A five-long non-coding RNA signature to improve prognosis prediction of clear cell renal cell carcinoma

Crosstalk among disulfidptosis-related lncRNAs in lung adenocarcinoma reveals a correlation with immune profile and clinical prognosis

Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.

Long noncoding RNA UNC5B-AS1 suppresses cell proliferation by sponging miR-24-3p in glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most common primary CNS tumor, characterized by high mortality and heterogeneity. However, the related lncRNA signatures and their target microRNA (miRNA) for GBM are still mostly unknown. Therefore, it is critical that we discover lncRNA markers in GBM and their biological activities.

MATERIALS AND METHODS

GBM-related RNA-seq data were obtained from the Cancer Genome Atlas (TCGA) database. The "edger" R package was used for differently expressed lncRNAs (DELs) identification. Then, we forecasted prospective miRNAs that might bind to lncRNAs by Cytoscape software. Survival analysis of those miRNAs was examined by the starBase database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the miRNAs' target genes was conducted by the Gene Set Enrichment Analysis (GSEA) database and R software. Moreover, the proliferative ability of unc-5 netrin receptor B antisense RNA 1 (UNC5B-AS1) cells was evaluated by Cell Counting Kit-8 (CCK-8) analysis. Mechanistically, the regulatory interaction between UNC5B-AS1 and miRNA in GBM biological processes was studied using CCK-8 analysis.

RESULTS

Our results indicated that overexpression of UNC5B-AS1 has been shown to suppress GBM cell growth. Mechanistically, miR-24-3p in GBM was able to alleviate the anti-oncogenic effects of UNC5B-AS1 on cell proliferation.

CONCLUSION

The discovery of the novel UNC5B-AS1-miR-24-3p network suggests possible lncRNA and miRNA roles in the development of GBM, which may have significant ramifications for the analysis of clinical prognosis and the development of GBM medications.

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Background

Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC.

Methods

Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs.

Results

Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I–II) and late (III–IV) HCC detection were 0.88 and 0.93, respectively.

Conclusions

The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.

Emerging role of long noncoding RNAs in recurrent hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains one of the most frequent types of liver cancer and is characterized by a high recurrence rate. Recent studies have proposed that long non-coding RNAs (lncRNAs) are potential biomarkers in several recurrent tumor types. It is now well understood that invasion, migration, and metastasis are important factors for tumor recurrence. Moreover, some of the known risk factors for HCC may affect the expression levels of several types of lncRNAs and thus affect the recurrence of liver cancer through lncRNA regulation. In this paper, we review the biological functions, molecular mechanisms, and roles of lncRNAs in HCC and summarize current knowledge about lncRNAs as potential biomarkers in recurrent HCC.

Effect of Aberrant Long Noncoding RNA on the Prognosis of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a kind of lethal cancer. Although there are mature treatment methods, there is still a lack of rigorous and scientific means for cancer diagnosis. Long noncoding RNAs (lncRNAs) are a kind of noncoding RNA (ncRNA). Recent studies find that alteration of lncRNA expression is related to the occurrence of many cancers. In order to find lncRNAs which can effectively predict the prognosis of ccRCC, RNA-seq count data and clinical information were downloaded from TCGA-KIRC, and gene expression profiles from 530 patients were included. Then, K-means was used for clustering, and the number of clusters was determined to be 5. The R-package "edgeR" was used to perform differential expression analysis. Subsequently, a risk model composed of 10 lncRNA biomarkers significantly related to prognosis was identified via Cox and LASSO regression analyses. Then, patients were divided into two groups according to the model-based risk score, and then, GSEA pathway enrichment was performed. The results showed that metabolism- and mTOR-related pathways were activated while immune-related pathways were inhibited in the high-risk patients. Combined with previous studies, it is believed that these 10 lncRNAs are potential targets for the treatment of ccRCC. In addition, Cox regression analysis was used to verify the independence of the risk model, and as results revealed, the risk model can be used to independently predict the prognosis of patients. In conclusion, our study found 10 lncRNAs related to the prognosis of ccRCC and provided new ideas for clinical diagnosis and drug development.

