A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.     

Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

Nowadays, an increasing number of studies illustrated that bladder urothelial cancer (BLCA) may act as the most common subtype of urological malignancies with a high rate of recurrence and metastasis. In this study, we attempted to establish a prognostic model and identify the possible pathway crosstalk. Long noncoding RNAs (lncRNAs) and mRNA expression and corresponding clinical information of patients with BLCA were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes analysis, univariate Cox analysis, the least absolute shrinkage, and selection operator Cox (LASSO Cox) regression model were then applied to identify five crucial lncRNAs (AC092725.1, AC104071.1, AL023584.1, AL132642.1, and AL137804.1). The multivariate cox analysis was utilized to calculate the regression coefficients (β_i). The risk-score model was subsequently constructed as follows: (0.13541AC092725.1) + (0.20968AC104071.1) + (0.1525AL023584.1) − (0.14768AL132642.1) + (0.14387AL137804.1). Nomogram and assessment of overall survival (OS) prediction were verificated by the receiver operating characteristic curve in the testing group. As to 3-, 5-year OS prediction, the area under curve (AUC) for the nomogram of training data set was 0.83 and 0.86. Besides, the AUC (0.883 and 0.879) presented excellent predictive power in the testing group. In addition, the calibration plots validated the predictive performance of the nomogram. Weighted correlation network analysis (WGCNA) coupled with functional enrichment analysis contributed to explore the potential pathways, including PI3K-Akt, HIF-1, and Jak-STAT signaling pathways. Construction of the risk-score model and data analysis were both derived from multiple packages on the basis of the R platform chiefly.     

LncRNA and mRNA signatures associated with neoadjuvant chemoradiotherapy downstaging effects in rectal cancer

Radiotherapy plays a crucial role in combined treatment modality in local advanced rectal cancer (LARC). While neoadjuvant chemoradiotherapy responses were variable in LARC patients, so, it is important to identify genes that closely associated with short-term and long-term responses to radiotherapy. In this study, we profiled long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) expression values of LARC patients with different neoadjuvant chemoradiotherapy downstaging depth score based on Agilent Arraystar Human LncRNA V3.0 Array(Agilent, CA). LncRNAs and mRNAs with aberrant expression values between the two groups of LARC patients were identified and lncRNA-miRNA-mRNA regulation network was also obtained through the combination of miRcode and miRTarBase database. Gene interaction network and module analysis of differential expression mRNAs contained in the lncRNA-miRNA-mRNA network identified five hub genes, including KRAS, PDPK1, PPP2R5C, PPP2R1B, and YES1, that should be closely associated with LARC’s response to chemoradiotherapy. Besides, Kaplan-Meier analysis based on the Cyber Research Center (CRC) data set from The Cancer Genome Atlas indicated that aberrant expression of the five hub genes is significantly associated with CRC overall survival. In conclusion, we obtained several biomarkers that should be associated with neoadjuvant chemoradiotherapy response in LARC, which should be helpful for individual treatment and prognosis improvement.     

