Construction of a specific SVM classifier and identification of molecular markers for lung adenocarcinoma based on lncRNA-miRNA-mRNA network

Combining single-cell profiling and functional analysis explores the role of pseudogenes in human early embryonic development

More and more studies have demonstrated that pseudogenes possess coding ability, and the functions of their transcripts in the development of diseases have been partially revealed. However, the role of pseudogenes in maintenance of normal physiological states and life activities has long been neglected. Here we identify pseudogenes that are dynamically expressed during human early embryogenesis, showing different expression pattern from that of adult tissues. We explore the expression correlation between pseudogenes and the parent genes, part due to their shared gene regulatory elements or the potential regulation network between them. The essential role of three pseudogenes, PI4KAP1, TMED10P1, and FBXW4P1, in maintaining self-renewal of human embryonic stem cells is demonstrated. We further find that the three pseudogenes might perform their regulatory functions by binding to proteins or microRNAs. The pseudogene-related single-nucleotide polymorphisms are significantly associated with human congenital disease, further illustrating their importance during early embryonic development. Overall, this study is an excavation and exploration of functional pseudogenes during early human embryonic development, suggesting that pseudogenes are not only capable of being specifically activated in pathological states, but also play crucial functions in the maintenance of normal physiological states.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

MicroRNA-204-5p Inhibits Hepatocellular Carcinoma by Targeting the Regulator of G Protein Signaling 20

Although the oncogenic roles of regulator of G protein signaling 20 (RGS20) and its upstream microRNAs (miRNAs) have been reported, their involvement in hepatocellular carcinoma (HCC) remains unexplored. We utilized the starBase, miRDB, TargetScan, and mirDIP databases, along with a dual-luciferase reporter assay and cDNA chip analysis to identify miRNAs targeting RGS20. miR-204-5p was selected for further experiments to confirm its direct targeting and downregulation of the RGS20 expression. To study the miR-204-5p/RGS20 axis in HCC, RGS20 and miR-204-5p were increased in PLC/PRF/5/Hep3B cells, and the viability, hyperplasia, apoptosis, cell cycle, and invasion/migration of the cells were assessed. RGS20 exhibited optimism, while miR-204-5p exhibited pessimism in tumors. miR-204-5p directly targeted RGS20 and downregulated its expression, whereas high RGS20 expression indicated a poor prognosis. Transfection of miR-204-5p inhibited the hyperplasia, migration, and invasion of HCC cells, but promoted apoptosis and influenced the levels of cyclin-dependent kinase 2 (CDK2), cyclin E1, B-cell lymphoma-2 (Bcl-2), Bax, and cleaved caspase-3/8. These effects were reversed by overexpression of RGS20. We recognized miR-204-5p as an upstream regulator targeting RGS20, thereby inhibiting HCC progression by downregulating RGS20 expression. RGS20 may prove to be a potential target for HCC treatment, and miR-204-5p might seem like to be a potential miRNA in gene therapy.

Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations

An increasing number of studies have shown that dysregulation of lncRNAs is related to the occurrence of various diseases. Most of the previous methods, however, are designed based on homogeneity assumption that the representation of a target lncRNA (or disease) node should be updated by aggregating the attributes of its neighbor nodes. However, the assumption ignores the affinity nodes that are far from the target node. We present a novel prediction method, GAIRD, to fully leverage the heterogeneous information in the network and the decoupled node features. The first major innovation is a random walk strategy based on width-first searching and depth-first searching. Different from previous methods that only focus on homogeneous information, our new strategy learns both the homogeneous information within local neighborhoods and the heterogeneous information within higher-order neighborhoods. The second innovation is a representation decoupling module to extract the purer attributes and the purer topologies. Third, a module based on group convolution and deep separable convolution is developed to promote the pairwise intrachannel and interchannel feature learning. The experimental results show that GAIRD outperforms comparing state-of-the-art methods, and the ablation studies prove the contributions of major innovations. We also performed case studies on 3 diseases to further demonstrate the effectiveness of the GAIRD model in applications.

Prognostic value of CASC15 and LINC01600 as competitive endogenous RNAs in lung adenocarcinoma: An observational study

Long noncoding RNAs (lncRNAs) can directly or indirectly regulate gene expression through interacting with microRNAs (miRNAs). Competitive endogenous RNAs render the roles of lncRNAs more complicated in the process of tumor occurrence and progression. However, the prognostic value of lncRNAs as potential biomarkers and their functional roles as competitive endogenous RNAs have not been clearly described for lung adenocarcinoma (LUAD). In the present study, the aberrant expression profiles of lncRNAs and miRNAs were analyzed at cBioPortal by interrogating LUAD dataset from The Cancer Genome Atlas (TCGA) database with 517 tissue samples. A total of 92 lncRNAs and 125 miRNAs with highly genetic alterations were identified. Further bioinformatics analysis was performed to construct a LUAD-related lncRNA-miRNA-mRNA ceRNA network, which included 24 highly altered lncRNAs, 21 miRNAs and 142 mRNAs. Some key lncRNAs in this network were subsequently identified as LUAD prognosis-related, and of those, CASC15 and LINC01600 both performed the potential prognostic characteristics with LUAD regarding OS and recurrence. Comprehensive analysis indicated that the expression of LINC01600 was significantly associated with KRAS mutation and lymph node metastasis, and CASC15 and LINC01600 were significantly tended towards co-occurrence, which may be due to the similarity of genes co-expressed by these 2 lncRNAs. Our findings provided novel insight into better understanding of ceRNA regulatory mechanisms in the pathogenesis of LUAD and facilitated the identification of potential biomarkers for prognosis.

