Mixomics analysis of breast cancer: Long non-coding RNA linc01561 acts as ceRNA involved in the progression of breast cancer

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial roles in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Constructing lncRNA-miRNA-mRNA networks specific to individual cancer patients and finding prognostic biomarkers

BACKGROUND

The competitive endogenous RNA (ceRNA) hypothesis suggests that microRNAs (miRNAs) mediate a regulatory relation between long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) which share similar miRNA response elements (MREs) to bind to the same miRNA. Since the ceRNA hypothesis was proposed, several studies have been conducted to construct a network of lncRNAs, miRNAs and mRNAs in cancer. However, most cancer-related ceRNA networks are intended for representing a general relation of RNAs in cancer rather than for a patient-specific relation. Due to the heterogeneous nature of cancer, lncRNA-miRNA-mRNA interactions can vary in different patients.

RESULTS

We have developed a new method for constructing a ceRNA network of lncRNAs, miRNAs and mRNAs, which is specific to an individual cancer patient and for finding prognostic biomarkers consisting of lncRNA-miRNA-mRNA triplets. We tested our method on extensive data sets of three types of cancer (breast cancer, liver cancer, and lung cancer) and obtained potential prognostic lncRNA-miRNA-mRNA triplets for each type of cancer.

CONCLUSIONS

Analysis of expression patterns of the RNAs involved in the triplets and survival rates of cancer patients revealed several interesting findings. First, even for the same cancer type, prognostic lncRNA-miRNA-mRNA triplets can be different depending on whether lncRNA and mRNA show opposite or similar expression patterns. Second, prognostic lncRNA-miRNA-mRNA triplets are often more predictive of survival rates than RNA pairs or individual RNAs. Our approach will be useful for constructing patient-specific lncRNA-miRNA-mRNA networks and for finding prognostic biomarkers from the networks.

Expression profiles of lncRNAs, miRNAs, and mRNAs and interaction analysis indicate their potential involvement during testicular fusion in Spodoptera litura

Testicular fusion of Spodoptera litura occures during metamorphosis, which benefits sperms development. Previous research identified involvement of ECM-integrin interaction pathways, MMPs in testicular fusion, but the regulatory mechanism remains unclear. RNA-seq was performed to analyze long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in testes, aiming to uncover potential regulatory mechanisms of testicular fusion. 2150 lncRNAs, 2742 targeted mRNAs, and 347 miRNAs were identified in testes at three different developmental stages. Up-regulated DElncRNAs and DEmRNAs, as well as down-regulated DEmiRNAs, were observed during testicular fusion, while the opposite expression pattern was observed after fusion. Enrichment analysis of DEmRNAs revealed that cAMP signal pathway, ECM remodeling enzymes, ECM-integrin interaction pathways, and cell adhesion molecules were potentially associated with testicular fusion. The identified DElncRNA-DEmiRNA-DEmRNA regulatory network related to cAMP signal pathway, ECM remodeling enzymes suggests their roles during testicular fusion. Our research will provide new targets for studying the mechanism of testicular fusion.

Constructing Integrative ceRNA Networks and Finding Prognostic Biomarkers in Renal Cell Carcinoma

Inspired by a newly discovered gene regulation mechanism known as competing endogenous RNA (ceRNA) interactions, several computational methods have been proposed to generate ceRNA networks. However, most of these methods have focused on deriving restricted types of ceRNA interactions such as lncRNA-miRNA-mRNA interactions. Competition for miRNA-binding occurs not only between lncRNAs and mRNAs but also between lncRNAs or between mRNAs. Furthermore, a large number of pseudogenes also act as ceRNAs, thereby regulate other genes. In this study, we developed a general method for constructing integrative networks of all possible interactions of ceRNAs in renal cell carcinoma (RCC). From the ceRNA networks we derived potential prognostic biomarkers, each of which is a triplet of two ceRNAs and miRNA (i.e., ceRNA-miRNA-ceRNA). Interestingly, some prognostic ceRNA triplets do not include mRNA at all, and consist of two non-coding RNAs and miRNA, which have been rarely known so far. Comparison of the prognostic ceRNA triplets to known prognostic genes in RCC showed that the triplets have a better predictive power of survival rates than the known prognostic genes. Our approach will help us construct integrative networks of ceRNAs of all types and find new potential prognostic biomarkers in cancer.

ncRNA-mediated ceRNA regulatory network: Transcriptomic insights into breast cancer progression and treatment strategies

