Relapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinoma

Effects of Helicobacter pylori Infection on the Development of Chronic Gastritis

BACKGROUND/AIMS

Based on the gene expression profiles of gastric epithelial tissue at different stages of Helicobacter pylori-infected gastritis, key long noncoding RNAs and genes in the development of Helicobacter pylori infection-induced gastritis were screened to provide a basis for early diagnosis and treatment.

MATERIALS AND METHODS

We downloaded 2 sets of sample data from the database, including gastric epithelial tissue samples from gastritis patients from Bhutan and Dominican, and screened mRNAs in the differentially expressed RNAs of the 2 regions. Mfuzz clustering algorithm was used to screen RNAs related to the 3 stages of chronic gastritis. The competing endogenous RNA (ceRNA) regulation network was constructed, and the selected key RNAs were verified. Samples from Bhutan and Dominican were subdivided into the chronic gastritis/ normal comparison groups, and the differentially expressed RNAs were screened to obtain 1067 overlapping RNAs, containing 21 long noncoding RNAs and 1046 mRNAs.

RESULTS

Thirty-eight significant gene ontology functional nodes and 6 expression pattern clusters were obtained. Two ceRNA regulatory networks were constructed, and 4 shared miRNAs (hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, and hsa-miR-155-5p) were obtained. Eleven important long noncoding RNAs (AFAP1-AS1, MIR155HG, LINC00472, and FAM201A) and mRNAs (CASP10, SLC26A2, TRIB1, BMP2K, SCAMP1, TNKS1BP1, and MBOAT2) regulated by these 4 miRNAs were obtained. These results indicated that Helicobacter pylori infection had a certain influence on the development of gastritis.

CONCLUSIONS

The 11 key RNAs can provide a target for the early diagnosis and treatment of chronic gastritis following Helicobacter pylori infection.

A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.

Costimulatory molecule-related lncRNA model as a potential prognostic biomarker in non-small cell lung cancer

OBJECTIVE

Costimulatory molecules have been demonstrated to exert essential roles in multiple cancers. However, their role in lung cancer remains elusive. Here, we sought to identify costimulatory molecule-related lncRNAs in non-small cell lung cancer (NSCLC) and establish a prognostic signature to predict the prognosis of patients with NSCLC.

METHODS

A total of 535 lung adenocarcinoma (LUAD) and 502 lung squamous cell carcinoma (LUSC) patients from the cancer genome atlas (TCGA) database were recruited. A novel costimulatory molecule-based lncRNA prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival. The Homo_sapiens.GRCh38 data set was set as a reference file for probe annotation.

RESULTS

A total of 593 costimulatory molecule-related lncRNAs were extracted. After analysis, six costimulatory molecule-related lncRNAs (AC084859.1, AC079949.2, HSPC324, LINC01150, LINC01150, and AC090617.5) were screened. A prognostic model based on the six lncRNAs was established using systematic bioinformatics analyses. The prognostic model had a prognostic value in NSCLC patients. Furthermore, a prognostic nomogram was established based on clinical parameters and a risk-score model. Patients with different risk scores had considerably different tumor-infiltrating immune cells, somatic mutational loading, clinical outcomes, signaling pathways, and immunotherapy efficacy. In addition, LINC01137 was associated with unfavorable disease outcomes and fueled tumor progression in NSCLC.

CONCLUSION

Taken together, our study demonstrated that a costimulatory molecule-related lncRNA model could be a potential prognostic biomarker in NSCLC. Moreover, LINC01137 could facilitate the proliferation and invasion of lung cancer.

Forecasting determinants of recurrence in lung cancer patients exploiting various machine learning models

Lung cancer recurrence seems to be the most leading cause of death as well as deterioration of lifespan. Proper assessment of the probability of recurrence in early-stage lung cancer is necessary to push up the treatment progress. We therefore employed machine-learning technologies to forecast post-operative recurrence risks using 174 lung cancer patient records. Six classification algorithms logistic regression, SVM, decision tree classification, random forest classification, XGBoost and lightGBM were used to predict the cancer recurrence. The patient samples were divided into training and test group with the split ratio of 3:1 for model generation and the accuracy were validated using k-fold cross-validation method. It is worth noting that the logistic regression model outperformed all the models in both training (Accuracy = 0.82) and test set (Accuracy = 0.79) on k-fold validation. Further, the optimal features (n = 7) identified using the RFE method is certainly helpful to improve the model in a high precision. The imperative risk factors associated with recurrence were identified using three feature selection methods. Importantly, our research showed that age is an important prognostic factor to be considered during the recurrence prediction. Indeed, severe concern on the identified risk factors combined with predictive models assists the physician to reduce the cancer recurrence rate in patients with lung cancer.

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.

A seven-lncRNA signature for predicting prognosis in breast carcinoma

Background

Long non-coding RNAs (lncRNAs) play an important part in tumorigenesis and cancer metastasis and can serve as a potential biosignature for cancer prognosis. However, the use of lncRNA signatures to predict survival in breast carcinoma is yet unreported.

Methods

The lncRNA expression profiles and homologous clinical data of 913 breast carcinoma samples from the Cancer Genome Atlas (TCGA), were analyzed to obtain 2,547 differentially expressed lncRNAs. Univariate Cox proportional risk regression was applied to both the training and testing datasets to screen the common prognostic lncRNAs. Potential prognostic LncRNAs were screened by multivariate Cox proportional risk regression in the training data set of the selected LncRNAs.

Results

Seven lncRNAs (LINC02037, MAPT-AS1, RP1-37C10.3, RP11-344E13.4, RP11-454P21.1, RP11-616M22.1, SPACA6P-AS) were prominently associated with overall survival. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves indicated that these indicators were sensitive and specific for survival prediction. The areas under the ROC curve of the seven-lncRNA signature in predicting 3- and 5-year survival rates were 0.771 and 0.780 respectively in the combined cohort. Furthermore, enrichment analysis revealed that these seven lncRNAs might participate multiple pathways related to tumorigenesis and prognosis.

Conclusions

The proposed seven-lncRNA signature could serve as a latent prognostic biomarker for survival prediction in patients with breast carcinoma.

A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma

Purpose

Long noncoding RNAs (lncRNAs) and glycolysis regulate multiple types of cancer. However, the prognostic roles and biological functions of glycolysis-related lncRNAs in lung adenocarcinoma (LUAD) remain unclear. In this study, we investigated the role of glycolysis-related lncRNAs in LUAD.

Patients and Methods

We retrieved glycolysis-related genes from the Molecular Signatures Database and screened for prognostic glycolysis-related lncRNAs from The Cancer Genome Atlas.

Results

We identified three LUAD subtypes (clusters 1–3) by univariate Cox regression analysis and consensus clustering. Patients in cluster 1 had the best overall survival rates. Immune, stromal, and cytolytic-activity scores were the highest in cluster 1. The expression of immune checkpoint molecules (programmed cell death protein 1 and cytotoxic T-lymphocyte-associated protein 4) and other immune-related indicators was the highest in cluster 1, whereas that of epithelial cell biomarkers (Cadherin 1, Cadherin 2, and MET) was the lowest. Therefore, patients in cluster 1 may benefit from immunotherapy. Lasso–Cox regression and multivariate Cox regression analyses were used to select nine lncRNAs to build a robust prognostic model of LUAD. The area under the curve classifier values and a nomogram performed well in predicting survival times for patients with LUAD. The expression levels of nine lncRNAs were validated by quantitative reverse transcriptase-polymerase chain reaction analysis, and most of these lncRNAs were significantly related to immune-related mRNAs. Gene set enrichment analysis revealed that the high-risk group was enriched for cell cycle-related pathways and the low-risk group was enriched for pathways associated with immunity or immune-related diseases.

Conclusion

The LUAD subtypes and prognostic model developed here may help in clinical risk stratification, prognosis management, and treatment decisions for patients with LUAD.

