A large-scale transcriptome analysis identified ELANE and PRTN3 as novel methylation prognostic signatures for clear cell renal cell carcinoma

DNA methylation was involved in the progress of many types of cancer including clear cell renal cell carcinomas (ccRCCs). This study aimed to identify the prognostic DNA methylation biomarkers for the ccRCCs by a large-scale RNA-seq analysis. The DNA methylation data and the corresponding clinical information of the patients with ccRCCs were extracted from TCGA database and randomly divided into the training group and the validation group. The differentially expressed CpG sites and the survival-related CpG sites were further identified, which was combined into CpG sites pair and followed by screening the survival-related pairs. The C-index and the forward search algorithms were constructed to identify the prognostic signatures for the patients with ccRCCs. The prognostic signatures were verified by the validation dataset and the protein–protein interactions (PPI) network analysis was performed on the CPG sites of the signature. A total of 9,861 differentially expressed CPG sites were identified and 567 CpG sites were found to relate to the overall survival (OS) of the patients with ccRCCs. Besides, 1,146 CPG sites pairs were found to be related to the OS of the ccRCCs samples and the signature composed of seven CpG sites pairs were obtained to predict the prognosis of patients with ccRCCs and the results were verified in the validation dataset. Besides, the PPI network analysis showed that ELANE and PRTN3 gene may be associated with the invasion and metastasis of ccRCCs and could function as potential prognostic and therapeutic signatures for ccRCCs.     

VNN3 is a potential novel biomarker for predicting prognosis in clear cell renal cell carcinoma

Although pathological observations provide approximate prognoses, it is difficult to achieve prognosis in patients with existing prognostic factors. Therefore, it is very important to find appropriate biomarkers to achieve accurate cancer prognosis. Renal cell carcinoma (RCC) has several subtypes, the discrimination of which is crucial for proper treatment. Here, we present a novel biomarker, VNN3, which is used to prognose clear cell renal cell carcinoma (ccRCC), the most common and aggressive subtype of kidney cancer. Patient information analyzed in our study was extracted from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. VNN3 expression was considerably higher in stages III and IV than in stages I and II. Moreover, Kaplan–Meier curves associated high VNN3 expression with poor prognoses (TCGA, p?p?=?.00076), confirming that ccRCC prognosis can be predicted via VNN3 expression patterns. Consistent with all patient results, the prognosis of patients with higher VNN3 expression was worse in both low stage (I and II) and high stage (III and IV) (TCGA, p < 0.0001 in stage I and II; ICGC, p = 0.028 in stage I and II; TCGA, p = 0.005 in stage III and IV). Area under the curve and receiver operating characteristic curves supported our results that highlighted VNN3 expression as a suitable ccRCC biomarker. Multivariate analysis also verified the prognostic performance of VNN3 expression (TCGA, p?p?=?.017). Altogether, we suggest that VNN3 is applicable as a new biomarker to establish prognosis in patients with ccRCC.     

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.     

Genome-wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma with varied prognosis. We aimed to identify and assess the possible prognostic long noncoding RNA (lncRNA) biomarkers. LncRNAs expression data and corresponding clinical information of 619 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes analysis, univariate Cox regression, the least absolute shrinkage and selection operator Cox regression model were utilized to identify hub lncRNAs. Multivariate Cox regression was used to establish the risk model. Statistical analysis was performed using R 3.5.3. The expression value of five lncRNAs and the risk-score levels were significantly associated with a survival prognosis of ccRCC patients (all P < .001). In the TCGA validation cohort, the area under the curve (AUC) for the integrated nomogram was 0.905 and 0.91 for 3-, 5-year prediction separately. The AUC reached up to 0.757 in an independent ICGC cohort. Besides, the calibration plots also illustrated well curve-fitting between observation values and predictive values. Weighted gene co-expression network analysis and subsequent pathway analysis revealed that the PI3K-Akt-mTOR and hypoxia-inducible factor signaling crosstalk might function as the most essential mechanisms related to the five-lncRNAs signature. Our study suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234, and LINC01885 were significantly associated with ccRCC prognosis. The prognostic model based on this five lncRNA may predict the overall survival of ccRCC.     

利用TCGA数据库构建肾透明细胞癌相关miRNA预后模型

利用TCGA数据库中肾透明细胞癌的miRNA与mRNA数据及临床信息,构建由miRNA组成的预后风险评分模型,并筛选与生存预后相关的miRNA-mRNA调控关系对,为研究提供理论依据。下载并整理TCGA[JP+1]数据库中肾透明细胞癌的miRNA与mRNA数据;对数据进行差异分析,将差异表达的miRNA与临床信息进行合并,利用单因素与多因素Cox回归分析,构建预后模型并进行模型评价;对模型中的miRNA进行靶基因预测,结果与差异表达的mRNA进行取交集,构建miRNA-mRNA调控网络;对网络中的mRNA进行生存分析,筛选生存相关的miRNA-mRNA调控关系对。共得到49个差异表达的miRNA与3 613个差异表达的mRNA;预后模型计算公式为:风险值(risk score)=hsa-miR-21-5p表达量×0.603＋hsa-miR-1251-5p表达量×-0.093;调控网络中共纳入31个miRNA-mRNA调控关系对;对mRNA进行生存分析,共得到7个有价值的关系对。所构建预后模型可有效预测肾透明细胞癌患者生存预后情况,筛选到的miRNA-mRNA调控关系对可为相关研究与治疗提供参考。     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

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.     

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。     

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.     

