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.     

Analysis of long noncoding RNA expression profiles in the whole blood of neonates with hypoxic-ischemic encephalopathy

Long noncoding RNAs (lncRNAs) have been shown to participate in many biological processes. To investigate the expression profiles of lncRNAs and their potential functions in neonatal hypoxic-ischemic encephalopathy (HIE), we detected the lncRNA and messenger RNA (mRNA) expression in the peripheral blood samples from HIE patients and controls using a microarray. A total of 376 lncRNAs and 126 mRNAs were differentially expressed between the HIE and the non-HIE samples (fold change > 2). Quantitative real-time polymerase chain reaction was used to validate the microarray data. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed to determine the gene function. Furthermore, the lncRNA-mRNA coexpression network was generated to predict the potential targets of lncRNAs. In conclusion, our study first demonstrated the differential expression profiles of lncRNAs in the whole blood of infants with HIE and may provide a new view of the distinct lncRNA functions in HIE.     

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNVs in breast cancer to predict the prognosis of patients has become a promising approach to accurate treatment in recent years. The differential analysis was performed on CNVs of long noncoding RNAs (lncRNAs) as well as the expression of lncRNAs, microRNAs (miRNAs) and mRNAs in normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. The CNV-driven lncRNAs were identified by the Kruskal–Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways, including the Focal Adhesion-PI3K-Akt–mTOR-signaling pathway, the neuronal system, metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between the CNVs of five lncRNAs and their gene expression in the ceRNA network was analyzed via a chi-square test, which confirmed that except for LINC00243, the expression of four lncRNAs was notably correlated with the CNVs. The survival analysis revealed that only the copy number gain of LINC00536 was evidently related to the poor prognosis of patients. The CIBERSORT algorithm showed that five lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.     

Genome-wide identification of a competing endogenous RNA network in cholangiocarcinoma

Cholangiocarcinoma (CCA) is the second widespread liver tumor with relatively poor survival. Increasing evidence in recent studies showed long noncoding RNAs (lncRNAs) exert a crucial impact on the development and progression of CCA based on the mechanism of competing endogenous RNAs (ceRNAs). However, functional roles and regulatory mechanisms of lncRNA-regulated ceRNA in CCA, are only partially understood. The expression profile of messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) downloaded from The Cancer Genome Atlas were comprehensively investigated. Differential expression of these three types of RNA between CCA and corresponding precancerous tissues were screened out for further analysis. On the basis of interactive information generated from miRDB, miRTarBase, TargetScan, and miRcode public databases, we then constructed an mRNA-miRNA-lncRNA regulatory network. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were conducted to identify the biological function of the ceRNA network involved in CCA. As a result, 2883 mRNAs, 136 miRNAs, and 993 lncRNAs were screened out as differentially expressed RNAs in CCA. In addition, a ceRNA network in CCA was constructed, composing of 50 up and 27 downregulated lncRNAs, 14 up and 7 downregulated miRNAs, 29 up and 25 downregulated mRNAs. Finally, gene set enrichment and pathway analysis indicated our CCA-specific ceRNA network was related with cancer-related pathway and molecular function. In conclusion, our research identified a novel lncRNA-related ceRNA network in CCA, which might act as a potential therapeutic target for patients with CCA.     

Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

Objective: To identify immune-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC).Methods: The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to obtain the gene expression profile. Immune-related lncRNAs were screened from the Molecular Signatures Database v4.0 (MsigDB). We performed a survival analysis of critical lncRNAs. Further, the function of prognostic lncRNAs was inferred using the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) to clarify the possible mechanisms underlying their predictive ability. The assessment was performed in clinical samples and PTC cells.Results: We obtained 4 immune-related lncRNAs, 15 microRNAs (miRNAs), and 375 mRNAs as the key mediators in the pathophysiological processes of PTC from the GEO database. Further, Lasso regression analysis identified seven prognostic markers (LINC02550, SLC26A4-AS1, ACVR2B-AS1, {"type":"entrez-nucleotide","attrs":{"text":"AC005479.2","term_id":"4508155","term_text":"AC005479.2"}}AC005479.2, LINC02454, and AL136366.1), most of which were related to tumor development. The KEGG pathway enrichment analysis showed different, changed genes mainly enriched in the cancer-related pathways, PI3K-Akt signaling pathway, and focal adhesion. Only SLC26A4-AS1 had an intersection in the results of the two databases.Conclusion: LncRNA SLC26A4-AS1, which is the most associated with prognosis, may play an oncogenic role in the development of PTC.     

