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Integrative Multi-Omics Analysis for Cancer Biomarkers

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 1564

Special Issue Editors


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Guest Editor
Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, 651 Ilalo Street, Honolulu, HI 96813, USA
Interests: bioinformatics; genomics; biomarker; systems biology; biomedical informatics; cancer
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Center for Epigenetics & Disease Prevention, Institute of Biosciences & Technology, College of Medicine, Texas A&M University, Houston, TX, USA
Interests: maternal nutrition; environmental health; development; bioinformatics; epigenetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue “Cancer Biomarkers and Bioinformatics”. Cancer biomarkers include any measurable molecular indicators of cancer risk, cancer occurrence, or patient outcome, and can be used as tools for cancer risk assessment, screening, and early detection; accurate diagnosis; patient prognosis; prediction of response to therapy; and cancer surveillance and response monitoring. They can also be developed as targeted therapies. Cancer biomarkers can be measured in tissue and liquid biopsy. Due to the development of advanced technologies such as omics and nanotechnologies, a great quantity of data related to cancer biomarkers have been generated. Bioinformatics plays a key role in correctly generating firsthand data and in leveraging secondary cancer biomarker data. Bioinformatics is involved in the biomarker discovery process, bridging the gap between initial discovery phases such as experimental design, clinical study execution, and bioanalytics, including sample preparation, separation, and high-throughput profiling, as well as data integration and the independent validation of identified candidates.

Led by Prof. Dr. Youping Deng and Dr. Kurt K. Zhang, and assisted by Dr. Yuanyuan Fu, this Special Issue welcomes all kinds of submissions which include the use of bioinformatics to process experimentally generated and/or published datasets for cancer biomarker studies.

Prof. Dr. Youping Deng
Dr. Kurt K. Zhang
Guest Editors

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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

  • bioinformatics
  • biomedical informatics
  • cancer
  • genomics
  • systems biology

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Published Papers (1 paper)

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Research

16 pages, 18536 KiB  
Article
Molecular Landscape of Bladder Cancer: Key Genes, Transcription Factors, and Drug Interactions
by Danishuddin, Md Azizul Haque, Shawez Khan, Jong-Joo Kim and Khurshid Ahmad
Int. J. Mol. Sci. 2024, 25(20), 10997; https://doi.org/10.3390/ijms252010997 - 12 Oct 2024
Viewed by 1091
Abstract
Bladder cancer is among the most prevalent tumors in the urinary system and is known for its high malignancy. Although traditional diagnostic and treatment methods are established, recent research has focused on understanding the molecular mechanisms underlying bladder cancer. The primary objective of [...] Read more.
Bladder cancer is among the most prevalent tumors in the urinary system and is known for its high malignancy. Although traditional diagnostic and treatment methods are established, recent research has focused on understanding the molecular mechanisms underlying bladder cancer. The primary objective of this study is to identify novel diagnostic markers and discover more effective targeted therapies for bladder cancer. This study identified differentially expressed genes (DEGs) between bladder cancer tissues and adjacent normal tissues using data from The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of these genes. A protein–protein interaction (PPI) network was also constructed to identify and analyze hub genes within this network. Gene set variation analysis (GSVA) was conducted to investigate the involvement of these genes in various biological processes and pathways. Ten key genes were found to be significantly associated with bladder cancer: IL6, CCNA2, CCNB1, CDK1, PLK1, TOP2A, AURKA, AURKB, FOXM1, and CALML5. GSVA analyses revealed that these genes are involved in a variety of biological processes and signaling pathways, including coagulation, UV-response-down, apoptosis, Notch signaling, and Wnt/beta-catenin signaling. The diagnostic relevance of these genes was validated through ROC curve analysis. Additionally, potential therapeutic drug interactions with these key genes were identified. This study provides valuable insights into key genes and their roles in bladder cancer. The identified genes and their interactions with therapeutic drugs could serve as potential biomarkers, presenting new opportunities for enhancing the diagnosis and prognosis of bladder cancer. Full article
(This article belongs to the Special Issue Integrative Multi-Omics Analysis for Cancer Biomarkers)
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