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Kawaguchi M, Kato H, Koie T, Noda Y, Hyodo F, Miyazaki T, Matsuo M. CT and MRI findings of small cell neuroendocrine carcinoma of the urinary bladder: comparison with urothelial carcinoma. Abdom Radiol (NY) 2024; 49:2672-2682. [PMID: 38584191 PMCID: PMC11300546 DOI: 10.1007/s00261-024-04274-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/27/2024] [Accepted: 03/01/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVE This study aimed to evaluate the efficacy of CT and MRI findings to differentiate small cell neuroendocrine carcinoma (SCNEC) from urothelial carcinoma (UC) of the urinary bladder. MATERIALS AND METHODS This study included 90 patients with histopathologically confirmed bladder cancer (10 SCNECs and 80 UCs). Eight patients with bladder SCNEC and 80 with UC underwent CT and MRI, whereas the remaining two patients with SCNEC underwent CT alone before treatment. CT and MRI findings were retrospectively evaluated and compared between the two pathologies. RESULTS The maximum diameter (36.5 mm vs. 19.0 mm, p < 0.01) and height (22.0 mm vs. 14.0 mm, p < 0.01) of the tumor in bladder SCNEC were higher than in UC. The pedunculated configuration (20% vs. 61%, p < 0.05) and irregular tumor margins (20% vs. 76%, p < 0.01) in bladder SCNEC were less common than in UC. The CT attenuation of the solid component in unenhanced CT images was higher in bladder SCNEC than in UC (37 Hounsfield unit [HU] vs. 34 HU, p < 0.01). The apparent diffusion coefficient (ADC) of the solid component in bladder SCNEC was lower than in UC (0.49 × 10-3 mm2/s vs. 1.02 × 10-3 mm2/s, p < 0.01). CONCLUSION In comparison with UC, bladder SCNEC was larger, had higher unenhanced CT attenuation, and had a lower ADC value. The pedunculated configuration and irregular tumor margins were typical of bladder UC.
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Affiliation(s)
- Masaya Kawaguchi
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
- Department of Radiology, Ogaki Municipal Hospital, 4-86 Minaminokawacho, Ogaki, 503-0864, Japan.
| | - Hiroki Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Takuya Koie
- Department of Urology, Gifu University, Gifu, Japan
| | - Yoshifumi Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Fuminori Hyodo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
- Center for One Medicine Innovative Translational Research (COMIT), Institute for Advanced Study, Gifu University, Gifu, Japan
| | | | - Masayuki Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
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Panwoon C, Seubwai W, Thanee M, Sangkhamanon S. Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning. PLoS One 2024; 19:e0305252. [PMID: 38857246 PMCID: PMC11164351 DOI: 10.1371/journal.pone.0305252] [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: 04/01/2024] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with therapeutic implications. This study aims to identify novel biomarkers to differentiate ccRCC from non-ccRCC using bioinformatics and machine learning. The gene expression profiles of ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe RCC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAVID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistochemistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly specific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC enriched in PD-1 signaling, immune system, and cytokine signaling in the immune system, whereas TCA cycle and respiratory, signaling by insulin receptor, and metabolism were enriched in chRCC. Feature selection based on Decision Tree Classifier revealed that the model with two genes, including NDUFA4L2 and DAT, had an accuracy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best performance with 82% accuracy and 0.9 AUC. NDUFA4L2 expression was associated with lymphovascular invasion, pathologic stage and pT stage in ccRCC. Using integrated bioinformatics and machine learning analysis, NDUFA4L2 and DAT were identified as novel biomarkers to differential diagnosis ccRCC from non-ccRCC.
