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Chen W, Li S, Huang D, Su Y, Wang J, Liang Z. Identification of prognostic RNA editing profiles for clear cell renal carcinoma. Front Med (Lausanne) 2024; 11:1390803. [PMID: 39091293 PMCID: PMC11291244 DOI: 10.3389/fmed.2024.1390803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 07/04/2024] [Indexed: 08/04/2024] Open
Abstract
Objective Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments. Methods Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2. Results An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated. Conclusion RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.
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Affiliation(s)
- Weihong Chen
- Department of Anxi County Hospital, Quanzhou, China
| | - Shaobin Li
- Department of Anxi County Hospital, Quanzhou, China
| | | | - Yuchao Su
- Department of Anxi County Hospital, Quanzhou, China
| | - Jing Wang
- Xilin Gol League Central Hospital, Xilin Hot, China
| | - Zhiru Liang
- Xilin Gol League Central Hospital, Xilin Hot, China
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2
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Jung AW, Holm PC, Gaurav K, Hjaltelin JX, Placido D, Mortensen LH, Birney E, Brunak SR, Gerstung M. Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study. Lancet Digit Health 2024; 6:e396-e406. [PMID: 38789140 DOI: 10.1016/s2589-7500(24)00062-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/19/2023] [Accepted: 03/13/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank. METHODS In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16-86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16-75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50-75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance. FINDINGS From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65-0·67] for cervix uteri cancer to 0·91 [0·90-0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44-0·66] for cervix uteri cancer to 0·78 [0·77-0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagnoses and health factors. The best overall performance was seen for cancers of the digestive system (oesophageal, stomach, colorectal, liver, and pancreatic) but also thyroid, kidney, and uterine cancers. INTERPRETATION Data available in national electronic health databases can be used to approximate cancer risk factors and enable risk predictions in most cancer types. Model predictions generalise between the Danish and UK health-care systems. With the emergence of multi-cancer early detection tests, electronic health record-based risk models could supplement screening efforts. FUNDING Novo Nordisk Foundation and the Danish Innovation Foundation.
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Affiliation(s)
- Alexander W Jung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK; University of Cambridge, Cambridge, UK
| | - Peter C Holm
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kumar Gaurav
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Laust Hvas Mortensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Statistics Denmark, Copenhagen, Denmark
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - S Ren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK; Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany; Robert Bosch Center for Tumor Diseases, Stuttgart, Germany; Medical Faculty, Eberhard-Karls-University, Tübingen, Germany; University Hospital Tübingen, Tübingen, Germany.
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Zhu J, Ye Y, Zhao X, Wu M, Wang X, Shu P, Liu J, Zhang X. Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration. ENVIRONMENTAL TOXICOLOGY 2024; 39:3694-3709. [PMID: 38511791 DOI: 10.1002/tox.24225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/22/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.
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Affiliation(s)
- Junlan Zhu
- The Precision Medicine Laboratory, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Yun Ye
- Department of Pharmacy, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Xin Zhao
- Department of Pharmacy, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Meiling Wu
- Department of Pharmacy, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Xuyao Wang
- The Precision Medicine Laboratory, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Peng Shu
- The Precision Medicine Laboratory, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Jian Liu
- The Precision Medicine Laboratory, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Xingguo Zhang
- Department of Pharmacy, Beilun People's Hospital, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
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Zhao S, He G, Li J, Yang X, Hou X, Wu T, Zhang S. Ecological risk assessment and spatial-temporal differentiation of soil and water resources in the Hefei metropolitan area. Sci Rep 2024; 14:8462. [PMID: 38605176 PMCID: PMC11009403 DOI: 10.1038/s41598-024-59246-6] [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: 01/31/2024] [Accepted: 04/08/2024] [Indexed: 04/13/2024] Open
Abstract
There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of "ST-QS-RR", the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of "high in the north and low in the south, high in the west and low in the east". (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.
