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Bioinformatics Analysis for Cancers
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Bioinformatics Analysis for Cancers

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 5489

Special Issue Editors

Prof. Dr. Yongsheng Li

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Guest Editor
College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
Interests: computational biology; cancer; tumor microenvironments; biomarkers; network analysis; genetic mutations
Prof. Dr. Juan Xu

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Guest Editor
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Interests: bioinformatics; cancer; noncoding RNAs; systems biology; resource; databases
Dr. Yan Zhang

E-Mail Website
Guest Editor
Cardiovascular Data Analysis Center, Harvard Medical School, Boston, MA, USA
Interests: bioinformatics; single-cell study

Special Issue Information

Dear Colleagues,

Cancer is a complex disease with widespread genetic and epigenetic alterations. To understand the potential mechanism of cancer, large-scale genomes, transcriptomes and proteomes have been generated for various types of cancer. However, it is still a challenge to analyze the huge omics data. Bioinformatics analysis can greatly help mining the knowledge hidden in the biomedical big data. An increasing number of methods, tools, webservers and databases have been proposed to help with data analysis. In addition, bioinformatics analyses have also uncovered the perturbed signaling, immune and metabolism pathways in cancer. The interactions of genetic and epigenetic alterations also can rewire the tumor microenvironments. Numerous biomarkers for diagnosis and prognosis have been prioritized by using a bioinformatics analysis for cancer.

In this Special Issue, we will focus on bioinformatics analyses for cancer, including computational methods, tools, and software or webserver development. We also encourage comprehensive analyses of public genome, transcriptome and proteome data in cancer for finding novel hypotheses. Integration of multiple omics datasets to reveal new biomarkers, and findings on tumor microenvironments or drug development are also suitable for this Special Issue. We welcome submissions of reviews, research articles, short communications and concept papers.

Prof. Dr. Yongsheng Li
Prof. Dr. Juan Xu
Dr. Yan Zhang
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. Genes 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

  • bioinformatics
  • computational biology
  • genetic mutations
  • noncoding RNAs
  • methods
  • resources, webservers and databases
  • cancer immunity
  • complex diseases
  • network analysis

Published Papers (2 papers)

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Research

15 pages, 35375 KiB  
Article
scDR: Predicting Drug Response at Single-Cell Resolution
by Wanyue Lei, Mengqin Yuan, Min Long, Tao Zhang, Yu-e Huang, Haizhou Liu and Wei Jiang
Genes 2023, 14(2), 268; https://doi.org/10.3390/genes14020268 - 19 Jan 2023
Cited by 4 | Viewed by 3007
Abstract
Heterogeneity exists inter- and intratumorally, which might lead to different drug responses. Therefore, it is extremely important to clarify the drug response at single-cell resolution. Here, we propose a precise single-cell drug response (scDR) prediction method for single-cell RNA sequencing (scRNA-seq) data. We [...] Read more.
Heterogeneity exists inter- and intratumorally, which might lead to different drug responses. Therefore, it is extremely important to clarify the drug response at single-cell resolution. Here, we propose a precise single-cell drug response (scDR) prediction method for single-cell RNA sequencing (scRNA-seq) data. We calculated a drug-response score (DRS) for each cell by integrating drug-response genes (DRGs) and gene expression in scRNA-seq data. Then, scDR was validated through internal and external transcriptomics data from bulk RNA-seq and scRNA-seq of cell lines or patient tissues. In addition, scDR could be used to predict prognoses for BLCA, PAAD, and STAD tumor samples. Next, comparison with the existing method using 53,502 cells from 198 cancer cell lines showed the higher accuracy of scDR. Finally, we identified an intrinsic resistant cell subgroup in melanoma, and explored the possible mechanisms, such as cell cycle activation, by applying scDR to time series scRNA-seq data of dabrafenib treatment. Altogether, scDR was a credible method for drug response prediction at single-cell resolution, and helpful in drug resistant mechanism exploration. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Cancers)
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12 pages, 2885 KiB  
Article
Analysis of Breast Cancer Differences between China and Western Countries Based on Radiogenomics
by Yuanyuan Zhang, Lifeng Yang and Xiong Jiao
Genes 2022, 13(12), 2416; https://doi.org/10.3390/genes13122416 - 19 Dec 2022
Cited by 1 | Viewed by 1691
Abstract
Using radiogenomics methods, the differences between tumor imaging data and genetic data in Chinese and Western breast cancer (BC) patients were analyzed, and the correlation between phenotypic data and genetic data was explored. In this paper, we analyzed BC patients’ image characteristics and [...] Read more.
Using radiogenomics methods, the differences between tumor imaging data and genetic data in Chinese and Western breast cancer (BC) patients were analyzed, and the correlation between phenotypic data and genetic data was explored. In this paper, we analyzed BC patients’ image characteristics and transcriptome data separately, then correlated the magnetic resonance imaging (MRI) phenotype with the transcriptome data through a computational method to develop a radiogenomics feature. The data was fed into the designed random forest (RF) model, which used the area under the receiver operating curve (AUC) as the evaluation index. Next, we analyzed the hub genes in the differentially expressed genes (DEGs) and obtained seven hub genes, which may cause Chinese and Western BC patients to behave differently in the clinic. We demonstrated that combining relevant genetic data and imaging features could better classify Chinese and Western patients than using genes or imaging characteristics alone. The AUC values of 0.74, 0.81, and 0.95 were obtained separately using the image characteristics, DEGs, and radiogenomics features. We screened SYT4, GABRG2, CHGA, SLC6A17, NEUROG2, COL2A1, and MATN4 and found that these genes were positively or negatively correlated with certain imaging characteristics. In addition, we found that the SLC6A17, NEUROG2, CHGA, and MATN4 genes were associated with clinical features. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Cancers)
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