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Biomedicines
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Bioinformatics: From Methods to Applications
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Bioinformatics: From Methods to Applications

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 13665

Special Issue Editors

Dr. Tian-Huei Chu

E-Mail Website
Guest Editor
Medical Laboratory, Medical Education and Research Center, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
Interests: cancer biology; hepatocellular carcinoma; cancer stem cell; cell biology; signal transduction; tumor immunology; inflammation; gene therapy; bioinformatics
Dr. Yung-Kuo Lee

E-Mail Website
Guest Editor
Medical Laboratory, Medical Education and Research Center, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
Interests: glycobiology; cancer biology; carcinogenesis; environmental toxicology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, with the growth of modern data science approaches, disease marker identification has been rapidly modernized. Databases are high-throughput and economic tools for the process of selecting candidate disease genes. Omics data, including genomics, proteomics, epigenomics, metabolomics, and microbiomics, introduce a remarkable opportunity for scientists to understand disease molecular networks via a comprehensive approach. The application of bioinformatics has accelerated the understanding of the human disease mechanism. Recently, artificial intelligence and machine learning through accumulation have been widely applied in disease diagnosis, prognosis, and drug development. However, the accuracy of molecular target selection depends on the skills and approaches of the operating researcher. Thus, optimization from methods to application is urgently needed for precision medicine in the bioinformatics field.

For this Special Issue of the journal Biomedicines, we welcome contributions focusing on—but not limited to—methods and applications of bioinformatics to advance precision medicine for human diseases. We encourage the submission of preclinical, translational, and clinical studies utilizing data set-based approaches in early detection, diagnosis, and screening of cancer. All article types within the scope of Biomedicines are welcomed, and we particularly encourage the submission of wet lab validated data of open data studies.

Dr. Tian-Huei Chu
Dr. Yung-Kuo Lee
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. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). 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

  • human diseases
  • bioinformatics
  • multi-omics
  • precision medicine
  • prognosis
  • diagnosis
  • drug development
  • artificial intelligence
  • machine learning

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Published Papers (6 papers)

