Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes

The breast cancer tumor microenvironment and precision medicine: immunogenicity and conditions favoring response to immunotherapy

Some main recent researches that have dissected tumor microenvironment (TME) by imaging mass cytometry (IMC) in different subtypes of primary breast cancer samples were considered. The many phenotypic variants, clusters of epithelial tumor and immune cells, their structural features as well as the main genetic aberrations, sub-clonal heterogeneity and their systematic classification also have been examined. Mutational evolution has been assessed in primary and metastatic breast cancer samples. Overall, based on these findings the current concept of precision medicine is questioned and challenged by alternative therapeutic strategies. In the last two decades, immunotherapy as a powerful and harmless tool to fight cancer has received huge attention. Thus, the tumor immune microenvironment (TIME) composition, its prognostic role for clinical course as well as a novel definition of immunogenicity in breast cancer are proposed. Investigational clinical trials carried out by us and other findings suggest that G0-G1 state induced in endocrine-dependent metastatic breast cancer is more suitable for successful immune manipulation. Residual micro-metastatic disease seems to be another specific condition that can significantly favor the immune response in breast and other solid tumors.

Bioinformatics analysis proposes a possible role for long noncoding RNA MIR17HG in retinoblastoma

BACKGROUND

Retinoblastoma (RB) is the most common prevalent intraocular malignancy among infants and children, particularly in underdeveloped countries. With advancements in genomics and transcriptomics, noncoding RNAs have been increasingly utilized to investigate the molecular pathology of diverse diseases.

AIMS

This study aims to establish the competing endogenous RNAs network associated with RB, analyse the function of mRNAs and lncRNAs, and finds the relevant regulatory network.

METHODS AND RESULTS

This study establishes a network of competing endogenous RNAs by Spearman correlation analysis and prediction based on RB patients and healthy children. Enrichment analyzes based on Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes are conducted to analyze the potential biological functions of lncRNA and mRNA networks. Weighted gene co-expression network analysis (WGCNA) is employed to identify gene cluster modules exhibiting the strongest correlation with RB. The results indicate a significant correlation between the lncRNA MIR17HG (R = .73, p = .02) and the RB phenotype. ceRNA networks reveal downstream miRNAs (hsa-mir-425-5p and hsa-mir455-5p) and mRNAs (MDM2, IPO11, and ITGA1) associated with MIR17Hg. As an inhibitor of the p53 signaling pathway, MDM2 can suppress the development of RB.

CONCLUSION

In conclusion, lncRNAs play a role in RB, and the MIR17HG/hsa-mir-425-5p/MDM2 pathway may contribute to RB development by inhibiting the p53 signaling pathway.

Multivariate competing endogenous RNA network characterization for cancer MicroRNA biomarker discovery: a novel bioinformatics model with application to prostate cancer metastasis

Background

MicroRNAs (miRNAs) are post-transcriptional regulators with potential as biomarkers for cancer management. Data-driven competing endogenous RNA (ceRNA) network modeling is an effective way to decipher the complex interplay between miRNAs and spongers. However, there are currently no general rules for ceRNA network-based biomarker prioritization.

Methods and results

In this study, a novel bioinformatics model was developed by integrating gene expression with multivariate miRNA-target data for ceRNA network-based biomarker discovery. Compared with traditional methods, the structural vulnerability in the human long non-coding RNA (lncRNA)–miRNA–messenger RNAs (mRNA) network was comprehensively analyzed, and the single-line regulatory or competing mode among miRNAs, lncRNAs, and mRNAs was characterized and quantified as statistical evidence for miRNA biomarker identification. The application of this model to prostate cancer (PCa) metastasis identified a total of 12 miRNAs as putative biomarkers from the metastatic PCa-specific lncRNA–miRNA–mRNA network and nine of them have been previously reported as biomarkers for PCa metastasis. The receiver operating characteristic curve and cell line qRT-PCR experiments demonstrated the power of miR-26b-5p, miR-130a-3p, and miR-363-3p as novel candidates for predicting PCa metastasis. Moreover, PCa-associated pathways such as prostate cancer signaling, ERK/MAPK signaling, and TGF-β signaling were significantly enriched by targets of identified miRNAs, indicating the underlying mechanisms of miRNAs in PCa carcinogenesis.

