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Non-clear cell renal cell carcinoma (non-ccRCC) is a highly heterogeneous disease group, accounting for approximately 25% of all RCC cases. Due to its rarity and especially heterogeneity, phase III trial data is limited and treatment options generally follow those of clear cell RCC. In the literature, there exists a number of studies with sunitinib, cabozantinib, and everolimus, but data on the efficacy of pazopanib are limited. Our aim in this study was to compare the efficacy of pazopanib and sunitinib, in a multicenter retrospective cohort of non-ccRCC patients. Our study included patients diagnosed with non-ccRCC who received pazopanib or sunitinib treatment as first-line therapy from 22 tertiary hospitals. We compared the progression-free survival (PFS), overall survival (OS), and response rates of pazopanib and sunitinib treatments. Additionally, we investigated prognostic factors in non-ccRCC. PFS and response rates of sunitinib and pazopanib were found to be similar, while a numerical difference was observed in OS. Being 65 years and older, being in the intermediate or poor risk group according to the International Metastatic Renal Cell Carcinoma Database Consortium, having liver metastases, presence of a sarcomatoid component, and having de novo metastatic disease were found to be significantly associated with shorter PFS. Pazopanib treatment appears to have similar efficacy in the treatment of non-ccRCC compared to sunitinib. Though randomized controlled trials are lacking and will probably be never be available, we suggest that pazopanib could be a preferred agent like sunitinib and cabozantinib.

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Pazopanib and sunitinib treatments show similar progression free survival, overall survival and objective response rate.IMDC risk group, liver metastasis, sarcomatoid component and de novo metastatic disease were determined as prognostic factors.

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BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC. PURPOSE: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets. METHODS: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients. RESULTS: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients. CONCLUSIONS: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

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Biomarcadores de Tumor , Carcinoma de Células Renales , Regulación Neoplásica de la Expresión Génica , Neoplasias Renales , Fosfatidilinositol 3-Quinasas , Proteínas Proto-Oncogénicas c-akt , Transducción de Señal , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/mortalidad , Proteínas Proto-Oncogénicas c-akt/genética , Proteínas Proto-Oncogénicas c-akt/metabolismo , Pronóstico , Neoplasias Renales/genética , Neoplasias Renales/mortalidad , Neoplasias Renales/patología , Fosfatidilinositol 3-Quinasas/genética , Fosfatidilinositol 3-Quinasas/metabolismo , Biomarcadores de Tumor/genética , Transducción de Señal/genética , Masculino , Femenino , Biología Computacional , Perfilación de la Expresión Génica , Bases de Datos Genéticas , Persona de Mediana Edad , Estimación de Kaplan-Meier

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Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and has a poor prognosis. Despite the impressive advancements in treating ccRCC using immune checkpoint (IC) blockade, such as PD-1/PD-L1 inhibitors, a considerable number of ccRCC patients experience adaptive resistance. Therefore, exploring new targetable ICs will provide additional treatment options for ccRCC patients. We comprehensively analyzed multi-omics data and performed functional experiments, such as pathologic review, bulk transcriptome data, single-cell sequencing data, Western blotting, immunohistochemistry and in vitro/in vivo experiments, to explore novel immunotherapeutic targets in ccRCC. It was found that immune-related genes VSIG4, SAA1, CD7, FOXP3, IL21, TNFSF13B, BATF, CD72, MZB1, LTB, CCL25 and KLRK1 were significantly upregulated in ccRCC (Student's t test and p-value < 0.05; 36 normal and 267 ccRCC tissues in raining cohort; 36 normal and 266 ccRCC tissues in validation cohort) and correlated with the poor prognosis of ccRCC patients (Wald test and p-value < 0.05 in univariate cox analysis; log-rank test and p-value < 0.05 in Kaplan-Meier method; 267 patients in training cohort and 266 in validation cohort). In particular, we found the novel IC target VSIG4 was specifically expressed in inhibitory immune cells M2-biased tumor-associated macrophages (TAMs), conventional dendritic cell 2 (cDC2) cells, and cycling myeloid cells in ccRCC microenvironment. Moreover, VSIG4 showed a closely relation with resistance of Ipilimumab/Nivolumab immunotherapy in ccRCC. Furthermore, VSIG4 promoted the infiltration of M2 macrophages, Tregs, and cDC2 in ccRCC tissues. VSIG4+ TAMs and VSIG4+ cDC2s may be a kind of immune cell subtypes related to immunosuppression. VSIG4 may play similar roles with other IC ligands, as it is highly expressed on the surface of antigen-presenting cells and ccRCC cells to inhibit T cells activity and facilitate immune escape. Targeting IC gene VSIG4 may provide a novel immunotherapeutic strategy to ccRCC patients with resistance to existing targeted therapy options.

