Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

Four long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

The study aimed to identify the long noncoding RNAs (lncRNAs) biomarkers for occurrence and prognosis of patients with hepatocellular carcinoma (HCC), and simultaneously to investigate the potential role of lncRNAs in the oncogenesis of HCC. The lncRNAs expression data and the corresponding clinical information of HCC samples were extracted from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes and lncRNAs were identified and the correlation networks were constructed. In this study, we identified 212 differentially expressed lncRNAs and 7,577 differentially expressed genes between liver HCC tumor tissues and normal tissue samples. And then, combining with clinical information, a total of 11 lncRNAs and 162 genes as HCC biomarkers were identified by comprehensive bioinformatics analysis. Further, through coexpress network analysis, we confirmed four lncRNAs (lncRNA_ANKRD10.IT1, lncRNA_CTD.2583A14.8, lncRNA_RP11.404P21.3, and lncRNA_RP11.488L18.10), which can serve as prognostic biomarkers for HCC. The four lncRNAs identified in this study may serve as a potential therapy target for HCC.     

Identification of m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression and Kaplan–Meier (K–M) analysis were performed to identify m6A methyltransferase-related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes (BPs) and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. The present study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.     

Comprehensive analysis of a ceRNA network reveals potential prognostic cytoplasmic lncRNAs involved in HCC progression

The aberrant expression of long noncoding RNAs (lncRNAs) has drawn increasing attention in the field of hepatocellular carcinoma (HCC) biology. In the present study, we obtained the expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in 371 HCC tissues and 50 normal tissues from The Cancer Genome Atlas (TCGA) and identified hepatocarcinogenesis-specific differentially expressed genes (DEGs, log fold change ≥ 2, FDR < 0.01), including 753 lncRNAs, 97 miRNAs, and 1,535 mRNAs. Because the specific functions of lncRNAs are closely related to their intracellular localizations and because the cytoplasm is the main location for competitive endogenous RNA (ceRNA) action, we analyzed not only the interactions among these DEGs but also the distributions of lncRNAs (cytoplasmic, nuclear or both). Then, an HCC-associated deregulated ceRNA network consisting of 37 lncRNAs, 10 miRNAs, and 26 mRNAs was constructed after excluding those lncRNAs located only in the nucleus. Survival analysis of this network demonstrated that 15 lncRNAs, 3 miRNAs, and 16 mRNAs were significantly correlated with the overall survival of HCC patients (p < 0.01). Through multivariate Cox regression and lasso analysis, a risk score system based on 13 lncRNAs was constructed, which showed good discrimination and predictive ability for HCC patient survival time. This ceRNA network-construction approach, based on lncRNA distribution, not only narrowed the scope of target lncRNAs but also provided specific candidate molecular biomarkers for evaluating the prognosis of HCC, which will help expand our understanding of the ceRNA mechanisms involved in the early development of HCC.     

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.     

lncRNA‐POIR promotes epithelial–mesenchymal transition and suppresses sorafenib sensitivity simultaneously in hepatocellular carcinoma by sponging miR‐182‐5p

Sorafenib (SOR) resistance remains a major obstacle in the effective treatment of hepatocellular carcinoma (HCC). A number of long noncoding RNAs (lncRNAs) are responsible for this chemoresistance. This study aimed to reveal the essential function of a recently defined lncRNA, lncRNA‐POIR, in the epithelial–mesenchymal transition (EMT) and SOR sensitivity of HCC cells. SOR‐induced cytotoxicity was analyzed via cell counting kit‐8 and ethynyl‐2'‐deoxyuridine incorporation assays, whereas immunoblotting and confocal immunofluorescence were used to determine the expression levels of EMT markers. Furthermore, loss‐ or gain‐of‐function approaches were used to demonstrate the role of lncRNA‐POIR/miR‐182‐5p on EMT and SOR sensitivity in HCC. The direct interaction between lncRNA‐POIR and miR‐182‐5p was verified using a luciferase reporter assay. We found that knockdown of lncRNA‐POIR sensitized HCC cells to SOR and simultaneously reversed EMT. As expected, miR‐182‐5p was confirmed as the downstream target of lncRNA‐POIR. Moreover, miR‐182‐5p overexpression clearly reversed EMT and promoted SOR‐induced cytotoxicity in representative HCC cells, whereas miR‐182‐5p downregulation played a contrasting role; miR‐182‐5p knockdown abolished the modulatory effects of lncRNA‐POIR siRNA on EMT and SOR sensitivity. Together, these pieces of data suggest that lncRNA‐POIR promotes EMT progression and suppresses SOR sensitivity simultaneously by sponging miR‐182‐5p. Thus, we proposed a compelling rationale for the use of lncRNA‐POIR as a promising predictor of SOR response and as a potential therapeutic target for HCC treatment in the future.     

CRlncRNA: a manually curated database of cancer-related long non-coding RNAs with experimental proof of functions on clinicopathological and molecular features

Background

Recent studies demonstrated that long non-coding RNAs (lncRNAs) could be intricately implicated in cancer-related molecular networks, and related to cancer occurrence, development and prognosis. However, clinicopathological and molecular features for these cancer-related lncRNAs, which are very important in bridging lncRNA basic research with clinical research, fail to well settle to integration.

