Atlas Kidney Renal Clear Cell Research Articles

Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma

Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma

Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

Differential expression analysis identifies a prognostically significant extracellular matrix–enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial–mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein–protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers

Introduction: We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real-world population renal cancer data from the National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data are used as an anchor for prevalence rate bias assessment. Specific gene expression and, therefore, biology of RCC differ by self-reported race, especially between the African American and Caucasian populations. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. Methods: Data elements evaluated included gender, demographics, reported pathologic grading, and cancer staging. American Urological Association risk levels were used. Poisson regression was performed to estimate the population-based and sample-specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. Results: Compared to PUF, KiTS19 and TCGA KIRC oversampled Caucasian by 9.5% (95% CI, −3.7 to 22.7%) and 15.1% (95% CI, 1.5 to 28.8%), undersampled African American by −6.7% (95% CI, −10% to −3.3%), and −5.5% (95% CI, −9.3% to −1.8%). Tertiary also undersampled African American by −6.6% (95% CI, −8.7% to −4.6%). The tertiary cohort largely undersampled aggressive cancers by −14.7% (95% CI, −20.9% to −8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate; however, heterogeneities in risk are notable. Conclusion: Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.

Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study

Introduction: This study investigates how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with clear cell renal cell carcinoma (ccRCC) biomarkers. Methods: The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive. 2,824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built MATLAB software package. Robust radiomic features with strong inter-scanner reproducibility were selected. Random forest, AdaBoost, and elastic net machine learning (ML) algorithms evaluated the ability of the selected radiomic features to predict the presence of 12 clinically relevant molecular biomarkers identified from the literature. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. Results: Before stratification by tumor grade and stage, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60–0.68). Once stratified, radiomics predicted KDM5C, SETD2, PBRM1, and mTOR mutation status with acceptable to excellent predictive discrimination (AUC ranges from 0.70 to 0.86). Conclusions: Radiomic texture analysis can potentially identify a variety of clinically relevant biomarkers in patients with ccRCC and may have a prognostic implication.

Comprehensive analyses of A 12-metabolism-associated gene signature and its connection with tumor metastases in clear cell renal cell carcinoma

BackgroundThe outcomes of patients with clear cell renal cell carcinoma (ccRCC) were dreadful due to lethal local recurrence and distant metastases. Accumulating evidence suggested that ccRCC was considered a metabolic disease and metabolism-associated genes (MAGs) exerted essential functions in tumor metastases. Thus, this study intends to seek whether the dysregulated metabolism promotes ccRCC metastases and explores underlying mechanisms.MethodWeighted gene co-expression network analysis (WGCNA) was employed based on 2131 MAGs to select genes mostly associated with ccRCC metastases for subsequent univariate Cox regression. On this basis, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression were employed to create a prognostic signature based on the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) cohort. The prognostic signature was confirmed using E-MTAB-1980 and GSE22541 cohorts. Kaplan–Meier, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox regression were applied to detect the predictability and independence of the signature in ccRCC patients. Functional enrichment analyses, immune cell infiltration examinations, and somatic variant investigations were employed to detect the biological roles of the signature.ResultA 12-gene-metabolism-associated prognostic signature, termed the MAPS by our team, was constructed. According to the MAPS, patients were divided into low- and high-risk subgroups and high-risk patients displayed inferior outcomes. The MAPS was validated as an independent and reliable biomarker in ccRCC patients for forecasting the prognosis and progression of ccRCC patients. Functionally, the MAPS was closely associated with metabolism dysregulation, tumor metastases, and immune responses in which the high-risk tumors were in an immunosuppressive status. Besides, high-risk patients benefited more from immunotherapy and held a higher tumor mutation burden (TMB) than low-risk patients.ConclusionThe 12-gene MAPS with prominent biological roles could independently and reliably forecast the outcomes of ccRCC patients, and provide clues to uncover the latent mechanism in which dysregulated metabolism controlled ccRCC metastases.

