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

Computational identification of DNA damage-relevant lncRNAs for predicting therapeutic efficacy and clinical outcomes in cancer

OBJECTIVES

The role of long non-coding RNAs (lncRNAs) in cancer treatment, particularly in modulating DNA repair programs, is an emerging field that warrants systematic exploration. This study aimed to systematically identify the lncRNA regulators that potentially regulate DNA damage response (DDR).

METHODS

Using genome-wide mRNA and lncRNA expression profiles of the same tumor patients, we proposed a novel computational framework. This framework performed Gene Set Variation Analysis to calculate DDR pathway enrichment score, which relies on weighting by tumor purity to obtain DDR activity score for each patient. Then, an in-depth differential expression profiling was conducted to identify pathway activity lncRNAs between high- and low-activity groups, utilizing a bootstrap-based randomization method.

RESULTS

We comprehensively charted the landscape of DDR-relevant lncRNAs across 23 epithelial-based cancer types. Its effectiveness was validated by assessing the consistency of these lncRNAs within various datasets of the same cancer type (hypergeometric test P < 0.001), examining the expression perturbation of these lncRNAs in response to treatment and demonstrating its application in prioritizing clinically-related lncRNAs. Furthermore, leveraging 820 epithelial ovarian cancer patients from four public datasets, we applied these lncRNAs identified by DDRLnc to establish and validate a risk stratification model to evaluate the benefits of platinum-based adjuvant chemotherapy for the improvement of clinical treatment outcomes.

CONCLUSIONS

Comprehensive pan-cancer analysis illustrates the utility of computational framework in identifying potentially lncRNAs involved in DDR, thereby offering novel insights into the complex realm of cancer research, and it will become a valuable tool for identifying potential therapeutic targets for cancer.

Identification and Validation of Key Gene Modules and Pathways in Coronary Artery Disease Development and Progression

The development and progression of atherosclerosis represent a chronic process involving complex molecular interactions. Therefore, identifying the potential hub genes and pathways contributing to coronary artery disease (CAD) development is essential for understanding its underlying molecular mechanisms. To this end, we performed transcriptome analysis of peripheral venous blood collected from 100 patients who were divided into four groups according to disease severity, including 27 patients in the atherosclerosis group, 22 patients in the stable angina group, 35 patients in the acute myocardial infarction group, and 16 controls. Weighted gene co-expression network analysis was performed using R programming. Significant module-trait correlations were identified according to module membership and genetic significance. Metascape was used for the functional enrichment of differentially expressed genes between groups, and the hub genes were identified via protein-protein interaction network analysis. The hub genes were further validated by analyzing Gene Expression Omnibus (GSE48060 and GSE141512) datasets. A total of 9,633 messenger ribonucleic acids were detected in three modules, among which the blue module was highly correlated with the Gensini score. The hub genes were significantly enriched in the myeloid leukocyte activation pathway, suggesting its important role in the progression of atherosclerosis. Among these genes, the Mediterranean fever gene (MEFV) may play a key role in the progression of atherosclerosis and CAD severity.

Long Noncoding RNA MAGI2-AS3 Represses Cell Progression in Clear Cell Renal Cell Carcinoma by Modulating the miR-629-5p/PRDM16 Axis

The objective of this study was to determine the regulatory mechanism of MAGI2-AS3 in clear cell renal cell carcinoma (ccRCC), thereby supplying a new insight for ccRCC treatment. Expression data in TCGA-KIRC were obtained. Target gene lncRNA for research was determined using expression analysis and clinical analysis. lncRNA's downstream regulatory miRNA and mRNA were predicted by bioinformatics databases. ccRCC cell malignant phenotypes were detected via CCK-8, colony formation, Transwell migration, and invasion assays. The targeting relationship between genes was assessed through dual-luciferase reporter gene analysis. Kaplan-Meier (K-M) analysis was carried out to verify the effect of MAGI2-AS3, miR-629-5p, and PRDM16 on the survival rate of ccRCC patients. MAGI2-AS3 expression in ccRCC tissue and cells was shown to be markedly decreased and its expression to continuously decline with tumor progression. MAGI2-AS3 suppresses ccRCC proliferation and migration. Dual-luciferase assay showed that MAGI2-AS3 binds miR-629-5p and that miR-629-5p binds PRDM16. In addition, functional experiments showed that MAGI2-AS3 facilitates PRDM16 expression by repressing miR-629-5p expression, thereby suppressing ccRCC cell aggression. K-M analysis showed that upregulation of either MAGI2-AS3 or PRDM16 significantly improves ccRCC patient survival, while upregulation of miR-629-5p has no significant impact. MAGI2-AS3 sponges miR-629-5p to modulate PRDM16 to mediate ccRCC development. Meanwhile, the MAGI2-AS3/miR-629-5p/PRDM16 axis, as a regulatory pathway of ccRCC progression, may be a possible therapeutic target and prognostic indicator of ccRCC.

