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Said Ali-Samani F, Shahrisa A, Tahmasebi-Birgani M, Hajjari M, Ghandil P. Study of the genomics and transcriptomics profiles of male-infertility genes in human prostate cancer: an in silico analysis. Syst Biol Reprod Med 2024; 70:139-149. [PMID: 38870367 DOI: 10.1080/19396368.2024.2354305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/30/2024] [Indexed: 06/15/2024]
Abstract
The World Health Organization has considered the infertility as an international public health problem. Infertility affect nearly 1 in 7 couples and male component contributes to 50% of infertility cases. There is a clear link between male infertility and some cancers such as testicular germ cell, prostate and colon cancers. Two possibilities support this finding: 1) Cancer treatments can affect the fertility factors 2) Genetic profile of infertility genes have been altered in cancer patients. Although the previously published researches have mostly focused on the first factor, no article has yet confirmed the role of genetic factors. In this in silico study, we collected the large number of genes (n = 17703) involved in infertility. These genes were collected from NGS panel tests of male infertility and comprehensive literature review or online data base. The Prostate Adenocarcinoma genomic and transcriptomics raw data were downloaded from the cBioPortal Cancer dataset. This included with 494 patients of Prostate Cancer with 494 mutation data, 489 with CNA and 493 with RNA seqV2 data. TCGA RNA-Seq raw data was extracted in R using the cgdsr extension package with a threshold of ±2 relative to normal samples. The observed data showed that male infertility genes have been distributed through the human genome. Among the 17703 analyzed genes of this study, the genomic profile of three genes including OR9Q1, H4C6 and PSG7 were changed approximately in 100% of (n = 493) patients. In most of patients (>98%), genetic alteration was related to change in gene expression. In conclusion, this study showed that the genomic and transcriptomics patterns of some male-infertility genes are notably altered in patients of prostate cancer and suggested a possible role of genetic factors in occurrence of infertility in cancer patients. Our information can be used as a source for the design of genetic database of male-infertility.
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Affiliation(s)
- Farima Said Ali-Samani
- Department of Medical Genetics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Arman Shahrisa
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Maryam Tahmasebi-Birgani
- Department of Medical Genetics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Cellular and Molecular Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mohammadreza Hajjari
- Department of Biology, Faculty of Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Pegah Ghandil
- Department of Medical Genetics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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2
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Manouchehri L, Zinati Z, Nazari L. Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA). World J Surg Oncol 2024; 22:177. [PMID: 38970097 PMCID: PMC11225268 DOI: 10.1186/s12957-024-03459-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024] Open
Abstract
This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.
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Affiliation(s)
- Laleh Manouchehri
- School of Medicine, Alma Mater Studiorum, Università Di Bologna, Via Zamboni, 33, 40126, Bologna, Italy
| | - Zahra Zinati
- Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran.
| | - Leyla Nazari
- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran.
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3
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Ozaki Y, Broughton P, Abdollahi H, Valafar H, Blenda AV. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis. Cancers (Basel) 2024; 16:2448. [PMID: 39001510 PMCID: PMC11240413 DOI: 10.3390/cancers16132448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.
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Affiliation(s)
- Yousaku Ozaki
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Phil Broughton
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Hamed Abdollahi
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
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4
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Satturwar S, Parwani AV. Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications. Adv Anat Pathol 2024; 31:136-144. [PMID: 38179884 DOI: 10.1097/pap.0000000000000425] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.
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Affiliation(s)
- Swati Satturwar
- The Ohio State University, Wexner Medical Center, Columbus, OH
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Jain S, Malhotra KPK, Patiyal S, Raghava GPS. A Highly Accurate Model for Screening Prostate Cancer Using Propensity Index Panel of Ten Genes. J Comput Biol 2023; 30:1305-1314. [PMID: 37917795 DOI: 10.1089/cmb.2023.0040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023] Open
Affiliation(s)
- Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi, India
| | - Kawal Preet Kaur Malhotra
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi, India
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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Ramadanov N. SuperPATH-Current Status of Evidence and Further Investigations: A Scoping Review and Quality Assessment. J Clin Med 2023; 12:5395. [PMID: 37629436 PMCID: PMC10455631 DOI: 10.3390/jcm12165395] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND SuperPATH is a novel minimally invasive technique for hip replacement that is gaining increasing attention. The aim of this review was to determine the nature, extent, and quality of current research evidence on SuperPATH and to identify areas for further investigations. METHODS A bibliometric search was conducted in PubMed up to 1 August 2023 using the search term "SuperPATH". Data extraction and quality assessment were performed for relevant articles. RESULTS The bibliometric search yielded 51 articles on SuperPATH, 9 of which were meta-analyses, 11 were randomized controlled trials (RCTs), 4 were prospective non-RCTs, 12 were retrospective comparative studies, 11 were case series, and 4 were other article types. Most articles were published between 2015 and 2023, with a steady increase in publications per year. The articles originated from 13 countries, of which China was the most productive (35%). The quality assessment of the meta-analyses showed that 22.2% were of moderate quality, 66.7% were of low quality, and 11.1% were of critically low quality. The quality assessment of the RCTs showed that 36.4% had a low risk of bias (RoB), 27.2% revealed some concerns, and 36.4% had a high RoB. All studies were evaluated for content and taken into account in the formulation of recommendations and conclusions. CONCLUSIONS The SuperPATH evidence varies from low to high quality. There is a steady increase in SuperPATH publications in the English-language literature and an uneven distribution of the article origins, with most articles coming from China. Consistent terminology should be used in the future, referring to the surgical approach as the direct superior approach (DSA) and to the surgical technique as SuperPATH. This review provides further concrete suggestions for future investigations and recommendations to improve study quality.
