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

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

Downregulation of lncRNA HCP5 has inhibitory effects on gastric cancer cells by regulating DDX21 expression

LncRNA HCP5 has been confirmed to play crucial roles in many types of cancers. However, the role of lncRNA HCP5 in regulating the occurrence and development of gastric cancer (GC) remains unknown. In the current study, we aimed to investigate the precise effects of lncRNA HCP5 on cell proliferation, migration and invasion and molecular mechanisms in gastric cancer. Using RT-qPCR analysis, we found that lncRNA HCP5 was differentially expressed in GC cell lines. CCK-8, wound healing and transwell assay indicated that the proliferation, migration and invasion of gastric cancer cells were inhibited by downregulation of lncRNA HCP5 and lncRNA HCP5 overexpression exhibited the opposite effects in gastric cancer cells. Mechanistically, RNA binding protein immunoprecipitation and dual luciferase reporter assay confirmed the interaction between lncRNA HCP5 and DDX21. The effects of lncRNA HCP5 overexpression the proliferation, migration and invasion of GC cells were partly rescued by DDX21 silencing. Taken together, downregulation of lncRNA HCP5 exerted inhibitory effects on GC cell proliferation, migration and invasion through modulation of DDX21 expression, demonstrating the function of lncRNA HCP5 and DDX21 in GC progression.     

Downregulation of long noncoding RNA LINC00683 associated with unfavorable prognosis in prostate cancer based on TCGA

Prostate cancer (PCa) is the third most common reason of cancer-related deaths in men. Accumulating evidence has shown that dysregulation of long noncoding RNAs (lncRNAs) is closely related to cancer initiation and development. Although large numbers of lncRNAs have been discovered, knowledge regarding their function and physiological/pathological significance remains limited. In this study, we aimed to reveal functional lncRNAs and identify prognosis-related RNAs in PCa by analyzing data from The Cancer Genome Atlas (TCGA). To achieve this, an lncRNA-mRNA coexpression network was constructed by weighted correlation network analysis. Additionally, a subnetwork was extracted from this weighted correlation network, and seven lncRNAs were identified as core nodes. Further Kaplan-Meier survival analysis showed that three lncRNAs (LINC00683, LINC00857, and FENDRR) were significantly downregulated in PCa samples, and there was a strong positive correlation with patient survival. Importantly, LINC00683 has not been fully reported as related with PCa. Additionally, gene set enrichment analysis indicated that LINC00683 might be involved in cancer-related pathways such as the Wnt pathway. Based on the findings of this study, lncRNA LINC00683 is likely to provide a new diagnostic biomarker and therapeutic target for future PCa treatments.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer

Autophagy-related long non-coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy-related lncRNA can predict the prognosis of breast cancer patients. The autophagy-related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy-related lncRNAs (MAPT-AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy-related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381-2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low-risk. Gene set enrichment analysis showed that the high-risk group was enriched in autophagy and cancer-related pathways, and the low-risk group was enriched in regulatory immune-related pathways. These results indicated that the ALPS model composed of five autophagy-related lncRNAs could predict the prognosis of breast cancer patients.     

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

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

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.     

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.     

Survival analysis for long noncoding RNAs identifies TP53TG1 as an antioncogenic target for the breast cancer

Breast carcinoma is one of the most commonly diagnosed tumors and also one of the deadliest cancers in the female. Long noncoding RNAs (lncRNAs) are emerging as novel targets and biomarkers for breast cancer diagnosis and treatment. In this study, we aimed to study the lncRNAs associated with the outcomes in patients using the breast invasive carcinoma datasets from The Cancer Genome Atlas. The Cox proportional hazards regression model was fitted to each lncRNA. Hierarchy clustering was carried out using these survival-related lncRNAs and the log-rank test was carried out for the clustered groups. DNA methylation status was utilized to identify the lncRNAs regulated by epigenetics. Finally, the coexpressed messenger RNA with the potential lncRNAs were utilized to study the possible functions and mechanisms of lncRNAs. In total, 182 lncRNAs had an impact on the survival time of the patients with a cutoff <0.01. The patients were clustered into three groups using these survival-related genes, which performed significantly different prognosis. Two lncRNAs, which were significantly correlated with the outcomes of breast cancer and were regulated by methylation status, were obtained. These two lncRNAs were TP53TG1 and RP5-1061H20.4. We proposed that TP53TG1 was activated by the wild-type TP53 and performed an impact on the PI3Ks family by binding YBX2 in breast cancer.     

