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Pharmaceuticals
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Data-Driven Biomarker and Drug Discovery for Complex Disease
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Data-Driven Biomarker and Drug Discovery for Complex Disease

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Pharmacology".

Deadline for manuscript submissions: 22 October 2024 | Viewed by 2701

Special Issue Editors

Dr. Yin Huang

E-Mail Website
Guest Editor
Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
Interests: network pharmacology; metabolomics; machine learning; chemotherapy toxicity; Chinese medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Ban

E-Mail Website
Guest Editor
Department of Pharmacology, College of Pharmacy, Harbin Medical University, Harbin, China
Interests: cardiovascular disease; ion channel; post-translational modification
Dr. Huimin Guo

E-Mail Website
Guest Editor
Center for Biotechnology, Anhui Agricultural University, Hefei 230036, China
Interests: mass spectrometry; natural products; antioxidation; diabetes; lipidomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Complex diseases, such as cardiovascular disorders, neurodegenerative conditions, and cancer, present formidable challenges to the medical community. These multifaceted ailments are not governed by single genetic factors or isolated environmental triggers. Instead, they arise from intricate interactions between genetic variations, environmental influences, and lifestyle factors. The era of big data, coupled with cutting-edge computational techniques, has revolutionized our ability to dissect the intricate landscape of complex diseases. The data-driven paradigm allows us to harness the wealth of information contained within diverse datasets, unravel intricate disease mechanisms, pinpoint novel biomarkers, and identify promising drugs.  This Special Issue serves as a platform for researchers to present innovative insights, methodologies, and discoveries that leverage extensive datasets in the quest for more effective solutions for complex disease. By leveraging vast datasets encompassing genomics, proteomics, metabolomics, clinical records, and other critical information, we aim to accelerate the translation of research findings into actionable solutions for patients. We invite contributions that elucidate the intricate web of factors underpinning complex diseases and offer potential biomarkers that can revolutionize diagnostics, prognostics, and treatment decisions. Moreover, we seek papers presenting novel drug targets and therapeutic compounds designed through data-driven insights. This Special Issue provides a platform to share your pioneering work, foster collaboration, and contribute to the advancement of healthcare. We welcome original research articles and reviews that resonate with the theme of data-driven biomarkers and drug discovery for complex diseases. We look forward to your submissions, anticipating that this Special Issue will inspire groundbreaking developments in the fight against complex diseases and bring us closer to effective medical interventions.

Dr. Yin Huang
Dr. Tao Ban
Dr. Huimin Guo
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. Pharmaceuticals 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 2900 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

  • cardiovascular disease
  • diabetes
  • neurodegenerative diseases
  • genomics
  • proteomics
  • metabolomics
  • systems biology
  • data mining
  • biomarker
  • drug discovery

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Published Papers (3 papers)

