Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma

BACKGROUND

Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI).

METHODS

The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology.

RESULTS

GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients.

CONCLUSION

This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.

M6A RNA methylation-mediated RMRP stability renders proliferation and progression of non-small cell lung cancer through regulating TGFBR1/SMAD2/SMAD3 pathway

Non-small cell lung cancer (NSCLC) has the highest mortality rate among all malignancies worldwide. The role of long noncoding RNAs (lncRNAs) in the progression of cancers is a contemporary research hotspot. Based on an integrative analysis of The Cancer Genome Atlas database, we identified lncRNA-RNA Component of Mitochondrial RNA Processing Endoribonuclease (RMRP) as one of the most highly upregulated lncRNAs that are associated with poor survival in NSCLC. Furthermore, N(6)-methyladenosine (m6A) was highly enriched within RMRP and enhanced its RNA stability. In vitro and in vivo experiments showed that RMRP promoted NSCLC cell proliferation, invasion, and migration. In terms of mechanism, RMRP recruited YBX1 to the TGFBR1 promotor region, leading to upregulation of the transcription of TGFBR1. The TGFBR1/SMAD2/SMAD3 pathway was also regulated by RMRP. In addition, RMRP promoted the cancer stem cells properties and epithelial mesenchymal transition, which promote the resistance to radiation therapy and cisplatin. Clinical data further confirmed a positive correlation between RMRP and TGFBR1. In short, our work reveals that m6A RNA methylation-mediated RMRP stability renders proliferation and progression of NSCLC through regulating TGFBR1/SMAD2/SMAD3 pathway.

LncRNA NCK1-AS1-mediated regulatory functions in human diseases

Disease development requires the activation of complex multi-factor processes involving numerous long noncoding RNAs (lncRNAs), which describe non-protein-coding RNAs longer than 200 nucleotides. Emerging evidence indicates that lncRNAs act as essential regulators that perform pivotal roles in the pathogenesis and progression of human diseases. The mechanisms underlying lncRNA involvement in diverse diseases have been extensively explored, and lncRNAs are considered powerful biomarkers for clinical practice. The lncRNA noncatalytic region of tyrosine kinase adaptor protein 1 (NCK1) antisense 1 (NCK1-AS1), also known as NCK1 divergent transcript (NCK1-DT), is encoded on human chromosome 3q22.3 and produces a 27,274-base-long transcript. NCK1-AS1 has increasingly been characterized as a causative agent for multiple diseases. The abnormal expression and involvement of NCK1-AS1 in various biological processes have been associated with several diseases. Further exploration of the mechanisms through which NCK1-AS1 contributes to disease development and progression will provide a foundation for potential clinical applications of NCK1-AS1 in the diagnosis and treatment of various diseases. This review summarizes the current understanding of the various functions and mechanisms through which NCK1-AS1 contributes to various diseases and the clinical application prospects for NCK1-AS1.

Establishment of a prognostic ferroptosis- and immune-related long noncoding RNAs profile in kidney renal clear cell carcinoma

Background: Ferroptosis and immunity are novel treatments that target several cancers, including kidney renal clear cell carcinoma (KIRC). Long noncoding RNAs (lncRNAs) are an important class of gene expression regulators that play fundamental roles in the regulation of ferroptosis and immunity. We aimed to identify ferroptosis- and immune-related lncRNAs as biomarkers in patients with KIRC.

Methods: Corresponding data for each patient with KIRC were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were used to identify candidate biomarkers followed by least absolute shrinkage and selection operator (LASSO) regression analyses, weighted gene coexpression network analysis (WGCANA), and gene set enrichment analysis (GSEA).

Results: Three ferroptosis- and immune-related differentially expressed lncRNAs (FI-DELs) (AC124854.1, LINC02609, and ZNF503-AS2) were markedly and independently correlated with the overall survival (OS) of patients with KIRC. The area under the curve (AUC) value of the prognostic model in the entire group using the three FI-DELs was > 0.70. The sensitivity and specificity of the diagnostic model using the three FI-DELs were 0.8586 and 0.9583, respectively.

Conclusion: The present study found that AC124854.1, LINC02609, and ZNF503-AS2 were considerably and independently correlated with the OS of patients with KIRC, suggesting that the three FI-DELs could be used as prognostic and diagnostic biomarkers for patients with KIRC.

The emerging potentials of lncRNA DRAIC in human cancers

Long non-coding RNA (lncRNA) is a subtype of noncoding RNA that has more than 200 nucleotides. Numerous studies have confirmed that lncRNA is relevant during multiple biological processes through the regulation of various genes, thus affecting disease progression. The lncRNA DRAIC, a newly discovered lncRNA, has been found to be abnormally expressed in a variety of diseases, particularly cancer. Indeed, the dysregulation of DRAIC expression is closely related to clinicopathological features. It was also reported that DRAIC is key to biological functions such as cell proliferation, autophagy, migration, and invasion. Furthermore, DRAIC is of great clinical significance in human disease. In this review, we discuss the expression signature, clinical characteristics, biological functions, relevant mechanisms, and potential clinical applications of DRAIC in several human diseases.

