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Bioinformatics in Human Disease Network Analysis

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A Special Issue of Current Issues in Molecular Biology (ISSN 1467-3045) belonging to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 4573

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Dr. Li Liao

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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 (6 papers)

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Research

17 pages, 2961 KB

Open AccessArticle

Candidate Regulatory Relationship and Expression Correlation Between miR-33a-5p and ANK3 in Chronic Myeloid Leukemia

by Nurgul Kar, Sema Misir, Serap Ozer Yaman, Osman Akidan, Ceylan Hepokur and Yuksel Aliyazicioglu

Curr. Issues Mol. Biol. 2026, 48(9), 949; https://doi.org/10.3390/cimb48090949 - 17 Sep 2026

Abstract

Chronic myeloid leukemia (CML) is a type of bone marrow cancer characterized by the uncontrolled proliferation of myeloid cells. MicroRNAs (miRNAs) are small, non-coding RNA molecules that play a crucial role in the post-transcriptional regulation of gene expression. This study aims to examine the association between miR-33a-5p and Ankyrin 3 (ANK3) in CML and to investigate the regulatory mechanisms of miR-33a-5p in the progression of this disease. mRNA expression profiles were obtained from the GSE100026 dataset within the Gene Expression Omnibus (GEO) repository. Quantitative real-time polymerase chain reaction (RT-qPCR) was conducted to assess the expression levels of miR-33a-5p and ANK3. To investigate the regulatory mechanism of miR-33a-5p/ANK3, various databases such as miRNet, miRDIP, TargetScan, BioGRID, and CancerSEA were utilized. The expression of miR-33a-5p was markedly elevated, while ANK3 expression was significantly reduced. Furthermore, pathway clustering and functional assessments of ANK3 demonstrated its involvement in regulating the cell cycle and apoptosis. The findings identify an inverse expression pattern between miR-33a-5p and ANK3 across the two cell models and support a candidate regulatory relationship that warrants direct functional validation. This relationship may merit further investigation as a potential molecular target in CML. Full article

(This article belongs to the Special Issue Bioinformatics in Human Disease Network Analysis)

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27 pages, 8257 KB

Open AccessArticle

Matrix Architecture and Integrin Branch Balance Distinguish Immune-Regulatory States in Clear Cell Renal Cell Carcinoma

by Caner Karaca, Mehmet Emin Arayici, Hüseyin Salih Semiz, Hulya Ellidokuz and Yasemin Basbinar

Curr. Issues Mol. Biol. 2026, 48(8), 789; https://doi.org/10.3390/cimb48080789 - 2 Aug 2026

