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Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Second Edition)

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 July 2025 | Viewed by 4875

Special Issue Editors

Dr. Hung-Yu Lin

E-Mail
Guest Editor
Research Assistant Center, Show Chwan Memorial Hospital, Changhua 500, Taiwan
Interests: lncRNA; miRNA; epigenetics; cancers; ageing-related diseases; RNA-targeting therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Pei-Yi Chu

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Guest Editor
Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402, Taiwan
Interests: lncRNA; miRNA; epigenetics; cancers; ageing-related diseases; RNA-targeting therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, an unprecedented breakthrough has been made in the wake of the application of next-generation sequencing technologies and advanced artificial intelligence methods. The advances accordingly bring a remarkable opportunity for the determination of key companion biomarkers and biosignatures, efficient diagnoses, prognostic predictions, potent drug identification, and possible mechanisms in a comprehensive way based on multi-omics data.

Bioinformatics is widely employed to cope with the above challenges. A variety of bioinformatic tools and open access datasets have been developed to promote the integration of omics data, such as epigenomics, metabolomics, transcriptomics, and genomics, in a comprehensive way. In contrast to the traditional approach, bioinformatics has considerably boosted the accuracy of diagnoses and prognoses, expedited the discovery of novel drug targets, and deepened the functional understanding of disease mechanisms, paving a path to advance precision medicine.

We are pleased to invite you to contribute original research articles with reference (but not limited) to the bioinformatics-centric identification of biomarkers/biosignatures/drugs/mechanisms/prediction algorithms for diagnoses, prognoses, and potential therapeutics in human diseases. Critical review manuscripts with a perspective vision, setting the stage for future research, are also especially welcome.

Please kindly note that new methods must be compared with existing state-of-the-art methods, using real biological data. The inclusion of experimental data is very much encouraged.

We look forward to receiving your contributions.

Dr. Hung-Yu Lin
Dr. Pei-Yi Chu
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • cancers
  • ageing-related diseases
  • bioinformatics
  • multi-omics
  • pharmacogenetics
  • deep learning
  • evolutionary learning
  • diagnosis
  • prognosis
  • companion biomarker
  • drug screening
  • mechanism dissection

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Published Papers (5 papers)

