scholarly journals Genomic Analysis Reveals Novel Specific Metastatic Mutations in Chinese Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hui Meng ◽  
Xuewen Jiang ◽  
Jianfeng Cui ◽  
Gang Yin ◽  
Benkang Shi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Genomic Analysis ◽  
Renal Clear Cell Carcinoma ◽  
Genomic Feature ◽  
Cell Renal Cell Carcinoma ◽  
Hybridization Capture ◽  
Significant Difference

Clear cell renal cell carcinoma (ccRCC) accounts for more than 75% of renal cell carcinoma. Nearly 25% of ccRCC patients were diagnosed with metastasis. Though the genomic profile of ccRCC has been widely studied, the difference between localized and metastatic ccRCC was not clarified. Primary tumor samples and matched whole blood were collected from 106 sporadic patients diagnosed with renal clear cell carcinoma at Qilu Hospital of Shandong University from January 2017 to November 2019, and 17 of them were diagnosed with metastasis. A hybridization capture-based next-generation sequencing of 618 cancer-related genes was performed to investigate the somatic and germline variants, tumor mutation burden (TMB), and microsatellite instability (MSI). Five genes with significantly different prevalence were identified in the metastatic group, especially TOP1 (17.65% vs. 0%) and SNCAIP (17.65% vs. 0%). The altered frequency of PBRM1 (0% vs. 27%) and BAP1 (24% vs. 10%) differed between the metastatic and nonmetastatic groups, which may relate to the prognosis. Of these 106 patients, 42 patients (39.62%) had at least one alteration in DNA damage repair (DDR) genes, including 58.82% of metastatic ccRCC patients and 35.96% of ccRCC patients without metastasis. Ten pathogenic or likely pathogenic (P/LP) variants were identified in 11 sporadic clear cell renal cell carcinoma patients (10.38%), including rarely reported ATM (n=1), MUTYH (n=1), NBN (n=1), RAD51D (n=1), and BRCA2 (n=1). No significant difference in the ratio of P/LP variant carriers or TMB was identified between the metastatic and nonmetastatic groups. We found a unique genomic feature of Chinese metastatic ccRCC patients with a higher prevalence of alterations in DDR, TOP1, and SNCAIP. Further investigated studies and drug development are needed in the future.

scholarly journals LRRC19—A Bridge between Selenium Adjuvant Therapy and Renal Clear Cell Carcinoma: A Study Based on Datamining

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 440
Author(s):  
Yitong Zhang ◽  
Jiaxing Wang ◽  
Xiqing Liu
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Independent Risk Factor ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Cell Renal Cell Carcinoma ◽  
Low Expression

Kidney renal clear cell carcinoma (KIRC) is the most common and fatal subtype of renal cancer. Antagonistic associations between selenium and cancer have been reported in previous studies. Selenium compounds, as anti-cancer agents, have been reported and approved for clinical trials. The main active form of selenium in selenoproteins is selenocysteine (Sec). The process of Sec biosynthesis and incorporation into selenoproteins plays a significant role in biological processes, including anti-carcinogenesis. However, a comprehensive selenoprotein mRNA analysis in KIRC remains absent. In the present study, we examined all 25 selenoproteins and identified key selenoproteins, glutathione peroxidase 3 (GPX3) and type 1 iodothyronine deiodinase (DIO1), with the associated prognostic biomarker leucine-rich repeat containing 19 (LRRC19) in clear cell renal cell carcinoma cases from The Cancer Genome Atlas (TCGA) database. We performed validations for the key gene expression levels by two individual clear cell renal cell carcinoma cohorts, GSE781 and GSE6344, datasets from the Gene Expression Omnibus (GEO) database. Multivariate survival analysis demonstrated that low expression of LRRC19 was an independent risk factor for OS. Gene set enrichment analysis (GSEA) identified tyrosine metabolism, metabolic pathways, peroxisome, and fatty acid degradation as differentially enriched with the high LRRC19 expression in KIRC cases, which are involved in selenium therapy of clear cell renal cell carcinoma. In conclusion, low expression of LRRC19 was identified as an independent risk factor, which will advance our understanding concerning the selenium adjuvant therapy of clear cell renal cell carcinoma.