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Affiliation(s)
- Chanita Panwoon
- Faculty of Medicine, Department of Pathology, Khon Kaen University, Khon Kaen, Thailand
| | - Wunchana Seubwai
- Faculty of Medicine, Department of Forensic Medicine, Khon Kaen University, Khon Kaen, Thailand
- Faculty of Medicine, Center for Translational Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Malinee Thanee
- Faculty of Medicine, Department of Pathology, Khon Kaen University, Khon Kaen, Thailand
| | - Sakkarn Sangkhamanon
- Faculty of Medicine, Department of Pathology, Khon Kaen University, Khon Kaen, Thailand
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Du L, Wang B, Wen J, Zhang N. No causal association between insomnia and bladder cancer: a bidirectional two-sample Mendelian randomization study. Eur J Med Res 2024; 29:316. [PMID: 38849949 PMCID: PMC11161941 DOI: 10.1186/s40001-024-01920-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 06/03/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Previous observational studies have indicated a potential link between insomnia and bladder cancer, yet the underlying causal relationship remains uncertain. The current study employed a bidirectional two-sample Mendelian randomization (MR) analysis to investigate this association. METHODS A two-sample MR analysis was conducted utilizing publicly available summary data from genome-wide association studies (GWAS) on insomnia and bladder cancer. Various regression methods including the inverse variance weighted (IVW), weighted median, MR-Egger, weighted mode, and simple mode methods were employed for the MR analysis. The presence of pleiotropy and heterogeneity in the MR results was also assessed. Furthermore, additional sensitivity tests were performed to mitigate potential biases. RESULTS No significant causal relationship was detected between insomnia and bladder cancer using IVW method (OR = 0.761, 95% CI 0.996-1.005; P = 0.76). Similarly, the IVW model did not reveal any causal effect of bladder cancer on the risk of insomnia (OR = 1.47, 95% CI 0.772-2.799; P = 0.24). Consistent results were obtained from the other four methods employed. There was no evidence of horizontal pleiotropy or heterogeneity in our MR analysis (P > 0.05). The sensitivity analyses further supported the reliability of the estimated causal effects. CONCLUSIONS This study presents no evidence for a causal relationship between insomnia and bladder cancer.
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Affiliation(s)
- Lihuan Du
- Department of Urology, The Second Affiliated Hospital of Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, China.
| | - Bohan Wang
- Department of Urology, The Second Affiliated Hospital of Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, China
| | - Jiaming Wen
- Department of Urology, The Second Affiliated Hospital of Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, China
| | - Nan Zhang
- Department of Urology, The Second Affiliated Hospital of Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, China
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Campobasso D, Panizzi M, Bellini V, Ferretti S, Amparore D, Castellani D, Fiori C, Puliatti S, Pietropaolo A, Somani BK, Micali S, Porpiglia F, Maestroni UV, Bignami EG. Application of AI in urolithiasis risk of infection: a scoping review. Minerva Urol Nephrol 2024; 76:295-302. [PMID: 38920010 DOI: 10.23736/s2724-6051.24.05686-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
INTRODUCTION Artificial intelligence and machine learning are the new frontier in urology; they can assist the diagnostic work-up and in prognostication bring superior to the existing nomograms. Infectious events and in particular the septic risk, are one of the most common and in some cases life threatening complication in patients with urolithiasis. We performed a scoping review to provide an overview of the current application of AI in prediction the infectious complications in patients affected by urolithiasis. EVIDENCE ACQUISITION A systematic scoping review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) guidelines by screening Medline, PubMed, and Embase to detect pertinent studies. EVIDENCE SYNTHESIS A total of 467 articles were found, of which nine met the inclusion criteria and were considered. All studies are retrospective and published between 2021 and 2023. Only two studies performed an external validation of the described models. The main event considered is urosepsis in four articles, urinary tract infection in two articles and diagnosis of infection stones in three articles. Different AI models were trained, each of which exploited several types and numbers of variables. All studies reveal good performance. Random forest and artificial neural networks seem to have higher AUC, specificity and sensibility and perform better than the traditional statistical analysis. CONCLUSIONS Further prospective and multi-institutional studies with external validation are needed to better clarify which variables and AI models should be integrated in our clinical practice to predict infectious events.