Transcriptomics-Based Study of Differentially Expressed Genes Related to Fat Deposition in Tibetan and Yorkshire Pigs

Fat deposition traits are one of the key factors in pig production and breeding. The fat deposition capacity of pigs mainly affects the quality of pork and pig productivity. The aim of this study was to analyze the differential expression of mRNA levels in dorsal adipose tissue of Tibetan and York pigs at different growth stages using transcriptomic data to estimate key genes that regulate fat deposition in pigs. The results showed that a total of 32,747 positively expressed genes were present in the dorsal adipose tissue of the two breeds. Differentially expressed gene (DEG) screening of multiple combinations between the two breeds yielded 324 DEGS. Gene ontology (GO) biofunctional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that these DEGS were mainly involved in lipid metabolic pathways, steroid biosynthetic pathways and lipid biosynthetic processes, sterol biosynthetic processes, brown adipocyte differentiation, and other pathways related to lipid deposition and metabolism. The results showed that ACACA, SLC2A4 and THRSP genes positively regulated the lipid deposition ability and CHPT1 gene negatively regulated the lipid deposition ability in pigs. The results of this experiment suggest a theoretical basis for further studies on the regulatory mechanisms of fat deposition in pigs.

Development and Validation of Ferroptosis-Related LncRNA Biomarker in Bladder Carcinoma

Bladder cancer (BC) is a highly prevalent cancer form of the genitourinary system; however, the effective biomarkers are still ambiguous and deserve deeper investigation. Long non-coding RNA (lncRNA) occupies a prominent position in tumor biology and immunology, and ferroptosis-related genes participate in regulatory processes of cancer. In this study, 538 differentially expressed ferroptosis-related lncRNAs were identified from the The Cancer Genome Atlas database through co-expression method and differential expression analysis. Then, the samples involved were equally and randomly divided into two cohorts for the construction of gene model and accuracy verification. Subsequently, a prediction model containing five ferroptosis-related lncRNAs was constructed by LASSO and Cox regression analysis. Furthermore, in terms of predictive performance, consistent results were achieved in the training set, testing set, and entire set. Kaplan-Meier curve, receiver operating characteristic area under the curve, and principal component analysis results verified the good predictive ability of model, and the gene model was confirmed as an independent prognostic indicator. To further investigate the mechanism, we explored the upstream of five lncRNAs and found that they may be modified by m6A to increase or decrease their expression in BC. Importantly, the low-risk group displayed higher mutation burden of tumors and lower Tumor Immune Dysfunction and Exclusion score, which may be predicted to have a higher response rate to immunotherapy. Interestingly, the patients in the high-risk group appeared to have a higher sensitivity to traditional chemotherapeutic agents through pRRophetic analysis. In general, our research established a five-ferroptosis-related lncRNA signature, which can be served as a promising prognostic biomarker for BC.

The LncRNA DUXAP10 Could Function as a Promising Oncogene in Human Cancer

Cancer is one of the most prevalent and deadliest diseases globally, with an increasing morbidity of approximately 14 million new cancer cases per year. Identifying novel diagnostic and prognostic biomarkers for cancers is important for developing cancer therapeutic strategies and lowering mortality rates. Long noncoding RNAs (lncRNAs) represent a group of noncoding RNAs of more than 200 nucleotides that have been shown to participate in the development of human cancers. The novel lncRNA DUXAP10 was newly reported to be abnormally overexpressed in several cancers and positively correlated with poor clinical characteristics of cancer patients. Multiple studies have found that DUXAP10 widely regulates vital biological functions related to the development and progression of cancers, including cell proliferation, apoptosis, invasion, migration, and stemness, through different molecular mechanisms. The aim of this review was to recapitulate current findings regarding the roles of DUXAP10 in cancers and evaluate the potential of DUXAP10 as a novel biomarker for cancer diagnosis, treatment, and prognostic assessment.

The LMCD1-AS1/miR-526b-3p/OSBPL5 axis promotes cell proliferation, migration and invasion in non-small cell lung cancer

PURPOSE

To explore the specific role and regulatory mechanism of oxysterol binding protein like 5 (OSBPL5) in non-small cell lung cancer (NSCLC).

METHODS AND RESULTS

Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrated that OSBPL5 expression was notably elevated in NSCLC tissues and cell lines, and Kaplan-Meier analysis manifested that high OSBPL5 expression was closely related to the poor prognosis of NSCLC patients. Besides, according to the results from western blot analysis, cell counting kit-8, EdU and Transwell assays, knockdown of OSBPL5 suppressed NSCLC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) process. Additionally, by performing qRT-PCR analysis, luciferase reporter and RNA pull-down assays, we verified that OSBPL5 was a downstream target of miR-526b-3p and long noncoding RNA (lncRNA) LMCD1-AS1 served as a sponge for miR-526b-3p. Moreover, from rescue assays, we observed that OSBPL5 overexpression offset LMCD1-AS1 knockdown-mediated inhibition in cell proliferation, migration, invasion and EMT in NSCLC.

CONCLUSIONS

This paper was the first to probe the molecular regulatory mechanism of OSBPL5 involving the LMCD1-AS1/miR-526b-3p axis in NSCLC and our results revealed that the LMCD1-AS1/miR-526b-3p/OSBPL5 axis facilitates NSCLC cell proliferation, migration, invasion and EMT, which may offer a novel therapeutic direction for NSCLC.

Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

Background

Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction.

Methods

The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs.

Results

Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells.

Conclusions

The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

LncRNA SNHG17 promotes tumor progression and predicts poor survival in human renal cell carcinoma via sponging miR-328-3p

Accumulating data shows that dysregulation of long non-coding RNAs (lncRNAs) are involved in human tumors' occurrence and progression. Small nucleolar RNA host genes (SNHGs) are recently revealed to play a carcinogenic role in various human neoplasms. However, the functions and underlying mechanisms of lncRNA SNHG17 in renal cell carcinoma (RCC) are still elusive. We analyzed the relationship between SNHG17 expression levels and clinicopathologic characteristics and prognosis in patients with RCC according to TCGA RNA-sequencing data and our cohort data. Loss-of-function and gain-of-function experiments were conducted to examine the biological behaviors of SNHG17 on RCC cell proliferation, migration, invasion, apoptosis, and tumor growth in vivo. The interaction between SNHG17, miR-328-3p, and Histone’sH2Avariant (H2AX) was verified by bioinformatics, dual-luciferase reporter gene, and RNA immunoprecipitation (RIP). Highly expressed SNHG17 was evident in RCC tissue samples and cell lines, and SNHG17 overexpression was related to advanced TNM stage and reduced relapse-free and overall survival of patients with RCC. Knockdown of SNHG17 prohibited malignant phenotypes, whereas ectopic SNHG17 expression showed the opposite effects. More importantly, SNHG17 could upregulate the expression of H2AX by acting as a miR-328-3p sponge. In vivo experiments confirmed that SNHG17 promoted the growth of RCC tumors. SNHG17/miR-328-3p/H2AXaxis might be involved in RCC progression, which provided a potential therapeutic target for RCC.

Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network

Introduction

Kidney renal clear cell carcinoma (KIRC) has a high incidence globally, and its pathogenesis remains unclear. Long non-coding RNA (lncRNA), as a molecular sponge, participates in the regulation of competitive endogenous RNA (ceRNA). We aimed to construct a ceRNA network and screened out possible lncRNAs to predict KIRC prognosis.

Material and methods

All KIRC data were downloaded from the TCGA database and screened to find the possible target lncRNA; a ceRNA network was designed. Next, GO functional enrichment and KEGG pathway of differentially expressed mRNA related to lncRNA were performed. We used Kaplan-Meier curve analysis to predict the survival of these RNAs. We used Cox regression analysis to construct a model to predict KIRC prognosis.

Results

In the KIRC datasets, 1457 lncRNA, 54 miRNA and 2307 mRNA were screened out. The constructed ceRNA network contained 81 lncRNAs, nine miRNAs, and 17 mRNAs differentially expressed in KIRC. Survival analysis of all differentially expressed RNAs showed that 21 lncRNAs, four miRNAs, and two mRNAs were related to the overall survival rate. Cox regression analysis was performed again, and we found that eight lncRNAs were related to prognosis and used to construct predictive models. Three lnRNAs from independent samples were meaningful.

Conclusion

The construction of ceRNA network was involved in the process and transfer of KIRC, and three lncRNAs may be potential targets for predicting KIRC prognosis.

Construction and validation of an autophagy-related long noncoding RNA signature for prognosis prediction in kidney renal clear cell carcinoma patients

Purpose

The purpose of this study was to identify autophagy‐associated long noncoding RNAs (ARlncRNAs) using the kidney renal clear cell carcinoma (KIRC) patient data from The Cancer Genome Atlas (TCGA) database and to construct a prognostic risk‐related ARlncRNAs signature to accurately predict the prognosis of KIRC patients.

Methods

The KIRC patient data were originated from TCGA database and were classified into a training set and testing set. Seven prognostic risk‐related ARlncRNAs, identified using univariate, lasso, and multivariate Cox regression analysis, were used to construct prognostic risk‐related signatures. Kaplan–Meier curves and receiver operating characteristic (ROC) curves as well as independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate and verify the specificity and sensitivity of the signature in training set and testing set, respectively. Two nomograms were established to predict the probable 1‐, 3‐, and 5‐year survival of the KIRC patients. In addition, the lncRNA‐mRNA co‐expression network was constructed and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify biological functions of ARlncRNAs.

Results

We constructed and verified a prognostic risk‐related ARlncRNAs signature in training set and testing set, respectively. We found the survival time of KIRC patients with low‐risk scores was significantly better than those with high‐risk scores in training set and testing set. ROC curves suggested that the area under the ROC (AUC) value for prognostic risk score signature was 0.81 in training set and 0.705 in testing set. And AUC values corresponding to 1‐, 3‐, and 5 years of OS were 0.809, 0.753, and 0.794 in training set and 0.698, 0.682, and 0.754 in testing set, respectively. We established the two nomograms that confirmed high C‐index and accomplished good prediction accuracy.

Conclusions

We constructed a prognostic risk‐related ARlncRNAs signature that could accurately predict the prognosis of KIRC patients.

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.

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA-miRNA-mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network was highly enriched in some cancer-associated signaling pathways. Next, lasso-Cox model was run 1,000 times to recognize the potential survival-related combinations of the candidate lncRNAs in the ceRNA network, followed by the "best subset selection" to further optimize these lncRNA-based combinations, and a seven-lncRNA prognostic signature with the best performance was determined. Based on the median risk score, LUAD patients could be well distinguished into high-/low-risk subgroups. The Kaplan-Meier survival curve showed that LUAD patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (log-rank test P = 4.52 × 10^-9). The ROC curve indicated that the clinical genomic model including both the TNM staging system and the signature had a superior performance in predicting the patients' overall survival compared to the clinical model with the TNM staging system only. Further stratification analysis suggested that the signature could work well in the different strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.

A Competing Endogenous RNA Network and an 8-lncRNA Prognostic Signature Identify MYO16-AS1 as an Oncogenic lncRNA in Bladder Cancer

Recently, growing evidence has shed light on the competitive endogenous RNAs (ceRNAs) activity of long noncoding RNAs (lncRNAs) in carcinogenesis and tumor progression. To better elucidate the regulatory mechanisms of lncRNA in muscle-invasive bladder cancer (MIBC), we identified aberrantly expressed mRNAs, lncRNAs, and miRNAs in tumor tissues by using RNA sequence profiles from The Cancer Genome Atlas. The MIBC-specific ceRNA network, including 58 lncRNAs, 22 miRNAs, and 52 mRNAs, was constructed and visualized in Cytoscape. Further, using the univariate and multivariate Cox regression model, we screened 8 lncRNAs (AC078778.1, LINC00525, AC008676.1, AP000553.1, SACS-AS1, AC009065.1, AC127496.3, and MYO16-AS1) to construct an lncRNA signature for predicting the overall survival of MIBC patients. Kaplan-Meier analysis and a receiver operating characteristic curve were applied to evaluate the performance of the signature. Real-time quantitative PCR analysis was carried out to test expression levels of the 8 lncRNAs in MIBC patient tissues. Transwell assays demonstrated that overexpressing MYO16-AS1 can enhance UMUC2 migration and invasion. Our study offers a novel lncRNA-correlated ceRNA model to better understand the molecular mechanisms involved in MIBC. In addition, we developed an independent 8-lncRNAs biomarker for prognostic prediction and identified MYO16-AS1 as an oncogenic lncRNA in bladder cancer.

LINC02738 Participates in the Development of Kidney Cancer Through the miR-20b/Sox4 Axis

Background

Long non-coding RNAs (lncRNAs) can affect tumorigenesis. Data from The Cancer Genome Atlas (TCAG) suggest that LINC02783 is highly expressed in renal cell carcinoma (RCC) and is expected to be a potential biological target. We conducted this study to verify this.

Patients and Methods

We conducted this study to verify the opinion that "LINC02783 is highly expressed in renal cell carcinoma (RCC) and is expected to be a potential biological target". We employed quantitative real-time polymerase chain reaction (qRT-PCR) to test LINC02783 expression in RCC tissues, CKK-8 assay and transwell assay to assess the viability and invasion of RCC cells, Western blot to quantify Sox-4 expression, dual-luciferase reporter (DLR) assay and RNA immunoprecipitation (RIP) assay to analyze the interaction between LINC02783 and miR-20b, in vivo experiments to test tumor formation.

Results

We detected high LINC02783 expression in RCC patients. Patients with higher LINC02783 levels had a markedly poorer prognosis. In vitro and in vivo, the down-regulation of LINC02783 suppressed the viability and invasion of RCC cells. The DLR assay results revealed that LINC02783 enhanced Sox-4 expression by regulating miR-20b. LINC02783 can act as a sponge for miR-20b to inhibit Sox-4 expression.

Conclusion

LINC02783 is highly expressed in RCC patients and indicates a poor prognosis. LINC02783 can affect the occurrence and progression of RCC through the miR-20b/Sox-4 axis, making it a promising target for the treatment of RCC.

Integrated analysis of immune-related genes in endometrial carcinoma

Background

Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis.

Methods

Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results.

Results

A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-prognosis-related lncRNAs were screened out. Kaplan-Meier survival analysis and time-dependent ROC analysis revealed a satisfactory predictive potential of the 4-lncRNA model. Consistency was achieved among the results from the training set, testing set and entire cohort. The distributed patterns between the high- and low-risk groups could be distinguished in principal component analysis. Comparisons of the risk score and clinical factors confirmed the four-lncRNA-based signature as an independent prognostic indicator. Last, the reliability of the results was verified by qRT-PCR in 29 cases of endometrial carcinoma and in cells.

Conclusions

Overall, our study constructed a network of transcription factors and immune-related genes and explored a four immune-related lncRNA signature that could serve as a novel potential biomarker of EC.

An immune-related lncRNA prognostic model in papillary renal cell carcinoma: A lncRNA expression analysis

OBJECTIVE

To screen several immune-related long non-coding RNAs (lncRNAs) and construct a prognostic model for papillary renal cell carcinoma (pRCC).

METHODS

Transcriptome-sequencing data of pRCC was downloaded and a prognostic model was constructed. Time-dependent receiver operating characteristic (ROC) curve was plotted and the area under curve (AUC) was calculated. We conducted quantitative reverse transcription polymerase chain reaction (RT-PCR) to verify the model. The gene set enrichment analysis (GSEA) was used to show the connection of our model with immune pathways.

RESULT

We identified four lncRNAs to constructed the model. The model was significantly associated with the survival time and survival state. The expression-levels of the four lncRNAs were measured and the prognosis of high-risk patients was significantly worse. The two immune-gene sets had an active performance in the high-risk patients.

CONCLUSION

We constructed a prognostic model in pRCC which provided more reference for treatment.

Identification of 12 immune-related lncRNAs and molecular subtypes for the clear cell renal cell carcinoma based on RNA sequencing data

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Despite the existing extensive research, the molecular and pathogenic mechanisms of ccRCC are elusive. We aimed to identify the immune-related lncRNA signature and molecular subtypes associated with ccRCC. By integrating 4 microarray datasets from Gene Expression Omnibus database, we identified 49 immune-related genes. The corresponding immune-related lncRNAs were further identified in the TCGA dataset. 12-lncRNAs prognostic and independent signature was identified through survival analysis and survival difference between risk groups was further identified based on the risk score. Besides, we identified 3 molecular subtypes and survival analysis result showed that cluster 2 has a better survival outcome. Further, ssGSEA enrichment analysis for the immune-associated gene sets revealed that cluster 1 corresponded to a high immune infiltration level. While cluster 2 and cluster 3 corresponded to low and medium immune infiltration level, respectively. In addition, we validated the 12-lncRNA prognostic signature and molecular subtypes in an external validation dataset from the ICGC database. In summary, we identified a 12-lncRNA prognostic signature which may provide new insights into the molecular mechanisms of ccRCC and the molecular subtypes provided a theoretical basis for personalized treatment by clinicians.

UBE2T promotes radiation resistance in non-small cell lung cancer via inducing epithelial-mesenchymal transition and the ubiquitination-mediated FOXO1 degradation

Radiation resistance affects survival in non-small-cell lung cancer (NSCLC) patients. Further exploration of mechanisms and targets is urgently needed. Using bioinformatic analyses, we found that UBE2T is associated with survival, tumor size, lymph node metastasis and distant metastasis. Then, real-time PCR and immunohistochemistry were performed to explore the differentially expressed genes between normal and NSCLC tissues. Furthermore, we used colony formation, EdU incorporation, scratch, transwell assays, flow cytometry, immunofluorescence and western blot to assess the role of UBE2T in vitro and in vivo. RNA-Seq and coimmunoprecipitation were used to explore the mechanism. The results showed that UBE2T promotes proliferation, migration, invasion, and radiation resistance in vitro and in vivo by accelerating the G2/M transition and inhibiting apoptosis. Mechanistically, UBE2T promotes epithelial-mesenchymal transition (EMT) via ubiquitination-mediated FOXO1 degradation and Wnt/β-catenin signaling pathway activation. Moreover, FOXO1 reversed radiation resistance and EMT. Therefore, UBE2T may be a potential target for enhancing radiotherapy sensitivity and serve as a biomarker to predict prognosis.

Identification and validation of seven prognostic long non-coding RNAs in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, due to poor diagnosis and treatment. There is increasing evidence that demonstrates the involvement of long non-coding RNAs (lncRNAs) in carcinogenesis and cancer progression. Therefore, the aim of the present study was to explore potential lncRNA-associated features of patients with OSCC as a valuable and independent prognostic biomarker. A total of 268 lncRNA expression profiles and clinical patient information on OSCC were downloaded from The Cancer Genome Atlas database. The clinical information was exploited for prescreening, using Cox regression analysis, and differentially expressed lncRNAs (DElncRNAs) were identified using edgeR software. Using the 'caret' package, the datasets were categorized into test datasets and training datasets, respectively. Through bioinformatics, seven prognostic DElncRNAs were selected. Using the regression coefficients, a risk score based on the seven-DElncRNA signature was developed to assess the prognostic function of key DElncRNAs. According to the median risk score, patients were classified into high-risk and low-risk groups in the training and test datasets. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cut-off point was obtained from ROC analysis. Based on the optimal cut-off point, the patients were also categorized into high-risk and low-risk groups. Notably, the optimal cut-off point was more sensitive than the median risk score, particularly in the test dataset. The Kaplan-Meier survival and log rank test analysis results indicated that the P-value, based on the optimal cut-off, was less than the median risk cut-off. Additionally, stratified analysis results revealed that the seven-DElncRNAs signature was also independent of OSCC age. Furthermore, the findings of the present study suggested that the seven-DElncRNA signature can be used as a potential prognostic indicator and may have important clinical significance in OSCC.

Comprehensive analysis of aberrantly expressed long non‑coding RNAs, microRNAs, and mRNAs associated with the competitive endogenous RNA network in cervical cancer

Cervical cancer is a common malignant disease that poses a serious health threat to women worldwide. Growing research efforts have focused on protein‑coding and non‑coding RNAs involved in the tumorigenesis and prognosis of various types of cancer. The potential molecular mechanisms and the interaction among long non‑coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs require further investigation in cervical cancer. In the present study, lncRNA, miRNA, and mRNA expression profiles of 304 primary tumor tissues from patients with cervical cancer and 3 solid normal tissues from The Cancer Genome Atlas (TCGA) dataset were studied via RNA sequencing (RNA‑seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using R package clusterProfiler to annotate the principal functions of differentially expressed (DE) mRNAs. Kaplan‑Meier analysis was also conducted to investigate the effects of DElncRNAs, DEmiRNAs, and DEmRNAs on overall survival. A total of 2,255 mRNAs, 133 miRNAs, and 150 lncRNAs that were differentially expressed were identified with a threshold of P<0.05 and |fold change (FC)|>2. Functional enrichment analysis indicated that DEmRNAs were enriched in cancer‑associated KEGG pathways. Furthermore, 255 mRNAs, 15 miRNAs, and 12 lncRNAs that were significantly associated with overall survival in cervical carcinoma were also identified. Importantly, an miRNA‑mediated competitive endogenous RNA (ceRNA) network was successfully constructed based on the expression profiles of DElncRNAs and DEmRNAs. More importantly, it was found that the lncRNA EPB41L4A‑AS1 may function as a pivotal regulator in carcinoma of the uterine cervix. Taken together, the present study has provided novel insights into investigating the potential mechanisms underlying tumorigenesis, development, and prognosis of cervical cancer, and presented new potential avenues for cancer therapeutics.

The construction and analysis of competitive endogenous RNA (ceRNA) networks in metastatic renal cell carcinoma: a study based on The Cancer Genome Atlas

Background

The pathogenesis of metastatic renal cell carcinoma (mRCC), one of the most common malignant neoplasms, remains unknown. Studies on competitive endogenous RNAs (ceRNAs) have uncovered new interactions between RNAs, which suggest their roles in cancer pathogenesis. However, the role of ceRNA networks in mRCC has not yet been studied. Thus, this study aims to explore the role of ceRNA networks in mRCC development and identify potential prognostic indicators.

Methods

We analyzed RNA sequencing data of mRCC patients, which had been obtained from The Cancer Genome Atlas (TCGA) database. Next, differentially expressed long-noncoding RNAs (DElncRNAs), differentially expressed micro RNAs (DEmiRNAs), and differentially expressed messenger RNAs (DEmRNAs) in mRCC and clear cell RCC (ccRCC) samples were identified using the edgeR package that is available in R software. Moreover, based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID), enrichment analyses for biological processes and pathways functional were performed. As such, we built a ceRNA network and performed a survival analysis using the survival package in R.

Results

A total of 369 DElncRNAs, 12 DEmiRNAs, and 728 DEmRNAs were identified for further analysis. Of these, 11 lncRNAs, 20 mRNAs, and 2 miRNAs were included in the ceRNA network. Moreover, 7 of the 11 lncRNAs and 3 of the 20 mRNAs were associated with the overall survival of mRCC patients (P<0.05).

Conclusions

Collectively, our findings allow a deepened understanding of the molecular mechanism of the ceRNA network and its role in mRCC development, which can guide both mRCC therapy and related future research.

Long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4

Non-small cell lung cancer (NSCLC) , as the most prevalent type of lung carcinoma with high severity, is of urgent necessity to be investigated for novel therapeutic strategies. Long non-coding RNAs (lncRNAs) are notable for their participation in cancer regulation, and lncRNA long intergenic non-protein coding RNA 641 (LINC00641) has been found to have an inhibitory influence on bladder cancer, but its role in NSCLC has not yet been studied. In this research, we launched an investigation into the biological functions and the underlying molecular mechanisms of LINC00641 in NSCLC. At first, downregulation of LINC00641 was identified in NSCLC cells. Functionally, LINC00641 suppressed cell proliferation and induced cell apoptosis in NSCLC, indicating that LINC00641 exerted tumor-suppressive role in NSCLC. Through mechanism investigation, we determined that LINC00641 acted as a competing endogenous RNA (ceRNA) in NSCLC by sponging miR-424-5p to upregulate phospholipid scramblase (PLSCR4) expression. Further rescue assays indicated that miR-424-5p and PLSCR4 could reverse LINC00641-mediated cellular processes. Taken together, it is demonstrated in our study that LINC00641 can function as a tumor suppressor in NSCLC via a ceRNA network.

Noncoding RNAs orchestrate cell growth, death and drug resistance in renal cell carcinoma

Aim: We aimed to explore the roles of noncoding RNAs (ncRNAs) in renal cell carcinoma. Materials & methods: The altered expressions of miR-196a2, miR-499a, H19, MALAT1 and GAS5, as well as some target transcripts were identified by quantitative real-time reverse transcription polymerase chain reaction. Results: Up-regulation of miR-196a2, E2F3, HSPA4 and MALAT1 (median fold change: 5.69, 25.6, 4.15 and 19.6, respectively) and down-regulation of miR-499a, GAS5, PDCD4, ANXA1 and DFFA (median fold change: 0.28, 0.25, 0.12, 0.09 and 0.08, respectively) were reported compared with paired non-cancer tissue. PDCD4, DFFA and GAS5 down-regulation was associated with poor prognosis in terms of high grade, larger tumor, nodal invasion, capsular and pelvic infiltration. Conclusion: The identified ncRNAs could represent potential theranostic biomarkers for renal cell carcinoma.

Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in squamous cell carcinoma of tongue

BACKGROUND

Numerous studies have highlighted that long non-coding RNAs (lncRNAs) can bind to microRNA (miRNA) sites as competing endogenous RNAs (ceRNAs), thereby affecting and regulating the expression of mRNAs and target genes. These lncRNA-associated ceRNAs have been theorized to play a significant role in cancer initiation and progression. However, the roles and functions of the lncRNA-miRNA-mRNA ceRNA network in squamous cell carcinoma of the tongue (SCCT) are still unclear.

METHODS

The miRNA, mRNA and lncRNA expression profiles from 138 patients with SCCT were downloaded from The Cancer Genome Atlas database. We identified the differential expression of miRNAs, mRNAs, and lncRNAs using the limma package of R software. We used the clusterProfiler package for GO and KEGG pathway annotations. The survival package was used to estimate survival analysis according to the Kaplan-Meier curve. Finally, the GDCRNATools package was used to construct the lncRNA-miRNA-mRNA ceRNA network.

RESULTS

In total, 1943 SCCT-specific mRNAs, 107 lncRNAs and 100 miRNAs were explored. Ten mRNAs (CSRP2, CKS2, ADGRG6, MB21D1, GMNN, RIPOR3, RAD51, PCLAF, ORC1, NAGS), 9 lncRNAs (LINC02560, HOXC13 - AS, FOXD2 - AS1, AC105277.1, AC099850.3, STARD4 - AS1, SLC16A1 - AS1, MIR503HG, MIR100HG) and 8 miRNAs (miR - 654, miR - 503, miR - 450a, miR - 379, miR - 369, miR - 190a, miR - 101, and let-7c) were found to be significantly associated with overall survival (log-rank p < 0.05). Based on the analysis of the lncRNA-miRNA-mRNA ceRNA network, one differentially expressed (DE) lncRNA, five DEmiRNAs, and three DEmRNAs were demonstrated to be related to the pathogenesis of SCCT.

CONCLUSIONS

In this study, we described the gene regulation by the lncRNA-miRNA-mRNA ceRNA network in the progression of SCCT. We propose a new lncRNA-associated ceRNA that could help in the diagnosis and treatment of SCCT.

Comprehensive analysis of competing endogenous RNA network and 3-mRNA signature predicting survival in papillary renal cell cancer

Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to exert significant roles in regulating the expression of mRNAs by sequestering and binding miRNAs. To elucidate the functional roles and regulatory mechanism of lncRNAs in papillary renal cell cancer (pRCC), we conducted a comprehensive analysis of ceRNA network and constructed a mRNA signature to predict prognosis of pRCC.We collected mRNAs and lncRNAs expression profiles of 289 pRCC samples and 32 normal renal tissues, and miRNA expression profiles of 292 pRCC samples and 34 normal samples from The Cancer Genome Atlas (TCGA) database. Differential expressions of RNAs were evaluated by the "edgeR" package in R. Functional enrichment analysis of DEmRNA was performed by DAVID 6.8 and KEGG, while PPI network of top 200 DEmRNAs was conducted using the STRING database. The univariate and multivariate Cox regression were conducted to figure out the candidate DEmRNAs with predictive values in prognosis. Receiver operator characteristic (ROC) curve estimation was performed to achieve the area under the curve (AUC) of the ROC curve to judge mRNA-associated prognosic model. A ceRNA network was established relying on the basis of combination of lncRNA-miRNA interactions and miRNA-mRNA interactions.A total of 1928 DEmRNAs, 981 DElncRNAs, and 52 DEmiRNAs were identified at significance level of |log2Fold Change |>2 and adjusted P-value < .01. A 3-mRNA signatures consisting of ERG, RRM2, and EGF was constructed to predict survival in pRCC. Moreover, a pRCC-associated ceRNA network was constructed, with 57 lncRNAs, 11 miRNAs, and 28 mRNAs.Our study illustrated the regulatory mechanism of ceRNA network in papillary renal cancer. The identified mRNA signatures could be used to predict survival of pRCC.

miR-130b Promotes Sunitinib Resistance through Regulation of PTEN in Renal Cell Carcinoma

BACKGROUND

MicroRNAs are a class of small noncoding RNAs that play an important role in progression and drug resistance in cancer. Several reports have shown that miR-130b modulates cell growth and drug resistance in some cancers. However, the expression and biological role of miR-130b in renal cell carcinoma (RCC) remain poorly understood. This study aimed to examine the expression and functional role of miR-130b and to analyze the association between miR-130b and sunitinib resistance in RCC.

METHODS

The expression of miR-130b in 32 RCC tissues and their corresponding normal kidney tissues was determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR). We performed a 4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT) assay in RCC cell lines transfected with miR-130b inhibitor or miR-130b mimics. We evaluated the relationship between miR-130b and PTEN and also analyzed the effect of miR-130b on sunitinib resistance.

RESULTS

qRT-PCR analysis showed that the expression of miR-130b was higher in RCC tissues than in corresponding normal kidney tissues. The MTT assay revealed that miR-130b modulated cell growth. qRT-PCR revealed an inverse correlation between miR-130b and PTEN in RCC. Western blotting demonstrated that miR-130b regulated the expression of PTEN in the RCC cell line. Additionally, miR-130b was associated with sunitinib resistance through regulation of PTEN. We established the sunitinib-resistant Caki-1 (Caki-1-SR) cells and observed that the expression of miR-130b was elevated in Caki-1-SR cells compared with parental Caki-1 cells. Knockdown of miR-130b improved sunitinib resistance in Caki-1-SR cells.

CONCLUSION

The expression of miR-130b was upregulated in RCC. miR-130b promoted cell growth and was associated with sunitinib resistance through regulating PTEN expression. Collectively, these results suggest that miR-130b may play an oncogenic role and be a promising therapeutic target.

LINC00473/miR-374a-5p regulates esophageal squamous cell carcinoma via targeting SPIN1 to weaken the effect of radiotherapy

Esophageal squamous cell carcinoma (ESCC) is the most prevalent type in esophageal cancers. Despite accumulating achievements in treatments of ESCC, patients still suffer from recurrence because of the treatment failures, one of the reasons for which is radioresistance. Therefore, it is a necessity to explore the molecular mechanism underlying ESCC radioresistance. Long intergenic noncoding RNA 473 (LINC00473) has been reported to be aberrantly expressed in several human malignancies. However, its biological function in radiosensitivity of ESCC remains to be fully understood. This study explored the role of LINC00473 in radiosensitivity of ESCC cells and whether LINC00473 acted as a competing endogenous RNA to realize its modulation on radioresistance. We found that LINC00473 was markedly upregulated in ESCC tissues and cell lines, and its expression was remarkably related to cellular response to irradiation. In addition, knockdown of LINC00473 could sensitize ESCC cells to radiation in vitro. As for the underlying mechanism, we uncovered that there was a mutual inhibition between LINC00473 and miR-374a-5p. Spindlin1 (SPIN1) was verified as a downstream target of miR-374a-5p, and LINC00473 upregulated SPIN1 expression through negatively modulating miR-374a-5p expression. Furthermore, we revealed that SPIN1 could aggravate the radioresistance of ESCC cells. Finally, overexpression of SPIN1 reversed the LINC00473 silencing-enhanced radiosensitivity in ESCC cells. To sum up, we demonstrated that LINC00473 facilitated radioresistance by regulating the miR-374a-5p/SPIN1 axis in ESCC.

Molecular Mechanisms in Clear Cell Renal Cell Carcinoma: Role of miRNAs and Hypermethylated miRNA Genes in Crucial Oncogenic Pathways and Processes

Clear cell renal cell carcinoma (ccRCC) is the third most common urological cancer, and it has the highest mortality rate. The increasing drug resistance of metastatic ccRCC has resulted in the search for new biomarkers. Epigenetic regulatory mechanisms, such as genome-wide DNA methylation and inhibition of protein translation by interaction of microRNA (miRNA) with its target messenger RNA (mRNA), are deeply involved in the pathogenesis of human cancers, including ccRCC, and may be used in its diagnosis and prognosis. Here, we review oncogenic and oncosuppressive miRNAs, their putative target genes, and the crucial pathways they are involved in. The contradictory behavior of a number of miRNAs, such as suppressive and anti-metastatic miRNAs with oncogenic potential (for example, miR-99a, miR-106a, miR-125b, miR-144, miR-203, miR-378), is examined. miRNAs that contribute mostly to important pathways and processes in ccRCC, for instance, PI3K/AKT/mTOR, Wnt-β, histone modification, and chromatin remodeling, are discussed in detail. We also separately consider their participation in crucial oncogenic processes, such as hypoxia and angiogenesis, metastasis, and epithelial-mesenchymal transition (EMT). The review also considers the interactions of long non-coding RNAs (lncRNAs) and miRNAs of significance in ccRCC. Recent advances in the understanding of the role of hypermethylated miRNA genes in ccRCC and their usefulness as biomarkers are reviewed based on our own data and those available in the literature. Finally, new data and perspectives concerning the clinical applications of miRNAs in the diagnosis, prognosis, and treatment of ccRCC are discussed.

Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high‐risk and low‐risk groups according to the 9 lncRNA and low‐risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9‐lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9‐lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA‐related ceRNA network in ccRCC and suggests that the 9‐lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.