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OBJECTIVE: To assess the distribution of key mutations across tumour sizes in clear-cell renal cell carcinoma (ccRCC), and secondarily to examine the prognostic impact of aggressive mutations in smaller ccRCCs. PATIENT AND METHODS: The distribution of mutations (VHL, PBRM1, SETD2, BAP1 and CDKN2A loss) across tumour sizes was assessed in 1039 ccRCCs treated with nephrectomy in cohorts obtained from the Tracking Cancer Evolution (TRACERx), The Cancer Genome Atlas (TCGA) and the Cancer Genomics of the Kidney (CAGEKID) projects. Logistic regression was used to model the presence of each mutation against size. In our secondary analysis, we assessed a subset of ccRCCs ≤7 cm for associations of key aggressive mutations (SETD2, BAP1, and CDKN2A loss) with metastasis, invasive disease and overall survival, while controlling for size. A subset of localised tumours ≤7 cm was also used to assess associations with recurrence after nephrectomy. RESULTS: On logistic regression, each 1-cm increase in tumour size was associated with aggressive mutations, SETD2, BAP1, and CDKN2A loss, at odds ratios (ORs) of 1.09, 1.10 and 1.19 (P < 0.001), whereas no significant association was observed between tumour size and PBRM1 (OR 1.02; P = 0.23). VHL was mildly negatively associated with a 1-cm increase in size (OR 0.95; P = 0.01). Among tumours ≤7 cm, SETD2 and CDKN2A loss were associated with metastatic disease at ORs of 3.86 and 3.84 (P < 0.05) while controlling for tumour size. CDKN2A loss was associated with worse overall survival, with a hazard ratio (HR) of 2.19 (P = 0.03). Among localised tumours ≤7 cm, SETD2 was associated with worse recurrence-free survival (HR 2.00; P = 0.03). CONCLUSION: Large and small ccRCCs are genomically different. Aggressive mutations, namely, SETD2, BAP1, and CDKN2A loss, are rarely observed in small ccRCCs and are observed more frequently in larger tumours. However, when present in tumours ≤7 cm, SETD2 mutations and CDKN2A loss were still independently associated with invasive disease, metastasis, worse survival, and recurrence after resection, after controlling for size.