Construction of a genomic instability-derived predictive prognostic signature for non-small cell lung cancer patients

BACKGROUND

Genomic instability (GI) is an effective prognostic marker of cancer. Thus, in this work, we aimed to explore the impact of GI derived signature on prognosis in non-small cell lung cancer (NSCLC) patients using bioinformatics methods.

METHODS

The data of NSCLC patients were collected from The Cancer Genome Atlas. Totally 1794 immune related genes were downloaded from immport database. The optimal prognosis related genes were identified by univariate and LASSO Cox analyses. The risk score model was built to predict the NSCLC patients' prognosis. The immune cell infiltration was analyzed in CIBERSORT.

RESULTS

The 951 differentially expressed genes (DEGs) between the genomic stability (GS) and GI groups were enriched in 862 Gene ontology terms and 32 Kyoto Encyclopedia of Genes and Genomes pathways. Based on the 13 optimal genes, a prognostic risk score mode for NSCLC was established, and the high-risk patients exhibited worse overall survival. Moreover, the nomogram could reliably predict the clinical outcomes. The immune cell infiltration and checkpoints were significantly differential between the two groups (high-risk and low-risk).

CONCLUSION

The GI related 13-gene signature (TMPRSS11E, TNNC2, HLF, FOXM1, PKMYT1, TCN1, RGS20, SYT8, CD1B, LY6K, MFSD4A, KLRG2 APCDD1L) could reliably predict the prognosis of NSCLC patients.

Specific topology and topological connection sensitivity enhanced graph learning for lncRNA-disease association prediction

Predicting disease-related candidate long noncoding RNAs (lncRNAs) is beneficial for exploring disease pathogenesis due to the close relations between lncRNAs and the occurrence and development of human diseases. It is a long-term and challenging task to adequately extract specific and local topologies in individual lncRNA network and individual disease network, and integrate the information of the connection relationships. We propose a new graph learning-based prediction method to encode specific and local topologies from each individual network, neighbor topologies with different connection relationships, and pairwise attributes. We first construct a lncRNA network composed of all the lncRNA nodes and their similarities, and a single disease network that contains all the disease nodes and disease similarities. Then, a network-aware graph convolutional autoencoder is constructed to encode the specific and local topologies of each network. Secondly, a heterogeneous network is established to embed all lncRNA, disease, and miRNA nodes and their various connections. Afterwards, a connection-sensitive graph neural network is designed to deeply integrate the neighbor node attributes and connection characteristics in the heterogeneous network and learn neighbor topological representations. We also construct both connection-level and topology representation-level attention mechanisms to extract informative connections and topological representations. Finally, we build a multi-layer convolutional neural networks with weighted residuals to adaptively complement the detailed features to pairwise attribute encoding. Comprehensive experiments and comparison results demonstrated that NCPred outperforms seven state-of-the-art prediction methods. The ablation studies demonstrated the importance of local topology learning, neighbor topology learning, and pairwise attribute encoding. Case studies on prostate, lung, and breast cancers further revealed NCPred's capacity to screen potential candidate disease-related lncRNAs.

MiR-210 regulates lung adenocarcinoma by targeting HIF-1α

Object

This study sought to elucidate the role of microRNA-210 (miR-210) in the occurrence and development of lung adenocarcinoma (LUAD).

Methods

The levels of lncRNA miR-210HG and miR-210 in LUAD tissues and corresponding normal tissues were analyzed by real-time quantitative PCR. The expression of the anti-hypoxia factor hypoxia inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) were measured by qRT-PCR and Western blot. The target of miR-210 on HIF-1α was confirmed using TCGA, Western blot and luciferase reporter assay. The regulatory role of miR-210 on HIF-1α and VEGF in LUAD was investigated. The correlation of genes with clinical prognosis was analyzed using bioinformatics methods. The effect of miR-210 on LUAD cells was verified through apoptosis assays.

Results

The expression of miR-210 and miR-210HG was significantly higher in LUAD tissues than in normal tissues. The expression of hypoxia-related indicators HIF-1α and VEGF was also significantly higher in LUAD tissues. MiR-210 suppressed HIF-1α expression by targeting site 113 of HIF-1α, thereby affecting VEGF expression. Overexpression of miR-210 inhibited HIF-1 expression by targeting the 113 site of HIF-1, thereby affecting VEGF expression. Conversely, inhibition of miR-210 resulted in a significant increase in HIF-1α and VEGF expression in LUAD cells. In TCGA-LUAD cohorts, the expression of VEGF-c and VEGF-d genes in LUAD tissues was significantly lower than in normal tissues, while overall survival was worse in LUAD patients with high expression of HIF-1α, VEGF-c and VEGF-d. Apoptosis was significantly lower in H1650 cells after miR-210 inhibition.

Conclusion

This study reveals that miR-210 exerts an inhibitory effect on VEGF expression by down-regulating HIF-1α expression in LUAD. Conversely, inhibition of miR-210 significantly reduced H1650 apoptosis and led to worse patient survival by upregulating HIF-1α and VEGF. These results suggest that miR-210 could serve as a potential therapeutic target for the treatment of LUAD.