Conclusions

A novel ceRNA-based bioinformatics model was proposed and applied to screen candidate miRNA biomarkers for PCa metastasis. Functional validations using human samples and clinical data will be performed for future translational studies on the identified miRNAs.

Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer

Single-cell RNA sequencing is a powerful tool to explore the heterogeneity of breast cancer. The identification of the cell subtype that responds to estrogen has profound significance in breast cancer research and treatment. The transcriptional regulation of estrogen is an intricate network involving crosstalk between protein-coding and non-coding RNAs, which is still largely unknown, particularly at the single cell level. Therefore, we proposed a novel strategy to specify cell subtypes based on a cell-specific ceRNA network (CCN). The CCN was constructed by integrating a cell-specific RNA-RNA co-expression network (RCN) with an existing ceRNA network. The cell-specific RCN was built based on single cell expression profiles with predefined reference cells. Heterogeneous cell subtypes were inferred by enriching RNAs in CCN to the estrogen response hallmark. Edge biomarkers were identified in the early estrogen response subtype. Topological analysis revealed that NEAT1 was a hub lncRNA for the early response subtype, and its ceRNAs could predict patient survival. Another hub lncRNA, DLEU2, could potentially be involved in GPCR signaling, based on CCN. The CCN method that we proposed here facilitates the inference of cell subtypes from a network perspective and explores the function of hub lncRNAs, which are promising targets for RNA-based therapeutics.

Hsa_circ_0000199 facilitates chemo-tolerance of triple-negative breast cancer by interfering with miR-206/613-led PI3K/Akt/mTOR signaling

Increasing attentions have been paid to the role of circRNAs in the etiology of triple-negative breast cancer (TNBC), and we strived to figure out the association of circRNA AKT3/miRNA axis with TNBC chemo-resistance. Altogether 207 BC patients were divided into TNBC group (n=83) and non-TNBC group (n=124), and MCF-10A, MDA-MB-231, MDA-MB-468, SK-BR-3 and MCF-7 cell lines were prepared in advance. Expressions of AKT3-derived circRNAs and relevant miRNAs in the TNBC tissues and cell lines were determined by employing real-time polymerase chain reaction (PCR). It was indicated that hsa_circ_0000199 expression was higher in TNBC tissues than in non-TNBC tissues, and high hsa_circ_0000199 expression was predictive of large tumor size, advanced TNM grade, high Ki-67 level and poor 3-year survival of TNBC patients (all P<0.05). Furthermore, miR-613 and miR-206 were sponged and negatively regulated by hsa_circ_0000199 (P<0.001), and PI3K/Akt/mTOR signaling was depressed by si-hsa_circ_0000199 in TNBC cell lines (P<0.01). Ultimately, miR-206/miR-613 inhibitor reversed impacts of si-hsa_circ_0000199 on PI3K/Akt/mTOR signaling, proliferation, migration, invasion, chemo-sensitivity and autophagy of TNBC cells (all P<0.01). Conclusively, silencing of hsa_circ_0000199 enhanced TNBC chemo-sensitivity by promoting miR-206/miR-613 expression and deactivating PI3K/Akt/mTOR signaling, which was conducive to improving chemotherapeutic efficacy of TNBC patients.

Comprehensive RNA expression profile of therapeutic adipose‑derived mesenchymal stem cells co‑cultured with degenerative nucleus pulposus cells

Stem cell-based therapy is a promising alternative to conventional approaches to treating intervertebral disc degeneration (IDD). However, comprehensive understanding of stem cell-based therapy at the gene level is still lacking. In the present study, we identified the expression profiles of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) expressed within a co-culture system of adipose-derived mesenchymal stem cells (ASCs) and degenerative nucleus pulposus cells (NPCs) and explored the signaling pathways involved and their regulatory networks. Microarray analysis was used to compare ASCs co-cultured with degenerative NPCs to ASCs cultured alone, and the underlying regulatory pattern, including the signaling pathways and competing endogenous RNA (ceRNA) network, was analyzed with robust bioinformatics methods. The results showed that 360 lncRNAs and 1757 mRNAs were differentially expressed by ASCs, and the microarray results were confirmed by quantitative PCR. Moreover, 589 Gene Ontology terms were upregulated, whereas 661 terms were downregulated. A total of 299 signaling pathways were significantly altered. A Path-net and a Signal-net were built to show interactions among differentially expressed genes. An mRNA-lncRNA co-expression network was constructed to reveal the interplay among differentially expressed mRNAs and lncRNAs, whereas a ceRNA network was built to investigate their connections with microRNAs involved in IDD. To the best of our knowledge, this original and comprehensive exploration reveals differentially expressed lncRNAs and mRNAs of ASCs stimulated by degenerative NPCs, underscoring the regulation pattern within the co-culture system at the gene level. These data may further understanding of NPC-directed differentiation of ASCs and facilitate the application of ASCs in future treatments for IDD.

