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Bioinformatics and Machine Learning for Cancer Biology (Volume II)
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Bioinformatics and Machine Learning for Cancer Biology (Volume II)

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 14143

Special Issue Editors

Dr. Shibiao Wan

E-Mail Website
Guest Editor
Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Interests: bioinformatics; machine learning; computational biology; single cell analysis; genomics
Special Issues, Collections and Topics in MDPI journals
Dr. Chunjie Jiang

E-Mail Website
Guest Editor
Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX, USA
Interests: cancer epigenomics; gene regulation
Special Issues, Collections and Topics in MDPI journals
Dr. Shengli Li

E-Mail Website
Guest Editor
Precision Research Center for Refractory Disease, Shanghai Jiao Tong University, Shanghai 200000, China
Interests: bioinformatics; multi-omics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is part of a series with, “Bioinformatics and Machine Learning for Cancer Biology----Volume I” (https://www.mdpi.com/journal/biology/special_issues/Bioinformatics_Machine_Learning).

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field which is studied to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enable the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.

For this Special Issue, we particularly encourage the submission of manuscripts which deal with any aspect of bioinformatics analyses and machine learning methods for cancer biology, including, but not limited to, the following: tumor subtype classification, T cell exhaustion, immunotherapy, drug response, cancer transcriptomics, cancer epigenomics, gene regulation, cancer prognosis, and cancer prediction. We welcome manuscripts in the form of original research, reviews, short communications, perspectives, and commentaries of the aforementioned topics and domains.

Dr. Shibiao Wan
Dr. Chunjie Jiang
Dr. Shengli Li
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. Biology 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 2700 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

  • tumor classification
  • immunotherapy
  • T cell exhaustion
  • drug response
  • cancer transcriptomics
  • cancer epigenomics
  • gene regulation
  • supervised learning
  • unsupervised learning

Published Papers (6 papers)