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Affiliation(s)
- Shuhang Zhao
- School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
| | - Gang He
- School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Jie Li
- School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
| | - Xia Yang
- School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Xiaoyu Hou
- School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Ting Wu
- School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Shangyun Zhang
- School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
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5
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Huang Y, Guo W, Zeng Y, Wang X, Fan B, Zhang Y, Yan L, Gu G, Liu Z. Identification and validation of a gap junction protein related signature for predicting the prognosis of renal clear cell carcinoma. Front Oncol 2024; 14:1354049. [PMID: 38454924 PMCID: PMC10919056 DOI: 10.3389/fonc.2024.1354049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
Background Gap junction proteins (GJPs) are a class of channel proteins that are closely related to cell communication and tumor development. The objective of this study was to screen out GJPs related prognostic signatures (GRPS) associated with clear cell renal cell carcinoma (ccRCC). Materials and Methods GJPs microarray data for ccRCC patients were obtained from The Gene Expression Omnibus (GEO) database, along with RNA sequencing data for tumor and paired normal tissues from The Cancer Genome Atlas (TCGA) database. In the TCGA database, least absolute shrinkage and selection Operator (LASSO) and Cox regression models were used to identify GJPs with independent prognostic effects as GRPS in ccRCC patients. According to the GRPS expression and regression coefficient from the multivariate Cox regression model, the risk score (RS) of each ccRCC patient was calculated, to construct the RS prognostic model to predict survival. Overall survival (OS) and progression-free survival (PFS) analyses; gene pan-cancer analysis; single gene survival analysis; gene joint effect analysis; functional enrichment analysis; tumor microenvironment (TME) analysis; tumor mutational burden (TMB) analysis; and drug sensitivity analysis were used to explore the biological function, mechanism of action and clinical significance of GRPS in ccRCC. Further verification of the genetic signature was performed with data from the GEO database. Finally, the cytofunctional experiments were used to verify the biological significance of GRPS associated GJPs in ccRCC cell lines. Results GJA5 and GJB1, which are GRPS markers of ccRCC patients, were identified through LASSO and Cox regression models. Low expression of GJA5 and GJB1 is associated with poor patient prognosis. Patients with high-RS had significantly shorter OS and PFS than patients with low-RS (p< 0.001). The risk of death for individuals with high-RS was 1.695 times greater than that for those with low-RS (HR = 1.695, 95%CI= 1.439-1.996, p< 0.001). Receiver Operating Characteristic (ROC) curve showed the great predictive power of the RS prognostic model for the survival rate of patients. The area under curve (AUC) values for predicting 1-year, 3-year and 5-year survival rates were 0.740, 0.781 and 0.771, respectively. The clinical column chart was also reliable for predicting the survival rate of patients, with AUC values of 0.859, 0.846 and 0.796 for predicting 1-year, 3-year and 5-year survival, respectively. The GRPS was associated with immune cell infiltration, the TME, the TMB, and sensitivity to chemotherapy drugs. Further in vitro experiments showed that knockdown of GJA5 or GJB1 could promote the proliferation, migration and epithelial-mesenchymal transition (EMT) and inhibit apoptosis of ccRCC cells. Conclusion GJA5 and GJB1 could be potential biological markers for predicting survival in patients with ccRCC.
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Affiliation(s)
- Yongsheng Huang
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wenyi Guo
- Department of Pancreatic Surgery, General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuan Zeng
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xinrong Wang
- Department of Anatomy and Neurobiology, Shandong Provincial Key Laboratory of Mental Disorders, School of Basic Medical Sciences and Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bohao Fan
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ying Zhang
- Department of Surgery, Qihe County Traditional Chinese Medicine Hospital, Dezhou, China
| | - Lei Yan
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Gangli Gu
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhao Liu
- Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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Espressivo A, Pan ZS, Usher-Smith JA, Harrison H. Risk Prediction Models for Oral Cancer: A Systematic Review. Cancers (Basel) 2024; 16:617. [PMID: 38339366 PMCID: PMC10854942 DOI: 10.3390/cancers16030617] [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: 11/25/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.
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Affiliation(s)
- Aufia Espressivo
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK; (Z.S.P.); (J.A.U.-S.); (H.H.)
