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Bioinformatics and Machine Learning for Cancer Biology
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Bioinformatics and Machine Learning for Cancer Biology

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 40853

Printed Edition Available!
A printed edition of this Special Issue is available here.

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: machine learning; data analysis; cancer transcriptomics
Special Issues, Collections and Topics in MDPI journals
Dr. Yiping Fan

E-Mail Website
Guest Editor
Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
Interests: T cell exhaustion; cancer immunotherapy; machine learning
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,

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field 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 enables 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 dealing 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. Yiping Fan
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 (13 papers)

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Editorial

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2 pages, 191 KiB  
Editorial
Special Issue on Bioinformatics and Machine Learning for Cancer Biology
by Shibiao Wan, Chunjie Jiang, Shengli Li and Yiping Fan
Biology 2022, 11(3), 361; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11030361 - 24 Feb 2022
Viewed by 1709
Abstract
Cancer is a leading cause of death worldwide, claiming millions of lives each year [...] Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)

Research

Jump to: Editorial, Other

18 pages, 3815 KiB  
Article
A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania
by Cristiana Tudor
Biology 2022, 11(6), 857; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11060857 - 03 Jun 2022
Cited by 7 | Viewed by 2220
Abstract
Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer [...] Read more.
Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of “oncotourism”. This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models—the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR—are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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10 pages, 1871 KiB  
Article
Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks
by Limin Jiang, Jijun Tang, Fei Guo and Yan Guo
Biology 2022, 11(6), 848; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11060848 - 01 Jun 2022
Cited by 1 | Viewed by 2285
Abstract
As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates [...] Read more.
As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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15 pages, 2545 KiB  
Article
Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes
by Saraswati Koppad, Annappa Basava, Katrina Nash, Georgios V. Gkoutos and Animesh Acharjee
Biology 2022, 11(3), 365; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11030365 - 25 Feb 2022
Cited by 19 | Viewed by 4705
Abstract
Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and [...] Read more.
Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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18 pages, 6172 KiB  
Article
Identification of DPP4/CTNNB1/MET as a Theranostic Signature of Thyroid Cancer and Evaluation of the Therapeutic Potential of Sitagliptin
by Sheng-Yao Cheng, Alexander T. H. Wu, Gaber El-Saber Batiha, Ching-Liang Ho, Jih-Chin Lee, Halimat Yusuf Lukman, Mohammed Alorabi, Abdullah N. AlRasheedi and Jia-Hong Chen
Biology 2022, 11(2), 324; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11020324 - 17 Feb 2022
Cited by 5 | Viewed by 2834
Abstract
In recent years, the incidence of thyroid cancer has been increasing globally, with papillary thyroid cancer (PTCa) being the most prevalent pathological type, accounting for approximately 80% of all cases. Although PTCa has been regarded to be slow growing and has a good [...] Read more.
In recent years, the incidence of thyroid cancer has been increasing globally, with papillary thyroid cancer (PTCa) being the most prevalent pathological type, accounting for approximately 80% of all cases. Although PTCa has been regarded to be slow growing and has a good prognosis, in some cases, PTCa can be aggressive and progress despite surgery and radioactive iodine treatment. In addition, most cancer treatment drugs have been shown to be cytotoxic and nonspecific to cancer cells, as they also affect normal cells and consequently cause harm to the body. Therefore, searching for new targets and therapies is required. Herein, we explored a bioinformatics analysis to identify important theranostic markers for THCA. Interestingly, we identified that the DPP4/CTNNB1/MET gene signature was overexpressed in PTCa, which, according to our analysis, is associated with immuno-invasive phenotypes, cancer progression, metastasis, resistance, and unfavorable clinical outcomes of thyroid cancer cohorts. Since most cancer drugs were shown to exhibit cytotoxicity and to be nonspecific, herein, we evaluated the anticancer effects of the antidiabetic drug sitagliptin, which was recently shown to possess anticancer activities, and is well tolerated and effective. Interestingly, our in silico molecular docking results exhibited putative binding affinities of sitagliptin with DPP4/CTNNB1/MET signatures, even higher than standard inhibitors of these genes. This suggests that sitagliptin is a potential THCA therapeutic, worthy of further investigation both in vitro and in vivo and in clinical settings. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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18 pages, 4774 KiB  
