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Applications of Bioinformatics in Human Disease
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Applications of Bioinformatics in Human Disease

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1789

Special Issue Editor

Dr. Maria Rosaria De Filippo

E-Mail Website
Guest Editor
Division of Pathology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
Interests: machine learning; omics data analysis; data integration; spatial proteomics; single cell data analysis; markers prediction in the context of cancer

Special Issue Information

Dear Colleagues,

Bioinformatics has become a cornerstone of modern biomedical research, transforming how we understand, diagnose, and treat human diseases. With the advent and explosive growth of high-throughput technologies, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics, researchers now have unprecedented access to multidimensional data that capture the complexity of biological systems. More than generating these data, the challenge lies in analysing and integrating them with other data to yield meaningful biological insights. Furthermore, the use of machine learning models and Artificial Intelligence has completely changed the way bioinformaticians investigate human disease, providing powerful tools for biomarker discovery, disease classification, and prediction of therapeutic response. This Special Issue aims to bring together research that highlights the diversity of computational approaches used to investigate disease biology. The contributions span multiple areas of application, from single-technology data analysis to the integration of different technologies, to the use of machine learning to gain biological insights to accelerate biomarker discovery, support personalized medicine, and promote reproducible, data-driven biological research.

Dr. Maria Rosaria De Filippo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformatics
  • omics data
  • machine learning
  • biomarker prediction

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Published Papers (3 papers)

