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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: 14 June 2024 | Viewed by 9449

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Dr. Camelia Quek

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Guest Editor

Melanoma Institute Australia, The University of Sydney, Sydney, Australia
Interests: cancer; immunotherapy; gene expression profiling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Translational bioinformatics is an emerging field that focusses on providing efficient translation of basic science (molecular, genetic and cellular) and clinical data into clinical practice. As a multidisciplinary field of science, translational bioinformatics combines biology, computer science, data engineering, mathematics, statistics and medicine to analyse and interpret the molecular basis of disease that has strong implications in clinical products or human health. New technologies and the increasingly voluminous amounts of scientific and clinical data are posing challenges in different fields, such as single-cell analysis, spatial image analysis, genomics, proteomics, transcriptomics, multi-omics data analysis, multimodal integration of molecular and clinical data, drug discovery, personalised medicine and biomarkers. In this Special Issue, manuscripts focusing on topics including but not limited to single-cell sequencing, spatial imaging, machine learning, artificial intelligence, next-generation sequencing for personalised medicine and biomarkers, rare and common variants of disease, drug discovery, system biology, pharmacogenomics, biomedical data mining and data visualisation are invited. Original research articles, reviews, opinions/commentaries, perspectives and short communications are welcome.

Dr. Camelia Quek
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 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.

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Keywords

translational research
sequencing
single cell
spatial biology
treatment
diagnostics
machine learning
artificial intelligence
model
computational biology

Published Papers (7 papers)

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Research

16 pages, 19366 KiB

Open AccessArticle

Multi-Omics Analysis Reveals the IFI6 Gene as a Prognostic Indicator and Therapeutic Target in Esophageal Cancer

by Nguyen-Kieu Viet-Nhi, Tran Minh Quan, Vu Cong Truc, Tran Anh Bich, Pham Hoang Nam, Nguyen Quoc Khanh Le, Po-Yueh Chen and Shih-Han Hung

Int. J. Mol. Sci. 2024, 25(5), 2691; https://doi.org/10.3390/ijms25052691 - 26 Feb 2024

Viewed by 1164

Abstract

The role of the IFI6 gene has been described in several cancers, but its involvement in esophageal cancer (ESCA) remains unclear. This study aimed to identify novel prognostic indicators for ESCA-targeted therapy by investigating IFI6’s expression, epigenetic mechanisms, and signaling activities. We utilized public data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) to analyze IFI6’s expression, clinical characteristics, gene function, pathways, and correlation with different immune cells in ESCA. The TIMER2.0 database was employed to assess the pan-cancer expression of IFI6, while UALCAN was used to examine its expression across tumor stages and histology subtypes. Additionally, the KEGG database helped identify related pathways. Our findings revealed 95 genes positively correlated and 15 genes negatively correlated with IFI6 in ESCA. IFI6 was over-expressed in ESCA and other cancers, impacting patient survival and showing higher expression in tumor tissues than normal tissues. IFI6 was also correlated with CD4+ T cells and B cell receptors (BCRs), both essential in immune response. GO Biological Process (GO BP) enrichment analysis indicated that IFI6 was primarily associated with the Type I interferon signaling pathway and the defense response to viruses. Intriguingly, KEGG pathway analysis demonstrated that IFI6 and its positively correlated genes in ESCA were mostly linked to the Cytosolic DNA-sensing pathway, which plays a crucial role in innate immunity and viral defense, and the RIG-I-like receptor (RLR) signaling pathway, which detects viral infections and activates immune responses. Pathways related to various viral infections were also identified. It is important to note that our study relied on online databases. Given that ESCA consists of two distinct subgroups (ESCC and EAC), most databases combine them into a single category. Future research should focus on evaluating IFI6 expression and its impact on each subgroup to gain more specific insights. In conclusion, inhibiting IFI6 using targeted therapy could be an effective strategy for treating ESCA considering its potential as a biomarker and correlation with immune cell factors. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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18 pages, 9886 KiB

Open AccessArticle

amer1 Regulates Zebrafish Craniofacial Development by Interacting with the Wnt/β-Catenin Pathway

by Le Sun, Lu Ping, Xinmiao Fan, Yue Fan, Bo Zhang and Xiaowei Chen

Int. J. Mol. Sci. 2024, 25(2), 734; https://doi.org/10.3390/ijms25020734 - 5 Jan 2024

Viewed by 954

Abstract

Microtia-atresia is a rare type of congenital craniofacial malformation causing severe damage to the appearance and hearing ability of affected individuals. The genetic factors associated with microtia-atresia have not yet been determined. The AMER1 gene has been identified as potentially pathogenic for microtia-atresia in two twin families. An amer1 mosaic knockdown zebrafish model was constructed using CRISPR/Cas9. The phenotype and the development process of cranial neural crest cells of the knockdown zebrafish were examined. Components of the Wnt/β-catenin pathway were examined by qPCR, Western blotting, and immunofluorescence assay. IWR-1-endo, a reversible inhibitor of the Wnt/β-catenin pathway, was applied to rescue the abnormal phenotype. The present study showed that the development of mandibular cartilage in zebrafish was severely compromised by amer1 knockdown using CRISPR/Cas9. Specifically, amer1 knockdown was found to affect the proliferation and apoptosis of cranial neural crest cells, as well as their differentiation to chondrocytes. Mechanistically, amer1 exerted an antagonistic effect on the Wnt/β-catenin pathway. The application of IWR-1-endo could partially rescue the abnormal phenotype. We demonstrated that amer1 was essential for the craniofacial development of zebrafish by interacting with the Wnt/β-catenin pathway. These findings provide important insight into the role of amer1 in zebrafish mandibular development and the pathology of microtia-atresia caused by AMER1 gene mutations in humans. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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18 pages, 6270 KiB