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Affiliation(s)
| | - Matteo Panizzi
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Stefania Ferretti
- Department of Urology, University of Modena e Reggio Emilia, Modena, Italy
| | - Daniele Amparore
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Daniele Castellani
- Department of Urology, Azienda Ospedaliera Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy
| | - Cristian Fiori
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Stefano Puliatti
- Department of Urology, University of Modena e Reggio Emilia, Modena, Italy
| | - Amelia Pietropaolo
- Department of Urology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Bhaskar K Somani
- Department of Urology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Salvatore Micali
- Department of Urology, University of Modena e Reggio Emilia, Modena, Italy
| | - Francesco Porpiglia
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | | | - Elena G Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
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Borna MR, Sepehri MM, Shadpour P, Khaleghi Mehr F. Enhancing bladder cancer diagnosis through transitional cell carcinoma polyp detection and segmentation: an artificial intelligence powered deep learning solution. Front Artif Intell 2024; 7:1406806. [PMID: 38873177 PMCID: PMC11169928 DOI: 10.3389/frai.2024.1406806] [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: 03/25/2024] [Accepted: 05/08/2024] [Indexed: 06/15/2024] Open
Abstract
Background Bladder cancer, specifically transitional cell carcinoma (TCC) polyps, presents a significant healthcare challenge worldwide. Accurate segmentation of TCC polyps in cystoscopy images is crucial for early diagnosis and urgent treatment. Deep learning models have shown promise in addressing this challenge. Methods We evaluated deep learning architectures, including Unetplusplus_vgg19, Unet_vgg11, and FPN_resnet34, trained on a dataset of annotated cystoscopy images of low quality. Results The models showed promise, with Unetplusplus_vgg19 and FPN_resnet34 exhibiting precision of 55.40 and 57.41%, respectively, suitable for clinical application without modifying existing treatment workflows. Conclusion Deep learning models demonstrate potential in TCC polyp segmentation, even when trained on lower-quality images, suggesting their viability in improving timely bladder cancer diagnosis without impacting the current clinical processes.
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Affiliation(s)
- Mahdi-Reza Borna
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Mehdi Sepehri
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Pejman Shadpour
- Hasheminejad Kidney Center (HKC), Iran University of Medical Sciences, Tehran, Iran
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Xiao S, Chen J, Wei Y, Song W. BHLHE41 inhibits bladder cancer progression via regulation of PYCR1 stability and thus inactivating PI3K/AKT signaling pathway. Eur J Med Res 2024; 29:302. [PMID: 38811952 PMCID: PMC11134742 DOI: 10.1186/s40001-024-01889-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND The basic helix-loop-helix family member e41 (BHLHE41) is frequently dysregulated in tumors and plays a crucial role in malignant progression of various cancers. Nevertheless, its specific function and underlying mechanism in bladder cancer (BCa) remain largely unexplored. METHODS The expression levels of BHLHE41 in BCa tissues and cells were examined by qRT-PCR and western blot assays. BCa cells stably knocking down or overexpressing BHLHE41 were constructed through lentivirus infection. The changes of cell proliferation, cell cycle distribution, migration, and invasion were detected by CCK-8, flow cytometry, wound healing, transwell invasion assays, respectively. The expression levels of related proteins were detected by western blot assay. The interaction between BHLHE41 and PYCR1 was explored by co-immunoprecipitation analysis. RESULTS In this study, we found that BHLHE41 was lowly expressed in bladder cancer tissues and cell lines, and lower expression of BHLHE41 was associated with poor overall survival in bladder cancer patients. Functionally, by manipulating the expression of BHLHE41, we demonstrated that overexpression of BHLHE41 significantly retarded cell proliferation, migration, invasion, and induced cell cycle arrest in bladder cancer through various in vitro and in vivo experiments, while silence of BHLHE41 caused the opposite effect. Mechanistically, we showed that BHLHE41 directly interacted with PYCR1, decreased its stability and resulted in the ubiquitination and degradation of PYCR1, thus inactivating PI3K/AKT signaling pathway. Rescue experiments showed that the effects induced by BHLHE41 overexpression could be attenuated by further upregulating PYCR1. CONCLUSION BHLHE41 might be a useful prognostic biomarker and a tumor suppressor in bladder cancer. The BHLHE41/PYCR1/PI3K/AKT axis might be a potential therapeutic target for bladder cancer intervention.