The Immune Heterogeneity Between Pulmonary Adenocarcinoma and Squamous Cell Carcinoma: A Comprehensive Analysis Based on lncRNA Model

Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are both classified as major forms of non-small cell lung cancer, but differences in clinical prognoses and molecular mechanisms are remarkable. Recent studies have supported the importance of understanding immune status in that it influences clinical outcomes of cancer, and immunotherapies based on the theory of “immune editing” have had notable clinical success. Our study aimed to identify specific long non-coding (lnc) RNAs that control key immune-related genes and to use them to construct risk models for AD and SCC. Risk scores were used to separate patients into high- and low-risk groups, and we validated the prognostic significance of both risk scores with our own cohorts. A Gene Set Enrichment Analysis suggested that the immune responses of patients in the AD high-risk group and the SCC low-risk group tended to be weakened. Evaluation of immune infiltration revealed that the degree of infiltration of dendritic cells is of particular importance in AD. In addition, prediction of responses to immune checkpoint inhibitor (ICI) treatments, based on the T Cell Immune Dysfunction and Exclusion and immunophenoscore models, indicated that deterioration of the immune microenvironment is due mainly to T cell exclusion in AD patients and T cell dysfunction in SCC patients and that high-risk patients with SCC might benefit from ICI treatment. The prediction of downstream targets via The Cancer Proteome Atlas and RNA-seq analyses of a transfected lung cancer cell line indicated that the lncRNA LINC00996 is a potential therapeutic target in AD.

Screening of tumor grade-related mRNAs and lncRNAs for Esophagus Squamous Cell Carcinoma

Background

The goal of our study was to screen tumor grade‐related lncRNAs and mRNAs to reveal the underlying molecular mechanism of esophagus squamous cell carcinoma (ESCC).

Methods

The lncRNA and mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA). Tumor grade correlation analysis of lncRNAs and mRNAs was executed, followed by the functional enrichment analysis of all tumor grade‐related mRNAs. The differentially expression mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) were obtained. PPI network and DEmRNA‐DElncRNA interaction analysis were constructed. The functional annotation of the DEmRNAs co‐expressed with DElncRNAs was performed. The expression levels of the candidate genes were validated using qRT‐PCR.

Results

A total of 1864 tumor grade‐related mRNAs (846 positively related and 1018 negatively related) and 552 tumor grade‐related lncRNAs (331 positively related and 221 negatively related) were obtained. The top 10 significantly grade‐related mRNAs and lncRNAs included CA12, FABP4, DECR1, BAIAP2, IL1RAPL2, PPARD, LAD1, TSPAN10, LDOC1, ZNF853, RP11‐25G10.2, RP11‐557H15.3, RP11‐521D12.5, CHKB‐AS1, RP11‐219B4.3, CH17‐335B8.4, RP11‐99 J16‐A.2, CTB‐111H14.1, ADNP‐AS1, and JHDM1D‐AS1. SFN, IL1RAPL2, and RP11‐25G10.2 were overlapped from grade 1, grade 2, and grade 3. PPI network showed that top 10 proteins with higher degrees, including GNAI1, RAP2B, GNAZ, SHH, ADCY1, PRKAR2B, SH3GL1, GNA15, and ARRB1. A DElncRNAs‐nearby DEmRNAs network was constructed to obtain hub lncRNAs including ADAMTS9‐AS2, RP11‐210 M15.2, RP11‐13 K12.1, ZBED3‐AS1, and RP11‐25G10.2. Except for RP11‐25G10.2, ADAMTS9‐AS1, ZBED3‐AS1, SFN, ATP1A2, and GNA15 were consistent with our TCGA analysis.

Conclusions

Alterations of DEmRNAs and DElncRNAs may provide key insights into the molecular mechanisms of ESCC.

Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.

Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.

Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.

Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

A novel five-lncRNA signature panel improves high-risk survival prediction in patients with cholangiocarcinoma

Cholangiocarcinoma (CCA) is a fatal disease with dismal survival rates. Long non-coding RNA (lncRNA) expression profiling as potential prognostic biomarkers play critical roles in tumor initiation, development, and poor prognosis. Identifying specific lncRNA to predict the prognosis of CCA patients in the early stages is very important for improving a patient’s survival. In the current study, we aimed to establish a novel risk-stratification lncRNA signature panel in CCA. The initial lncRNA discovery was identified in The Cancer Genome Atlas database (TCGA cohort). The Cox regression analysis was used to establish the lncRNA prognostic model and the receiver operating characteristic (ROC) curve analysis was performed to assess the specificity and sensitivity of the model. This was followed by independent validation of the lncRNA signature in the CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU cohort). Furthermore, by using the Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Finally, five lncRNA (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish the predictive model that significantly associated with poor overall survival(HR:4.879;95%CI,1.587-14.996;p=0.006). This five-lncRNA signature model showed excellent accuracy in the TCGA cohort (AUC=0.938), and also robustly predicted survival in the validation WMU cohort(AUC=0.816). Functional enrichment analysis suggested prognostic lncRNA was primarily associated with CCA-related biological processes. Our data established a novel lncRNA signature model for CCA risk-stratification and robust identification of CCA patients with poor molecular genotypes. Moreover, it revealed new molecular mechanisms of CCA.

lncRNAs-mRNAs Co-Expression Network Underlying Childhood B-Cell Acute Lymphoblastic Leukaemia: A Pilot Study

Simple Summary

Acute lymphoblastic leukemia (ALL) is one of the most common childhood cancers. The ALL onset involves abnormal proliferation and arrest of differentiation of B or T cell progenitors. Recently, long non–coding RNAs (lncRNAs) gained great interest in the B–ALL leukemogenesis, however, so far few “omic” studies investigate lncRNAs and protein–coding gene networks. In our retrospective study, we conceived an integrated bioinformatic approach, by using NGS platform, to discover lncRNAs strongly correlated with aberrantly expressed protein–coding genes. We provided dysregulated lncRNA–mRNA pairs potentially underlying B–ALL pathogenesis. Diagnosis incidence peak of ALL appears approximatively between 1 and 19 years old. lncRNAs may be of clinical utility as non–invasive biomarker for B–ALL onset or therapy response in support of precision medicine. The identification of lncRNA as key regulators in B–ALL could lead to the identification of the altered pathways able to sustain the leukemic growth.

Abstract

Long non–coding RNAs (lncRNAs) are emerging as key gene regulators in the pathogenesis and development of various cancers including B lymphoblastic leukaemia (B–ALL). In this pilot study, we used RNA–Seq transcriptomic data for identifying novel lncRNA–mRNA cooperative pairs involved in childhood B–ALL pathogenesis. We conceived a bioinformatic pipeline based on unsupervised PCA feature extraction approach and stringent statistical criteria to extract potential childhood B–ALL lncRNA signatures. We then constructed a co–expression network of the aberrantly expressed lncRNAs (30) and protein–coding genes (754). We cross–validated our in–silico findings on an independent dataset and assessed the expression levels of the most differentially expressed lncRNAs and their co–expressed mRNAs through ex vivo experiments. Using the guilt–by–association approach, we predicted lncRNA functions based on their perfectly co–expressed mRNAs (Spearman’s correlation) that resulted closely disease–associated. We shed light on 24 key lncRNAs and their co–expressed mRNAs which may play an important role in B–ALL pathogenesis. Our results may be of clinical utility for diagnostic and/or prognostic purposes in paediatric B–ALL management.

