three-lncRNA Signature Research Articles

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer.

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.

Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery

Accurately predicting patient risk and identifying survival biomarkers are two important tasks in survival analysis. For the emerging high-throughput gene expression data, random survival forest (RSF) is attracting more and more attention as it not only shows excellent performance on survival prediction problems with high-dimensional variables, but also is capable of identifying important variables according to variable importance automatically calculated within the algorithm. However, RSF still suffers from some problems such as limited predictive accuracy on independent datasets and limited biological interpretation of survival biomarkers. In this study, we integrated gene interaction information into a Reweighted RSF model (RRSF) to improve predictive accuracy and identify biologically meaningful survival markers. We applied RRSF to the prediction of patients with glioblastoma multiforme (GBM) and esophageal squamous cell carcinoma (ESCC). With a reconstructed global pathway network and an mRNA-lncRNA co-expression network as the prior gene interaction information, RRSF showed better overall predictive performance than RSF on three GBM and two ESCC datasets. In addition, RRSF identified a two-gene and three-lncRNA signature, which showed robust prognostic values and had high biological relevance to the development of GBM and ESCC, respectively.

Three-lncRNA signature is a potential prognostic biomarker for pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly aggressive and metastatic cancer characterized by poor survival rates. Long non-coding RNAs (lncRNAs) play important roles in various biological processes, including cancer and PAAD. To identify the specific lncRNAs associated with PAAD and analyze their function, we constructed a global triple network based on the competitive endogenous RNA (ceRNA) theory and RNA-seq data from The Cancer Genome Atlas. Using 182 PAAD cases, we established a lncRNA–miRNA–mRNA co-expression network, which was composed of 43 lncRNA nodes, 253 mRNA nodes, 11 miRNA nodes, and 475 edges. Six lncRNAs in the ceRNA network were closely related to overall survival, and a three-lncRNA signature predicted survival of PAAD patients. Protein–protein interaction network data revealed that five genes were associated with overall survival. These results provide novel insight into the function of a lncRNA-associated ceRNA network in the pathogenesis of PAAD, and indicate that the identified three-lncRNA signature may serve as an independent prognostic marker in PAAD.

A three-lncRNA signature predicts overall survival and disease-free survival in patients with esophageal squamous cell carcinoma

BackgroundIncreasing evidence shows that dysregulated long non-coding RNAs (lncRNAs) can serve as potential biomarkers for cancer prognosis. However, lncRNA signatures, as potential prognostic biomarkers for esophageal squamous cell carcinoma (ESCC), have been seldom reported.MethodsBased on our previous transcriptome RNA sequencing analysis from 15 paired ESCC tissues and adjacent normal tissues, we selected 10 lncRNAs with high score rank and characterized the expression of those lncRNAs, by qRT-PCR, in 138 ESCC and paired adjacent normal samples. These 138 patients were divided randomly into training (n = 77) and test (n = 59) groups. A prognostic signature of lncRNAs was identified in the training group and validated in the test group and in an independent cohort (n = 119). Multivariable Cox regression analysis evaluated the independence of the signature in overall survival (OS) and disease-free survival (DFS) prediction. GO and KEGG pathway analysis, combined with cell transwell and proliferation assays, are applied to explore the function of the three lncRNAs.ResultsA novel three-lncRNA signature, comprised of RP11-366H4.1.1 (ENSG00000248370), LINC00460 (ENSG00000233532) and AC093850.2 (ENSG00000230838), was identified. The signature classified patients into high-risk and low-risk groups with different overall survival (OS) and disease-free survival (DFS). For the training group, median OS: 23.1 months vs. 39.1 months, P < 0.001; median DFS: 15.2 months vs. 33.3 months, P < 0.001. For the test group, median OS: 23 months vs. 59 months, P < 0.001; median DFS: 16.4 months vs. 50.8 months, P < 0.001. For the independent cohort, median OS: 22.4 months vs. 60.4 months, P < 0.001). The signature indicates that patients in the high-risk group show poor OS and DFS, whereas patients with a low-risk group show significantly better outcome. The independence of the signature was validated by multivariable Cox regression analysis. GO and KEGG pathway analysis for 588 protein-coding genes-associated with the three lncRNAs indicated that the three lncRNAs were involved in tumorigenesis. In vitro assays further demonstrated that the three lncRNAs promoted the migration and proliferation of ESCC cells.ConclusionsThe three-lncRNA signature is a novel and potential predictor of OS and DFS for patients with ESCC.