A 7-lncRNA signature predict prognosis of Uterine corpus endometrial carcinoma

Current research indicate that long noncoding RNAs (lncRNAs) are associated with the progression of various cancers and can be used as prognostic biomarkers. This study aims to construct a prognostic lncRNA signature for the risk assessment of Uterine corpus endometrial carcinoma (UCEC). The RNA-Seq expression profile and corresponding clinical data of UCEC patients obtained from The Cancer Genome Atlas database. First, some prognosis-related lncRNAs were obtained by univariate Cox analysis. The minimum absolute contraction and selection operator (LASSO) regression and the Cox proportional hazard regression method were used to further identify the lncRNA prognostic model. Finally, seven lncRNAs (AC110491.1, AL451137.1, AC005381.1, AC103563.2, AC007422.2, AC108025.2, and MIR7-3HG) were identified as potential prognostic factors. According to the model constructed by the above analysis, the risk score of each UCEC patient was calculated, and the patients were classified into high and low-risk groups. The low-risk group had significant survival benefits. Moreover, we constructed a nomogram that incorporated independent prognostic factors (age, tumor stage, tumor grade, and risk score). The c-index value for evaluating the predictive nomogram model was 0.801. The area under the curve was 0.797 (3-year survival). The calibration curve also showed that there was a satisfactory agreement between the predicted and observed values in the probability of 1-, 3-, and 5-year overall survival. On the basis of the coexpression relationship, we established a coexpression network of lncRNA-messenger RNA (mRNA) of the 7-lncRNA. The Kyoto Encyclopedia of Genes and Genomes analysis of the coexpressing mRNAs showed that the main pathways related to the 7-lncRNA signature were neuroactive ligand-receptor interaction, serotonergic synapse, and gastric cancer pathway. Therefore, our study revealed that the 7-lncRNA could be used to predict the prognosis of UCEC and for postoperative treatment and follow-up.     

Expression profiling of long noncoding RNAs associated with vasculogenic mimicry in osteosarcoma

Osteosarcoma (OS) is the most common highly malignant bone tumor in teens. Vasculogenic mimicry (VM) is defined as de novo extracellular matrix-rich vascular-like networks formed by highly aggressive tumor cells. We previously reported the presence of VM and it is an unfavorable prognostic factor in OS patients. Long noncoding RNAs (lncRNAs) are aberrantly expressed in OS and involved in cancer cell VM. However, lncRNAs in VM formation of OS have not been investigated. We, therefore, profiled the expression of lncRNAs in highly aggressive OS cell line 143B compared with its parental poorly aggressive cell line HOS. The differentially expressed (DE) lncRNAs and messenger RNA (mRNAs) were subjected to constructed lncRNA-mRNA coexpressed network. The top-ranked hub gene lncRNA n340532 knockdown 143B cells were used for in vitro and in vivo VM assays. The annotation of DE lncRNAs was performed according to the coexpressed mRNAs by Gene Ontology and pathway analysis. A total of 1360 DE lncRNAs and 1353 DE mRNAs were screened out. lncRNA MALAT1 and FTX, which have known functions related to VM formation and tumorigenesis were identified in our data. The coexpression network composed of 226 lncRNAs and 118 mRNAs in which lncRNA n340532 had the highest degree number. lncRNA n340532 knockdown reduced VM formation in vitro. The suppression of n340532 also exhibited potent anti-VM and antimetastasis effect in vivo, suggesting its potential role in OS VM and metastasis. Furthermore, n340532 coexpressed with 10 upregulation mRNAs and 3 downregulation mRNAs. The enriched transforming growth factor-β signaling pathway, angiogenesis and so forth were targeted by those coexpressed mRNAs, implying n340532 may facilitate VM formation in OS through these pathways and gene functions. Our findings provide evidence for the potential role of lncRNAs in VM formation of OS that could be used in the clinic for anti-VM therapy in OS.     

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.     

Long noncoding RNA POU3F3 promotes cancer cell proliferation in prostate carcinoma by upregulating rho-associated protein kinase 1

Long noncoding RNAs (lncRNAs) POU3F3 is overexpressed in esophageal squamous-cell carcinomas, while its role in other human cancers is unclear. In this study we found that POU3F3 and rho-associated protein kinase 1 (ROCK1) were both increased in tumor tissues than in adjacent healthy tissues of patients with prostate carcinoma. Expression levels of POU3F3 increased with increase in the diameter of tumor but were not significantly affected by lymph node metastasis or distant metastasis. Expression levels of POU3F3 and ROCK1 were positive correlated in tumor tissues but not in adjacent healthy tissues. POU3F3 and ROCK1 overexpression promoted, while ROCK1 knockdown inhibited the proliferation of prostate carcinoma cells. ROCK1 knockdown reduced the enhancing effect of POU3F3 overexpression on cancer cell proliferation. POU3F3 overexpression led to ROCK1 overexpression in prostate carcinoma cells, while ROCK1 overexpression did not significantly affect POU3F3 expression. Therefore, lncRNA POU3F3 may promote cancer cell proliferation in prostate carcinoma by upregulating ROCK1.     