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Campi R, Rebez G, Klatte T, Roussel E, Ouizad I, Ingels A, Pavan N, Kara O, Erdem S, Bertolo R, Capitanio U, Mir MC. Effect of smoking, hypertension and lifestyle factors on kidney cancer - perspectives for prevention and screening programmes. Nat Rev Urol 2023; 20:669-681. [PMID: 37328546 DOI: 10.1038/s41585-023-00781-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/18/2023]
Abstract
Renal cell carcinoma (RCC) incidence has doubled over the past few decades. However, death rates have remained stable as the number of incidental renal mass diagnoses peaked. RCC has been recognized as a European health care issue, but to date, no screening programmes have been introduced. Well-known modifiable risk factors for RCC are smoking, obesity and hypertension. A direct association between cigarette consumption and increased RCC incidence and RCC-related death has been reported, but the underlying mechanistic pathways for this association are still unclear. Obesity is associated with an increased risk of RCC, but interestingly, improved survival outcomes have been reported in obese patients, a phenomenon known as the obesity paradox. Data on the association between other modifiable risk factors such as diet, dyslipidaemia and physical activity with RCC incidence are conflicting, and potential mechanisms underlying these associations remain to be elucidated.
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Affiliation(s)
- Riccardo Campi
- Department of Urology, University of Florence, Careggi Hospital, Florence, Italy
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
| | - Giacomo Rebez
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Cattinara Hospital, University of Trieste, Trieste, Italy
| | - Tobias Klatte
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Royal Bournemouth Hospital, Bournemouth, UK
| | - Eduard Roussel
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, KU Leuven, Leuven, Belgium
| | - Idir Ouizad
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Bichat-Claude Bernard Hospital, Paris, France
| | - Alexander Ingels
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Henri Mondor Hospital, Créteil, France
| | - Nicola Pavan
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Cattinara Hospital, University of Trieste, Trieste, Italy
| | - Onder Kara
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Faculty of Medicine, Kocaeli University, İzmit, Turkey
| | - Selcuk Erdem
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Istanbul University, Istanbul, Turkey
| | - Riccardo Bertolo
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Urology Unit, Department of Surgery, Tor Vergata University of Rome, Rome, Italy
| | - Umberto Capitanio
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, San Raffaele Scientific Institute, Milan, Italy
- Division of Experimental Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Milan, Italy
| | - Maria Carmen Mir
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands.
- Department of Urology, Hospital Universitario La Ribera, Valencia, Spain.
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Xiao C, Ma X. Comparison of the efficacy of robotic-assisted retroperitoneal laparoscopy and traditional retroperitoneal laparoscopy in partial nephrectomy among patients with renal tumors: A retrospective cohort study. Investig Clin Urol 2023; 64:579-587. [PMID: 37932569 PMCID: PMC10630689 DOI: 10.4111/icu.20230168] [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: 05/19/2023] [Revised: 07/23/2023] [Accepted: 08/07/2023] [Indexed: 11/08/2023] Open
Abstract
PURPOSE The efficacy of robotic-assisted retroperitoneal laparoscopy (RARL) and traditional retroperitoneal laparoscopy (TRL) in patients undergoing partial nephrectomy for treatment of a renal tumor were compared in this study. MATERIALS AND METHODS The retrospective study reviewed patients with renal tumors who underwent partial nephrectomy in our hospital between January 2020 and February 2022. According to different surgical methods, the patients were enrolled into the RARL (experimental group, n=65) or the TRL (control group, n=63) partial nephrectomy group. RESULTS One hundred twenty-eight patients were reviewed. The number of patients with collecting system injuries in the experimental group (19 cases) was significantly less than in the control group (32 cases; p<0.05). The operative time (115.7±48.2 min vs. 143.1±25.5 min) and heat ischemia time (18.7±4.9 min vs. 26.4±5.2 min) were significantly shorter in the experimental group than in the control group. The intraoperative blood loss (35.4±13.2 mL vs. 96.1±34.3 mL) and postoperative drainage volume (55.9±26.4 mL vs. 75.2±32.6 mL) were significantly less in the experimental group than in the control group (p<0.05). The change in postoperative glomerular filtration rate decrease in the experimental group was significantly lower than that in the control group (p<0.05). The change level in postoperative creatinine increase in the experimental group were significantly lower than those in the control group (p<0.05). There were no considerable differences in other clinical indicators or follow-up results between the two groups. CONCLUSIONS RARL was superior to TRL for renal tumor treatment with respect to operative time, intraoperative blood loss, warm ischemia time, and postoperative renal function recovery.