Bioinformatics analysis of potential Key lncRNA-miRNA-mRNA molecules as prognostic markers and important ceRNA axes in gastric cancer

Gastric cancer (GC), the fifth most common malignancy worldwide, has an extremely poor prognosis at the advanced stage or the early stage if inadequately treated. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs all function as competing endogenous RNAs (ceRNAs) that target and regulate each other. Changes in their expression and their regulatory bioprocesses play important roles in GC. However, the roles of key RNAs and their regulatory networks remain unclear. In this study, RNA profiles were extracted from The Cancer Genome Atlas database, and R language was used to discover the differentially expressed (DE) lncRNAs, miRNAs and mRNAs in GC. Then, the DERNAs were paired by miRcode, miRDB, TargetScan and DIANA, and the ceRNA network was further constructed and visualized using Cytoscape. Moreover, a functional enrichment analysis was performed using Metascape. Afterward, the "survival" package was employed to identify candidate prognostic targets (DERNA-os) in the ceRNA network. Ultimately, the ceRNA network was analyzed to identify crucial lncRNA/miRNA/mRNA axes. Based on 374 gastric adenocarcinoma and gastric adenoma samples, 283 DEceRNAs (69 lncRNAs, 10 miRNAs, and 204 mRNAs) were identified. The 204 mRNAs were significantly enriched in some interesting functional clusters, such as the trans-synaptic signaling cluster and the protein digestion and absorption cluster. The ceRNA network consisted of 43 ceRNAs (13 lncRNAs, 2 miRNAs, and 28 mRNAs) that were related to prognosis. Among them, 2 lncRNAs (LNC00469 and AC010145.1) and 1 mRNA (PRRT4) were potential new biomarkers. In addition, according to the lncRNA/miRNA/mRNA regulatory relationships among the 43 ceRNAs, we identified four axes that might play important roles in the progression of GC and investigated the potential mechanism of the most promising axis (POU6F2-AS2/hsa-mir-137/OPCML) in promoting the proliferation and invasiveness of GC.

Functional network analysis of p85 and PI3K as potential gene targets and mechanism of oleanolic acid in overcoming breast cancer resistance to tamoxifen

Background

Tamoxifen resistance in estrogen receptor positive (ER+) breast cancer therapy increases, which is the leading cause of cancer treatment failure, as it can impair patients’ prognoses, cause cancer recurrence, metastasis, and death. Combination therapy with compounds is needed to overcome tamoxifen resistance. Oleanolic acid (OA) was known to increase tamoxifen sensitivity in tamoxifen-resistant breast cancer; however, the molecular mechanism of OA and its involvement in overcoming tamoxifen resistance remain unknown and need further investigation. This study was conducted to identify the potential gene targets and molecular mechanisms of OA in overcoming tamoxifen resistance.

Results

A bioinformatic approach for functional network analysis was used in silico by utilizing secondary data in the Gene Expression Omnibus (GEO) database and analyzing them with GEO2R to obtain data on differentially expressed genes (DEGs). The DEG data were further examined with Database for Annotation, Visualization, and Integrated Discovery (DAVID), STRING, cBioPortal website, and Cytoscape with its plugin CytoHubba. Molecular docking was performed to predict the binding properties of OA on the protein encoded by the potential gene. CD44, FGFR2, PIK3R1, and MDM2 were designated as potential target genes (PTGs), and PIK3R1 was suspected as the potential gene for OA to overcome tamoxifen resistance. Molecular docking confirms that OA can inhibit p85 activation. PIK3R1 is suggested to be the potential gene for OA in overcoming tamoxifen resistance in breast cancer therapy.

Conclusion

The predicted molecular mechanism of OA in overcoming tamoxifen resistance involves inhibiting p85 activation, leading to the inhibition of the downstream activity of the PI3K signaling pathway, causing breast cancer to respond to tamoxifen therapy once again. Results of this study need to be validated by further studies, including in vitro and in vivo.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00341-4.

lncRNA ADAMTS9-AS1/circFN1 Competitively Binds to miR-206 to Elevate the Expression of ACTB, Thus Inducing Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease and can result in substantial disability. The current study explored the potentials of long noncoding RNA- (lncRNA-) circular RNA- (circRNA-) microRNA- (miRNA-) messenger RNA (mRNA) networks in HCM. Firstly, HCM-related microarray data were procured from the GEO database, with differentially expressed genes (DEGs) obtained. HCM-related target genes were retrieved in combination with GeneCards and CTD databases, and candidate target genes were subsequently obtained by intersection screening. Further, an interaction network diagram of candidate target genes was constructed using the STRING database, and the hub genes in the network were determined according to the core degree. The “ClusterProfiler” package of the R software was adopted for GO and KEGG analyses of candidate target genes, to analyze the potential molecular pathways in HCM. Next, upstream miRNA, lncRNA, and circRNA of ACTB were predicted with RNAInter, mirDIP, TargetScan, DIANA-LncBase, and StarBase databases, followed by construction of lncRNA/circRNA-miRNA-mRNA coexpression networks. ACTB, miR-206, circFN1, and ADAMTS9-AS1 expression in peripheral blood samples from HCM patients and normal healthy controls were detected using RT-qPCR. Moreover, rat cardiomyocyte cell lines H9c2 and HEK293 cells were selected for in vitro verification of competitive endogenous RNA (ceRNA) regulation mechanism. A total of 15 candidate target genes related to HCM were screened using the online databases. Further protein-protein interaction analysis identified ACTB as the hub gene for HCM. The targeted binding relationship between miR-206, miR-145-5p, miR-1-3p, and ACTB was found. Furthermore, ADAMTS9-AS1 and circFN1 were discovered as the upstream genes of miR-206. Moreover, ADAMTS9-AS1, circFN1, and ACTB were found to be poorly expressed, and miR-206 was highly expressed in HCM. In vitro experimentation further confirmed that ADAMTS9-AS1 and circFN1 could competitively bind to miR-206, thereby augmenting ACTB expression. Taken all, ADAMTS9-AS1/circFN1-miR-206-ACTB regulatory network may involve in HCM occurrence, providing a novel theoretical basis for in-depth understanding of mechanism of HCM.