Open AccessArticle

Preclinical Repurposing of Sitagliptin as a Drug Candidate for Colorectal Cancer by Targeting CD24/CTNNB1/SOX4-Centered Signaling Hub

by Jing-Wen Shih, Alexander T. H. Wu, Ntlotlang Mokgautsi, Po-Li Wei and Yan-Jiun Huang

Int. J. Mol. Sci. 2024, 25(1), 609; https://doi.org/10.3390/ijms25010609 - 3 Jan 2024

Viewed by 1212

Abstract

Despite significant advances in treatment modalities, colorectal cancer (CRC) remains a poorly understood and highly lethal malignancy worldwide. Cancer stem cells (CSCs) and the tumor microenvironment (TME) have been shown to play critical roles in initiating and promoting CRC progression, metastasis, and treatment resistance. Therefore, a better understanding of the underlying mechanisms contributing to the generation and maintenance of CSCs is crucial to developing CSC-specific therapeutics and improving the current standard of care for CRC patients. To this end, we used a bioinformatics approach to identify increased CD24/SOX4 expression in CRC samples associated with poor prognosis. We also discovered a novel population of tumor-infiltrating CD24+ cancer-associated fibroblasts (CAFs), suggesting that the CD24/SOX4-centered signaling hub could be a potential therapeutic target. Pathway networking analysis revealed a connection between the CD24/SOX4-centered signaling, β-catenin, and DPP4. Emerging evidence indicates that DPP4 plays a role in CRC initiation and progression, implicating its involvement in generating CSCs. Based on these bioinformatics data, we investigated whether sitagliptin, a DPP4 inhibitor and diabetic drug, could be repurposed to inhibit colon CSCs. Using a molecular docking approach, we demonstrated that sitagliptin targeted CD24/SOX4-centered signaling molecules with high affinity. In vitro experimental data showed that sitagliptin treatment suppressed CRC tumorigenic properties and worked in synergy with 5FU and this study thus provided preclinical evidence to support the alternative use of sitagliptin for treating CRC. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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16 pages, 1434 KiB

Open AccessArticle

Prioritization of Fluorescence In Situ Hybridization (FISH) Probes for Differentiating Primary Sites of Neuroendocrine Tumors with Machine Learning

by Lucas Pietan, Hayley Vaughn, James R. Howe, Andrew M. Bellizzi, Brian J. Smith, Benjamin Darbro, Terry Braun and Thomas Casavant

Int. J. Mol. Sci. 2023, 24(24), 17401; https://doi.org/10.3390/ijms242417401 - 12 Dec 2023

Viewed by 803

Abstract

Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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21 pages, 2843 KiB

Open AccessArticle

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information

by Mingwei Sun, Haoyuan Hu, Wei Pang and You Zhou

Int. J. Mol. Sci. 2023, 24(20), 15447; https://doi.org/10.3390/ijms242015447 - 22 Oct 2023

Cited by 4 | Viewed by 1061

Abstract

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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16 pages, 6610 KiB

Open AccessArticle

A Customized Human Mitochondrial DNA Database (hMITO DB v1.0) for Rapid Sequence Analysis, Haplotyping and Geo-Mapping

by Jane Shen-Gunther, Rutger S. Gunther, Hong Cai and Yufeng Wang

Int. J. Mol. Sci. 2023, 24(17), 13505; https://doi.org/10.3390/ijms241713505 - 31 Aug 2023

Viewed by 1620

Abstract

The field of mitochondrial genomics has advanced rapidly and has revolutionized disciplines such as molecular anthropology, population genetics, and medical genetics/oncogenetics. However, mtDNA next-generation sequencing (NGS) analysis for matrilineal haplotyping and phylogeographic inference remains hindered by the lack of a consolidated mitogenome database and an efficient bioinformatics pipeline. To address this, we developed a customized human mitogenome database (hMITO DB) embedded in a CLC Genomics workflow for read mapping, variant analysis, haplotyping, and geo-mapping. The database was constructed from 4286 mitogenomes. The macro-haplogroup (A to Z) distribution and representative phylogenetic tree were found to be consistent with published literature. The hMITO DB automated workflow was tested using mtDNA-NGS sequences derived from Pap smears and cervical cancer cell lines. The auto-generated read mapping, variants track, and table of haplotypes and geo-origins were completed in 15 min for 47 samples. The mtDNA workflow proved to be a rapid, efficient, and accurate means of sequence analysis for translational mitogenomics. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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Graphical abstract

9 pages, 1534 KiB

Open AccessArticle

Non-Antigenic Modulation of Antigen Receptor (TCR) Cβ-FG Loop Modulates Signalling: Implications of External Factors Influencing T-Cell Responses

by Nicholas Manolios, Son Pham, Guojiang Hou, Jonathan Du, Camelia Quek and David Hibbs

Int. J. Mol. Sci. 2023, 24(11), 9334; https://doi.org/10.3390/ijms24119334 - 26 May 2023

Viewed by 1269

Abstract

T-cell recognition of antigens is complex, leading to biochemical and cellular events that impart both specific and targeted immune responses. The end result is an array of cytokines that facilitate the direction and intensity of the immune reaction—such as T-cell proliferation, differentiation, macrophage activation, and B-cell isotype switching—all of which may be necessary and appropriate to eliminate the antigen and induce adaptive immunity. Using in silico docking to identify small molecules that putatively bind to the T-cell Cβ-FG loop, we have shown in vitro using an antigen presentation assay that T-cell signalling is altered. The idea of modulating T-cell signalling independently of antigens by directly targeting the FG loop is novel and warrants further study. Full article

(This article belongs to the Special Issue New Insights in Translational Bioinformatics)

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