Semantic Computed Tomography Features for Predicting BRCA1-associated Protein 1 and/or Tumor Protein p53 Gene Mutation Status in Clear Cell Renal Cell Carcinoma

The purpose of this study was to explore the semantic computed tomography (CT) features associated with BRCA1-associated protein 1 (BAP1) and/or tumor protein p53 (TP53) mutation in clear cell renal cell carcinoma (ccRCC). Clinical characteristics and gene mutation information of 336 ccRCC patients were retrieved from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database (TCGA-KIRC). Kaplan-Meier analysis was performed to examine prognosis by gene mutation. The CT imaging data and gene mutation information of 156 ccRCC patients treated between January 2019 and January 2021 (the training cohort) were retrospectively analyzed. The CT imaging information and gene mutation data of 123 patients with ccRCC were downloaded from The Cancer Imaging Archive and The Cancer Genome Atlas database (the external validation cohort). Univariate Chi-square test and multivariate binary logistic regression analysis were performed to determine predictors of gene mutation; a nomogram was developed using these predictors. Receiver operating characteristic curve analysis and the Hosmer-Lemeshow test were performed to evaluate the performance of the nomogram. Kaplan-Meier analysis showed that BAP1 and/or TP53 mutation was significantly correlated with worse survival outcome. Multivariate binary logistic regression analysis indicated ill-defined margin (P=.001), spiculated margin (P=.018), renal vein invasion (P=.002), and renal pelvis invasion (P=.001) were independent predictors of BAP1 and/or TP53 mutation. A nomogram containing these 4 semantic CT features was constructed; the area under the receiver operating characteristic curves was 0.872 (95% CI, 0.809-0.920). The Hosmer-Lemeshow test showed acceptable goodness-of-fit for the nomogram (X2=1.194, P=.742). The nomogram was validated in the validation cohort; it showed good accuracy (area under the receiving operating characteristic curve=0.819, 95% CI, 0.740-0.883) and was well calibrated (X2=3.934, P=.559). Semantic CT features are a potential and promising method for predicting BAP1 and/or TP53 mutation status in ccRCC patients.

Comprehensive analysis of necroptosis-related long noncoding RNA to predict prognosis, immune status, and immunotherapeutic response in clear cell renal cell carcinoma.

Necroptosis has been found to be associated with tumorigenesis and tumor progression. However, the prognostic effect of long noncoding RNAs (lncRNAs) associated with necroptosis in clear cell renal cell carcinoma (ccRCC) is still unclear. Pearson correlation analysis was used to identify necroptosis-related genes and lncRNAs obtained from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were used to identify a novel necroptosis-associated lncRNAs signature that significantly correlated with survival of ccRCC. Next, single sample gene set enrichment analysis (ssGSEA) was employed to assess the extent of infiltration with immune cells. Analyses to predict the half-maximal inhibitory concentration (IC50) of patients in different risk groups were also conducted. Moreover, follow-up data of an immunotherapy cohort were used to test for differences in the immunotherapeutic efficiency between two risk groups. Finally, patients with ccRCC were divided into two groups based on 6 prognostic lncRNAs. We developed a signature of necroptosis-related lncRNAs, which was verified as an independent prognostic factor that can predict prognosis up to 7 years. Patients with higher risk scores were shown to have higher immune suppressive cell infiltration levels and expression of immune checkpoint genes, which suggests that these patients were in a state of immunosuppression. Patients in the low-risk group were found to have an increased response to immunotherapy. A prognostic prediction nomogram was conducted to predict long-term survival of patients. Cluster A tumors were considered hot tumors, since they were correlated with higher levels of immune infiltration and were more sensitive to immunotherapy. A comprehensive bioinformatics analysis was conducted, which found that the necroptosis-associated lncRNA signature might be a potent prognostic factor for patients with ccRCC, which could contribute to improved prognosis of these patients.

The Prognosis and Immune Microenvironment Related Factor Interleukin-23 in Clear Cell Renal Cell Carcinoma: A Radiological Investigation.