Identification of biomarkers and pathways in hypertensive nephropathy based on the ceRNA regulatory network

BACKGROUND

Hypertensive nephropathy (HTN) is a kind of renal injury caused by chronic hypertension, which seriously affect people's life. The purpose of this study was to identify the potential biomarkers of HTN and understand its possible mechanisms.

METHODS

The dataset numbered GSE28260 related to hypertensive and normotensive was downloaded from NCBI Gene Expression Omnibus. Then, the differentially expressed RNAs (DERs) were screened using R limma package, and functional analyses of DE-mRNA were performed by DAVID. Afterwards, a ceRNA network was established and KEGG pathway was analyzed based on the Gene Set Enrichment Analysis (GSEA) database. Finally, a ceRNA regulatory network directly associated with HTN was proposed.

RESULTS

A total of 947 DERs were identified, including 900 DE-mRNAs, 20 DE-lncRNAs and 27 DE-miRNAs. Based on these DE-mRNAs, they were involved in biological processes such as fatty acid beta-oxidation, IRE1-mediated unfolded protein response, and transmembrane transport, and many KEGG pathways like glycine, serine and threonine metabolism, carbon metabolism. Subsequently, lncRNAs KCTD21-AS1, LINC00470 and SNHG14 were found to be hub nodes in the ceRNA regulatory network. KEGG analysis showed that insulin signaling pathway, glycine, serine and threonine metabolism, pathways in cancer, lysosome, and apoptosis was associated with hypertensive. Finally, insulin signaling pathway was screened to directly associate with HTN and was regulated by mRNAs PPP1R3C, PPKAR2B and AKT3, miRNA has-miR-107, and lncRNAs SNHG14, TUG1, ZNF252P-AS1 and MIR503HG.

CONCLUSIONS

Insulin signaling pathway was directly associated with HTN, and miRNA has-miR-107 and lncRNAs SNHG14, TUG1, ZNF252P-AS1 and MIR503HG were the biomarkers of HTN. These results would improve our understanding of the occurrence and development of HTN.

Identification of mRNA and non-coding RNA hubs using network analysis in organ tropism regulated triple negative breast cancer metastasis

Triple negative breast cancer (TNBC) is aggressive in nature, resistant to conventional therapy and often ends in organ specific metastasis. In this study, publicly available datasets were used to identify miRNA, mRNA and lncRNA hubs. Using validated mRNA-miRNA, mRNA-mRNA and lncRNA-miRNA interaction information obtained from various databases, RNA interaction networks for TNBC and its subtype specific as well as organ tropism regulated metastasis were generated. Further, miRNA-mRNA-lncRNA triad classification was performed using social network analysis from subnetworks and visualized using Cytoscape. Survival analysis of the RNA hubs, oncoprint analysis for mRNAs and pathway analysis of the lncRNAs were also performed. Results indicated that two lncRNAs (NEAT1 and CASC7) and four miRNAs (hsa-miR-106b-5p, hsa-miR-148a-3p, hsa-miR-25-3p and hsa-let-7i-5p) were common between hubs identified in TNBC and TNBC associated metastasis. The exclusive hubs for TNBC associated metastasis were hsa-miR-200b-3p, SP1, HSPA4 and RAB1B. HMGA1 was the top ranked hub in mesenchymal subtype associated lung metastasis, while hsa-miR-27a-3p was identified as the top ranked hub mRNA in luminal androgen receptor subtype associated bone metastasis. When lncRNA associated pathway analysis was performed, Hs Cytoplasmic Ribosomal Protein pathway was found to be the most significant and among the selected hubs, CTNND1, SON and hsa-miR-29c emerged as TNBC survival markers. TP53, FOXA1, MTDH and HDGF were found as the top ranked mRNAs in oncoprint analysis. The pipeline proposed for the first time in this study with validated RNA interaction data integration and graph-based learning for miRNA-mRNA-lncRNA triad classification from RNA hubs may aid experimental cost reduction and its successful execution will allow it to be extended to other diseases too.