LINC01232 promotes lung squamous cell carcinoma progression through modulating miR-181a-5p/SMAD2 axis

BACKGROUND

LINC01232 has been implicated in the progression of multiple malignancies. Yet, the function of LINC01232 in the carcinogenesis of lung squamous cell carcinoma (LUSC) remains unclear. This study aims to examine the role LINC01232 plays in LUSC progression.

METHODS

mRNA and protein levels were assessed using qRT-PCR and western blot, respectively. Cell proliferation was assessed by CCK-8 and colony formation assays. Cell migration and invasion were evaluated by transwell assay. The interactions between LINC01232, miR-181a-5p, and SMAD2 were assessed using luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays. The subcellular distribution of LINC01232 was examined by cytosolic/nuclear fractionation assay RESULTS: LINC01232 was upregulated in both LUSC tissues and cell lines. Knockdown of LINC01232 impaired cell proliferation, migration and invasion capability in H1229 and A549 cells, a phenotype that could be reversed by miR-181a-5p silencing. In addition, LINC01232 silencing reduced levels of N-cadherin, Vimentin, and Snail in H1229 and A549 cells, but increased the level of E-cadherin, which can be abrogated by miR-181a-5p inhibitors.

CONCLUSIONS

In summary, our study demonstrates that LINC01232 expression increases in LUSC tissues and cell lines and promotes LUSC progression by modulating the miR-181a-5p/SMAD2 signaling, providing new potential drug targets for LUSC treatment.

Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction

Since abnormal expression of long non-coding RNAs (lncRNAs) is associated with various human diseases, identifying disease-related lncRNAs helps reveal the pathogenesis of diseases. Existing methods for lncRNA-disease association prediction mainly focus on multi-sourced data related to lncRNAs and diseases. The rich semantic information of meta-paths, composed of multiple kinds of connections between lncRNA and disease nodes, is neglected. We propose a new prediction method, MGLDA, to encode and integrate the semantics of multiple meta-paths, the global topology of heterogeneous graph, and pairwise attributes of lncRNA and disease nodes. First, a tri-layer heterogeneous graph is constructed to associate multi-sourced data across the lncRNA, disease, and miRNA nodes. Afterwards, we establish multiple meta-paths connecting the lncRNA and disease nodes to derive and denote various semantics. Each meta-path contains its specific semantics formulated by an embedding strategy, and each embedding covers local topology formed by the diverse semantic connections among the lncRNA, disease, and miRNA nodes. We construct multiple graph convolutional autoencoders (GCA) with topology-level attention to learn global and multiple local topologies from the tri-layer graph and each meta-path, respectively. The topology-level attention mechanism can learn the importance of various global and local topologies for adaptive pairwise topology fusion. Finally, a convolutional autoencoder learns the attribute representations of lncRNA-disease pairs, which integrates the learnt detailed and representative pairwise features. Experimental results show that MGLDA outperforms other state-of-the-art prediction methods in comparison and retrieves more real lncRNA-disease associations in the top-ranked candidates. The ablation study also demonstrates the important contributions of the local and global topology learning, and pairwise attribute learning. Case studies on three diseases further demonstrate MGLDA's ability to identify potential disease-related lncRNAs.

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD.

Methods

RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465.

Results

A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues.

Conclusion

Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs

Motivation

Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of diseases. Predicting disease-related lncRNAs can help to understand the pathogenesis of diseases deeply. The existing methods mainly rely on multi-source data related to lncRNAs and diseases when predicting the associations between lncRNAs and diseases. There are interdependencies among node attributes in a heterogeneous graph composed of all lncRNAs, diseases and micro RNAs. The meta-paths composed of various connections between them also contain rich semantic information. However, the existing methods neglect to integrate attribute information of intermediate nodes in meta-paths.

Results

We propose a novel association prediction model, GSMV, to learn and deeply integrate the global dependencies, semantic information of meta-paths and node-pair multi-view features related to lncRNAs and diseases. We firstly formulate the global representations of the lncRNA and disease nodes by establishing a self-attention mechanism to capture and learn the global dependencies among node attributes. Second, starting from the lncRNA and disease nodes, respectively, multiple meta-pathways are established to reveal different semantic information. Considering that each meta-path contains specific semantics and has multiple meta-path instances which have different contributions to revealing meta-path semantics, we design a graph neural network based module which consists of a meta-path instance encoding strategy and two novel attention mechanisms. The proposed meta-path instance encoding strategy is used to learn the contextual connections between nodes within a meta-path instance. One of the two new attention mechanisms is at the meta-path instance level, which learns rich and informative meta-path instances. The other attention mechanism integrates various semantic information from multiple meta-paths to learn the semantic representation of lncRNA and disease nodes. Finally, a dilated convolution-based learning module with adjustable receptive fields is proposed to learn multi-view features of lncRNA-disease node pairs. The experimental results prove that our method outperforms seven state-of-the-art comparing methods for lncRNA-disease association prediction. Ablation experiments demonstrate the contributions of the proposed global representation learning, semantic information learning, pairwise multi-view feature learning and the meta-path instance encoding strategy. Case studies on three cancers further demonstrate our method’s ability to discover potential disease-related lncRNA candidates.

Contact

zhang@hlju.edu.cn or peiliangwu@ysu.edu.cn

Supplementary information

Supplementary data are available at Briefings in Bioinformatics online.

Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers Using Machine Learning Techniques Applied to Lung Cancer

Background:

Nowadays, gene expression analysis is one of the most promising pillars for understanding and uncovering the mechanisms underlying the development and spread of cancer. In this sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained from older technologies, such as Microarray, which could still be useful to extract relevant knowledge.

