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Johnson JR, Mavingire N, Woods-Burnham L, Walker M, Lewis D, Hooker SE, Galloway D, Rivers B, Kittles RA. The complex interplay of modifiable risk factors affecting prostate cancer disparities in African American men. Nat Rev Urol 2024; 21:422-432. [PMID: 38307952 DOI: 10.1038/s41585-023-00849-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2023] [Indexed: 02/04/2024]
Abstract
Prostate cancer is the second most commonly diagnosed non-skin malignancy and the second leading cause of cancer death among men in the USA. However, the mortality rate of African American men aged 40-60 years is almost 2.5-fold greater than that of European American men. Despite screening and diagnostic and therapeutic advances, disparities in prostate cancer incidence and outcomes remain prevalent. The reasons that lead to this disparity in outcomes are complex and multifactorial. Established non-modifiable risk factors such as age and genetic predisposition contribute to this disparity; however, evidence suggests that modifiable risk factors (including social determinants of health, diet, steroid hormones, environment and lack of diversity in enrolment in clinical trials) are prominent contributing factors to the racial disparities observed. Disparities involved in the diagnosis, treatment and survival of African American men with prostate cancer have also been correlated with low socioeconomic status, education and lack of access to health care. The effects and complex interactions of prostate cancer modifiable risk factors are important considerations for mitigating the incidence and outcomes of this disease in African American men.
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Affiliation(s)
- Jabril R Johnson
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, USA.
| | - Nicole Mavingire
- Department of Physiology, Morehouse School of Medicine, Atlanta, GA, USA
| | | | - Mya Walker
- Department of Diabetes and Cancer Metabolism, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Deyana Lewis
- Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA, USA
| | - Stanley E Hooker
- Department of Population Sciences, Division of Health Equities, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Dorothy Galloway
- Department of Population Sciences, Division of Health Equities, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Brian Rivers
- Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA, USA
| | - Rick A Kittles
- Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA, USA
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2
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Drożdż A, Duggan B, Ruddock MW, Reid CN, Kurth MJ, Watt J, Irvine A, Lamont J, Fitzgerald P, O’Rourke D, Curry D, Evans M, Boyd R, Sousa J. Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics. Front Oncol 2024; 14:1401071. [PMID: 38779086 PMCID: PMC11109371 DOI: 10.3389/fonc.2024.1401071] [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: 03/14/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
Background Detailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones. Materials and methods We applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes. Results Results showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16. Conclusions CACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals.
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Affiliation(s)
- Anna Drożdż
- Personal Health Data Science Group, Sano – Centre for Computational Personalised Medicine - International Research Foundation, Krakow, Poland
| | - Brian Duggan
- South Eastern Health and Social Care Trust, Ulster Hospital Dundonald, Belfast, United Kingdom
| | - Mark W. Ruddock
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - Cherith N. Reid
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - Mary Jo Kurth
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - Joanne Watt
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - Allister Irvine
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - John Lamont
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - Peter Fitzgerald
- Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
| | - Declan O’Rourke
- Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, United Kingdom
| | - David Curry
- Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, United Kingdom
| | - Mark Evans
- Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, United Kingdom
| | - Ruth Boyd
- Northern Ireland Clinical Trials Network, Belfast City Hospital, Belfast, United Kingdom
| | - Jose Sousa
- Personal Health Data Science Group, Sano – Centre for Computational Personalised Medicine - International Research Foundation, Krakow, Poland
- Centre for Public Health, Institute of Clinical Sciences, Queen’s University, Belfast, United Kingdom
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3
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Guo J, Gu L, Johnson H, Gu D, Lu Z, Luo B, Yuan Q, Zhang X, Xia T, Zeng Q, Wu AHB, Johnson A, Dizeyi N, Abrahamsson PA, Zhang H, Chen L, Xiao K, Zou C, Persson JL. A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis. Prostate Cancer Prostatic Dis 2024:10.1038/s41391-023-00758-z. [PMID: 38308042 DOI: 10.1038/s41391-023-00758-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 02/04/2024]
Abstract
BACKGROUND Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. METHODS An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. RESULTS An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. CONCLUSIONS The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.