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Affiliation(s)
- Shuai Xiao
- Department of Urology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, 410011, China
| | - Junjie Chen
- Department of Urology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, 410011, China
| | - Yongbao Wei
- Department of Urology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, 350001, China.
| | - Wei Song
- Department of Urology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, 410011, China.
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Zhang W, Chen XS, Wei Y, Wang XM, Chen XJ, Chi BT, Huang LQ, He RQ, Huang ZG, Li Q, Chen G, He J, Wu M. Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer. Eur J Med Res 2024; 29:257. [PMID: 38689322 PMCID: PMC11059691 DOI: 10.1186/s40001-024-01844-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND This study aimed to explore the expression, molecular mechanism and its biological function of potassium two pore domain channel subfamily K member 1 (KCNK1) in bladder cancer (BC). METHODS We integrated large numbers of external samples (n = 1486) to assess KCNK1 mRNA expression levels and collected in-house samples (n = 245) for immunohistochemistry (IHC) experiments to validate at the KCNK1 protein level. Single-cell RNA sequencing (scRNA-seq) analysis was performed to further assess KCNK1 expression and cellular communication. The transcriptional regulatory mechanisms of KCNK1 expression were explored by ChIP-seq, ATAC-seq and ChIA-PET data. Highly expressed co-expressed genes (HECEGs) of KCNK1 were used to explore potential signalling pathways. Furthermore, the immunoassay, clinical significance and molecular docking of KCNK1 were calculated. RESULTS KCNK1 mRNA was significantly overexpressed in BC (SMD = 0.58, 95% CI [0.05; 1.11]), validated at the protein level (p < 0.0001). Upregulated KCNK1 mRNA exhibited highly distinguishing ability between BC and control samples (AUC = 0.82 [0.78-0.85]). Further, scRNA-seq analysis revealed that KCNK1 expression was predominantly clustered in BC epithelial cells and tended to increase with cellular differentiation. BC epithelial cells were involved in cellular communication mainly through the MK signalling pathway. Secondly, the KCNK1 transcription start site (TSS) showed promoter-enhancer interactions in three-dimensional space, while being transcriptionally regulated by GRHL2 and FOXA1. Most of the KCNK1 HECEGs were enriched in cell cycle-related signalling pathways. KCNK1 was mainly involved in cellular metabolism-related pathways and regulated cell membrane potassium channel activity. KCNK1 expression was associated with the level of infiltration of various immune cells. Immunotherapy and chemotherapy (docetaxel, paclitaxel and vinblastine) were more effective in BC patients in the high KCNK1 expression group. KCNK1 expression correlated with age, pathology grade and pathologic_M in BC patients. CONCLUSIONS KCNK1 was significantly overexpressed in BC. A complex and sophisticated three-dimensional spatial transcriptional regulatory network existed in the KCNK1 TSS and promoted the upregulated of KCNK1 expression. The high expression of KCNK1 might be involved in the cell cycle, cellular metabolism, and tumour microenvironment through the regulation of potassium channels, and ultimately contributed to the deterioration of BC.