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Affiliation(s)
- Chengsheng Xiao
- Department of Urology, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Urology, The First People's Hospital of Changde City, Changde, China
| | - Xin Ma
- Department of Urology, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Urology, State Key Laboratory of Kidney Diseases, Chinese People's Liberation Army General Hospital, PLA Medical School, Beijing, China.
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9
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Alcala K, Zahed H, Cortez Cardoso Penha R, Alcala N, Robbins HA, Smith-Byrne K, Martin RM, Muller DC, Brennan P, Johansson M. Kidney Function and Risk of Renal Cell Carcinoma. Cancer Epidemiol Biomarkers Prev 2023; 32:1644-1650. [PMID: 37668600 PMCID: PMC10618735 DOI: 10.1158/1055-9965.epi-23-0558] [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] [Received: 05/16/2023] [Revised: 07/13/2023] [Accepted: 08/31/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND We evaluated the temporal association between kidney function, assessed by estimated glomerular filtration rate (eGFR), and the risk of incident renal cell carcinoma (RCC). We also evaluated whether eGFR could improve RCC risk discrimination beyond established risk factors. METHODS We analyzed the UK Biobank cohort, including 463,178 participants of whom 1,447 were diagnosed with RCC during 5,696,963 person-years of follow-up. We evaluated the temporal association between eGFR and RCC risk using flexible parametric survival models, adjusted for C-reactive protein and RCC risk factors. eGFR was calculated from creatinine and cystatin C levels. RESULTS Lower eGFR, an indication of poor kidney function, was associated with higher RCC risk when measured up to 5 years prior to diagnosis. The RCC HR per SD decrease in eGFR when measured 1 year before diagnosis was 1.26 [95% confidence interval (95% CI), 1.16-1.37], and 1.11 (95% CI, 1.05-1.17) when measured 5 years before diagnosis. Adding eGFR to the RCC risk model provided a small improvement in risk discrimination 1 year before diagnosis with an AUC of 0.73 (95% CI, 0.67-0.84) compared with the published model (0.69; 95% CI, 0.63-0.79). CONCLUSIONS This study demonstrated that kidney function markers are associated with RCC risk, but the nature of these associations are consistent with reversed causality. Markers of kidney function provided limited improvements in RCC risk discrimination beyond established risk factors. IMPACT eGFR may be of potential use to identify individuals in the extremes of the risk distribution.
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Affiliation(s)
- Karine Alcala
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Hana Zahed
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | | | - Nicolas Alcala
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Hilary A. Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Richard M. Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | | | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
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10
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Harrison H, Li N, Saunders CL, Rossi SH, Dennis J, Griffin SJ, Stewart GD, Usher‐Smith JA. The current state of genetic risk models for the development of kidney cancer: a review and validation. BJU Int 2022; 130:550-561. [PMID: 35460182 PMCID: PMC9790357 DOI: 10.1111/bju.15752] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. METHODS Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). RESULTS A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. CONCLUSIONS Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.
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Affiliation(s)
- Hannah Harrison
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Nicole Li
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
- Deanary of Biomedical SciencesUniversity of EdinburghEdinburghUK
| | | | - Sabrina H. Rossi
- Department of SurgeryUniversity of CambridgeAddenbrooke’s HospitalCambridgeUK
| | - Joe Dennis
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Simon J. Griffin
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Grant D. Stewart
- Department of SurgeryUniversity of CambridgeAddenbrooke’s HospitalCambridgeUK
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11
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Peng L, Cao Z, Wang Q, Fang L, Yan S, Xia D, Wang J, Bi L. Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis. Front Oncol 2022; 12:963483. [PMID: 36313709 PMCID: PMC9606658 DOI: 10.3389/fonc.2022.963483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 09/29/2022] [Indexed: 11/24/2022] Open
Abstract
Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene Expression Omnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What’s more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to the OS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping genes were AQP2 and KCNJ1. To sum up, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC.
