A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.     

An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.     

Clinicopathological and prognostic value of lncRNA PANDAR expression in solid tumors: Evidence from a systematic review and meta-analysis

PANDAR (promoter of CDKN1A antisense DNA damage activated RNA) has been shown to be aberrantly expressed in many types of cancer. Considering conflicting data, the current study was aimed to assess its potential role as a prognostic marker in malignant tumors. A comprehensive literature search of PubMed, Medline, and Web of Science was performed to identify all eligible studies describing the use of PANDAR as a prognostic factor for different types of cancer. Data related to overall survival (OS) and clinicopathologic features were collected and analyzed. The pooled hazard ratio (HR) and odds radio (OR) with a 95% confidence interval (CI) were used to estimate associations. Ten original studies containing 1,231 patients were included. The results showed that in patients with cancer, high PANDAR expression is correlated with lymph node metastasis (LNM; OR = 2.57; 95% CI, 1.76–3.81; p < 0.001), tumor stage (OR = 2.90; 95% CI, 1.25–6.75; p = 0.013), and tumor size (OR = 1.79; 95% CI, 1.11–2.91; p = 0.018). However, sensitivity analysis further demonstrated a significant association between high PANDAR expression and OS, both in multivariate and univariate analysis models (pooled HR 2.01; 95% CI, 1.17–3.44 and pooled HR 2.62; 95% CI, 1.98–3.47, respectively), after omitting one study. These results suggested that PANDAR expression might be indicative of advanced disease and poor prognosis in patients with cancer. Further studies are necessary to determine the value of this risk stratification biomarker in clinical management of patients with cancer.     

Orchestrating a biomarker panel with lncRNAs and mRNAs for predicting survival in pancreatic ductal adenocarcinoma

The low survival of patients with pancreatic ductal adenocarcinoma (PDAC) makes the treatment of this disease one of the most challenging task in modern medicine. Here, by mining a large‐scale cancer genome atlas data set of pancreatic cancer tissues, we identified 21 long noncoding RNAs (lncRNAs) that significantly associated with overall survival in patients with PDAC (P < .01). Further analysis revealed that 8 lncRNAs turned out to be independently correlated with patients’ overall survival, and the risk score could be calculated based on their expression. To obtain a better predicting power, we integrated lncRNA data with a total of 410 differently expressed messenger RNAs (mRNAs) screened from PDAC and normal tissues in gene expression omnibus (GEO) database. The integration resulted in a much better panel including 8 lncRNAs (RP3.470B24.5, CTA.941F9.9, RP11.557H15.3, LINC00960, AP000479.1, LINC00635, LINC00636, and AC073133.1) and 8 mRNAs (DHRS9, ONECUT1, OR8D4, MT1M, TCN1, MMP9, DPYSL3, and TTN) to predict prognosis. A functional evaluation showed that these lncRNAs might play roles in pancreatic secretion, cell adhesion, and proteolysis. Using normal and pancreatic cancer cell lines, we confirmed that a majority of identified lncRNAs and mRNAs showed altered expressions in pancreatic cancer cells. Especially, LINC01589, LINC00960, TCN1, and MT1M showed a profoundly increased expression in pancreatic cancer cells, which suggests their potentially important role in pancreatic cancer. The results of our work indicate that lncRNAs have vital roles in PADC and provide new insights to integrate multiple kinds of markers in clinical practices.     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer

The immune system and the tumor interact closely during tumor development. Aberrantly expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. The present study aimed to build a risk scoring system to improve the prognosis of GC patients. In the present study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. About 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. About 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, ‘clusterProfiler’. The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.     

