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Cells
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Cutting Edge in Bioinformatics of Cancer Immunotherapy
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Cutting Edge in Bioinformatics of Cancer Immunotherapy

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Methods".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 13214

Special Issue Editor

Prof. Dr. Anyuan Guo

E-Mail Website
Guest Editor
Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science andTechnology, Wuhan 430074, China
Interests: tumor bioinformatics; systems biology; bioinformatics method; databases and their application in tumor research; extracellular vesicle and gene expression regulation

Special Issue Information

Dear Colleagues, 

Cancer immunotherapy is a revolutionary therapy approach in cancers. Bioinformatics is widely used in cancer immunotherapy-related data analysis and target identification. This issue is to highlight recent findings on Cutting Edge in Bioinformatics of Cancer Immunotherapy, including but not limited to the following: 1) bioinformatics algorithms/methods/software/databases in cancer immunotherapy; 2) bioinformatics data analysis in cancer immunotherapy, including CAR-T and immune checkpoint blockade therapy; 3) bioinformatics related to immune cells, TCR, and BCR; 4) reviews of bioinformatics progression in cancer immunotherapy; 5) assessment, evaluation, and benchmark for bioinformatics software/methods in cancer immunotherapy, immune cells, and TCR/BCR; 6) methods, models, and biomarker identification for immunotherapy response and prognosis; 7) immunotherapy drug and drugs combined with immunotherapy identification by bioinformatics.

Prof. Dr. Anyuan Guo
Guest Editor

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. Cells is an international peer-reviewed open access semimonthly 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

  • bioinformatics
  • immunotherapy
  • cell methods
  • biomarker
  • drug
  • CAR-T

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Published Papers (4 papers)