To explore the ccRCC clinical and immune characteristics correlated with IL-23 expression level and build pre-operative prediction models based on contrast CT scans. The study included the cancer genome atlas kidney renal clear cell carcinoma cases to build a bioinformatics cohort. The cases with qualified contrast CT images were selected as radiographic and radiomics cohort. The IL-23 expression level groups were defined by median-based thresholding. The clinical characteristics were compared between groups. The impacts of IL-23 on immune microenvironment composition were measured via the CIBERSORT. Two radiologists evaluated the pre-operative contrast CT images. The radiomics features were automatically extracted. IL-23 group-specific radiographic and radiomics features were collected and used for prediction model establishment via Orange Data Mining Toolbox. P < 0.05 was set as statistically significant. For total, 530 ccRCC cases were included. The IL-23 group was significantly associated with survival, histologic grade, AJCC tumor stage, AJCC cancer stage, and plasma calcium level. Except for Treg and other T cells, IL-23 showed correlation with NK cell, mast cell, monocyte infiltration. Axial length was the only significant radiographic measurement between IL-23 groups. The radiomics features established an IL-23 group prediction model with the highest 10-fold cross-verification AUC of 0.842. The clear cell renal cell carcinoma IL-23 expression level had prognosis and immune microenvironment correlation and could be predicted by pre-operative radiomics features.

SLC22A8: An indicator for tumor immune microenvironment and prognosis of ccRCC from a comprehensive analysis of bioinformatics.

Clear cell renal cell carcinoma (ccRCC) is one of the most common renal malignancies worldwide. SLC22A8 plays a key role in renal excretion of organic anions. However, its role in ccRCC remains unclear; therefore, this study aimed to elucidate the relationship between SLC22A8 and ccRCC. The The Cancer Genome Atlas-kidney renal clear cell carcinoma cohort was included in this study. The Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between SLC22A8 expression and clinicopathological characteristics. Multifactorial analysis and Kaplan–Meier survival curves were adopted for correlation between SLC22A8 expression and clinicopathological parameters and overall survival. Utilizing the UALCAN database, the correlation of the expression levels of SLC22A8 DNA methylation in ccRCC was explored. Immunological characterization of SLC22A8 regarding the ccRCC tumor microenvironment was carried out by the single sample Gene Set Enrichment Analysis algorithm and the CIBERSORT algorithm. With the CellMiner database, the analysis of the association between SLC22A8 gene expression and drug sensitivity was further performed. Eventually, gene ontology and Kyoto Encyclopedia of Gene and Genome enrichment analyses were applied to identify the functional and signaling pathways involved in SLC22A8. SLC22A8 expression is associated with age, grade, stage, and tumor status. SLC22A8 protein expression levels, phosphorylated protein levels, and DNA methylation expression levels were lower in ccRCC tissues than in normal tissues, and low methylation levels predicted poor overall survival. Comprehensive analysis of tumor immune infiltration and the tumor microenvironment indicated a higher level of overall immunity in the SLC22A8 low expression group. Gene Enrichment Analysis results showed that low expression of SLC22A8 was associated with immune pathways, such as phagocytosis recognition and humoral immune response. SLC22A8 expression was significantly correlated with survival and immune infiltration in ccRCC and can be used as a prognostic biomarker for ccRCC.

Identification of Ligand-Receptor Pairs Associated With Tumour Characteristics in Clear Cell Renal Cell Carcinoma.

The tumour microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) comprises multiple cell types, which promote tumour progression and modulate drug resistance and immune cell infiltrations via ligand-receptor (LR) interactions. However, the interactions, expression patterns, and clinical relevance of LR in the TME in ccRCC are insufficiently characterised. This study characterises the complex composition of the TME in ccRCC by analysing the single-cell sequencing (scRNA-seq) data of patients with ccRCC from the Gene expression omnibus database. On analysing the scRNA-seq data combined with the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) dataset, 46 LR-pairs were identified that were significantly correlated and had prognostic values. Furthermore, a new molecular subtyping model was proposed based on these 46 LR-pairs. Molecular subtyping was performed in two ccRCC cohorts, revealing significant differences in prognosis between the subtypes of the two ccRCC cohorts. Different molecular subtypes exhibited different clinicopathological features, mutational, pathway, and immune signatures. Finally, the LR.score model that was constructed using ten essential LR-pairs that were identified based on LASSO Cox regression analysis revealed that the model could accurately predict the prognosis of patients with ccRCC. In addition, the differential expression of ten LR-pairs in tumour and normal cell lines was identified. Further functional experiments showed that CX3CL1 can exert anti-tumorigenic role in ccRCC cell line. Altogether, the effects of immunotherapy were connected to LR.scores, indicating that potential medications targeting these LR-pairs could contribute to the clinical benefit of immunotherapy. Therefore, this study identifies LR-pairs that could be effective biomarkers and predictors for molecular subtyping and immunotherapy effects in ccRCC. Targeting LR-pairs provides a new direction for immunotherapy regimens and prognostic evaluations in ccRCC.