Identification of lncRNAs and Their Regulatory Relationships with mRNAs in Response to Cryptococcus neoformans Infection of THP-1 Cells

Aims. Cryptococcosis is an invasive fungal disease that is associated with an increasing prevalence along with a very high fatality and is primarily caused by Cryptococcus. However, its mechanism to cause pathogenicity is not yet completely understood. In this study, we aim to screen the lncRNA markers in human monocytic (THP-1) cells infected by Cryptococcus neoformans (C. neoformans) through high-throughput sequencing technology and to explore its effects on biological functions. Methods. We initially conducted an lncRNA microarray analysis of the THP-1 cells infected by C. neoformans and normal THP-1 cells. Based upon these data, RT-qPCR was used to verify the expressions of the selected lncRNAs and mRNAs. We then performed functional and pathway enrichment analyses. Lastly, target prediction was performed by using the lncRNA target tool which was based on the differentially expressed lncRNAs. Results. We determined 81 upregulated and 96 downregulated lncRNAs using microarray. In addition, the profiling data showed 42 upregulated and 57 downregulated genes and discovered that neuroactive ligand-receptor interaction, tyrosine metabolism, and phenylalanine metabolism are extremely impaired in the regulation of C. neoformans infection. GO enrichment analysis of the 99 differentially expressed mRNAs exhibited that these modules showed different signaling pathways and biological mechanisms like protein binding and metal ion binding. Moreover, lncRNAs and mRNAs were analyzed for their coexpression relations. A qRT-PCR analysis confirmed that the expression of the top 10 differently expressed mRNA and lincRNA. The expressions of the lncRNAs after C. neoformans infection in THP-1 cells were detected by RNA-sequence, suggesting that microarray analysis could reveal lncRNAs having functional significance that might be linked with the progression of patients. Conclusion. The current study analyzed the differential lncRNAs and mRNAs in C. neoformans infection and predicted the corresponding pathways and their correlations that can offer new potential insights into the mechanistic basis of this condition.

Long noncoding RNA ADAMTS9-AS1 represses ferroptosis of endometrial stromal cells by regulating the miR-6516-5p/GPX4 axis in endometriosis

Endometriosis (EMs) is one of the most frequent diseases of reproductive-age women and is characterized by the growth of endometrial tissues beyond the uterus. The enhanced proliferative and migratory potential of endometrial stromal cells (ESCs) plays an important role in the progression of EMs. Mounting studies have demonstrated that long noncoding RNAs (lncRNAs) exert an important role in regulating the development and progression of EMs. Given the aberrant expression of lncRNA ADAMTS9-AS1 in ectopic endometrium (ecEM), we investigated the biological effect of ADAMTS9-AS1 on ESC proliferation and migration and explored the underlying mechanism. The current data showed that ADAMTS9-AS1 expression was significantly upregulated in ecEM compared with eutopic endometrium (euEM) in patients with EMs and in a murine model of EMs. Functionally, ADAMTS9-AS1 knockdown in ectopic ESCs (EESCs) decreased cell viability and migration, whereas ADAMTS9-AS1 overexpression in normal ESCs (NESCs) enhanced cell viability and migration. More importantly, the effect of ADAMTS9-AS1 inhibition on decreasing ESC viability was significantly blocked by ferrostatin-1 (Fer-1, a ferroptosis inhibitor), and ADAMTS9-AS1 overexpression repressed erastin (a ferroptosis activator)-induced cell death. Furthermore, the regulatory role of ADAMTS9-AS1 in ferroptosis was defined and evidenced by increased reactive oxygen species (ROS) levels and malonyl dialdehyde (MDA) content and decreased expression of glutathione peroxidase 4 (GPX4) after ADAMTS9-AS1 inhibition. Mechanistically, ADAMTS9-AS1 functioned as a competing endogenous RNA (ceRNA) by sponging miR-6516-5p to derepress the expression of GPX4, the critical repressor of ferroptosis. Taken together, these results demonstrate that upregulated ADAMTS9-AS1 accelerates ESC proliferation and migration by regulating miR-6516-5p/GPX4-dependent ferroptosis and may be a potential target for the treatment of EMs.

Identification of Potential Genetic Biomarkers and Target Genes of Peri-Implantitis Using Bioinformatics Tools

Objectives

To investigate potential genetic biomarkers of peri-implantitis and target genes for the therapy of peri-implantitis by bioinformatics analysis of publicly available data.

Methods

The GSE33774 microarray dataset was downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between peri-implantitis and healthy gingival tissues were identified using the GEO2R tool. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database and the Metascape tool, and the results were expressed as a bubble diagram. The protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using Cytoscape. The hub genes were screened by the cytoHubba plugin of Cytoscape. The potential target genes associated with peri-implantitis were obtained from the DisGeNET database and the Open Targets Platform. The intersecting genes were identified using the Venn diagram web tool.