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Affiliation(s)
- Nikolai Ramadanov
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, 14770 Brandenburg an der Havel, Germany
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8
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Liu Z, Yang H, Chen Z, Jing C. A novel chromatin regulator-related immune checkpoint related gene prognostic signature and potential candidate drugs for endometrial cancer patients. Hereditas 2022; 159:40. [PMID: 36253800 PMCID: PMC9578220 DOI: 10.1186/s41065-022-00253-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/22/2022] [Indexed: 11/14/2022] Open
Abstract
Background Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries and its prevalence is increasing. As an emerging therapy with a promising efficacy, immunotherapy has been extensively applied in the treatment of solid tumors. In addition, chromatin regulators (CRs), as essential upstream regulators of epigenetics, play a significant role in tumorigenesis and cancer development. Methods CRs and immune checkpoint-related genes (ICRGs) were obtained from the previous top research. The Genome Cancer Atlas (TCGA) was utilized to acquire the mRNA expression and clinical information of patients with EC. Correlation analysis was utilized for screen CRs-related ICRGs (CRRICRGs). By Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, prognosis related CRRICRGs were screened out and risk model was constructed. The Kaplan–Meier curve was used to estimate the prognosis between high- and low-risk group. By comparing the IC50 value, the drugs sensitivity difference was explored. We obtained small molecule drugs for the treatment of UCEC patients based on CAMP dataset. Results We successfully constructed a 9 CRRICRs-based prognostic signature for patients with UCEC and found the riskscore was an independent prognostic factor. The results of functional analysis suggested that CRRICRGs may be involved in immune processes associated with cancer. Immune characteristics analysis provided further evidence that the CRRICRGs-based model was correlated with immune cells infiltration and immune checkpoint. Eight small molecule drugs that may be effective for the treatment of UCEC patients were screened. Effective drugs identified by drug sensitivity profiling in high- and low-risk groups. Conclusion In summary, our study provided novel insights into the function of CRRICRGs in UCEC. We also developed a reliable prognostic panel for the survival of patients with UCEC. Supplementary Information The online version contains supplementary material available at 10.1186/s41065-022-00253-w.
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Affiliation(s)
- Zesi Liu
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning Province, China
| | - Hongxia Yang
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning Province, China
| | - Ziyu Chen
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning Province, China
| | - Chunli Jing
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning Province, China.
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Mou Z, Spencer J, Knight B, John J, McCullagh P, McGrath JS, Harries LW. Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer. Front Oncol 2022; 12:914078. [PMID: 36033512 PMCID: PMC9413154 DOI: 10.3389/fonc.2022.914078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.
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Affiliation(s)
- Zhuofan Mou
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
| | - Jack Spencer
- Translational Research Exchange at Exeter, Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Bridget Knight
- National Institute for Health and Care Research (NIHR) Exeter Clinical Research Facility, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - Joseph John
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
- Exeter Surgical Health Services Research Unit, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Exeter, United Kingdom
| | - Paul McCullagh
- Department of Pathology, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Exeter, United Kingdom
| | - John S. McGrath
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
- Exeter Surgical Health Services Research Unit, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Exeter, United Kingdom
| | - Lorna W. Harries
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
- *Correspondence: Lorna W. Harries,
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Eshun RB, Kamrul Islam AKM, Bikdash MU. Identification of Significantly Expressed Gene Mutations for Automated Classification of Benign and Malignant Prostate Cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2437-2443. [PMID: 34891773 DOI: 10.1109/embc46164.2021.9630460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Among males, prostate cancer (Pca) is the cancer type with the highest prevalence and the second leading cause of cancer deaths. The current screening methods for prostate cancer lack effectiveness such as prostate-specific antigen (PSA) and digital rectal exam (DRE). Machine learning models have been used to predict Pca progression, Gleason score, and laterality. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machines (SVM), Decision Trees, Logistic Regression, K-Nearest Neighbors, Random Forest and AdaBoost for detecting malignant prostate cancers from benign ones. Moreover, different feature extracting strategies are proposed to improve the detection performance and identify potential genomic biomarkers. The results show the Lasso feature set yielded high performance from the models with SVM achieving exemplary classification accuracy of 97%. The Lasso and SVM combination reported many significant biomarker genes and gene mutations including but not restricted to CA2320112, CA2328529, and CA2436168.
