Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.

A Computationally Constructed lncRNA-Associated Competing Triplet Network in Clear Cell Renal Cell Carcinoma

Long noncoding RNAs (lncRNAs) are revealed to be involved in the tumorigenesis and progression of human malignancies mediated by microRNA (miRNA) via the competing endogenous RNA (ceRNA) mechanism, a newly proposed “RNA language.” However, the lncRNA-associated competing triplet (lncACT) network among ceRNA transcripts in clear cell renal cell carcinoma (ccRCC) is currently lacking. We carried out differential expression analysis to identify aberrantly expressed lncRNAs, miRNAs, and mRNAs by analyzing the RNA-seq data of 420 ccRCC tissues and 71 noncancerous kidney tissues obtained from The Cancer Genome Atlas (TCGA). Then, a ccRCC-specific ceRNA network was built using computational algorithms, including miRcode, TargetScan, miRanda, and miRTarBase. In total, 1491 dysregulated lncRNAs were found between normal renal tissues and ccRCC (fold $\text{change}>4$ and false discovery $\text{rate}<0.01$ ). A ceRNA network that comprised of 46 DElncRNAs, 11 DEmiRNAs, and 55 DEmRNAs was established by integrating the lncRNA/miRNA and miRNA/mRNA interactions into lncACTs. Several lncRNAs were identified to be significantly associated with clinical features of ccRCC patients. Notably, four key lncRNAs (TCL6, HOTTIP, HULC, and PCGEM1) were tightly correlated with both patients’ clinical characteristics and overall survival (log-rank $P<0.05$ ), indicating their potential important roles in ccRCC. HOTTIP may be a potential prognostic and therapeutic molecular marker for ccRCC patients. Collectively, our results provide a comprehensive view of the lncRNA-associated ceRNA regulatory network for a better understanding of the mechanisms and prognosis biomarkers for ccRCC.

Dysregulation of KRT19, TIMP1, and CLDN1 gene expression is associated with thyroid cancer

Thyroid nodules are the main indicators of thyroid cancer, their malignancy is evaluated by cytological analysis and imaging technology, however, there are still cases where the result is not enough to classify thyroid cancer. Therefore, there is a necessity for accurate molecular biomarkers to collaborate in the diagnosis. Here, we analyzed the mRNA relative expression of CLDN1, TIMP1, and KRT19 genes in FNA of malignant (n = 48) and benign (n = 49) thyroid nodules by RT-qPCR analysis to assess their predictive value as cancer biomarkers. We identified a significant overexpression of the three transcripts in malignant nodules, therefore, the evaluation of their predictive capacity to distinguish between benign and malignant nodule as individual biomarkers were evaluated by logistic regression tests, obtaining promising prediction results to rule out cancer; later by random forest to create a stronger model, we included expression results with clinicopathological characteristics, the best model consists of the three-mRNA level expression with patient's history of cancer (AUC = 0.821, accuracy = 85.4% and sensitivity of 81.1%). These results demonstrate a dysregulated expression of CLDN1, KRT19 and TIMP1 in thyroid cancer, thus, represent a promising panel of biomarkers to be evaluated in indeterminate thyroid nodules.

Overexpression of miR‑375 reverses the effects of dexamethasone on the viability, migration, invasion and apoptosis of human airway epithelial cells by targeting DUSP6

