Systematic analysis of survival-associated alternative splicing signatures in clear cell renal cell carcinoma

Alternative splicing (AS) constitutes a major reason for messenger RNA (mRNA) and protein diversity. Increasing studies have shown a link to splicing dysfunction associated with malignant neoplasia. Systematic analysis of AS events in kidney cancer remains poorly reported. Therefore, we generated AS profiles in 533 kidney renal clear cell carcinoma (KIRC) patients in The Cancer Genome Atlas (TCGA) database using RNA-seq data. Then, prognostic models were developed in a primary cohort (N = 351) and validated in a validation cohort (N = 182). In addition, splicing networks were built by integrating bioinformatics analyses. A total of 11 268 and 8083 AS variants were significantly associated with patient overall survival time in the primary and validation KIRC cohorts, respectively, including STAT1, DAZAP1, IDS, NUDT7, and KLHDC4. The AS events in the primary KIRC cohorts served as candidate AS events to screen the independent risk factors associated with survival in the primary cohort and to develop prognostic models. The area under the curve of the receiver-operator characteristic curve for prognostic prediction in the primary and validation KIRC cohorts was 0.84 and 0.82 at 2500 days of overall survival, respectively. In addition, splicing correlation networks revealed key splicing factors (SFs) in KIRC, such as HNRNPH1, HNRNPU, KHDBS1, KHDBS3, SRSF9, RBMX, SFQ, SRP54, HNRNPA0, and SRSF6. In this study, we analyzed the AS landscape in the TCGA KIRC cohort and detected predictors (prognostic) based on AS variants with high performance for risk stratification of the KIRC cohort and revealed key SFs in splicing networks, which could act as underlying mechanisms.     

Immune-pyroptosis-related genes predict the prognosis of kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) is a common cancer of the adult urological system. Recent developments in tumor immunology and pyroptosis biology have provided new directions for kidney cancer treatment. Therefore, there is an urgent need to identify potential targets and prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy.MethodsThe expression of immune-pyroptosis-related differentially expressed genes (IPR-DEGs) between KIRC and healthy tissues was examined using the Gene Expression Omnibus datasets. The GSE168845 dataset was selected for subsequent analyses. Data of 1793 human immune-related genes were downloaded from the ImmPort database (https://www.immport.org./home), while those of 33 pyroptosis-related genes were extracted from previous reviews. The independent prognostic value of IPR-DEGs was determined using differential expression, prognostic, and univariate and multivariate Cox regression analyses. The GSE53757 dataset was used to further verify the GSDMB and PYCARD levels. In our cohorts, the association among DEGs and clinicopathological features and overall survival was analyzed. The least absolute shrinkage and selection operator Cox regression model was established to evaluate the correlation of IPR-DEGs with the immune score, immune checkpoint gene expression, and one-class logistic regression (OCLR) score. KIRC cells and clinical tissue samples were subjected to quantitative real-time polymerase chain reaction to examine the GSDMB and PYCARD mRNA levels. The GSDMB and PYCARD levels in a healthy kidney cell line (HK-2 cells) and two KIRC cell lines (786-O and Caki-1 cells) were verified. The tissue levels of GSDMB and PYCARD were evaluated using immunohistochemical analysis. GSDMB and PYCARD were knocked down in 786-O cells using short-interfering RNA. Cell proliferation was examined using the cell counting kit-8 assay. Cell migration was measured by transwell migration assaysResultsGSDMB and PYCARD were determined to be IPR-DEGs with independent prognostic values. A risk prognostic model based on GSDMB and PYCARD was successfully established. In the GSE53757 dataset, the GSDMB and PYCARD levels in KIRC tissues were significantly higher than those in healthy tissues. The GSDMB and PYCARD expression was related to T stage and OS in our cohort. The GSDMB and PYCARD levels were significantly correlated with the immune score, immune checkpoint gene expression, and OCLR score. The results of experimental studies were consistent with those of bioinformatics analysis. The GSDMB and PYCARD levels in KIRC cells were significantly upregulated when compared with those in healthy kidney cells. Consistently, GSDMB and PYCARD in KIRC tissues were significantly upregulated when compared with those in adjacent healthy kidney tissues. GSDMB and PYCARD knockdown significantly decreased 786-O cell proliferation (p < 0.05). Transwell migration result reflects that silencing GSDMB and PYCARD inhibited 786-O cell migration (p < 0.05) .ConclusionsGSDMB and PYCARD are potential targets and effective prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy in KIRC.     

