr A. Nikiforov and Stanislav N. Kotlyarov
Curr. Issues Mol. Biol. 2026, 48(4), 401; https://doi.org/10.3390/cimb48040401 - 14 Apr 2026
Viewed by 183
Abstract
(1) Objective. To conduct a comparative bioinformatics analysis of the transcriptomic profiles of peri-implantitis and periodontitis to identify common and specific molecular signatures underlying their pathogenesis, as well as molecular parallels with atherosclerosis. (2) Methods: We used datasets from the Gene Expression Omnibus [...] Read more.
(1) Objective. To conduct a comparative bioinformatics analysis of the transcriptomic profiles of peri-implantitis and periodontitis to identify common and specific molecular signatures underlying their pathogenesis, as well as molecular parallels with atherosclerosis. (2) Methods: We used datasets from the Gene Expression Omnibus (GEO) database: dataset GSE223924 (30 gingival tissue samples from patients with peri-implantitis, periodontitis, and healthy subjects) and GSE100927 (atherosclerotic and control tissue; n = 104). Differentially expressed genes (DEGs) were identified based on the criteria: |logFC| > 1 and FDR < 0.05. To quantitatively assess the relative abundance of immune cells, we used the xCell deconvolution algorithm. (3) Results: In the peri-implantitis group, 3669 DEGs with upregulated expression and 3106 with downregulated expression were identified; in the periodontitis group, 1968 and 1250 DEGs, respectively. Functional analysis of the upregulated DEGs revealed activation of inflammatory processes, cell adhesion, and angiogenesis in both diseases. Key differences lay in the activation of adaptive immune mechanisms in peri-implantitis (enrichment of the “graft rejection” and “T-cell receptor signaling”) and innate immunity in periodontitis (enrichment of the “lipopolysaccharide response” and “Toll-like receptors (TLR) signaling” pathways). Analysis of downregulated DEGs revealed more profound disruptions in cytoskeletal organization and epithelial differentiation in periodontitis, as well as suppression of xenobiotic and lipid metabolism in both diseases. xCell deconvolution confirmed a significant increase in B cells, neutrophils, monocytes, M1 macrophages, and dendritic cells in peri-implantitis, and also revealed a trend toward an increase in these cells in periodontitis (p > 0.05), which is consistent with the activation of TLR signaling. In periodontitis, a significant increase in M2 macrophages and a decrease in Th1 cells were observed. Comparison with atherosclerosis revealed 272 common DEGs with peri-implantitis and 173 common DEGs with periodontitis. Functional analysis of the common genes confirmed their role in leukocyte transendothelial migration, cytokine production, and the “Lipids and Atherosclerosis” pathway. (4) Conclusions: Functional analysis and immune deconvolution consistently demonstrate that peri-implantitis is characterized by statistically significant activation of both adaptive and innate immunity, whereas in periodontitis, the activation of innate immunity manifests primarily at the level of signaling pathways. The significant overlap found between the transcriptional profiles of both diseases and atherosclerosis may indicate the presence of common pathogenetic links. Full article
(This article belongs to the Special Issue Bioinformatics in Human Disease Network Analysis)
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