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Research

17 pages, 2249 KiB  
Article
Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics
by Wei Yuan, Yaming Li, Zhengpan Han, Yu Chen, Jinnan Xie, Jianguo Chen, Zhisheng Bi and Jianing Xi
Biomedicines 2024, 12(9), 2086; https://doi.org/10.3390/biomedicines12092086 - 12 Sep 2024
Viewed by 968
Abstract
The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, [...] Read more.
The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher’s exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10−4 ± 3.06 × 10−5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10−1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data. Full article
(This article belongs to the Special Issue Bioinformatics: From Methods to Applications)
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12 pages, 1201 KiB  
Article
Prognosis Prediction in Head and Neck Squamous Cell Carcinoma by Radiomics and Clinical Information
by Shing-Yau Tam, Fuk-Hay Tang, Mei-Yu Chan, Hiu-Ching Lai and Shing Cheung
Biomedicines 2024, 12(8), 1646; https://doi.org/10.3390/biomedicines12081646 - 24 Jul 2024
Viewed by 1510
Abstract
(1) Background: head and neck squamous cell carcinoma (HNSCC) is a common cancer whose prognosis is affected by its heterogeneous nature. We aim to predict 5-year overall survival in HNSCC radiotherapy (RT) patients by integrating radiomic and clinical information in machine-learning models; (2) [...] Read more.
(1) Background: head and neck squamous cell carcinoma (HNSCC) is a common cancer whose prognosis is affected by its heterogeneous nature. We aim to predict 5-year overall survival in HNSCC radiotherapy (RT) patients by integrating radiomic and clinical information in machine-learning models; (2) Methods: HNSCC radiotherapy planning computed tomography (CT) images with RT structures were obtained from The Cancer Imaging Archive. Radiomic features and clinical data were independently analyzed by five machine-learning algorithms. The results were enhanced through a voted ensembled approach. Subsequently, a probability-weighted enhanced model (PWEM) was generated by incorporating both models; (3) Results: a total of 299 cases were included in the analysis. By receiver operating characteristic (ROC) curve analysis, PWEM achieved an area under the curve (AUC) of 0.86, which outperformed both radiomic and clinical factor models. Mean decrease accuracy, mean decrease Gini, and a chi-square test identified T stage, age, and disease site as the most important clinical factors in prognosis prediction; (4) Conclusions: our radiomic–clinical combined model revealed superior performance when compared to radiomic and clinical factor models alone. Further prospective research with a larger sample size is warranted to implement the model for clinical use. Full article
(This article belongs to the Special Issue Bioinformatics: From Methods to Applications)
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19 pages, 3145 KiB  
Article
Sirtuin Inhibitor Cambinol Induces Cell Differentiation and Differently Interferes with SIRT1 and 2 at the Substrate Binding Site
by Deborah Giordano, Bernardina Scafuri, Luigi De Masi, Lucia Capasso, Viviana Maresca, Lucia Altucci, Angela Nebbioso, Angelo Facchiano and Paola Bontempo
Biomedicines 2023, 11(6), 1624; https://doi.org/10.3390/biomedicines11061624 - 2 Jun 2023
Cited by 4 | Viewed by 2199
Abstract
Epigenetic mechanisms finely regulate gene expression and represent potential therapeutic targets. Cambinol is a synthetic heterocyclic compound that inhibits class III histone deacetylases known as sirtuins (SIRTs). The acetylating action that results could be crucial in modulating cellular functions via epigenetic regulations. The [...] Read more.
Epigenetic mechanisms finely regulate gene expression and represent potential therapeutic targets. Cambinol is a synthetic heterocyclic compound that inhibits class III histone deacetylases known as sirtuins (SIRTs). The acetylating action that results could be crucial in modulating cellular functions via epigenetic regulations. The main aim of this research was to investigate the effects of cambinol, and its underlying mechanisms, on cell differentiation by combining wet experiments with bioinformatics analyses and molecular docking simulations. Our in vitro study evidenced the ability of cambinol to induce the differentiation in MCF-7, NB4, and 3T3-L1 cell lines. Interestingly, focusing on the latter that accumulated cytoplasmic lipid droplets, the first promising results related to the action mechanisms of cambinol have shown the induction of cell cycle-related proteins (such as p16 and p27) and modulation of the expression of Rb protein and nuclear receptors related to cell differentiation. Moreover, we explored the inhibitory mechanism of cambinol on human SIRT1 and 2 performing in silico molecular simulations by protein–ligand docking. Cambinol, unlike from other sirtuin inhibitors, is able to better interact with the substrate binding site of SIRT1 than with the inhibition site. Additionally, for SIRT2, cambinol partially interacts with the substrate binding site, although the inhibition site is preferred. Overall, our findings suggest that cambinol might contribute to the development of an alternative to the existing epigenetic therapies that modulate SIRTs. Full article
(This article belongs to the Special Issue Bioinformatics: From Methods to Applications)
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13 pages, 1731 KiB  
Article
Direct Effects of Mifepristone on Mice Embryogenesis: An In Vitro Evaluation by Single-Embryo RNA Sequencing Analysis
by Yu-Ting Su, Jia-Shing Chen, Kuo-Chung Lan, Yung-Kuo Lee, Tian-Huei Chu, Yu-Cheng Ho, Cheng-Chun Wu and Fu-Jen Huang
Biomedicines 2023, 11(3), 907; https://doi.org/10.3390/biomedicines11030907 - 15 Mar 2023
Cited by 2 | Viewed by 3431
Abstract
The clinical use of mifepristone for medical abortions has been established in 1987 in France and since 2000 in the United States. Mifepristone has a limited medical period that lasts <9 weeks of gestation, and the incidence of mifepristone treatment failure increases with [...] Read more.
The clinical use of mifepristone for medical abortions has been established in 1987 in France and since 2000 in the United States. Mifepristone has a limited medical period that lasts <9 weeks of gestation, and the incidence of mifepristone treatment failure increases with gestation time. Mifepristone functions as an antagonist for progesterone and glucocorticoid receptors. Studies have confirmed that mifepristone treatments can directly contribute to endometrium disability by interfering with the endometrial receptivity of the embryo, thus causing decidual endometrial degeneration. However, whether mifepristone efficacy directly affects embryo survival and growth is still an open question. Some women choose to continue their pregnancy after mifepristone treatment fails, and some women express regret and seek medically unapproved mifepristone antagonization with high doses of progesterone. These unapproved treatments raise the potential risk of embryonic fatality and developmental anomalies. Accordingly, in the present study, we collected mouse blastocysts ex vivo and treated implanted blastocysts with mifepristone for 24 h. The embryos were further cultured to day 8 in vitro to finish their growth in the early somite stage, and the embryos were then collected for RNA sequencing (control n = 3, mifepristone n = 3). When we performed a gene set enrichment analysis, our data indicated that mifepristone treatment considerably altered the cellular pathways of embryos in terms of viability, proliferation, and development. The data indicated that mifepristone was involved in hallmark gene sets of protein secretion, mTORC1, fatty acid metabolism, IL-2-STAT5 signaling, adipogenesis, peroxisome, glycolysis, E2F targets, and heme metabolism. The data further revealed that mifepristone interfered with normal embryonic development. In sum, our data suggest that continuing a pregnancy after mifepristone treatment fails is inappropriate and infeasible. The results of our study reveal a high risk of fetus fatality and developmental problems when pregnancies are continued after mifepristone treatment fails. Full article
(This article belongs to the Special Issue Bioinformatics: From Methods to Applications)
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13 pages, 1323 KiB  
Article
Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parameters
by Ivona Mitu, Cristina-Daniela Dimitriu, Ovidiu Mitu, Cristina Preda, Florin Mitu and Manuela Ciocoiu
Biomedicines 2023, 11(2), 489; https://doi.org/10.3390/biomedicines11020489 - 8 Feb 2023
Cited by 2 | Viewed by 1785
Abstract
Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy [...] Read more.
Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy to measure parameters in any setting. The study included 104 apparently healthy subjects, but with a higher-than-normal percent of adiposity or waist circumference. Multiple linear regression (MLR) and artificial neural network (ANN) models were built for predicting abdominal fat and fat-free masses in patients with relatively low cardiometabolic risks. The data were divided into training, validation and test sets, and this process was repeated 20 times per each model to reduce the bias of data division on model accuracy. The best performance models used a maximum number of five anthropometric inputs, with higher R2 values for ANN models than for MLR models (R2 = 0.96–0.98 vs. R2 = 0.80–0.94, p = 0.006). The root mean square error (RMSE) for all predicted parameters was significantly lower for ANN models than for MLR models, suggesting a higher accuracy for ANN models. From all body masses predicted, trunk fat mass and fat-free mass registered the best performance with ANN, allowing a possible error of 1.84 kg for predicting the correct trunk fat mass and 1.48 kg for predicting the correct trunk fat-free mass. The developed algorithms represent cost-effective prediction tools for the most relevant adipose and lean tissues involved in the physiopathology of cardiometabolic risks. Full article
(This article belongs to the Special Issue Bioinformatics: From Methods to Applications)
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10 pages, 951 KiB  
Article
Atrial Fibrillation Risk and Urate-Lowering Therapy in Patients with Gout: A Cohort Study Using a Clinical Database
by Ching-Han Liu, Shih-Chung Huang, Chun-Hao Yin, Wei-Chun Huang, Jin-Shuen Chen, Yao-Shen Chen, Su-Ting Gan, Shiow-Jyu Tzou, Ching-Tsai Hsu, Hao-Ming Wu and Wen-Hwa Wang
Biomedicines 2023, 11(1), 59; https://doi.org/10.3390/biomedicines11010059 - 26 Dec 2022
Cited by 3 | Viewed by 2491
Abstract
Individuals of Asian descent are at higher risk for developing hyperuricemia and gout as compared to Western populations. Urate-lowering therapy (ULT) is an effective treatment for hyperuricemia and gout. It was reported that febuxostat, one of the ULTs, raises the risk of atrial [...] Read more.
Individuals of Asian descent are at higher risk for developing hyperuricemia and gout as compared to Western populations. Urate-lowering therapy (ULT) is an effective treatment for hyperuricemia and gout. It was reported that febuxostat, one of the ULTs, raises the risk of atrial fibrillation (AF) in elderly populations. Nevertheless, this association has not been properly investigated in Asian populations. We aimed to investigate the development of AF after ULT with different drugs in an Asian population. We conducted a retrospective cohort study using the clinical database at Kaohsiung Veterans General Hospital. Patients newly diagnosed with gout between 1 January 2013 and 31 December 2020 and with a documented baseline serum uric acid (sUA) level but no prior diagnosis of AF were identified. Patients were divided into three groups—allopurinol, benzbromarone, and febuxostat users. During the follow-up period, the risks of incident AF following the initiation of ULT with different drugs were assessed. Development of incident AF was noted in 43 (6%) of the 713 eligible patients during the follow-up period (mean, 49.4 ± 26.6 months). Febuxostat-treated patients had a higher prevalence of certain comorbidities (diabetes mellitus, heart failure, and chronic kidney disease) and higher CHA2DS2-VASc scores. Compared with allopurinol, neither febuxostat nor benzbromarone was associated with increased adjusted hazard ratios (HR) for incident AF (HR: 1.20, 95% confidence interval [CI]: 0.43–3.34; HR: 0.68, 95% CI: 0.22–2.08). There was no difference in the risk of incident AF among Asian patients with gout who received febuxostat, allopurinol, or benzbromarone. Further studies are needed to evaluate long-term cardiovascular outcomes in patients receiving different ULT drugs. Full article
(This article belongs to the Special Issue Bioinformatics: From Methods to Applications)
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