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Research

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19 pages, 6508 KiB  
Article
Identification of an Immune-Related Prognostic Signature for Glioblastoma by Comprehensive Bioinformatics and Experimental Analyses
by Shengda Ye, Bin Yang, Tingbao Zhang, Wei Wei, Zhiqiang Li, Jincao Chen and Xiang Li
Cells 2022, 11(19), 3000; https://doi.org/10.3390/cells11193000 - 26 Sep 2022
Cited by 9 | Viewed by 2417
Abstract
Background: Glioblastoma (GBM), which has a poor prognosis, accounts for 31% of all cancers in the brain and central nervous system. There is a paucity of research on prognostic indicators associated with the tumor immune microenvironment in GBM patients. Accurate tools for risk [...] Read more.
Background: Glioblastoma (GBM), which has a poor prognosis, accounts for 31% of all cancers in the brain and central nervous system. There is a paucity of research on prognostic indicators associated with the tumor immune microenvironment in GBM patients. Accurate tools for risk assessment of GBM patients are urgently needed. Methods: In this study, we used weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) methods to screen out GBM-related genes among immune-related genes (IRGs). Then, we used survival analysis and Cox regression analysis to identify prognostic genes among the GBM-related genes to further establish a risk signature, which was validated using methods including ROC analysis, stratification analysis, protein expression level validation (HPA), gene expression level validation based on public cohorts, and RT-qPCR. In order to provide clinicians with a useful tool to predict survival, a nomogram based on an assessment of IRGs and clinicopathological features was constructed and further validated using DCA, time-dependent ROC curve, etc. Results: Three immune-related genes were found: PPP4C (p < 0.001, HR = 0.514), C5AR1 (p < 0.001, HR = 1.215), and IL-10 (p < 0.001, HR = 1.047). An immune-related prognostic signature (IPS) was built to calculate risk scores for GBM patients; patients classified into different risk groups had significant differences in survival (p = 0.006). Then, we constructed a nomogram based on an assessment of the IRG-based signature, which was validated as a potential prediction tool for GBM survival rates, showing greater accuracy than the nomogram without the IPS when predicting 1-year (0.35 < Pt < 0.50), 3-year (0.65 < Pt < 0.80), and 5-year (0.65 < Pt < 0.80) survival. Conclusions: In conclusion, we integrated bioinformatics and experimental approaches to construct an IPS and a nomogram based on IPS for predicting GBM prognosis. The signature showed strong potential for prognostic prediction and could help in developing more precise diagnostic approaches and treatments for GBM. Full article
(This article belongs to the Special Issue Cutting Edge in Bioinformatics of Cancer Immunotherapy)
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14 pages, 2204 KiB  
Article
Biological Pathway-Derived TMB Robustly Predicts the Outcome of Immune Checkpoint Blockade Therapy
by Ya-Ru Miao, Chun-Jie Liu, Hui Hu, Mei Yang and An-Yuan Guo
Cells 2022, 11(18), 2802; https://doi.org/10.3390/cells11182802 - 8 Sep 2022
Cited by 5 | Viewed by 2720
Abstract
Although immune checkpoint blockade (ICB) therapies have achieved great progress, the patient response varies among cancers. In this study, we analyzed the potential genomic indicators contributing to ICB therapy response. The results showed that high tumor mutation burden (TMB) failed to predict response [...] Read more.
Although immune checkpoint blockade (ICB) therapies have achieved great progress, the patient response varies among cancers. In this study, we analyzed the potential genomic indicators contributing to ICB therapy response. The results showed that high tumor mutation burden (TMB) failed to predict response in anti-PD1 treated melanoma. SERPINB3 was the most significant response-related gene in melanoma and mutations in either SERPINB3 or PEG3 can serve as an independent risk factor in melanoma. Some recurrent mutations in CSMD3 were only in responders or non-responders, indicating their diverse impacts on patient response. Enrichment scores (ES) of gene mutations in 12 biological pathways were significantly higher in responders or non-responders. Next, the P-TMB calculated from genes in these pathways was significantly related to patient response with prediction AUC 0.74–0.82 in all collected datasets. In conclusion, our work provides new insights into the application of TMB in predicting patient response, which will benefit to immunotherapy research. Full article
(This article belongs to the Special Issue Cutting Edge in Bioinformatics of Cancer Immunotherapy)
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21 pages, 7471 KiB  
Article
Transcriptomic-Based Identification of the Immuno-Oncogenic Signature of Cholangiocarcinoma for HLC-018 Multi-Target Therapy Exploration
by Bashir Lawal, Yu-Cheng Kuo, Sung-Ling Tang, Feng-Cheng Liu, Alexander T. H. Wu, Hung-Yun Lin and Hsu-Shan Huang
Cells 2021, 10(11), 2873; https://doi.org/10.3390/cells10112873 - 25 Oct 2021
Cited by 20 | Viewed by 3410
Abstract
Cholangiocarcinomas (CHOLs), hepatobiliary malignancies, are characterized by high genetic heterogeneity, a rich tumor microenvironment, therapeutic resistance, difficulty diagnosing, and poor prognoses. Current knowledge of genetic alterations and known molecular markers for CHOL is insufficient, necessitating the need for further evaluation of the genome [...] Read more.
Cholangiocarcinomas (CHOLs), hepatobiliary malignancies, are characterized by high genetic heterogeneity, a rich tumor microenvironment, therapeutic resistance, difficulty diagnosing, and poor prognoses. Current knowledge of genetic alterations and known molecular markers for CHOL is insufficient, necessitating the need for further evaluation of the genome and RNA expression data in order to identify potential therapeutic targets, clarify the roles of these targets in the tumor microenvironment, and explore novel therapeutic drugs against the identified targets. Consequently, in our attempt to explore novel genetic markers associated with the carcinogenesis of CHOL, five genes (SNX15, ATP2A1, PDCD10, BET1, and HMGA2), collectively termed CHOL-hub genes, were identified via integration of differentially expressed genes (DEGs) from relatively large numbers of samples from CHOL GEO datasets. We further explored the biological functions of the CHOL-hub genes and found significant enrichment in several biological process and pathways associated with stem cell angiogenesis, cell proliferation, and cancer development, while the interaction network revealed high genetic interactions with a number of onco-functional genes. In addition, we established associations between the CHOL-hub genes and tumor progression, metastasis, tumor immune and immunosuppressive cell infiltration, dysfunctional T-cell phenotypes, poor prognoses, and therapeutic resistance in CHOL. Thus, we proposed that targeting CHOL-hub genes could be an ideal therapeutic approach for treating CHOLs, and we explored the potential of HLC-018, a novel benzamide-linked small molecule, using molecular docking of ligand-receptor interactions. To our delight, HLC-018 was well accommodated with high binding affinities to binding pockets of CHOL-hub genes; more importantly, we found specific interactions of HLC-018 with the conserved sequence of the AT-hook DNA-binding motif of HMGA2. Altogether, our study provides insights into the immune-oncogenic phenotypes of CHOL and provides valuable information for our ongoing experimental validation. Full article
(This article belongs to the Special Issue Cutting Edge in Bioinformatics of Cancer Immunotherapy)
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Review

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16 pages, 1057 KiB  
Review
Research Progress of Gliomas in Machine Learning
by Yameng Wu, Yu Guo, Jun Ma, Yu Sa, Qifeng Li and Ning Zhang
Cells 2021, 10(11), 3169; https://doi.org/10.3390/cells10113169 - 15 Nov 2021
Cited by 10 | Viewed by 3442
Abstract
In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed [...] Read more.
In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed. Full article
(This article belongs to the Special Issue Cutting Edge in Bioinformatics of Cancer Immunotherapy)
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