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12
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Abhari RE, Thomson B, Yang L, Millwood I, Guo Y, Yang X, Lv J, Avery D, Pei P, Wen P, Yu C, Chen Y, Chen J, Li L, Chen Z, Kartsonaki C. External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank. BMC Med 2022; 20:302. [PMID: 36071519 PMCID: PMC9454206 DOI: 10.1186/s12916-022-02488-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/17/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population. This study aimed to assess the performance of risk scores in predicting CRC using the China Kadoorie Biobank (CKB), one of the largest and geographically diverse prospective cohort studies in China. METHODS Nine models were externally validated in 512,415 participants in CKB and included 2976 cases of CRC. Model discrimination was assessed, overall and by sex, age, site, and geographic location, using the area under the receiver operating characteristic curve (AUC). Model discrimination of these nine models was compared to a model using age alone. Calibration was assessed for five models, and they were re-calibrated in CKB. RESULTS The three models with the highest discrimination (Ma (Cox model) AUC 0.70 [95% CI 0.69-0.71]; Aleksandrova 0.70 [0.69-0.71]; Hong 0.69 [0.67-0.71]) included the variables age, smoking, and alcohol. These models performed significantly better than using a model based on age alone (AUC of 0.65 [95% CI 0.64-0.66]). Model discrimination was generally higher in younger participants, males, urban environments, and for colon cancer. The two models (Guo and Chen) developed in Chinese populations did not perform better than the others. Among the 10% of participants with the highest risk, the three best performing models identified 24-26% of participants that went on to develop CRC. CONCLUSIONS Several risk models based on easily obtainable demographic and modifiable lifestyle factor have good discrimination in a Chinese population. The three best performing models have a higher discrimination than using a model based on age alone.
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Affiliation(s)
- Roxanna E Abhari
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Blake Thomson
- Department of Surveillance and Health Equity Science, American Cancer Society, Atlanta, GA, USA
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Iona Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Pei Pei
- Chinese Academy of Medical Sciences, Building C, NCCD, Shilongxi Rd., Mentougou District, Beijing, 102308, China
| | - Peng Wen
- Maiji CDC, No. 29 Shangbu Road, Maiji, Tianshui, 741020, Gansu, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 37 Guangqu Road, Beijing, 100021, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK.
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
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Kidney Cancer Biomarker Selection Using Regularized Survival Models. Cells 2022; 11:cells11152311. [PMID: 35954157 PMCID: PMC9367278 DOI: 10.3390/cells11152311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 01/27/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC showing a significant percentage of mortality. One of the priorities of kidney cancer research is to identify RCC-specific biomarkers for early detection and screening of the disease. With the development of high-throughput technology, it is now possible to measure the expression levels of thousands of genes in parallel and assess the molecular profile of individual tumors. Studying the relationship between gene expression and survival outcome has been widely used to find genes associated with cancer survival, providing new information for clinical decision-making. One of the challenges of using transcriptomics data is their high dimensionality which can lead to instability in the selection of gene signatures. Here we identify potential prognostic biomarkers correlated to the survival outcome of ccRCC patients using two network-based regularizers (EN and TCox) applied to Cox models. Some genes always selected by each method were found (COPS7B, DONSON, GTF2E2, HAUS8, PRH2, and ZNF18) with known roles in cancer formation and progression. Afterward, different lists of genes ranked based on distinct metrics (logFC of DEGs or β coefficients of regression) were analyzed using GSEA to try to find over- or under-represented mechanisms and pathways. Some ontologies were found in common between the gene sets tested, such as nuclear division, microtubule and tubulin binding, and plasma membrane and chromosome regions. Additionally, genes that were more involved in these ontologies and genes selected by the regularizers were used to create a new gene set where we applied the Cox regression model. With this smaller gene set, we were able to significantly split patients into high/low risk groups showing the importance of studying these genes as potential prognostic factors to help clinicians better identify and monitor patients with ccRCC.