Long non‑coding RNA SNHG12 regulates cell proliferation, invasion and migration in endometrial cancer by targeting miR‑4429

Long non-coding RNA small nucleolar RNA host gene 12 (SNHG12) has been demonstrated to be oncogenic. The aim of the present study was to examine the effects of SNHG12 on the progression of endometrial cancer (EC). The expression levels of SNHG12 and microRNA (miR)-4429 were assessed in EC cell lines by reverse transcription-quantitative PCR. Plasmids, including SNHG12 short hairpin RNAs (shRNAs), shRNA negative control (NC), SNHG12 overexpression (OV), OV-NC, miR-4429 mimic and mimic-NC, were transfected into RL95-2 cells. Post-transfection, Cell Counting Kit-8, Transwell Matrigel and wound-healing assays were performed to assess cell proliferation, invasion and migration, respectively. Cell cycle phase distribution was assessed by flow cytometry. The protein expression levels of matrix metalloproteinase (MMP)2 and MMP9 were detected by western blotting. miR-4429 target genes were predicted by bioinformatics analysis using target prediction online tools; the findings of this analysis were verified using a dual-luciferase reporter system. Identified as a target of miR-4429, SNHG12 was overexpressed in EC cell lines with decreased expression of miR-4429. Further experiments demonstrated that SNHG12 silencing and overexpression of miR-4429 markedly suppressed proliferation, migration and invasion of RL95-2 cells, arrested cells in the G₁ phase, and markedly downregulated the expression of MMP2 and MMP9. The opposite effects were observed in miR-4429 mimic-transfected RL95-2 cells after SNHG12 was overexpressed. The findings of the present study established the role of SNHG12 and miR-4429 in EC. Therefore, targeting the SNHG12/miR-4429 axis could serve as a potential future therapeutic target for treatment of EC.

SOD1 Promotes Cell Proliferation and Metastasis in Non-small Cell Lung Cancer via an miR-409-3p/SOD1/SETDB1 Epigenetic Regulatory Feedforward Loop

Superoxide dismutase 1(SOD1) is a major antioxidant with oncogenic effects in many human cancers. Although SOD1 is overexpressed in various cancers, the clinical significance and functions of SOD1 in non-small cell lung cancer (NSCLC), particularly the epigenetic regulation of SOD1 in NSCLC carcinogenesis and progression have been less well investigated. In this study, we found that SOD1 expression was upregulated in NSCLC cell lines and tissues. Further, elevated SOD1 expression could promote NSCLC cell proliferation, invasion and migration. While inhibition of SOD1 expression induced NSCLC G1-phase cell cycle arrest and promoted apoptosis. In addition, miR-409-3p could repress SOD1 expression and significantly counteract its oncogenic activities. Bioinformatics analysis indicated that SET domain bifurcated histone lysine methyltransferase1 (SETDB1) was involved in the epigenetic regulation of miR-409-3p and SOD1 expression and functions in NSCLC cells. Identification of this miR-409-3p/SOD1/SETDB1 epigenetic regulatory feedforward loop may provide new insights into further understanding of NSCLC tumorigenesis and progression. Additionally, our results incicate that SOD1 may be a potential new therapeutic target for NSCLC treatment.

Long Non-Coding RNA Taurine Upregulated Gene 1 (TUG1) Downregulation Constrains Cell Proliferation and Invasion through Regulating Cell Division Cycle 42 (CDC42) Expression Via MiR-498 in Esophageal Squamous Cell Carcinoma Cells

Background

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor of the gastrointestinal tract. Taurine upregulated gene 1 (TUG1), a long non-coding (lnc) RNA, also known as LIN00080 or TI-227H, was connected with the tumorigenesis of various diseases. Hence, we plumed the role and molecular mechanism of TUG1 in the progression of ESCC.

Material/Methods

Expression patterns of TUG1, microRNA-498 (miR-498), and cell division cycle 42 (CDC42) mRNA were assessed using quantitative real time polymerase chain reaction (qRT-PCR). The expression level of CDC42 protein was evaluated via western blot analysis. Cell proliferation and invasion were determined with Cell Counting Kit-8 (CCK-8) assay or Transwell assay. The relationship between miR-498 and TUG1 or CDC42 was predicted by online bioinformatics database LncBase Predicted v.2 or microT-CDS and confirmed through dual-luciferase reporter system or RNA immunoprecipitation assay (RIP).

Results

TUG1 and CDC42 were upregulated while miR-498 was strikingly decreased in ESCC tissues and cells (P<0.0001). Besides, TUG1 suppression blocked the proliferation and invasion of ESCC cells (P<0.001). Importantly, TUG1 decrease restrained CDC42 expression via binding to miR-498 in ESCC cells. Also, the suppressive impacts of TUG1 silencing on the proliferation and invasion of ESCC cells were mitigated by miR-498 reduction. Meanwhile, the repression of proliferation and invasion induced by miR-498 elevation was weakened by CDC42 overexpression.

Conclusions

Inhibition of TUG1 hampered cell proliferation and invasion by downregulating CDC42 via upregulating miR-498 in ESCC cells. Thus, TUG1 might be an underlying therapeutic target for ESCC.

Integration of multi-omics data to mine cancer-related gene modules

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.

Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer

Gastric cancer (GC) is one of the most common types of malignant cancer and is associated with poor prognosis. Although the prognosis of patients with GC is associated with grade, stage and lymph node metastases, these traditional clinical features are inadequate to predict the outcome of GC. Therefore, there has been an increased focus on identifying novel molecular biomarkers for early diagnosis and prognosis, in order to improve outcomes in GC. In the present study, an integrative analysis of microRNA (miRNA) expression profiles, mRNA expression profiles and clinical characteristics was performed in a large cohort of patients with GC in order to identify an integrative prognostic model for improving postoperative risk classification. An integrative mRNA/miRNA signature (IMMIS), comprised of three miRNAs and one mRNA, was identified from a large number of differentially expressed miRNAs and mRNAs using univariate and multivariate Cox regression analysis. The prognostic value of the IMMIS was validated in the discovery cohort, testing cohort and The Cancer Genome Atlas (TCGA) cohort. The present results suggested that the identified signature had a reliable predictive performance and could classify the patients into high- and low-risk groups with significantly different overall survival times. In the discovery cohort, the hazard ratio (HR) was 2.805 with a 95% CI=1.722–4.567 (P<0.001). The median overall survival time as 1.49 vs. 3.85 years. In the testing cohort, the HR was 1.625 with a 95% CI=1.004–2.638 (P=0.039) and the median overall survival time was 2.17 vs. 4.62 years. In the TCGA cohort, the HR was 2.139 with a 95% CI=1.519–3.012 (P<0.001) and the median overall survival time was 1.53 vs. 4.62 years. The IMMIS constituted a reliable independent prognostic factor compared with clinical covariates, including age, sex, grade and stage, as indicated by multivariate and stratified analyses. Furthermore, comparative analysis revealed that the predictive value of the IMMIS was superior to the mRNA-based signature alone. The present results suggested the potential value of the IMMIS as a promising novel biomarker for improving the clinical management of patients with GC.

A long non-coding RNA signature to improve prognostic prediction in clear cell renal cell carcinoma

BACKGROUND

Accumulating research reports have indicated that long non-coding RNAs (lncRNAs) are abnormally expressed in many types of cancers. However, few lncRNA signatures for predicting cancer prognosis have been established. Our goal is to establish a lncRNA signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC).

METHODS

We downloaded KIRC lncRNA FPKM (Fragments Per Kilobase of transcript per Million Fragments) standardized expression data from The Cancer Genome Atlas (TCGA) by using the TANRIC tool. We established an 11-lncRNA signature that was clearly linked to the overall survival (OS) rates in the training and test sets.

RESULTS

The training set was divided into the high-risk and low-risk subgroups, between which the OS was disparate (HR = 1.51, 95%CI = 1.39-1.64, P < 0.0001). The accuracy of the 11-lncRNA signature for predicting prognosis was confirmed in the test set. Further analysis revealed that the prognostic value of this signature was independent of the neoplasm grade and TNM stage. Gene set enrichment analysis (GSEA) was performed, and a summary of 4 gene sets related to canonical pathway, biological process, molecular function and cellular component was obtained. We demonstrated the biological function of these lncRNAs in ccRCC cell lines and found that LINC00488 and HOTTIP promoted tumour proliferation and inhibited apoptosis. However, LINC-PINT had the opposite effect.