Temporal expression and functional analysis of long non‐coding RNAs in colorectal cancer initiation

Long non‐coding RNAs (lncRNAs) have potential applications in clinical diagnosis and targeted cancer therapies. However, the expression profile of lncRNAs in colorectal cancer (CRC) initiation is still unclear. In this study, the expression profiles of lncRNAs and mRNAs were determined by microarray at specific tumour stages in an AOM/DSS‐induced primary colon cancer model. The temporal expression of lncRNAs was analysed by K‐means clustering. Additionally, weighted correlation network analysis (WGCNA) and gene ontology analysis were performed to construct co‐expression networks and establish functions of the identified lncRNAs and mRNAs. Our results suggested that 4307 lncRNAs and 5798 mRNAs are deregulated during CRC initiation. These differential expression genes (DEGs) exhibited a clear correlation with the differential stage of tumour initiation. WGCNA results suggested that a series of hub lncRNAs are involved in regulating cell stemness, colon inflammation, oxidative stress response and cell death at each stage. Among them, lncRNA H19 was up‐regulated in colon tumours and correlated with poor patient prognosis. Collectively, we have been the first to demonstrate the temporal expression and function of lncRNAs in CRC initiation. These results provide novel diagnosis and therapy targets for CRC.     

Molecular characterization of lung cancer: A two-miRNA prognostic signature based on cancer stem-like cells related genes

Lung cancer is one of the deadliest cancers worldwide. To increase the survival rate of lung cancer, it is necessary to explore specific prognosis markers. More and more evidence finds that noncoding RNA is closely associated with the survival of lung cancer, and cancer stem cells (CSCs) also play a significant role in the progress of lung cancer. The objective of this study is to find CSLCs genes that affect the prognosis of lung cancer. The differential expression of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), messenger RNAs (mRNAs) in the Cancer Genome Atlas (TCGA) database and differential expression data from microarray of CD326⁺ and CD326⁻ A549 cell are intersected to identify stable and consistent expression genes (2 lncRNAs, 15 miRNAs, and 134 mRNAs). The intersection of lncRNAs and miRNAs is analyzed by univariate and multivariate Cox regression to obtained prognostic genes. Two miRNAs (miR-30b-5p and miR-29c-3p) are significantly correlated with the overall survival rate. Then using these two miRNAs to construct a risk score model as a prognosis signature of lung cancer. Subsequently, we analyzed the association between two miRNAs and clinical information of lung cancer patients, of which T stage, Neoplasm cancer and risk score (P < .05) can be used as independent prognostic indicators of lung cancer. Finally, target genes of 2 miRNAs and 134 mRNAs were annotated with Gene Ontology and analyzed with Kyoto Encyclopedia of Genes and Genomes pathway, and verified with the GEO database. In summary, this study illustrates the role of miRNAs in the promotion of lung cancer by CSCs, which is important to find molecular biomarkers of lung cancer.     

A comprehensive study of construction and analysis of competitive endogenous RNA networks in lung adenocarcinoma

BackgroundLong noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network.MethodsRNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors.ResultsAfter compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively.ConclusionsThis study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches.     

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.     

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

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

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.     

Systematic identification of lncRNAs and circRNAs-associated ceRNA networks in human lumbar disc degeneration

ABSTRACT

Lumbar disc degeneration (LDD) is a common cause of low back and neck pain. The molecular mechanisms underlying LDD, however, are unclear. Noncoding RNAs have been reported to participate in human diseases. We investigated a series of public datasets (GSE67566, GSE56081 and GSE63492) and identified 568 mRNAs, 55 microRNAs (miRNAs), 765 long noncoding RNAs (lncRNAs), and 586 circular RNAs (circRNAs) that were expressed differently in LDD than in normal discs. We constructed lncRNAs and circRNAs regulated competing endogenous RNAs (ceRNA) networks in LDD. Four lncRNAs, DANCR, CASK-AS1, SCARNA2, and LINC00638), and three circRNAs, hsa_circ_0005139, hsa_circ_0037858, and hsa_circ_0087890, were identified as key regulators of LDD progression. We found that hsa-miR-486-5p regulated the crosstalk among circRNA hsa_circ_0000189, lncRNA DANCR and 6 mRNAs, PYCR2, TOB1, ARHGAP5, RBPJ, CD247, SLC34A1. Gene ontology (GO) analysis demonstrated that these differently expressed lncRNAs and circRNAs were involved in cellular component organization or biogenesis, gene expression and negative regulation of metabolic processes. Our findings provide useful information for exploring new mechanisms for LDD and candidates for therapeutic targets.     