Identification of LINC01234 and MIR210HG as novel prognostic signature for colorectal adenocarcinoma

This study aimed to identify potential biomarkers and the therapeutic targets for colorectal adenocarcinoma by systematically evaluate a large scale of long noncoding RNAs (lncRNAs) expression data from TCGA. The algorithm t-distributed stochastic neighbor embedding and hierarchical clustering were utilized to group the samples into three clusters that showed a different prognosis. To identify the relationship between the clustered groups and different histoclinical features, different statistical methods were used. The functions of LINC01234 and MIR210HG were investigated with the help of the public database. The results showed that the expression levels of lncRNAs were able to distinguish the tumor samples from the normal tissues and in further they were able to predict the prognosis of the patients. We proposed two potential lncRNAs, which might serve as a biomarker or therapeutic targets. LINC01234 can be a good biomarker. In contrast, MIR210HG participated in the progression of colorectal adenocarcinoma by regulating hypoxia. It might function through an lncRNA–microRNA–messenger RNA regulatory network with MIR210 and RASSF7.     

Aberrant expression of lncRNAs SNHG6, TRPM2-AS1, MIR4435-2HG,and hypomethylation of TRPM2-AS1 promoter in colorectal cancer

Accumulating evidence has indicated that deregulation of lncRNAs plays essential roles in colorectal cancer (CRC) carcinogenesis. The goal of this study was to analyze the expression of lncRNAs in colorectal cancer and their association with clinicopathological variables. Bioinformatics analysis of published CRC microarray data was performed to identify the important lncRNAs. The expression levels of candidate genes were assessed in the human colon cancer/normal cell lines, CRC, adenomatous colorectal polyps, and their marginal tissues by qRT-PCR. Moreover, the methylation status of the TRPM2-AS1 promoter was studied using qMSP assay. Furthermore, we investigated the molecular mechanisms of these lncRNAs in CRC progression using in silico analysis. Microarray analysis revealed that lncRNAs SNHG6, MIR4435-2HG, and TRPM2-AS1 were upregulated in CRC. These results were validated in colon cell lines. Moreover, qRT-PCR showed that the expression levels of SNHG6 and TRPM2-AS1 were upregulated in the colorectal tumor tissues compared with their paired tissues. Nonetheless, there was no significant increase in MIR4435-2HG expression in CRC samples. Furthermore, we observed a significant hypomethylation of TRPM2-AS1 promoter and its activation in CRC tissues. By in silico analysis, we found that the lncRNAs upregulation could promote proliferation and drug resistance of colorectal cancer cells via miRNAs sponging and modulation of their targets expression. In conclusion, based on our results upregulation of SNHG6 and TRPM2-AS1, and hypomethylation of TRPM2-AS1 promoter might be considered as potential diagnostic biomarkers for CRC initiation and development.     

长链非编码RNA的功能与疾病

长链非编码RNAs（long noncoding RNAs,lncRNAs）是一类长度大于200 nt且不表现出任何蛋白质编码潜能的RNAs,在总非编码RNAs(ncRNA)中占有相当大的比例.对lncRNAs的研究将有助于理解生命体多层次的表达调控网络,并有望为复杂疾病的预测、诊断、和治疗提供新的分子依据.本文在简要介绍lncRNAs的基础上,综合分析了lncRNAs与表观遗传、基因表达调控和疾病发生的关系,以期为进一步的研究提供参考.     

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.     

Long noncoding RNAs biomarker-based cancer assessment

Cancer diagnosis have mainly relied on the incorporation of molecular biomarkers as part of routine diagnostic tool. The molecular alteration ranges from those involving DNA, RNA, noncoding RNAs (microRNAs and long noncoding RNAs [lncRNAs]) and proteins. lncRNAs are recently discovered noncoding endogenous RNAs that critically regulates the development, invasion, and metastasis of cancer cells. They are dysregulated in different types of malignancies and have the potential to serve as diagnostic markers for cancer. The expression of noncoding RNAs is altered following many diseases, and besides, some of them can be secreted from the cells into the circulation following the apoptotic and necrotic cell death. These secreted noncoding RNAs are known as cell free RNA. These RNAs can be secreted from the cell through the apoptotic body, extracellular vesicles including microvesicle and exosome, and bind to proteins. Since, lncRNAs display high organ and cell specificity, can be found in the blood, urine, tumor tissue, or other tissues or bodily fluids of some patients with cancer, this review summarizes the most significant and up-to-date findings of research on lncRNAs involvement in different cancers, focusing on the potential of cancer-related lncRNAs as biomarkers for diagnosis, prognosis, and therapy.     