CONCLUSIONS

The establishment of the 11-lncRNA signature indicated the underlying biochemical functional roles of the selected lncRNAs in ccRCC. Our results may provide a reliable theoretical basis for clinical evaluation of ccRCC prognosis.

Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung

Background

Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data.

Material/Methods

The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time.

Results

From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74–7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets.

Conclusions

This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.

Screening key lncRNAs for human rectal adenocarcinoma based on lncRNA-mRNA functional synergistic network

Background

Rectal adenocarcinoma (READ) is one of the deadliest malignancies, and the molecular mechanisms underlying the initiation and development of READ remain largely unknown. In this study, we aimed to find key long noncoding RNAs (lncRNAs) and mRNAs in READ by RNA sequencing.

Methods

RNA sequencing was performed to identify differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) between READ and normal tissue. READ‐specific protein‐protein interaction (PPI), DElncRNA‐DEmRNA coexpression, and DElncRNA‐nearby DEmRNA interaction networks were constructed. DEmRNAs and DEmRNAs coexpressed with DElncRNAs were functionally annotated.

Results

A total of 2113 DEmRNAs and 150 DElncRNAs between READ and normal tissue were identified. The PPI network identified several hub proteins, including CDK1, AURKB, CDC6, FOXQ1, NUF2, and TOP2A. The DElncRNA‐DEmRNA coexpression and DElncRNA‐nearby DEmRNA interaction networks identified some hub lncRNAs, including CCAT1, LOC105374879, GAS5, and B3GALT5‐AS1. The colorectal cancer pathway, the intestinal immune network for IgA production and the p53 signaling pathway were three pathways significantly enriched in DEmRNAs and DEmRNAs coexpressed with DElncRNAs. MSH6 coexpressed with two DElncRNAs (LOC105374879 and CASC15) and BCL2 coexpressed with B3GALT5‐AS1 were significantly enriched in the colorectal cancer signaling pathway. TNFRSF17 coexpressed with B3GALT5‐AS1 was enriched in the intestinal immune network for IgA production. CCNB2 coexpressed with LOC105374879 was enriched in the p53 signaling pathway.

Conclusion

A total of four DEmRNAs (MSH6, BCL2, TNFRSF17, and CCNB2) and three DElncRNAs (LOC105374879, CASC15, and B3GALT5‐AS1) may be involved in the pathogenesis of READ; this data may contribute to understanding the mechanisms of READ and the development of therapeutic strategies for READ.

A 9‑lncRNA risk score system for predicting the prognosis of patients with hepatitis B virus‑positive hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and can be induced by hepatitis B virus (HBV) infection. The aim of the present study was to screen prognosis‑associated long noncoding RNAs (lncRNAs) and construct a risk score system for the disease. The RNA‑sequencing data of patients with HCC (including 100 HCC samples and 26 normal samples) were extracted from The Cancer Genome Atlas (TCGA) database. In addition, GSE55092, GSE19665 and GSE10186 datasets were downloaded from the Gene Expression Omnibus database. Combined with weighted gene co‑expression network analysis, the identification and functional annotation of stable modules was performed. Using the MetaDE package, the consensus differentially expressed RNAs (DE‑RNAs) were analyzed. To construct a risk score system, prognosis‑associated lncRNAs and the optimal lncRNA combination were separately analyzed by survival and penalized packages. Finally, pathway enrichment analysis for the nodes in an lncRNA‑mRNA network was conducted via Gene Set Enrichment Analysis. A total of four stable modules and 3,051 consensus DE‑RNAs were identified. The stable modules were significantly associated with the histological grades of HCC, tumor, node and metastasis stage, pathological stage, recurrence and exposure to radiation therapy. A 9‑lncRNA optimal combination [DiGeorge syndrome critical region gene 9, glucosidase, β, acid 3 (GBA3), HLA complex group 4, N‑acetyltransferase 8B, neighbor of breast cancer 1 gene 2, prostate androgen‑regulated transcript 1, ret finger protein like 1 antisense RNA 1, solute carrier family 22 member 18 antisense and T‑cell leukemia/lymphoma 6] was selected from the 14 prognosis‑associated lncRNAs, and was further supported by the validation dataset, GSE10186. The lncRNA‑mRNA co‑expression network revealed lncRNA GBA3 as a positive regulator of phosphoenolpyruvate carboxykinase 2, an important enzyme in the metabolic pathway of gluconeogenesis. A risk score system was established based on the optimal 9 lncRNAs, which may be valuable for predicting the prognosis of patients with HBV‑positive HCC and improving understanding of mechanisms associated with the pathogenesis of this disease. On the contrary, a larger, independent cohort of patients is required to further validate the risk‑score system.

Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods.

Loss of ARID1A Expression Correlates With Tumor Differentiation and Tumor Progression Stage in Pancreatic Ductal Adenocarcinoma

Mutations in the AT-rich interactive domain 1A gene, which encodes a subunit of the Switch/Sucrose nonfermentable chromatin remodeling complex, can result in loss of protein expression and are associated with different cancers. Here, we used immunohistochemistry to investigate the significance of AT-rich interactive domain 1A loss in 73 pancreatic ductal adenocarcinoma cases with paired paracancerous normal pancreatic tissues. The relationship between levels of the AT-rich interactive domain 1A protein product, BAF250a, and clinicopathological parameters in the 73 pancreatic cancer specimens was also analyzed. We found that the expression of AT-rich interactive domain 1A in normal pancreatic tissue was higher than that in tumor tissue. Loss of AT-rich interactive domain 1A expression in pancreatic tumors was associated with tumor differentiation (P = .002) and tumor stage (P = .048). Meanwhile, BAF250a protein levels were not related to lymph node metastasis, distant metastasis, sex, or age and were not associated with survival. Transfection of the pancreatic cancer cell lines AsPC-1 and PANC-1 with small-interfering RNA specific for AT-rich interactive domain 1A resulted in elevated messenger RNA and protein expression levels of B-cell lymphoma-2 (Bcl-2), CyclinD1, and Kirsten rat sarcoma viral oncogene (KRAS). The AT-rich interactive domain 1A expression level in the cells was increased following microRNA-31 (miR-31) inhibitor transfection. Our data provide additional evidence that AT-rich interactive domain 1A might function as a tumor suppressor gene in pancreatic carcinogenesis.

Five-long non-coding RNA risk score system for the effective prediction of gastric cancer patient survival

The prognosis for patients with gastric cancer (GC) is usually poor, as the majority of patients have reached the advanced stages of disease at the point of diagnosis. Therefore, revealing the mechanisms of GC is necessary for the identification of key biomarkers and the development of effective targeted therapies. The present study aimed to identify long non-coding RNAs (lncRNAs) prominently expressed in patients with GC. The GC dataset (including 384 GC samples) was downloaded from The Cancer Genome Atlas database as the training set. A number of other GC datasets were obtained from the Gene Expression Omnibus database as validation sets. Following data processing, lncRNAs were annotated, followed by co-expression module analysis to identify stable modules, using the weighted gene co-expression network analysis (WGCNA) package. Prognosis-associated lncRNAs were screened using the ‘survival’ package. Following the selection of the optimal lncRNA combinations using the ‘penalized’ package, risk score systems were constructed and assessed. Consensus differentially-expressed RNAs (DE-RNAs) were screened using the MetaDE package, and an lncRNA-mRNA network was constructed. Additionally, pathway enrichment analysis was conducted for the network nodes using gene set enrichment analysis (GSEA). A total of seven modules (blue, brown, green, grey, red, turquoise and yellow) were obtained following WGCNA analysis, among which the green and turquoise modules were stable and associated with the histological grade of GC. A total of 12 prognosis-associated lncRNAs were identified in the two modules. Combined with the optimal lncRNA combinations, risk score systems were constructed. The risk score system based on the green module [including ITPK1 antisense RNA 1 (ITPK1-AS1), KCNQ1 downstream neighbor (KCNQ1DN), long intergenic non-protein coding RNA 167 (LINC00167), LINC00173 and LINC00307] was the more efficient at predicting risk compared with those based on the turquoise, or the green + turquoise modules. A total of 1,105 consensus DE-RNAs were identified; GSEA revealed that LINC00167, LINC00173 and LINC00307 had the same association directions with 4 pathways and the 32 genes involved in those pathways. In conclusion, a risk score system based on the green module may be applied to predict the survival of patients with GC. Furthermore, ITPK1-AS1, KCNQ1DN, LINC00167, LINC00173 and LINC00307 may serve as biomarkers for GC pathogenesis.