CeRNASeek: an R package for identification and analysis of ceRNA regulation

Competitive endogenous RNA (ceRNA) represents a novel layer of gene regulation that controls both physiological and pathological processes. However, there is still lack of computational tools for quickly identifying ceRNA regulation. To address this problem, we presented an R-package, CeRNASeek, which allows identifying and analyzing ceRNA-ceRNA interactions by integration of multiple-omics data. CeRNASeek integrates six widely used computational methods to identify ceRNA-ceRNA interactions, including two global and four context-specific ceRNA regulation prediction methods. In addition, it provides several downstream analyses for predicted ceRNA-ceRNA pairs, including regulatory network analysis, functional annotation and survival analysis. With examples of cancer-related ceRNA prioritization and cancer subtyping, we demonstrate that CeRNASeek is a valuable tool for investigating the function of ceRNAs in complex diseases. In summary, CeRNASeek provides a comprehensive and efficient tool for identifying and analysis of ceRNA regulation. The package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=CeRNASeek.

Computational Identification of Cross-Talking ceRNAs

Competing endogenous RNAs (ceRNAs) are kinds of RNAs that regulate each other at post-transcription level through competing for miRNA regulators. CeRNA-ceRNA networks provide another type of function for protein-coding mRNAs, which link non-coding RNAs such as miRNA, long non-coding RNA, pseudogenes and circular RNAs. In this chapter, we will introduce the definition of ceRNAs, mainly provide the computational method to predict ceRNA interactions in general condition and complex diseases. In addition, we also illustrated several computational methods that are commonly used to identify the perturbed ceRNA networks in human diseases compared to normal conditions. Finally, we also summarized the principles of methods that integrated ceRNA theory to identify human disease biomarkers. Understanding of RNA-RNA crosstalk will provide significant insights into gene regulatory network that has been implicated in human development and/or diseases.

Breast Cancer and miR-SNPs: The Importance of miR Germ-Line Genetics

Recent studies in cancer diagnostics have identified microRNAs (miRNAs) as promising cancer biomarkers. Single nucleotide polymorphisms (SNPs) in miRNA binding sites, seed regions, and coding sequences can help predict breast cancer risk, aggressiveness, response to stimuli, and prognosis. This review also documents significant known miR-SNPs in miRNA biogenesis genes and their effects on gene regulation in breast cancer, taking into account the genetic background and ethnicity of the sampled populations. When applicable, miR-SNPs are evaluated in the context of other patient factors, including mutations, hormonal status, and demographics. Given the power of miR-SNPs to predict patient cancer risk, prognosis, and outcomes, further study of miR-SNPs is warranted to improve efforts towards personalized medicine.

Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis

BACKGROUND

Basal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression.

OBJECTIVE

The aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis.

METHODS AND RESULTS

The differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter.

CONCLUSION

Our results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients.

[Differences in expression profiles of circular RNA between luminal breast cancer cells and normal breast cells]

OBJECTIVE

To investigate the differences in the expression profiles of circular RNA (circRNA) between luminal breast cancer cells and normal breast cells.

METHODS

Total RNA extracted from luminal breast cancer cells MCF7 and normal breast cells MCF10A was digested with Rnase R to remove linear RNAs and enrich circRNAs. The enriched circRNAs were amplified and transcribed into fluorescent cRNAs using a random priming method, and were hybridized onto the circRNA hybridization array. The circRNA expression profiles of MCF7 and MCF10A cells were analyzed using Agilent Feature Extraction software. Quantile normalization and subsequent data processing were performed, and volcano plot filtering and hierarchical clustering were utilized to analyze the circRNA expression patterns. The expressions of 3 circRNAs with significant log fold changes were validated using qPCR.