Identification of SHMT2 as a Potential Prognostic Biomarker and Correlating with Immune Infiltrates in Lung Adenocarcinoma

It has attracted growing attention that the role of serine hydroxy methyl transferase 2 (SHMT2) in various types of cancers. However, the prognostic role of SHMT2 in lung adenocarcinoma (LUAD) and its relationship with immune cell infiltration is not clear. In this study, the information of mRNA expression and clinic data in LUAD were, respectively, downloaded from the GEO and TCGA database. We conducted a biological analysis to select the signature gene SHMT2. Online databases including Oncomine, GEPIA, TISIDB, TIMER, and HPA were applied to analyze the characterization of SHMT2 expression, prognosis, and the correlation with immune infiltration in LUAD. The mRNA expression and protein expression of SHMT2 in LUAD tissues were higher than in normal tissue. A Kaplan-Meier analysis showed that patients with lower expression level of SHMT2 had a better overall survival rate. Multivariate analysis and the Cox proportional hazard regression model revealed that SHMT2 expression was an independent prognostic factor in patients with LUAD. Meanwhile, the gene SHMT2 was highly associated with tumor-infiltrating lymphocytes in LUAD. These results suggest that the SHMT2 gene is a promising candidate as a potential prognostic biomarker and highly associated with different types of immune cell infiltration in LUAD.

Immune-Related Long Non-coding RNA Signature and Clinical Nomogram to Evaluate Survival of Patients Suffering Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) turns out to be one of the most prevalent cancer types, leading to a relatively high mortality among worldwide sufferers. In this study, gene microarray data of ESCC patients were obtained from the GEO database, with the samples involved divided into a training set and a validation set. Based on the immune-related differential long non-coding RNAs (lncRNAs) we identified, a prognostic eight-lncRNA-based risk signature was constructed following regression analyses. Then, the predictive capacity of the model was evaluated in the training set and validation set using survival curves and receiver operation characteristic curves. In addition, univariate and multivariate regression analyses based on clinical information and the model-based risk score also demonstrated the ability of the risk score in independently determining the prognosis of patients. Besides, based on the CIBERSORT tool, the abundance of immune infiltrates in tumor samples was scored, and a significant difference was presented between the high- and low- risk groups. Correlation analysis with immune checkpoints (PD1, PDL1, and CTLA4) indicated that the eight-lncRNA signature–based risk score was negatively correlated with PD1 expression, suggesting that the eight-lncRNA signature may have an effect in immunotherapy for ESCC. Finally, GO annotation was performed for the differential mRNAs that were co-expressed with the eight lncRNAs, and it was uncovered that they were remarkably enriched in immune-related biological functions. These results suggested that the eight-lncRNA signature–based risk model could be employed as an independent biomarker for ESCC prognosis and might play a part in evaluating the response of ESCC to immunotherapy with immune checkpoint blockade.

Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

Objective

Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need.

Methods

The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape.

Results

Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions.

Conclusion

This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

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.

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p < 0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent and invasive histological subtypes among all renal cell carcinomas (RCC). Cancer cell metabolism, particularly glucose metabolism, has been reported as a hallmark of cancer. However, the characteristics of glucose metabolism-related gene sets in ccRCC have not been systematically profiled.

Methods: In this study, we downloaded a gene expression profile and glucose metabolism-related gene set from TCGA (The Cancer Genome Altas) and MSigDB, respectively, to analyze the characteristics of glucose metabolism-related gene sets in ccRCC. We used a multivariable Cox regression analysis to develop a risk signature, which divided patients into low- and high- risk groups. In addition, a nomogram that combined the risk signature and clinical characteristics was created for predicting the 3- and 5-year overall survival (OS) of ccRCC. The accuracy of the nomogram prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and a calibration plot.

Results: A total of 231 glucose metabolism-related genes were found, and 68 differentially expressed genes (DEGs) were identified. After screening by univariate regression analysis, LASSO regression analysis and multivariable Cox regression analysis, six glucose metabolism-related DEGs (FBP1, GYG2, KAT2A, LGALS1, PFKP, and RGN) were selected to develop a risk signature. There were significant differences in the clinical features (Fuhrman nuclear grade and TNM stage) between the high- and low-risk groups. The multivariable Cox regression indicated that the risk score was independent of the prognostic factors (training set: HR=3.393, 95% CI [2.025, 5.685], p<0.001; validation set: HR=1.933, 95% CI [1.130, 3.308], p=0.016). The AUCs of the nomograms for the 3-year OS in the training and validation sets were 0.808 and 0.819, respectively, and 0.777 and 0.796, respectively, for the 5- year OS.

Conclusion: We demonstrated a novel glucose metabolism-related risk signature for predicting the prognosis of ccRCC. However, additional in vitro and in vivo research is required to validate our findings.