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11
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A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes (Basel) 2021. [DOI: 10.3390/pr9081466] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.
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Wang X, Ma J, Bhosale P, Ibarra Rovira JJ, Qayyum A, Sun J, Bayram E, Szklaruk J. Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging. Abdom Radiol (NY) 2021; 46:3378-3386. [PMID: 33580348 PMCID: PMC8215028 DOI: 10.1007/s00261-021-02964-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/17/2020] [Accepted: 01/16/2021] [Indexed: 02/07/2023]
Abstract
Introduction Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, “DLR” in improving image quality and mitigating artifacts, which is now commercially available as AIRTM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts. Methods This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERCDLR, ERCConv, Non-ERCDLR, and Non-ERCConv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor. Results The Non-ERCDLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor. Conclusion Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.
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Affiliation(s)
- Xinzeng Wang
- MR Clinical Solutions and Research Collaborations, GE Healthcare, Houston, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Priya Bhosale
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Juan J Ibarra Rovira
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Aliya Qayyum
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Ersin Bayram
- MR Clinical Solutions and Research Collaborations, GE Healthcare, Houston, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA
| | - Janio Szklaruk
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA.
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Liang X, Peng J, Chen D, Tang L, Liu A, Fu Z, Shi L, Wang K, Shao C. Identification of novel hub genes and lncRNAs related to the prognosis and progression of pancreatic cancer by microarray and integrated bioinformatics analysis. Gland Surg 2021; 10:1104-1117. [PMID: 33842254 PMCID: PMC8033078 DOI: 10.21037/gs-21-151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/22/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Pancreatic cancer (PC) is one of the most invasive and metastatic neoplasms among the fatal malignancies of the digestive system. Abnormal expression of genes and long non-coding RNAs (lncRNAs) are reportedly linked to multiple cancers. However, the lncRNA-mRNA expression profiles and their molecular mechanisms in PC progression are poorly known. This study aimed to map the hub genes and lncRNAs which might play core roles in the development of PC. METHODS This study used microarray expression analysis to screen for both differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between PC and matched adjacent non-tumor (AN) tissues. In order to clarify the functional classification of DEGs, we conducted GO and KEGG pathway enrichment analyses via the Enrichr database. LncRNA-mRNA co-expressed networks were also constructed to explore the probable core regulating DEGs and DElncRNAs. Subsequently, the hub genes and lncRNAs were validated via the ONCOMINE and GEPIA databases and the co-expressed networks. RESULTS By analyzing an mRNA-lncRNA microarray, we identified 943 mRNAs and 1,138 lncRNAs differentially expressed in PC tumors compared with the matched AN tissues. GO analysis confirmed that both up-regulated and down-regulated DEGs were enriched in multiple terms. The KEGG pathways enrichment analyses revealed that DEGs were mostly enriched in the focal adhesion and glutathione metabolism pathways, amongst others. Co-expressed networks were established to reveal the differential interactions between DEGs and DElncRNAs, and to indicate the core regulatory factors located at the core nodes of the co-expressed networks. The expression levels of potential core-regulating DEGs were validated by the GEPIA and ONCOMINE databases, and the relationship between overall survival and tumor stage and the potential core-regulating DEGs was analyzed using the GEPIA database. As a result, five genes and sixteen lncRNAs were finally considered as the hub transcripts in PC. CONCLUSIONS This study identified DEGs and DElncRNAs between PC tumors and matched AN tissues, and these transcripts were connected with malignant phenotypes in PC through different BPs and signaling pathways. Furthermore, five hub genes and sixteen lncRNAs were identified, which are expected to represent candidate diagnostic biomarkers or potential therapeutic targets for PC.
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Affiliation(s)
- Xing Liang
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Junfeng Peng
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Danlei Chen
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Liang Tang
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Anan Liu
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Zhiping Fu
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Ligang Shi
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Keqi Wang
- Department of Gastroenterology, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Chenghao Shao
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
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14
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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15
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Valenzuela O, Rojas F, Rojas I, Glosekotter P. Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018. BMC Bioinformatics 2020; 21:153. [PMID: 32366219 PMCID: PMC7199304 DOI: 10.1186/s12859-020-3467-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.
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Affiliation(s)
- Olga Valenzuela
- Faculty of Sciences, Applied Mathematics, University of Granada, Avenida de Fuente Nueva, Granada, 18071 Spain
| | - Fernando Rojas
- Information and Communications Technology Centre (CITIC and ETSIIT-UGR) University of Granada, Periodista Daniel Saucedo Aranda, Granada, 18071 Spain
| | - Ignacio Rojas
- Information and Communications Technology Centre (CITIC and ETSIIT-UGR) University of Granada, Periodista Daniel Saucedo Aranda, Granada, 18071 Spain
| | - Peter Glosekotter
- Department of Electrical Engineering and Computer Science, University of Applied Sciences of Munster, Stegerweldstr 39, Steinfurt, 48565 Germany
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