Methods:

Throughout this research, a complete machine learning methodology to cross-evaluate the compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented. In order to show a real application of the designed pipeline, a lung cancer case study is addressed by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic datasets considered for our study have been obtained from the public repositories NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples belonging to these cancer subtypes have been developed.

Results:

The predictive models built using one technology are capable of discerning samples from a different technology. The classification results are evaluated in terms of accuracy, F1-score and ROC curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.

Conclusion:

Our method has the capability of finding strong gene signatures which are also independent of the transcriptomic technology used to develop the analysis. In addition, our article highlights the potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies, increasing the statistical significance of the results.

Integrated Analysis of Circular RNA-Associated ceRNA Network Reveals Potential circRNA Biomarkers in Human Breast Cancer

Circular RNA (circRNA) is closely related to tumorigenesis and cancer progression. Yet, the roles of cancer-specific circRNAs in the circRNA-related ceRNA network of breast cancer (BRCA) remain unclear. The aim of this study was to construct a ceRNA network associated with circRNA and to explore new therapeutic and prognostic targets and biomarkers for breast cancer. We downloaded the circRNA expression profile of BRCA from Gene Expression Omnibus (GEO) microarray datasets and downloaded the miRNA and mRNA expression profiles of BRCA from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were identified, and a competitive endogenous RNA (ceRNA) regulatory network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Gene ontology and pathway enrichment analyses were performed on mRNAs regulated by circRNAs in ceRNA networks. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network were performed. A total of 72 DEcircRNAs, 158 DEmiRNAs, and 2762 DE mRNAs were identified. The constructed ceRNA network contains 60 circRNA-miRNA pairs and 140 miRNA-mRNA pairs, including 40 circRNAs, 30 miRNAs, and 100 mRNAs. Functional enrichment indicated that DEmRNAs regulated by DEcircRNAs in ceRNA networks were significantly enriched in the PI3K-Akt signaling pathway, microRNAs in cancer, and proteoglycans in cancer. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network showed that 13 mRNAs and 6 miRNAs were significantly associated with overall survival, and 48 miRNA-mRNA interaction pairs had a significant negative correlation. A PPI network was established, and 21 hub genes were determined from the network. This study provides an effective bioinformatics basis for further understanding of the molecular mechanisms and predictions of breast cancer. A better understanding of the circRNA-related ceRNA network in BRCA will help identify potential biomarkers for diagnosis and prognosis.

Graph Triple-Attention Network for Disease-related LncRNA Prediction

Abnormal expressions of long non-coding RNAs (lncRNAs) are associated with various human diseases. Identifying disease-related lncRNAs can help clarify complex disease pathogeneses. The latest methods for lncRNA-disease association prediction rely on diverse data about lncRNAs and diseases. These methods, however, cannot adequately integrate the neighbour topological information of lncRNA and disease nodes. Moreover, more intrinsic features of lncRNA-disease node pairs can be explored to better predict the latent associations between lncRNAs and diseases. We developed a novel method, named GTAN, to predict the association propensities between lncRNAs and diseases. GTAN integrates various information about lncRNAs and diseases, including similarities, associations and interactions among lncRNAs, diseases and miRNAs, and exploits neighbour topology and attribute representations of a pair of lncRNA-disease nodes. We adopted in GTAN a graph neural network architecture with three attention mechanisms and multi-layer convolutional neural networks. First, a neighbour-level self-attention mechanism is constructed to learn the importance of each neighbour for an interested lncRNA or disease node. Second, topology-level attention is proposed to enhance contextual dependencies among multiple local topology representations of the lncRNA or disease node. An attention-enhanced graph neural network framework is then established to learn a topology representation of top-ranked neighbours for a pair of lncRNA-disease nodes. GTAN also has attribute-level attention to distinguish various contributions of attributes of the lncRNA-disease pair. Finally, attribute representation is learned by multi-layer CNN to integrate detailed features and representative features of the pair. Extensive experimental results demonstrated that GTAN outperformed state-of-the-art methods. The improved recall rates also showed GTANs capacity for retrieving more actual lncRNA-disease associations in the top-ranked candidates. The ablation studies confirmed the important contributions of three attention mechanisms. Case studies on lung cancer, prostate cancer and colon cancer further showed GTANs ability in discovering potential lncRNA candidates related to diseases.

Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion

BACKGROUND

Adenocarcinoma and squamous cell carcinoma are the two most prevalent lung cancer types, and their distinction requires different screenings, such as the visual inspection of histology slides by an expert pathologist, the analysis of gene expression or computer tomography scans, among others. In recent years, there has been an increasing gathering of biological data for decision support systems in the diagnosis (e.g. histology imaging, next-generation sequencing technologies data, clinical information, etc.). Using all these sources to design integrative classification approaches may improve the final diagnosis of a patient, in the same way that doctors can use multiple types of screenings to reach a final decision on the diagnosis. In this work, we present a late fusion classification model using histology and RNA-Seq data for adenocarcinoma, squamous-cell carcinoma and healthy lung tissue.

RESULTS

The classification model improves results over using each source of information separately, being able to reduce the diagnosis error rate up to a 64% over the isolate histology classifier and a 24% over the isolate gene expression classifier, reaching a mean F1-Score of 95.19% and a mean AUC of 0.991.

