scholarly journals Identification of adhesion-associated extracellular matrix component thrombospondin 3 as a prognostic signature for clear cell renal cell carcinoma

2022 ◽  
Vol 63 (1) ◽  
pp. 107
Author(s):  
Xiangling Chen ◽  
Jiatian Lin ◽  
Min Chen ◽  
Qiaoling Chen ◽  
Zhiming Cai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Extracellular Matrix ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Extracellular Matrix Component ◽  
Prognostic Signature ◽  
Matrix Component ◽  
Cell Renal Cell Carcinoma

scholarly journals N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tianming Ma ◽  
Xiaonan Wang ◽  
Jiawen Wang ◽  
Xiaodong Liu ◽  
Shicong Lai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differential Expression ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.

scholarly journals Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Lu Zhang ◽  
Jianlong Li ◽  
Mengzhao Zhang ◽  
Lu Wang ◽  
Tao Yang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Risk Stratification ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Prognostic Signature ◽  
Characteristic Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Clear cell renal cell carcinoma (ccRCC) is widely acknowledged to be extremely sensitive to immunotherapy, emphasizing the tremendous impacts on which the tumor microenvironment (TME) has shown. However, the molecular subgroups characterized by the TME features scarcely serve as the risk stratification guides in clinical practice for survival outcomes and immunotherapy response prediction. This study generated fresh insights into a novel TME-related prognostic signature derived from The Cancer Genome Atlas database using integrated bioinformatics analyses. Subsequently, Kaplan–Meier survival analysis, receiver operating characteristic analysis, and univariate and multivariate Cox regression analysis were performed to evaluate and validate the efficacy and the accuracy of the signature in ccRCC prognosis. Furthermore, we discovered that the risk score presented an increased likelihood of correlation with miscellaneous clinicopathological characteristics, natural killer cell-mediated cytotoxicity, immune cell infiltration levels, and immune checkpoint expression. These findings highlighted the notion that the six-gene signature characterized by the TME features may have implications on the risk stratification for personalized and precise immunotherapeutic management.

scholarly journals Development of an Individualized Ubiquitin Prognostic Signature for Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yue Wu ◽  
Xi Zhang ◽  
Xian Wei ◽  
Huan Feng ◽  
Bintao Hu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Good Prediction ◽  
Tumor Type ◽  
Prognostic Signature ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Good Prediction Accuracy

Clear cell renal cell carcinoma (ccRCC) is a common tumor type in genitourinary system and has a poor prognosis. Ubiquitin dependent modification systems have been reported in a variety of malignancies and have influenced tumor genesis and progression. However, the molecular characteristics and prognostic value of ubiquitin in ccRCC have not been systematically reported. In our study, 204 differentially expressed ubiquitin related genes (URGs) were identified from The Cancer Genome Atlas (TCGA) cohort, including 141 up-regulated and 63 down-regulated URGs. A total of seven prognostic related URGs (CDCA3, CHFR, CORO6, RNF175, TRIM72, VAV3, and WDR72) were identified by Cox regression analysis of differential URGs and used to construct a prognostic signature. Kaplan-Meier analysis confirmed that high-risk patients had a worse prognosis (P = 1.11e-16), and the predicted area under the receiver operating characteristic (ROC) curves were 0.735 at 1 year, 0.702 at 3 years, and 0.744 at 5 years, showing good prediction accuracy. Stratified analysis showed that the URGs-based prognostic signature could be used to evaluate tumor progression in ccRCC. Further analysis confirmed that the signature is an independent prognostic factor related to the prognosis of ccRCC patients, which may help to reveal the molecular mechanism of ccRCC and provide potential diagnostic and prognostic markers for ccRCC.

scholarly journals Identification and validation of a hypoxia-related prognostic signature in clear cell renal cell carcinoma patients

Medicine ◽  
2021 ◽  
Vol 100 (39) ◽  
pp. e27374
Author(s):  
Zhengtian Li ◽  
Gang Du ◽  
Rong Zhao ◽  
Wenkang Yang ◽  
Chan Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Prognostic Signature ◽  
Cell Renal Cell Carcinoma

scholarly journals The Extracellular Matrix Environment of Clear Cell Renal Cell Carcinoma Determines Cancer Associated Fibroblast Growth

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5873
Author(s):  
Kyle H. Bond ◽  
Takuto Chiba ◽  
Kieran P. H. Wynne ◽  
Calvin P. H. Vary ◽  
Sunder Sims-Lucas ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Extracellular Matrix ◽  
Cell Carcinoma ◽  
Tumor Cells ◽  
Stromal Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Primary Cultures ◽  
Kidney Cortex ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and is often caused by mutations in the oxygen-sensing machinery of kidney epithelial cells. Due to its pseudo-hypoxic state, ccRCC recruits extensive vasculature and other stromal components. Conventional cell culture methods provide poor representation of stromal cell types in primary cultures of ccRCC, and we hypothesized that mimicking the extracellular environment of the tumor would promote growth of both tumor and stromal cells. We employed proteomics to identify the components of ccRCC extracellular matrix (ECM) and found that in contrast to healthy kidney cortex, laminin, collagen IV, and entactin/nidogen are minor contributors. Instead, the ccRCC ECM is composed largely of collagen VI, fibronectin, and tenascin C. Analysis of single cell expression data indicates that cancer-associated fibroblasts are a major source of tumor ECM production. Tumor cells as well as stromal cells bind efficiently to a nine-component ECM blend characteristic of ccRCC. Primary patient-derived tumor cells bind the nine-component blend efficiently, allowing to us to establish mixed primary cultures of tumor cells and stromal cells. These miniature patient-specific replicas are conducive to microscopy and can be used to analyze interactions between cells in a model tumor microenvironment.

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