A cluster of long non-coding RNAs exhibit diagnostic and prognostic values in renal cell carcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Cheng G
PROVIDER: S-EPMC6874440 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
Publications
A cluster of long non-coding RNAs exhibit diagnostic and prognostic values in renal cell carcinoma.
Cheng Gong G Liu Di D Liang Huageng H Yang Hongmei H Chen Ke K Zhang Xiaoping X
Aging 20191114 21
Kidney cancer ranked in the top 10 for both men and women in the estimated numbers of new cancer cases in the United States in 2018. Targeted therapies have recently been administered to patients with clear cell renal cell carcinoma (ccRCC), but the overall survival of patients at the terminal stage of the disease has not been as good as expected. It is therefore necessary to uncover efficient biomarkers for early diagnosis, and to clarify the molecular mechanisms underlying ccRCC progression an ...[more]
Similar Datasets
Project description:The aim of the present study was to identify long non-coding RNA (lncRNA)-based prognostic biomarkers in papillary renal cell carcinoma (pRCC). lncRNA expression data and corresponding clinical data from patients with pRCC were obtained from The Cancer Genome Atlas. R software and packages were used for data analysis. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression were performed to identify key lncRNAs, which were then used to construct a prognostic model using multivariate Cox regression analysis. Patients were divided into high- and low-risk groups, and Kaplan-Meier (KM) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted. The C-index was calculated to estimate the model's prognostic power. The hazard ratio (HR), 95% confidence interval (CI), and statistical significance of each key lncRNA were also calculated by multivariate Cox regression. Based on the result of the multivariate Cox regression analysis, KM survival plots were plotted for each significantly associated lncRNA. The subcellular locations of the prognostic biomarkers were predicted using lncRNAMap and lncLocator. A total of 17 lncRNA signatures were identified as key lncRNAs. Overall survival rate was significantly higher in the low-risk group compared with the high-risk group. The areas under the ROC curve were 0.93 (3-year ROC) and 0.902 (5-year ROC), and the C-index was 0.915. A forest plot was used to illustrate the HR and 95% CI of key lncRNAs. KM survival analysis revealed the prognostic significance of two protective biomarkers, AC024022.1 and GAS6-AS1, and three adverse biomarkers, AC087379.2, AL352984.1, and AL499627.1. It was predicted that AC024022.1 and AC087379.2 may be located in the cytoplasm and GAS6-AS1 may be located in the cytosol. The present study may contribute to the management of pRCC and serve as a foundation for further investigations into the underlying mechanism of tumorigenesis and progression of pRCC.
| S-EPMC6732993 | biostudies-literature
Project description:Background: N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) have been proven to be involving in regulating tumorigenesis, invasion, and metastasis for a variety of tumors. The present study aimed to screen lncRNAs with m6A modification and investigate their biological signatures and prognostic values in kidney renal clear cell carcinoma (KIRC). Materials and Methods: lncRNA-seq, miRNA-seq, and mRNA-seq profiles of KIRC samples and the clinical characteristics of corresponding patients were downloaded from The Cancer Genome Atlas (TCGA). The R package "edgeR" was utilized to perform differentially expressed analysis on these profiles to gain DElncRNAs, DEmiRNAs, and DEmRNAs, respectively. The results of intersection of DElncRNAs and m6A-modified genes were analyzed by the weighted gene co-expression network analysis (WGCNA) to screen hub m6A-lncRNAs. Then, WGCNA was also used to construct an lncRNA-miRNA-mRNA (ceRNA) network. The Cox regression analysis was conducted on hub m6A-lncRNAs to construct the m6A-lncRNAs prognostic index (m6AlRsPI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of m6AlRsPI. The m6AlRsPI model was tested by internal and external cohorts. The molecular signatures and prognosis for hub m6A-lncRNAs and m6AlRsPI were analyzed. The expression level of hub m6A-lncRNAs in KIRC cell lines were quantified by qRT-PCR. Results: A total of 21 hub m6A-lncRNAs associated with tumor metastasis were identified in the light of WGCNA. The ceRNA network for 21 hub m6A-lncRNAs was developed. The Cox regression analysis was performed on the 21 hub m6A-lncRNAs, screening two m6A-lncRNAs regarded as independent prognostic risk factors. The m6AlRsPI was established based on the two m6A-lncRNAs as follows: (0.0006066 × expression level of LINC01820) + (0.0020769 × expression level of LINC02257). The cutoff of m6AlRsPI was 0.96. KM survival analysis for m6AlRsPI showed that the high m6AlRsPI group could contribute to higher mortality. The area under ROC curve for m6AlRsPI for predicting 3- and 5-year survival was 0.760 and 0.677, respectively, and the m6AlRsPI was also tested. The mutation and epithelial-mesenchymal transition (EMT) analysis for m6AlRsPI showed that the high m6AIRsPI group had more samples with gene mutation and had more likely caused EMT. Finally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for mRNAs interacted with the two m6A-lncRNAs, showing they were involved in the process of RNA splicing and regulation of the mRNA surveillance pathway. qRT-PCR analysis showed that the two m6A-lncRNAs were upregulated in KIRC. Conclusion: In the present study, hub m6A-lncRNAs were determined associated with metastasis in KIRC, and the ceRNA network demonstrated the potential carcinogenic regulatory pathway. Two m6A-lncRNAs associated with the overall survival were screened and m6AlRsPI was constructed and validated. Finally, the molecular signatures for m6AlRsPI and the two m6A-lncRNAs were analyzed to investigate the potential modulated processes in KIRC.
| S-EPMC8290079 | biostudies-literature
Project description:BackgroundThe immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC.MethodsThe raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score.ResultsThere were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC. We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine.ConclusionsOur study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.
| S-EPMC8261450 | biostudies-literature
Project description:BackgroundAs the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.MethodsThe RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.ResultsA total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.ConclusionsWe established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.
| S-EPMC9741795 | biostudies-literature
Project description:Circulating RNAs in serum, plasma or other body liquid have emerged as useful and highly promising biomarkers for noninvasive diagnostic application. Herein, we aimed to establish a serum long non-coding RNAs (lncRNAs) signature for diagnosing nasopharyngeal carcinoma (NPC). In this study, we recruited a cohort of 101 NPC patients, 20 patients with chronic nasopharyngitis (CN), 20 EBV carriers (EC) and 101 healthy controls. qRT-PCR was performed with NPC cells and serum samples to screen a pool of 38 NPC-related lncRNAs obtained from the LncRNADisease database. A profile of three circulating lncRNAs (MALAT1, AFAP1-AS1 and AL359062) was established for NPC diagnosis. By Receiver Operating Characteristic (ROC) curve analysis, this three-lncRNA signature showed high accuracy in discriminating NPC from healthy controls (AUC = 0.918), CN (AUC = 0.893) or EC (AUC = 0.877). Furthermore, high levels of these three lncRNAs were closely related to advanced NPC tumor node metastasis stages and EBV infection. Serum levels of these three lncRNAs declined significantly in patients after therapy. Our present study indicates that circulating MALAT1, AFAP1-AS1 and AL359062 may represent novel serum biomarkers for NPC diagnosis and prognostic prediction after treatment.