A Panel of Four-lncRNA Signature as a Potential Biomarker for Predicting Survival in Clear Cell Renal Cell Carcinoma

Long non-coding RNAs (lncRNAs) have been considered as biomarkers for the carcinogenesis and development of various cancers. However, the prognostic significance of lncRNAs in renal cell carcinoma (RCC) remains unclear. This study aimed to determine the predictive ability of lncRNAs in clear cell RCC (ccRCC). Among the cohort of kidney renal clear cell carcinoma (KIRC) of the The Cancer Genome Atlas (TCGA), 525 patients were enrolled in our study. Expression of lncRNAs based on RNAseq was obtained from TCGA. Kaplan-Meier prognostic analysis and a Cox proportional hazards regression model were used to assess related factors. The lncRNA signature was then validated in an independent cohort of an additional 60 ccRCC patients. Hierarchical clustering of the KIRC TCGA dataset identified 26 differentially expressed lncRNAs (11 down-regulated and 15 up-regulated) using average linkage clustering. Kaplan-Meier survival analysis identified 30 statistically significant lncRNAs that strongly predicted prognosis, with 4 ccRCC-specific lncRNAs (TCL6, PVT1, MIR155HG, and HAR1B) being differentially expressed and correlating significantly with OS. Patients assigned to the high-risk group were associated with poor OS compared with patients in the low-risk group (HR = 2.57; 95%CI, 1.89-3.50; p < 0.001). This finding was validated in the Tongji Hospital cohort, and the four-lncRNA signature was shown to be significantly predictive of ccRCC prognosis (p < 0.001). In this study, we constructed an applicable four-lncRNA-based classifier as a reliable prognostic and predictive tool for OS in patients with ccRCC.

A novel prognostic nomogram based on 5 long non-coding RNAs in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common and invasive histological subtype of all kidney malignancies, with high levels of incidence and mortality. In the present study, long non-coding (lnc)RNA expression profiles of patients with ccRCC from The Cancer Genome Atlas database were comprehensively analyzed to identify differentially expressed lncRNAs (DElncRNAs). The patients with ccRCC were then divided into training and validation cohorts. Univariate and LASSO regression analyses were performed to select the most significant survival-associated candidate DElncRNAs in the training cohort. Multivariate Cox regression analysis was then performed to develop a risk score formula and a prognostic nomogram for predicting 3- and 5-year overall survival (OS). The accuracies of the nomogram predictions were evaluated by determining the area under the receiver operating characteristic curve (AUC) and a calibration plot. Finally, functional enrichment analysis and protein-protein interaction network prediction were implemented to predict the functions and molecular mechanisms of the candidate DElncRNAs in ccRCC. A total of 1,553 DElncRNAs were identified, and 5 candidate DElncRNAs (AC026992.2, AC245041.2, LINC00524, LINC01956 and LINC02080) were included in the nomogram. The AUC values for 3- and 5-year overall survival in the training cohort were 0.768 and 0.814, respectively, which were increased compared with that based on the clinical index (0.760 and 0.694, respectively). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the 521 mRNAs highly associated with 5 DElncRNAs were primarily involved in 17 terms and 25 pathways, respectively. Based on the 5 DElncRNAs, a novel and convenient prognostic nomogram for predicting 3- and 5-year OS for patients with ccRCC was developed. The results of the present study may be conducive to the development of a precise predictive tool for the prognosis of ccRCC and may provide information regarding the molecular mechanisms of ccRCC. However, additional experimental in vitro and in vivo studies investigating lncRNAs may be required.

Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy

Circular RNAs (circRNAs) may act as novel cancer biomarkers. However, a genome-wide evaluation of circRNAs in clear cell renal cell carcinoma (ccRCC) has yet to be conducted. Therefore, the objective of this study was to identify and validate circRNAs in ccRCC tissue with a focus to evaluate their potential as prognostic biomarkers. A genome-wide identification of circRNAs in total RNA extracted from ccRCC tissue samples was performed using microarray analysis. Three relevant differentially expressed circRNAs were selected (circEGLN3, circNOX4, and circRHOBTB3), their circular nature was experimentally confirmed, and their expression-along with that of their linear counterparts-was measured in 99 malignant and 85 adjacent normal tissue samples using specifically established RT-qPCR assays. The capacity of circRNAs to discriminate between malignant and adjacent normal tissue samples and their prognostic potential (with the endpoints cancer-specific, recurrence-free, and overall survival) after surgery were estimated by C-statistics, Kaplan-Meier method, univariate and multivariate Cox regression analysis, decision curve analysis, and Akaike and Bayesian information criteria. CircEGLN3 discriminated malignant from normal tissue with 97% accuracy. We generated a prognostic for the three endpoints by multivariate Cox regression analysis that included circEGLN3, circRHOBT3 and linRHOBTB3. The predictive outcome accuracy of the clinical models based on clinicopathological factors was improved in combination with this circRNA-based signature. Bootstrapping as well as Akaike and Bayesian information criteria confirmed the statistical significance and robustness of the combined models. Limitations of this study include its retrospective nature and the lack of external validation. The study demonstrated the promising potential of circRNAs as diagnostic and particularly prognostic biomarkers in ccRCC patients.