Airway epithelial cell (AEC) dysfunction has been proven to be involved in the pathogenesis of asthma, which may be induced by the use of dexamethasone (Dex). The altered expression of microRNAs (miRNAs/miRs) has been found in asthma. However, the detailed mechanisms responsible for the effects of miR-375 on Dex-induced AEC dysfunction remain elusive. Thus, the present study aimed to elucidate these mechanisms. Following treatment with Dex for 0, 6, 12 and 24 h, AEC viability, migration, invasion and apoptosis were examined using Cell Counting Kit-8 (CCK-8), wound healing and Transwell assays, and flow cytometry, respectively. The expression levels of miR-375, dual specificity phosphatase 6 (DUSP6) and apoptosis-related proteins (Bcl-2, Bax, cleaved caspase-3) were measured using reverse transcription-quantitative polymerase chain reaction and western blot analysis. The target genes and potential binding sites of miR-375 and DUSP6 were predicted using TargetScan and confirmed using dual-luciferase reporter assay. The viability, migration, invasion and apoptosis of Dex-treated AECs were further assessed with or without miR-375 and DUSP6. In the AECs (9HTE cells), Dex treatment suppressed cell viability and miR-375 expression, whereas it promoted cell apoptosis and the expression of DUSP6, the target gene of miR-375. The overexpression of miR-375 reversed the effects of Dex treatment on miR-375 expression, cell viability, migration and invasion, and apoptosis-related protein expression; in turn, these effects were reversed by the overexpression of DUSP6, with the exception of miR-375 expression. On the whole, the present study demonstrates that the overexpression of miR-375 counteracts the effects of Dex treatment on AEC viability, migration, invasion and apoptosis by targeting DUSP6. Thus, it was suggested that the downregulated expression of miR-375 may be a therapeutic target for AEC dysfunction.

Silencing long noncoding RNA LINC01138 inhibits aerobic glycolysis to reduce glioma cell proliferation by regulating the microRNA‑375/SP1 axis

Glioma is a primary cerebral neoplasm that originates from glial tissue and spreads to the central nervous system. Long noncoding RNAs are known to play a role in glioma cells by regulating cell proliferation, migration and invasion. The aim of the present study was to investigate the mechanism by which long intergenic non‑protein coding RNA (LINC) 01138 affects glycolysis and proliferation in glioma cells via the microRNA (miR)‑375/specificity protein 1 (SP1) axis. LINC01138 expression was assessed in glioma tissues and cells using reverse transcription‑quantitative PCR and the association between LINC01138 and patient clinicopathological features was analyzed. Glucose uptake, lactic acid secretion, cell proliferation, and glycolysis‑related enzyme levels were detected following LINC01138 silencing using CCK‑8, EDU assay and western blot analysis. miR‑375 and SP1 expression levels were also assessed, and the distribution of LINC01138 in the nucleus and cytoplasm was investigated using subcellular fractionation localization. Furthermore, the binding relationships between LINC01138 and miR‑375, and between miR‑375 and SP1 were assessed via dual‑luciferase experiment, RIP and RNA pull‑down assays. Finally, xenograft transplantation models were used to verify the in vitro results. LINC01138 was highly expressed in glioma, which was independent of patient sex or age but was significantly related to tumor diameter, the World Health Organization tumor grade and lymph node metastasis. Silencing LINC01138 significantly reduced glioma glycolysis and cell proliferation. Moreover, LINC01138 acted as a competing endogenous RNA to sponge miR‑375 and promote SP1 expression. miR‑375 inhibition significantly reversed the effect of LINC01138 silencing. In addition, silencing LINC01138 significantly reduced tumor growth in vivo. The present study demonstrated that silencing LINC01138 inhibited aerobic glycolysis and thus reduced glioma cell proliferation, potentially by modulating the miR‑375/SP1 axis.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.

Long Noncoding RNA OR7E156P/miR-143/HIF1A Axis Modulates the Malignant Behaviors of Glioma Cell and Tumor Growth in Mice

Gliomas are characterized by high incidence, recurrence and mortality all of which are significant challenges to efficacious clinical treatment. The hypoxic microenvironment in the inner core and intermediate layer of the tumor mass of gliomas is a critical contributor to glioma pathogenesis. In this study, we identified an upregulated lncRNA, OR7E156P, in glioma was identified. The silencing of OR7E156P inhibited cell invasion and DNA synthesis in vitro and tumor growth in vivo. OR7E156P was intricately linked to the HIF1A pathway. Hypoxia could induce OR7E156P expression, whereas OR7E156P silencing decreased HIF1A protein levels under hypoxic conditions. Hypoxia promoted glioma cell invasion and DNA synthesis, and HUVEC tube formation, whereas OR7E156P silencing partially reversed the cellular effects of hypoxia. HIF1A overexpression promoted, whereas OR7E156P silencing inhibited tumor growth; the inhibitory effects of OR7E156P silencing on tumor growth were partially reversed by HIF1A overexpression. miR-143 directly targeted OR7E156P and HIF1A, respectively. miR-143 inhibition increased HIF1A protein levels, promoted glioma cell invasion and DNA synthesis. Moreover, they enhanced HUVEC tube formation, whereas OR7E156P silencing partially reversed the cellular effects of miR-143 inhibition. HIF1A targeted the promoter region of miR-143 and inhibited miR-143 expression. Altogether a regulatory axis consisting of OR7E156P, miR-143, and HIF1A, was identified which is deregulated in glioma, and the process of the OR7E156P/miR-143/HIF1A axis modulating glioma cell invasion through ZEB1 and HUVEC tube formation through VEGF was demonstrated.