CONCLUSIONS

These findings suggest that a classification model using a late fusion methodology can considerably help clinicians in the diagnosis between the aforementioned lung cancer cancer subtypes over using each source of information separately. This approach can also be applied to any cancer type or disease with heterogeneous sources of information.

A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer

Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox's regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future.

Functional multigenic variations associated with hodgkin lymphoma

INTRODUCTION

The current study aimed to describe genotypes associated with Hodgkin lymphoma (HL) in a cohort of Saudi and non-Saudi patients and discuss their possible susceptibility to HL.

METHODS

We studied clinical, histopathological, and laboratory findings of HL patients admitted over 12 years duration, at King Fahd University Hospital, KSA. The genomic DNAs of HL patients (n = 61) and normal control subjects (n = 36) were extracted, and genotyping was performed using the Illumina human exome bead chip. Set of HL patients and set of normal controls were included in this study.

RESULTS

A total of 35 DNA variants were found to be highly significant with the P-value <9.90 × 10^-11 among 243 345 exonic biomarkers and obeying the Hardy-Weinberg equilibrium. Nine, MEGF11-rs150945752 (P-value 1.20 × 10^-12 ), CACNA1I- s58055559 (P-value 1.93 × 10^-12 ), DECR2-rs146760080 (P-value 2.19 × 10^-12 ), STAB1-rs143894786 (P-value 2.45 × 10^-12 ), ZNF526-rs144433879 (P-value 2.76 × 10^-12 ), CPLANE1-rs200612080 (P-value 3.77 × 10^-12 ), DLK1-rs1058009 (P-value 5.95 × 10^-12 ), RTN4RL2-rs61745214 (P-value 7.71 × 10^-12 ), and PGRMC1-rs145582672 (P-value 8.56 × 10^-12 ), exonic variants on chromosomes 15, 22, and 16 were highly associated with HL cases. THE HIGHLY SIGNIFICANT HAPLOTYPES AT CHROMOSOME 3: rs143894786G; rs149982219G with P-value = 3.43 × 10^-14 was found to be the risk haplotype for the HL patients. The opposite alleles at chromosome 3: rs143894786A; rs149982219G is protective with P-value = 2.46 × 10^-12 . Maximum number of SNPs at the chromosome 19: rs144433879C; rs181265966G; rs201144421C; rs145591797G; rs200560875G; rs77270337G (risk P-value = 2.24 × 10^-12 ) and its opposite allele rs144433879A; rs181265966A; rs201144421T; rs145591797A; rs200560875A; rs77270337A (protective P-value = 2.60 × 10^-9 ) were found to be associated haplotype with the HL and controls, respectively, in Saudi population.

CONCLUSION

Our study concludes that the HL is genetically heterogeneous with multigene causation.

HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis

The emergence of high-throughput RNA-seq data has offered unprecedented opportunities for cancer diagnosis. However, capturing biological data with highly nonlinear and complex associations by most existing approaches for cancer diagnosis has been challenging. In this study, we propose a novel hierarchical feature selection and second learning probability error ensemble model (named HFS-SLPEE) for precision cancer diagnosis. Specifically, we first integrated protein-coding gene expression profiles, non-coding RNA expression profiles, and DNA methylation data to provide rich information; afterward, we designed a novel hierarchical feature selection method, which takes the CpG-gene biological associations into account and can select a compact set of superior features; next, we used four individual classifiers with significant differences and apparent complementary to build the heterogeneous classifiers; lastly, we developed a second learning probability error ensemble model called SLPEE to thoroughly learn the new data consisting of classifiers-predicted class probability values and the actual label, further realizing the self-correction of the diagnosis errors. Benchmarking comparisons on TCGA showed that HFS-SLPEE performs better than the state-of-the-art approaches. Moreover, we analyzed in-depth 10 groups of selected features and found several novel HFS-SLPEE-predicted epigenomics and epigenetics biomarkers for breast invasive carcinoma (BRCA) (e.g., TSLP and ADAMTS9-AS2), lung adenocarcinoma (LUAD) (e.g., HBA1 and CTB-43E15.1), and kidney renal clear cell carcinoma (KIRC) (e.g., IRX2 and BMPR1B-AS1).

A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

Background

Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD.

Method

RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs.

Results

Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection.

Conclusion

Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.

Identification of LINC02310 as an enhancer in lung adenocarcinoma and investigation of its regulatory network via comprehensive analyses

BACKGROUND

Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer and has one of the highest mortality rates. An increasing number of long non-coding RNAs (LncRNAs) were reported to be associated with the occurrence and progression of LADC. Thus, it is necessary and reasonable to find new prognostic biomarkers for LADC among LncRNAs.

METHODS

Differential expression analysis, survival analysis, PCR experiments and clinical feature analysis were performed to screen out the LncRNA which was significantly related to LADC. Its role in LADC was verified by CCK-8 assay and colony. Furthermore, competing endogenous RNA (ceRNA) regulatory network construction, enrichment analysis and protein-protein interaction (PPI) network construction were performed to investigate the downstream regulatory network of the selected LncRNA.