Article
Identification and Validation of an Annexin-Related Prognostic Signature and Therapeutic Targets for Bladder Cancer: Integrative Analysis
by Xitong Yao, Xinlei Qi, Yao Wang, Baokun Zhang, Tianshuai He, Taoning Yan, Lu Zhang, Yange Wang, Hong Zheng, Guosen Zhang and Xiangqian Guo
Biology 2022, 11(2), 259; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11020259 - 07 Feb 2022
Cited by 6 | Viewed by 2195
Abstract
Abnormal expression and dysfunction of Annexins (ANXA1-11, 13) have been widely found in several types of cancer. However, the expression pattern and prognostic value of Annexins in bladder cancer (BC) are currently still unknown. In this study, survival analysis by our in-house OSblca [...] Read more.
Abnormal expression and dysfunction of Annexins (ANXA1-11, 13) have been widely found in several types of cancer. However, the expression pattern and prognostic value of Annexins in bladder cancer (BC) are currently still unknown. In this study, survival analysis by our in-house OSblca web server revealed that high ANXA1/2/3/5/6 expression was significantly associated with poor overall survival (OS) in BC patients, while higher ANXA11 was associated with increased OS. Through Oncomine and GEPIA2 database analysis, we found that ANXA2/3/4/13 were up-regulated, whereas ANXA1/5/6 were down-regulated in BC compared with normal bladder tissues. Further LASSO analysis built an Annexin-Related Prognostic Signature (ARPS, including four members ANXA1/5/6/10) in the TCGA BC cohort and validated it in three independent GEO BC cohorts (GSE31684, GSE32548, GSE48075). Multivariate COX analysis demonstrated that ARPS is an independent prognostic signature for BC. Moreover, GSEA results showed that immune-related pathways, such as epithelial–mesenchymal transition and IL6/JAK/STAT3 signaling were enriched in the high ARPS risk groups, while the low ARPS risk group mainly regulated metabolism-related processes, such as adipogenesis and bile acid metabolism. In conclusion, our study comprehensively analyzed the mRNA expression and prognosis of Annexin family members in BC, constructed an Annexin-related prognostic signature using LASSO and COX regression, and validated it in four independent BC cohorts, which might help to improve clinical outcomes of BC patients, offer insights into the underlying molecular mechanisms of BC development and suggest potential therapeutic targets for BC. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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29 pages, 14063 KiB  
Article
Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network
by Khalil ur Rehman, Jianqiang Li, Yan Pei, Anaa Yasin, Saqib Ali and Yousaf Saeed
Biology 2022, 11(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11010015 - 23 Dec 2021
Cited by 13 | Viewed by 4727
Abstract
Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms [...] Read more.
Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI’s detection, training deep learning, and machine learning networks to classify AD’s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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18 pages, 2862 KiB  
Article
Comprehensive Analysis of CPA4 as a Poor Prognostic Biomarker Correlated with Immune Cells Infiltration in Bladder Cancer
by Chengcheng Wei, Yuancheng Zhou, Qi Xiong, Ming Xiong, Yaxin Hou, Xiong Yang and Zhaohui Chen
Biology 2021, 10(11), 1143; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10111143 - 06 Nov 2021
Cited by 5 | Viewed by 2512
Abstract
Carboxypeptidase A4 (CPA4) has shown the potential to be a biomarker in the early diagnosis of certain cancers. However, no previous research has linked CPA4 to therapeutic or prognostic significance in bladder cancer. Using data from The Cancer Genome Atlas (TCGA) database, we [...] Read more.
Carboxypeptidase A4 (CPA4) has shown the potential to be a biomarker in the early diagnosis of certain cancers. However, no previous research has linked CPA4 to therapeutic or prognostic significance in bladder cancer. Using data from The Cancer Genome Atlas (TCGA) database, we set out to determine the full extent of the link between CPA4 and BLCA. We further analyzed the interacting proteins of CPA4 and infiltrated immune cells via the TIMER2, STRING, and GEPIA2 databases. The expression of CPA4 in tumor and normal tissues was compared using the TCGA + GETx database. The connection between CPA4 expression and clinicopathologic characteristics and overall survival (OS) was investigated using multivariate methods and Kaplan–Meier survival curves. The potential functions and pathways were investigated via gene set enrichment analysis. Furthermore, we analyze the associations between CPA4 expression and infiltrated immune cells with their respective gene marker sets using the ssGSEA, TIMER2, and GEPIA2 databases. Compared with matching normal tissues, human CPA4 was found to be substantially expressed. We confirmed that the overexpression of CPA4 is linked with shorter OS, DSF(Disease-specific survival), PFI(Progression-free interval), and increased diagnostic potential using Kaplan–Meier and ROC analysis. The expression of CPA4 is related to T-bet, IL12RB2, CTLA4, and LAG3, among which T-bet and IL12RB2 are Th1 marker genes while CTLA4 and LAG3 are related to T cell exhaustion, which may be used to guide the application of checkpoint blockade and the adoption of T cell transfer therapy. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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13 pages, 39201 KiB  