Predicting LncRNA–Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.

RUNX3 Expression Level Is Correlated with the Clinical and Pathological Characteristics in Endometrial Cancer: A Systematic Review and Meta-analysis

Human Runt-associated transcription factor 3 (RUNX3) plays an important role in the development and progression of endometrial cancer (EC). However, the clinical and pathological significance of RUNX3 in EC needs to be further studied. In order to clarify the clinical and pathological significance of RUNX3, a systematic review and meta-analysis was conducted in EC patients. Keywords RUNX3, endometrial cancer, and uterine cancer were searched in Cochrane Library, Web of Knowledge, PubMed, CBM, MEDLINE, and Chinese CNKI database for data up to Dec 31, 2018. References, abstracts, and meeting proceedings were manually searched in supplementary. Outcomes were various clinical and pathological features. The two reviewers performed the literature searching, data extracting, and method assessing independently. Meta-analysis was performed by RevMan5.3.0. A total of 563 EC patients were enrolled from eight studies. Meta-analysis results showed that the expression of RUNX3 has significant differences in these comparisons: lymph node (LN) metastasis vs. non-LN metastasis (P = 0.26), EC tissues vs. normal tissues (P < 0.00001), clinical stages I/II vs. II/IV (P < 0.00001), muscular infiltration < 1/2 vs. muscular infiltration ≥ 1/2 (P < 0.00001), and G1 vs. G2/G3 (P < 0.00001). The decreasing expression of RUNX3 is associated with poor TNM stage and muscular infiltration. It is indicated that RUNX3 was involved in the suppression effect of EC. However, further multicenter randomized controlled trials are needed considering the small sample size of the included trials.

Differentially expressed circular RNAs and the competing endogenous RNA network associated with preeclampsia

INTRODUCTION

Circular RNAs (circRNAs) are non-coding RNAs that are implicated in preeclampsia (PE) pathogenesis; however, their expression and functions in PE remain unclear. In this study, we aimed to investigate the expression of circRNAs in PE and construct a competing endogenous RNA (ceRNA) network, and analyze the associated pathways in PE pathogenesis.

METHODS

We performed circRNA sequencing to identify the differential expression profile of circRNAs in PE as compared to normal pregnancy. The circRNA candidates were validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Subsequently, we used datasets from the GEO database to generate the interaction network between circRNAs, microRNAs (miRNAs), and mRNAs. GO and KEGG enrichment analyses were performed to understand the functional significance of the differentially expressed circRNAs in PE.

RESULTS

We identified 361 differentially expressed circRNAs (252 upregulated and 109 downregulated) in preeclamptic placentas. Within the selected 31 circRNAs, 6 of them were verified by qRT-PCR. GO and KEGG analyses revealed the potential pathways affected by these circRNAs, e.g., T cell receptor signaling and MAP kinase pathways. A total of 134 miRNAs and 199 mRNAs were revealed to be differentially expressed in PE by analyzing datasets from the GEO database. The circRNA-miRNA-mRNA network comprised 206 circRNAs, 50 miRNAs, and 38 mRNAs. KEGG analysis of the 38 mRNAs included pathways involved in AMPK and PI3K-Akt signaling.

DISCUSSION

Our results reported the differential expression profile of circRNAs and the circRNA-miRNA-mRNA network in PE, which provides potential therapeutic targets for this disease.