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Emerging evidence suggests that the APOBEC family is implicated in multiple cancers and might be utilized as a new target for cancer detection and treatment. However, the dysregulation and clinical implication of the APOBEC family in clear cell renal cell cancer (ccRCC) remain elusive. TCGA multiomics data facilitated a comprehensive exploration of the APOBEC family across cancers, including ccRCC. Remodeling analysis classified ccRCC patients into two distinct subgroups: APOBEC family pattern cancer subtype 1 (APCS1) and subtype 2 (APCS2). The study investigated differences in clinical parameters, tumor immune microenvironment, therapeutic responsiveness, and genomic mutation landscapes between these subtypes. An APOBEC family-related risk model was developed and validated for predicting ccRCC patient prognosis, demonstrating good sensitivity and specificity. Finally, the overview of APOBEC3B function was investigated in multiple cancers and verified in clinical samples. APCS1 and APCS2 demonstrated considerably distinct clinical features and biological processes in ccRCC. APCS1, an aggressive subtype, has advanced clinical stage and a poor prognosis. APCS1 exhibited an oncogenic and metabolically active phenotype. APCS1 also exhibited a greater tumor mutation load and immunocompromised condition, resulting in immunological dysfunction and immune checkpoint treatment resistance. The genomic copy number variation of APCS1, including arm gain and loss, was much more than that of APCS2, which may help explain the tired immune system. Furthermore, the two subtypes have distinct drug sensitivity patterns in clinical specimens and matching cell lines. Finally, we developed a predictive risk model based on subtype biomarkers that performed well for ccRCC patients and validated the clinical impact of APOBEC3B. Aberrant APOBEC family expression patterns might modify the tumor immune microenvironment by increasing the genome mutation frequency, thus inducing an immune-exhausted phenotype. APOBEC family-based molecular subtypes could strengthen the understanding of ccRCC characterization and guide clinical treatment. Targeting APOBEC3B may be regarded as a new therapeutic target for ccRCC.

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Desaminasas APOBEC , Carcinoma de Células Renales , Neoplasias Renales , Microambiente Tumoral , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/inmunología , Carcinoma de Células Renales/patología , Microambiente Tumoral/inmunología , Microambiente Tumoral/genética , Neoplasias Renales/genética , Neoplasias Renales/inmunología , Neoplasias Renales/patología , Desaminasas APOBEC/genética , Pronóstico , Mutación , Antígenos de Histocompatibilidad Menor/genética , Biomarcadores de Tumor/genética

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Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.

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Background: Clear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer associated with higher death rates. However, traditional anti-angiogenic therapies have limited effectiveness due to drug resistance. Vascular mimicry (VM) provides a different way for tumors to develop blood vessels without relying on endothelial cells or angiogenesis. However, the intricate mechanisms and interplay between it and the immune microenvironment in ccRCC remain unclear. Methods: A PubMed and GeneCards literature review was conducted to identify VM-related genes (VMRGs). VMRGs expression profiles were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), developing a novel VM risk score model and nomogram for ccRCC. The EBI ArrayExpress database (the validation set) was obtained to validate the prognostic model. The relationship between VMRGs risk score clinical characteristics and immune infiltration was investigated. Finally, the expression of six model VMRGs was validated using single-cell analysis, GEPIA, Human Protein Atlas (HPA), and quantitative Real-time PCR (qRT-PCR). Results: Cox regression analysis and nomogram identified L1CAM, TEK, CLDN4, EFNA1, SERPINF1, and MALAT1 as independent prognostic risk factors, which could be used to stratify the ccRCC population into two risk groups with distinct immune profiles and responsiveness to immunotherapy. The results of single-cell analysis, GEPIA, HPA, and qRT-PCR validated the model genes' expression. Conclusions: Our novel findings constructed a convenient and reliable 6 gene signatures as potential immunologic and prognostic biomarkers of VM in ccRCC.

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BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a serious threat to human life. It is very important to clarify the pathogenesis of ccRCC. In this study we evaluated the clinical value of HADH and explored its role and mechanism in the malignant progression of ccRCC. METHODS: HADH expression and its relationship with prognosis were analyzed using bioinformatics database. RT-PCR, Western blot and immunohistochemistry were used to examine the expression of HADH in ccRCC tissues and tissue microarrays. To examine the cell proliferation, apoptosis, migration and invasion ability, ccRCC cells with HADH overexpressed were constructed. Xenograft experiments were performed to determine the role of HADH. Non-target metabolomics was applied to explore the potential metabolic pathway by which HADH inhibited ccRCC progression. Plasmid pcDNA3.1-NRF2 was used to confirm whether HADH inhibited the process of ccRCC cells through NRF2-related glutathione (GSH) synthesis. RESULTS: Bioinformatics database analysis showed that HADH expression was significantly decreased in ccRCC tissues, and its low expression predicted a poor prognosis. Both ccRCC tissues and tissue microarrays exhibited a significantly decreased HADH level compared with adjacent normal renal tissues. HADH overexpression inhibited the malignant behaviors of ccRCC cells. Furthermore, HADH overexpression attenuated GSH synthesis and induced oxidative stress damage. Exogenously increased NRF2 effectively attenuated the inhibitive effect of HADH overexpression on ccRCC cells. CONCLUSION: Our data revealed that HADH suppressed the malignant behaviors of ccRCC cells by attenuating GSH synthesis through inhibition of NRF2 nuclear translocation, and HADH might be a novel therapeutic target for ccRCC treatment.