Comprehensive analysis of lower mitochondrial complex I expression is associated with cell metastasis of clear cell renal cell carcinoma.

BackgroundClear cell renal cell carcinoma (ccRCC) is characterized by high metastasis potential. It is of great importance to explore the mechanisms underlying ccRCC metastasis and to enable development of potent therapeutics. The mitochondrial complex I (CI) had been considered to play an important role in the development of cancers, but less known in ccRCC.MethodsWe utilized available public databases of ccRCC, including single-cell RNA sequencing (scRNA-seq) data GSE73121 and The Cancer Genome Atlas-kidney renal clear cell carcinoma (TCGA-KIRC). Principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (tSNE) analysis were evaluated the heterogeneity of metastatic renal cell carcinoma (mRCC) and primary renal cell carcinoma (pRCC). Protein-protein interaction (PPI) network identified critical gene. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) performed to explore the potential biologic pathways.ResultsOur study revealed a significant gene expression heterogeneity between pRCC and mRCC. A PPI network based on differentially expressed genes (DEGs) identified electron transport chain (ETC), especially mitochondrial CI, as the key network hub. Further analysis revealed that the role of mitochondrial CI is associated with tumor metastasis and immune responds of ccRCC. Although CI had low frequency mutations in ccRCC, CI expression is associated with the high frequency mutated genes. A prognosis model included 7 CI genes, and these had a significant effect on overall survival (OS). The area under the curve at 1, 3, and 5 years was 0.717, 0.685, and 0.728, respectively. Transcription factor analysis predicted that PPARG possibly is a potential transcription activator of CI genes in ccRCC.ConclusionsOverall, we found that CI expression is associated with ccRCC progress. CI and PPARG may be potential biomarkers for metastatic ccRCC.

Nanoparticles targeting at methylases with high correlation to N6-methyladenosine-related lncRNA signatures as potential therapy of kidney clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous malignancy with poor prognosis. Methylation of the N6 position of adenosine (m6A), the most common epigenetic modification in both messenger RNAs and noncoding RNAs, has been reported to regulate the initiation and progression of ccRCC. However, whether and how m6A-related long noncoding RNAs (m6ArlncRNAs) signify the progression of ccRCC remain unclear. We found m6ArlncRNAs are effective signatures illustrating immune landscape and risk stratification in ccRCC. We identified two differently expressed m6ArlncRNAs (DEm6ArlncRNAs), AC008870.2 and EMX2OS, as independent risk factors for overall survival of ccRCC patients, by applying stringent variable selection procedure to data from the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma project. The risk score generated from the DEm6ArlncRNA expression categorizes patients into either high or low-risk groups, between which, enrichment analysis indicated an enrichment in immune-related pathways. Under different DEm6ArlncRNA transcription pattern, the two risk groups differ in immune cell population composition and expression levels of therapy targeting genes. Nanoparticle is satisfactory strategy to delivering therapeutic drugs. For further clinical translation, we designed a novel nanoparticle delivery system packaged STM2457 (STM@8P4 NPs), which selectively inhibits AC008870.2-correlated m6A writer. STM@8P4 NPs loaded drug successfully with uniform particle size, long-term stability and high release efficiency. STM@8P4 NPs can easily enter ccRCC cells and showed a highly efficient ccRCC killing activity in vitro. Our results therefore indicate that m6ArlncRNAs expression can depict tumor microenvironment, predict prognosis for ccRCC patient and give hint to therapeutic strategies in ccRCC.