Role of miR-221/222 in Tumor Development and the Underlying Mechanism

MicroRNA-221/222 (miRNA-221/222, miR-221/222) is a noncoding microRNA which is widely distributed in eukaryotic organisms and deeply involved in the posttranscriptional regulation of gene expressions. According to recent studies, abnormal expressions of miR-221/222 are closely related to the occurrence and development of various kinds of malignant tumors. The role of miR-221/222 in tumor development and their potential molecular mechanism in various cancers, including liver cancer, colorectal cancer, cervical cancer, ovarian cancer, and endometrial carcinoma, are summarized and reviewed in this paper. Moreover, the potential translational biomarker role of abnormal miR-221/222 level in tumor or blood circulation for tumor diagnosis is also discussed.

Emerging roles of the long non-coding RNA 01296/microRNA-143-3p/MSI2 axis in development of thyroid cancer

Thyroid cancer (TC) is an endocrine malignancy with rising incidence. Long non-coding RNAs (lncRNAs) can serve as diagnostic and prognostic biomarkers for TC. Thus, we studied roles of LINC01296 in TC progression. Initially, the Gene Expression Omnibus (GEO) database was used to detect the differentially expressed genes in human TC samples and the potential mechanism. Expression of LINC01296 and miR-143-3p in TC tissues and cells was measured. The transfection of TC cells was conducted with si-LINC01296, si-Musashi 2 (MSI2), mimic or inhibitor of miR-143-3p to determine their effects on TC cell proliferation, migration, invasion, apoptosis and the AKT/STAT3 signaling pathway. Finally, in vivo assay was performed to verify role of miR-143-3p in tumorigenesis of TC cells in nude mice. LINC01296 was predicted to bind to miR-143-3p to modulate MSI2 expression, thus regulating the occurrence and development of TC. LINC01296 was up-regulated, while miR-143-3p was down-regulated in TC cells and tissues. LNC01296 specifically bound to miR-143-3p and MSI2 was a target of miR-143-3p. Besides, LINC01296 silencing or miR-143-3p overexpression inhibited migration, invasion, proliferation and advanced apoptosis of TC cells. Additionally, silenced LINC01296 or overexpressed miR-143-3p reduced phosphorylated STAT3/STAT3, phosphorylated AKT/AKT, B-cell lymphoma-2 (Bcl-2) and CyclinD1 levels but elevated BCL2-associated X (Bax), Cleaved Caspase3 and Caspase3 levels. Also, tumorigenesis of TC cells in nude mice was inhibited with the silencing of LINC01296. In summary, LINC01296/miR-143-3p/MSI2 axis regulated development of TC through the AKT/STAT3 signaling pathway.

Biomarker expression analysis in different age groups revealed age was a risk factor for breast cancer

The relationship between age and breast cancer is ambiguous. Here, we analyzed the differential expression pattern of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in different age groups to provide an effective association between age and breast cancer risk at the molecular level. We integrated the microarray information from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. The patients were divided into young ( < 50 years) and old ( ≥ 50 years) age groups and evaluated by differential gene expression, weighted gene correlation network analysis (WGCNA), functional enrichment analyses, and coexpression analysis. To determine their potential clinical significance, univariate Cox regression analysis and survival assessment were conducted. We identified two lncRNAs (AL139280.1 and AP000851.1) and three mRNAs (MT1M, HBB, and TFPI2) as the risk markers, and Gene set enrichment analysis (GSEA) focusing on a single gene revealed that "pyrimidine metabolism," "cell cycle," and "P53 signaling pathway" were coenriched. These data demonstrated that age may be a risk factor for breast carcinogenesis and prognosis and provide an in-depth molecular characterization based on the expression patterns of lncRNAs and mRNAs.

Systematic analysis identifies three-lncRNA signature as a potentially prognostic biomarker for lung squamous cell carcinoma using bioinformatics strategy

Background

Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients.

Methods

Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction. Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes.