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BACKGROUND: Clear cell renal cell carcinoma (ccRCC) remains one of the most lethal urological malignancies even though a great number of improvements in diagnosis and management have achieved over the past few decades. Accumulated evidence revealed that histone deacetylases (HDACs) play vital role in cell proliferation, differentiation and apoptosis. Nevertheless, the biological functions of histone deacetylation modification related genes in ccRCC remains poorly understood. METHOD: Bulk transcriptomic data and clinical information of ccRCC patients were obtained from the TCGA database and collected from the Chinese PLA General Hospital. A total of 36 histone deacetylation genes were selected and studied in our research. Univariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, random forest (RF) analysis, and protein-protein interaction (PPI) network analysis were applied to identify key genes affecting the prognosis of ccRCC. The 'oncoPredict' algorithm was utilized for drug-sensitive analysis. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore the potential biological function. The ssGSEA algorithm was used for tumor immune microenvironment analysis. The expression levels of HDAC10 were validated by RT-PCR and immunohistochemistry (IHC). 5-ethynyl-2'-deoxyuridine (EdU assay), CCK-8 assay, cell transwell migration and invasion assay and colony formation assay were performed to detect the proliferation and invasion ability of ccRCC cells. A nomogram incorporating HDAC10 and clinicopathological characteristics was established to predict the prognosis of ccRCC patients. RESULT: Two machine learning algorithms and PPI analysis identified four histone deacetylation genes that have a significant association with the prognosis of ccRCC, with HDAC10 being the key gene among them. HDAC10 is highly expressed in ccRCC and its high expression is associated with poor prognosis for ccRCC patients. Pathway enrichment and the experiments of EdU staining, CCK-8 assay, cell transwell migration and invasion assay and colony formation assay demonstrated that HDAC10 mediated the proliferation and metastasis of ccRCC cells and involved in reshaping the tumor microenvironment (TME) of ccRCC. A clinically reliable prognostic predictive model was established by incorporating HDAC10 and other clinicopathological characteristics ( https://nomogramhdac10.shinyapps.io/HDAC10_Nomogram/ ). CONCLUSION: Our study found the increased expression of HDAC10 was closely associated with poor prognosis of ccRCC patients. HDAC10 showed a pro-tumorigenic effect on ccRCC and promote the proliferation and metastasis of ccRCC, which may provide new light on targeted therapy for ccRCC.

Asunto(s)

Carcinoma de Células Renales , Proliferación Celular , Histona Desacetilasas , Neoplasias Renales , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Humanos , Neoplasias Renales/genética , Neoplasias Renales/patología , Proliferación Celular/genética , Histona Desacetilasas/genética , Histona Desacetilasas/metabolismo , Masculino , Femenino , Persona de Mediana Edad , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Regulación Neoplásica de la Expresión Génica , Movimiento Celular/genética , Pronóstico , Microambiente Tumoral/genética , Línea Celular Tumoral , Mapas de Interacción de Proteínas , Oncogenes/genética , Anciano