DNA methylation and gene expression profiles characterize epigenetic regulation of lncRNAs in colon adenocarcinoma

The long noncoding RNAs (lncRNAs) are associated with tumorigenesis and progression of cancer. While DNA methylation is a common epigenetic regulator of gene expression, the methylation of lncRNAs was rarely studied. To address this gap, we integrated DNA methylation and RNA-seq data to characterize the landscape of lncRNA methylation in colon adenocarcinoma (COAD). We collected and analyzed the lncRNA expression and methylation data from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia to identify the epigenetically regulated lncRNAs. We further investigated the biological and clinical relevance of the identified lncRNAs via bioinformatics analysis. We identified 20 epigenetically upregulated lncRNAs in COAD, including several well-studied lncRNAs whose methylation regulation were poorly investigated, such as PVT1 and UCA1. We also revealed several novel tumor-associated lncRNAs in COAD, including GATA2-As1 and CYTOR. Next, we explored their biology function using gene set enrichment analysis and competitive endogenous RNA analysis. We characterized the methylation landscape of lncRNA in COAD and identified 20 epigenetically upregulated lncRNAs. Our findings will shed new light on the epigenetic regulation of lncRNA expression by DNA methylation.     

Identification of mitochondrial function‐associated lncRNAs in septic mice myocardium

The present study aimed to analyze long noncoding RNA (lncRNA) and messenger RNA (mRNA) expression profiles in septic mice heart and to identify potential lncRNAs and mRNAs that be responsible for cardiac mitochondrial dysfunction during sepsis. Mice were treated with 10 mg/kg of lipopolysaccharides to induce sepsis. LncRNAs and mRNAs expression were evaluated by using lncRNA and mRNA microarray or real‐time polymerase chain reaction technique. LncRNA‐mRNA coexpression network assay, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. The results showed that 1275 lncRNAs were differentially expressed in septic myocardium compared with those in the control group. A total of 2769 mRNAs were dysregulated in septic mice heart, most of which are mainly related to the process of inflammation, mitochondrial metabolism, oxidative stress, and apoptosis. Coexpression network analysis showed that 14 lncRNAs were highly correlated with 11 mitochondria‐related differentially expressed mRNA. Among all lncRNAs and their cis‐acting mRNAs, 41 lncRNAs‐mRNA pairs (such as NONMMUG004378 and Apaf1 gene) were enriched in GO terms and KEGG pathways. In summary, we gained some specific lncRNAs and their potential target mRNAs that might be involved in mitochondrial dysfunction in septic myocardium. These findings provide a panoramic view of lncRNA and might allow developing new treatment strategies for sepsis.     

Identification of pathological grade and prognosis-associated lncRNA for ovarian cancer

Ovarian carcinoma (OC) is one of the most common malignant tumors in female genitals. In recent years, the therapeutic effect of OC has been significantly improved through the application of effective chemotherapy regimen. However, the 5-year survival rate is also lower than 30% due to high rate of relapse. So, it is needed to screen reliable predictive and prognostic markers of OC. Ovarian cancer gene expression data and corresponding clinical data used were downloaded from Gene Expression Omnibus database. Weighted gene expression network analysis (WGCNA) and Cox proportional hazards regression (PHR) were used to screen Pathological Grade and Prognosis-associated long noncoding RNA (lncRNA). Kaplan-Meier analysis and receiver operating characteristic curves analysis were performed to evaluate the predictive ability of the selected lncRNA. Gene Ontology (GO) enrichment and Gene Set Enrichment Analysis (GSEA) enrichment analysis methods were used to explore the possible mechanisms of the selected lncRNA affecting the development of OC. Five reliably lncRNAs (LINC00664, LINC00667, LINC01139, LINC01419, and LOC286437) was identified through a series of bioinformatics methods. In testing cohorts, we found that the five lncRNAs in predicting the risk of OC recurrence is robustness, and multivariate Cox PHR analysis indicate that the five lncRNAs is an independent risk factor for OC recurrence. Moreover, GO and GSEA enrichment analysis showed that the five lncRNAs are involved in multiple ovarian cancer occurrence mechanism. In summary, all these findings indicated that the five lncRNAs can effectively predict the risk of recurrence of ovarian cancer.