Results

WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P<1e-200), and differentially expressed RNAs in the module were used to subsequently analyze. PPI network analysis identified FPR2, GNG11 and ADCY4 as critical genes in LUSC, and survival analysis revealed that low mRNA expression of FPR2 and GNG11 resulted in a higher OS rate of LUSC patients. A lncRNA-miRNA-mRNA ceRNA network including 121 DElncRNAs, 18 DEmiRNAs and 3 DEmRNAs was established, and univariate and multivariate Cox regression analysis of those 121 DElncRNAs showed a group of 3 DElncRNAs (TTTY16, POU6F2-AS2 and CACNA2D3-AS1) had significantly prognostic value in OS of LUSC patients. ROC analysis showed that the area under the curve (AUC) of the 3-lncRNA signature associated with 3-year survival was 0.629.

Conclusions

The current study provides novel insights into the lncRNA-related regulatory mechanisms underlying LUSC, and identifying 3-lncRNA signature may serve as a potentially prognostic biomarker in predicting the OS of LUSC patients.

Long noncoding RNA MALAT1 knockdown inhibits progression of anaplastic thyroid carcinoma by regulating miR-200a-3p/FOXA1

Long noncoding RNAs (lncRNAs) have been reported to play essential roles in progression of thyroid carcinoma. However, the roles of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in anaplastic thyroid carcinoma (ATC) process and its mechanism remain not been fully established. In this study, we focused on the effect of MALAT1 on cell proliferation, apoptosis, migration, invasion, and autophagy formation in ATC and explored the interaction between miR-200a-3p and MALAT1 or FOXA1. Moreover, murine xenograft model was established to investigate the roles and mechanism of MALAT1 in ATC progression in vivo. Results showed that MALAT1 expression was enhanced and miR-200a-3p was reduced in ATC tissues and cells. Knockdown of MALAT1 or overexpression of miR-200a-3p inhibited cell proliferation, migration and invasion but increased apoptosis and autophagy formation in ATC cells. Moreover, miR-200a-3p was directly bound to MALAT1 and its inhibition reversed the inhibitory effect of MALAT1 knockdown on progression of ATC. In addition, FOXA1 was indicated as a target of miR-200a-3p and its restoration attenuated the anti-cancer role of miR-200a-3p in ATC cells. Furthermore, MALAT1 functioned as a competing endogenous RNA (ceRNA) via sponging miR-200a-3p to derepress FOXA1 expression. Besides, interference of MALAT1 decreased tumor growth by upregulating miR-200a-3p and downregulating FOXA1. Collectively, MALAT1 knockdown suppressed ATC progression by regulating miR-200a-3p/FOXA1, providing a novel avenue for treatment of ATC.

Identification of prognostic signatures associated with long-term overall survival of thyroid cancer patients based on a competing endogenous RNA network

Competing endogenous RNAs (ceRNAs) are considered as transcripts that can regulate each other at post-transcription level by competing for shared miRNAs. Considering the key roles of lncRNAs acting as ceRNAs in progression of solid tumors, to develop prognostic signatures in thyroid cancer (THCA), patients with corresponding clinical data were selected and two ceRNA networks were constructed using online databases. Two prognostic signatures (Lnc5m4 and Lnc2mi1m2) were found to be more efficient in predicting long-term survival of THCA patients. However, the high-risk score of Lnc2mi1m2 was not an independent factor.The risk score of Lnc5m4 was able to effectively stratify patients in stage III-IV into low- and high-risk groups, and also could predict poor prognosis of patients in stage III-IV. In conclusion, these findings indicate that Lnc5m4 is a novel prognostic signature for predicting long-term overall survival of THCA patients, which could provide a new approach to lncRNA research in THCA progression.

Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants

It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological traits, and this interaction is more common. Many studies focus mainly on the analysis of microRNA(miRNA) and message RNA (mRNA) interactions. In this study, we investigated miRNA and long non-coding RNA (lncRNA) interactions using support vector regression (SVR) for prediction of new target genes in Arabidopsis thaliana and identify some regulatory roles in stress response. The networks of miRNA-mRNA, miRNA-lncRNA and miRNA-mRNA-lncRNA were constructed. They were further analyzed and interpreted in R. We showed that miRNA with low sequence number, targeted lncRNA with high sequence number and miRNA with high sequence number targeted lncRNA with low sequence number. The experimental results showed that there is a regulatory relationship between miRNA-lncRNA. New RNA targets were predicted using SVR with new gene expression mechanism and the stress related functions were annotated.