Prognosis of clear cell renal cell carcinoma (ccRCC) based on a six-lncRNA-based risk score: an investigation based on RNA-sequencing data

Background

The scientific understanding of long non-coding RNAs (lncRNAs) has improved in recent decades. Nevertheless, there has been little research into the role that lncRNAs play in clear cell renal cell carcinoma (ccRCC). More lncRNAs are assumed to influence the progression of ccRCC via their own molecular mechanisms.

Methods

This study investigated the prognostic significance of differentially expressed lncRNAs by mining high-throughput lncRNA-sequencing data from The Cancer Genome Atlas (TCGA) containing 13,198 lncRNAs from 539 patients. Differentially expressed lncRNAs were assessed using the R packages edgeR and DESeq. The prognostic significance of lncRNAs was measured using univariate Cox proportional hazards regression. ccRCC patients were then categorized into high- and low-score cohorts based on the cumulative distribution curve inflection point the of risk score, which was generated by the multivariate Cox regression model. Samples from the TCGA dataset were divided into training and validation subsets to verify the prognostic risk model. Bioinformatics methods, gene set enrichment analysis, and protein–protein interaction networks, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were subsequently used.

Results

It was found that the risk score based on 6 novel lncRNAs (CTA-384D8.35, CTD-2263F21.1, LINC01510, RP11-352G9.1, RP11-395B7.2, RP11-426C22.4) exhibited superior prognostic value for ccRCC. Moreover, we categorized the cases into two groups (high-risk and low-risk), and also examined related pathways and genetic differences between them. Kaplan–Meier curves indicated that the median survival time of patients in the high-risk group was 73.5 months, much shorter than that of the low-risk group (112.6 months; P < 0.05). Furthermore, the risk score predicted the 5-year survival of all 539 ccRCC patients (AUC at 5 years, 0.683; concordance index [C-index], 0.853; 95% CI 0.817–0.889). The training set and validation set also showed similar performance (AUC at 5 years, 0.649 and 0.681, respectively; C-index, 0.822 and 0.891; 95% CI 0.774–0.870 and 0.844–0.938).

Conclusions

The results of this study can be applied to analyzing various prognostic factors, leading to new possibilities for clinical diagnosis and prognosis of ccRCC.

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

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

Identification and characterization of the lncRNA signature associated with overall survival in patients with neuroblastoma

Neuroblastoma (NB) is a commonly occurring cancer among infants and young children. Recently, long non-coding RNAs (lncRNAs) have been using as prognostic biomarkers for therapeutics and interventions in various cancers. Considering the poor survival of NB, the lncRNA-based therapeutic strategies must be improved. This work proposes an overall survival time estimator called SVR-NB to identify the lncRNA signature that is associated with the overall survival of patients with NB. SVR-NB is an optimized support vector regression (SVR)-based method that uses an inheritable bi-objective combinatorial genetic algorithm for feature selection. The dataset of 231 NB patients that contains overall survival information and expression profiles of 783 lncRNAs was used to design and evaluate SVR-NB from the database of gene expression omnibus accession GSE62564. SVR-NB identified a signature of 35 lncRNAs and achieved a mean squared correlation coefficient of 0.85 and a mean absolute error of 0.56 year between the actual and estimated overall survival time using 10-fold cross-validation. Further, we ranked and characterized the 35 lncRNAs according to their contribution towards the estimation accuracy. Functional annotations and co-expression gene analysis of LOC440896, LINC00632, and IGF2-AS revealed the association of co-expressed genes in Kyoto Encyclopedia of Genes and Genomes pathways.

