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The Application of Bioinformatics Methods in Cancer Biology
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The Application of Bioinformatics Methods in Cancer Biology

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 11761

Special Issue Editor

Prof. Dr. Hsien-Da Huang

E-Mail Website
Guest Editor
School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
Interests: bioinformatics; genomics; intelligent biomedical technologies (drug development; genetic test & precision medicine); AI & machine learning; biological database design & development

Special Issue Information

Dear Colleagues,

Bioinformatics originated in the 1970s and, with the advancement of molecular biology and computer science and technology, has developed rapidly. It has the advantages of scale, programming and specialization in data processing. With the popularization and application of sequencing methods such as microarray technology and high-throughput sequencing, the use of bioinformatics methods to process and analyze omics data provides the possibility for clinical molecular pathology research and cancer molecular targeted therapy. The in-depth application of bioinformatics in cancer research has greatly promoted the advancement of cancer diagnosis and treatment technology.

The purpose of this Special Issue is to collect bioinformatics research as the basis, combined with laboratory data analysis, to explain the research results of cancer molecular biology. To provide a basis for in-depth understanding of cancer pathogenesis, finding new molecular targets, and the development of new treatment technologies.

Reviews and research papers, covering the molecular aspects of the current trends in the application of bioinformatics methods in cancer biology, are all welcome.

Prof. Dr. Hsien-Da Huang
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • next-generation sequencing
  • emerging bioinformatic techniques
  • cancer genome atlas
  • bioinformatics technology
  • cancer molecular biology
  • cancer pathogenesis
  • cancer molecular targeted therapy

Published Papers (4 papers)

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Research

16 pages, 2789 KiB  
Article
Identifying Hub Genes Associated with Neoadjuvant Chemotherapy Resistance in Breast Cancer and Potential Drug Repurposing for the Development of Precision Medicine
by Trishna Saha Detroja, Rajesh Detroja, Sumit Mukherjee and Abraham O. Samson
Int. J. Mol. Sci. 2022, 23(20), 12628; https://doi.org/10.3390/ijms232012628 - 20 Oct 2022
Cited by 5 | Viewed by 2803
Abstract
Breast cancer is the second leading cause of morbidity and mortality in women worldwide. Despite advancements in the clinical application of neoadjuvant chemotherapy (NAC), drug resistance remains a major concern hindering treatment efficacy. Thus, identifying the key genes involved in driving NAC resistance [...] Read more.
Breast cancer is the second leading cause of morbidity and mortality in women worldwide. Despite advancements in the clinical application of neoadjuvant chemotherapy (NAC), drug resistance remains a major concern hindering treatment efficacy. Thus, identifying the key genes involved in driving NAC resistance and targeting them with known potential FDA-approved drugs could be applied to advance the precision medicine strategy. With this aim, we performed an integrative bioinformatics study to identify the key genes associated with NAC resistance in breast cancer and then performed the drug repurposing to identify the potential drugs which could use in combination with NAC to overcome drug resistance. In this study, we used publicly available RNA-seq datasets from the samples of breast cancer patients sensitive and resistant to chemotherapy and identified a total of 1446 differentially expressed genes in NAC-resistant breast cancer patients. Next, we performed gene co-expression network analysis to identify significantly co-expressed gene modules, followed by MCC (Multiple Correlation Clustering) clustering algorithms and identified 33 key hub genes associated with NAC resistance. mRNA–miRNA network analysis highlighted the potential impact of these hub genes in altering the regulatory network in NAC-resistance breast cancer cells. Further, several hub genes were found to be significantly involved in the poor overall survival of breast cancer patients. Finally, we identified FDA-approved drugs which could be useful for potential drug repurposing against those hub genes. Altogether, our findings provide new insight into the molecular mechanisms of NAC resistance and pave the way for drug repurposing techniques and personalized treatment to overcome NAC resistance in breast cancer. Full article
(This article belongs to the Special Issue The Application of Bioinformatics Methods in Cancer Biology)
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24 pages, 4471 KiB  
Article
Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis
by Yu Deng, He Huang, Jiangcheng Shi and Hongyan Jin
Int. J. Mol. Sci. 2022, 23(19), 11892; https://doi.org/10.3390/ijms231911892 - 6 Oct 2022
Cited by 4 | Viewed by 2771
Abstract
Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not [...] Read more.
Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17β-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT. Full article
(This article belongs to the Special Issue The Application of Bioinformatics Methods in Cancer Biology)
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18 pages, 2248 KiB  
Article
A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants
by Erda Qorri, Bertalan Takács, Alexandra Gráf, Márton Zsolt Enyedi, Lajos Pintér, Ernő Kiss and Lajos Haracska
Int. J. Mol. Sci. 2022, 23(14), 7946; https://doi.org/10.3390/ijms23147946 - 19 Jul 2022
Cited by 5 | Viewed by 2742
Abstract
The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing [...] Read more.
The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms. Full article
(This article belongs to the Special Issue The Application of Bioinformatics Methods in Cancer Biology)
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15 pages, 3458 KiB  
Article
A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases
by Giulia Fiscon, Federica Conte, Lorenzo Farina and Paola Paci
Int. J. Mol. Sci. 2022, 23(7), 3703; https://doi.org/10.3390/ijms23073703 - 28 Mar 2022
Cited by 4 | Viewed by 2324
Abstract
Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time [...] Read more.
Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit. Full article
(This article belongs to the Special Issue The Application of Bioinformatics Methods in Cancer Biology)
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