With the rapid development of next-generation sequencing technology, several studies have shown that ncRNAs can act as competitive endogenous RNAs (ceRNAs) and are involved in various biological processes, such as proliferation, differentiation, apoptosis, and migration of breast cancer (BC) cells, and plays an important role in BC progression as a molecular target for its diagnosis, treatment, prognosis, and differentiation of subtypes and age groups of BC patients. Based on the description of ceRNA-related biological functions, this study screened and sorted the sequencing analysis and experimental verification conclusions of BC-related ceRNAs and found that the ncRNAs mediated ceRNA networks can promote the development of BC by promoting the expression of genes related to BC proliferation, drug resistance, and apoptosis, inducing the production of epithelial-mesenchymal transition (EMT) to promote metastasis and activating cancer-related signaling pathways.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.

Coding roles of long non-coding RNAs in breast cancer: Emerging molecular diagnostic biomarkers and potential therapeutic targets with special reference to chemotherapy resistance

Dysregulation of epigenetic mechanisms have been depicted in several pathological consequence such as cancer. Different modes of epigenetic regulation (DNA methylation (hypomethylation or hypermethylation of promotor), histone modifications, abnormal expression of microRNAs (miRNAs), long non-coding RNAs, and small nucleolar RNAs), are discovered. Particularly, lncRNAs are known to exert pivot roles in different types of cancer including breast cancer. LncRNAs with oncogenic and tumour suppressive potential are reported. Differentially expressed lncRNAs contribute a remarkable role in the development of primary and acquired resistance for radiotherapy, endocrine therapy, immunotherapy, and targeted therapy. A wide range of molecular subtype specific lncRNAs have been assessed in breast cancer research. A number of studies have also shown that lncRNAs may be clinically used as non-invasive diagnostic biomarkers for early detection of breast cancer. Such molecular biomarkers have also been found in cancer stem cells of breast tumours. The objectives of the present review are to summarize the important roles of oncogenic and tumour suppressive lncRNAs for the early diagnosis of breast cancer, metastatic potential, and chemotherapy resistance across the molecular subtypes.

A novel prognostic related lncRNA signature associated with amino acid metabolism in glioma

Background

Glioma is one of the deadliest malignant brain tumors in adults, which is highly invasive and has a poor prognosis, and long non-coding RNAs (lncRNAs) have key roles in the progression of glioma. Amino acid metabolism reprogramming is an emerging hallmark in cancer. However, the diverse amino acid metabolism programs and prognostic value remain unclear during glioma progression. Thus, we aim to find potential amino-related prognostic glioma hub genes, elaborate and verify their functions, and explore further their impact on glioma.

Methods

Glioblastoma (GBM) and low-grade glioma (LGG) patients' data were downloaded from TCGA and CCGA datasets. LncRNAs associated with amino acid metabolism were discriminated against via correlation analysis. LASSO analysis and Cox regression analysis were conducted to identify lncRNAs related to prognosis. GSVA and GSEA were performed to predict the potential biological functions of lncRNA. Somatic mutation data and CNV data were further built to demonstrate genomic alterations and the correlation between risk scores. Human glioma cell lines U251 and U87-MG were used for further validation in vitro experiments.

Results

There were eight amino-related lncRNAs in total with a high prognostic value that were identified via Cox regression and LASSO regression analyses. The high risk-score group presented a significantly poorer prognosis compared with the low risk-score group, with more clinicopathological features and characteristic genomic aberrations. Our results provided new insights into biological functions in the above signature lncRNAs, which participate in the amino acid metabolism of glioma. LINC01561 is one of the eight identified lncRNAs, which was adopted for further verification. In in vitro experiments, siRNA-mediated LINC01561 silencing suppresses glioma cells' viability, migration, and proliferation.

Conclusion

Novel amino-related lncRNAs associated with the survival of glioma patients were identified, and a lncRNA signature can predict glioma prognosis and therapy response, which possibly has vital roles in glioma. Meanwhile, it emphasized the importance of amino acid metabolism in glioma, particularly in providing deeper research at the molecular level.