A prognostic signature based on three non-coding RNAs for prediction of the overall survival of glioma patients

Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA expression profiles. We downloaded gene expression profiles of glioma patients along with their clinical information from the Gene Expression Omnibus and extracted ncRNA expression profiles via a microarray annotation file. Correlations between ncRNAs and glioma patients’ OS were first evaluated through univariate Cox regression analysis and a permutation test, followed by random survival forest analysis for further screening of valuable ncRNA signatures. Prognostic signatures could be established as a risk score formula by including ncRNA signature expression values weighted by their estimated regression coefficients. Patients could be divided into high risk and low risk subgroups by using the median risk score as cutoff. As a result, glioma patients with a high risk score tended to have shorter OS than those with low risk scores, which was confirmed by analyzing another set of glioma patients in an independent dataset. Additionally, gene set enrichment analysis showed significant enrichment of cancer development‐related biological processes and pathways. Our study may provide further insights into the evaluation of glioma patients’ prognosis.

Long noncoding RNAs in cancer cells

Long noncoding RNA (lncRNA) has recently been investigated as key modulators that regulate many biological processes in human cancers via diverse mechanisms. LncRNAs can interact with macromolecules such as DNA, RNA, or protein to exert cellular effects and to act as either tumor promoters or tumor suppressors in various malignancies. Moreover, the aberrant expression of lncRNAs may be detected in multiple cancer phenotypes by employing the rapidly developing modern gene chip technology and bioinformatics analysis. Herein, we highlight the mechanisms of action of lncRNAs, their functional cellular roles and their involvement in cancer progression. Finally, we provide an overview of recent progress in the lncRNA field and future potential for lncRNAs as cancer diagnostic markers and therapeutics.

Long noncoding RNA CAR10 promotes lung adenocarcinoma metastasis via miR-203/30/SNAI axis

Long noncoding RNAs (lncRNAs) play an important role in lung adenocarcinoma (LUAD) metastasis. Here, we found that lncRNA chromatin-associated RNA 10 (CAR10) was upregulated in the tumor tissue of patients with LUAD and enhanced tumor metastasis in vitro and in vivo. Mechanistically, CAR10 induced epithelial-to-mesenchymal transition (EMT) by directly binding with miR-30 and miR-203 and then regulating the expression of SNAI1 and SNAI2. CAR10 overexpression was positively correlated with a poor prognosis in LUAD patients, whereas overexpression of both CAR10 and SNAI was correlated with even worse clinical outcomes. In conclusion, the CAR10/miR-30/203/SNAI axis is a novel and potential therapeutic target for LUAD.

LASSO‑based Cox‑PH model identifies an 11‑lncRNA signature for prognosis prediction in gastric cancer

The present study aimed to identify a long non-coding (lnc) RNAs-based signature for prognosis assessment in gastric cancer (GC) patients. By integrating gene expression data of GC and normal samples from the National Center for Biotechnology Information Gene Expression Omnibus, the EBI ArrayExpress and The Cancer Genome Atlas (TCGA) repositories, the common RNAs in Genomic Spatial Event (GSE) 65801, GSE29998, E-MTAB-1338, and TCGA set were screened and used to construct a weighted correlation network analysis (WGCNA) network for mining GC-related modules. Consensus differentially expressed RNAs (DERs) between GC and normal samples in the four datasets were screened using the MetaDE method. From the overlapped lncRNAs shared by preserved WGCNA modules and the consensus DERs, an lncRNAs signature was obtained using L1-penalized (lasso) Cox-proportional hazard (PH) model. LncRNA-mRNA networks were constructed for these signature lncRNAs, followed by functional annotation. A total of 14,824 common mRNAs and 2,869 common lncRNAs were identified in the 4 sets and 5 GC-associated WGCNA modules were preserved across all sets. MetaDE method identified 1,121 consensus DERs. A total of 50 lncRNAs were shared by preserved WGCNA modules and the consensus DERs. Subsequently, an 11-lncRNA signature was identified by LASSO-based Cox-PH model. The lncRNAs signature-based risk score could divide patients into 2 risk groups with significantly different overall survival and recurrence-free survival times. The predictive capability of this signature was verified in an independent set. These signature lncRNAs were implicated in several biological processes and pathways associated with the immune response, the inflammatory response and cell cycle control. The present study identified an 11-lncRNA signature that could predict the survival rate for GC.

Novel three‑lncRNA signature predicts survival in patients with pancreatic cancer

A growing body of evidence confirms that long non-coding RNAs (lncRNAs) have an important role in biological processes by regulating gene expression at multiple levels. Dysregulated lncRNAs may be potential prognostic biomarkers or targets for the development of cancer treatments. However, the prognostic role of an lncRNA signature in pancreatic cancer has not been investigated. Pancreatic cancer lncRNA expression profiles from The Cancer Genome Atlas (TCGA) were analyzed in the current study. The prognostic value of differentially expressed lncRNAs (DElncRNAs) was evaluated via the Kaplan-Meier method. A risk score model was established based on the potential prognostic lncRNAs. The biological functions of lncRNAs were predicted by functional enrichment analysis. Then, an lncRNA-mRNA co-expression network was established and predicted the function of the lncRNAs. Seven DElncRNAs that were significantly associated with the prognosis of pancreatic cancer were identified. Patients were classified into high-risk and low-risk groups using a risk score based on a three-lncRNA signature. There was a significant difference in overall survival (OS) between the groups (median OS 1.33 vs. 3.65 years; log-rank test, P=0.0000). Cox regression analysis and ROC curves demonstrated that the three-lncRNA signature may be an effective independent prognostic biomarker in patients with pancreatic. The functional enrichment analysis showed that lncRNA AL137789.1, one component of the three-lncRNA signature, may be associated with tumor immune responses. In the present study, a novel three-lncRNA signature that was established that may be useful in predicting survival among patients with pancreatic cancer. These lncRNAs may be involved in tumor immunity and thus affect the prognosis of patients.

Genome-wide methylation analysis identifies novel prognostic methylation markers in colon adenocarcinoma

Previous studies have indicated that abnormal methylation is a critical and early event in the pathogenesis of most types of human cancer, which contributes to tumorigenesis. However, there has been little focus on the potential of DNA methylation patterns as predictive markers for the prognosis of colon adenocarcinoma (COAD). In the present study, a genome-wide comparative analysis of DNA methylation profiles was performed between 315 COAD samples and 38 matched tumor-adjacent normal tissue samples. A total of 675 differentially methylated regions (DMRs) associated with 630 genes were identified, including 654 hypermethylated regions (UMRs) and 21 hypomethylated regions, which were capable of distinguishing COAD samples from non-malignant tissue samples. Although most of the DMRs appeared to be located within the gene body or promoter regions, UMRs were mostly located within CpG islands. Functional analysis suggested that genes associated with DMRs were enriched in many of the core cancer-signaling pathways known to be important in COAD biology. A survival analysis was also performed, which identified 7 DMRs as potential candidate markers with the ability to classify patients into high and low-risk groups with significantly different overall survival. The present study provides a better understanding of the molecular mechanisms underlying COAD, and demonstrates the utility of aberrant DNA methylation in the prognosis of COAD.