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14
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Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagn Progn Res 2022; 6:13. [PMID: 35794668 PMCID: PMC9261114 DOI: 10.1186/s41512-022-00126-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/07/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain. METHODS We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately. RESULTS We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation. CONCLUSIONS The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Louvain, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- EPI-Centre, KU Leuven, Louvain, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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15
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Beyer K, Moris L, Lardas M, Haire A, Barletta F, Scuderi S, Molnar M, Herrera R, Rauf A, Campi R, Greco I, Shiranov K, Dabestani S, van den Broeck T, Arun S, Gacci M, Gandaglia G, Omar MI, MacLennan S, Roobol MJ, Farahmand B, Vradi E, Devecseri Z, Asiimwe A, Zong J, Maclennan SJ, Collette L, NDow J, Briganti A, Bjartell A, Van Hemelrijck M. Diagnostic and prognostic factors in patients with prostate cancer: a systematic review. BMJ Open 2022; 12:e058267. [PMID: 35379637 PMCID: PMC8981333 DOI: 10.1136/bmjopen-2021-058267] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES As part of the PIONEER Consortium objectives, we have explored which diagnostic and prognostic factors (DPFs) are available in relation to our previously defined clinician and patient-reported outcomes for prostate cancer (PCa). DESIGN We performed a systematic review to identify validated and non-validated studies. DATA SOURCES MEDLINE, Embase and the Cochrane Library were searched on 21 January 2020. ELIGIBILITY CRITERIA Only quantitative studies were included. Single studies with fewer than 50 participants, published before 2014 and looking at outcomes which are not prioritised in the PIONEER core outcome set were excluded. DATA EXTRACTION AND SYNTHESIS After initial screening, we extracted data following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of prognostic factor studies (CHARMS-PF) criteria and discussed the identified factors with a multidisciplinary expert group. The quality of the included papers was scored for applicability and risk of bias using validated tools such as PROBAST, Quality in Prognostic Studies and Quality Assessment of Diagnostic Accuracy Studies 2. RESULTS The search identified 6604 studies, from which 489 DPFs were included. Sixty-four of those were internally or externally validated. However, only three studies on diagnostic and seven studies on prognostic factors had a low risk of bias and a low risk concerning applicability. CONCLUSION Most of the DPFs identified require additional evaluation and validation in properly designed studies before they can be recommended for use in clinical practice. The PIONEER online search tool for DPFs for PCa will enable researchers to understand the quality of the current research and help them design future studies. ETHICS AND DISSEMINATION There are no ethical implications.