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Affiliation(s)
- Jinan Guo
- Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China
- Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- Shenzhen Urology Minimally Invasive Engineering Center, Shenzhen, China
- Shenzhen Public Service Platform on Tumor Precision Medicine and Molecular Diagnosis, Clinical Medicine Research Centre, Shenzhen, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Liangyou Gu
- Department of Urology, The Third Medical Centre, Chinese PLA General Hospital, Beijing, China
| | | | - Di Gu
- Department of Urology, The First affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhenquan Lu
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Binfeng Luo
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Qian Yuan
- Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Xuhui Zhang
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Taolin Xia
- Department of Urology, Foshan First People's Hospital, Foshan, China
| | - Qingsong Zeng
- Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Alan H B Wu
- Clinical Laboratories, San Francisco General Hospital, San Francisco, CA, USA
| | | | - Nishtman Dizeyi
- Department of Translational Medicine, Lund University, Clinical Research Centre, Malmö, Sweden
| | - Per-Anders Abrahamsson
- Department of Translational Medicine, Lund University, Clinical Research Centre, Malmö, Sweden
| | - Heqiu Zhang
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Lingwu Chen
- Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Kefeng Xiao
- Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Chang Zou
- Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China.
- Shenzhen Urology Minimally Invasive Engineering Center, Shenzhen, China.
- Shenzhen Public Service Platform on Tumor Precision Medicine and Molecular Diagnosis, Clinical Medicine Research Centre, Shenzhen, China.
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.
- Key Laboratory of Medical Electrophysiology of Education Ministry, School of Pharmacy, Southwest Medical University, Luzhou, China.
| | - Jenny L Persson
- Department of Molecular Biology, Umeå University, Umeå, Sweden.
- Department of Biomedical Sciences, Malmö University, Malmö, Sweden.
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4
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Grizzi F, Bax C, Hegazi MAAA, Lotesoriere BJ, Zanoni M, Vota P, Hurle RF, Buffi NM, Lazzeri M, Tidu L, Capelli L, Taverna G. Early Detection of Prostate Cancer: The Role of Scent. CHEMOSENSORS 2023; 11:356. [DOI: 10.3390/chemosensors11070356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.
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Affiliation(s)
- Fabio Grizzi
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
| | - Carmen Bax
- Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, 20133 Milan, Italy
| | - Mohamed A. A. A. Hegazi
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Beatrice Julia Lotesoriere
- Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, 20133 Milan, Italy
| | - Matteo Zanoni
- Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Italy
| | - Paolo Vota
- Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Italy
| | - Rodolfo Fausto Hurle
- Department of Urology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Nicolò Maria Buffi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
- Department of Urology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Massimo Lazzeri
- Department of Urology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Lorenzo Tidu
- Italian Ministry of Defenses, “Vittorio Veneto” Division, 50136 Firenze, Italy
| | - Laura Capelli
- Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, 20133 Milan, Italy
| | - Gianluigi Taverna
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
- Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Italy
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5
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Bettio V, Mazzucco E, Aleni C, Cracas S, Rinaldi C, Antona A, Varalda M, Venetucci J, Ferrante D, Rimedio A, Capello D. UPO Biobank: The Challenge of Integrating Biobanking into the Academic Environment to Support Translational Research. J Pers Med 2023; 13:911. [PMID: 37373900 DOI: 10.3390/jpm13060911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/18/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Biobanks are driving motors of precision and personalized medicine by providing high-quality biological material/data through the standardization and harmonization of their collection, preservation, and distribution. UPO Biobank was established in 2020 as an institutional, disease, and population biobank within the University of Piemonte Orientale (UPO) for the promotion and support of high-quality, multidisciplinary studies. UPO Biobank collaborates with UPO researchers, sustaining academic translational research, and supports the Novara Cohort Study, a longitudinal cohort study involving the population in the Novara area that will collect data and biological specimens that will be available for epidemiological, public health, and biological studies on aging. UPO Biobank has been developed by implementing the quality standards for the field and the ethical and legal issues and normative about privacy protection, data collection, and sharing. As a member of the "Biobanking and Biomolecular Resources Research Infrastructure" (BBMRI) network, UPO Biobank aims to expand its activity worldwide and launch cooperation with new national and international partners and researchers. The objective of this manuscript is to report an institutional and operational experience through the description of the technical and procedural solutions and ethical and scientific implications associated with the establishment of this university research biobank.