Viewed by 410

Abstract

Background/Objectives: Clear cell renal cell carcinoma (ccRCC) is frequently vascular and immune-infiltrated, yet durable responses to immune checkpoint blockade remain limited. This suggests that immune resistance may reflect tumor microenvironmental organization and mechanotransduction state rather than immune infiltration alone. We aimed to determine whether matrix reorganization and branch-specific integrin mechanosensing define immune-regulatory states in ccRCC, with particular attention to adenosine-associated immune resistance. Methods: We performed an integrative computational analysis of TCGA-KIRC bulk RNA-sequencing, clinical, survival, immune feature, and reverse-phase protein array data. Matrix- and mechanobiology-related programs were quantified using ssGSEA, compact z-score-based signatures, and principal component-based sensitivity analyses. Immune-regulatory programs, CAF and ECM scores, FAK/SRC activation features, and MINER-inferred transcriptional regulons were integrated using stage association, correlation, partial correlation, variance partitioning, survival, and transcriptional state analyses. Results: Matrix-centered transcriptional programs were the dominant stage-associated mechanobiology signal in ccRCC, including ECM deposition, collagen organization, matrix remodeling, fluid shear stress, and YAP/TAZ activity. A compact ECM-associated core (ECM_Stiffness_Core; a ten-gene signature whose highest-loading members include FN1, COL1A1, COL6A1, and LOX) captured a matrix reorganization program, indicating remodeling of ECM composition and architecture rather than uniform increases in tumor stiffness, pressure, or bulk mechanical load. Matrix remodeling was associated with CAF abundance, TGFβ signaling, CD276/B7-H3, CSF1-related myeloid biology, ENTPD1/CD39, and PRDM1, whereas associations with cytotoxic immune cells were weaker. Integrin mechanosensing separated into opposing branches: ITGA5/ILK/SRC-associated features aligned with higher-risk biology and adenosine-linked immune regulation, whereas PTK2/FAK–RHOA–ROCK components showed lower-risk directions. RPPA analyses supported SRC–FAK imbalance as an adverse signaling pattern. MINER analyses further separated matrix-associated immune-suppressive regulons from canonical integrin/focal adhesion states. Conclusions: Matrix reorganization and integrin branch imbalance appear to shift ccRCC toward distinct immune-regulatory states. We propose a conceptual model that matrix architecture may act as a directional suppressive amplifier, whereas the relative balance between ITGA5/ILK/SRC-associated signaling and canonical PTK2/FAK–RHOA–ROCK mechanosensing functions as an integrin branch rheostat. This framework identifies matrix remodeling, CD276/B7-H3, CSF1-related myeloid biology, adenosine signaling, and SRC–FAK imbalance as candidate biological axes for future investigation, including their potential relevance to combination strategies beyond PD-1/PD-L1 blockade. Future experimental, spatial, and treatment response studies may further clarify the mechanistic basis of these associations and evaluate their potential therapeutic relevance. Full article

(This article belongs to the Special Issue Bioinformatics in Human Disease Network Analysis)

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22 pages, 2397 KB

Open AccessArticle

FuDiCo: Gene Fusion-Initiated Path Propagation for Disease Comorbidity Prediction

by Ashwag Altayyar and Li Liao

Curr. Issues Mol. Biol. 2026, 48(6), 622; https://doi.org/10.3390/cimb48060622 - 16 Jun 2026

Viewed by 338

Abstract

Disease comorbidity—the co-occurrence of two or more diseases in the same individual—has gained growing attention due to its association with adverse clinical outcomes and increased treatment complexity. Recent subgraph-based approaches for disease comorbidity prediction model disease modules as subgraphs induced by disease-associated genes in the protein–protein interaction (PPI) network and learn disease representations from subgraph topology. However, these approaches are constrained by incomplete disease–gene annotations, which may obscure important molecular relationships between diseases. Accordingly, disease comorbidity may also be influenced by molecular events beyond annotated disease genes, such as gene fusion events that have emerged as important contributors to disease mechanisms. Motivated by the role of gene fusions in disease development, we introduce Gene Fusion-Initiated Path Propagation for Disease Comorbidity Prediction (FuDiCo), a framework that models comorbidity through influence propagation over the PPI network. FuDiCo represents fusion-associated genes as localized perturbation sources and learns how their influence propagates along interaction paths toward disease subgraphs, thereby capturing propagation patterns that link related diseases and contribute to their comorbidity. Experiments on a benchmark disease comorbidity dataset show that FuDiCo outperforms state-of-the-art methods, achieving statistically significant improvements. These results shed light on the importance of gene fusion events in understanding disease relationships. Full article

(This article belongs to the Special Issue Bioinformatics in Human Disease Network Analysis)

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11 pages, 1943 KB

Open AccessArticle

Transcriptomic Profiling Identifies Potential Prognostic Genes in Vietnamese Patients with Non-Small-Cell Lung Cancer

by Tuan Quoc Bach, Giang Thi Chau Truong, Bang Ngoc Dao, Thang Ba Ta and Thuy Thi Bich Vo

Curr. Issues Mol. Biol. 2026, 48(5), 491; https://doi.org/10.3390/cimb48050491 - 9 May 2026