Results

Between the peri-implantitis group and the healthy group, 205 DEGs were investigated including 140 upregulated genes and 65 downregulated genes. These DEGs were mainly enriched in functions such as the immune response, inflammatory response, cell adhesion, receptor activity, and protease binding. The results of KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the cytokine-cytokine receptor interaction, pathways in cancer, and the PI3K-Akt signaling pathway. The intersecting genes, including IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1, were revealed as potential genetic biomarkers and target genes of peri-implantitis.

Conclusions

This study provides supportive evidence that IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1 might be used as potential target biomarkers for peri-implantitis which may provide further therapeutic potentials for peri-implantitis.

Identification of novel biomarkers in breast cancer via integrated bioinformatics analysis and experimental validation

Breast cancer (BC), an extremely aggressive malignant tumor, causes a large number of deaths worldwide. In this study, we pooled profile datasets from three cohorts to illuminate the underlying key genes and pathways of BC. Expression profiles GSE42568, GSE45827, and GSE124646, including 244 BC tissues and 28 normal breast tissues, were integrated and analyzed. Differentially expressed genes (DEGs) were screened out based on these three datasets. Functional analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plugin were utilized to visualize protein protein interaction (PPI) of these DEGs. The module with the highest connectivity of gene interactions was selected for further analysis. All of these hub genes had a significantly worse prognosis in BC by survival analysis. Additionally, four genes (CDK1, CDC20, AURKA, and MCM4) dramatically were enriched in oocyte meiosis and cell cycle pathways through re-analysis of DAVID. Moreover, the mRNA and protein levels of CDK1, CDC20, AURKA, and MCM4 were significantly increased in BC patients. In addition, knockdown of CDK1 and CDC20 by small interfering RNA remarkably suppressed cell migration and invasion in MCF-7 and MDA-MB-231 cells. In conclusion, our results suggested that CDK1, CDC20, AURKA, and MCM4 were reliable biomarkers of BC via bioinformatics analysis and experimental validation and may act as prospective targets for BC diagnosis and treatment.

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Biologia Futura: progress and future perspectives of long non-coding RNAs in forest trees

Forest trees are affected by climate change, anthropogenic pressure, as well as abiotic and biotic stresses. Conventional tree breeding has so far been limited to enhance overall productivity, and our understanding of the genetic basis of quantitative traits is still inadequate. Quantum leaps in next-generation sequencing technologies and bioinformatics have permitted the exploration and identification of various non-coding regions of the genome other than protein coding genes. These genomic regions produce various types of non-coding RNAs and regulate myriads of biological functions at epigenetic, transcriptional and translational levels. Recently, long non-coding RNAs (lncRNAs) which act as molecular switch have been identified to be pivotal molecules in forest trees. This review focuses on progress made in regulatory mechanisms in various developmental phases like wood formation, adventitious rooting and flowering and stress responses. It was predicted that complex regulatory interactions among lncRNA, miRNA and gene exist. LncRNAs can function as a sponge for miRNAs, reducing the suppressive effect of miRNAs on target mRNAs and perhaps adding a new layer of regulatory interactions among non-coding RNA classes in trees. Furthermore, network analysis revealed the interactions of lncRNA and genes during the expression of several important genes. The insights generated about lncRNAs in forest trees would enable improvement of economically important traits including the devastating abiotic and biotic stresses. In addition, solid understanding on the wide range of regulatory functions of lncRNAs on traits influencing biomass productivity and adaptation would aid the applications of biotechnology in genetic improvement of forest trees.

Identification of key molecular markers of acute coronary syndrome using peripheral blood transcriptome sequencing analysis and mRNA-lncRNA co-expression network construction

Acute coronary syndrome (ACS) is a term used to describe major cardiovascular diseases, and treatment of in-stent restenosis in patients with ACS remains a major clinical challenge. Further investigation into molecular markers of ACS may aid early diagnosis, and the treatment of ACS and post-treatment recurrence. In the present study, total RNA was extracted from the peripheral blood samples of 3 patients with ACS, 3 patients with percutaneous coronary intervention (PCI)_non-restenosis, 3 patients with PCI_restenosis and 3 healthy controls. Subsequently, RNA library construction and high-throughput sequencing were performed. DESeq2 package in R was used to screen genes that were differentially expressed between the different samples. Moreover, the intersection of the differentially expressed mRNAs (DEmRNAs) and differentially expressed long noncoding RNAs (DElncRNAs) obtained. GeneCodis4.0 was used to perform function enrichment for DEmRNAs, and lncRNA-mRNA co-expression network was constructed. The GSE60993 dataset was utilized for diagnostic analysis, and the aforementioned investigations were verified using in vitro studies. Results of the present study revealed a large number of DEmRNAs and DElncRNAs in the different groups. We selected genes in the top 10 of differential expression and also involved in the co-expression of lncRNA-mRNA for diagnostic analysis in the GSE60993 dataset. The area under curve (AUC) of PDZK1IP1 (0.747), PROK2 (0.769) and LAMP3 (0.725) were all >0.7. These results indicated that the identified mRNAs and lncRNAs may act as potential clinical biomarkers, and more specifically, PDZK1IP1, PROK2 and LAMP3 may act as potential biomarkers for the diagnosis of ACS.

Identification of Key Biomarkers and Pathways in Small-Cell Lung Cancer Using Biological Analysis

Background

Small-cell lung cancer (SCLC) is a major cause of carcinoma-related deaths worldwide. The aim of this study was to identify the key biomarkers and pathways in SCLC using biological analysis.