RESULTS

The hybridization array data revealed significant differences in the circRNA expression profiles between MCF7 and MCF10A cells. Compared with those of MCF10A cells, the 12910 circRNAs expressed in MCF7 cells showed 5964 up-regulated, 81 consistently regulated, and 6865 down-regulated circRNAs; 343 circRNAs showed a log fold change by more than 2 folds, among which 213 circRNAs were up-regulated and 130 were down-regulated. Nine circRNAs showed differential expressions by more than 2 folds, including 8 up-regulated ones, namely hsa_circRNA_061260 (6.02 folds), hsa_circRNA_103933 (5.96 folds), hsa_circRNA_005239 (5.84 folds), hsa_circRNA_100689 (5.69 folds), hsa_circRNA_004087 (5.60 folds), hsa_circRNA_104420 (5.25 folds), hsa_circRNA_104421 (5.13 folds) and hsa_circRNA_101222 (5.03 folds); only one circRNA was down-regulated, namely hsa_circRNA_104864 (5.09 folds). The expressions of hsa_circRNA_100689, hsa_circRNA_005239 and hsa_circRNA_104864 were further validated by qPCR, which yielded consistent results with the microarray data.

CONCLUSIONS

The circRNA expression profiles differ significantly between luminal breast cancer cells and normal breast cells. These differentially expressed circRNAs may serve as potential novel targets for the diagnosis of luminal breast cancer.

Long non-coding antisense RNA GAS6-AS1 supports gastric cancer progression via increasing GAS6 expression

OBJECTIVE

As one broader class of non-coding RNAs (lncRNAs), non-coding antisense (AS) transcripts are functionally characterized to play pivotal roles in various pathophysiological processes, including tumor biology.

METHODS

In this study, the exact biological functions and regulation mechanisms of GAS6-AS1 in gastric cancer (GC) was examined.

RESULTS

The expression of GAS6-AS1 was markedly upregulated in GC tissues and is associated with advanced stage (III + IV) of GC patients. Gain-of-function and loss-of-function experiments showed that GAS6-AS1 promoted cell proliferation, migration, invasion ability in vitro and xenograft tumor growth in vivo by promoting entry into S-phase. The mechanistic investigations showed that GAS6-AS1 can control the expression of its cognate sense gene GAS6 at the transcriptional or translational levels by forming a RNA-RNA duplex, consequently inducing an increase of AXL level and driveling AXL signaling pathway activation.

CONCLUSIONS

Taken together, our studies indicate that GAS6-AS1 significantly driving the aggressive phenotype in GC through activating its cognate sense gene GAS6, and provides a more complete understanding of GAS6-AS1 as a potential therapeutic target for GC.

An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network

PURPOSE

To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients.

METHODS

A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves.

RESULTS

A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXP_AC007731.1) + (0.108 × EXP_AL513123.1) + (0.105 × EXP_C10orf126) + (0.065 × EXP_WT1-AS) + (- 0.126 × EXP_ADAMTS9-AS1) + (- 0.130 × EXP_SRGAP3-AS2) + (0.116 × EXP_TLR8-AS1) + (0.060 × EXP_HOTAIR) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database.

CONCLUSIONS

The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA.

Competing endogenous RNA (ceRNA) cross talk and language in ceRNA regulatory networks: A new look at hallmarks of breast cancer

Breast cancer (BC) is the most frequently occurring malignancy in women worldwide. Despite the substantial advancement in understanding the molecular mechanisms and management of BC, it remains the leading cause of cancer death in women. One of the main reasons for this obstacle is that we have not been able to find the Achilles heel for the BC as a highly heterogeneous disease. Accumulating evidence has revealed that noncoding RNAs (ncRNAs), play key roles in the development of BC; however, the involving of complex regulatory interactions between the different varieties of ncRNAs in the development of this cancer has been poorly understood. In the recent years, the newly discovered mechanism in the RNA world is "competing endogenous RNA (ceRNA)" which proposes regulatory dialogues between different RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs). In the latest BC research, various studies have revealed that dysregulation of several ceRNA networks (ceRNETs) between these ncRNAs has fundamental roles in establishing the hallmarks of BC development. And it is thought that such a discovery could open a new window for a better understanding of the hidden aspects of breast tumors. Besides, it probably can provide new biomarkers and potential efficient therapeutic targets for BC. This review will discuss the existing body of knowledge regarding the key functions of ceRNETs and then highlights the emerging roles of some recently discovered ceRNETs in several hallmarks of BC. Moreover, we propose for the first time the "ceRnome" as a new term in the present article for RNA research.

Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer

Background

Increasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients.

Methods

The RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA–miRNA–mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model.

Results

A total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA–miRNA–mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696.

Conclusions

The current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1640-2) contains supplementary material, which is available to authorized users.