| S-EPMC5522198 | biostudies-literature
Project description:BackgroundRenal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, of which the clear cell renal cell carcinoma (ccRCC) accounts for the most subtypes. The increasing discoveries of abundant autophagy-related long non-coding RNAs (ARLNRs) lead to a resurgent interest in evaluating their potential on prognosis prediction. Based on a large number of ccRCC gene samples from TCGA and clinics, ARLNRs analysis will provide a novel perspective into this field.MethodsWe calculated the autophagy scores of each sample according to the expression levels of autophagy-related genes (ARGs) and screened the survival-related ARLNRs (sARLNRs) of ccRCC patients by Cox regression analysis. The high-risk group and the low-risk group were distinguished by the median score of the autophagy-related risk score (ARRS) model. The functional annotations were detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA). The expression levels of two kinds of sARLNRs in the renal tumor and adjacent normal tissues and cell lines were verified.ResultsThere were 146 ARLNRs selected by Pearson analysis. A total of 30 sARLNRs were remarkably correlated with the clinical outcomes of ccRCC patients. Eleven sARLNRs (AC002553.1, AC092611.2, AL360181.2, AP002807.1, AC098484.1, AL513218.1, AC008735.2, MHENCR, AC020907.4, AC011462.4, and AC008870.2) with the highest prognosis value were recruited to establish the ARRS in which the overall survival (OS) in the high-risk group was shorter than that in the low-risk group. ARRS could be treated as an independent prognostic factor and has significant correlations with OS. The distributions of autophagy genes were different between the high-risk group and the low-risk group. In addition, we also found that the expression levels of AC098484.1 in ccRCC cell lines and tumor tissues were lower than those in HK-2 and adjacent normal tissues, but AL513218.1 showed the inverse level. Furthermore, the AC098484.1 expressed decreasingly with the more advanced T-stages, but AL513218.1 gradually increased.ConclusionOur study identified and verified some sARLNRs with clinical significances and revealed their potential values on predicting prognoses of ccRCC patients, which may provide a novel perspective for autophagy-related research and clinical decisions.
| S-EPMC8370088 | biostudies-literature
Project description:BackgroundStarvation-induced tumor microenvironment significantly alters genetic profiles including long non-coding RNAs (lncRNAs), further regulating the malignant biological characteristics (invasion and migration) of clear cell renal cell carcinoma (ccRCC).MethodsTranscriptome RNA-sequencing data of 539 ccRCC tumors and 72 normal tissues were acquired from the TCGA and paired clinical samples of 50 ccRCC patients. In vitro experiments, such as qPCR, migration and invasion assays were applied to reveal the clinical relevance of LINC-PINT, AC108449.2 and AC007637.1.Results170 lncRNAs were verified as starvation-related lncRNAs (SR-LncRs), of which 25 lncRNAs were associated with overall survival in ccRCC patients. Furthermore, a starvation-related risk score model (SRSM) was built based on the expression levels of LINC-PINT, AC108449.2, AC009120.2, AC008702.2 and AC007637.1. ccRCC patients with high level of LINC-PINT expression were divided into high-risk group and led to higher mortality, but AC108449.2 and AC007637.1 were contrary. Analogously, LINC-PINT was highly expressed in ccRCC cell lines and tumor tissues, especially in patients with advanced stage, T-stage and M-stage, while AC108449.2 and AC007637.1 showed the opposite results. In addition, the increased levels of AC108449.2 and AC007637.1 were significantly correlated with grade. Silencing LINC-PINT reduced the invasion and migration characteristics of ccRCC cells. SiR-AC108449.2 and siR-AC007637.1 enhanced the ability of invasion and migration in ccRCC cells.ConclusionsIn this study, we find the clinical significance of LINC-PINT, AC108449.2 and AC007637.1 in predicting the prognosis of ccRCC patients and verify their correlation with various clinical parameters. These findings provide an advisable risk score model for ccRCC clinical decision-making.
| S-EPMC10415547 | biostudies-literature
Project description:Current prostate cancer (PCa) biomarkers such as PSA are not optimal in distinguishing cancer from benign prostate diseases and predicting disease outcome. To discover additional biomarkers, we investigated PCa-specific expression of novel unannotated transcripts. Using the unique probe design of Affymetrix Human Exon Arrays, we identified 334 candidates (EPCATs), of which 15 were validated by RT-PCR. Combined into a diagnostic panel, 11 EPCATs classified 80% of PCa samples correctly, while maintaining 100% specificity. High specificity was confirmed by in situ hybridization for EPCAT4R966 and EPCAT2F176 (SChLAP1) on extensive tissue microarrays. Besides being diagnostic, EPCAT2F176 and EPCAT4R966 showed significant association with pT-stage and were present in PIN lesions. We also found EPCAT2F176 and EPCAT2R709 to be associated with development of metastases and PCa-related death, and EPCAT2F176 to be enriched in lymph node metastases. Functional significance of expression of 9 EPCATs was investigated by siRNA transfection, revealing that knockdown of 5 different EPCATs impaired growth of LNCaP and 22RV1 PCa cells. Only the minority of EPCATs appear to be controlled by androgen receptor or ERG. Although the underlying transcriptional regulation is not fully understood, the novel PCa-associated transcripts are new diagnostic and prognostic markers with functional relevance to prostate cancer growth.