Ferroptosis-related gene CHAC1 is a valid indicator for the poor prognosis of kidney renal clear cell carcinoma

To evaluate the validity of CHAC1 for predicting the prognosis of kidney renal clear cell carcinoma (KIRC) and to explore its therapeutic potential for KIRC, we conducted several bioinformatic analyses using the sequencing data and clinical information derived from online databases. We found CHAC1 is down-regulated in KIRC samples when compared with normal samples but up-regulated in KIRC samples with relatively higher malignancy and later stages. Univariate cox analysis and multivariate cox regression analysis were conducted and the results revealed up-regulated CHAC1 is an independent risk factor for poor prognosis of KIRC. Further, the nomogram model based on the result of multivariate cox regression analysis was constructed and effectively predicted patients' 1-year, 3-year and 5-year survival respectively. The correlation analyses showed CHAC1 is associated with the immune pathway markers of memory B cell, natural killer cell and type1 T helper cell as well as the checkpoint genes like ADORA2A, CD200, CD44, CD70, HHLA2, NRP1, PDCD1LG2 and TNFRSF18. Furthermore, experiments in vitro indicated CHAC1 could induce cell death in KIRC cell lines but had limited influence on cell migration and cell invasion. In conclusion, CHAC1 is found a valid indicator for poor prognosis of kidney renal clear cell carcinoma.     

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

ABSTRACT

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.     

lncRNA MSC-AS1 activates Wnt/β-catenin signaling pathway to modulate cell proliferation and migration in kidney renal clear cell carcinoma via miR-3924/WNT5A

Kidney renal clear cell carcinoma (KIRC) is the most general subtype of renal cell carcinoma, which composes about 1/20 of adult malignancies. The anomaly of long noncoding RNAs (lncRNAs) expression is proved to mediate cancer progression of various types. The function and mediation mechanism of MSC-AS1 has rarely been detected in KIRC before. This study started with the mediation of MSC-AS1 on cell function. In this study, MSC-AS1 was dramatically upregulated in KIRC and correlated with dismal prognosis of KIRC patients. Knockdown of MSC-AS1 would suppress the proliferative and migratory properties of KIRC cells. MSC-AS1 was found to directly downregulate miR-3924 expression while miR-3924 directly downregulated WNT5A expression. Meanwhile, MSC-AS1 could promote the expression of WNT5A, indicating the existence of MSC-AS1/miR-3924/WNT5A. Further assays indicated that MSC-AS1 could enhance Wnt/β-catenin pathway. By means of rescue assays, the mediation of MSC-AS1/miR-3924/WNT5A/β-catenin axis on KIRC cell proliferation, migration and migration was verified. This study revealed that MSC-AS1 regulates KIRC cell proliferation and migration via miR-3924/WNT5A/β-catenin axis. MSC-AS1 might contribute to new strategies for KIRC treatment.     

Identification of a universal 6-lncRNA prognostic signature for three pathologic subtypes of renal cell carcinoma

Renal cell carcinoma (RCC) is the most common adult renal epithelial cancer susceptible to metastasis and patients with irresectable RCC always have a poor prognosis. Long noncoding RNAs (lncRNAs) have recently been documented as having critical roles in the etiology of RCC. Nevertheless, the prognostic significance of lncRNA-based signature for outcome prediction in patients with RCC has not been well investigated. Therefore, it is essential to identify a lncRNA-based signature for predicting RCC prognosis. In the current study, we comprehensively analyzed the RNA sequencing data of the three main pathological subtypes of RCC (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], and kidney chromophobe carcinoma [KICH]) from The Cancer Genome Atlas (TCGA) database, and identified a 6-lncRNA prognostic signature with the help of a step-wise multivariate Cox regression model. The 6-lncRNA signature stratified the patients into low- and high-risk groups with significantly different prognosis. Multivariate Cox regression analysis showed that predictive value of the 6-lncRNA signature was independent of other clinical or pathological factors in the entire cohort and in each cohort of RCC subtypes. In addition, the three independent prognostic clinical factors (including age, pathologic stage III, and stage IV) was also stratified into low- and high-risk groups basis on the risk score, and the stratification analyses demonstrated that the high-risk score was a poor prognostic factor. In conclusion, these findings indicate that the 6-lncRNA signature is a novel prognostic biomarker for all three subtypes of RCC, and can increase the accuracy of predicting overall survival.     