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Affiliation(s)
- Valentina Bettio
- UPO Biobank, University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Eleonora Mazzucco
- UPO Biobank, University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Chiara Aleni
- Department of Sustainable Development and Ecological Transition, University of Piemonte Orientale, 13100 Vercelli, Italy
| | - Silvia Cracas
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Carmela Rinaldi
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
- Learning and Research Area, A.O.U. Maggiore della Carità, 28100 Novara, Italy
| | - Annamaria Antona
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Marco Varalda
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Jacopo Venetucci
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Daniela Ferrante
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Antonio Rimedio
- Ethics Committee of the University "Hospital Major of Charity" in Novara, Local Health Authorities Biella, 28100 Novara, Italy
| | - Daniela Capello
- UPO Biobank, University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
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6
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Heidegger I, Hamdy FC, van den Bergh RCN, Heidenreich A, Sedelaar M, Roupret M. Intermediate-risk Prostate Cancer-A Sheep in Wolf's Clothing? Eur Urol Oncol 2023; 6:103-109. [PMID: 34305038 DOI: 10.1016/j.euo.2021.07.004] [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: 04/06/2021] [Revised: 06/23/2021] [Accepted: 07/07/2021] [Indexed: 11/16/2022]
Abstract
This case-based discussion describes a 65-year-old man newly diagnosed with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa). According to the European Association of Urology classification system, the patient harbors an intermediate-risk cancer. In step-by step discussion, we elaborate guideline-based treatment modalities for intermediate-risk PCa focused on debating active surveillance versus active treatment. Thereby, we discuss the importance of patient characteristics, including age, hereditary factors, life expectancy and comorbidity status, findings of multiparametric magnetic resonance imaging, as well as prostate-specific antigen (PSA) density and PSA kinetics, in predicting the clinical course of the disease. In addition, we focus on cribriform pathology as a predictor of adverse outcomes and critically discuss its relevance in patient management. Lastly, we outline genomic stratification in ISUP 2 cancer as a future tool to predict PCa aggressiveness. PATIENT SUMMARY: Based on current guidelines, patients with intermediate-risk prostate cancer are treated actively or can alternatively undergo an active surveillance approach when favorable risk factors are present. One major issue is to discriminate between patients who benefit from an active therapy approach and those who benefit from a deferred treatment. Therefore, reliable biomarkers and early predictors of disease progression are needed urgently.
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Affiliation(s)
- Isabel Heidegger
- Department of Urology, Medical University Innsbruck, Innsbruck, Austria.
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Axel Heidenreich
- Department of Urology, Uro-Oncology, Robot Assisted and Reconstructive Urologic Surgery, University Hospital Cologne, Cologne, Germany; Department of Urology, Medical University Vienna, Vienna, Austria
| | - Michiel Sedelaar
- Department of Urology, Radboud University, Medical Center, Nijmegen, The Netherlands
| | - Morgan Roupret
- Sorbonne Université, Urology Department, Hôpital Pitié-Salpêtrière, Paris, France
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7
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Prostate cancer screening: Continued controversies and novel biomarker advancements. Curr Urol 2022; 16:197-206. [PMID: 36714234 PMCID: PMC9875204 DOI: 10.1097/cu9.0000000000000145] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/23/2022] [Indexed: 02/01/2023] Open
Abstract
Prostate cancer (PCa) screening remains one of the most controversial topics in clinical and public health. Despite being the second most common cancer in men worldwide, recommendations for screening using prostate-specific antigen (PSA) are unclear. Early detection and the resulting postscreening treatment lead to overdiagnosis and overtreatment of otherwise indolent cases. In addition, several unwanted harms are associated with PCa screening process. This literature review focuses on the limitations of PSA-specific PCa screening, reasons behind the screening controversy, and the novel biomarkers and advanced innovative methodologies that improve the limitations of traditional screening using PSA. With the verdict of whether or not to screen not yet unanimous, we hope to aid in resolution of the long-standing debate.
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8
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Johnson H, El-Schich Z, Ali A, Zhang X, Simoulis A, Wingren AG, Persson JL. Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients. Cancers (Basel) 2022; 14:2045. [PMID: 35454952 PMCID: PMC9030299 DOI: 10.3390/cancers14082045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.