Tamoxifen resistance-related ceRNA network for breast cancer

Background: Tamoxifen (TMX) is one of the most widely used drugs to treat breast cancer (BC). However, acquired drug resistance is still a major obstacle to its application, rendering it crucial to explore the mechanisms of TMX resistance in BC. This aims of this study were to identify the mechanisms of TMX resistance and construct ceRNA regulatory networks in breast cancer. Methods: GEO2R was used to screen for differentially expressed mRNAs (DEmRNAs) leading to drug resistance in BC cells. MiRTarbase and miRNet were used to predict miRNAs and lncRNAs upstream, and the competing endogenous RNA (ceRNA) regulatory network of BC cell resistance was constructed by starBase. We used the Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the expression and prognostic differences of genes in the ceRNA network with core axis, and qRT-PCR was used to further verify the above conclusions. Results: We found that 21 DEmRNAs were upregulated and 43 DEmRNA downregulated in drug-resistant BC cells. DEmRNAs were noticeably enriched in pathways relevant to cancer. We then constructed a protein-protein interaction (PPI) network based on the STRING database and defined 10 top-ranked hub genes among the upregulated and downregulated DEmRNAs. The 20 DEmRNAs were predicted to obtain 113 upstream miRNAs and 501 lncRNAs. Among them, 7 mRNAs, 22 lncRNAs, and 11 miRNAs were used to structure the ceRNA regulatory network of drug resistance in BC cells. 4 mRNAs, 4 lncRNAs, and 3 miRNAs were detected by GEPIA and the Kaplan-Meier plotter to be significantly associated with BC expression and prognosis. The differential expression of the genes in BC cells was confirmed by qRT-PCR. Conclusion: The ceRNA regulatory network of TMX-resistant BC was successfully constructed and confirmed. This will provide an important resource for finding therapeutic targets for TMX resistance, where the discovery of candidate conventional mechanisms can aid clinical decision-making. In addition, this resource will help discover the mechanisms behind this type of resistance.

Analysis of Competitive Endogenous RNA Regulatory Network of Exosomal Breast Cancer Based on exoRBase

Objective:

To construct a competitive endogenous RNA (ceRNA) regulatory network derived from exosomes of human breast cancer (BC) by using the exoRbase database, to explore the possible pathogenesis of BC, and to develop new targets for future diagnosis and treatment.

Methods:

The exosomal gene sequencing data of BC patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were analyzed by using R language. Use Targetscan and miRanda database to jointly predict and differentially express miRNA (microRNA), miRNA combined with mRNA. The miRcode database was used to predict the miRNA combined with differentially expressed lncRNA, and the starBase database was used to predict the miRNA combined with circRNA in the difference table. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Enrichment analysis and visualization of KEGG were carried out using KOBAS. Hub gene was determined by Cytohubba plug-in.

Results:

Forty-two differentially expressed mRNA, 43 differentially expressed circRNA, and 26 differentially expressed lncRNA were screened out. The ceRNA network was constructed by using Cytoscape software, including 19 mRNA nodes, 2 lncRNA nodes, 8 circRNA nodes, and 41 miRNA nodes. KEGG enrichment analysis showed that differentially expressed mRNA in the regulatory network mainly enriched the p53 signaling pathway. Find the key Hub gene PTEN.

Conclusion:

The ceRNA regulatory network in blood exosomes of BC patients has been successfully constructed in this study, which provides an exact target for the diagnosis and treatment of BC.

Regulation of the TUG1/miR‑145‑5p/SOX2 axis on the migratory and invasive capabilities of melanoma cells

Melanoma is the most prevalent malignancy of cutaneous carcinomas. Taurine-upregulated gene 1 (TUG1), a lncRNA, is a pivotal regulator of cutaneous malignancies. The present study aimed to investigate the impact and possible mechanisms of action of TUG1 behind the progression of melanomas. Reverse transcription-quantitative PCR was conducted to detect the expression levels of TUG1, microRNA (miR)-145-5p and SOX2 in melanoma tissues and cell lines. Cell Counting Kit-8 (CCK-8) assays were performed to measure the proliferative ability of melanoma cells and transwell assays were used to examine the migration and invasion of melanoma cells. Dual luciferase reporter and RNA immunoprecipitation (RIP) assays were utilized to identify the interactions among TUG1, miR-145-5p and SOX2. Western blotting and immunohistochemical assays were performed to determine the expression profile of SOX2. The impact of TUG1 on melanoma tumorigenesis was assessed using tumorigenicity assays. TUG1 expression levels were elevated in melanoma tumor tissues and cell lines. Reduced TUG1 expression levels significantly inhibited the proliferative, migratory and invasive abilities of melanoma cells. The expression levels of miR-145-5p were decreased in melanoma tumor tissues and cell lines. TUG1 directly targeted miR-145-5p and downregulated miR-145-5p. Upregulation of TUG1 counteracted the promotion of the proliferative, migratory and invasive abilities of melanoma cells induced by the overexpression of miR-145-5p. SOX2 was a target of miR-145-5p and its expression was negatively regulated by miR-145-5p, while positively regulated by TUG1. TUG1 regulated SOX2 expression through sponging miR-145-5p. Silencing of TUG1 also inhibited melanoma tumorigenesis in mice. In conclusion, the TUG1/miR-145-5p/SOX2 axis regulated the migration and invasion of melanoma cells.