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Research

24 pages, 3473 KB  
Article
Prognostic Genes Linked to Asparagine Metabolism in Hepatocellular Carcinoma: Identification, Validation, and Regulatory Mechanisms Based on Transcriptome and Single-Cell RNA Sequencing
by Jianting Feng, Kaihua Wei, Nana Li, Yinshi Li, Fei Du, Mengjiao Lv, Lifei Ma, Suwen Wang, Shuliang Niu and Liang Feng
Int. J. Mol. Sci. 2026, 27(10), 4425; https://doi.org/10.3390/ijms27104425 (registering DOI) - 15 May 2026
Abstract
Metabolic reprogramming is closely linked to tumor proliferation, invasion, and immune escape. Despite its central role in amino acid metabolism, the regulatory mechanisms of asparagine metabolism in hepatocellular carcinoma (HCC) progression remain poorly characterized. Rather than focusing on canonical metabolic genes, prognostic markers [...] Read more.
Metabolic reprogramming is closely linked to tumor proliferation, invasion, and immune escape. Despite its central role in amino acid metabolism, the regulatory mechanisms of asparagine metabolism in hepatocellular carcinoma (HCC) progression remain poorly characterized. Rather than focusing on canonical metabolic genes, prognostic markers were identified from co-expression modules associated with asparagine metabolism signatures. Using the TCGA database and asparagine metabolism-related gene sets, a prognostic risk-scoring model was developed through differential expression analysis, univariate Cox regression, and the LASSO algorithm and externally validated with the GEO dataset (GSE14620). Survival analysis, ROC curve evaluation, nomogram construction, scRNA-seq, GSEA, and drug sensitivity analysis were performed to systematically delineate the molecular mechanisms by which asparagine metabolism drives HCC progression. A three-gene signature comprising BOP1, SAC3D1, and PDE2A effectively stratified patients into high- and low-risk groups. High-risk patients exhibited markedly poorer overall survival, enrichment in tumor proliferation-associated pathways, increased tumor purity, reduced immune cell infiltration, and a substantially higher TP53 mutation rate (38% vs. 13%). In contrast, the low-risk group showed enrichment in pathways linked to hepatoblastoma suppression and liver function, alongside improved predicted response to immunotherapy. Single-cell analysis identified NK cells and endothelial cells as central mediators of asparagine metabolism-driven HCC progression, with BOP1, SAC3D1, and PDE2A displaying dynamic expression patterns during differentiation. Furthermore, the high-risk group was predicted to be more sensitive to chemotherapeutics such as cyclophosphamide and 5-fluorouracil. These findings highlight a potential interplay between nitrogen metabolism and asparagine metabolism in HCC and suggest mechanisms by which these pathways may influence NK cell and endothelial cell function to promote disease progression. This study establishes a novel prognostic model and identifies potential chemotherapeutic vulnerabilities in high-risk patients, warranting further experimental and clinical validation. Full article
(This article belongs to the Special Issue Applications of Bioinformatics in Human Disease)
25 pages, 12236 KB  
Article
Screening and Validation of LTBP1 as a Key Target of Oxymatrine in Inhibiting Cardiac Fibroblast Differentiation Under High Glucose Conditions: In Vitro and Bioinformatic Studies
by Lianqing Tian, Shiquan Gan, Youqi Du, Chaowen Long, Churui Chang and Xiangchun Shen
Int. J. Mol. Sci. 2026, 27(8), 3481; https://doi.org/10.3390/ijms27083481 - 13 Apr 2026
Viewed by 481
Abstract
Diabetic cardiomyopathy (DCM) features progressive fibrotic remodeling, but the shared molecular circuitry connecting diabetes mellitus (DM) to cardiomyopathy (CM) remains unclear. We integrated three DM- and three CM-related Gene Expression Omnibus (GEO) datasets and corrected batch effects with sva, verified by violin plots, [...] Read more.
Diabetic cardiomyopathy (DCM) features progressive fibrotic remodeling, but the shared molecular circuitry connecting diabetes mellitus (DM) to cardiomyopathy (CM) remains unclear. We integrated three DM- and three CM-related Gene Expression Omnibus (GEO) datasets and corrected batch effects with sva, verified by violin plots, principal component analysis (PCA), and silhouette coefficients computed on all common genes (DM: 0.9489 to −0.1016; CM: 0.9693 to −0.045; PC1/PC2 inter-batch differences abolished after normalization). Differential expression analysis identified 2562 DM Differentially expressed genes (DEGs) and 1414 CM DEGs, and their intersection yielded 91 common DEGs (51 upregulated, 40 downregulated). Protein–protein interaction (PPI) analysis prioritized 25 hub genes, whose enrichment profiles implicated insulin resistance/insulin signaling and adrenergic signaling in cardiomyocytes. TRRUST-based inference further defined a regulatory network centered on seven key genes (HIF-1α, ACTN4, ABCB1, LTBP1, CLU, TIMP2, and MYH11). To nominate a candidate target of oxymatrine (OMT), we performed docking and molecular dynamics (MD) simulations for representative complexes; OMT showed the most stable interaction with LTBP1, maintaining a consistently short pocket distance (~0.2 nm), the highest contact frequency, and the lowest MM/PBSA binding free energy (−15.32 kcal/mol), with favorable contributions dominated by van der Waals and nonpolar solvation terms. In primary cardiac fibroblasts (CFs), high glucose (HG, 30 mM glucose) induced proliferative and profibrotic activation, whereas OMT (0.4–0.8 mM) reduced HG-driven proliferation without detectable toxicity below 1.2 mM, suppressed FN, collagen I/III, and α-SMA expression, and inhibited migration. OMT also normalized HG-induced cell-cycle skewing by restoring G0/G1-phase occupancy and reducing S-phase entry, with effects comparable to metformin. Finally, HG increased LTBP1 expression and upregulated SMAD3/SMAD4, while OMT attenuated LTBP1 induction and suppressed downstream TGF-β/SMAD activation. Together, these data integrate cross-dataset transcriptomics with mechanistic validation to position LTBP1 as a putative antifibrotic node targeted by OMT, supporting inhibition of the LTBP1/TGF-β/SMAD axis as a candidate strategy to counter DCM-associated fibrosis. Full article
(This article belongs to the Special Issue Applications of Bioinformatics in Human Disease)
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20 pages, 1521 KB  
Article
IFNAR2 p.F8S Variant Associates with Severe COVID-19 and Adaptive Immune Cell Activation Modulation
by Francesco Malvestiti, Angela Lombardi, Francesco Gentile, Veronica Torcianti, Elena Trombetta, Alessandro Cherubini, Giuseppe Lamorte, Sara Colonia Uceda Renteria, Daniele Marchelli, Lorenzo Rosso, Alessandra Bandera, Flora Peyvandi, Francesco Blasi, Giacomo Grasselli, Laura Porretti, Saleh Alqahtani, Daniele Prati, Roberta Gualtierotti, Blagoje Soskic, Valentina Vaira, Luisa Ronzoni and Luca Valentiadd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2026, 27(2), 992; https://doi.org/10.3390/ijms27020992 - 19 Jan 2026
Cited by 1 | Viewed by 987
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a wide range of clinical manifestations modulated by genetic factors. The aim of this study was to identify genetic determinants of severe COVID-19 affecting protein sequence to gain insight into disease pathogenesis. Variants prioritized [...] Read more.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a wide range of clinical manifestations modulated by genetic factors. The aim of this study was to identify genetic determinants of severe COVID-19 affecting protein sequence to gain insight into disease pathogenesis. Variants prioritized in two patients requiring lung transplant were tested in the Milan FOGS cohort (487/869 cases/controls), highlighting an independent association between the p.F8S low-frequency variant of interferon alpha receptor 2 gene (IFNAR2) and severe disease (OR = 1.73 [1.24–2.42], p = 0.001), replicated in the COVID-19 Host Genetics Initiative cohort (26,167/2,061,934 cases/controls). In the FOGS cohort, the p.F8S variant was linked to higher circulating IL-6 levels. In keeping, bulk transcriptomic analysis in PBMCs at the peak of infection (n = 57) showed that carriers of the p.F8S variant had upregulation of immune signaling and pathogens response (p < 0.05). Functional flow cytometry experiments in healthy donors (n = 12) revealed that membrane IFNAR2 protein expression was reduced in B lymphocytes, but higher in dendritic cells (p < 0.05). Finally, by interrogating a public scRNAseq resource of PBMC of people with COVID-19, we showed that p.F8S carriers had upregulation of immune pathways specifically in dendritic cells (p < 0.05). These results suggest that the p.F8S variant may influence COVID-19 severity by enhancing adaptive immune response, thereby favoring inflammation. Full article
(This article belongs to the Special Issue Applications of Bioinformatics in Human Disease)
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