Plasmatic carbonic anhydrase IX as a diagnostic marker for clear cell renal cell carcinoma

Carbonic anhydrase (CA, EC 4.2.1.1) IX is regarded as a tumour hypoxia marker and CA inhibitors have been proposed as a new class of antitumor agents, with one such agent in Phase II clinical trials. The expression of some CAs, in particular the isoforms CA IX and CA XII, has been correlated with tumour aggressiveness and progression in several cancers. The aim of this study was to evaluate the possibility that CA IX could represent a marker related to clear cell Renal Cell Carcinoma (ccRCC). Bcl-2 and Bax, and the activity of caspase-3, evaluated in tissue biopsies from patients, were congruent with resistance to apoptosis in ccRCCs with respect to healthy controls, respectively. In the same samples, the CA IX and pro-angiogenic factor VEGF expressions revealed that both these hypoxia responsive proteins were strongly increased in ccRCC with respect to controls. CA IX plasma concentration and CA activity were assessed in healthy volunteers and patients with benign kidney tumours and ccRCCs. CA IX expression levels were found strongly increased only in plasma from ccRCC subjects, whereas, CA activity was found similarly increased both in plasma from ccRCC and benign tumour patients, compared to healthy volunteers. These results show that the plasmatic level of CA IX, but not the CA total activity, can be considered a diagnostic marker of ccRCCs. Furthermore, as many reports exist relating CA IX inhibition to a better outcome to anticancer therapy in ccRCC, plasma levels of CA IX could be also predictive for response to therapy.     

Calcium-activated nucleotidase 1 silencing inhibits proliferation,migration, and invasion in human clear cell renal cell carcinoma

Calcium-activated nucleotidase 1 (CANT1, belongs to the apyrase family, is widely expressed in various organs. However, the biological function of CANT1 remains poorly explored. In this study, we aimed to investigate the expression profile and functions of CANT1 in clear cell renal cell carcinoma (ccRCC). Our data show that the protein level of CANT1 was significantly higher in tumor tissues than in adjacent normal tissues. CANT1 silencing suppressed cell proliferation, migration, and invasion obviously in 769-P and 786-O cells, arrested cell cycle in S phase and promoted apoptosis in 769-P cells. In conclusion, the present study shows the different expression mode of CANT1 in human ccRCC tumor tissue and adjacent normal tissue, denotes the function of CANT1 in ccRCC cells and provides potential molecular mechanisms and pathways of CANT1 antitumor function in ccRCC.     

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.     

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.     

Identification of a four immune-related genes signature based on an immunogenomic landscape analysis of clear cell renal cell carcinoma

Renal clear cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which has strong immunogenicity. A comprehensive study of the role of immune-related genes (IRGs) in ccRCC is of great significance in finding ccRCC treatment targets and improving patient prognosis. In this study, we comprehensively analyzed the expression of IRGs in ccRCC based on The Cancer Genome Atlas datasets. The mechanism of differentially expressed IRGs in ccRCC was analyzed by bioinformatics. In addition, Cox regression analysis was used to screen prognostic related IRGs from differentially expressed IRGs. We also identified a four IRGs signature consisting of four IRGs (CXCL2, SEMA3G, PDGFD, and UCN) through lasso regression and multivariate Cox regression analysis. Further analysis results showed that the four IRGs signature could effectively predict the prognosis of patients with ccRCC, and its predictive power is independent of other clinical factors. In addition, the correlation analysis of immune cell infiltration showed that this four IRGs signature could effectively reflect the level of immune cell infiltration of ccRCC. We also found that the expression of immune checkpoint genes CTLA-4, LAG3, and PD-1 in the high-risk group was higher than that in the low-risk group. Our research revealed the role of IRGs in ccRCC, and developed a four IRGs signature that could be used to evaluate the prognosis of patients with ccRCC, which will help to develop personalized treatment strategies for patients with ccRCC and improve their prognosis. In addition, these four IRGs may be effective therapeutic targets for ccRCC.     

Serum exosomal miR-210 as a potential biomarker for clear cell renal cell carcinoma

Exosomal microRNAs (miRNAs) are suggested to reflect molecular changes occurring in their cells of origin and are potential indicators in the early detection of cancers. This study aimed to determine whether certain exosomal miRNAs from tumor tissue can be used as noninvasive biomarkers for clear cell renal cell carcinoma (ccRCC). Based on ccRCC miRNA expression profiles and the literature, we selected six miRNAs (miR-210, miR-224, miR-452, miR-155, miR-21, and miR-34a) and analyzed their expression in tissues, sera, and serum exosomes through quantitative real-time polymerase chain reaction in hypoxia-induced (with CoCl₂) renal cell lines. miR-210, miR-224, miR-452, miR-155, and miR-21 were upregulated in tumor tissues compared with normal tissues. Serum miR-210 and miR-155 levels were higher in patients with ccRCC than in healthy controls (HCs). Furthermore, only exosomal miR-210 was significantly upregulated in patients with ccRCC than in HCs. Moreover, receiver operating characteristic (ROC) curve analysis revealed an area under the ROC curve of 0.8779 (95% confidence interval, 0.7987-0.9571) and a sensitivity and specificity of 82.5% and 80.0%, respectively. Moreover, exosomal miR-210 was upregulated at an advanced stage, and Fuhrman grade and metastasis decreased significantly one month after surgery. Acute hypoxia exposure activates miR-210 and release of exosomes with upregulated miR-210 in both normal and tumor RCC cell lines and interferes with vacuole membrane protein 1 mRNA expression, especially in the metastatic ccRCC cell line. In conclusion, Serum exosomal miR-210 originating from tumor tissue has potential as a novel noninvasive biomarker for the detection and prognosis of ccRCC.     

Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.     

Integrated glycoproteomic characterization of clear cell renal cell carcinoma

1. Download : Download high-res image (269KB)
2. Download : Download full-size image