RESULTS

A total of 2431 differentially expressed LncRNAs (DELncRNAs) and 2227 differentially expressed mRNAs (DEmRNAs) were from The Cancer Genome Atlas database. Survival analysis results indicated that lnc-YARS2-5, lnc-NPR3-2 and LINC02310 were significantly related to overall survival. Their overexpression indicated poor prognostic. PCR experiments and clinical feature analysis suggested that LINC02310 was significantly correlated with TNM-stage and T-stage. CCK-8 assay and colony formation assay demonstrated that LINC02310 acted as an enhancer in LADC. In addition, 3 targeted miRNAs of LINC02310 and 414 downstream DEmRNAs were predicted. The downstream DEmRNAs were then enriched in 405 Gene Ontology terms and 11 Kyoto Encyclopedia of Genes and Genomes pathways, which revealed their potential functions and mechanisms. The PPI network showed the interactions among the downstream DEmRNAs.

CONCLUSIONS

This study verified LINC02310 as an enhancer in LADC and performed comprehensive analyses on its downstream regulatory network, which might benefit LADC prognoses and therapies.

Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling

Background

Idiopathic pulmonary fibrosis (IPF) is associated with an increased risk for lung cancer, but the underlying mechanisms driving malignant transformation remain largely unknown. This study aimed to identify differentially expressed genes (DEGs) distinguishing IPF and lung cancer from healthy individuals and common genes driving the transformation from healthy to IPF and lung cancer.

Methods

The gene expression data for IPF and non-small cell lung cancer (NSCLC) were retrieved from the Gene Expression Omnibus (GEO) database. The DEG signatures were identified via unsupervised two-way clustering (TWC) analysis, supervised support vector machine analysis, dimensional reduction, and mutual exclusivity analysis. Gene enrichment and pathway analyses were performed to identify common signaling pathways. The most significant signature genes in common among IPF and lung cancer were further verified by immunohistochemistry.

Results

The gene expression data from GSE24206 and GSE18842 were merged into a super array dataset comprising 86 patients with lung disorders (17 IPF and 46 NSCLC) and 51 healthy controls and measuring 23,494 unique genes. Seventy-nine signature DEGs were found among IPF and NSCLC. The peroxisome proliferator-activated receptor (PPAR) signaling pathway was the most enriched pathway associated with lung disorders, and matrix metalloproteinase-1 (MMP-1) in this pathway was mutually exclusive with several genes in IPF and NSCLC. Subsequent immunohistochemical analysis verified enhanced MMP1 expression in NSCLC associated with IPF.

Conclusions

For the first time, we defined common signature genes for IPF and NSCLC. The mutually exclusive sets of genes were potential drivers for IPF and NSCLC.

LncRNA NNT-AS1 promotes lung squamous cell carcinoma progression by regulating the miR-22/FOXM1 axis

Background

Recent studies have revealed that dysregulated expression of long non-coding RNA nicotinamide nucleotide transhydrogenase antisense RNA 1 (lncRNA NNT-AS1) is associated with cell tumorigenicity in non-small cell lung cancer. However, the exact molecular mechanisms of NNT-AS1 in lung squamous cell carcinoma (LUSC) remain largely unknown.

Methods

The expression of NNT-AS1, microRNA (miR)-22 and Forkhead box protein M1 (FOXM1) was measured using quantitative real-time polymerase chain reaction or western blot, respectively. The interaction between miR-22 and NNT-AS1 or FOXM1 was confirmed using a dual-luciferase reporter assay and RNA immunoprecipitation assay. Cell migration and invasion abilities were measured by Transwell assay. Flow cytometry was used to detect apoptotic cells.

Results

NNT-AS1 and FOXM1 were up-regulated but miR-22 was down-regulated in LUSC tissues and cell lines. NNT-AS1 was a sponge of miR-22, and NNT-AS1 deletion suppressed the migration and invasion but induced apoptosis in LUSC cells. FOXM1 was a target of miR-22, and overexpression of miR-22 inhibited cell carcinogenesis in LUSC by targeting FOXM1. Additionally, NNT-AS1 could directly regulate FOXM1 expression by binding to miR-22 in LUSC cells.

Conclusion

LncRNA NNT-AS1 contributes to cell carcinogenesis in LUSC by regulating the miR-22/FOXM1 axis, providing a novel insight into the pathogenesis of LUSC and a new potential therapeutic target for LUSC treatment.

SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA-gene associations

MOTIVATION

MircroRNAs (miRNAs) regulate target genes and are responsible for lethal diseases such as cancers. Accurately recognizing and identifying miRNA and gene pairs could be helpful in deciphering the mechanism by which miRNA affects and regulates the development of cancers. Embedding methods and deep learning methods have shown their excellent performance in traditional classification tasks in many scenarios. But not so many attempts have adapted and merged these two methods into miRNA-gene relationship prediction. Hence, we proposed a novel computational framework. We first generated representational features for miRNAs and genes using both sequence and geometrical information and then leveraged a deep learning method for the associations' prediction.

RESULTS

We used long short-term memory (LSTM) to predict potential relationships and proved that our method outperformed other state-of-the-art methods. Results showed that our framework SG-LSTM got an area under curve of 0.94 and was superior to other methods. In the case study, we predicted the top 10 miRNA-gene relationships and recommended the top 10 potential genes for hsa-miR-335-5p for SG-LSTM-core. We also tested our model using a larger dataset, from which 14 668 698 miRNA-gene pairs were predicted. The top 10 unknown pairs were also listed.

AVAILABILITY

Our work can be download in https://github.com/Xshelton/SG_LSTM.