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Affiliation(s)
- Wei Zhang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xiao-Song Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Ying Wei
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xiao-Min Wang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xian-Jin Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Bang-Teng Chi
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Lin-Qing Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Rong-Quan He
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Zhi-Guang Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Qi Li
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Juan He
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
| | - Mei Wu
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong RD, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
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Pan W, Liu X, Liu S. ALYREF m5C RNA methylation reader predicts bladder cancer prognosis by regulating the tumor immune microenvironment. Medicine (Baltimore) 2024; 103:e37590. [PMID: 38579085 PMCID: PMC10994465 DOI: 10.1097/md.0000000000037590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/22/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND 5-Methylcytidine (m5C) methylation is a recently emerging epigenetic modification that is closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the clinicopathological characteristics and prognostic value of m5C regulators in bladder cancer (BLCA), and their correlation with the tumor immune microenvironment. METHODS Thirteen m5C RNA methylation regulators were analyzed using RNA-sequencing and corresponding clinical information obtained from the TCGA database. The Cluster Profiler package was used to analyze the gene ontology function of potential targets and enriched the Kyoto Encyclopedia of Genes and Genomes pathway. Kaplan-Meier survival analysis was used to compare survival differences using the log-rank test and univariate Cox proportional hazards regression. The correlation between signature prognostic m5C regulators and various immune cells was analyzed. Univariate and multivariate Cox regression analyses identified independence of the ALYREF gene signature. RESULTS Nine out of the 13 m5C RNA methylation regulators were differentially expressed in BLCA and normal samples and were co-expressed. These 9 regulators were associated with clinicopathological tumor characteristics, particularly high or low tumor risk, pT or pTNM stage, and migration. Consensus clustering analysis divides the BLCA samples into 4 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment annotation and gene ontology function analysis identified 273 upregulated and 594 downregulated genes in BLCA. Notably, only ALYREF was significantly correlated with OS (P < .05). ALYREF exhibited significant infiltration levels in macrophage cells. Therefore, we constructed a nomogram for ALYREF as an independent prognostic factor. Additionally, we observed that both the mRNA and protein levels of ALYREF were upregulated, and immunofluorescence showed that ALYREF was mainly distributed in nuclear speckles. ALYREF overexpression was significantly associated with poor OS. CONCLUSION Our findings demonstrated the potential of ALYREF to predict clinical prognostic risks in BLCA patients and regulate the tumor immune microenvironment. As such, ALYREF may serve as a novel prognostic indicator in BLCA patients.
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Affiliation(s)
- Wengu Pan
- Kidney Transplantation of The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Kidney Transplantation, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Jinan, China
| | - Xiaoli Liu
- Kidney Transplantation of The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Kidney Transplantation, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Jinan, China
| | - Shuangde Liu
- Kidney Transplantation of The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Kidney Transplantation, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Jinan, China
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Yu SH, Kim SS, Kim S, Lee H, Kang TW. FGFR3 Mutations in Urothelial Carcinoma: A Single-Center Study Using Next-Generation Sequencing. J Clin Med 2024; 13:1305. [PMID: 38592174 PMCID: PMC10931944 DOI: 10.3390/jcm13051305] [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: 01/20/2024] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Mutations of fibroblast growth factor receptor 3 (FGFR3) are associated with urothelial carcinoma (UC) oncogenesis and are considered an important therapeutic target. Therefore, we evaluated the FGFR3 mutation rate and its clinical significance in urothelial carcinoma (UC) using next-generation sequencing. Methods: A total of 123 patients with UC who were treated at Chonnam National University Hospital (Gwang-ju, Korea) from January 2018 to December 2020 were enrolled. We performed NGS using the Oncomine panel with tumor specimens and blood samples corresponding to each specimen. We analyzed the FGFR3 mutation results according to the type of UC and the effects on early recurrence and progression. Results: The mean age of the patients was 71.39 ± 9.33 years, and 103 patients (83.7%) were male. Overall, the FGFR3 mutation rate was 30.1% (37 patients). The FGFR3 mutation rate was the highest in the non-muscle-invasive bladder cancer (NMIBC) group (45.1%), followed by the muscle-invasive bladder cancer (22.7%) and upper tract UC (UTUC) (14.3%) groups. Patients with FGFR3 mutations had a significantly lower disease stage (p = 0.019) but a high-risk of NMIBC (p < 0.001). Conclusions: Our results revealed that FGFR3 mutations were more prevalent in patients with NMIBC and lower stage UC and associated with a high-risk of NMIBC. Large multicenter studies are needed to clarify the clinical significance of FGFR3 mutations in UC.