Article
The Identification of RNA Modification Gene PUS7 as a Potential Biomarker of Ovarian Cancer
by Huimin Li, Lin Chen, Yunsong Han, Fangfang Zhang, Yanyan Wang, Yali Han, Yange Wang, Qiang Wang and Xiangqian Guo
Biology 2021, 10(11), 1130; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10111130 - 03 Nov 2021
Cited by 14 | Viewed by 2329
Abstract
RNA modifications are reversible, dynamically regulated, and involved in a variety of diseases such as cancers. Given the lack of efficient and reliable biomarkers for early diagnosis of ovarian cancer (OV), this study was designed to explore the role of RNA modification genes [...] Read more.
RNA modifications are reversible, dynamically regulated, and involved in a variety of diseases such as cancers. Given the lack of efficient and reliable biomarkers for early diagnosis of ovarian cancer (OV), this study was designed to explore the role of RNA modification genes (RMGs) in the diagnosis of OV. Herein, 132 RMGs were retrieved in PubMed, 638 OV and 18 normal ovary samples were retrieved in The Cancer Genome Atlas (TCGA), and GSE18520 cohorts were collected for differential analysis. Finally, PUS7 (Pseudouridine Synthase 7) as differentially expressed RMGs (DEGs-RMGs) was identified as a diagnostic biomarker candidate and evaluated for its specificity and sensitivity using Receiver Operating Characteristic (ROC) analysis in TCGA and GEO data. The protein expression, mutation, protein interaction networks, correlated genes, related pathways, biological processes, cell components, and molecular functions of PUS7 were analyzed as well. The upregulation of PUS7 protein in OV was confirmed by the staining images in HPA and tissue arrays. Collectively, the findings of the present study point towards the potential of PUS7 as a diagnostic marker and therapeutic target for ovarian cancer. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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10 pages, 903 KiB  
Article
R-Score: A New Parameter to Assess the Quality of Variants’ Calls Assessed by NGS Using Liquid Biopsies
by Roberto Serna-Blasco, Estela Sánchez-Herrero, María Berrocal Renedo, Silvia Calabuig-Fariñas, Miguel Ángel Molina-Vila, Mariano Provencio and Atocha Romero
Biology 2021, 10(10), 954; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10100954 - 24 Sep 2021
Cited by 1 | Viewed by 2518
Abstract
Next-generation sequencing (NGS) has enabled a deeper knowledge of the molecular landscape in non-small cell lung cancer (NSCLC), identifying a growing number of targetable molecular alterations in key genes. However, NGS profiling of liquid biopsies risk for false positive and false negative calls [...] Read more.
Next-generation sequencing (NGS) has enabled a deeper knowledge of the molecular landscape in non-small cell lung cancer (NSCLC), identifying a growing number of targetable molecular alterations in key genes. However, NGS profiling of liquid biopsies risk for false positive and false negative calls and parameters assessing the quality of NGS calls remains lacking. In this study, we have evaluated the positive percent agreement (PPA) between NGS and digital PCR calls when assessing EGFR mutation status using 85 plasma samples from 82 EGFR-positive NSCLC patients. According to our data, variant allele fraction (VAF) was significantly lower in discordant calls and the median of the absolute values of all pairwise differences (MAPD) was significantly higher in discordant calls (p < 0.001 in both cases). Based on these results, we propose a new parameter that integrates both variables, named R-score. Next, we sought to evaluate the PPA for EGFR mutation calls between two independent NGS platforms using a subset of 40 samples from the same cohort. Remarkably, there was a significant linear correlation between the PPA and the R-score (r = 0.97; p < 0.001). Specifically, the PPA of samples with an R-score ≤ −1.25 was 95.83%, whereas PPA falls to 81.63% in samples with R-score ≤ 0.25. In conclusion, R-score significantly correlates with PPA and can assist laboratory medicine specialists and data scientists to select reliable variants detected by NGS. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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16 pages, 10237 KiB  
Article
Role of Persistent Organic Pollutants in Breast Cancer Progression and Identification of Estrogen Receptor Alpha Inhibitors Using In-Silico Mining and Drug-Drug Interaction Network Approaches
by Bibi Zainab, Zainab Ayaz, Umer Rashid, Dunia A. Al Farraj, Roua M. Alkufeidy, Fatmah S. AlQahtany, Reem M. Aljowaie and Arshad Mehmood Abbasi
Biology 2021, 10(7), 681; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10070681 - 19 Jul 2021
Cited by 5 | Viewed by 2875
Abstract