MiR-96-5p targets PTEN to mediate sunitinib resistance in clear cell renal cell carcinoma

A multiple receptor tyrosine kinase inhibitor, sunitinib, is a first-line therapy for clear cell renal cell carcinoma (CCRCC). Unfortunately, it has the major challenges of low initial response rate and resistance after about one year of treatment. Here we evaluated a microRNA (miRNA) and its target responsible for sunitinib resistance. Using miRNA profiling, we identified miR-96-5p upregulation in tumors from sunitinib-resistant CCRCC patients. By bioinformatic analysis, PTEN was selected as a potential target of miR-96-5p, which showed low levels in tumors from sunitinib-resistant CCRCC patients. Furthermore, PTEN and miR-96-5p levels were negatively correlated in a large The Cancer Genome Atlas kidney renal clear cell carcinoma cohort and high miR-96 and low PTEN represented poor prognosis in this cohort. Additionally, four-week sunitinib treatment increased miR-96-5p and decreased PTEN only in tumors from a sunitinib-resistant patient-derived xenograft model. We found a novel miR-96-5p binding site in the PTEN 3′ UTR and confirmed direct repression by luciferase reporter assay. Furthermore, we demonstrated that repression of PTEN by miR-96-5p increased cell proliferation and migration in sunitinib-treated cell lines. These results highlight the direct suppression of PTEN by miR-96-5p and that high miR-96-5p and low PTEN are partially responsible for sunitinib resistance and poor prognosis in CCRCC.

MiR-21-5p serves as a promoter in renal cell carcinoma progression through ARHGAP24 downregulation.

Renal cell carcinoma (RCC) is a highly recurrent aggressive tumor. This study works for the regulation of miR-21-5p on RCC cell functions and novel ideas for therapies of RCC. Isoform expression quantification data were offered by The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) to investigate differentially expressed miRNAs. The way miR-21-5p works on biological functions of RCC was examined with MTT and Transwell assays. The downstream targets of miR-21-5p were predicted using bioinformatics analysis. The binding of two researched objects was verified by the dual-luciferase method. TCGA data manifested a considerably high level of miR-21-5p in RCC tissue, while ARHGAP24 was significantly lowly expressed. miR-21-5p bound ARHGAP24 and stimulated RCC cell functions, whereas ARHGAP24 mimic could reverse such promotion. This work observed miR-21-5p, a stimulator in RCC, and it deteriorated this cancer via repressing its downstream target gene ARHGAP24 expression.

Identification of Survival-Specific Genes in Clear Cell Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel.

Purpose: Although mutations are associated with carcinogenesis, little is known about survival-specific genes in clear cell renal cell carcinoma (ccRCC). We developed a customized next-generation sequencing (NGS) gene panel with 156 genes. The purpose of this study was to investigate whether the survival-specific genes we found were present in Korean ccRCC patients, and their association with clinicopathological findings. Materials and Methods: DNA was extracted from the formalin-fixed, paraffin-embedded tissue of 22 ccRCC patients. NGS was performed using our survival-specific gene panel with an Illumina MiSeq. We analyzed NGS data and the correlations between mutations and clinicopathological findings and also compared them with data from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) and Renal Cell Cancer-European Union (RECA-EU). Results: We found a total of 100 mutations in 37 of the 156 genes (23.7%) in 22 ccRCC patients. Of the 37 mutated genes, 11 were identified as clinicopathologically significant. Six were novel survival-specific genes (ADAMTS10, CARD6, NLRP2, OBSCN, SECISBP2L, and USP40), and five were top-ranked mutated genes (AKAP9, ARID1A, BAP1, KDM5C, and SETD2). Only CARD6 was validated as an overall survival-specific gene in this Korean study (p = 0.04, r = −0.441), TCGA-KIRC cohort (p = 0.0003), RECA-EU (p = 0.0005). The 10 remaining gene mutations were associated with clinicopathological findings; disease-free survival, mortality, nuclear grade, sarcomatoid component, N-stage, sex, and tumor size. Conclusions: We discovered 11 survival-specific genes in ccRCC using data from TCGA-KIRC, RECA-EU, and Korean patients. We are the first to find a correlation between CARD6 and overall survival in ccRCC. The 11 genes, including CARD6, NLRP2, OBSCN, and USP40, could be useful diagnostic, prognostic, and therapeutic markers in ccRCC.

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis.

BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

Identification of a Four Hypoxia-Associated Long Non-Coding RNA Signature and Establishment of a Nomogram Predicting Prognosis of Clear Cell Renal Cell Carcinoma

To identify novel hypoxia-associated long non-coding RNAs (lncRNAs) as potential biomarkers, we developed a risk stratification signature and constructed a prognosis prediction nomogram of clear cell renal cell carcinoma (ccRCC). Hypoxia-related lncRNAs were identified through Pearson correlation analysis between the expression profiles of hypoxia-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Then, a signature of four key lncRNAs (COMETT, EMX2OS, AC026462.3, and HAGLR) was developed. The four lncRNAs were downregulated in high-grade, advanced stage, and high-risk ccRCC. The signature had an independent and long-standing prognosis prediction ability up to a 10-year follow-up. Notably, the risk score was significantly positively correlated with the infiltration abundances of six immune cells from the Tumor IMmune Estimation Resource (TIMER). The gene set enrichment analysis (GSEA) also suggested that the signature was involved in metabolism and tumorigenesis, which were closely related to the hypoxic tumor microenvironment. Ultimately, a nomogram of signature, age, stage, and grade, was built to predict the individual long-term survival possibility. Finally, the expressions of four lncRNAs were validated by quantitative real-time PCR (qRT-PCR). Our study identified a four-lncRNA signature and established a prognostic nomogram that reliably predicts survival in ccRCC. The findings may be beneficial to therapeutic customization and medical decision-making.

Uncontrolled Confounders May Lead to False or Overvalued Radiomics Signature: A Proof of Concept Using Survival Analysis in a Multicenter Cohort of Kidney Cancer.

PurposeWe aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them.Materials and MethodsPreoperative contrast enhanced abdominal CT scan of ccRCC patients along with pathological grade/stage, gene mutation status, and survival outcomes were retrieved from The Cancer Imaging Archive (TCIA)/The Cancer Genome Atlas—Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database, a publicly available dataset. A semi-automatic segmentation method was applied to segment ccRCC tumors, and 1,160 radiomics features were extracted from each segmented tumor on the CT images. Non-parametric principal component decomposition (PCD) and unsupervised hierarchical clustering were applied to build the radiomics signature models. The factors confounding the radiomics signature were investigated and controlled sequentially. Kaplan–Meier curves and Cox regression analyses were performed to test the association between radiomics signatures and survival outcomes.Results183 patients of TCGA-KIRC cohort with available imaging, pathological, and clinical outcomes were included in this study. All 1,160 radiomics features were included in the first radiomics signature. Three additional radiomics signatures were then modelled in successive steps removing redundant radiomics features first, removing radiomics features biased by CT slice thickness second, and removing radiomics features dependent on tumor size third. The final radiomics signature model was the most parsimonious, unbiased by CT slice thickness, and independent of tumor size. This final radiomics signature stratified the cohort into radiomics phenotypes that are different by cancer-specific and recurrence-free survival; HR (95% CI) = 3.0 (1.5–5.7), p <0.05 and HR (95% CI) = 6.6 (3.1–14.1), p <0.05, respectively.ConclusionRadiomics signature can be confounded by multiple factors, including feature redundancy, image acquisition parameters like slice thickness, and tumor size. Attention to and proper control for these potential confounders are necessary for a reliable and clinically valuable radiomics signature.

CORO6 Promotes Cell Growth and Invasion of Clear Cell Renal Cell Carcinoma via Activation of WNT Signaling.

Renal cell carcinoma (RCC) constitutes the most lethal type of genitourinary cancer. Understanding of RCC tumor biology helps to identify novel targets and develop directed treatments for patients with this type of cancer. Analysis from both The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset and our RCC samples demonstrated that the expression level of CORO6 was significantly higher in RCC patients than in normal kidney tissues, and its level was highly associated with tumor stage and grade. Importantly, CORO6 expression level was an independent predictor of tumor metastasis and overall survival in RCC patients. Our cell line data also confirmed that CORO6 knockdown could suppress RCC cell growth as well as cell migration and invasion. The depletion of CORO6 led to cell cycle arrest at the G0/G1 phase and caused cell apoptosis. Further, mechanistic dissection showed that CORO6 mediated RCC cell growth, and cell invasion relied on WNT signaling. Moreover, the in vivo data suggested that CORO6 knockdown indeed suppressed RCC tumor growth. Overall, our study defines the oncogenic role of CORO6 in RCC progression and provides a rationale for developing CORO6-targeted therapies for improved treatment of RCC patients.