CONTACT

luojiawei@hnu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Data Mining and Expression Analysis of Differential lncRNA ADAMTS9-AS1 in Prostate Cancer

Long noncoding RNAs (lncRNAs) play important roles in the regulation of gene expression by acting as competing endogenous RNAs (ceRNAs). However, the roles of lncRNA-associated ceRNAs in oncogenesis are not fully understood. The present study aims to determine whether a ceRNA network can serve as a prognostic marker in human prostate cancer (PCa). In order to identify a ceRNA network and the key lncRNAs in PCa, we constructed a differentially expressed lncRNAs (DELs)-differentially expressed miRNAs (DEMis)-differentially expressed mRNAs (DEMs) regulatory network based on the ceRNA theory using data from the Cancer Genome Atlas (TCGA). We found that the DELs-DEMis-DEMs network was composed of 27 DELs nodes, seven DEMis nodes, and three DEMs nodes. The 27 DELs were further analyzed with several public databases to provide meaningful information for understanding the functional roles of lncRNAs in regulatory networks in PCa. We selected ADAMTS9-AS1 to determine its role in PCa and found that ADAMTS9-AS1 significantly influences tumor cell growth and proliferation, suggesting that it plays a tumor suppressive role. In addition, ADAMTS9-AS1 functioned as ceRNA, effectively becoming a sponge for hsa-mir-96 and modulating the expression of PRDM16. These results suggest that ceRNAs could accelerate biomarker discovery and therapeutic strategies for PCa.

Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients

BACKGROUND

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

METHODS

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance.

RESULTS

As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results.

CONCLUSIONS

Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.

Identifying prognostic biomarkers in endometrial carcinoma based on ceRNA network

PURPOSE

Endometrial carcinoma (EC), a common gynecological malignancy with high incidence, affects the mental and physical health of women. Mounting evidence shows that long noncoding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) have instrumental roles in various biological processes associated with the pathogenesis of EC. In this research, we intend to further study the mechanism of EC and the potential predictive markers of EC.

METHODS

First, we obtained original data of EC RNA transcripts from The Cancer Genome Atlas database and performed differential analysis. Subsequently, according to the miRcode online software, relationship pairs of lncRNA-miRNA were constructed, and miRNA-mRNA pairs were established based on miRDB, TargetScan, and miRTarBase. Then, we constructed the competing endogenous RNA (ceRNA) network based on lncRNA-miRNA and miRNA-mRNA pairs. To further explain the function of the ceRNA network and explore the potential prognostic markers, functional enrichment analysis, and survival analysis were carried out.

RESULTS

The research showed that there were 744 differential expression lncRNAs (DElncRNAs), 164 differential expression miRNAs (DEmiRNAs), and 2447 differential expression mRNAs (DEmRNAs) between EC tissues and normal tissues. Subsequently, we built 103 DEmiRNA-DEmRNA interaction pairs and 369 DElncRNA-DEmiRNA pairs. Then, we established the ceRNA network of EC, including 62 DElncRNAs, 26 DEmiRNAs, and 70 DEmRNAs. Moreover, 10 of 62 lncRNAs, 19 of 70 mRNAs, and 4 of 26 miRNAs that closely related to the survival of EC with P < .05 were obtained. Notably, based on this network, it was found that LINC00261-hsa-mir-31 pair and LINC00261-hsa-mir-211 target pairs could be used as the potential prognostic markers of EC.

CONCLUSION

This research recommended an available basis for the molecular mechanism of EC and prognosis prediction, which could help guide the subsequent treatments and predict the prognosis for patients with EC.

FGB and FGG derived from plasma exosomes as potential biomarkers to distinguish benign from malignant pulmonary nodules

Previous proteomic analysis (label-free) of plasma exosomes revealed that the expression of FGG and FGB was significantly higher in the malignant pulmonary nodules group, compared to the benign pulmonary nodules group. The present study was performed to evaluate the role of plasma exosomal proteins FGB and FGG in the diagnosis of benign and malignant pulmonary nodules. We examined the expression levels of FGB and FGG in plasma exosomes from 63 patients before surgery. Postoperative pathological diagnosis confirmed that 43 cases were malignant and 20 cases were benign. The ROC curve was used to describe the sensitivity, specificity, area under the curve (AUC) of the biomarker and the corresponding 95% confidence interval. We confirmed that the expression levels of FGB and FGG were higher in the plasma exosomes of malignant group than in the benign group. The sensitivity and AUC of FGB combined with FGG detection to determine the nature of pulmonary nodules are superior to single FGB or FGG detection. FGB and FGG might represent novel and sensitive biomarker to distinguish benign from malignant pulmonary nodules.

Signature microRNAs and long noncoding RNAs in laryngeal cancer recurrence identified using a competing endogenous RNA network

The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE-miRs), lncRNAs (DE-lncRs) and mRNAs (DEGs). miRNA-mRNA and lncRNA-miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA-mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA-mRNA network, consisting of 32 optimal feature-coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE-lncRs, 507 DEGs and 55 DE-miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)-miR-33b, HOX transcript antisense RNA (HOTAIR)-miR-1-MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)-miR-124-calcitonin related polypeptide α (CALCA) and EMX2OS-miR-124-γ-aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR-1, miR-33b, miR-124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4-miR-33b, HOTAIR-miR-1-MAGEA2 and EMX2OS-miR-124-CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.