In silico identification of novel lncRNAs with a potential role in diagnosis of gastric cancer

Abstract

Gastric cancer (GC) is the second leading cause of cancer-related deaths in the world. Due to the shortage of adequate symptoms in the early stages, it is diagnosed when the tumor has spread to distant organs. Early recognition of GC enhances the chance of successful treatment. Molecular mechanisms of GC are still poorly understood. LncRNAs are emerging as new players in cancer in both oncogene and tumor suppressor roles. High-throughput technologies such as RNA-Seq, have revealed thousands of lncRNAs which are dysregulated in GC. In this study, we retrieved lncRNAs obtained by High-throughput technologies from OncoLnc database. Consequently, retrieved lncRNAs were compared in literature-based databases including PubMed. As a result, two lists, including experimentally validated lncRNAs and predicted lncRNAs were provided. We found 43 predicted lncRNAs that had not been experimentally validated in GC, so far. Further Bioinformatics analyses were performed to obtain the expression profile of predicted lncRNAs in tumor and normal tissues. Also, the roles and targets of predicted lncRNAs in GC were identified by related databases. Finally, using the GEPIA database was reviewed the significant relationship of predicted lncRNAs with the survival of GC patients. By recognizing the lncRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in the GC early diagnosis or targeted treatment and lead to novel therapeutic strategies.

Communicated by Ramaswamy H. Sarma     

Identification of functional lncRNAs in atrial fibrillation by integrative analysis of the lncRNA-mRNA network based on competing endogenous RNAs hypothesis

A mounting body of evidence has suggested that long noncoding RNAs (lncRNAs) play critical roles in human diseases by acting as competing endogenous RNAs (ceRNAs). However, the functions and ceRNA mechanisms of lncRNAs in atrial fibrillation (AF) remain to date unclear. In this study, we constructed an AF-related lncRNA-mRNA network (AFLMN) based on ceRNA theory, by integrating probe reannotation pipeline and microRNA (miRNA)-target regulatory interactions. Two lncRNAs with central topological properties in the AFLMN were first obtained. By using bidirectional hierarchical clustering, we identified two modules containing four lncRNAs, which were significantly enriched in many known pathways of AF. To elucidate the ceRNA interactions in certain disease or normal condition, the dysregulated lncRNA-mRNA crosstalks in AF were further analyzed, and six hub lncRNAs were obtained from the network. Furthermore, random walk analysis of the AFLMN suggested that lncRNA RP11-296O14.3 may function importantly in the pathological process of AF. All these eight lncRNAs that were identified from previous steps (RP11-363E7.4, GAS5, RP11-410L14.2, HAGLR, RP11-421L21.3, RP11-111K18.2, HOTAIRM1, and RP11-296O14.3) exhibited a strong diagnostic power for AF. The results of our study provide new insights into the functional roles and regulatory mechanisms of lncRNAs in AF, and facilitate the discovery of novel diagnostic biomarkers or therapeutic targets.     

State of the art technologies to explore long non‐coding RNAs in cancer

Long non‐coding RNAs (lncRNAs) comprise a vast repertoire of RNAs playing a wide variety of crucial roles in tissue physiology in a cell‐specific manner. Despite being engaged in myriads of regulatory mechanisms, many lncRNAs have still remained to be assigned any functions. A constellation of experimental techniques including single‐molecule RNA in situ hybridization (sm‐RNA FISH), cross‐linking and immunoprecipitation (CLIP), RNA interference (RNAi), Clustered regularly interspaced short palindromic repeats (CRISPR) and so forth has been employed to shed light on lncRNA cellular localization, structure, interaction networks and functions. Here, we review these and other experimental approaches in common use for identification and characterization of lncRNAs, particularly those involved in different types of cancer, with focus on merits and demerits of each technique.