Methods

Key genes involved in the development of SCLC were identified by downloading three datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the GEO2R online analyzer; for the functional annotation and pathway enrichment analysis of genes, Funrich software was used. Construction of protein-to-protein interaction (PPI) networks was accomplished using the Search Tool for the Retrieval of Interacting Genes (STRING), and network visualization and module identification were performed using Cytoscape.

Results

A total of 268 DEGs were ultimately obtained. The enriched functions and pathways of the upregulated DEGs included cell cycle, mitotic, and DNA replication, and the downregulated DEGs were enriched in epithelial-to-mesenchymal transition, serotonin degradation, and noradrenaline. Analysis of significant modules demonstrated that the upregulated genes are primarily concentrated in functions related to cell cycle and DNA replication. Kaplan-Meier analysis of hub genes revealed that they may promote the carcinogenesis and progression of SCLC. The result of ONCOMINE demonstrated that these 10 hub genes were significantly overexpressed in SCLC compared with normal samples.

Conclusion

Identification of the molecular functions and signaling pathways of participating DEGs can deepen the current understanding of the molecular mechanisms of SCLC. The knowledge gained from this work may contribute to the development of treatment options and improve the prognosis of SCLC in the future.

Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

Background

The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach.

Methods

We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log₂ fold change (FC) | >2.

Results

A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape.

Conclusions

We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

LncRNA FGF14-AS2 represses growth of prostate carcinoma cells via modulating miR-96-5p/AJAP1 axis

OBJECTIVE

This investigation devoted to lncRNA FGF14 antisense RNA 2 (FGF14-AS2) in prostate carcinoma progression.

METHODS

The levels of lncRNA FGF14-AS2, miR-96-5p, and Adherens junction-associated protein-1 (AJAP1) in prostate carcinoma were tested by Western blot and qRT-PCR. How these two genes interacted was confirmed by RNA immunoprecipitation and dualluciferase gene methods. The effect of FGF14-AS2/miR-96-5p/AJAP1 axis in prostate carcinoma progression was determined by MTT, Transwell, and nude mice tumor model.

RESULTS

FGF14-AS2 was a downregulated lncRNA in prostate carcinoma tissue and cells. FGF14-AS2 could restrain miR-96-5p expression while miR-96-5p hampered AJAP1. FGF14-AS2 could effectively decrease the biological behaviors of prostate carcinoma cells, while knock-down of FGF14-AS2 triggered opposite results. Moreover, miR-96-5p mimic presented a cancer promoter role in prostate carcinoma cells. AJAP1 expression level could affect levels of proteins related to epithelial-mesenchymal transition. In vivo experiment suggested that overexpressing FGF14-AS2 could reverse the promotion of silenced AJAP1 on prostate carcinoma cell metastasis, thus to inhibit tumor growth.

CONCLUSION

lncRNA FGF14-AS2 was a downregulated lncRNA in prostate carcinoma and influenced cell proliferation and metastasis. The influence relied on modulating miR-96-5p and its target gene AJAP1.

Biomarkers of Blood from Patients with Atherosclerosis Based on Bioinformatics Analysis

Atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that attach to arteries and cause cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, computerized tomography, and nuclear scans, assess cardiovascular disease risk and treatment targets. However, there is currently no simple blood biochemical index or biological target for the diagnosis of atherosclerosis. Therefore, it is of interest to find a biochemical blood marker for atherosclerosis. Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using the Robustrankaggreg algorithm. The genes considered more critical by the Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification. Twenty-one possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY, and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cells. In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the analysis of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

Identification of Vital Hub Genes and Potential Molecular Pathways of Dermatomyositis by Bioinformatics Analysis

Dermatomyositis is an autoimmune disease characterized by severe symmetrical muscle dysfunction and pain. This study was aimed at discovering vital hub genes and potential molecular pathways of DM through bioinformatics analysis, which contributes to identifying potential diagnostic or therapeutic biomarkers and targets. In this study, a total of 915 DEGs in DM samples including 167 upregulated genes and 748 downregulated genes were screened out by the limma package based on the GSE142807 dataset from the Gene Expression Omnibus (GEO) database. Furthermore, the results of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these downregulated genes were highly associated with the immune-related biological processes and pathways. Therefore, 41 genes closely related to DM were extracted for further study based on the subcluster analysis through the Molecular Complex Detection (MCODE) software plugin in Cytoscape. Ultimately, 10 hub genes (including ISG15, DDX58, IFIT3, CXCL10, and STAT1) were identified as the potential candidate biomarkers and targets. Besides, we found that the identified hub genes directly or indirectly communicated with each other via molecular signaling pathways on the protein and transcription level. In general, under the guidance of bioinformatics analysis, 10 vital hub genes and molecular mechanisms in DM were identified and the expression of proinflammatory factors and interferon family proteins and genes showed high association with DM, which might help provide a theoretical foundation for the development of point-to-point targeted therapy in the future treatment of DM.

Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the “limma” package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and “cytoHubba” were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

Bio-informatics and in Vitro Experiments Reveal the Mechanism of Schisandrin A Against MDA-MB-231 cells

Schisandrin A (SchA) has been reported to have good anti-cancer effects. However, its anti-cancer mechanism in breast cancer remains unknown. This study aimed to explore the mechanism of SchA in breast cancer treatment using bio-informatics analysis and in vitro experiments. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Cards, and PharmMapper databases were used to screen the candidate targets of SchA against MDA-MB-231 cells selected as the tested cell line through MTT analysis. The functions and pathways of the targets were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and further analyzed using DAVID 6.8.1 database. Network pharmacology analysis revealed 77 candidate targets, 31 signal pathways, and 208 GO entries (P < 0.05). The targets regulated serine-type endopeptidase and protein tyrosine kinase activities, thereby promoting the migration and inhibiting the apoptosis of MDA-MB-231 cells. Comprehensive analysis of the ‘Protein–Protein Interaction’ (PPI) and ‘Component-Targets-Pathways’ (C-T-P) networks constructed using Cytoscape 3.7.1 software revealed four core targets: EGFR, PIK3R1, MMP9 and Caspase 3. Their docking scores with SchA were subsequently investigated through molecular docking. The wound healing, Hoechst 33342/PI, and western blot assays confirmed that SchA significantly down-regulated EGFR, PIK3R1, and MMP9, but up-regulated cleaved-caspase 3, thus inhibiting the migration and promoting the apoptosis of MDA-MB-231 cells. Reckoning the findings of the study, SchA is a potential adjuvant treatment for breast cancer.

lncRNA ADAMTS9-AS1 promotes bladder cancer cell invasion, migration, and inhibits apoptosis and autophagy through PI3K/AKT/mTOR signaling pathway

Bladder cancer is the most common cancer in the urinary system which has threatened lives. Increasing evidence has shown that lncRNAs were expressed in cancer cells as a biomarker and might regulate cancer cell development and progression. It was reported that ADAMTS9-AS1 plays a suppressive role in tumor cell growth and proliferation in prostate cancer. However, it remains unclear whether ADAMTS9-AS1 influences the function of bladder cancer cells. In this study, we detected that ADAMTS9-AS1 is highly expressed in bladder cancer. We observed that the up-regulation of ADAMTS9-AS1 promoted cell proliferation, migration and invasion, and reduced the apoptosis and autophagy of 5637 and T42 cell lines. Meanwhile, the down-regulation of ADAMTS9-AS1 increased the apoptosis and autophagy, and suppressed their proliferation, migration and invasion. Interestingly, the up-regulation of ADAMTS9-AS1 was accompanied by the activation of PI3K/AKT/mTOR signaling pathway, while the down-regulation of ADAMTS9-AS1 lead to an opposite effect. Together, these results demonstrated that ADAMTS9-AS1 promotes bladder cancer cell invasion and migration, and negatively regulates bladder cancer cell apoptosis and autophagy, which might be through PI3K/AKT/mTOR signaling pathway. Targeting ADAMTS9-AS1 might become a potential therapeutic approach in treating bladder cancer.

Bioinformatics Analysis and Functional Verification of ADAMTS9-AS1/AS2 in Lung Adenocarcinoma

Long non-coding RNAs (lncRNAs), as competitive endogenous RNAs (ceRNAs), play a critical role in biological processes of cancer. However, the roles of specific lncRNAs in ceRNA network of lung adenocarcinoma (LUAD) remains largely unclear. Herein, we identified the roles of lncRNA ADAMTS9-AS1/AS2 (ADAMTS-AS1/AS2) in lung adenocarcinoma by bioinformatics analyses and functional verification. First, differentially expressed genes ADAMTS9-AS1, ADAMTS9-AS2 and ADAMTS9 were screened out from GSE130779. Then the expression correlation of these three genes was analyzed. The results showed that ADAMTS9-AS1, ADAMTS9-AS2 and ADAMTS9 were down-regulated in LUAD, and were positively correlated with each other. After that, miRcode was used to find miR-150 which binds to ADAMTS9-AS1/ADAMTS9-AS2/ADAMTS9. Next, co-expression analysis and functional enrichment analyses were performed to further analyze differentially expressed genes. The results showed that the differentially expressed genes were mainly enriched in Beta3 integrin cell surface interactions and epithelial-to-mesenchymal transition. Finally, the cell functions of ADAMTS9-AS1 and ADAMTS9-AS2 in A549 and NCI-H1299 cell lines were verified. In vitro cell studies confirmed that ADAMTS9-AS1 and ADAMTS9-AS2 play an inhibitory role in LUAD cells.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy

Background

Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.

Materials and Methods

The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.

Results

Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.

Conclusions

AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Identification of potential autophagy-associated lncRNA in prostate cancer

Background: Long non-coding RNAs (lncRNAs) have been linked to autophagy. It is urgent to identify and assess the hub autophagy-associated lncRNA in prostate cancer.

Methods: Differentially expressed lncRNAs associated with autophagy were identified in prostate cancer based on The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) data. An autophagy-mediated competing endogenous RNA network was constructed to screen for autophagy-associated lncRNA, and the preselected lncRNAs were further validated using Gene Expression Omnibus (GEO) datasets. Furthermore, a prognostic lncRNA signature was established and assessed. Additionally, Gene Set Enrichment Analysis (GSEA) revealed the underlying molecular mechanisms.

Results: Using a competing endogenous RNA network, 66 differentially expressed lncRNAs associated with autophagy were identified, and the differential expression of 7 lncRNAs were verified using the TCGA-PRAD, GSE21034, and GSE94767 datasets. Additionally, a lncRNA signature associated with autophagy, including MKNK1-AS1 and INE1, was identified as an independent indicator of survival with a C-index of 0.882. The GSEA analysis indicated that several autophagy-related signaling pathways were enriched in different risk groups.