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Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignancy of the urinary system. Despite the integration of immune checkpoint inhibitors (ICIs) into the treatment paradigm for advanced RCC, resistance to immunotherapy has emerged as a pivotal determinant impacting the clinical outlook of ccRCC. Accumulating evidence underscores the pivotal role of immune evasion-related genes and pathways in enabling tumor escape from host immune surveillance, consequently influencing patients' responsiveness to immunotherapy. Nonetheless, the clinical relevance of immune evasion-related genes in ccRCC patients undergoing immunotherapy remains inadequately understood. In this study, we aggregated RNA sequencing and clinical data from ccRCC patients across three cohorts: the Cancer Genome Atlas (TCGA), CheckMate cohorts, and the JAVELIN Renal 101 trial. Leveraging a curated immune evasion-related gene set from Lawson et al., we employed the LASSO algorithm and Cox regression analysis to identify eight genes (LPAR6, RGS5, NFYC, PCDH17, CENPW, CNOT8, FOXO3, SNRPB) significantly associated with immune therapy prognosis (HR, 3.57; 95 % CI, 2.38-5.35; P<0.001). A predictive algorithm developed utilizing these genes exhibited notable accuracy in forecasting patients' progression-free survival in the training set (AUC, 0.835). Furthermore, stratification of patients by risk score revealed discernible differences in immunotherapy response and tumor microenvironment. In summary, we present a prognostic model intricately linked with immune status and treatment response. For ccRCC patients undergoing immunotherapy, this approach holds promise in aiding clinical decision-making by providing more precise and tailored treatment recommendations.

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PURPOSE: We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment. METHODS: The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results. RESULTS: At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05). CONCLUSION: The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

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Carcinoma de Células Renales , Neoplasias Renales , Tomografía Computarizada por Rayos X , Microambiente Tumoral , Humanos , Carcinoma de Células Renales/diagnóstico por imagen , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/mortalidad , Neoplasias Renales/diagnóstico por imagen , Neoplasias Renales/patología , Neoplasias Renales/genética , Neoplasias Renales/mortalidad , Femenino , Masculino , Persona de Mediana Edad , Pronóstico , Tomografía Computarizada por Rayos X/métodos , Aprendizaje Profundo , Anciano , Estudios Retrospectivos , Radiómica

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Background: In recent decades, there has been increasing evidence that Go-Ichi-Nii-San (GINS) subunits play an important role in the development and progression of various tumors. However, little research has been conducted on the role of GINS subunits in clear cell renal cell carcinoma (ccRCC). This study sought to explore the differential expression, prognosis, and immunological significance of GINS subunits in ccRCC. Methods: We used various analysis packages of R (version 3.6.3), the University of ALabama at Birmingham CANcer (UALCAN) data analysis portal, the Cancer Cell Line Encyclopedia (CCLE), the cBio Cancer Genomics Portal (cBioPortal), and the Tumor Immune Estimation Resource (TIMER) to study the gene expression, promoter methylation level, gene mutations, prognostic and diagnostic value, immune infiltration, pathway enrichment, and other aspects of the GINS subunits. Next, the genes related to the GINS subunits were analyzed using the STRING and GeneMANIA platforms, and the correlation between GINS subunits and the functions involved were investigated. Results: The expression level of GINS1/2/3/4 was significantly higher in ccRCC tumor tissues than normal tissues, and was significantly related to tumor grade and stage. The expression of GINS1/2/4 may be related to the methylation degree of the promoter region. The prognostic and diagnostic analyses showed that the increased expression of GINS1 was associated with various poor prognoses and had diagnostic value. The GINS subunit mutation also significantly affected the clinical prognosis of ccRCC patients. Finally, the correlation analysis of the immune infiltration level, co-expression, and enrichment of related genes indicated that GINS subunit expression was associated with different levels of ccRCC immune infiltration. Conclusions: The analysis results showed that the differential expression of GINS subunits in ccRCC, which had prognostic and diagnostic value, was correlated with clinicopathological stage, immune infiltration, and other related aspects. GINS1 may serve as a new potential prognostic biomarker for ccRCC patients and be used to guide treatment.