RNA-sequencing investigation identifies an effective risk score generated by three novel lncRNAs for the survival of papillary thyroid cancer patients.

Scholars are striving to apply molecular biology involving long non-coding RNA (lncRNA) in the prognostication of papillary thyroid cancer (PTC). However, the clinical role of lncRNAs in the prognostic setting of PTC is still unclear. Herein, a comprehensive inquiry was performed to screen lncRNA expression profiling with 507 PTC patients from The Cancer Genome Atlas RNA-sequencing datasets. A total of 734 lncRNAs were detected to be aberrantly expressed, among which three novel lncRNAs including AC079630.2, CRNDE and CTD-2171N6.1 were markedly related to the progression and survival of PTC. Furthermore, the aberrant expression of these lncRNAs could be verified by other cohorts from gene expression omnibus (GEO) as detected by microarrays. Next, we established a three-lncRNA signature and divided the PTC patients into two subgroups of high- and low-risk. Interestingly, patients with high-risk tended to gain obviously poorer outcome. Most importantly, this three-lncRNA signature was an independent biomarker to predict the patient survival of PTC. The accurate molecular roles of these three lncRNAs remains unclarified and warrants further characterization, but our current data propose that they might play pivotal roles in PTC tumorigenesis and more importantly, these novel lncRNAs are closely related to patients’ survival. These discoveries will have far-reaching consequences with respect to molecular prediction of PTC.

Serum long non-coding RNAs MALAT1, AFAP1-AS1 and AL359062 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma.

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.

A three-lncRNA signature derived from the Atlas of ncRNA in cancer (TANRIC) database predicts the survival of patients with head and neck squamous cell carcinoma

ObjectiveLong non-coding RNAs (lncRNAs) have important biological functions and can be used as prognostic biomarkers in cancer. To identify a lncRNA prognostic signature for head and neck squamous cell carcinoma (HNSCC). MethodWe analysed RNA-seq data derived from the TANRIC database to identify a lncRNA prognostic signature model using the orthogonal partial least squares discrimination analysis (OPLS-DA) and 1.5-fold expression change criterion methods. The prognosis prediction model based on the lncRNA signatures and clinical parameters were evaluated using the 5-fold cross validation method. ResultsA total of 84 out of 3199 lncRNAs were significantly associated with the survival of patients with HNSCC (log-rank test P<0.01). Using the OPLS-DA and 1.5-fold change selection criterion, 5 lncRNAs (KTN1-AS1, LINC00460, GUSBP11, LINC00923 and RP5-894A10.6) were further selected. The prediction power of each combination of the 5 lncRNAs was evaluated through the receiver operating characteristic (ROC) curve and a three-lncRNA panel (KTN1-AS1, LINC00460 and RP5-894A10.6) achieved the highest prognostic prediction power (AUC 0.68, 95% CI 0.60–0.76, P<0.0001) in the cohort. The patients were categorized into high- and low-risk groups based on their three-lncRNA profiles. Patients with high-risk scores had worse overall survival than those with low risk scores in the cohort (log-rank test P=0.0003). Multivariable Cox regression analyses showed that the lncRNA signature and tumour grade were independent prognostic factors for patients with HNSCC. ConclusionsOur findings showed that the three-lncRNA signature might be a novel biomarker for the accurate prognosis prediction of patients with HNSCC.

P2.01-020 Identification of a Three-lncRNA Signature for Lung Cancer Diagnosis and Prognosis

P2.01-020 Identification of a Three-lncRNA Signature for Lung Cancer Diagnosis and Prognosis

Transcriptome sequencing uncovers a three-long noncoding RNA signature in predicting breast cancer survival.