Modulation of cancer cell signaling by long noncoding RNAs

Cellular signaling pathways play a very important role in almost all molecular processes in the cell, and are generally composed of a complex set of cascades in which enzymes and proteins play a key role. These signaling pathways include different types of cellular signaling classified based on their receptors and effector proteins such as enzyme-linked receptors, cytokine receptors, and G-protein-coupled receptors each of which is subdivided into different classes. Signaling pathways are tightly controlled by different mechanisms mostly thorough inhibiting/activating their receptors or effector proteins. In the last two decades, our knowledge of molecular biology has changed dramatically and today we know that more than 85% of the human genome expresses noncoding RNAs most of which are crucial in the cellular and molecular mechanisms of cells. One of these noncoding RNAs are long noncoding RNAs (lncRNA) containing more than 200 nucleotides. LncRNAs participate in the progression of cancer growth through several mechanism including signaling pathways. In this review, we summarize some of the most important of lncRNAs and their effect on important signaling pathways.     

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.     

Identification of functional lncRNAs in gastric cancer by integrative analysis of GEO and TCGA data

Gastric cancer (GC) is a prevalent malignant cancer of digestive system, identification of novel diagnostic and prognostic biomarkers for GC is urgently demanded. The aim of this study was to determine potential long noncoding RNAs (lncRNAs) associated with the pathogenesis and prognosis of GC. Raw noncoding RNA microarray data (GSE53137, GSE70880, and GSE99417) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by The Cancer Genome Atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway, survival analysis were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. In total of 246 integrated differential genes including 15 lncRNAs and 241 messenger RNAs (mRNAs) were obtained after intersections of differential genes between GEO and TCGA database. ceRNA network comprised of three lncRNAs (UCA1, HOTTIP, and HMGA1P4), 26 microRNAs (miRNAs) and 72 mRNAs. Functional analysis revealed that three lncRNAs were mainly dominated in cell cycle and cellular senescence. Survival analysis showed that HMGA1P4 was statistically related to the overall survival rate. For the first time, we identified that HMGA1P4, a target of miR-301b/miR-508, is involved in cell cycle and senescence process by regulating CCNA2 in GC. Finally, the expression levels of three lncRNAs were validated to be upregulated in GC tissues. Thus, three lncRNAs including UCA1, HOTTIP, and HMGA1P4 may contribute to GC development and their potential functions might be associated with the prognosis of GC.     

ANRIL and ANRASSF1 long noncoding RNAs are upregulated in gastric cancer

Gastric cancer (GC) is the fifth most frequent cancer and the third-leading cause of cancer-related death worldwide. It is a highly heterogeneous disease regarding the morphological and molecular viewpoints. Since it is curable in primary stages, early detection could improve the survival rate. Long noncoding RNAs contribute to a variety of cellular mechanisms, and their dysregulation is reported in various diseases such as cancer. Thus, they have a great potential to be used as diagnostic and prognostic biomarkers and therapeutic targets as well. In the current study, ANRIL and ANRASSF1 expression levels were compared between GC tumors and the adjacent normal tissues collected from 39 Iranian patients using the quantitative real-time polymerase chain reaction method. Correlation between ANRIL and ANRASSF1 expression levels and other clinical parameters was also evaluated. ANRIL and ANRASSF1 were significantly overexpressed in GC tumors compared with adjacent tissues ( P < 0.0001 and P = 0.001, respectively). No significant correlation between ANRIL and ANRASSF1 expression levels and demographic information was found. This study suggests that ANRIL and ANRASSF1 may play a critical role in GC progression and can be considered as a potential diagnostic or therapeutics biomarkers.