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Research

13 pages, 935 KiB  
Article
Measurement of Disease Comorbidity Using Semantic Profiling of Disease Genes
by Seong Beom Cho
Int. J. Mol. Sci. 2025, 26(8), 3906; https://doi.org/10.3390/ijms26083906 - 21 Apr 2025
Viewed by 169
Abstract
The identification of overlapping disease genes between different diseases is the first step in the elucidation of the biological mechanism of disease comorbidity; however, in the absence of common genes, it is difficult to determine the mechanism of comorbidity even if clinical evidence [...] Read more.
The identification of overlapping disease genes between different diseases is the first step in the elucidation of the biological mechanism of disease comorbidity; however, in the absence of common genes, it is difficult to determine the mechanism of comorbidity even if clinical evidence of disease co-occurrence exists. In this research, a gene-set-based measurement of the comorbidity of diseases (GS.CoMoD) was proposed. The underlying assumption of GS.CoMoD is that if the p-value vectors obtained from the enrichment analyses of different disease gene lists indicate similarity, the diseases are possibly comorbid. Therefore, comorbidity can be detected even without overlapping genes. A simulation analysis showed that GS.CoMoD yielded higher scores for comorbid disease pairs vs. random disease pairs. Moreover, comparison analyses revealed that GS.CoMoD outperformed the pre-existing methods for detecting comorbidity. Full article
(This article belongs to the Special Issue Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Second Edition))
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11 pages, 2769 KiB  
Article
In Silico Analysis Identified Putative Pathogenic Missense Single Nucleotide Polymorphisms (SNPs) in the Human HNF1A Gene
by Hitham Aldharee and Hamdan Z. Hamdan
Int. J. Mol. Sci. 2025, 26(8), 3768; https://doi.org/10.3390/ijms26083768 - 16 Apr 2025
Viewed by 352
Abstract
Maturity-onset diabetes of the young (MODY) is a rare genetic condition that affects children, adolescents, and adults. Studies have shown that genetic changes in the HNF1A gene are associated with MODY-3. However, most of the causative variants and the molecular mechanisms remain underexplored. [...] Read more.
Maturity-onset diabetes of the young (MODY) is a rare genetic condition that affects children, adolescents, and adults. Studies have shown that genetic changes in the HNF1A gene are associated with MODY-3. However, most of the causative variants and the molecular mechanisms remain underexplored. This study aims to better understand MODY-3 by investigating HNF1A-missense variants with clinical uncertainty. Various bioinformatics tools were utilised to address the clinical uncertainty of missense variants in the HNF1A gene that have not been linked with HNF1A-related conditions, sourced from the Genome Aggregation Database (GnomAD v4.1.0). Among the clinically uncertain 2444 variants, only 138 were classified as missense with clinically uncertain significance. Results show that four variants (Arg168Cys, Glu275Ala, Gly375Asp and Val411Phe) were consistently predicted as pathogenic by all tools. The allele frequency (AF) of the commonly predicted disease-causing variants was very low in the global population. The assessment of the secondary structure of filtered variants indicates that variants (Arg168Cys and Glu275Ala) are located in the helical region of the HNF1A protein. At the same time (Gly375Asp and Val411Phe) are found in the protein’s coil, suggesting structural changes at the site of variations. The prediction of protein stability was conducted using I-Mutant and MuPro. Both tools collectively indicate decreased protein stability for the variants (Arg168Cys, Glu275Ala, Gly375Asp and Val411Phe). Predicting the protein’s 3D structure for the HNF1A wild-type and mutants indicates potential structural damages in Arg168Cys and Gly375Asp. Additionally, results show that the amino acids at the variation sites of the variants (Arg168Cys, Glu275Ala, Gly375Asp and Val411Phe) were highly conserved. To conclude, 4 out of the 138 missense variants labelled as uncertain significance were found to be consistently pathogenic using in silico tools in this study. Our findings aim to support variant interpretation, understand the genotype–phenotype association of diabetes, and provide better healthcare services for patients with diabetes. Full article
(This article belongs to the Special Issue Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Second Edition))
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30 pages, 7566 KiB  
Article
One Health Approach to the Computational Design of a Lipoprotein-Based Multi-Epitope Vaccine Against Human and Livestock Tuberculosis
by Robert Adamu Shey, Gordon Takop Nchanji, Tangan Yanick Aqua Stong, Ntang Emmaculate Yaah, Cabirou Mounchili Shintouo, Bernis Neneyoh Yengo, Derrick Neba Nebangwa, Mary Teke Efeti, Joan Amban Chick, Abey Blessings Ayuk, Ketura Yaje Gwei, Arnaud Azonpi Lemoge, Luc Vanhamme, Stephen Mbigha Ghogomu and Jacob Souopgui
Int. J. Mol. Sci. 2025, 26(4), 1587; https://doi.org/10.3390/ijms26041587 - 13 Feb 2025
Viewed by 817
Abstract
Tuberculosis (TB) remains a major cause of ill health and one of the leading causes of death worldwide, with about 1.25 million deaths estimated in 2023. Control measures have focused principally on early diagnosis, the treatment of active TB, and vaccination. However, the [...] Read more.