LINC00221 silencing prevents the progression of hepatocellular carcinoma through let-7a-5p-targeted inhibition of MMP11

Background

Microarray profiles of hepatocellular carcinoma (HCC) identified that long intergenic noncoding RNA 00221 (LINC00221) was upregulated. Herein, we aimed to identify the functional significance and underlying mechanisms of LINC00221 in HCC.

Methods and results

Human HCC samples had increased expression of LINC00221. Effects of LINC00221 on HCC cellular functions were analyzed using gain- and loss-function approaches. LINC00221 knockdown repressed HCC cell growth, migration, and invasion and enhanced their apoptosis. This anti-tumor effect was validated in vivo. Online prediction showed the potential binding relationship between LINC00221 and let-7a-5p, as well as that between let-7a-5p and matrix metalloproteinase 11 (MMP11). The results of luciferase, RNA immunoprecipitation, and RNA pull-down assays identified that LINC00221 interacted with let-7a-5p to increase expression of MMP11. Furthermore, we demonstrated that LINC00221 silencing increased let-7a-5p and inhibited MMP11 expression, thereby delaying the progression of HCC in vitro.

Conclusions

Silencing of LINC00221 could prevent HCC progression via upregulating let-7a-5p and downregulating MMP11. As such, LINC00221 inhibition presents a promising antitumor strategy for the treatment of HCC.

Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies

Scope

Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health.

Methods and results

The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects (n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone.

Conclusion

The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.

lncRNA PART1 Promotes Breast Cancer Cell Progression by Directly Targeting miR-4516

Introduction

Breast cancer is a serious threat to human health. It is meaningful to study the pathogenesis of breast cancer. lncRNAs have been found to play vital roles in numerous biological processes including development, immunology and cancer.

Methods

qRT-PCR was performed to examine the expressions of PART1 and miR-4516. CCK-8 assay, colony formation assay and transwell assay were used to examine the progression of breast cancer cells.

Results

In this study, we showed that lncRNA PART1 was highly expressed in breast cancer cells. Knockdown of PART1 induced decreased proliferation, invasion and migration of breast cancer cells. Moreover, we found that PART1 can bind to miR-4516 directly. We also found that inhibition of miR-4516 could rescue the decreased proliferation, migration and invasion of breast cancer cells induced by knockdown of PART1.

Discussion

lncRNA PART1 and miR-4516 were proven to be involved in the progression of many cancers. However, the roles of lncRNA PART1 and miR-4516 in the regulation of breast cancer remain unknown. Here, we demonstrated that PART1 can bind to miR-4516 to decrease the expression of miR-4516 and promote the development of breast cancer.

SOX2-induced upregulation of lncRNA LINC01561 promotes non-small-cell lung carcinoma progression by sponging miR-760 to modulate SHCBP1 expression

Long noncoding RNAs (lncRNAs) have been shown to have critical regulatory roles in tumorigenesis. lncRNA LINC01561 (LINC01561) is a newly identified tumor-related lncRNA and its dysregulation has been demonstrated in several tumors. However, whether LINC01561 is involved in the progression of non-small-cell lung carcinoma (NSCLC) and its underlying mechanisms remain unknown. In this study, we first provided evidence that LINC01561 expressions were distinctly upregulated in NSCLC tissues and cell lines. Combining with bioinformatics assays and mechanism experiments, our group demonstrated that LINC01561 was activated by SOX2 in NSCLC. Clinical research revealed that upregulation of LINC01561 was related to poorer clinicopathologic features and shorter survival time. Functionally, suppression of LINC01561 exhibited tumor-suppressive functions through impairing cell proliferation, migration, and invasion as well as inducing apoptosis. Moreover, we verified that LINC01561 could directly bind to miR-760, isolating miR-760 from its target gene SHC SH2 domain-binding protein 1 (SHCBP1). We also found that SHCBP1 was lowly expressed in NSCLC and served as a tumor promoter. A functional study indicated that LINC01561 regulated SHCBP1 expression by competitively binding to miR-760. In summary, our findings indicated that SOX2-induced overexpression of LINC01561 promoted the proliferation and metastasis by acting as a competing endogenous RNA to modulate SHCBP1 by sponging miR-760.