The strong association between POPs and breast cancer in humans has been suggested in various epidemiological studies. However, the interaction of POPs with the ERα protein of breast cancer, and identification of natural and synthetic compounds to inhibit this interaction, is mysterious yet. [...] Read more.
The strong association between POPs and breast cancer in humans has been suggested in various epidemiological studies. However, the interaction of POPs with the ERα protein of breast cancer, and identification of natural and synthetic compounds to inhibit this interaction, is mysterious yet. Consequently, the present study aimed to explore the interaction between POPs and ERα using the molecular operating environment (MOE) tool and to identify natural and synthetic compounds to inhibit this association through a cluster-based approach. To validate whether our approach could distinguish between active and inactive compounds, a virtual screen (VS) was performed using actives (627 compounds) as positive control and decoys (20,818 compounds) as a negative dataset obtained from DUD-E. Comparatively, short-chain chlorinated paraffins (SCCPs), hexabromocyclododecane (HBCD), and perfluorooctanesulfonyl fluoride (PFOSF) depicted strong interactions with the ERα protein based on the lowest-scoring values of −31.946, −18.916, −17.581 kcal/mol, respectively. Out of 7856 retrieved natural and synthetic compounds, sixty were selected on modularity bases and subsequently docked with ERα. Based on the lowest-scoring values, ZINC08441573, ZINC00664754, ZINC00702695, ZINC00627464, and ZINC08440501 (synthetic compounds), and capsaicin, flavopiridol tectorgenin, and ellagic acid (natural compounds) showed incredible interactions with the active sites of ERα, even more convening and resilient than standard breast cancer drugs Tamoxifen, Arimidex and Letrozole. Our findings confirm the role of POPs in breast cancer progression and suggest that natural and synthetic compounds with high binding affinity could be more efficient and appropriate candidates to treat breast cancer after validation through in vitro and in vivo studies. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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12 pages, 7914 KiB  
Article
DNA Damage Repair Gene Set as a Potential Biomarker for Stratifying Patients with High Tumor Mutational Burden
by To-Yuan Chiu, Ryan Weihsiang Lin, Chien-Jung Huang, Da-Wei Yeh and Yu-Chao Wang
Biology 2021, 10(6), 528; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10060528 - 14 Jun 2021
Cited by 4 | Viewed by 3003
Abstract
Tumor mutational burden (TMB) is a promising predictive biomarker for cancer immunotherapy. Patients with a high TMB have better responses to immune checkpoint inhibitors. Currently, the gold standard for determining TMB is whole-exome sequencing (WES). However, high cost, long turnaround time, infrastructure requirements, [...] Read more.
Tumor mutational burden (TMB) is a promising predictive biomarker for cancer immunotherapy. Patients with a high TMB have better responses to immune checkpoint inhibitors. Currently, the gold standard for determining TMB is whole-exome sequencing (WES). However, high cost, long turnaround time, infrastructure requirements, and bioinformatics demands have prevented WES from being implemented in routine clinical practice. Panel-sequencing-based estimates of TMB have gradually replaced WES TMB; however, panel design biases could lead to overestimation of TMB. To stratify TMB-high patients better without sequencing all genes and avoid overestimating TMB, we focused on DNA damage repair (DDR) genes, in which dysfunction may increase somatic mutation rates. We extensively explored the association between the mutation status of DDR genes and TMB in different cancer types. By analyzing the mutation data from The Cancer Genome Atlas, which includes information for 33 different cancer types, we observed no single DDR gene/pathway in which mutation status was significantly associated with high TMB across all 33 cancer types. Therefore, a computational algorithm was proposed to identify a cancer-specific gene set as a surrogate for stratifying patients with high TMB in each cancer. We applied our algorithm to skin cutaneous melanoma and lung adenocarcinoma, demonstrating that the mutation status of the identified cancer-specific DDR gene sets, which included only 9 and 14 genes, respectively, was significantly associated with TMB. The cancer-specific DDR gene set can be used as a cost-effective approach to stratify patients with high TMB in clinical practice. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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Other

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13 pages, 2576 KiB  
Technical Note
DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets
by Alyssa Obermayer, Li Dong, Qianqian Hu, Michael Golden, Jerald D. Noble, Paulo Rodriguez, Timothy J. Robinson, Mingxiang Teng, Aik-Choon Tan and Timothy I. Shaw
Biology 2022, 11(2), 260; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11020260 - 08 Feb 2022
Cited by 4 | Viewed by 3152
Abstract
High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated [...] Read more.
High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery. Full article
(This article belongs to the Special Issue Bioinformatics and Machine Learning for Cancer Biology)
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