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BACKGROUND: Lipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients. METHODS: We conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan-Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response. RESULTS: We constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs, suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes. CONCLUSIONS: Our findings suggested that the LD-related gene signature could potentially predict the prognosis of ccRCC patients. Additionally, it showed promise for predicting responses to immunotherapy and targeted therapy in ccRCC patients. These insights might potentially have guided the clinical management of these patients, but further validation and broader data analysis are needed to confirm these preliminary observations.

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Carcinoma de Células Renales , Inmunoterapia , Neoplasias Renales , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/inmunología , Carcinoma de Células Renales/terapia , Neoplasias Renales/genética , Neoplasias Renales/terapia , Pronóstico , Microambiente Tumoral/genética , Microambiente Tumoral/inmunología , Femenino , Masculino , Persona de Mediana Edad , Transcriptoma , Nomogramas

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Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

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Biomarcadores de Tumor , Carcinoma de Células Renales , Regulación Neoplásica de la Expresión Génica , Neoplasias Renales , Biología de Sistemas , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/mortalidad , Neoplasias Renales/genética , Neoplasias Renales/patología , Neoplasias Renales/mortalidad , Biomarcadores de Tumor/genética , Biología de Sistemas/métodos , Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes , Ontología de Genes , Pronóstico

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BACKGROUND: In metastatic clear cell renal cell carcinoma (ccRCC), recent studies have shown promising efficacy of immune checkpoint inhibitor (ICI) combination therapy. However, there are insufficient evidences about clinical efficacy and safety of ICI combination therapy in metastatic non-ccRCC (nccRCC). METHODS: We retrospectively investigated 44 patients treated with nivolumab plus ipilimumab (ICI + ICI group) or anti-PD-1/PD-L1 inhibitor plus tyrosine kinase inhibitors (TKI) (ICI + TKI group), and assessed clinical efficacy in both groups. RESULTS: Of all patients, overall response rate and disease control rate for ICI combination treatments were 36.3% and 75%, respectively. The median progression-free survival (PFS) and overall survival (OS) was 8.8 and 23.9 months, respectively. Multivariate analysis revealed that the presence of liver metastasis significantly affected worse PFS and OS (p = 0.035 and p = 0.049). Importantly, PFS and OS seemed similar in ICI + ICI group and ICI + TKI group (p = 0.778 and p = 0.559). Although the discontinuation rate of the combination therapy due to adverse effects in patients aged ≥ 75 years was significantly higher compared to that in patients aged < 75 years (45% versus 12%, p = 0.017), there were no significant differences in PFS and OS between two groups (p = 0.290 and p = 0.257, respectively). CONCLUSION: This study confirms clinical benefit of ICI combination therapy for metastatic nccRCC patients in real-world settings. Furthermore, the effectiveness of combination therapy was comparable between patients aged < 75 and those ≥75 years with respect to clinical prognosis.

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Clear cell renal cell carcinoma (ccRCC) is characterized by its aggressive behavior and complex molecular heterogeneity, posing significant challenges for treatment and prognostication. This study offers a comprehensive analysis of ccRCC by leveraging both bulk and single-cell RNA sequencing data, with a specific aim to unravel the complexities of sphingolipid metabolism and the intricate dynamics within the tumor microenvironment (TME). By examining ccRCC samples sourced from public databases, our investigation delves deep into the genetic and transcriptomic landscape of this cancer type. Employing advanced analytical techniques, we have identified pivotal patterns in gene expression and cellular heterogeneity, with a special focus on the roles and interactions of various immune cells within the TME. Significantly, our research has unearthed insights into the dynamics of sphingolipid metabolism in ccRCC, shedding light on its potential implications for tumor progression and strategies for immune evasion. A novel aspect of this study is the development of a risk score model designed to enhance prognostic predictions for ccRCC patients, which is currently pending external validation to ascertain its clinical utility. Despite its contributions, the study is mindful of its limitations, including a reliance on observational data from public sources and a primary focus on RNA sequencing data, which may constrain the depth and generalizability of the findings. The study does not encompass critical aspects, such as protein expression, posttranslational modifications, and comprehensive metabolic profiles. Moreover, its retrospective design underscores the necessity for future prospective studies to solidify these preliminary conclusions. Our findings illuminate the intricate interplay between genetic alterations, sphingolipid metabolism, and immune responses in ccRCC. This research not only enhances our understanding of the molecular foundations of ccRCC but also paves the way for the development of targeted therapies and personalized treatment modalities. The study underlines the importance of cautious interpretation of results and champions ongoing research using diverse methodologies to thoroughly comprehend and effectively combat this formidable cancer type.