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Affiliation(s)
- Katharina Beyer
- Translational and Oncology Research (TOUR), King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Lisa Moris
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Michael Lardas
- Department of Urology, Metropolitan Hospital, Athens, Greece
| | - Anna Haire
- Translational and Oncology Research (TOUR), King's College London, Faculty of Life Sciences and Medicine, London, UK
| | - Francesco Barletta
- Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Simone Scuderi
- Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | | | - Abdul Rauf
- Department of Urology, Mid Cheshire Hospitals, NHS Foundation Trust, Crewe, UK
| | - Riccardo Campi
- Department of Minimally Invasive and Robotic Urologic Surgery and Kidney Transplantation, University of Florence, Florence, Italy
| | - Isabella Greco
- Department of Minimally Invasive and Robotic Urologic Surgery and Kidney Transplantation, University of Florence, Florence, Italy
| | | | - Saeed Dabestani
- Dept. of Translational Medicine, Division of Urological Cancers, Lund University, Kristianstad Central Hospital, Malmo, Sweden
| | | | | | - Mauro Gacci
- Department of Minimally Invasive and Robotic Urologic Surgery and Kidney Transplantation, University of Florence, Florence, Italy
| | - Giorgio Gandaglia
- Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | | | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Jihong Zong
- Global Medical Affairs Oncology, Real World Evidence, Bayer HealthCare Pharmaceuticals Inc, Whippany, New Jersey, USA
| | | | | | - James NDow
- Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Alberto Briganti
- Unit of Urology/Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
- Department of Urology, University Vita e Salute-San Raffaele, Milan, Italy
| | - Anders Bjartell
- Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Mieke Van Hemelrijck
- Translational and Oncology Research (TOUR), King's College London, Faculty of Life Sciences and Medicine, London, UK
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16
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Screening programs for renal cell carcinoma: a systematic review by the EAU young academic urologists renal cancer working group. World J Urol 2022; 41:929-940. [PMID: 35362747 PMCID: PMC10160199 DOI: 10.1007/s00345-022-03993-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/12/2022] [Indexed: 11/27/2022] Open
Abstract
PURPOSE To systematically review studies focused on screening programs for renal cell carcinoma (RCC) and provide an exhaustive overview on their clinical impact, potential benefits, and harms. METHODS A systematic review of the recent English-language literature was conducted according to the European Association of Urology guidelines and the PRISMA statement recommendations (PROSPERO ID: CRD42021283136) using the MEDLINE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases. Risk-of-bias assessment was performed according to the QUality In Prognosis Studies (QUIPS) tool. RESULTS Overall, nine studies and one clinical trials were included. Eight studies reported results from RCC screening programs involving a total of 159 136 patients and four studies reported screening cost-analysis. The prevalence of RCC ranged between 0.02 and 0.22% and it was associated with the socio-demographic characteristics of the subjects; selection of the target population decreased, overall, the screening cost per diagnosis. CONCLUSIONS Despite an increasing interest in RCC screening programs from patients and clinicians there is a relative lack of studies reporting the efficacy, cost-effectiveness, and the optimal modality for RCC screening. Targeting high-risk individuals and/or combining detection of RCC with other health checks represent pragmatic options to improve the cost-effectiveness and reduce the potential harms of RCC screening.
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17
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Fitzgerald RC, Antoniou AC, Fruk L, Rosenfeld N. The future of early cancer detection. Nat Med 2022; 28:666-677. [PMID: 35440720 DOI: 10.1038/s41591-022-01746-x] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/22/2022]
Abstract
A proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival. However, as detection methods become increasingly sensitive, it can be difficult to distinguish inconsequential changes from lesions that will lead to life-threatening cancer. Progress relies on a detailed understanding of individualized risk, clear delineation of cancer development stages, a range of testing methods with optimal performance characteristics, and robust evaluation of the implications for individuals and society. In the future, advances in sensors, contrast agents, molecular methods, and artificial intelligence will help detect cancer-specific signals in real time. To reduce the burden of cancer on society, risk-based detection and prevention needs to be cost effective and widely accessible.
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Affiliation(s)
- Rebecca C Fitzgerald
- Early Detection Programme, Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health & Primary Care, University of Cambridge, Cambridge, UK
| | - Ljiljana Fruk
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Nitzan Rosenfeld
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
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18
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Harrison H, Pennells L, Wood A, Rossi SH, Stewart GD, Griffin SJ, Usher-Smith JA. Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank. BJU Int 2022; 129:498-511. [PMID: 34538014 DOI: 10.1111/bju.15598] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/20/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme. METHODS We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6-year risk: 0.1-1.0%). RESULTS In all, 10 models had reasonable discrimination (area under the receiver-operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age- and sex-based screening. All the models performed less well in women than men. CONCLUSIONS The present study is the first comprehensive external validation of risk models for kidney cancer. The best-performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme.