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Affiliation(s)
- Seong Hyeon Yu
- Department of Urology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju 61469, Republic of Korea;
| | - Sung sun Kim
- Department of Pathology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju 61469, Republic of Korea;
| | - Shinseung Kim
- MediCloud Corporation, Hwasun 58128, Republic of Korea; (S.K.); (H.L.)
| | - Hyungki Lee
- MediCloud Corporation, Hwasun 58128, Republic of Korea; (S.K.); (H.L.)
| | - Taek Won Kang
- Department of Urology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju 61469, Republic of Korea;
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Shi S, Peng G, Luo L, Li D. Predictive nomograms for risk and prognostic factors in metastatic bladder cancer: a population-based study. Transl Cancer Res 2023; 12:3284-3302. [PMID: 38192983 PMCID: PMC10774037 DOI: 10.21037/tcr-23-1229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/08/2023] [Indexed: 01/10/2024]
Abstract
Background Given the poor prognosis of patients with metastatic bladder cancer (MBC), the development of an effective diagnostic and prognostic model is significant in cancer management and for guidance in clinical practice. Methods We acquired data of 23,180 bladder cancer patients from Surveillance Epidemiology and End Results (SEER) database registered from 2010 to 2019. The optimal cut-off value for patient age and tumor size was determined by x-tile software. Independent risk factors for MBC were identified by univariate and multivariate logistic regression analyses and prognosis factors were identified by univariate and multivariate cox regression analyses, and risk and prognostic nomograms were constructed. The accuracy of the nomograms was verified by receiver operating characteristic (ROC) curves, calibration curves, and its clinical utility was determined by decision curve analysis (DCA) curves and clinical impact curves (CIC). Kaplan-Meier (K-M) survival curves further confirmed the clinical validity of the prognostic model. Results Through logistic regression analyses, we derived that age, histological type, tumor size, T stage, and N stage were independent risk factors for metastasis in bladder cancer patients. By cox regression analyses, age, chemotherapy, histological type, bone, lung and liver metastases were identified as risk factors influencing prognosis of MBC patients. Area under the curve (AUC) of the risk nomogram was 0.80, the AUC values of 1/2/3 years were 0.74/0.71/0.71 in the training group and 0.81/0.77/0.77 in the validation group. Based on calibration curves, DCA curves, CIC and K-M curves, the nomograms were validated with excellent predictive performance and clinical utility for MBC. Conclusions The nomograms we constructed have perfect predictive accuracy and clinical practicality for MBC patients, enabling clinicians to provide treatment advice and clinical guidance to patients.
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Affiliation(s)
- Shuibo Shi
- Department of Urology, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Guangbei Peng
- Children’s Medical Center of Jiangxi Province, Nanchang, China
| | - Longhua Luo
- Department of Urology, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Dongshui Li
- Department of Urology, the First Affiliated Hospital of Nanchang University, Nanchang, China
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Kuang A, Kouznetsova VL, Kesari S, Tsigelny IF. Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics. Metabolites 2023; 14:11. [PMID: 38248814 PMCID: PMC10818630 DOI: 10.3390/metabo14010011] [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: 10/27/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.
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Affiliation(s)
- Alyssa Kuang
- Haas Business School, University of California at Berkeley, Berkeley, CA 94720, USA;
| | - Valentina L. Kouznetsova
- San Diego Supercomputer Center, University of California at San Diego, La Jolla, CA 92093, USA;
- BiAna, La Jolla, CA 92038, USA
- CureScience Institute, San Diego, CA 92121, USA
| | - Santosh Kesari
- Pacific Neuroscience Institute, Santa Monica, CA 90404, USA;
| | - Igor F. Tsigelny
- San Diego Supercomputer Center, University of California at San Diego, La Jolla, CA 92093, USA;
- BiAna, La Jolla, CA 92038, USA
- CureScience Institute, San Diego, CA 92121, USA
- Department of Neurosciences, University of California at San Diego, La Jolla, CA 92093, USA
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Iacobas DA, Obiomon EA, Iacobas S. Genomic Fabrics of the Excretory System's Functional Pathways Remodeled in Clear Cell Renal Cell Carcinoma. Curr Issues Mol Biol 2023; 45:9471-9499. [PMID: 38132440 PMCID: PMC10742519 DOI: 10.3390/cimb45120594] [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: 10/19/2023] [Revised: 11/18/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most frequent form of kidney cancer. Metastatic stages of ccRCC reduce the five-year survival rate to 15%. In this report, we analyze the ccRCC-induced remodeling of the five KEGG-constructed excretory functional pathways in a surgically removed right kidney and its metastasis in the chest wall from the perspective of the Genomic Fabric Paradigm (GFP). The GFP characterizes every single gene in each region by these independent variables: the average expression level (AVE), relative expression variability (REV), and expression correlation (COR) with each other gene. While the traditional approach is limited to only AVE analysis, the novel REV analysis identifies the genes whose correct expression level is critical for cell survival and proliferation. The COR analysis determines the real gene networks responsible for functional pathways. The analyses covered the pathways for aldosterone-regulated sodium reabsorption, collecting duct acid secretion, endocrine and other factor-regulated sodium reabsorption, proximal tubule bicarbonate reclamation, and vasopressin-regulated water reabsorption. The present study confirms the conclusion of our previously published articles on prostate and kidney cancers that even equally graded cancer nodules from the same tumor have different transcriptomic topologies. Therefore, the personalization of anti-cancer therapy should go beyond the individual, to his/her major cancer nodules.