Aberrantly expressed messenger RNAs and long noncoding RNAs in degenerative nucleus pulposus cells co-cultured with adipose-derived mesenchymal stem cells

Background

Stem cell therapy is considered as a promising alternative to treat intervertebral disc degeneration (IDD). Extensive work had been done on identifying and comparing different types of candidate stem cells, both in vivo and in vitro. However, few studies have shed light on degenerative nucleus pulposus cells (NPCs), especially their biological behavior under the influence of exogenous stem cells, specifically the gene expression and regulation pattern. In the present study, we aimed to determine messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs), which are differentially expressed during the co-culturing process with adipose-derived mesenchymal stem cells (ASCs) and to explore the involved signaling pathways and the regulatory networks.

Methods

We compared degenerative NPCs co-cultured with ASCs with those cultured solely using lncRNA-mRNA microarray analysis. Based on these data, we investigated the significantly regulated signaling pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. Moreover, 23 micro RNAs (miRNAs), which were demonstrated to be involved in IDD were chosen; we investigated their theoretic regulatory importance associated with our microarray data.

Results

We found 632 lncRNAs and 1682 mRNAs were differentially expressed out of a total of 40,716 probes. We then confirmed the microarray data by real-time PCR. Furthermore, we demonstrated 197 upregulated, and 373 downregulated Gene Ontology terms and 176 significantly enriched pathways, such as the mitogen-activated protein kinase (MAPK) pathway. Also, a signal-net was constructed to reveal the interplay among differentially expressed genes. Meanwhile, a mRNA-lncRNA co-expression network was constructed for the significantly changed mRNAs and lncRNAs. Also, the competing endogenous RNA (ceRNA) network was built.

Conclusion

Our results present the first comprehensive identification of differentially expressed lncRNAs and mRNAs of degenerative NPCs, altered by co-culturing with ASCs, and outline the gene expression regulation pattern. These may provide valuable information for better understanding of stem cell therapy and potential candidate biomarkers for IDD treatment.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1677-x) contains supplementary material, which is available to authorized users.

Construction of implantation failure related lncRNA-mRNA network and identification of lncRNA biomarkers for predicting endometrial receptivity

Insufficient endometrial receptivity is a major factor leading to implantation failure (IF), and the traditional way of morphological observation of endometrium cannot determine the condition of receptivity sufficiently. Considering that long-noncoding RNAs (lncRNAs) regulate endometrial receptivity and competing endogenous RNA (ceRNA) mechanism works in plenty of biological processes, ceRNA is likely to function in the pathology of IF. In the present study, we aim to construct an implantation failure related lncRNA-mRNA network (IFLMN), and to identify the key lncRNAs as the candidates for predicting endometrial receptivity. The global background network was constructed based on the presumed lncRNA-miRNA and miRNA-mRNA pairs obtained from lncRNASNP and miRTarBase. Differentially expressed genes (DEGs) of IF were calculated using the data of GSE26787, and then re-annotated as differentially expressed mRNAs (DEMs) and lncRNAs (DELs). IFLMN was constructed by hypergeometric test, including 255 lncRNA-mRNA pairs, 10 lncRNAs, and 212 mRNAs. Topological analysis determined the key lncRNAs with the highest centroid. Functional enrichment analyses were performed by unsupervised clustering, GO classification, KEGG pathway, and co-expression module analyses, achieving six key lncRNAs and their ceRNA sub-networks, which were involved in immunological activity, growth factor binding, vascular proliferation, apoptosis, and steroid biosynthesis in uterus and prepared endometrium for embryo implantation. Sixteen endometrial samples were collected during mid-luteal phase, including 8 recurrent implantation failure (RIF) or recurrent miscarriage (RM) women and 8 controls who conceived successfully. Quantitative real-time PCR was performed to compare the expression of the above six lncRNAs, which validated that the expression of all these lncRNAs was significantly elevated in endometrium of RIF/RM patients. Further studies are needed to investigate the underlying mechanism, and the lncRNAs may be developed into predictive biomarkers for endometrial receptivity.

Systematical analysis of lncRNA-mRNA competing endogenous RNA network in breast cancer subtypes

BACKGROUND

Breast cancer is one of the most common solid tumors in women involving multiple subtypes. However, the mechanism for subtypes of breast cancer is still complicated and unclear. Recently, several studies indicated that long non-coding RNAs (lncRNAs) could act as sponges to compete miRNAs with mRNAs, participating in various biological processes.