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OBJECTIVE: Contrast-enhanced computed tomography (CECT) improves lesion contrast with surrounding tissues through the injection of contrast agents. This enhancement allows for more precise lesion characterization, aiding in the early diagnosis of clear cell renal cell carcinoma (ccRCC). This meta-analysis aims to assess the diagnostic efficacy of CECT in ccRCC and to provide an ideal imaging examination method for the preoperative diagnosis of ccRCC. METHODS: We conducted a comprehensive search across six major online databases: PubMed, Web of Science, Cochrane Library, WANFANG DATA, China National Knowledge Infrastructure, and Chinese BioMedical Literature Database (CBM). The objective was to collate and analyze studies that evaluate the diagnostic utility of CECT in the identification of ccRCC. Meta-disc 1.4 and Stata 16.0 were used to conduct a meta-analysis and evaluate the diagnostic accuracy of CECT for ccRCC. RESULTS: The meta-analysis included 17 relevant studies investigating the diagnostic value of CECT for ccRCC. The combined sensitivity and specificity of CECT were 0.88 (95% confidence interval: 0.83-0.91) and 0.82 (95%CI: 0.75-0.87), respectively. Positive diagnostic likelihood ratio = 4.87 (95%CI: 3.47-6.84), negative diagnostic likelihood ratio = 0.15 (95%CI: 0.11-0.21), and diagnostic odds ratio = 32.67 (95%CI: 18.21-58.61). In addition, the area under the ROC curve was 0.92 (95%CI: 0.89-0.94), indicating that CECT has a decent discriminative ability in diagnosing ccRCC. CONCLUSIONS: CECT is recognized as a highly effective imaging tool for diagnosing ccRCC. It provides valuable guidance in the preoperative assessment and planning of surgical strategies for patients with ccRCC.

Asunto(s)

Carcinoma de Células Renales , Medios de Contraste , Neoplasias Renales , Tomografía Computarizada por Rayos X , Humanos , Carcinoma de Células Renales/diagnóstico por imagen , Neoplasias Renales/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos

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Background: Renal transplant recipients confront a substantially elevated susceptibility to renal cell carcinoma (RCC), particularly in their native kidneys as opposed to allografts. Methods: In this systematic scoping review, exhaustive searches were conducted of the MEDLINE and EMBASE databases. Information was gathered on clinical manifestations, donor demographics, diagnostic intervals, tumor dimensions, histopathological characteristics, and therapeutic outcomes associated with RCC arising in allograft kidneys. Results: The searches yielded a corpus of 42 case reports and 11 retrospective cohorts, encompassing a cohort of 274 patients. The majority of cases (75.4%) were clinically latent, discerned primarily through imaging modalities. Symptomatic presentations encompassed manifestations such as hematuria, elevated serum creatinine levels, abdominal discomfort, and graft-related pain. The mean temporal interval between renal transplantation and RCC diagnosis was calculated at 11.6 years, albeit displaying considerable variance. Notably, papillary and clear cell RCC emerged as the prevailing histopathological subtypes. However, the paucity of longitudinal follow-up data represents a notable caveat. Conclusion: This investigation underscores the imperative of rigorous posttransplant surveillance regimes owing to the substantial prevalence of asymptomatic RCC instances. Future research should focus on clinical outcomes and cost-effectiveness of screening practices to develop preventive strategies.