Immune associated LncRNAs identify novel prognostic subtypes of renal clear cell carcinoma

Kidney Renal Clear Cell Carcinoma (KIRC) is a significant cause of cancer-related deaths. Here, we aim to identify the LncRNAs associated with the immune system and characterise their clinical utility in KIRC. A total of 504 patients' data was used from TCGA-GDC. In silico correlation analysis identified 143 LncRNAs associated with immune-related genes (r > 0.7, P < 0.05). K-means consensus method clustered KIRC samples in three immune clusters, namely cluster C1, C2, and C3 based on the expression of 143 immune-related LncRNAs. Kaplan-Meier analysis showed that C3 patients survived significantly worse than the other two clusters (P < 0.0001). A comparison of TCGA miRNA, mRNA cluster with immune cluster showed the independence and robustness of immune clusters (HR = 2.02 and P = 2.12 × 10^-8 ). The GSEA and CIBERSORT analysis showed high enrichment of poorly activated T-cells in C3 patients. To define LncRNA immune prognostic signature, we randomly divided the TCGA sample into discovery and validation sets. By utilising multivariate Cox regression analysis, we identified and validated a seven LncRNA immune prognostic signature score (LIPS score) (HR = 1.43 and P = 2.73 × 10^-6 ) in KIRC. Comparison of LIPS score with all the clinical factors validated its independence and superiority in KIRC prognosis. In summary, we identified LncRNAs associated with the immune system and showed the presence of prognostic subtypes of KIRC patients based on immune-related LncRNA expression. We also identified a novel immune LncRNA based gene-signature for KIRC patients' prognostication.

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.

Identification of a six-gene signature with prognostic value for patients with endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P-value <0.01 in the training set. Next, using robust likelihood-based survival modeling, we developed a six-gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six-gene signature. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer overall survival (OS) outcomes than those in the low-risk group (log-rank test P-value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six-gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.

Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma

AIM

To construct a long non-coding RNA (lncRNA) signature for predicting hepatocellular carcinoma (HCC) prognosis with high efficiency.

METHODS

Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were identified using the edgeR package to analyze The Cancer Genome Atlas (TCGA) LIHC dataset. Univariate Cox proportional hazards regression was performed to obtain the DELs significantly associated with overall survival (OS) in a training set. These OS-related DELs were further analyzed using a stepwise multivariate Cox regression model. Those lncRNAs fitted in the multivariate Cox regression model and independently associated with overall survival were chosen to build a prognostic risk formula. The prognostic value of this formula was then validated in the test group and the entire cohort and further compared with two previously identified prognostic signatures for HCC. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to explore the potential biological functions of the lncRNAs in the signature.

RESULTS

Based on lncRNA expression profiling of 370 HCC patients from the TCGA database, we constructed a 5-lncRNA signature (AC015908.3, AC091057.3, TMCC1-AS1, DCST1-AS1 and FOXD2-AS1) that was significantly associated with prognosis. HCC patients with high-risk scores based on the expression of the 5 lncRNAs had significantly shorter survival times compared to patients with low-risk scores in both the training and test groups. Multivariate Cox regression analysis demonstrated that the prognostic value of the 5 lncRNAs was independent of clinicopathological parameters. A comparison study involving two previously identified prognostic signatures for HCC demonstrated that this 5-lncRNA signature showed improved prognostic power compared with the other two signatures. Functional enrichment analysis indicated that the 5 lncRNAs were potentially involved in metabolic processes, fibrinolysis and complement activation.

CONCLUSION

Our present study constructed a 5-lncRNA signature that improves survival prediction and can be used as a prognostic biomarker for HCC patients.

LncRNA SNHG16 drives proliferation, migration, and invasion of hemangioma endothelial cell through modulation of miR-520d-3p/STAT3 axis

It has been verified that long noncoding RNAs (lncRNAs) have great effects on various biological behaviors of human diseases. Although more and more lncRNAs have been studied in human cancers, countless lncRNAs still need to be excavated. This study aims to investigate the impacts of lncRNA SNHG16 on proliferation and metastasis of human hemangioma endothelial cell (HemECs). qRT-PCR analysis was carried out to explore the expression pattern of SNHG16, miR-520d-3p, and STAT3. The effect of SNHG16 on cell proliferation was detected by MTT and colony formation assay. Flow cytometry analysis was performed to test the apoptosis of HemECs cells. Migration and invasion of HemECs cells were determined and examined by transwell assays. Tube formation assay helped to observe the influence of SNHG16 expression on the vasoformation of HemECs cells. The correlations among SNHG16, miR-520d-3p, and STAT3 were certified by bioinformatics analysis, pull-down assay, and dual-luciferase reporter assay. Finally, rescue assays were conducted to demonstrate the effects of SNHG16-miR-520d-3p-STAT3 axis on biological behaviors of HemECs cell. SNHG16 was strongly expressed in proliferating phase hemangioma tissues and HemECs cells. Silenced SNHG16 negatively affected proliferation, migration, and invasion of HemECs cell. LncRNA SNHG16 acted as a ceRNA to upregulate STAT3 through binding with miR-520d-3p in HemECs cell. LncRNA SNHG16 acted as a ceRNA to drive proliferation, vasoformation, migration, and invasion of HemECs cells through modulating miR-520d-3p/STAT3 axis.