Conclusions: The lncRNAs associated with autophagy were identified, and a prediction model was developed that could be used as a prognostic predictor for prostate cancer, indicating the critical role of lncRNA in the regulation of prostate cancer autophagy regulation.

Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis

Background

Breast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective targets for breast cancer patients via bioinformatical analysis.

Methods

GSE86374, GSE5364, and GSE70947 were chosen from the GEO database. DEGs between breast cancer tissues and normal breast tissues were picked out by GEO2R and Venn diagram software. Then, DAVID (Database for Annotation, Visualization, and Integrated Discovery) was used to analyze these DEGs in gene ontology (GO) including molecular function (MF), cellular component (CC), and biological process (BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. Next, STRING (Search Tool for the Retrieval of Interacting Genes) was used to investigate potential protein-protein interaction (PPI) relationships among DEGs and these DEGs were analyzed by Molecular Complex Detection (MCODE) in Cytoscape. After that, UALCAN, GEPIA (gene expression profiling interactive analysis), and KM (Kaplan-Meier plotter) were used for the prognostic information and core genes were qualified.

Results

There were 96 upregulated genes and 98 downregulated genes in this study. 55 upregulated genes were selected as hub genes in the PPI network. For validation in UALCAN, GEPIA, and KM, 5 core genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) were found to highly expressed in breast cancer tissues with poor prognosis. They differentially expressed between different subclasses of breast cancer.

Conclusion

These five genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) could be potential targets for therapy in breast cancer and prediction of prognosis on the basis of bioinformatical analysis.

CXCL1 Clone Evolution Induced by the HDAC Inhibitor Belinostat Might Be a Favorable Prognostic Indicator in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer due to its lack of treatment options. Patients with TNBC frequently develop resistance to chemotherapy. As epigenetic-based antineoplastic drugs, histone deacetylase inhibitors (HDACis) have achieved particular efficacy in lymphoma but are less efficacious in solid tumors, and the resistance mechanism remains poorly understood. In this study, the GSE129944 microarray dataset from the Gene Expression Omnibus database was downloaded, and fold changes at the transcriptome level of a TNBC line (MDA-MB-231) after treatment with belinostat were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to identify the critical biological processes. Construction and analysis of the protein-protein interaction (PPI) network were performed to screen candidate genes related to cancer prognosis. A total of 465 DEGs were identified, including 240 downregulated and 225 upregulated genes. The cytokine-cytokine receptor pathway was identified as being significantly changed. Furthermore, the expression of CXCL1 was implicated as a favorable factor in the overall survival of breast cancer patients. With in vitro approaches, we also showed that belinostat could induce the expression of CXCL1 in another 2 TNBC cell lines (BT-549 and HCC-1937). We speculate that belinostat-induced CXCL1 expression could be one of the results of the stress clone evolution of cells after HDACi treatment. These findings provide new insights into clone evolution during HDACi treatment, which might guide us to a novel perspective that various mutation-targeted treatments should be implemented during the whole treatment cycle.

Long non-coding RNA ADAMTS9-AS1 inhibits the progression of prostate cancer by modulating the miR-142-5p/CCND1 axis

BACKGROUND

Emerging evidence has implied the importance of long non-coding RNAs in cancer development, including prostate cancer (PCa). Bioinformatic analyses have identified that ADAMTS9-AS1 may play a role in cancer progression.

METHODS

A quantitative real-time polymerase chain reaction was conducted to measure ADAMTS9-AS1 expression level at messenger RNA level. In vitro functional analyses were performed to investigate cell behaviors status under different conditions. Moreover, rescue assays were conducted to explore the potential mechanisms of ADAMTS9-AS1 in PCa progression.

RESULTS

ADAMTS9-AS1 expression is down-regulated in PCa. Forcing ADAMTS9-AS1 expression impedes PCa cell proliferation via initiating cell apoptosis. Importantly, microRNA-142-5p (miR-142-5p) mimic and small-interfering RNA targeting cyclin D1 (CCND1, si-CCND1) could attenuate the inhibitory effects of ADAMTS9-AS1 overexpression on PCa cell growth.

CONCLUSIONS

Collectively, our results indicate that ADAMTS9-AS1 suppresses PCa progression by regulating the miR-142-5p/CCND1 axis, which provides a new mechanism for the progression of PCa.

Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

Identification of long non-coding RNA signatures for squamous cell carcinomas and adenocarcinomas

Studies have demonstrated that both squamous cell carcinomas (SCCs) and adenocarcinomas (ACs) possess some common molecular characteristics. Evidence has accumulated to support the theory that long non-coding RNAs (lncRNAs) serve as novel biomarkers and therapeutic targets in complex diseases such as cancer.

In this study, we aimed to identify pan lncRNA signatures that are common to squamous cell carcinomas or adenocarcinomas with different tissues of origin. With the aid of elastic-net regularized regression models, a 35-lncRNA pan discriminative signature and an 11-lncRNA pan prognostic signature were identified for squamous cell carcinomas, whereas a 6-lncRNA pan discriminative signature and a 5-lncRNA pan prognostic signature were identified for adenocarcinomas. Among them, many well-known cancer relevant genes such as MALAT1 and PVT1 were included.