METHODS

We concentrated on the competing interactions between lncRNAs and mRNAs in four subtypes of breast cancer (basal-like, HER2+, luminal A and luminal B), and analyzed the impacts of competing endogenous RNAs (ceRNAs) on each subtype systematically. We constructed four breast cancer subtype-related lncRNA-mRNA ceRNA networks by integrating the miRNA target information and the expression data of lncRNAs, miRNAs and mRNAs.

RESULTS

We constructed the ceRNA network for each breast cancer subtype. Functional analysis revealed that the subtype-related ceRNA networks were enriched in cancer-related pathways in KEGG, such as pathways in cancer, miRNAs in cancer, and PI3k-Akt signaling pathway. In addition, we found three common lncRNAs across the four subtype-related ceRNA networks, NEAT1, OPI5-AS1 and AC008124.1, which played specific roles in each subtype through competing with diverse mRNAs. Finally, the potential drugs for treatment of basal-like subtype could be predicted through reversing the differentially expressed lncRNA in the ceRNA network.

CONCLUSION

This study provided a novel perspective of lncRNA-involved ceRNA network to dissect the molecular mechanism for breast cancer.

Integrative analysis of lncRNAs and miRNAs with coding RNAs associated with ceRNA crosstalk network in triple negative breast cancer

Triple negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Currently, there is increasing focus on long non-coding RNAs (lncRNAs), which can act as competing endogenous RNAs (ceR-NAs) and suppress miRNA functions involved in post-transcriptional regulatory networks in the tumor. Therefore, to investigate specific mechanisms of TNBC carcinogenesis and improve treatment efficiency, we comprehensively integrated expression profiles, including data on mRNAs, lncRNAs and miRNAs obtained from 116 TNBC tissues and 11 normal tissues from The Cancer Genome Atlas. As a result, we selected the threshold with |log2FC|>2.0 and an adjusted p-value >0.05 to obtain the differentially expressed mRNAs, miRNAs and lncRNAs. Hereafter, weighted gene co-expression network analysis was performed to identify the expression characteristics of dysregulated genes. We obtained five co-expression modules and related clinical feature. By means of correlating gene modules with protein-protein interaction network analysis that had identified 22 hub mRNAs which could as hub target genes. Eleven key dysregulated differentially expressed micro RNAs (DEmiRNAs) were identified that were significantly associated with the 22 hub potential target genes. Moreover, we found that 14 key differentially expressed lncRNAs could interact with the key DEmiRNAs. Then, the ceRNA crosstalk network of TNBC was constructed by utilizing key lncRNAs, key miRNAs, and hub mRNAs in Cytoscape software. We analyzed and described the potential characteristics of biological function and pathological roles of the TNBC ceRNA co-regulatory network; also, the survival analysis was performed for each molecule. These findings revealed that ceRNA crosstalk network could play an important role in the development and progression for TNBC. In addition, we also identified that some molecules in the ceRNA network possess clinical correlation and prognosis.

Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk

Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA–target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA–target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies.

The landscape of DNA methylation-mediated regulation of long non-coding RNAs in breast cancer

Although systematic studies have identified a host of long non-coding RNAs (lncRNAs) which are involved in breast cancer, the knowledge about the methyla-tion-mediated dysregulation of those lncRNAs remains limited. Here, we integrated multi-omics data to analyze the methylated alteration of lncRNAs in breast invasive carcinoma (BRCA). We found that lncRNAs showed diverse methylation patterns on promoter regions in BRCA. LncRNAs were divided into two categories and four subcategories based on their promoter methylation patterns and expression levels be-tween tumor and normal samples. Through cis-regulatory analysis and gene ontology network, abnormally methylated lncRNAs were identified to be associated with can-cer regulation, proliferation or expression of transcription factors. Competing endog-enous RNA network and functional enrichment analysis of abnormally methylated lncRNAs showed that lncRNAs with different methylation patterns were involved in several hallmarks and KEGG pathways of cancers significantly. Finally, survival analysis based on mRNA modules in networks revealed that lncRNAs silenced by high methylation were associated with prognosis significantly in BRCA. This study enhances the understanding of aberrantly methylated patterns of lncRNAs and pro-vides a novel insight for identifying cancer biomarkers and potential therapeutic tar-gets in breast cancer.