A Competing Endogenous RNA Network Reveals Novel lncRNA, miRNA and mRNA Biomarkers With Diagnostic and Prognostic Value for Early Breast Cancer

BACKGROUND

This study aims to reveal early breast cancer (BC) specific competing endogenous RNA (ceRNA) network through the expression profiles of microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and mRNAs.

METHODS

Based on The Cancer Genome Atlas (TCGA), we obtained the differentially expressed mRNAs, miRNAs, and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) between early BC and normal samples. The lncRNA-miRNA-mRNA interaction network was constructed using Cytoscape. Functional enrichment were performed using GeneCoDis3. The expression of selected genes were validated by qRT-PCR. Based on the published dataset, we validated the result of TCGA integration analysis. The diagnostic and prognostic value of candidate genes was evaluated by ROC curve analysis and survival analysis, respectively.

RESULTS

Totally, 1207 DEmRNAs, 194 DElncRNAs and 37 DEmiRNAs were obtained. Functional enrichment analysis results showed that all of DEmRNAs were enriched in pathway of cytokine-cytokine receptor interaction, PPAR signaling pathway and pathways in cancer. The DEmRNA-DEmiRNA-DElncRNA interaction network in early BC was consisted of 23 DEmiRNAs, 95 DElncRNAs and 309 DEmRNAs. Among ceRNA network, IL-6-hsa-miR-182-5p-ADAMTS9-AS1 interactions, LIFR-hsa-miR-21-5p-ADAMTS9-AS1 interactions and MMP1/MMP11-hsa-miR-145-5p-CDKN2B-AS1 interactions were speculated to involve in the development of early BC. The qRT-PCR results were consistent with our integrated analysis. Except for ADAMTS9-AS1 and CDKN2B-AS1, expression of the others results in the Gene Expression Omnibus (GEO) dataset were generally consistent with TCGA integrated analysis. The area under curve (AUC) of the ADAMTS9-AS1, CDKN2B-AS1, IL-6, MMP11, hsa-miR-145-5p and hsa-miR-182-5p were 0.947, 0.862, 0.842, 0.993, 0.960 and 0.944, and the specificity and sensitivity of the 6 biomarkers were 83.4% and 95.6%, 72.2% and 90.3%, 80.1% and 74.3%, 96.2% and 96.5%, 90.1% and 92.3%, and 88.7% and 90.4%, respectively. In addition, IL-6 had potential prognostic value for early BC.

CONCLUSION

These findings may provide novel insights into the lncRNA-miRNA-mRNA network and uncover potential therapeutic targets in early BC.

Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis

Lung cancer is the leading cause of cancer-related mortality worldwide. Despite progress in the treatment of non-small-cell lung cancer, there are limited treatment options for lung squamous cell carcinoma (LUSC), compared with lung adenocarcinoma. The present study investigated the disease mechanism of LUSC in order to identify key candidate genes for diagnosis and therapy. A total of three gene expression profiles (GSE19188, GSE21933 and GSE74706) were analyzed using GEO2R to identify common differentially expressed genes (DEGs). The DEGs were then investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins, and visualized using Cytoscape software. The expression levels of the hub genes identified using CytoHubba were validated using the University of California, Santa Cruz (UCSC) database and the Human Protein Atlas. A Kaplan-Meier curve and Gene Expression Profiling Interactive Analysis were then employed to evaluate the associated prognosis and clinical pathological stage of the hub genes. Furthermore, non-coding RNA regulatory networks were constructed using the Gene-Cloud Biotechnology information website. A total of 359 common DEGs (155 upregulated and 204 downregulated) were identified, which were predominantly enriched in 'mitotic nuclear division', 'cell division', 'cell cycle' and 'p53 signaling pathway'. The PPI network consisted of 257 nodes and 2,772 edges, and the most significant module consisted of 66 upregulated genes. A total of 19 hub genes exhibited elevated RNA levels, and 10 hub genes had elevated protein levels compared with normal lung tissues. The upregulation of five hub genes (CCNB1, CEP55, FOXM1, MKI67 and TYMS; defined in Table I) were significantly associated with poor overall survival and unfavorable clinical pathological stages. Various ncRNAs, such as C1orf220, LINC01561 and MGC39584, may also play important roles in hub-gene regulation. In conclusion, the present study provides further understanding of the pathogenesis of LUSC, and reveals CCNB1, CEP55, FOXM1, MKI67 and TYMS as potential biomarkers or therapeutic targets.