Long noncoding RNAs (lncRNAs) play a crucial role in tumorigenesis. The aim of this study is to identify lncRNA signature that can predict breast cancer patient survival. RNA expression data from 1064 patients were downloaded from The Cancer Genome Atlas project. Cox regression, Kaplan–Meier, and receiver operating characteristic (ROC) analyses were performed to construct a model for predicting the overall survival (OS) of patients and evaluate it. A model consisting of three lncRNA genes (CAT104, LINC01234, and STXBP5-AS1) was identified. The Kaplan–Meier analysis and ROC curves proved that the model could predict the prognostic survival with good sensitivity and specificity in both the validation set (AUC = 0.752, 95% confidence intervals (CI): 0.651–0.854) and the microarray dataset (AUC = 0.714, 95%CI: 0.615–0.814). Further study showed the three-lncRNA signature was not only pervasive in different breast cancer stages, subtypes and age groups, but also provides more accurate prognostic information than some widely known biomarkers. The results suggested that RNA-seq transcriptome profiling provides that the three-lncRNA signature is an independent prognostic biomarker, and have clinical significance. In addition, lncRNA, miRNA, and mRNA interaction network indicated lncRNAs may intervene in breast cancer pathogenesis by binding to miR-190b, acting as competing endogenous RNAs.

Circulating CUDR, LSINCT-5 and PTENP1 long noncoding RNAs in sera distinguish patients with gastric cancer from healthy controls.

The examination of circulating nucleic acids (CNAs) is an emerging noninvasive diagnostic technique. However, it is unclear if serum long noncoding RNAs (lncRNAs) represent a novel marker to detect gastric cancer (GC). In this study, we measured 39 candidate cancer-associated lncRNAs by reverse transcription and quantitative polymerase chain reaction (RT-qPCR) in sera from 110 patients with GC, 106 age- and sex-matched healthy subjects and 15 patients with gastric peptic ulcer, markers were validated and assessed by RT-qPCR. The correlation of the expression levels of the candidate serum lncRNAs with clinical parameters of GC patients was performed. A three-lncRNA signature, including CUDR, LSINCT-5 and PTENP1, was identified that may be potential diagnostic marker for GC. The areas under the receiver operating characteristic (ROC) curve for this serum three-lncRNA signature were 0.920 and 0.829 for the two sets of serum samples. Moreover, a risk model for the serum three-lncRNA signature demonstrated that healthy samples can be distinguished from early GC samples. Three-lncRNA signature in serum was identified as diagnostic marker for GC. This work may facilitate the detection of GC and serve as the basis for further studies of the clinical value of serum lncRNAs in maintaining surveillance and forecasting prognosis.

LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma

BackgroundOesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles.ObjectiveTo assess the lncRNA expression profile of...

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<h3>Background</h3> Oesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles. <h3>Objective</h3> To assess the lncRNA expression profile of oesophageal squamous cell carcinoma (OSCC) and identify prognosis-related lncRNAs. <h3>Method</h3> LncRNA expression profiles were studied by microarray in paired tumour and normal tissues from 119 patients with OSCC and validated by qRT-PCR. The 119 patients were divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random Forest supervised classification algorithm and a nearest shrunken centroid algorithm, then validated in a test group and further, in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by multivariable Cox regression analysis. <h3>Results</h3> LncRNAs showed significantly altered expression in OSCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1) which classified the patients into two groups with significantly different overall survival (median survival 19.2 months vs &gt;60 months, p&lt;0.0001). The signature was applied to the test group (median survival 21.5 months vs &gt;60 months, p=0.0030) and independent cohort (median survival 25.8 months vs &gt;48 months, p=0.0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with OSCC. Stratified analysis suggested that the signature was prognostic within clinical stages. <h3>Conclusions</h3> Our results suggest that the three-lncRNA signature is a new biomarker for the prognosis of patients with OSCC, enabling more accurate prediction of survival.