Abstract 346: Integrative genomic analysis of sporadic clear cell renal cell carcinoma

2011 ◽  
Author(s):  
Vijay R. Dondeti ◽  
Bradley Wubbenhorst ◽  
Priti Lal ◽  
John D. Gordan ◽  
Kurt D'Andrea ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Genomic Analysis ◽  
Cell Renal Cell Carcinoma

scholarly journals Comprehensive Genomic Analysis of Metastatic Non–Clear-Cell Renal Cell Carcinoma to Identify Therapeutic Targets

2019 ◽  
pp. 1-18 ◽  
Author(s):  
Maria I. Carlo ◽  
Nabeela Khan ◽  
Ahmet Zehir ◽  
Sujata Patil ◽  
Yasser Ged ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Somatic Mutations ◽  
Clear Cell ◽  
De Novo ◽  
Genomic Analysis ◽  
Precision Oncology ◽  
Single Nucleotide Variants ◽  
Cell Renal Cell Carcinoma

PURPOSENon–clear-cell renal cell carcinoma (nccRCC) encompasses approximately 20% of renal cell carcinomas and includes subtypes that vary in clinical and molecular biology. Compared with clear cell renal cell carcinoma, nccRCC demonstrates limited sensitivity to conventional vascular endothelial growth factor– and mammalian target of rapamycin–directed agents, indicating a need for better therapies. Characterizing the genomic landscape of metastatic nccRCC variants may help define novel therapeutic strategies.PATIENTS AND METHODSWe retrospectively analyzed tumor tissue from patients with metastatic nccRCC who consented to genomic analysis of their tumor and germline DNA. A hybridization capture–based assay was used to identify single nucleotide variants and small insertions and deletions across more than 340 cancer-associated genes with germline comparison. Clinical actionability of somatic mutations was assessed using OncoKB levels of evidence. Microsatellite instability (MSI) in the tumor was investigated.RESULTSOf 116 patients included in the analysis, 57 (49%) presented with de novo metastatic disease, and 59 (51%) presented with localized disease that later metastasized. Subtype classifications included unclassified (n = 41; 35%), papillary (n = 26; 22%), chromophobe (n = 17; 15%), translocation associated (n = 13; 11%), and other (n = 19; 16%). Of all tumors, 15 (13%) had putative driver somatic alterations amenable to targeted therapies, including alterations in MET, TSC1/2, and an ALK translocation. Of 45 patients who had germline testing, 11 (24%) harbored mutations, seven of which could potentially guide therapy. Of 115 available tumors for analysis, two (1.7%) had high and six (5%) had intermediate MSI status.CONCLUSIONThe mutation profiles of metastatic nccRCC vary by subtype. Comprehensive analysis of somatic mutations, germline mutations, and MSI, interpreted via an annotated precision oncology knowledge base, identified potentially targetable alterations in 22% of patients, which merits additional investigation.

scholarly journals A Proteogenomic Atlas of Clear Cell Renal Cell Carcinoma in a Chinese Population

2021 ◽  
Author(s):  
Chen Ding ◽  
Yuan-Yuan Qu ◽  
Jinwen Feng ◽  
Xiaohui Wu ◽  
Lin Bai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Clear Cell ◽  
Genomic Analysis ◽  
Genomic Alterations ◽  
Cell Renal Cell Carcinoma ◽  
Genetic Features ◽  
One Carbon Metabolism

Abstract Renal cell carcinoma (RCC) is among the top 10 malignant carcinomas1. Clear cell (cc)RCC, accounting for ~ 75% of RCC cases, is an aggressive histological RCC subtype. In the last decade, large-scale multiomics studies have profoundly enhanced our understanding of this disease2,3. However, despite the differences of genomic alterations between Western and Eastern ccRCC4,5, these studies mostly focused on patients in Western populations. Here we conducted a comprehensive proteogenomic analysis of 232 tumor and adjacent non-tumor tissue pairs from Chinese ccRCC patients. Genomic analysis revealed unique genetic features of Chinese ccRCC and distinct mutation patterns associated with copy number alterations. Based on proteomic profiles, ccRCC showed extensive metabolic dysregulation, especially in one-carbon metabolism. We classified ccRCC into three subtypes (GP1–3), among which the most aggressive GP1 exhibited dominant immune response, metastasis, and metabolic imbalance, linking the proteomic features, genomic alterations, and clinical outcomes of ccRCC. Nicotinamide N-methyltransferase (NNMT) and NNMT mediated protein homocysteinylation were identified as a poor prognosis indicator and a drug target for GP1, respectively. We demonstrated that NNMT induces DNA-dependent protein kinase catalytic subunit (DNA-PKcs) homocysteinylation, increases DNA repair, and promotes tumor growth in ccRCC. Treatment of N-acetyl-cysteine (NAC), an inhibitor of homocysteinylation, markedly reduced the NNMT overexpression induced radioresistance of tumor cells. This study provided valuable insights into the biological underpinnings and prognosis assessment of ccRCC, revealing a targetable metabolic vulnerability.

