Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.     

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.     

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.MethodsThe mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.FindingsA novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).InterpretationThe model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.FundingThis study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).     

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.     

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients’ prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509–3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601–4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113–8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243–4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.     

Semaphorin 6D as an independent predictor for better prognosis in clear cell renal cell carcinoma

IntroductionClear cell renal cell carcinoma (ccRCC) is the most common type of RCC and is associated with poor survival. However, the mechanisms underlying its development have not been thoroughly investigated. Semaphorin 6D (SEMA6D) is differentially expressed in various cancers, including lung adenocarcinoma and colorectal cancer. However, the role and mechanism of SEMA6D in ccRCC remain unexplored.Materials and methodsWe obtained 25 pairs of ccRCC tissue samples and 57 urine samples from patients with ccRCC and 52 urine samples from healthy volunteers. We performed RNA sequencing and compared the results with data from The Cancer Genome Atlas database to identify our gene of interest, SEMA6D. To verify the differential expression of SEMA6D, we used real-time quantitative polymerase chain reaction, immunohistochemistry, and enzyme-linked immunosorbent assay. Finally, we conducted in vitro proliferation, migration and invasion experiments.ResultsSEMA6D expression was significantly lower in ccRCC tissue compared to that in normal tissue. Comparative analysis of our results with data from online databases revealed that the expression level of SEMA6D in ccRCC tissue correlated with the clinical stage and pathological grade of ccRCC. Furthermore, higher SEMA6D expression was associated with improved quality of life of patients with ccRCC. In addition, the diagnostic value of SEMA6D was confirmed using data from two Gene Expression Omnibus ccRCC databases. The results showed that SEMA6D can be used as a predictor for ccRCC diagnosis, with an area under the curve of 0.9642.ConclusionSEMA6D may serve as a diagnostic and prognostic biomarker for ccRCC.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

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.     

Expression signature of six‐snoRNA serves as novel non‐invasive biomarker for diagnosis and prognosis prediction of renal clear cell carcinoma

Increasing evidence has verified that small nucleolar RNAs (snoRNAs) play significant roles in tumorigenesis and exhibit prognostic value in clinical practice. In the study, we analysed the expression profile and clinical relevance of snoRNAs from TCGA database including 530 ccRCC (clear cell renal cell carcinoma) and 72 control cases. By using univariate and multivariate Cox analysis, we established a six‐snoRNA signature and divided patients into high‐risk or low‐risk groups. We found patients in high‐risk group had significantly shorter overall survival and recurrence‐free survival than those in low‐risk group in test series, validation series and entire series by Kaplan‐Meier analysis. We also confirmed this signature had a great accuracy and specificity in 64 clinical tissue cases and 50 serum samples. Then, depending on receiver operating characteristic curve analysis we found the six‐snoRNA signature was an superior indicator better than conventional clinical factors (AUC = 0.732). Furthermore, combining the signature with TNM stage or Fuhrman grade were the optimal indicators (AUC = 0.792; AUC = 0.800) and processed the clinical applied value for ccRCC. Finally, we found the SNORA70B and its hose gene USP34 might directly regulate Wnt signalling pathway to promote tumorigenesis in ccRCC. In general, our study established a six‐snoRNA signature as an independent and superior diagnosis and prognosis indicator for ccRCC.     

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 novel apoptosis-associated prognostic signature related to osteosarcoma metastasis and immune infiltration

BackgroundApoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma.MethodsThe differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan–Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry.ResultsHSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients’ samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas.ConclusionsThe present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.     

FBXL20 promotes breast cancer malignancy by inhibiting apoptosis through degradation of PUMA and BAX

Apoptosis is a programmed cell death that efficiently removes damaged cells to maintain tissue homeostasis. Defect in apoptotic machinery can lead to tumor development, progression, and resistance to chemotherapy. PUMA (p53 upregulated modulator of apoptosis) and BAX (BCL2-associated X protein) are among the most well-known inducers of apoptosis. It has been reported that expression levels of BAX and PUMA are controlled at the posttranslational level by phosphorylation. However, the posttranslational regulation of these proapoptotic proteins remains largely unexplored. In this study, using biochemical, molecular biology, flow cytometric, and immunohistochemistry techniques, we show that PUMA and BAX are the direct target of the F-box protein FBXL20, which restricts their cellular levels. FBXL20 directs the proteasomal degradation of PUMA and BAX in a protein kinase AKT1-dependent manner to promote cancer cell proliferation and tumor growth. Interestingly, inactivation of AKT1 results in activation of another protein kinase GSK3α/β, which facilitates the proteasomal degradation of FBXL20 by another F-box protein, FBXO31. Thus, a switch between two signaling kinases AKT1 and GSK3α/β modulates the functional activity of these proapoptotic regulators, thereby determining cell survival or death. RNAi-mediated ablation of FBXL20 results in increased levels of PUMA as well as BAX, which further enhances the sensitivity of cancer cells to chemotherapeutic drugs. We showed that high level expression of FBXL20 in cancer cells reduces therapeutic drug-induced apoptosis and promotes chemoresistance. Overall, this study highlights the importance of targeting FBXL20 in cancers in conjunction with chemotherapy and may represent a promising anticancer strategy to overcome chemoresistance.     