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Affiliation(s)
- Hannah Harrison
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Lisa Pennells
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Angela Wood
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sabrina H Rossi
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Simon J Griffin
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Juliet A Usher-Smith
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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19
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Campi R, Tellini R, Grosso AA, Pecoraro A, Mari A, Raspollini MR, Gacci M, Carini M, Serni S, Minervini A. Exploring the Diversity and Predictors of Histopathological Findings Across the European Association of Urology Guidelines Office Rapid Reaction Group Priority Groups for Patients with Renal Tumors: Implications for Individualized Prioritization of Renal Cancer Care. EUR UROL SUPPL 2021; 34:5-9. [PMID: 34761237 PMCID: PMC8567362 DOI: 10.1016/j.euros.2021.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 12/17/2022] Open
Abstract
In response to the COVID-19 pandemic, the European Association of Urology (EAU) Guidelines Office Rapid Reaction Group (GORRG) defined priority groups to guide the prioritization of surgery for nonmetastatic renal cell carcinoma (RCC). In this study we explored the diversity and predictors of histopathological findings across the EAU GORRG priority groups using a large database of 1734 consecutive patients undergoing elective surgery for nonmetastatic renal masses between 2017 and 2020 at a referral institution. Overall, 940 (54.2%), 358 (20.6%), and 436 (25.2%) patients were classified as low-, intermediate-, and high-priority, respectively. The low-, intermediate-, and high-risk groups significantly differed regarding all primary histopathological outcomes: benign histology (21.6% vs 15.9% vs 6.4%; p < 0.001); non–organ-confined disease (5.0% vs 19.0% vs 45.4%; p < 0.001); and adverse pathological features according to validated prognostic models (including the median Leibovich score for clear-cell RCC: 0 vs 2 vs 4; p < 0.001). On multivariable analysis, beyond the EAU GORRG priority groups, specific patient and/or tumor-related characteristics were independent predictors of the aforementioned histopathological outcomes. To the best of our knowledge, our study shows for the first time the value of the EAU GORRG priority groups from a histopathological standpoint and supports implementation of such a prioritization scheme beyond the COVID-19 pandemic. Patient summary During the COVID-19 pandemic, the European Association of Urology designed a scheme to prioritize patients needing surgery for kidney cancer according to their tumor characteristics and symptoms. We used results from our hospital database to test the scheme and found that the priority classification can be used to predict cancer outcomes after surgery. This scheme may be useful in prioritizing kidney cancer surgeries after the COVID-19 pandemic.
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Affiliation(s)
- Riccardo Campi
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.,Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Riccardo Tellini
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.,Unit of Urological Oncologic Minimally-Invasive Robotic Surgery and Andrology, University of Florence, Careggi Hospital, Florence, Italy
| | - Antonio Andrea Grosso
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.,Unit of Urological Oncologic Minimally-Invasive Robotic Surgery and Andrology, University of Florence, Careggi Hospital, Florence, Italy
| | - Alessio Pecoraro
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.,Unit of Urological Oncologic Minimally-Invasive Robotic Surgery and Andrology, University of Florence, Careggi Hospital, Florence, Italy
| | - Andrea Mari
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Unit of Urological Oncologic Minimally-Invasive Robotic Surgery and Andrology, University of Florence, Careggi Hospital, Florence, Italy
| | - Maria Rosaria Raspollini
- Histopathology and Molecular Diagnostics, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Mauro Gacci
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy
| | - Marco Carini
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Unit of Urological Oncologic Minimally-Invasive Robotic Surgery and Andrology, University of Florence, Careggi Hospital, Florence, Italy
| | - Sergio Serni
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.,Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Andrea Minervini
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Unit of Urological Oncologic Minimally-Invasive Robotic Surgery and Andrology, University of Florence, Careggi Hospital, Florence, Italy
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Dhiman P, Ma J, Navarro CA, Speich B, Bullock G, Damen JA, Kirtley S, Hooft L, Riley RD, Van Calster B, Moons KGM, Collins GS. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol 2021; 138:60-72. [PMID: 34214626 PMCID: PMC8592577 DOI: 10.1016/j.jclinepi.2021.06.024] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/15/2021] [Accepted: 06/25/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. RESULTS Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). CONCLUSION Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Constanza Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK; Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna Aa Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK. ST5 5BG
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.; EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
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