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Affiliation(s)
| | - Zahra El-Schich
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Amjad Ali
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
| | - Xuhui Zhang
- Department of Bio-Diagnosis, Institute of Basic Medical Sciences, Beijing 100005, China;
| | - Athanasios Simoulis
- Department of Clinical Pathology and Cytology, Skåne University Hospital, SE-205 02 Malmö, Sweden;
| | - Anette Gjörloff Wingren
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Jenny L. Persson
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
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9
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Peng Y, Song Y, Wang H. Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer. Front Cell Dev Biol 2022; 9:723466. [PMID: 35127694 PMCID: PMC8814427 DOI: 10.3389/fcell.2021.723466] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 12/20/2021] [Indexed: 12/24/2022] Open
Abstract
Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.
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Affiliation(s)
- Yun Peng
- Tianjin Institute of Urology, the 2nd Hospital of Tianjin Medical University, Tianjin, China
| | - Yuxuan Song
- Department of Urology, Peking University People’s Hospital, Beijing, China
| | - Haitao Wang
- Department of Oncology, the 2nd Hospital of Tianjin Medical University, Tianjin, China
- *Correspondence: Haitao Wang,
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10
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Ding L, Liu Z, Wang J. Role of cystatin C in urogenital malignancy. Front Endocrinol (Lausanne) 2022; 13:1082871. [PMID: 36589819 PMCID: PMC9794607 DOI: 10.3389/fendo.2022.1082871] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Urogenital malignancy accounts for one of the major causes of cancer-related deaths globally. Numerous studies have investigated novel molecular markers in the blood circulation, tumor tissue, or urine in order to assist in the clinical identification of tumors at early stages, predict the response of therapeutic strategies, and give accurate prognosis assessment. As an endogenous inhibitor of lysosomal cysteine proteinases, cystatin C plays an integral role in diverse processes. A substantial number of studies have indicated that it may be such a potential promising biomarker. Therefore, this review was intended to provide a detailed overview of the role of cystatin C in urogenital malignancy.
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Affiliation(s)
- Li Ding
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zijie Liu
- Department of Urology, Wuxi No.2 People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Junqi Wang
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- *Correspondence: Junqi Wang,
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Yang L, Lu P, Yang X, Li K, Qu S. Annexin A3, a Calcium-Dependent Phospholipid-Binding Protein: Implication in Cancer. Front Mol Biosci 2021; 8:716415. [PMID: 34355022 PMCID: PMC8329414 DOI: 10.3389/fmolb.2021.716415] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/08/2021] [Indexed: 12/20/2022] Open
Abstract
Annexin A3 (ANXA3), also known as lipocortin III and placental anticoagulant protein III, has been reported to be dysregulated in tumor tissues and cancer cell lines, and harbors pronounced diagnostic and prognostic value for certain malignancies, such as breast, prostate, colorectal, lung and liver cancer. Aberrant expression of ANXA3 promotes tumor cell proliferation, invasion, metastasis, angiogenesis, and therapy resistance to multiple chemotherapeutic drugs including platinum-based agents, fluoropyrimidines, cyclophosphamide, doxorubicin, and docetaxel. Genetic alterations on the ANXA3 gene have also been reported to be associated with the propensity to form certain inherited, familial tumors. These diverse functions of ANXA3 in tumors collectively indicate that ANXA3 may serve as an attractive target for novel anticancer therapies and a powerful diagnostic and prognostic biomarker for early tumor detection and population risk screening. In this review, we dissect the role of ANXA3 in cancer in detail.