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Clear Cell Renal Cell Carcinoma (ccRCC), the most prevalent form of renal cell carcinoma (RCC), poses a significant threat to human health due to its rising morbidity and mortality rates. Sunitinib, a pivotal targeted drug for the treatment of ccRCC, presents a significant challenge due to the high susceptibility of ccRCC to resistance. HSP90 inhibitor AUY922 has demonstrated anti-tumor activity in a range of cancer types. However, its efficacy in combination with sunitinib for ccRCC treatment has not been evaluated. In this study, we employed bioinformatics, network pharmacology, and in vitro assays to verify that AUY922 inhibits cell viability, proliferation, and migration of ccRCC cell lines 786-O and ACHN, with IC50s of 91.86 µM for 786-O and 115.5 µM for ACHN. The effect of AUY922 enhancing the inhibitory effect of sunitinib on ccRCC was further confirmed. The CCK-8 assay demonstrated that the IC50 of sunitinib was reduced from 15.10 µM to 11.91 µM for 786-O and from 17.65 µM to 13.66 µM for ACHN, after the combined application of AUY922. The EdU assay and wound healing assay indicated that AUY922 augmented the inhibitory impact of sunitinib on the proliferation and migration of ccRCC cells. Western blot and RT-PCR analyses demonstrated that AUY922 increased the sensitivity of ccRCC cells to sunitinib by targeting the HIF-1α/VEGFA/VEGFR pathway. Our study represents the first investigation into the role and mechanism of AUY922 in enhancing the sensitivity of ccRCC to sunitinib. In conclusion, the findings indicate the potential for AUY922 to enhance the therapeutic efficacy of sunitinib and overcome sunitinib resistance in ccRCC.

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BACKGROUND: The von Hippel-Lindau (VHL) mutation is an important alteration in clear cell renal cell carcinoma (ccRCC); however, its imaging phenotype remains unclear. PURPOSE: To investigate whether MRI features can reflect the VHL mutation status. STUDY TYPE: Retrospective. FIELD STRENGTH/SEQUENCE: 3 T/fast spin echo T2-weighted, spin-echo echo planar diffusion-weighted, gradient recalled echo T1-weighted, gradient recalled echo chemical-shift T1-weighted, and contrast-enhanced gradient recalled echo T1-weighted sequences. POPULATION: One hundred five patients with ccRCC who underwent preoperative contrast-enhanced MRI and subsequent genomic sequencing: 59 consecutive patients from our institution (38 [64.41%] with VHL mutations) formed a training cohort, and 46 from The Cancer Genome Atlas (TCGA) database (24 [52.17%] with VHL mutations) formed an independent test cohort. ASSESSMENT: Two radiologists, with 23 and 33 years of experience respectively, jointly evaluated the semantic MRI features of the primary lesion in ccRCCs to propose potential features related to VHL mutations in both cohorts. Three additional readers, with 5, 7, and 10 years of experience respectively, independently reviewed all lesions to assess the interobserver agreement of MRI features. A VHL mutational likelihood score (VHL-MULIS) system was constructed using the training cohort and validated using the independent test cohort. STATISTICAL TESTS: Fisher's test or chi-square test, t-test or Mann-Whitney U test, logistic regression, Cohen's kappa (κ), area under the receiver operating characteristic curve (AUC). A two-sided P value <0.05 was considered statistically significant. RESULTS: In both the local and public cohorts, T2-weighted signal intensity and presence of microscopic fat from primary lesions were significantly associated with VHL mutation status. The VHL-MULIS incorporated maximum diameter, T2-weighted signal intensity, and presence of microscopic fat in the training cohort and demonstrated promising diagnostic ability (AUC, 0.82; sensitivity, 0.79; specificity, 0.82) and substantial interobserver agreement (κ, 0.787) in the test cohort. DATA CONCLUSION: The VHL mutation exhibited a distinct MRI phenotype. Integrating multiple semantic MRI features has potential to reflect the mutation status in patients with ccRCC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.