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Research

15 pages, 16010 KiB  
Article
Cuproptosis- and m6A-Related lncRNAs for Prognosis of Hepatocellular Carcinoma
by Yuezhi Zhu, Jen Kit Tan and Jo Aan Goon
Biology 2023, 12(8), 1101; https://doi.org/10.3390/biology12081101 - 08 Aug 2023
Cited by 1 | Viewed by 1414
Abstract
Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis [...] Read more.
Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
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22 pages, 1019 KiB  
Article
Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature
by Minhyeok Lee
Biology 2023, 12(7), 893; https://doi.org/10.3390/biology12070893 - 21 Jun 2023
Cited by 6 | Viewed by 3072
Abstract
Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific [...] Read more.
Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
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24 pages, 13372 KiB  
Article
Dynamic Regulation Genes at Microtubule Plus Ends: A Novel Class of Glioma Biomarkers
by Wenwen Wang, Weilong Li, Lifang Pan, Lingjie Li, Yasi Xu, Yuqing Wang, Xiaochen Zhang and Shirong Zhang
Biology 2023, 12(3), 488; https://doi.org/10.3390/biology12030488 - 22 Mar 2023
Viewed by 1753
Abstract
Glioma is the most prevalent and aggressive primary nervous system tumor with an unfavorable prognosis. Microtubule plus-end-related genes (MPERGs) play critical biological roles in the cell cycle, cell movement, ciliogenesis, and neuronal development by coordinating microtubule assembly and dynamics. This research seeks to [...] Read more.
Glioma is the most prevalent and aggressive primary nervous system tumor with an unfavorable prognosis. Microtubule plus-end-related genes (MPERGs) play critical biological roles in the cell cycle, cell movement, ciliogenesis, and neuronal development by coordinating microtubule assembly and dynamics. This research seeks to systematically explore the oncological characteristics of these genes in microtubule-enriched glioma, focusing on developing a novel MPERG-based prognostic signature to improve the prognosis and provide more treatment options for glioma patients. First, we thoroughly analyzed and identified 45 differentially expressed MPERGs in glioma. Based on these genes, glioma patients were well distinguished into two subgroups with survival and tumor microenvironment infiltration differences. Next, we further screened the independent prognostic genes (CTTNBP2, KIF18A, NAV1, SLAIN2, SRCIN1, TRIO, and TTBK2) using 36 prognostic-related differentially expressed MPERGs to construct a signature with risk stratification and prognostic prediction ability. An increased risk score was related to the malignant progression of glioma. Therefore, we also designed a nomogram model containing clinical factors to facilitate the clinical use of the risk signature. The prediction accuracy of the signature and nomogram model was verified using The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets. Finally, we examined the connection between the signature and tumor microenvironment. The signature positively correlated with tumor microenvironment infiltration, especially immunoinhibitors and the tumor mutation load, and negatively correlated with microsatellite instability and cancer stemness. More importantly, immune checkpoint blockade treatment and drug sensitivity analyses confirmed that this prognostic signature was helpful in anticipating the effect of immunotherapy and chemotherapy. In conclusion, this research is the first study to define and validate an MPERG-based signature closely associated with the tumor microenvironment as a reliable and independent prognostic biomarker to guide personalized choices of immunotherapy and chemotherapy for glioma patients. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
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14 pages, 3328 KiB  
Article
Identification of Potential Hub Genes Related to Aflatoxin B1, Liver Fibrosis and Hepatocellular Carcinoma via Integrated Bioinformatics Analysis
by Hayam Hamdy, Yi Yang, Cheng Cheng and Qizhan Liu
Biology 2023, 12(2), 205; https://doi.org/10.3390/biology12020205 - 29 Jan 2023
Cited by 3 | Viewed by 2514
Abstract
The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy [...] Read more.
The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy of HCC caused by AFB1. In this study, expression profiles of AFB1, liver fibrosis and liver cancer-related datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by the GEO2R tool. The STRING database, CytoHubba, and Cytoscape software were used to create the protein-protein interaction and hub genes of the combined genes, and the ssGSEA score for inflammatory cells related gene sets, the signaling pathway, and immunotherapy were identified using R software and the GSEA database. The findings revealed that AFB1-associated liver fibrosis and HCC combined genes were linked to cell process disruptions, the BUB1B and RRM2 genes were identified as hub genes, and the BUB1B gene was significantly increased in JAK-STAT signaling gene sets pathways as well as having an immunotherapy-related impact. In conclusion, BUB1B and RRM2 were identified as potential biomarkers for AFB1-induced fibrosis and HCC progression. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
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0 pages, 6088 KiB  
Article
RETRACTED: Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing
by Amir Abbas Esmaeilzadeh, Mahdis Kashian, Hayder Mahmood Salman, Marwa Fadhil Alsaffar, Mustafa Musa Jaber, Siamak Soltani, Danial Amiri Manjili, Ahmet Ilhan, Abolfazl Bahrami and John W. Kastelic
Biology 2022, 11(12), 1851; https://doi.org/10.3390/biology11121851 - 19 Dec 2022
Cited by 1 | Viewed by 2648 | Retraction
Abstract
Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity [...] Read more.
Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network’s central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
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19 pages, 5903 KiB  
Article
Integrating Expression Data-Based Deep Neural Network Models with Biological Networks to Identify Regulatory Modules for Lung Adenocarcinoma
by Lei Fu, Kai Luo, Junjie Lv, Xinyan Wang, Shimei Qin, Zihan Zhang, Shibin Sun, Xu Wang, Bei Yun, Yuehan He, Weiming He, Wan Li and Lina Chen
Biology 2022, 11(9), 1291; https://doi.org/10.3390/biology11091291 - 30 Aug 2022
Cited by 2 | Viewed by 1473
Abstract
Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) [...] Read more.
Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. First, the mRNAs, lncRNAs and miRNAs with significant differences in the expression levels between tumor and non-tumor tissues were obtained. MRNA DNN models were established and optimized to mine candidate mRNAs that significantly contributed to the DNN models and were in the center of an interaction network. Another DNN model was then constructed and potential ceRNAs were screened out based on the contribution of each RNA to the model. Finally, three modules comprised of miRNAs and their regulated mRNAs and lncRNAs with the same regulation direction were identified as regulatory modules that regulated the initiation of lung adenocarcinoma through ceRNAs relationships. They were validated by literature and functional enrichment analysis. The effectiveness of these regulatory modules was evaluated in an independent lung adenocarcinoma dataset. Regulatory modules for lung adenocarcinoma identified in this study provided a reference for regulatory mechanisms during carcinogenesis. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology (Volume II))
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