Hypoxia-related lncRNA Signature to Predict the Survival of Patients with Clear Cell Renal Cell Carcinoma

Abstract Background: Renal cell carcinoma is the most common aggressive tumor of the genitourinary system. The main pathological subtype is clear cell renal cell carcinoma (ccRCC), and its treatment options are very limited. Therefore, identifying specific markers of renal clear cell carcinoma is of great significance for diagnosis and prognosis.Methods: From the TCGA database, we obtained information on 611 patients with renal clear cell carcinoma to analyze the relationship between hypoxia-related lncRNAs and overall survival. According to the coexpression of hypoxia genes and lncRNAs, genes related to hypoxia were identified. Difference analysis and Cox regression analysis were applied to assess survival-related risk factors. According to the median risk score of hypoxia-related genes, patients were divided into high-risk and low-risk groups. According to these gene characteristics and clinical parameters, a nomogram map was built, and GSEA was used for gene function annotation. RT-qRCR, Western Blot and Flow Cytometry were used to determine the role of SNHG19 in RCC cells.Results: By analyzing the coexpression of hypoxia genes and lncRNAs, 310 hypoxia-related genes were obtained. Six sHRlncRs were significantly correlated with the clinical outcomes of patients with ccRCC. Four sHRlncRs (AC011445.2, PTOV1-AS2, AP004609.3, and SNHG19) with the highest prognostic values were included in the group to construct the HRRS model. The high-risk group had a shorter OS than the low-risk group. HR-lncRNAs were considered to be an independent prognostic factor and associated with OS. The high- and low-risk groups showed different pathways in GSEA. Experiments showed that SNHG19 plays essential roles in autophagy and apoptosis of RCC cells.Conclusion: Our research shows that we established and verified a hypoxia-related lncRNA model that accurately correlates with ccRCC patients. This study also provides novel insights into hypoxia-based mechanisms and provides new biomarkers for the poor prognosis of ccRCC patients.

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

Genes ◽

10.3390/genes11040440 ◽

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.

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

Frontiers in Oncology ◽

10.3389/fonc.2020.605769 ◽

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.

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A Metastasis-Related lncRNA Signature Correlates With the Prognosis in Clear Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.692535 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qian Dou ◽

Shun Gao ◽

Hua Gan ◽

Zhao Kang ◽

Han Zhang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion ◽

Cell Renal Cell Carcinoma

To explore the role of metastasis-related long noncoding RNA (lncRNA) signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) patients. Firstly, metastasis-associated genes were identified to establish a metastasis-related lncRNA signature by statistical analysis. Secondly, the ccRCC patients were grouped into high-risk or low-risk group according to the established signature, and the different pathways between the 2 groups were identified by gene set enrichment analysis (GSEA). Finally, investigations involving PCR, transwell migration and invasion assay were carried out to further confirm our findings. The metastasis-related lncRNA signature was successfully constructed according to 7-metastasis-related genes (ADAM12, CD44, IL6, TFPI2, TGF-β1, THBS2, TIMP3). The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated. Most of the values of the area under the time‐dependent receiver‐operating characteristic (ROC) were greater than 0.70. The nomogram constructed by integrating clinical data and risk scores confirmed that the risk score calculated from our signature was a good prognosis predictor. GSEA analysis showed that some tumor-related pathways were enriched in the high-risk group, while metabolism-related pathways were enriched in the low-risk group. In carcinoma tissues, the SSR3-6, WISP1-2 were highly expressed, but the expression of UBAC2-6 was low there. Knocking down SSR3-6 decreased the ability of migration and invasion in ccRCC cells. In conclusion, we successfully constructed a metastasis-related lncRNA signature, which could accurately predict the survival and prognosis of ccRCC patients.

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

10.21203/rs.3.rs-944336/v1 ◽

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.

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Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

10.21203/rs.3.rs-17499/v2 ◽

2020 ◽

Author(s):

Yuanbin Jiang ◽

Xin Gou ◽

Zongjie Wei ◽

Jianyu Tan ◽

Haitao Yu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Cell Renal Cell Carcinoma ◽

Immune Score

Abstract Background: Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in urogenital system, and the clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma. Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IRlncRs analysis based on abundant ccRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. Methods: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immune-related genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines.Results: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRlncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient.Conclusion: Our results demonstrate some sIRlncRs with remark clinical relevance show the latent monitoring and prognosis values for ccRCC patients and may provide new insight in immunological researches and treatment strategies of ccRCC patients.

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Bioinformatics profiling intergrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

10.21203/rs.3.rs-17499/v1 ◽

2020 ◽

Author(s):

Xiang Zhou ◽

Xin Gou ◽

Zongjie Wei ◽

Jianyu Tan ◽

Haitao Yu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Cell Renal Cell Carcinoma ◽

Immune Score

Abstract Background: Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A complete and meaningful IRlncRs analysis based on abundant clear cell renal cell carcinoma (ccRCC) gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. Methods: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immune-related genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines. Results: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRlncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient. Conclusion: Our results demonstrated some sIRlncRs with remark clinical relevance shown the latent monitoring and prognosis value of ccRCC patients and may provide new insight for immunological research and treatment strategies in ccRCC patients.

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A Mitochondrial Dysfunction and Oxidative Stress Pathway-Based Prognostic Signature for Clear Cell Renal Cell Carcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/9939331 ◽

2021 ◽

Vol 2021 ◽

pp. 1-32

Author(s):

Yue Wu ◽

Xi Zhang ◽

Xian Wei ◽

Huan Feng ◽

Bintao Hu ◽

...

Keyword(s):

Oxidative Stress ◽

Renal Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Risk Group ◽

Prognostic Significance ◽

Individual Risk ◽

Cell Renal Cell Carcinoma

Mitochondria not only are the main source of ATP synthesis but also regulate cellular redox balance and calcium homeostasis. Its dysfunction can lead to a variety of diseases and promote cancer and metastasis. In this study, we aimed to explore the molecular characteristics and prognostic significance of mitochondrial genes (MTGs) related to oxidative stress in clear cell renal cell carcinoma (ccRCC). A total of 75 differentially expressed MTGs were analyzed from The Cancer Genome Atlas (TCGA) database, including 46 upregulated and 29 downregulated MTGs. Further analysis screened 6 prognostic-related MTGs (ACAD11, ACADSB, BID, PYCR1, SLC25A27, and STAR) and was used to develop a signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curve analyses showed that the signature could accurately distinguish patients with poor prognosis and had good individual risk stratification and prognostic potential. Stratified analysis based on different clinical variables indicated that the signature could be used to evaluate tumor progression in ccRCC. Moreover, we found that there were significant differences in immune cell infiltration between the low- and high-risk groups based on the signature and that ccRCC patients in the low-risk group responded better to immunotherapy than those in the high-risk group (46.59% vs 35.34%, P = 0.008 ). We also found that the expression levels of these prognostic MTGs were significantly associated with drug sensitivity in multiple ccRCC cell lines. Our study for the first time elucidates the biological function and prognostic significance of mitochondrial molecules associated with oxidative stress and provides a new protocol for evaluating treatment strategies targeting mitochondria in ccRCC patients.

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A novel 10 glycolysis-related genes signature could predict overall survival for clear cell renal cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08111-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qianwei Xing ◽

Tengyue Zeng ◽

Shouyong Liu ◽

Hong Cheng ◽

Limin Ma ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Overall Survival ◽

Cell Carcinoma ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Regression Analyses ◽

Cell Renal Cell Carcinoma

Abstract Background The role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration. Methods Clinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature. Results Finally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature. Conclusions A novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients’ survival.

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