Application of Bioinformatics to Unravel the Molecular Mechanisms of Cancer Biology

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Biochemistry and Molecular Biology".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 949

Special Issue Editor


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Guest Editor
Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, China
Interests: cancer biology; bioinformatics; genetics; epigenetics; transcriptomics; tumorigenesis
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Special Issue Information

Dear Colleagues,

Cancer is a heterogeneous group of diseases characterized by uncontrolled cell growth, metastasis, and invasiveness. Although significant progress has been made in cancer biology research, the underlying mechanisms of carcinogenesis remain incompletely understood. Bioinformatics can accelerate the elucidation of key molecular pathways and networks driving cancer progression. This Special Issue aims to focus on the application of bioinformatics in unraveling the genetic and epigenetic mechanisms underlying cancer initiation and advancement. We invite submissions of original research papers covering integrative analyses of cancer genomics, transcriptomics, and epigenomics data to gain deeper insights into the molecular mechanisms of cancer and facilitate the discovery of cancer biomarkers and novel drug targets.

Dr. Yuanyan Xiong
Guest Editor

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Keywords

  • cancer biology
  • bioinformatics
  • genetics
  • epigenetics
  • transcriptomics

Published Papers (1 paper)

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Research

16 pages, 5753 KiB  
Article
Identification and Validation of eRNA as a Prognostic Indicator for Cervical Cancer
by Lijing Huang, Jingkai Zhang, Zhou Songyang and Yuanyan Xiong
Biology 2024, 13(4), 227; https://doi.org/10.3390/biology13040227 - 29 Mar 2024
Viewed by 781
Abstract
The survival of CESC patients is closely related to the expression of enhancer RNA (eRNA). In this work, we downloaded eRNA expression, clinical, and gene expression data from the TCeA and TCGA portals. A total of 7936 differentially expressed eRNAs were discovered by [...] Read more.
The survival of CESC patients is closely related to the expression of enhancer RNA (eRNA). In this work, we downloaded eRNA expression, clinical, and gene expression data from the TCeA and TCGA portals. A total of 7936 differentially expressed eRNAs were discovered by limma analysis, and the relationship between these eRNAs and survival was analyzed by univariate Cox hazard analysis, LASSO regression, and multivariate Cox hazard analysis to obtain an 8-eRNA model. Risk score heat maps, KM curves, ROC analysis, robustness analysis, and nomograms further indicate that this 8-eRNA model is a novel indicator with high prognostic performance independent of clinicopathological classification. The model divided patients into high-risk and low-risk groups, compared pathway diversity between the two groups through GSEA analysis, and provided potential therapeutic agents for high-risk patients. Full article
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