HNRNPA2/B1 is upregulated in endocrine-resistant LCC9 breast cancer cells and alters the miRNA transcriptome when overexpressed in MCF-7 cells

MicroRNAs are dysregulated in breast cancer. Heterogeneous Nuclear Ribonucleoprotein A2/B1 (HNRNPA2/B1) is a reader of the N(6)-methyladenosine (m6A) mark in primary-miRNAs (pri-miRNAs) and promotes DROSHA processing to precursor-miRNAs (pre-miRNAs). We examined the expression of writers, readers, and erasers of m6A and report that HNRNPA2/B1 expression is higher in tamoxifen-resistant LCC9 breast cancer cells as compared to parental, tamoxifen-sensitive MCF-7 cells. To examine how increased expression of HNRNPA2/B1 affects miRNA expression, HNRNPA2/B1 was transiently overexpressed (~5.4-fold) in MCF-7 cells for whole genome miRNA profiling (miRNA-seq). 148 and 88 miRNAs were up- and down-regulated, respectively, 48 h after transfection and 177 and 172 up- and down-regulated, respectively, 72 h after transfection. MetaCore Enrichment analysis identified progesterone receptor action and transforming growth factor β (TGFβ) signaling via miRNA in breast cancer as pathways downstream of the upregulated miRNAs and TGFβ signaling via SMADs and Notch signaling as pathways of the downregulated miRNAs. GO biological processes for mRNA targets of HNRNPA2/B1-regulated miRNAs included response to estradiol and cell-substrate adhesion. qPCR confirmed HNRNPA2B1 downregulation of miR-29a-3p, miR-29b-3p, and miR-222 and upregulation of miR-1266-5p, miR-1268a, miR-671-3p. Transient overexpression of HNRNPA2/B1 reduced MCF-7 sensitivity to 4-hydroxytamoxifen and fulvestrant, suggesting a role for HNRNPA2/B1 in endocrine-resistance.

Identification of ceRNA network based on a RNA-seq shows prognostic lncRNA biomarkers in human lung adenocarcinoma

Previous studies have emphasized the significant functions of long non-coding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in tumor biology. However, the functions of certain cancer lncRNAs in the lncRNA-related ceRNA network in lung adenocarcinoma (LUAD) are unknown. A systematic and integrative survey of RNA-seq data from The Cancer Genome Atlas (TCGA) was performed to identify candidate lncRNAs for the prognosis of LUAD. In total, 20,502 genes that contain 181 lncRNAs were evaluated in a cohort of 570 LUAD cases. Initially, 6,280 differentially expressed genes (fold-change >2, P<0.05) were obtained using R package, which includes 75 lncRNAs. Next, by univariate regression and multivariate Cox proportional hazards analysis, 32 genes were associated with survival in LUAD. Using these 29 mRNAs and 3 lncRNAs, a prognosis index (PI) was calculated to accurately estimate the survival in LUAD: PI=∑exp_{risk gene} × HRrisk gene. Furthermore, the 32-gene signature was an independent prognostic indicator for LUAD (HR >1; P<0.05, by multivariate analysis). Weighted gene co-expression network analysis (WGCNA) of three risk lncRNAs-FAM138B, NHEG1 and TLX1NB-was performed, based on the P-values of the associated genes, and the top 27 miRNAs that bound to these lncRNAs were predicted by Miranda as target miRNAs. Next, these target miRNAs were transferred to the TarBase, miRTarBase, miRecards and starBase v2.0 databases to obtain their target genes. According to the previous miRNA-mRNA and miRNA-lncRNA data, three lncRNA-miRNA-mRNA ceRNA networks were established, based on the 29 prognostic mRNAs, forming a regulatory network in LUAD. The present study provided insight into the lncRNA-related ceRNA network in LUAD and has identified potential diagnostic and prognostic biomarkers.