急性髓系白血病mRNA与非编码RNA差异表达谱及CeRNA调控网络分析

利用GEO数据库(gene expression omnibus database)通过生物信息学分析方法探讨急性髓系白血病(acute myelogenous leukemia,AML)的发病机制。检索GEO数据库中AML相关芯片数据集GSE142698、GSE142699和GSE96535。利用GEO2R分析得到差异mRNAs、miRNAs以及差异lncRNAs。利用在线生物信息学分析工具DAVID对差异mRNAs进行GO富集分析和KEGG通路分析。利用miRWalk数据库预测AML相关miRNAs的靶向mRNAs,利用Spongescan数据库预测AML相关miRNAs的靶向lncRNAs,构建lncRNA-miRNA-mRNA竞争性内源RNA （competing endogenous RNA,ceRNA）调控网络。共筛选出29个显著差异mRNAs、70个显著差异miRNAs和20 005个显著差异lncRNAs。GO富集分析和KEGG通路分析显示,差异表达基因主要涉及蛋白磷酸化、细胞分裂、细胞增殖的负调控、基因表达的正向调节、周期蛋白依赖的丝氨酸/苏氨酸激酶活性的调节等生物过程以及细胞周期、细胞衰老、癌症通路、PI3K-Akt通路等信号通路。将miRWalk数据库预测的靶向mRNAs与差异mRNAs取交集,Spongescan数据库预测的靶向lncRNAs与差异lncRNAs取交集,分别确定了25个mRNAs、6个lncRNAs参与AML相关ceRNA调控网络的构建。结果表明,lncRNAs可能作为关键的ceRNA,通过调控miRNA和相关靶基因参与AML的发生与发展,研究结果为AML诊断和治疗的分子生物学研究提供了新的依据。     

The exploration of new therapeutic targets for HPV-negative head and neck squamous cell cancer through the construction of a ceRNA network and immune microenvironment analysis

Previous studies have shown that human papillomavirus (HPV)-negative patients with head and neck squamous cell cancer (HNSCC) suffer from an unsatisfactory prognosis. Long noncoding RNAs (lncRNAs) have been verified to participate in many biological processes, including regulating gene expression as competing endogenous RNAs (ceRNAs), while few studies focused the ceRNA network regulation mechanism in patients with HPV-negative HNSCC tumor. Meanwhile, the immune microenvironment may be critical in the development and prognosis of HPV-negative tumors. Our study aimed to further investigate the pathogenesis and potential biomarkers for the diagnosis, therapy and prognosis of HPV-negative HNSCC through a ceRNA network. Comprehensively analyzing the sequencing data of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in The Cancer Genome Atlas HNSCC dataset, we constructed a differentially expressed ceRNA network containing 131 lncRNAs, 35 miRNAs and 162 mRNAs. Then, survival analysis in the network was cited to explore the prognostic biomarkers. Eight mRNAs, nine lncRNAs, and one miRNA were identified to be associated with prognosis. Neuropilin (NRP) binding function, retinoid X receptor (RXR) binding, and the vascular endothelial growth factor (VEGF) signaling pathway were associated with the enrichment analysis, and they also related to the immune microenvironment. Combined with the analysis of the immune microenvironment differences, we obtained new targeted therapies using an RXR agonist, or a combination of the VEGF monoclonal antibody and an NRP antagonist, which may provide a promising future for HPV-negative HNSCC patients.     

Construction and analysis of dysregulated lncRNA-associated ceRNA network in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently diagnosed digestive system cancer. The aim of the present study was to investigate the interactions among messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) in CRC to reveal the mechanisms of CRC. Differentially expressed genes (DEGs) were identified from public gene expression data sets. One thousand eighty-one common dysregulated mRNAs in two data sets were identified. Gene function analysis and protein-protein interaction network analysis indicated that these DEGs might play important roles in CRC. LINC00365 was selected through coding- noncoding network analysis and its expression was validated upregulated in 22 paired clinical samples and four CRC cell lines. A competing endogenous RNA network composed of 70 miRNAs, nine mRNAs, and LINC00365 was constructed. Eight of nine mRNAs were validated upregulated in The Cancer Genome Atlas data set. Our results suggested that LINC00365 was an oncogene in CRC and it could regulate the expression of several mRNAs through sponging miRNAs.