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Advances in Computational Cancer Omics
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Advances in Computational Cancer Omics

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 3913

Special Issue Editors

Dr. Andreia Brandão

E-Mail Website
Guest Editor
Cancer Genetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal
Interests: cancer genomics; genetics; biomarkers; population genetics; bioinformatics
Dr. Luisa Ferreira

E-Mail Website
Guest Editor
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
Interests: cell-cell interactions; genetics; cancers
Prof. Dr. Quan Zou

grade E-Mail Website
Guest Editor
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; parallel computing; deep learning; protein classification; genome assembly
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to participate in this Special Issue on “Advances in Computational Cancer Omics”.

In the last decade, advances in high-throughput sequencing technologies and computational multi-omics approaches have provided unprecedented opportunities to integrate multiple omics datasets generated in cancer research, contributing to a better understanding of the molecular and clinical features of cancers.

This Special Issue aims to showcase the latest advances in computational omics methodologies and their potential contributions in the cancer research field.

We welcome contributions that may demonstrate the application of new bioinformatics and machine learning methods in cancer research, explore multiple omics data (including but not limited to genomics, transcriptomics, or proteomics), and identify new biomarkers for diagnosis, patient stratification or drug response. Original research, methods, and review articles will be considered for publication.

Dr. Andreia Brandão
Dr. Luisa Ferreira
Prof. Dr. Quan Zou
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

  • multi-omics cancer studies
  • cancer genomics
  • cancer transcriptomics (bulk and single-cell)
  • spatial transcriptomics
  • cancer immunotherapy
  • cancer biomarkers
  • computational advances in cancer diagnosis (tumor and liquid biopsies)
  • computational algorithms and tools for cancer patient stratification and drug response (e.g., machine learning methods)

Published Papers (3 papers)

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Research

17 pages, 5353 KiB  
Article
Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer
by Ruihao Xin, Qian Cheng, Xiaohang Chi, Xin Feng, Hang Zhang, Yueying Wang, Meiyu Duan, Tunyang Xie, Xiaonan Song, Qiong Yu, Yusi Fan, Lan Huang and Fengfeng Zhou
Genes 2023, 14(12), 2169; https://doi.org/10.3390/genes14122169 - 01 Dec 2023
Viewed by 1067
Abstract
A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without [...] Read more.
A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer. Full article
(This article belongs to the Special Issue Advances in Computational Cancer Omics)
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11 pages, 5137 KiB  
Article
Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches
by Jinmyung Jung and Sunyong Yoo
Genes 2023, 14(9), 1820; https://doi.org/10.3390/genes14091820 - 20 Sep 2023
Cited by 2 | Viewed by 1287
Abstract
Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from [...] Read more.
Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis. Full article
(This article belongs to the Special Issue Advances in Computational Cancer Omics)
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16 pages, 620 KiB  
Article
Towards In Silico Identification of Genes Contributing to Similarity of Patients’ Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia
by Declan J. Batten, Jonathan J. Crofts and Nadia Chuzhanova
Genes 2023, 14(9), 1795; https://doi.org/10.3390/genes14091795 - 13 Sep 2023
Viewed by 869
Abstract
We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients’ similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of [...] Read more.
We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients’ similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein–protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature. Full article
(This article belongs to the Special Issue Advances in Computational Cancer Omics)
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