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Affiliation(s)
- Dumitru Andrei Iacobas
- Personalized Genomics Laboratory, Undergraduate Medical Academy, Prairie View A&M University, Prairie View, TX 77446, USA;
| | - Ehiguese Alade Obiomon
- Personalized Genomics Laboratory, Undergraduate Medical Academy, Prairie View A&M University, Prairie View, TX 77446, USA;
| | - Sanda Iacobas
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA;
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Hu R, Li X, Zhou X, Ding S. Development and validation of a competitive risk model in patients with rectal cancer: based on SEER database. Eur J Med Res 2023; 28:362. [PMID: 37735712 PMCID: PMC10515244 DOI: 10.1186/s40001-023-01357-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 09/10/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Rectal cancer is one of the most common malignancies. To predict the specific mortality risk of rectal cancer patients, we constructed a predictive nomogram based on a competing risk model. METHODS The information on rectal cancer patients was extracted from the SEER database. Traditional survival analysis and specific death analysis were performed separately on the data. RESULTS The present study included 23,680 patients, with 16,580 in the training set and 7100 in the validation set. The specific mortality rate calculated by the competing risk model was lower than that of the traditional survival analysis. Age, Marriage, Race, Sex, ICD-O-3Hist/Behav, Grade, AJCC stage, T stage, N stage, Surgery, Examined LN, RX SUMM-SURG OTH, Chemotherapy, CEA, Deposits, Regional nodes positive, Brain, Bone, Liver, Lung, Tumor size, and Malignant were independent influencing factors of specific death. The overall C statistic of the model in the training set was 0.821 (Se = 0.001), and the areas under the ROC curve for cancer-specific survival (CSS) at 1, 3, and 5 years were 0.842, 0.830, and 0.812, respectively. The overall C statistic of the model in the validation set was 0.829 (Se = 0.002), and the areas under the ROC curve for CSS at 1, 3, and 5 years were 0.851, 0.836, and 0.813, respectively. CONCLUSIONS The predictive nomogram based on a competing risk model for time-specific mortality in patients with rectal cancer has very desirable accuracy. Thus, the application of the predictive nomogram in clinical practice can help physicians make clinical decisions and follow-up strategies.
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Affiliation(s)
- Ruobing Hu
- Department of Gastroenterology and Hepatology, People's Hospital of Zhengzhou University, No.7 Weiwu Road, Zhengzhou, 450003, Henan, China
| | - Xiuling Li
- Department of Gastroenterology and Hepatology, People's Hospital of Zhengzhou University, No.7 Weiwu Road, Zhengzhou, 450003, Henan, China
| | - Xiaomin Zhou
- Department of Infection Disease, Shanghai Jinshan District Tinglin Hospital, Shanghai, 201505, China
| | - Songze Ding
- Department of Gastroenterology and Hepatology, People's Hospital of Zhengzhou University, No.7 Weiwu Road, Zhengzhou, 450003, Henan, China.
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