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Research

14 pages, 5888 KiB  
Article
Comprehensive Analysis of Characteristics of Cuproptosis-Related LncRNAs Associated with Prognosis of Lung Adenocarcinoma and Tumor Immune Microenvironment
by Feihong Chen, Xin Wen, Jiani Wu, Min Feng and Shicheng Feng
Pharmaceuticals 2024, 17(9), 1244; https://doi.org/10.3390/ph17091244 - 21 Sep 2024
Viewed by 473
Abstract
As a novel discovered mechanism of cell death, cuproptosis is copper-dependent and induces protein toxicity related to advanced tumors, disease prognosis, and human innate and adaptive immune response. However, it has not yet been fully established how the prognosis of lung adenocarcinoma (LUAD) [...] Read more.
As a novel discovered mechanism of cell death, cuproptosis is copper-dependent and induces protein toxicity related to advanced tumors, disease prognosis, and human innate and adaptive immune response. However, it has not yet been fully established how the prognosis of lung adenocarcinoma (LUAD) is related to the immune microenvironment of cuproptosis-related lncRNAs using several bioinformatic techniques. In the study, 19 genes related to cuproptosis were collected. Subsequently, 783 lncRNAs related to the co-expression of cuproptosis were obtained. Moreover, the Cox model revealed and constructed four lncRNA (AC012020.1, AC114763.1, AL161431.1, AC010260.1) prognostic markers related to cuproptosis. Based on the median risk score (RS) values, patients were categorized into two groups: high risk and low risk. The Kaplan–Meier (KM) survival curve depicted a statistically significant overall survival (OS) rate among two groups. Principal component analysis (PCA) and receiver operator characteristic curve (ROC) proved that the model had promising ability in prognosis. The analysis of univariate and multivariate Cox regression revealed that RS served as an independent prognostic factor. Moreover, multivariate Cox regression was employed for the establishment of a nomogram of prognostic indicators. The tumor mutational burden (TMB) depicted a considerable difference between the two risk groups. The immunotherapy response of LUAD patients with high risk was improved compared to low risk patients. The study also revealed that drug sensitivity associated with LUAD was significantly linked to RS. The findings could be helpful to establish a good diagnosis, prognosis, and management regime for patients with LUAD. Full article
(This article belongs to the Special Issue Data-Driven Biomarker and Drug Discovery for Complex Disease)
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15 pages, 1520 KiB  
Article
Nuclear Receptors and Stress Response Pathways Associated with the Development of Oral Mucositis Induced by Antineoplastic Agents
by Moena Kagaya and Yoshihiro Uesawa
Pharmaceuticals 2024, 17(8), 1086; https://doi.org/10.3390/ph17081086 - 20 Aug 2024
Viewed by 780
Abstract
Oral mucositis (OM) is one of the common adverse events associated with cancer treatment that decreases the quality of life and affects treatment outcomes. However, the medications used to manage OM are generally only palliative, and our knowledge of the syndrome is limited. [...] Read more.
Oral mucositis (OM) is one of the common adverse events associated with cancer treatment that decreases the quality of life and affects treatment outcomes. However, the medications used to manage OM are generally only palliative, and our knowledge of the syndrome is limited. The etiology of the syndrome is thought to be complex and multifactorial. We investigated the trends and characteristics of OM and estimated molecular initiating events (MIEs) associated with the development of the syndrome using the FDA Adverse Event Reporting System. The study of trends and characteristics suggested that OM is significantly more likely to occur in females and nonelderly patients and is likely to be induced by protein kinase inhibitors such as afatinib and everolimus. Next, we used Toxicity Predictor, an in-house quantitative structure–activity relationship system, to estimate OM-associated MIEs. The results revealed that the agonist activity of the human pregnane X receptor, thyroid-stimulating hormone-releasing hormone receptor, and androgen receptor may be associated with OM development. Our study findings are expected to help avoid the risk of OM induction during the drug discovery process and clinical use of antineoplastic agents. Full article
(This article belongs to the Special Issue Data-Driven Biomarker and Drug Discovery for Complex Disease)
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27 pages, 11196 KiB  
Article
Improved Immunotherapy Outcomes via Cuproptosis Upregulation of HLA-DRA Expression: Promoting the Aggregation of CD4+ and CD8+T Lymphocytes in Clear Cell Renal Cell Carcinoma
by Bowen Wang, Yiwen Liu, Feng Xiong and Chunyang Wang
Pharmaceuticals 2024, 17(6), 678; https://doi.org/10.3390/ph17060678 - 24 May 2024
Viewed by 857
Abstract
Immunotherapy has shown promising clinical results in clear cell renal cell carcinoma (ccRCC), but low clinical target response rates due to dysfunction of the major histocompatibility complex (MHC) and an inhibitory tumor immune microenvironment (TIME) have largely limited the associated clinical benefits. In [...] Read more.
Immunotherapy has shown promising clinical results in clear cell renal cell carcinoma (ccRCC), but low clinical target response rates due to dysfunction of the major histocompatibility complex (MHC) and an inhibitory tumor immune microenvironment (TIME) have largely limited the associated clinical benefits. In the present study, we explored the feasibility of enhancing tumor-specific-MHC-II-HLA-DRA expression, counteracting the TIME’s suppressive effects, thereby improving the sensitivity of immune checkpoint inhibitor (ICI) therapy from the standpoint of cuproptosis. Immunohistochemical staining and in vitro experiments validated the expression of HLA-DRA in ccRCC and its positive impact on ICI therapy. Subsequently, we observed that cuproptosis upregulated HLA-DRA expression in a dose-dependent manner, further confirming the link between cuproptosis and HLA-DRA. In vivo experiments showed that cuproptosis increased the sensitivity to ICI treatment, and implementing cuproptosis alongside anti-PD-1 treatment curtailed tumor growth. Mechanistically, cuproptosis upregulates HLA-DRA expression at the transcriptional level in a dose-dependent manner by inducing the production of reactive oxygen species; high levels of HLA-DRA promote the expression of chemokines CCL5, CXCL9, and CXCL10 in the TIME, inhibiting the development of a pro-tumor microenvironment by promoting the infiltration of CD4+T and CD8+T cells, thereby synergizing ICI therapy and exerting anti-tumor effects. Taken together, this work highlights the role of cuproptosis in mediating TIME remodeling and synergistic immunotherapy, providing new evidence that cuproptosis can evoke effective anti-tumor immune responses. Full article
(This article belongs to the Special Issue Data-Driven Biomarker and Drug Discovery for Complex Disease)
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