Prognostic Value of Components of Body Composition in Patients Treated with Targeted Therapy for Advanced Renal Cell Carcinoma: A Retrospective Case Series

BackgroundTo evaluate the association between various components of body composition and overall survival of patients treated with targeted therapies for advanced renal cell carcinoma.MethodsThis retrospective study included 124 Chinese patients with advanced renal cell carcinoma who had been treated with targeted therapy from 2008 to 2012 at Fudan University Cancer Center. The L3 plane from a computed tomography scan was analyzed. Area and density were recorded as quantitative and quality measures. Univariate and multivariate Cox proportion hazard regression models were constructed to calculate the crude and adjusted hazard ratio (HR) of various components of body composition for overall survival. X-tile software was used to search for optimal cutoffs for male and female patients and the concordance index evaluated incremental changes in prognostication.ResultsAfter adjusting for age, sex and Heng risk stratification, only visceral adipose tissue index (HR 0.981, p = 0.002) and subcutaneous adipose tissue index (HR 0.987, p = 0.048) were independently associated with overall survival. Visceral adipose tissue remained a significant prognostic factor (HR 0.997, p = 0.005) when the influence of body mass index was included. Using defined cutoffs, patients with low VAT had double the death rate (p = 0.007). Visceral adipose tissue also added significant benefit to Heng risk stratification. Further exploratory analysis revealed that visceral adipose tissue may be an indicator of nutritional status in patients with advanced renal cell carcinoma.ConclusionRadiologic measurement of VAT is an independent prognostic factor for Asian patients treated with targeted therapy for advanced renal cell carcinoma.     

肾透明细胞癌患者铁死亡相关基因的预后作用

探讨铁死亡相关基因在肾透明细胞癌患者中的表达及其预后价值。通过TCGA数据库下载KIRC的相关测序数据与检索到的铁死亡相关基因取交集,进行铁死亡相关基因的差异分析。之后利用单变量和多变量Cox回归分析,筛选具有预后价值的基因,构建预测患者生存情况的风险评分模型,并对模型进行验证。对高低风险组进行GO与KEGG通路富集,探讨风险差异的可能原因;通过ssGSEA分析,评估高低风险组间的免疫浸润情况。在KIRC患者的肿瘤组织和正常组织中,共得到21个差异的铁死亡相关基因;通过单因素Cox回归分析,获得 28 个与KIRC预后相关的基因;之后进行Lasso回归与多因素Cox回归分析,结果显示有10个基因被纳入模型,计算公式为:风险值(Risk score)=(0.024 5)×ALOX5表达值+(0.126 0)×CBS表达值+(0.199 5)×CD44表达值+(0.218 3)×CHAC1表达值+(-0.295 9)×HMGCR表达值+(0.036 7)×MT1G表达值+(0.061 4)×SLC7A11表达值+(-0.080 7)×FDFT1表达值+(0.160 3)×PEBP1表达值+(-0.220 5)×GOT1表达值。生存状态图表明,高风险组死亡病例数多于低风险组;ROC曲线表明风险评分模型具备一定预测能力;K-M生存分析显示,高风险组总体生存率低于低风险组(P=5.73×10^-13)。GO与KEGG富集分析提示,高低风险组间免疫情况及IL-17信号通路存在显著差异;进一步的ssGSEA富集显示,高低风险组间大部分免疫细胞的评分存在显著差异。基于铁死亡相关基因的预后风险评分模型可用于KIRC的预后预测,针对铁死亡相关基因设计靶点可能是治疗KIRC的一种新选择。     

Mining TCGA database for screening and identification of hub genes in kidney renal clear cell carcinoma microenvironment