Identifying gene modules of thyroid cancer associated with pathological stage by weighted gene co-expression network analysis

Thyroid cancer is the most common type of endocrine tumor. The TNM classification remains a standard for treatment determination and predicting prognosis in thyroid cancer. The genes modules associated with the progression of papillary thyroid carcinoma (PTC) were not clear. We applied a weighted gene co-expression network analysis (WGCNA) and differential expression analysis to systematically identified co-expressed gene modules and hub genes associated with PTC progression based on The Cancer Genome Atlas (TCGA) PTC transcriptome sequencing data. An independent validation cohort, GSE27155, was used to evaluate the preservation of gene modules. We identified two co-expressed genes modules associated with progression of PTC. Enrichment analysis indicated that the two modules were enriched in angiogenesis and extracellular matrix organization. DCN, COL1A1, COL1A2, COL5A2 and COL3A1 were hub genes in the co-expressed network. We systematically identified co-expressed gene modules and hub genes associated with PTC progression for the first time, which provided insights into the mechanisms underlying PTC progression and some potential targets for the treatment of PTC.

Molecular Network-Based Drug Prediction in Thyroid Cancer

As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher’s exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells.

An Efficient Classifier for Alzheimer's Disease Genes Identification

Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.

Molecular network-based identification of competing endogenous RNAs and mRNA signatures that predict survival in prostate cancer

Background

The aim of the study is described the regulatory mechanisms and prognostic values of differentially expressed RNAs in prostate cancer and construct an mRNA signature that predicts survival.

Methods

The RNA profiles of 499 prostate cancer tissues and 52 non-prostate cancer tissues from TCGA were analyzed. The differential expression of RNAs was examined using the edgeR package. Survival was analyzed by Kaplan–Meier method. microRNA (miRNA), messenger RNA (mRNA), and long non-coding RNA (lncRNA) networks from the miRcode database were constructed, based on the differentially expressed RNAs between non-prostate and prostate cancer tissues.

Results

A total of 773 lncRNAs, 1417 mRNAs, and 58 miRNAs were differentially expressed between non-prostate and prostate cancer samples. The newly constructed ceRNA network comprised 63 prostate cancer-specific lncRNAs, 13 miRNAs, and 18 mRNAs. Three of 63 differentially expressed lncRNAs and 1 of 18 differentially expressed mRNAs were significantly associated with overall survival in prostate cancer (P value < 0.05). After the univariate and multivariate Cox regression analyses, 4 mRNAs (HOXB5, GPC2, PGA5, and AMBN) were screened and used to establish a predictive model for the overall survival of patients. Our ROC curve analysis revealed that the 4-mRNA signature performed well.

Conclusion

These ceRNAs may play a critical role in the progression and metastasis of prostate cancer and are thus candidate therapeutic targets and potential prognostic biomarkers. A novel model that incorporated these candidates was established and might provide more powerful prognostic information in predicting survival in prostate cancer.

Integrative Analysis of Dysregulated lncRNA-Associated ceRNA Network Reveals Functional lncRNAs in Gastric Cancer

Mounting evidence suggests that long noncoding RNAs (lncRNAs) play important roles in the regulation of gene expression by acting as competing endogenous RNA (ceRNA). However, the regulatory mechanisms of lncRNA as ceRNA in gastric cancer (GC) are not fully understood. Here, we first constructed a dysregulated lncRNA-associated ceRNA network by integrating analysis of gene expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs). Then, we determined three lncRNAs (RP5-1120P11, DLEU2, and DDX11-AS1) as hub lncRNAs, in which associated ceRNA subnetworks were involved in cell cycle-related processes and cancer-related pathways. Furthermore, we confirmed that the two lncRNAs (DLEU2 and DDX11-AS1) were significantly upregulated in GC tissues, promote GC cell proliferation, and negatively regulate miRNA expression, respectively. The hub lncRNAs (DLEU2 and DDX11-AS1) could have oncogenic functions, and act as potential ceRNAs to sponge miRNA. Our findings not only provide novel insights on ceRNA regulation in GC, but can also provide opportunities for the functional characterization of lncRNAs in future studies.