The identified pan lncRNA lists can help experimental biologists generate research hypotheses and adopt existing treatments for less prevalent cancers. Therefore, these signatures warrant further investigation.

LncRNA ADAMTS9-AS1 Restrains the Aggressive Traits of Breast Carcinoma Cells via Sponging miR-513a-5p

Purpose

Long noncoding RNAs (lncRNAs) exert important functions in the progression of cancers. Currently, we aim to investigate the potential roles of lncRNA ADAM Metallopeptidase with Thrombospondin Type 1 Motif 9 Antisense RNA 1 (ADAMTS9-AS1) in breast carcinoma.

Materials and Methods

The expressions of ADAMTS9-AS1 and miR-513a-5p in breast carcinoma tissues and cell lines were detected using qRT-PCR. Cell Counting Kit-8 (CCK-8) and transwell assays were used to assess the viability and invasive ability of breast cancer cells. The direct interaction between ADAMTS9-AS1 and miR-513a-5p was predicted using bioinformatics tools. The target of miR-513a-5p, ZFP36 Ring Finger Protein (ZFP36) was validated by luciferase assay. The expression of ZFP36 was measured using Western blot assay. Breast cancer MDA-MB-231 cells growth in vivo was evaluated using xenograft tumor assay.

Results

ADAMTS9-AS1 was downregulated in breast cancer tissues as well as cell lines. Upregulation of ADAMTS9-AS1 suppressed the growth and invasiveness of breast carcinoma cells in vitro as well as inhibiting cellgrowth in vivo. Furthermore, ZFP36 was manifested as the target gene of miR-513a-5p and negatively modulated by ADAMTS9-AS1. In addition, overexpression of ADAMTS9-AS1 neutralized the promoting impact of miR-513a-5p on the aggressiveness of breast cancer cells.

Conclusion

In conclusion, lncRNA ADAMTS9-AS1 inhibited the aggressive phenotypes of breast carcinoma cells via sponging miR-513a-5p and regulating ZFP36.

LncRNA ADAMTS9-AS1, as prognostic marker, promotes cell proliferation and EMT in colorectal cancer

The long non-coding RNA antisense 1 ADAMTS9-AS1 has been reported to predict the survival in several tumors, including bladder cancer and breast cancer. However, the clinical significance and biological behaviors of ADAMTS9-AS1 in colorectal cancer (CRC) have not been reported yet. In this study, the expression of ADAMTS9-AS1 was measured in CRC tissues and cell lines using quantitative real-time PCR analysis. The clinical significance of ADAMTS9-AS1 was evaluated with Chi-squared test, Kaplan-Meier method and Cox regression analysis in CRC patients. CCK8 assay, colony formation assay, flow cytometry and transwell assay were used to explore the biological function of ADAMTS9-AS1 knockdown in CRC cell lines (SW1116 and HT29). We further explore the role of ADAMTS9-AS1 in vivo though xenograft tumor assay. Our data showed that ADAMTS9-AS1 expression level was significantly up-regulated in CRC tissues and cell lines compared with corresponding controls. High ADAMTS9-AS1 level was associated with TNM stage, lymph node invasion and worse survival prognosis. Depletion of ADAMTS9-AS1 significantly suppressed cell proliferation, G1/S transition, migration and invasion, as well as suppressed CDK4/Cyclin D1 and epithelial-mesenchymal transition (EMT). To sum up, these findings illustrated that ADAMTS9-AS1 might be a promising therapeutic target and prognostic factor for CRC.

RNA delivery by extracellular vesicles in mammalian cells and its applications

The term 'extracellular vesicles' refers to a heterogeneous population of vesicular bodies of cellular origin that derive either from the endosomal compartment (exosomes) or as a result of shedding from the plasma membrane (microvesicles, oncosomes and apoptotic bodies). Extracellular vesicles carry a variety of cargo, including RNAs, proteins, lipids and DNA, which can be taken up by other cells, both in the direct vicinity of the source cell and at distant sites in the body via biofluids, and elicit a variety of phenotypic responses. Owing to their unique biology and roles in cell-cell communication, extracellular vesicles have attracted strong interest, which is further enhanced by their potential clinical utility. Because extracellular vesicles derive their cargo from the contents of the cells that produce them, they are attractive sources of biomarkers for a variety of diseases. Furthermore, studies demonstrating phenotypic effects of specific extracellular vesicle-associated cargo on target cells have stoked interest in extracellular vesicles as therapeutic vehicles. There is particularly strong evidence that the RNA cargo of extracellular vesicles can alter recipient cell gene expression and function. During the past decade, extracellular vesicles and their RNA cargo have become better defined, but many aspects of extracellular vesicle biology remain to be elucidated. These include selective cargo loading resulting in substantial differences between the composition of extracellular vesicles and source cells; heterogeneity in extracellular vesicle size and composition; and undefined mechanisms for the uptake of extracellular vesicles into recipient cells and the fates of their cargo. Further progress in unravelling the basic mechanisms of extracellular vesicle biogenesis, transport, and cargo delivery and function is needed for successful clinical implementation. This Review focuses on the current state of knowledge pertaining to packaging, transport and function of RNAs in extracellular vesicles and outlines the progress made thus far towards their clinical applications.

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

BACKGROUND

Long noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network.

METHODS

RNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors.

RESULTS

After compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively.

CONCLUSIONS

This study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches.