Biomarkers and Therapeutic Targets Based on Bioinformatical Studies

Topic Information

Dear Colleagues,

Bioinformatics is a rapidly evolving field that has revolutionized our understanding of biological systems and their functions. The ability to analyze large volumes of data and thereby extract meaningful information has led to the identification of biomarkers that can be used for the diagnosis and treatment of diseases. This has greatly improved patient outcomes and paved the way for personalized medicine. The integration of cutting-edge technologies into bioinformatics has greatly facilitated the identification of biomarkers and new drugs, the development of diagnostic models, the design of targeted drugs, and the personalized treatment of diseases. In the development of drug targets, bioinformatics can also be used to discover new targets, predict drug actions, evaluate drug efficacy and safety, etc. Among these tasks, drug target prediction based on bioinformatics is an incredibly important research area. By analyzing protein sequences, structures, and functions through molecular docking and molecular simulation, the interactions between drugs and proteins can be predicted, and molecules with potential drug activity can be screened. In addition, bioinformatics can also be used for the simulation and design of drug molecules, thereby optimizing the properties and effects of drugs. In summary, bioinformatics plays an important role in the development of drug targets, providing new ideas and methods for drug development.

The implementation of cutting-edge technologies such as machine learning, RNA-seq, epigenomics, and metabolomics has greatly improved our understanding of biological systems and their functions. These technologies have been used to identify biomarkers for diagnosis and treatment, develop prognostic models that can predict the outcomes of disease progression, and identify patients at high risk of developing complications. The integration of these technologies with clinical data has led to the development of personalized medicine, which has greatly improved patient outcomes and has the potential to revolutionize healthcare.

We welcome original research, reviews, and other articles relevant to the relevant Topics. Topics of interest include but are not limited to the following:

The identification of biomarkers using advanced sequencing technologies;
the development of prognostic and diagnostic models using machine learning and deep learning algorithms;
the design of targeted drugs using omics data;
the validation of biomarkers using clinical samples;
the application of advanced technologies in personalized medicine;
the integration of different omics data for a comprehensive understanding of diseases.

Dr. Qingjia Chi
Prof. Dr. Xiangcheng Chen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Bioengineering bioengineering	3.8	4.0	2014	15.6 Days	CHF 2700	Submit
Biology biology	3.6	5.7	2012	16.1 Days	CHF 2700	Submit
Biomimetics biomimetics	3.4	3.5	2016	20.3 Days	CHF 2200	Submit
Life life	3.2	4.3	2011	18 Days	CHF 2600	Submit
Molecules molecules	4.2	7.4	1996	15.1 Days	CHF 2700	Submit

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Published Papers (7 papers)

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All Bioengineering Biology Biomimetics Life Molecules

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Open AccessArticle

Identification of Clinical Value and Biological Effects of XIRP2 Mutation in Hepatocellular Carcinoma

by Dahuan Li, Xin Bao, Shan Lei, Wenpeng Cao, Zhirui Zeng and Tengxiang Chen

Biology 2024, 13(8), 633; https://doi.org/10.3390/biology13080633 - 19 Aug 2024

Viewed by 1048

Abstract

Hepatocellular carcinoma (HCC) is a prevalent malignant digestive tumor. Numerous genetic mutations have been documented in HCC, yet the clinical significance of these mutations remains largely unexplored. The objective of this study is to ascertain the clinical value and biological effects of xin [...] Read more.

Hepatocellular carcinoma (HCC) is a prevalent malignant digestive tumor. Numerous genetic mutations have been documented in HCC, yet the clinical significance of these mutations remains largely unexplored. The objective of this study is to ascertain the clinical value and biological effects of xin actin binding repeat containing 2 (XIRP2) mutation in HCC. The gene mutation landscape of HCC was examined using data from the Cancer Genome Atlas and the International Cancer Genome Consortium databases. The prognostic significance of the XIRP2 mutation was assessed through KM plot analysis. The association between drug sensitivity and the XIRP2 mutation was investigated using the TIDE algorithm and CCK-8 experiments. The biological effects of the XIRP2 mutation were evaluated through qRT-PCR, protein stability experiments, and relevant biological experiments. The XIRP2 mutation is one of the high-frequency mutations in HCC, and is associated with poor prognosis. A total of 72 differentially expressed genes (DEGs) were observed in HCC tissues with the XIRP2 mutation as compared to those with the XIRP2 wildtype, and these DEGs were closely related to ion metabolic processes. The XIRP2 mutation was linked to alterations in the sensitivity of fludarabine, oxaliplatin, WEHI-539, and LCL-161. CCK-8 assays demonstrated that HCC cells carrying the XIRP2 mutation exhibited increased resistance to fludarabine and oxaliplatin, but enhanced sensitivity to WEHI-539 and LCL-161 as compared with those HCC cells with the XIRP2 wildtype. The XIRP2 mutation was found to have no impact on the mRNA levels of XIRP2 in tissues and cells, but it did enhance the stability of the XIRP2 protein. Mechanically, the inhibition of XIRP2 resulted in a significant increase in sensitivity to oxaliplatin through an elevation in zinc ions and a calcium ion overload. In conclusion, the XIRP2 mutation holds potential as a biomarker for predicting the prognosis and drug sensitivity of HCC and serves as a therapeutic target to enhance the efficacy of oxaliplatin. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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19 pages, 7673 KiB  

Open AccessArticle

Hypoxia-Induced Adaptations of Embryonic Fibroblasts: Implications for Developmental Processes

by Zeyu Li, Delong Han, Zhenchi Li and Lingjie Luo

Biology 2024, 13(8), 598; https://doi.org/10.3390/biology13080598 - 8 Aug 2024

Viewed by 983

Abstract

Animal embryonic development occurs under hypoxia, which can promote various developmental processes. Embryonic fibroblasts, which can differentiate into bone and cartilage and secrete various members of the collagen protein family, play essential roles in the formation of embryonic connective tissues and basement membranes. [...] Read more.

Animal embryonic development occurs under hypoxia, which can promote various developmental processes. Embryonic fibroblasts, which can differentiate into bone and cartilage and secrete various members of the collagen protein family, play essential roles in the formation of embryonic connective tissues and basement membranes. However, the adaptations of embryonic fibroblasts under hypoxia remain poorly understood. In this study, we investigated the effects of hypoxia on mouse embryonic fibroblasts (MEFs). We found that hypoxia can induce migration, promote metabolic reprogramming, induce the production of ROS and apoptosis, and trigger the activation of multiple signaling pathways of MEFs. Additionally, we identified several hypoxia-inducible genes, including Proser2, Bean1, Dpf1, Rnf128, and Fam71f1, which are regulated by HIF1α. Furthermore, we demonstrated that CoCl₂ partially mimics the effects of low oxygen on MEFs. However, we found that the mechanisms underlying the production of ROS and apoptosis differ between hypoxia and CoCl₂ treatment. These findings provide insights into the complex interplay between hypoxia, fibroblasts, and embryonic developmental processes. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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11 pages, 254 KiB  

Open AccessArticle

Effect of Ultrasound-Guided Renal Biopsies on Urinary N-Acetyl-Beta-D-Glucosaminidase Index Activity in Dogs with Diffuse Parenchymal Nephropathies

by Andrei Răzvan Codea, Romeo Popa, Bogdan Sevastre, Alexandra Biriș, Daniela Neagu, Cristian Popovici, Mircea Mircean and Ciprian Ober

Life 2024, 14(7), 867; https://doi.org/10.3390/life14070867 - 11 Jul 2024

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Abstract

Background: Ultrasound-guided kidney biopsy is an essential diagnostics method that can increase the accuracy of the differential diagnosis between acute and chronic nephropathies. In addition, it will help clinicians perform an etiologic diagnosis, issue a prognosis, and orient therapy for the majority of [...] Read more.

Background: Ultrasound-guided kidney biopsy is an essential diagnostics method that can increase the accuracy of the differential diagnosis between acute and chronic nephropathies. In addition, it will help clinicians perform an etiologic diagnosis, issue a prognosis, and orient therapy for the majority of parenchymal nephropathies. Due to the relative invasiveness and potential adverse effects, the use of kidney biopsies is limited among practitioners. Results: Twenty-eight dogs, of mixed breed and variable ages, of which 11 (39, 29%) were males and 17 (60, 71%) were females, were examined and underwent an ultrasound-guided kidney biopsy to establish a definitive diagnosis. The patients were presented with a variety of diffuse nephropathies, such as kidney lymphoma: 1 (3.57%), glomerulonephritis: 13 (46.43%), tubulointerstitial nephritis: 11 (39.29%), and nephrocalcinosis. A total of 3 (10.71%) of 18 (64.29%) were in acute kidney injury, and 10 (35.71%) were CKD patients. The type and the severity of the kidney lesions were correlated with changes in the urinary n-acetyl-beta-d-glucosaminidase index (iNAG. To quantify the side effects of percutaneous kidney biopsy, the magnitude of post-biopsy hematuria and changes in urinary iNAG activity were evaluated. The results indicate a significant post-biopsy increase in the urinary iNAG activity in all the patients that underwent this procedure (100.08 ± 34.45 U/g), with a pre-biopsy iNAG vs. 147.65 ± 33.26 U/g post-biopsy iNAG (p < 0.001), suggesting an intensification in the kidney tubular damage that comes consecutives to kidney puncture and sampling. Transitory macro- or microhematuria were constant findings in all the dogs that underwent ultrasound-guided kidney biopsy, but the magnitude and extent could not be associated with the platelet count (PLT 109/L), aPTT (s), and PT (s) levels in our patients, and they were also resolved after 12–24 h without therapeutic interventions. Conclusions: Ultrasound-guided renal biopsy was shown to be a minimally invasive diagnostic procedure that causes transient and limited effects on kidney structures. Although these effects were minor and resolved without intervention, we feel that the benefit of obtaining higher-quality biopsied tissue outweighs the higher risks associated with this procedure. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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17 pages, 4515 KiB  

Open AccessArticle

Efficacy of Glycyrrhetinic Acid in the Treatment of Acne Vulgaris Based on Network Pharmacology and Experimental Validation

by Lingna Xie, Congwei Ma, Xinyu Li, Huixiong Chen, Ping Han, Li Lin, Weiqiang Huang, Menglu Xu, Hailiang Lu and Zhiyun Du

Molecules 2024, 29(10), 2345; https://doi.org/10.3390/molecules29102345 - 16 May 2024

Cited by 1 | Viewed by 1754

Abstract

Glycyrrhetinic acid (GA) is a saponin compound, isolated from licorice (Glycyrrhiza glabra), which has been wildly explored for its intriguing pharmacological and medicinal effects. GA is a triterpenoid glycoside displaying an array of pharmacological and biological activities, including anti-inflammatory, anti-bacterial, antiviral and antioxidative [...] Read more.

Glycyrrhetinic acid (GA) is a saponin compound, isolated from licorice (Glycyrrhiza glabra), which has been wildly explored for its intriguing pharmacological and medicinal effects. GA is a triterpenoid glycoside displaying an array of pharmacological and biological activities, including anti-inflammatory, anti-bacterial, antiviral and antioxidative properties. In this study, we investigated the underlying mechanisms of GA on acne vulgaris through network pharmacology and proteomics. After the intersection of the 154 drug targets and 581 disease targets, 37 therapeutic targets for GA against acne were obtained. A protein–protein interaction (PPI) network analysis highlighted TNF, IL1B, IL6, ESR1, PPARG, NFKB1, STAT3 and TLR4 as key targets of GA against acne, which is further verified by molecular docking. The experimental results showed that GA inhibited lipid synthesis in vitro and in vivo, improved the histopathological damage of skin, prevented mast cell infiltration and decreased the level of pro-inflammatory cytokines, including TNF-α, IL-1β and IL-6. This study indicates that GA may regulate multiple pathways to improve acne symptoms, and the beneficial effects of GA against acne vulgaris might be through the regulation of sebogenesis and inflammatory responses. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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15 pages, 3602 KiB  

Open AccessArticle

PRMT5 Mediated HIF1α Signaling and Ras-Related Nuclear Protein as Promising Biomarker in Hepatocellular Carcinoma

by Wafaa Abumustafa, Darko Castven, Fatemeh Saheb Sharif-Askari, Batoul Abi Zamer, Mawieh Hamad, Jens-Uwe Marquardt and Jibran Sualeh Muhammad

Biology 2024, 13(4), 216; https://doi.org/10.3390/biology13040216 - 27 Mar 2024

Cited by 1 | Viewed by 1557

Abstract

Protein arginine N-methyltransferase 5 (PRMT5) has been identified as a potential therapeutic target for various cancer types. However, its role in regulating the hepatocellular carcinoma (HCC) transcriptome remains poorly understood. In this study, publicly available databases were employed to investigate PRMT5 expression, its [...] Read more.

Protein arginine N-methyltransferase 5 (PRMT5) has been identified as a potential therapeutic target for various cancer types. However, its role in regulating the hepatocellular carcinoma (HCC) transcriptome remains poorly understood. In this study, publicly available databases were employed to investigate PRMT5 expression, its correlation with overall survival, targeted pathways, and genes of interest in HCC. Additionally, we utilized in-house generated NGS data to explore PRMT5 expression in dysplastic nodules compared to hepatocellular carcinoma. Our findings revealed that PRMT5 is significantly overexpressed in HCC compared to normal liver, and elevated expression correlates with poor overall survival. To gain insights into the mechanism driving PRMT5 overexpression in HCC, we analyzed promoter CpG islands and methylation status in HCC compared to normal tissues. Pathway analysis of PRMT5 knockdown in the HCC cells revealed a connection between PRMT5 expression and genes related to the HIF1α pathway. Additionally, by filtering PRMT5-correlated genes within the HIF1α pathway and selecting up/downregulated genes in HCC patients, we identified Ras-related nuclear protein (RAN) as a target associated with overall survival. For the first time, we report that PRMT5 is implicated in the regulation of HIF1A and RAN genes, suggesting the potential prognostic utility of PRMT5 in HCC. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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27 pages, 1435 KiB  

Open AccessReview

Molecular Assessment of Methylglyoxal-Induced Toxicity and Therapeutic Approaches in Various Diseases: Exploring the Interplay with the Glyoxalase System

by Muhanad Alhujaily

Life 2024, 14(2), 263; https://doi.org/10.3390/life14020263 - 17 Feb 2024

Cited by 2 | Viewed by 2542

Abstract

This comprehensive exploration delves into the intricate interplay of methylglyoxal (MG) and glyoxalase 1 (GLO I) in various physiological and pathological contexts. The linchpin of the narrative revolves around the role of these small molecules in age-related issues, diabetes, obesity, cardiovascular diseases, and [...] Read more.

This comprehensive exploration delves into the intricate interplay of methylglyoxal (MG) and glyoxalase 1 (GLO I) in various physiological and pathological contexts. The linchpin of the narrative revolves around the role of these small molecules in age-related issues, diabetes, obesity, cardiovascular diseases, and neurodegenerative disorders. Methylglyoxal, a reactive dicarbonyl metabolite, takes center stage, becoming a principal player in the development of AGEs and contributing to cell and tissue dysfunction. The dual facets of GLO I—activation and inhibition—unfold as potential therapeutic avenues. Activators, spanning synthetic drugs like candesartan to natural compounds like polyphenols and isothiocyanates, aim to restore GLO I function. These molecular enhancers showcase promising outcomes in conditions such as diabetic retinopathy, kidney disease, and beyond. On the contrary, GLO I inhibitors emerge as crucial players in cancer treatment, offering new possibilities in diseases associated with inflammation and multidrug resistance. The symphony of small molecules, from GLO I activators to inhibitors, presents a nuanced understanding of MG regulation. From natural compounds to synthetic drugs, each element contributes to a molecular orchestra, promising novel interventions and personalized approaches in the pursuit of health and wellbeing. The abstract concludes with an emphasis on the necessity of rigorous clinical trials to validate these findings and acknowledges the importance of individual variability in the complex landscape of health. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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40 pages, 7326 KiB  

Open AccessArticle

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data

by Dinesh Chellappan and Harikumar Rajaguru

Biomimetics 2023, 8(6), 503; https://doi.org/10.3390/biomimetics8060503 - 22 Oct 2023

Viewed by 1679

Abstract

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares [...] Read more.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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