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.     

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.     

Five human genes encoding F-box proteins: chromosome mapping and analysis in human tumors

Members of the F-box protein (Fbp) family are characterized by an approximately 40 amino acid F-box motif. SCF complexes (formed by Skp1, cullin, and one of many Fbps) act as protein-ubiquitin ligases that control the G(1)/S transition of the eukaryotic cell cycle. The substrate specificity of SCF complexes is determined by the presence of different Fbp subunits that recruit specific substrates for ubiquitination. Unchecked degradation of cellular regulatory proteins has been observed in certain tumors and it is possible that deregulated ubiquitin ligases play a role in the altered degradation of cell cycle regulators. We have recently identified a family of human Fbps. As a first step aimed at determining if FBP genes could be involved in human neoplasia, we have mapped the chromosome positions of 5 FBP genes by fluorescence in situ hybridization (FISH) to 10q24 (BTRC alias beta-TRCP/FBW1a), 9q34 (FBXW2 alias FBW2), 13q22 (FBXL3A alias FBL3a), 5p12 (FBXO4 alias FBX4) and 6q25-->q26 (FBXO5 alias FBX5). Since most of these are chromosomal loci frequently altered in tumors, we have screened 42 human tumor cell lines and 48 human tumor samples by Southern hybridization and FISH. While no gross alterations of the genes encoding beta-Trcp/Fbw1a, Fbw2, Fbx4 and Fbx5 were found, heterozygous deletion of the FBXL3A gene was found in four of 13 small cell carcinoma cell lines. This is the first evaluation of genes encoding Fbps in human tumors.     

The Ubiquitin E3 Ligase SCF-FBXO24 Recognizes Deacetylated Nucleoside Diphosphate Kinase A To Enhance Its Degradation

The Skp-Cul-F box (SCF) ubiquitin E3 ligase machinery recognizes predominantly phosphodegrons or, less commonly, an (I/L)Q molecular signature within substrates to facilitate their recruitment in mediating protein ubiquitination and degradation. Here, we examined the molecular signals that determine the turnover of the multifunctional enzyme nucleoside diphosphate kinase A (NDPK-A) that controls cell proliferation. NDPK-A protein exhibits a half-life of ∼6 h in HeLa cells and is targeted for ubiquitylation through actions of the F-box protein FBXO24. SCF-FBXO24 polyubiquitinates NDPK-A at K85, and two NH₂-terminal residues, L55 and K56, were identified as important molecular sites for FBXO24 interaction. Importantly, K56 acetylation impairs its interaction with FBXO24, and replacing K56 with Q56, an acetylation mimic, reduces NDPK-A FBXO24 binding capacity. The acetyltransferase GCN5 catalyzes K56 acetylation within NDPK-A, thereby stabilizing NDPK-A, whereas GCN5 depletion in cells accelerates NDPK-A degradation. Cellular expression of an NDPK-A acetylation mimic or FBXO24 silencing increases NDPK-A life span which, in turn, impairs cell migration and wound healing. We propose that lysine acetylation when presented in the appropriate context may be recognized by some F-box proteins as a unique inhibitory molecular signal for their recruitment to restrict substrate degradation.     

Circ_0101692 knockdown retards the development of clear cell renal cell carcinoma through miR-384/FN1 pathway

PurposeCircular RNA_0101692 (circ_0101692) is overexpressed in clear cell renal cell carcinoma (ccRCC) by microarray analyses. However, its function and action mechanism in ccRCC tumorigenesis is still elusive.MethodsWestern blotting and qRT-PCR were executed to assess the circ_0101692, miR-384 and FN1 expression in ccRCC cells and tissues. Target relationships among them were determined via dual luciferase reporter and/or RNA immunoprecipitation assays. Cell proliferation was evaluated by CCK-8 assay. Caspase-3 activity assay was utilized to analyze cell apoptosis. To find out whether ccRCC cells might migrate, a transwell assay was performed. To assess the effects of circ_0101692 on tumor development in vivo, a mouse xenograft model was used.ResultsHigh expression of circ_0101692 and FN1, and decreased miR-384 were determined in ccRCC. Cell growth, migration and viability were decreased whereas cell apoptosis was stimulated when circ_0101692 was knockdown. miR-384 inhibitor transfection attenuated the inhibiting impacts of circ_0101692 silencing on ccRCC cell progression. FN1 deletion further inverted the cancer-promoting effect of miR-384 downregulation on cell viability and migration. In addition, circ_0101692 could sponge miR-384 to relieve the inhibition of miR-384 on FN1 in ccRCC.ConclusionsCirc_0101692 targeted miR-384/FN1 axis to facilitate cell proliferation, migration and repress apoptosis, thereby accelerating the development of ccRCC. This points out that circ_0101692/miR-384/FN1 axis might be a prospective target implemented for the future treatment of ccRCC.