scholarly journals Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices

2021 ◽  
Vol 10 ◽  
Author(s):  
Jianhong Zhao ◽  
Jiangpeng Wu ◽  
Jinyan Wei ◽  
Xiaolu Su ◽  
Yanjun Chai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Test Data ◽  
Renal Cell ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Learning Approaches ◽  
Cell Renal Cell Carcinoma ◽  
Subtype Differentiation

Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.

scholarly journals A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaoli Meng ◽  
Jun Shu ◽  
Yuwei Xia ◽  
Ruwu Yang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Image Features ◽  
Regression Method ◽  
Cell Renal Cell Carcinoma ◽  
Significant Difference

This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001≤p≤0.0051) and the subjective findings model (0.0006≤p≤0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation.

scholarly journals Subcutaneous Adipose Tissue Reduction in Patients with Clear Cell Renal Cell Carcinoma and Peritumoral Collateral Vessels: A Retrospective Observational Study

2021 ◽  
Vol 11 (13) ◽  
pp. 6076
Author(s):  
Federico Greco ◽  
Luigi Giuseppe Quarta ◽  
Aldo Carnevale ◽  
Melchiore Giganti ◽  
Rosario Francesco Grasso ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Adipose Tissue ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Subcutaneous Adipose Tissue ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Significant Difference ◽  
Collateral Vessels ◽  
Subcutaneous Adipose

Background: peritumoral collateral vessels adjacent to renal cell carcinoma (RCC) can be encountered in clinical practice. Cancer cachexia is defined as a decrease of adipose and skeletal muscle tissues. In this study we evaluated, using a quantitative CT imaging-based approach, the distribution of abdominal adipose tissue in clear cell RCC (ccRCC) male patients with and without collateral vessels. Methods: between November 2019 and February 2020, in this retrospective study we enrolled 106 ccRCC male Caucasian patients divided into two groups: a ccRCCa group without collateral vessels (n = 48) and a ccRCCp group with collateral vessels (n = 58). The total adipose tissue (TAT), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) areas were measured in both groups. Moreover, the VAT/SAT ratio was calculated for each subject. Results: a statistically significant difference between the two groups was found in the SAT area (p < 0.05), while no significant differences were found in the TAT area, VAT area and VAT/SAT ratio. Conclusion: this study demonstrates a reduction of SAT in ccRCC patients with peritumoral collateral vessels.

scholarly journals A Risk Assessment Model of Immune-Associated lncRNA to Predict the Prognosis and Immune Landscape of Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Tiantian Ma ◽  
Cuiwen Zhu ◽  
Yiping Duan ◽  
Lingyue Chen ◽  
Jiacui Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Risk Groups ◽  
Renal Clear Cell Carcinoma ◽  
Assessment Model ◽  
Cell Renal Cell Carcinoma

Abstract Renal cell carcinoma (RCC) is one of the most common malignancies of the urinary system, accounting for 3% of adult malignancies. Long non-coding RNA (lncRNA) is abnormally regulated in many cancers and can be used as a molecular marker for early diagnosis and prognosis of RCC. Here, original lncRNA datas were retrieved from TCGA, differential co-expression analysis was performed to classify immune-related lncRNA (irlncRNA) with differential expression, and the improved 0 or 1 matrix cyclic single pairing method was used to verify lncRNA pairs. Then, we performed a univariate analysis in combination with an improved Lasso penalty regression that included cross-validation, multiple repetitions, and random stimulus procedures to determine different expression irlncRNA (DEirlncRNA) pairs. AUC values under Receiver Operating Characteristic curve (ROC) were calculated to obtain the optimal model, and AIC values of each point on AUC were calculated to obtain the optimal cut-off point to distinguish the high and low risk groups of Clear-cell renal cell carcinoma (ccRCC) patients. Finally, we evaluated the new model in a variety of clinical settings including survival, clinicopathological features, tumor-infiltrating immune cells, chemotherapy, and checkpoint related biomarkers, all showing promising clinical application.

Sign in / Sign up

Export Citation Format

Share Document