Viewed by 580

Abstract

Background/Objectives: Non-small-cell lung cancer (NSCLC) is one of the most common malignancies in Vietnam, yet its molecular mechanisms remain incompletely understood. This study aimed to identify prognostic genes in Vietnamese NSCLC patients using integrative transcriptomic and bioinformatics analyses. Methods: RNA-seq data from 30 Vietnamese NSCLC patients treated at Military Hospital 103 (January 2023–April 2024) were analyzed and cross-validated with the Gene Expression Omnibus (GEO) dataset GSE140343 to identify shared differentially expressed genes (DEGs). Subsequent analyses included functional enrichment (GO and KEGG), protein–protein interaction (PPI) network construction via STRING, and module/centrality analyses to pinpoint hub genes. Finally, prognostic significance was evaluated using overall survival data from The Cancer Genome Atlas (TCGA) via the GEPIA platform. Results: A total of 1900 shared DEGs were identified, most of which were enriched in cancer-related pathways. The resulting PPI network (comprising 1528 nodes and 8185 edges) yielded eight significant modules containing 64 high-centrality candidate genes. Survival analyses demonstrated that high expression of CCNA2 and S100A12, and low expression of ADRB2, ARRB1, PTGS2, and SMAD7 were significantly associated with poor overall survival in NSCLC patients. Conclusions: These findings highlight potential biomarkers for prognosis and may inform future therapeutic strategies in Vietnamese NSCLC patients. Full article

(This article belongs to the Special Issue Bioinformatics in Human Disease Network Analysis)

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20 pages, 2734 KB

Open AccessArticle

Identification of Common Molecular Signatures in Chronic Obstructive Pulmonary Disease and Pulmonary Tuberculosis

by Stanislav Kotlyarov and Dmitry Oskin

Curr. Issues Mol. Biol. 2026, 48(5), 462; https://doi.org/10.3390/cimb48050462 - 29 Apr 2026

Cited by 2 | Viewed by 1445

Abstract

Chronic obstructive pulmonary disease (COPD) and pulmonary tuberculosis (TB) are major causes of morbidity and mortality worldwide. Epidemiologic studies indicate an increased risk of tuberculosis in patients with COPD; however, the shared molecular mechanisms underlying the pathogenesis of these two diseases remain insufficiently understood. Objective. Based on a comparative bioinformatics analysis of peripheral blood transcriptomic profiles in patients with COPD and pulmonary tuberculosis, to identify common systemic immune mechanisms associated with the pathogenesis of both diseases. Gene expression data from the NCBI GEO public database were analyzed. GSE34608 included blood samples from 8 patients with tuberculosis and 18 healthy controls. The GSE76705 dataset contained peripheral-blood samples from 364 former smokers (225 with COPD and 139 without). Functional enrichment (GO Biological Process and KEGG) was run in ShinyGO; protein–protein interaction networks were built in STRING, and the top-15 hub genes were ranked by the MCC algorithm in CytoHubba. In tuberculosis, 892 up-regulated and 1448 down-regulated genes were identified; in COPD, 520 up-regulated and 1329 down-regulated. Common upregulated DEGs are involved in toll-like receptor signaling pathways, NOD-like receptor signaling pathways, neutrophil extracellular trap (NET) formation, phagosomes, and tuberculosis. Downregulated genes in each of the diseases were associated with processes of transcriptional regulation and RNA metabolism, which may indicate common transcriptional abnormalities in COPD and tuberculosis. COPD and tuberculosis share common pathogenic mechanisms, including the activation of innate immune signaling pathways (TLR, NOD), neutrophilic inflammation, the formation of neutrophil extracellular traps (NETosis), and phagocyte dysfunction. The identified common genes and signaling pathways may serve as a basis for the development of biomarkers and therapeutic targets; however, they require further validation in independent cohorts. Full article

(This article belongs to the Special Issue Bioinformatics in Human Disease Network Analysis)

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19 pages, 2981 KB

Open AccessArticle

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 1024

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 (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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