Results

After manually reviewing more than 2500 published literature, we collected the cancer-related lncRNAs with the experimental proof of functions. By integrating from literature and public databases, we constructed CRlncRNA, a database of cancer-related lncRNAs. The current version of CRlncRNA embodied 355 entries of cancer-related lncRNAs, covering 1072 cancer-lncRNA associations regarding to 76 types of cancer, and 1238 interactions with different RNAs and proteins. We further annotated clinicopathological features of these lncRNAs, such as the clinical stages and the cancer hallmarks. We also provided tools for data browsing, searching and download, as well as online BLAST, genome browser and gene network visualization service.

Conclusions

CRlncRNA is a manually curated database for retrieving clinicopathological and molecular features of cancer-related lncRNAs supported by highly reliable evidences. CRlncRNA aims to provide a bridge from lncRNA basic research to clinical research. The lncRNA dataset collected by CRlncRNA can be used as a golden standard dataset for the prospective experimental and in-silico studies of cancer-related lncRNAs. CRlncRNA is freely available for all users at http://crlnc.xtbg.ac.cn.

     

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Background

Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques.

Results

CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC?=?0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports.

Conclusions

Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

     

Increased long noncoding RNA LASP1-AS is critical for hepatocellular carcinoma tumorigenesis via upregulating LASP1

Aberrant long noncoding RNAs (lncRNA) have been proved to be associated with the many types of malignant tumors (including hepatocellular carcinoma [HCC]). In this study, a lncRNAs and mRNAs microarray analysis was performed in three pairs of HCC patitents’ tumor. We found lncRNA LIM and SH3 protein 1 antisense (LASP1-AS) and its sense-cognate gene LIM and SH3 protein 1 (LASP1) were upregulated in HCC and both are correlated with poorer prognosis and lower survival of HCC patients. Meanwhile, the expression of LASP1-AS correlated positively with LASP1 expression in HCC tissues. LASP1-AS promoted the proliferation, migration, and invasion abilities of HCC in vitro and vivo by enhancing LASP1 expression. Our study explored lncRNA LASP1-AS as an oncogene in HCC and promoted proliferation and metastasis capabilities of HCC via increasing the expression of its sense-cognate gene LASP1. LncRNA LASP1-AS might be a potential valuable prognostic biomarker and potential therapeutic target of HCC.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

Novel biomarkers for post-contrast acute kidney injury identified from long non-coding RNA expression profiles

Background: Post-contrast acute kidney injury (PC-AKI) is a severe complication of cardiac catheterization. Emerging evidence indicated that long non-coding RNAs (lncRNAs) could serve as biomarkers for various diseases. However, the lncRNA expression profile and potential biomarkers in PC-AKI remain unclear. This study aimed to investigate novel lncRNA biomarkers for the early detection of PC-AKI.Methods: lncRNA profile in the kidney tissues of PC-AKI rats was evaluated through RNA sequencing. Potential lncRNA biomarkers were identified through human-rat homology analysis, kidney and blood filtering in rats and verified in 112 clinical samples. The expression patterns of the candidate lncRNAs were detected in HK-2 cells and rat models to evaluate their potential for early detection.Results: In total, 357 lncRNAs were found to be differentially expressed in PC-AKI. We identified lnc-HILPDA and lnc-PRND were conservative and remarkably upregulated in both kidneys and blood from rats and the blood of PC-AKI patients; these lncRNAs can precisely distinguish PC-AKI patients (area under the curve (AUC) values of 0.885 and 0.875, respectively). The combination of these two lncRNAs exhibited improved accuracy for predicting PC-AKI, with 100% sensitivity and 83.93% specificity. Time-course experiments showed that the significant difference was first noted in the blood of PC-AKI rats at 12 h for lnc-HILPDA and 24 h for lnc-PRND.Conclusion: Our study revealed that lnc-HILPDA and lnc-PRND may serve as the novel biomarkers for early detection and profoundly affect the clinical stratification and strategy guidance of PC-AKI.     

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression–based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression–based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression–based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.     

Cancer Specific Long Noncoding RNAs Show Differential Expression Patterns and Competing Endogenous RNA Potential in Hepatocellular Carcinoma

Long noncoding RNAs (lncRNAs) regulate gene expression by acting with microRNAs (miRNAs). However, the roles of cancer specific lncRNA and its related competitive endogenous RNAs (ceRNA) network in hepatocellular cell carcinoma (HCC) are not fully understood. The lncRNA profiles in 372 HCC patients, including 372 tumor and 48 adjacent non-tumor liver tissues, from The Cancer Genome Atlas (TCGA) and NCBI GEO omnibus (GSE65485) were analyzed. Cancer specific lncRNAs (or HCC related lncRNAs) were identified and correlated with clinical features. Based on bioinformatics generated from miRcode, starBase, and miRTarBase, we constructed an lncRNA-miRNA-mRNA network (ceRNA network) in HCC. We found 177 cancer specific lncRNAs in HCC (fold change ≥ 1.5, P < 0.01), 41 of them were also discriminatively expressed with gender, race, tumor grade, AJCC tumor stage, and AJCC TNM staging system. Six lncRNAs (CECR7, LINC00346, MAPKAPK5-AS1, LOC338651, FLJ90757, and LOC283663) were found to be significantly associated with overall survival (OS, log-rank P < 0.05). Collectively, our results showed the lncRNA expression patterns and a complex ceRNA network in HCC, and identified a complex cancer specific ceRNA network, which includes 14 lncRNAs and 17 miRNAs in HCC.