A seven-lncRNA signature predicts overall survival in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common types of cancer and the leading causes of cancer-related mortality worldwide, especially in Eastern Asia. Here, we downloaded the microarray data of lncRNA expression profiles of ESCC patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets and divided into training, validation and test set. The random survival forest (RSF) algorithm and Cox regression analysis were applied to identify a seven-lncRNA signature. Then the predictive ability of the seven-lncRNA signature was evaluated in the validation and test set using Kaplan-Meier test, time-dependent receiver operating characteristic (ROC) curves and dynamic area under curve (AUC). Stratified analysis and multivariate Cox regression also demonstrated the independence of the signature in prognosis prediction from other clinical factors. Besides, the predict accuracy of lncRNA signature was much better than that of tumor-node-metastasis (TNM) stage in all the three sets. LncRNA combined with TNM displayed better prognostic predict ability than either alone. The role of LINC00173 from the signature in modulating the proliferation and cell cycle of ESCC cells was also observed. These results indicated that this seven-lncRNA signature could be used as an independent prognostic biomarker for prognosis prediction of patients with ESCC.

Genome-Wide Analysis of Uveal Melanoma Metastasis-Associated LncRNAs and Their Functional Network

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Up to 50% of primary UM cases will develop distant metastasis, but no effective therapies are currently available. The present study aimed to characterize the expression profile of the long noncoding RNAs (lncRNAs) and screen the potential metastasis-associated lncRNAs in UM. A genome-wide analysis of the transcriptome was performed on 11 primary UM tissues (6 metastasized and 5 nonmetastasized) through RNA sequencing. A total of 40,878 lncRNAs were detected in UM, 4,983 of which were novel candidates. We identified 329 differentially expressed lncRNAs (DELs) and 802 differentially expressed mRNAs (DEMs) by comparing the transcriptome profile between metastasized and nonmetastasized UM group. The DEL-DEM coexpression network revealed that the RP11-551L14.4, TCONS_00004101, and TCONS_00004845 DELs had the highest connectivity with the DEMs, coexpressed with 225, 28, and 10 DEMs, respectively, whereas the SPOCD1, PEA15, and SLC44A3 DEMs were most closely connected with the DELs, and were coexpressed with 89, 27, and 22 DELs, respectively. Moreover, 17 and 743 DEMs were targeted by the DELs through cis- or trans-action, respectively. These targeted DEMs were significantly enriched in D-Arginine and D-ornithine metabolism and glycerolipid metabolism of Kyoto Encyclopedia of Genes and Genomes pathways, and enriched in bradykinin receptor activity and haptoglobin binding of gene ontology biological processes. Quantitative real-time PCR confirmed the sequencing data. These findings have provided new insights into the molecular mechanism of UM metastasis and paved the way for further investigations regarding lncRNA in UM.

Identification of potential prognostic ceRNA module biomarkers in patients with pancreatic adenocarcinoma

Accumulating evidence suggested that long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to interact with other RNA transcripts and ceRNAs perturbation play important roles in cancer initiation and progression including pancreatic adenocarcinoma (PAAD). In this study, we constructed a PAAD-specific hallmark gene-related ceRNA network (HceNet) using paired genome-wide expression profiles of mRNA, lncRNA and miRNA and regulatory relationships between them. Based on “ceRNA hypothesis”, we analyzed the characteristics of HceNet and identified a ceRNA module comprising of 29 genes (12 lncRNAs, two miRNAs and 15 mRNAs) as potential prognostic biomarkers related to overall survival of patients with PAAD. The prognostic value of ceRNA module biomarkers was further validated in the train (Hazard Ratio (HR) =1.661, 95% CI: 1.275–2.165, p<1.00e-4), test (HR=1.546, 95% CI: 1.238-1.930, p<1.00e-4), and entire (HR=1.559, 95% CI: 1.321-1.839, p<1.00e-4) datasets. Our study provides candidate prognostic biomarkers for PAAD and increases our understanding of ceRNA-related regulatory mechanism in PAAD pathogenesis.