Differential Analysis of lncRNA, miRNA and mRNA Expression Profiles and the Prognostic Value of lncRNA in Esophageal Cancer

Integrative central axis of lncRNA-miRNA-mRNA plays pivotal roles in tumor development and progression. However, the regulatory role of lncRNA-miRNA-mRNA in esophageal cancer remains elusive. TCGA database was utilized to investigate the differential expression of lncRNA, miRNA and mRNA in esophageal cancer (ESCA) and normal esophageal tissues, and GEO database was used to further validate the expression profile of key genes. Differential lncRNAs in TCGA database were submitted to Starbase, and lncRNAs related to overall survival were analyzed using Kaplan-Meier and log-rank test. We found 145 lncRNAs, 112 miRNAs and 2000 protein coding mRNAs were differentially expressed in ESCA samples, which were tightly involved in chromosome segregation, extracellular matrix assembly by GO assay, and KEGG assay revealed the correlation of differentially expressed genes with cell cycle, apoptosis and cGMP-PKG signaling pathway. Furthermore, there were 291 nodes in ceRNA network, which consisted of 40 lncRNAs, 28 miRNAs and 233 mRNAs, and formed 677 relations. Furthermore, 6 of 10 lncRNAs in TCGA database were consistent with GEO database, and expressions of 10 mRNAs in TCGA database all exhibited the same tendency with GEO database. Notably, we found 8 lncRNAs (WDFY3-AS2, CASC8, UGDH-AS1, RAP2C-AS1, AC007128.1, AC016205.1, AC092803.2 and AC079949.2) were correlated with overall survival of the patients with ESCA. The key differentially expressed genes participate in the development and progression of ESCA, and thus the elucidation of functions of lncRNA-miRNA-mRNA will provide new novel therapeutic target for the patients with ESCA.

Early diagnosis of colorectal cancer via plasma proteomic analysis of CRC and advanced adenomatous polyp

AIM

This paper aimed to identify new candidate biomarkers in blood for early diagnosis of CRC.

BACKGROUND

Colorectal cancer (CRC) is the third most widespread malignancies increasing globally. The high mortality rate associated with colorectal cancer is due to the delayed diagnosis in an advanced stage while the metastasis has occurred. For better clinical management and subsequently to reduce mortality of CRC, early detection biomarkers are in high demand.

METHODS

A 2D-PAGE separation of proteins was performed followed by tandem mass Spectrometry (MALDI-TOF-TOF) to discover potential plasma protein markers for CRC and AA (advanced adenomas). Furthermore, western blot method was used to confirm a part of the results in colorectal tissue samples.

RESULTS

The significantly altered proteins including HPR, HP, ALB, KRT1, APOA1, FGB, IGJ and C4A were down-regulated in polyp relative to normal, and CRC compare to polyp surprisingly, and inversely, ORM2 was up-regulated with the fold change ≥ 2 and p-value ≤ 0.05. We also surveyed APOA1, FGB, and C4A for further confirmation of their expression changes by western blotting. All three of them showed a decreasing trend from normal toward CRC tissue samples as it mentioned before, but just changes of FGB and C4A were significant.

CONCLUSION

The results demonstrated that plasma proteins can be less invasive markers for the detection of CRC. FGB and C4A can be considered as plasma potential biomarkers to early diagnosis of CRC patients and understanding the underlying procedures in tumorigenesis. Undoubtedly, the additional study must be conducted on large scale cohorts to verify the results.

The construction and analysis of ceRNA networks in invasive breast cancer: a study based on The Cancer Genome Atlas

Background

Studies have shown that long noncoding RNAs (lncRNAs) make up the major proportion of the ceRNA network and can regulate gene expression by competitively binding to miRNAs. This reveals the existence of an RNA-miRNA regulatory pathway and is of great biological significance. CeRNAs, as competitive endogenous RNAs, have revealed a new mechanism of interaction between RNAs. Until now, the role of lncRNA-mediated ceRNAs in breast cancer and their regulatory mechanisms have been elucidated to some extent.

Purpose

In this study, comprehensive analysis of large-scale invasive breast cancer samples in TCGA were conducted to further explore the developmental mechanism of invasive breast cancer and the potential predictive markers for invasive breast cancer prognosis in the ceRNA network.

Methods

Abnormal expression profiles of invasive breast cancer associated mRNAs, lncRNAs and miRNAs were obtained from the TCGA database. Through further alignment and prediction of target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network was constructed for invasive breast cancer. Through the overall survival analysis, Identification prognostic bio-markers for invasive breast cancer patients. In addition, we used Cytoscape plug-in BinGo for the different mRNA performance functional cluster analysis.

Results

Differential analysis revealed that 1059 lncRNAs, 86 miRNAs, and 2138 mRNAs were significantly different in invasive breast cancer samples versus normal samples. Then we construct an abnormal lncRNA-miRNA-mRNA ceRNA network for invasive breast cancer, consisting of 90 DElncRNAs, 18 DEmiRNAs and 26 DEmRNAs.Further, 4 out of 90 lncRNAs, 3 out of 26 mRNAs, and 2 out of 18 miRNAs were useful as prognostic biomarkers for invasive breast cancer patients (P value < 0.05). It is worth noting that based on the ceRNA network, we found that the LINC00466-Hsa-mir-204- NTRK2 LINC00466-hsa-mir-204-NTRK2 axis was present in 9 RNAs associated with the prognosis of invasive breast cancer.

Conclusion

This study provides an effective bioinformatics basis for further understanding of the molecular mechanism of invasive breast cancerand for predicting outcomes, which can guide the use of invasive breast cancerdrugs and subsequent related research.