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.

Long non-coding RNA H19 promotes the migration and invasion of colon cancer cells via MAPK signaling pathway

Long non-coding RNA H19 has been identified to be dysregulated in a number of tumor types, and is closely associated with cancer progression. RAS/mitogen-activated protein kinase (MAPK) is an important intracellular signaling transduction pathway. Activation of the RAS-MAPK signaling pathway is one of the most frequent carcinogenic events in human cancer. However, the mechanism of H19 in promoting the migration and invasion of colorectal cancer (CRC) cells, and the association between H19 and RAS-MAPK signaling pathway is not well understood. The aim of the present study was to investigate the function of H19 on CRC metastasis and invasion, and assess the association between H19 and the RAS-MAPK signaling pathway. The migration and invasion of CRC cells were analyzed using Transwell migration and invasion assays. To elucidate the association between H19 and the RAS-MAPK signaling pathway and determine the expression level of active RAS in CRC cells, Ras activity assay and Western blotting were performed. It was indicated that the overexpression of H19 was able to increase the migration and invasion of CRC cells and this may be mediated by the regulation of RAS activation. Therefore, H19 may promote metastasis and invasion in colorectal cancer by activating the RAS-MAPK signaling pathway.

Recurrence-Associated Long Non-coding RNA Signature for Determining the Risk of Recurrence in Patients with Colon Cancer

Patients with colon cancer are often faced a high risk of disease recurrence within 5 years of treatment that is the major cause of cancer mortality. Reliable molecular markers were required to improve the most effective personalized therapy. Here, we identified a recurrence-associated six-lncRNA (long non-coding RNA) signature (LINC0184, AC105243.1, LOC101928168, ILF3-AS1, MIR31HG, and AC006329.1) that can effectively distinguish between high and low risk of cancer recurrence from 389 patients of a discovery dataset, and validated its robust performance in four independent datasets comprising a total of 906 colon cancer patients. We found that the six-lncRNA signature was an independent predictive factor of disease recurrence in multivariate analysis and was superior to the performance of clinical factors and known gene signature. Furthermore, in silico functional analysis showed that the six-lncRNA-signature-associated coding genes are significantly enriched in proliferation and angiogenesis, cell death, as well as critical cancer pathways that could play important roles in colon cancer recurrence. Together, the six-lncRNA signature holds great potential for recurrence risk assessment and personalized management of colon cancer patients.

Construction and analysis of a ceRNA‑ceRNA network reveals two potential prognostic modules regulated by hsa‑miR‑335‑5p in osteosarcoma

Osteosarcoma is an aggressive cancer of the skeletal system, which is associated with a poor prognosis due to the high recurrence rate. Although previous studies have revealed that competitive endogenous RNAs (ceRNAs) are involved in various biological processes in the physiology and development of osteosarcoma, the roles of ceRNAs in osteosarcoma recurrence remain largely unexplored. The present study constructed a ceRNA-ceRNA network for osteosarcoma by systematically integrating matched expression profiles for microRNAs (miRNAs/miRs) and mRNAs, and identified two ceRNA-mediated modules that were associated with recurrence in patients with osteosarcoma. A multivariate Cox regression analysis demonstrated that the recurrence-free prognosis associated with the expression of the two modules was independent of other clinical factors. In addition, hsa-miR-335-3p was identified as an upstream regulating factor for both modules. In conclusion, the results of the present study suggested that ceRNAs may act as potential therapeutic biomarkers for predicting the recurrence of osteosarcoma, and may help to identify patients with osteosarcoma at a high risk of recurrence, who may benefit from adjuvant therapy.

Reconstruction and analysis of the aberrant lncRNA-miRNA-mRNA network based on competitive endogenous RNA in CESC

A growing body of studies has demonstrated that long non-coding RNA (lncRNA) are regarded as the primary section of the ceRNA network. This is thought to be the case owing to its regulation of protein-coding gene expression by functioning as miRNA sponges. However, functional roles and regulatory mechanisms of lncRNA-mediated ceRNA in cervical squamous cell carcinoma (CESC), as well as their use for potential prediction of CESC prognosis, remains unknown. The aberrant expression profiles of mRNA, lncRNA, and miRNA of 306 cervical squamous cancer tissues and three adjacent cervical tissues were obtained from the TCGA database. A lncRNA-mRNA-miRNA ceRNA network in CESC was constructed. Meanwhile, Gene Ontology (GO) and KEGG pathway analysis were performed using Cytoscape plug-in BinGo and DAVID database. We identified a total of 493 lncRNA, 70 miRNA, and 1921 mRNA as differentially expressed profiles. An aberrant lncRNA-mRNA-miRNA ceRNA network was constructed in CESC, it was composed of 50 DElncRNA, 18 DEmiRNA, and 81 DEmRNA. According to the overall survival analysis, 3 out of 50 lncRNA, 10 out of 81 mRNA, and 1 out of 18 miRNA functioned as prognostic biomarkers for patients with CESC (P value < 0.05). We extracted the sub-network in the ceRNA network and found that two novel lncRNA were recognized as key genes. These included lncRNA MEG3 and lncRNA ADAMTS9-AS2. The present study provides a new insight into a better understanding of the lncRNA-related ceRNA network in CESC, and the novel recognized ceRNA network will help us to improve our understanding of lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of CESC.

Identification of novel long non-coding RNA biomarkers for prognosis prediction of papillary thyroid cancer

Papillary thyroid carcinoma (PTC) is the most frequent type of malignant thyroid tumor. Several lncRNA signatures have been established for prognosis prediction in some cancers. However, the prognostic value of lncRNAs has not been investigated in PTC yet. In this study, we performed genome-wide analysis of lncRNA expression profiles in a large cohort of PTC patients from The Cancer Genome Atlas and identified 111 differentially expressed lncRNAs between tumor and non-tumor samples and between recurrent and recurrence-free samples. From the 111 differentially expressed lncRNAs, four independent lncRNA biomarkers associated with prognosis were identified and were integrated into a four-lncRNA signature which classified the patients of training dataset into the high-risk group and low-risk group with significantly different overall survival (p=0.016, log-rank test). The prognostic value of the four-lncRNA signature was validated in the independent testing dataset. Multivariate analysis indicated that the four-lncRNA signature was an independent prognostic predictor. Moreover, identified four lncRNA biomarkers demonstrates good performance in predicting disease recurrence with AUC of 0.833 using leave one out cross-validation. Our study not only highlighted the potential role for lncRNAs to improve clinical prognosis prediction in patients with PTC and but also provided alternative biomarkers and therapeutic targets for PTC patients.

Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network

Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulation network (DLMCEN) by integrating aberrant lncRNAs, mRNAs and their co-dysregulation relationships, and found that some of mRNAs were previously reported to be involved in cardiovascular disease, suggesting the functional roles of dysregulated lncRNAs in the pathogenesis of MI. Therefore, using support vector machine (SVM) and leave one out cross-validation (LOOCV), we developed a 9-lncRNA signature (termed 9LncSigAMI) from the discovery cohort which could distinguish MI patients from healthy samples with accuracy of 95.96%, sensitivity of 93.88% and specificity of 98%, and validated its predictive power in early diagnosis of MI in another completely independent cohort. Functional analysis demonstrated that these nine lncRNA biomarkers in the 9LncSigAMI may be involved in myocardial innate immune and inflammatory response, and their deregulation may lead to the dysfunction of the inflammatory and immune system contributing to MI recurrence. With prospective validation, the 9LncSigAMI identified by our work will provide additional diagnostic information beyond other known clinical parameters, and increase the understanding of the molecular mechanism underlying the pathogenesis of MI.

IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity

Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations. In this article, we proposed an integrative framework, IntNetLncSim, to infer LFS by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). As a result, LFS by IntNetLncSim was significant positively correlated with the LmRSet (Pearson correlation γ2=0.8424) and LmiRSet (Pearson correlation γ2=0.2601). Particularly, the performance of IntNetLncSim is superior to several previous methods. In the case of applying the LFS to identify novel lncRNA-disease relationships, we achieved an area under the ROC curve (0.7300) in experimentally verified lncRNA-disease associations based on leave-one-out cross-validation. Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the excellent performance of IntNetLncSim. Finally, a web-accessible system was provided for querying LFS and potential lncRNA-disease relationships: http://www.bio-bigdata.com/IntNetLncSim.

Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and the 5‐year survival rate was only 7.7%. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. Long non‐coding RNA (lncRNA) expression profiles as potential cancer prognostic biomarkers play critical roles in development of tumorigenesis and metastasis of cancer. However, lncRNA signatures in predicting the survival of a patient with PDAC remain unknown. In the current study, we try to identify potential lncRNA biomarkers and their prognostic values in PDAC. LncRNAs expression profiles and corresponding clinical information for 182 cases with PDAC were acquired from The Cancer Genome Atlas (TCGA). A total of 14 470 lncRNA were identified in the cohort, and 175 PDAC patients had clinical variables. We obtained 108 differential expressed lncRNA via R packages. Univariate and multivariate Cox proportional hazards regression, lasso regression was performed to screen the potential prognostic lncRNA. Five lncRNAs have been recognized to significantly correlate with OS. We established a linear prognostic model of five lncRNA (C9orf139, MIR600HG, RP5‐965G21.4, RP11‐436K8.1, and CTC‐327F10.4) and divided patients into high‐ and low‐risk group according to the prognostic index. The five lncRNAs played independent prognostic biomarkers of OS of PDAC patients and the AUC of the ROC curve for the five lncRNAs signatures prediction 5‐year survival was 0.742. In addition, targeted genes of MIR600HG, C9orf139, and CTC‐327F10.4 were explored and functional enrichment was also conducted. These results suggested that this five‐lncRNAs signature could act as potential prognostic biomarkers in the prediction of PDAC patient's survival.

Construction of differential mRNA-lncRNA crosstalk networks based on ceRNA hypothesis uncover key roles of lncRNAs implicated in esophageal squamous cell carcinoma

Increasing evidence has indicated that lncRNAs acting as competing endogenous RNAs (ceRNAs) play crucial roles in tumorigenesis, metastasis and diagnosis of cancer. However, the function of lncRNAs as ceRNAs involved in esophageal squamous cell carcinoma (ESCC) is still largely unknown. In this study, clinical implications of two intrinsic subtypes of ESCC were identified based on expression profiles of lncRNA and mRNA. ESCC subtype-specific differential co-expression networks between mRNAs and lncRNAs were constructed to reveal dynamic changes of their crosstalks mediated by miRNAs during tumorigenesis. Several well-known cancer-associated lncRNAs as the hubs of the two networks were firstly proposed in ESCC. Based on the ceRNA mechanism, we illustrated that the"loss" of miR-186-mediated PVT1-mRNA and miR-26b-mediated LINC00240-mRNA crosstalks were related to the two ESCC subtypes respectively. In addition, crosstalks between LINC00152 and EGFR, LINC00240 and LOX gene family were identified, which were associated with the function of "response to wounding" and "extracellular matrix-receptor interaction". Furthermore, functional cooperation of multiple lncRNAs was discovered in the two differential mRNA-lncRNA crosstalk networks. These together systematically uncovered the roles of lncRNAs as ceRNAs implicated in ESCC.

Construction and analysis of dysregulated lncRNA-associated ceRNA network identified novel lncRNA biomarkers for early diagnosis of human pancreatic cancer

It is increasing evidence that ceRNA activity of long non-coding RNAs (lncRNAs) played critical roles in both normal physiology and tumorigenesis. However, functional roles and regulatory mechanisms of lncRNAs as ceRNAs in pancreatic ductal adenocarcinoma (PDAC), and their potential implications for early diagnosis remain unclear. In this study, we performed a genome-wide analysis to investigate potential lncRNA-mediated ceRNA interplay based on "ceRNA hypothesis". A dysregulated lncRNA-associated ceRNA network (DLCN) was constructed by utilizing sample-matched miRNA, lncRNA and mRNA expression profiles in PDAC and normal samples in combination with miRNA regulatory network. The results of network analysis uncovered seven novel lncRNAs as functional ceRNAs whose aberrant expression will result in the extensive variation in tumorigenic or tumor-suppressive gene expression through DLCN at the post-transcriptional level contributing to PDAC. Therefore, we developed a 7-lncRNA signature (termed LncRisk-7) based on the expression data of seven lncRNAs and SVM algorithm as a novel diagnostic tool to improve early diagnosis of PDAC. The LncRisk-7 achieved high performance in distinguishing PDAC patients from nonmalignant pancreas samples in the discovery cohort and was further confirmed in another two independent validation cohorts. Functional analysis demonstrated that seven lncRNA biomarkers act as ceRNAs involving the regulation of cell death, cell adhesion and cell cycle. This study will help to improve our understanding of the lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of PDAC and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.

A novel lncRNA-focus expression signature for survival prediction in endometrial carcinoma

Background

Endometrial cancer (UCEC) is a complex malignant tumor characterized by both genetic level and clinical trial. Patients with UCEC exhibit the similar clinical features, however, they have distinct outcomes due to molecular heterogeneity. The aim of this study was to access the prognostic value of long non-coding RNAs (lncRNAs) in UCEC patients and to identify potential lncRNA signature for predicting patients’ survival and improving patient-tailored treatment.

Methods

We performed a comprehensive genome-wide analysis of lncRNA expression profiles and clinical data in a large cohort of 301 UCEC patients. UCEC patients were randomly divided into the discovery cohort (n = 150) and validation cohort (n = 151). A novel lncRNA-focus expression signature was identified in the discovery cohort, and independently accessed in the validation cohort. Additionally, the lncRNA signature was evaluated by multivariable Cox regression and stratification analysis as well as functional enrichment analysis.

Results

We detected a novel lncRNA-focus expression signature (LFES) consisting of 11 lncRNAs that were associated with survival based on risk scoring strategy in UCEC. The risk score based on the LFES was able to separate patients of discovery cohort into high-risk and low-risk groups with significantly different overall survival and progression-free survival, and has been successfully confirmed in the validation cohort. Furthermore, the LFES is an independent prognostic predictor of survival and demonstrates superior prognostic performance compared with the clinical covariates for predicting 5-year survival (AUC = 0.887). Functional analysis has linked the expression of prognostic lncRNAs to well-known tumor suppressor or ontogenetic pathways in endometrial carcinogenesis.

Conclusions

Our study revealed a novel 11-lncRNA signature to predict survival of UCEC patient. This lncRNA signature may be a valuable and alternative marker for risk evaluation to aid patient-tailored treatment and improve the outcome of patients with UCEC.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3983-0) contains supplementary material, which is available to authorized users.

Measuring disease similarity and predicting disease-related ncRNAs by a novel method

Background

Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.

Methods

Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.

Results

The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ² = 0.1315, p = 2.2e-16). Finally, the disease similarity computed by infDisSim was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC (0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting novel disease-related lncRNAs and miRNAs, respectively.

Conclusions

The high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related lncRNAs and miRNAs.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12920-017-0315-9) contains supplementary material, which is available to authorized users.