To evaluate the diagnosis and prognosis of the tumor microenvironment (immunization and stromal cells) in kidney renal clear cell carcinoma (KIRC), KIRC cases selected from The Cancer Genome Atlas database were divided into two groups according to the ESTIMATE algorithm-derived immune scores. Our data suggested that the Von Hippel-Lindau mutations and pathologic grades are associated with immune scores. Importat ntly, we identified 173 differential expression genes (DEGs) associated with prognosis in patients with KIRC. Consequently, Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on these DEGs, which included immune response, defense response, intrinsic to the plasma membrane, positive regulation of immune system process, and cytokine binding. Next, the protein-protein interaction network of DEGs and the most significant module was constructed. Five hub genes were identified and analyzed using biological analysis. The survival analysis of the hub genes showed that KIRC patients with high gene expression of C2, MXRA8, TNFSF13B, and X-linked inhibitor of apoptosis protein-associated factor 1 (XAF1) had worse overall survival, and MXRA8, TNFSF13B, and XAF1 alteration were significantly associated with disease-free survival (DFS). In addition, high gene expression of XAF1 alteration showed better DFS. Conclusion: we identified a list of microenvironment-related genes that are useful for understanding the molecular mechanisms and prognosis of KIRC.     

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening

Background

In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC’s prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA).

Results

With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data.

Conclusions

Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients’ survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

     

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival-related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein-protein interaction analysis was conducted for the prognostic AS-related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival-specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival-specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <−0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.     

A panel of eight autophagy-related long non-coding RNAs is a good predictive parameter for clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) carries a variable prognosis. Prognostic biomarkers can stratify patients according to risk, and can provide crucial information for clinical decision-making. We screened for an autophagy-related long non-coding lncRNA (lncRNA) signature to improve postoperative risk stratification in The Cancer Genome Atlas (TCGA) database. We confirmed this model in ICGC and SYSU cohorts as a significant and independent prognostic signature. Western blotting, autophagic-flux assay and transmission electron microscopy were used to verify that regulation of expression of 8 lncRNAs related to autophagy affected changes in autophagic flow in vitro. Our data suggest that 8-lncRNA signature related to autophagy is a promising prognostic tool in predicting the survival of patients with ccRCC. Combination of this signature with clinical and pathologic parameters could aid accurate risk assessment to guide clinical management, and this 8-lncRNAs signature related to autophagy may serve as a therapeutic target.     

Tumor-Specific Hypermethylation of Epigenetic Biomarkers,Including SFRP1, Predicts for Poorer Survival in Patients from the TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Project

The recent publication of the TCGA Kidney Renal Clear Cell Carcinoma (KIRC) project has provided an immense wealth and breadth of data providing an invaluable tool for confirmation and expansion upon previous observations in a large data set containing multiple data types including DNA methylation, somatic mutation, and clinical information. In clear cell renal cell carcinoma (CCRCC) many genes have been demonstrated to be epigenetically inactivated by promoter hypermethylated and in a small number of cases to be associated with clinical outcome. This study created two cohorts based on the Illumina BeadChip array used to confirm the frequency of tumor-specific hypermethylation of these published hypermethylated genes, assess the impact of somatic mutation or chromosomal loss and provide the most comprehensive assessment to date of the association of this hypermethylation with patient survival. Hypermethylation of the Fibrillin 2 (FBN2) gene was the most consistent epigenetic biomarker for CCRCC across both cohorts in 40.2% or 52.5% of tumors respectively. Hypermethylation of the secreted frizzled-related protein 1 (SFRP1) gene and the basonuclin 1 (BNC1) gene were both statistically associated with poorer survival in both cohorts (SFRP1 - p = <0.0001 or 0.0010 and BNC1 - p = <0.0001 or 0.0380) and represented better independent markers of survival than tumor stage, grade or dimension in one cohort and tumor stage or dimension in the other cohort. Loss of the SFRP1 protein can potentially activate the WNT pathway and this analysis highlighted hypermethylation of several other WNT pathway regulating genes and demonstrated a poorer survival outcome for patients with somatic mutation of these genes. The success of demethylating drugs in hematological malignances and the current trials in solid tumors suggest that the identification of clinically relevant hypermethylated genes combined with therapeutic advances may improve the effectiveness and usefulness of such drugs in clear cell renal cell carcinoma.