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Affiliation(s)
- Liu Yang
- Key Laboratory of High-Incidence Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Pingan Lu
- Faculty of Medicine, Amsterdam Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Xiaohui Yang
- Key Laboratory of High-Incidence Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Kaiguo Li
- Key Laboratory of High-Incidence Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Song Qu
- Key Laboratory of High-Incidence Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
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Martínez-González LJ, Sánchez-Conde V, González-Cabezuelo JM, Antunez-Rodríguez A, Andrés-León E, Robles-Fernandez I, Lorente JA, Vázquez-Alonso F, Alvarez-Cubero MJ. Identification of MicroRNAs as Viable Aggressiveness Biomarkers for Prostate Cancer. Biomedicines 2021; 9:biomedicines9060646. [PMID: 34198846 PMCID: PMC8227559 DOI: 10.3390/biomedicines9060646] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/22/2021] [Accepted: 06/01/2021] [Indexed: 12/13/2022] Open
Abstract
MiRNAs play a relevant role in PC (prostate cancer) by the regulation in the expression of several pathways’ AR (androgen receptor), cellular cycle, apoptosis, MET (mesenchymal epithelium transition), or metastasis. Here, we report the role of several miRNAs’ expression patterns, such as miR-93-5p, miR-23c, miR-210-3p, miR-221-3p, miR-592, miR-141, miR-375, and miR-130b, with relevance in processes like cell proliferation and MET. Using Trizol® extraction protocol and TaqMan™ specific probes for amplification, we performed miRNAs’ analysis of 159 PC fresh tissues and 60 plasmas from peripheral blood samples. We had clinical data from all samples including PSA, Gleason, TNM, and D’Amico risk. Moreover, a bioinformatic analysis in TCGA (The Cancer Genome Atlas) was included to analyze the effect of the most relevant miRNAs according to aggressiveness in an extensive cohort (n = 531). We found that miR-210-3p, miR-23c, miR-592, and miR-93-5p are the most suitable biomarkers for PC aggressiveness and diagnosis, respectively. In fact, according with our results, miR-93-5p seems the most promising non-invasive biomarker for PC. To sum up, miR-210-3p, miR-23c, miR-592, and miR-93-5p miRNAs are suggested to be potential biomarkers for PC risk stratification that could be included in non-invasive strategies such as liquid biopsy in precision medicine for PC management.
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Affiliation(s)
- Luis Javier Martínez-González
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Genomics Unit, PTS Granada-Avenida de la Ilustración, 114-18016 Granada, Spain;
- Correspondence: author: (L.J.M.-G.); (M.J.A.-C.); Tel.: +34-958-715-500 (ext. 108) (L.J.M.-G.); +34-958-248-945 (M.J.A.-C.); Fax: +34-958-637-071 (L.J.M.-G.)
| | - Victor Sánchez-Conde
- Urology Department, Hospital Virgen de las Nieves, 18014 Granada, Spain; (V.S.-C.); (F.V.-A.)
| | | | - Alba Antunez-Rodríguez
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Genomics Unit, PTS Granada-Avenida de la Ilustración, 114-18016 Granada, Spain;
| | - Eduardo Andrés-León
- Bioinformatics Unit, Institute of Parasitology and Biomedicine “López-Neyra” (IPBLN), Spanish National Research Council (CSIC), 18016 Granada, Spain;
| | - Inmaculada Robles-Fernandez
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Liquid Biopsy and Cancer Interception Group, PTS Granada, 114-18016 Granada, Spain; (I.R.-F.); (J.A.L.)
| | - Jose Antonio Lorente
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Liquid Biopsy and Cancer Interception Group, PTS Granada, 114-18016 Granada, Spain; (I.R.-F.); (J.A.L.)
- University of Granada, Legal Medicine and Toxicology Department, Faculty of Medicine, PTS Granada, 18016 Granada, Spain
| | - Fernando Vázquez-Alonso
- Urology Department, Hospital Virgen de las Nieves, 18014 Granada, Spain; (V.S.-C.); (F.V.-A.)
| | - María Jesus Alvarez-Cubero
- GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Liquid Biopsy and Cancer Interception Group, PTS Granada, 114-18016 Granada, Spain; (I.R.-F.); (J.A.L.)
- University of Granada, Department of Biochemistry and Molecular Biology III, Faculty of Medicine, PTS Granada, 18016 Granada, Spain
- Nutrition, Diet and Risk Assessment Group, Bio-Health Research Institute (ibs.GRANADA Instituto de Investigación Biosanitaria), 18014 Granada, Spain
- Correspondence: author: (L.J.M.-G.); (M.J.A.-C.); Tel.: +34-958-715-500 (ext. 108) (L.J.M.-G.); +34-958-248-945 (M.J.A.-C.); Fax: +34-958-637-071 (L.J.M.-G.)
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