Tuberculosis (TB) remains a major cause of ill health and one of the leading causes of death worldwide, with about 1.25 million deaths estimated in 2023. Control measures have focused principally on early diagnosis, the treatment of active TB, and vaccination. However, the widespread emergence of anti-tuberculosis drug resistance remains the major public health threat to progress made in global TB care and control. Moreover, the Bacillus Calmette–Guérin (BCG) vaccine, the only licensed vaccine against TB in children, has been in use for over a century, and there have been considerable debates concerning its effectiveness in TB control. A multi-epitope vaccine against TB would be an invaluable tool to attain the Global Plan to End TB 2023–2030 target. A rational approach that combines several B-cell and T-cell epitopes from key lipoproteins was adopted to design a novel multi-epitope vaccine candidate. In addition, interactions with TLR4 were implemented to assess its ability to elicit an innate immune response. The conservation of the selected proteins suggests the possibility of cross-protection in line with the One Health approach to disease control. The vaccine candidate was predicted to be both antigenic and immunogenic, and immune simulation analyses demonstrated its ability to elicit both humoral and cellular immune responses. Protein–protein docking and normal-mode analyses of the vaccine candidate with TLR4 predicted efficient binding and stable interaction. This study provides a promising One Health approach for the design of multi-epitope vaccines against human and livestock tuberculosis. Overall, the designed vaccine candidate demonstrated immunogenicity and safety features that warrant further experimental validation in vitro and in vivo. Full article
(This article belongs to the Special Issue Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Second Edition))
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21 pages, 8402 KiB  
Article
Multi-Omics Analysis Reveals Immune Infiltration and Clinical Significance of Phosphorylation Modification Enzymes in Lung Adenocarcinoma
by Deyu Long, Yanheng Ding, Peng Wang, Lili Wei and Ketao Ma
Int. J. Mol. Sci. 2025, 26(3), 1066; https://doi.org/10.3390/ijms26031066 - 26 Jan 2025
Viewed by 774
Abstract
Protein phosphorylation is a dynamic and reversible modification involved in almost all cellular processes. Numerous investigations have shown that protein phosphorylation modification enzymes (PPMEs) that regulate protein phosphorylation play an important role in the occurrence and treatment of tumors. However, there is still [...] Read more.
Protein phosphorylation is a dynamic and reversible modification involved in almost all cellular processes. Numerous investigations have shown that protein phosphorylation modification enzymes (PPMEs) that regulate protein phosphorylation play an important role in the occurrence and treatment of tumors. However, there is still a lack of effective insights into the value of PPMEs in the classification and treatment of patients with lung adenocarcinoma (LUAD). Here, four topological algorithms identified 15 hub PPMEs from a protein–protein interaction (PPI) network. This PPI network was constructed using 124 PPMEs significantly correlated with 35 cancer hallmark-related pathways. Our study illustrates that these hub PPMEs can affect the survival of patients with LUAD in the form of somatic mutation or expression perturbation. Consistency clustering based on hub PPMEs recognized two phosphorylation modification subtypes (namely cluster1 and cluster2) from LUAD. Compared with patients in cluster1, the survival prognosis of patients in cluster2 is worse. This disparity is probably attributed to the higher tumor mutation burden, the higher male proportion, and the more significant expression disturbance in patients in cluster2. Moreover, phosphorylation modification subtypes also have different characteristics in terms of immune activity, immune infiltration level, immunotherapy response, and drug sensitivity. We constructed a PSig scoring system by using a principal component analysis algorithm to estimate the level of phosphorylation modification in individual LUAD patients. Patients in the high and low PSig score groups demonstrated different characteristics in terms of survival rate, tumor mutation burden, somatic gene mutation rate, immune cell abundance, and sensitivity to immunotherapy and drug treatment. This work reveals that phosphorylation plays a non-negligible role in the tumor microenvironment and immunotherapy of LUAD. Evaluating the phosphorylation status of individual LUAD patients by the PSig score can contribute to enhancing our cognition of the tumor microenvironment and guiding the formulation of more effective personalized treatment strategies. Full article
(This article belongs to the Special Issue Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Second Edition))
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17 pages, 3715 KiB  
Article
ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation
by Peng Jiang, Zuzhen Zhang, Qing Yu, Ze Wang, Lihong Diao and Dong Li
Int. J. Mol. Sci. 2024, 25(17), 9544; https://doi.org/10.3390/ijms25179544 - 2 Sep 2024
Cited by 1 | Viewed by 1577
Abstract
Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics [...] Read more.
Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body’s response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR’s architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety. Full article
(This article belongs to the Special Issue Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Second Edition))
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