Identification of a progression-associated long non-coding RNA signature for predicting the prognosis of lung squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have been indicated to have prognostic roles in various cancer types. However, the association between lncRNAs and lung squamous cell carcinoma (LSCC) progression, and the prognostic value of lncRNAs as a marker for early detection of LSCC have not been systematically investigated. The present study performed a genome-wide comparative analysis in order to determine the expression profiles of 10,207 lncRNAs to investigate the expression patterns between patients with early stages of LSCC (stage I–II) and those with late-stage disease (stage III–IV). It was found that 114 lncRNAs were significantly differentially expressed between late- and early-stage LSCC and therefore associated with the progression of the malignancy. By focusing on progression-associated lncRNAs, eight lncRNAs were found to be significantly associated with overall survival of LSCC patients according to univariate Cox proportional hazards regression analysis. These eight prognostic lncRNAs were integrated into a progression-associated eight-lncRNA signature, which stratified patients into two groups with significantly different overall survival (median survival, 2.25 vs. 7.68 years; P=2.06×10⁻⁵). In addition, the prognostic value of the progression-associated eight-lncRNA signature was independent of known clinical factors. Functional analysis suggested that eight prognostic lncRNAs may be involved in adipocytokine signaling pathway and glycerophospholipid metabolism. Taken together, the progression-associated eight-lncRNA signature identified by our study not only represents a candidate prognostic biomarker for LSCC patients but also provides insight into the molecular mechanisms in the progression of LSCC.

Analysis of long non-coding RNA expression profiles identifies functional lncRNAs associated with the progression of acute coronary syndromes

It has been demonstrated that long non-coding RNAs (lncRNAs) are important in the gene regulatory network and their dysregulated expression has been implicated in cardiovascular disease. However, little is known regarding lncRNA expression patterns and their function in the progression of acute coronary syndromes (ACSs). In the present study, the expression profiles of lncRNAs from 52 patients with ACS were analyzed by re-annotating existing microarray data. The lncRNA expression profiles in the two distinct clinical entities of ACS, myocardial infarction (MI) and unstable angina (UA), were examined. Out of the 2,332 lncRNAs assessed, it was identified that 18 lncRNAs were upregulated and 35 lncRNAs were downregulated in patients with MI compared to those with UA. Furthermore, the expression profiles of patients with ACS were compared at different time points and significantly altered lncRNA expression was observed during the progression of ACS. A total of 7 candidate lncRNA biomarkers were identified and an lncRNA-based classifier was developed to predict MI risk based on the expression data of the 7 lncRNAs using random forest and support vector machine strategies. This achieved a classification accuracy of 90.38% with a sensitivity of 100% and a specificity of 68.75%. Additionally, functional analysis suggested that these 7 lncRNAs may be involved in known MI-associated biological processes and pathways.

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.

A novel seven-long non-coding RNA signature predicts survival in early stage lung adenocarcinoma

Increasing evidence has revealed the significant association between dysregulated lncRNA expression and cancers. The prognostic value of lncRNAs in predicting the risk of disease recurrence and identifying high-risk subgroup of early stage lung adenocarcinoma (LUAD) is still unclear. In this study, we analyzed lncRNA expression profiles of 415 early-stage LUAD patients from Gene Expression Omnibus and identified a novel seven-lncRNA signature that was significantly associated with survival in patients with early-stage LUAD (HR = 2.718, CI = 2.054–3.597, p < 0.001). Based on the seven-lncRNA signature, we constructed a risk score model which is able to classify patients of training dataset into the high-risk group and the low-risk group with significantly different clinical outcome (p < 0.001). The robustness of the seven-lncRNA signature was successfully validated through application in other two independent patient datasets. Furthermore, the prognostic value of seven-lncRNA signature was independent of other clinicopathological factors including age, gender, stage and smoking status. Functional analysis suggested that the seven-lncRNA signature may be involved in a variety of biological pathways including cell cycle, ECM-receptor interaction, Focal adhesion and p53 signaling pathway. Taken together, our study not only provides insights into the lncRNA association with LUAD, but also provide alternative molecular markers in prognosis prediction for early-stage LUAD patients.

Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma

This study aims to comprehensively analyze the diagnosis and prognosis of lncRNAs in hepatocellular carcinoma (HCC). From the Gene Expression Omnibus database, we screened out and analyzed the differently expressed lncRNAs and related mRNAs using bioinformatics methods. The expressions of lncRNAs were validated in tumor tissues, cell lines and the Cancer Genome Atlas database. At the same time, we also conducted an exploratory analysis on the diagnostic and prognostic ability of lncRNAs in HCC. In this study, we found that most of the targeted mRNAs promote the biological process of cell division, cellular component of nucleoplasm, molecular function of protein binding, which were significantly associated with 12 KEGG pathways. LncRNA CRNDE and LINC01419 also had significant diagnostic value in HCC. In particular, the sensitivity, specificity and area under the curve of CRNDE were 71.0%, 87.1% and 0.701 (95% CI: 0.543-0.860), respectively. In addition, the high expression of CRNDE and GBAP1 predicated poor prognosis, while the high expression of LINC01093 suggested the opposite outcome. Through the comprehensive analysis of lncRNAs, it provided an important reference for the early diagnosis, prognosis evaluation, pathogenesis and targeted therapy of HCC.

Long non-coding RNA00544 serves as a potential novel predictive and prognostic marker for HR+ HER2- subtype breast cancer

Luminal breast cancers (BC) account for majority of breast cancer. Due to its heterogeneity and the development of treatment resistance, luminal BC patients can vary substantially. Long noncoding RNAs (lncRNAs), as we known, is involved in breast cancer progression. Here, we aim to identify the lncRNAs which are involved in the particular type luminal BC progression. By Gene Chips analysis, we found a novel lncRNA00544, which was highly expressed in the metastatic axillary nodes compared with corresponding luminal BC tissues (fold change = 2.26, P = 0.043). This result was confirmed in luminal BC cell lines (p = 0.0113) and 49 paired breast cancer samples compared with in corresponding controls (p = 0.011). Furthermore, Kaplan-Meier survival curves of 373 breast cancer patients indicated that disease-free survival was significantly poor in breast cancer patients with high lncRNA00544 expression (p < 0.001). Univariate and multivariate Cox regression analyses showed that lncRNA00544 was a significant independent prognostic biomarker in luminal BC patients. Further analysis showed that the prognosis of high lncRNA00544 expression in breast cancer patients was actually related to HR + HER2- subtype. Together, our studies indicate that lncRNA00544 may represent a novel predictive and prognostic indicator in luminal BC patients.

A long non-coding RNA expression signature to predict survival of patients with colon adenocarcinoma

Colon cancer is the most common type of gastrointestinal cancer and is still the leading cause of cancer-related mortality worldwide. Long non-coding RNAs (lncRNAs) have been proved to be superior biomarkers in cancer diagnosis and prognosis than miRNAs and protein-coding genes. In the current study, our objective was to detect novel lncRNA biomarkers by analyzing lncRNA expression profiles and clinical data in a large cohort of patients with colon patients from The Cancer Genome Atlas (TCGA). By using Cox regression analysis, we identified two lncRNAs (SNHG6 and CTD-2354A18.1) which could be independent prognostic factors for predicting clinical outcome in colon adenocarcinoma. Then a linear combination of these two lncRNA biomarkers (SNHG6 and CTD-2354A18.1), termed two-lncRNA signature, was developed in the training set as a predictor for OS in patients with colon adenocarcinoma, and validated in the testing set and the entire patient set. This two-lncRNA signature demonstrated significant prognostic performance in both the testing set and the entire patient set which classified the patients into two groups with significantly different OS. The multivariate and stratified analysis suggested that the prognostic value of the two-lncRNA signature was independent of other traditional clinical variables. Functional analysis suggested that these two lncRNA biomarkers might be mainly involved in transcription/translation-related or DNA repair-related biological processes. In summary, our results warrant further studies on these lncRNAs that will improve our understanding of the mechanisms associated with pathogenesis and progression of colon adenocarcinoma.