| S-EPMC4414171 | biostudies-literature
Project description:BackgroundLong non-coding RNAs (lncRNA) play an important role in carcinogenesis; knowledge on lncRNA expression in renal cell carcinoma is rudimental. As a basis for biomarker development, we aimed to explore the lncRNA expression profile in clear cell renal cell carcinoma (ccRCC) tissue.ResultsMicroarray experiments were performed to determine the expression of 32,183 lncRNA transcripts belonging to 17,512 lncRNAs in 15 corresponding normal and malignant renal tissues. Validation was performed using quantitative real-time PCR in 55 ccRCC and 52 normal renal specimens. Computational analysis was performed to determine lncRNA-microRNA (MiRTarget2) and lncRNA-protein (catRAPID omics) interactions. We identified 1,308 dysregulated transcripts (expression change >2-fold; upregulated: 568, downregulated: 740) in ccRCC tissue. Among these, aberrant expression was validated using PCR: lnc-BMP2-2 (mean expression change: 37-fold), lnc-CPN2-1 (13-fold), lnc-FZD1-2 (9-fold), lnc-ITPR2-3 (15-fold), lnc-SLC30A4-1 (15-fold), and lnc-SPAM1-6 (10-fold) were highly overexpressed in ccRCC, whereas lnc-ACACA-1 (135-fold), lnc-FOXG1-2 (19-fold), lnc-LCP2-2 (2-fold), lnc-RP3-368B9 (19-fold), and lnc-TTC34-3 (314-fold) were downregulated. There was no correlation between lncRNA expression with clinical-pathological parameters. Computational analyses revealed that these lncRNAs are involved in RNA-protein networks related to splicing, binding, transport, localization, and processing of RNA. Small interfering RNA (siRNA)-mediated knockdown of lnc-BMP2-2 and lnc-CPN2-1 did not influence cell proliferation.ConclusionsWe identified many novel lncRNA transcripts dysregulated in ccRCC which may be useful for novel diagnostic biomarkers.
| S-EPMC4326488 | biostudies-literature
Project description:Many long noncoding RNAs (lncRNAs) are deregulated in cancer and contribute to oncogenesis. In urothelial carcinoma (UC), several lncRNAs have been reported to be overexpressed and proposed as biomarkers. As most reports have not been confirmed independently in large tissue sets, we aimed to validate the diagnostic and prognostic value of lncRNA upregulation in independent cohorts of UC patients. Thus, expression of seven lncRNA candidates (GAS5, H19, linc-UBC1, MALAT1, ncRAN, TUG1, UCA1) was measured by RT-qPCR in cell lines and tissues and correlated to clinicopathological parameters including follow-up data (set 1: N n = 10; T n = 106). Additionally, publicly available TCGA data was investigated for differential expression in UC tissues (set 2: N n = 19; T n = 252,) and correlation to overall survival (OS). All proposed candidates tended to be upregulated in tumour tissues, with the exception of MALAT1, which was rather diminished in cancer tissues of both data sets. However, strong overexpression was generally limited to individual tumour tissues and statistically significant overexpression was only observed for UCA1, TUG1, ncRAN and linc-UBC1 in tissue set 2, but for no candidate in set 1. Altered expression of individual lncRNAs was associated with overall survival, but not consistently between both patient cohorts. Interestingly, lower expression of TUG1 in a subset of UC patients with muscle-invasive tumours was significantly correlated with worse OS in both cohorts. Further analysis revealed that tumours with low TUG1 expression are characterized by a basal-squamous-like subtype signature accounting for the association with poor outcome. In conclusion, our study demonstrates that overexpression of the candidate lncRNAs is found in many UC cases, but does not occur consistently and strongly enough to provide reliable diagnostic or prognostic value as an individual biomarker. Subtype-dependent expression patterns of lncRNAs like TUG1 could become useful to stratify patients by molecular subtype, thus aiding personalized treatments.
| S-EPMC5400278 | biostudies-literature