An Integrated Analysis of miRNA and mRNA Expressions of Non-small Cell Lung Cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Ma L
PROVIDER: S-ECPF-GEOD-29250 | biostudies-other | 2011
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Using DNA microarrays, we generated both mRNA and miRNA expression data from 6 non-small cell lung cancer (NSCLC) tissues and their matching normal control from adjacent tissues to identify potential miRNA markers for diagnostics. We demonstrated that hsa-miR-96 is significantly and consistently up-regulated in all 6 NSCLCs. We validated this result in an independent set of 35 paired tumors and their adjacent normal tissues, as well as their sera that are collected before surgical resection or chemotherapy, and the results suggested that hsa-miR-96 may play an important role in NSCLC development and has great potential to be used as a noninvasive marker for diagnosing NSCLC. We predicted potential miRNA target mRNAs based on different methods (TargetScan and miRanda). Further classification of miRNA regulated genes based on their relationship with miRNAs revealed that hsa-miR-96 and certain other miRNAs tend to down-regulate their target mRNAs in NSCLC development, which have expression levels permissive to direct interaction between miRNAs and their target mRNAs. In addition, we identified a significant correlation of miRNA regulation with genes coincide with high density of CpG islands, which suggests that miRNA may represent a primary regulatory mechanism governing basic cellular functions and cell differentiations, and such mechanism may be complementary to DNA methylation in repressing or activating gene expression.
| S-EPMC3203153 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series: GSE29248: Genome-wide analysis of microRNA expression in Non-small Cell Lung Cancer GSE29249: Genome-wide analysis of gene expression in Non-small Cell Lung Cancer Refer to individual Series
2012-09-16 | E-GEOD-29250 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2012-09-16 | GSE29250 | GEO
Project description:An Integrated Analysis of miRNA and mRNA Expressions of Non-small Cell Lung Cancer
| PRJNA139965 | ENA
Project description:ObjectiveNon-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers, but its pathogenesis has not been fully elucidated. Therefore, it is valuable to explore the pathogenesis of NSCLC to improve diagnosis and identify novel treatment biomarkers.MethodsCircular (circ)RNA, micro (mi)RNA, and gene expression datasets of NSCLC were analyzed to identify those that were differentially expressed between tumor and healthy tissues. Common genes were found and pathway enrichment analyses were performed. Survival analysis was used to identify hub genes, and their level of methylation and association with immune cell infiltration were analyzed. Finally, an NSCLC circRNA-miRNA-mRNA network was constructed.ResultsEight miRNAs and 211 common genes were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that cell projection morphogenesis, blood vessel morphogenesis, muscle cell proliferation, and synapse organization were enriched. Ten hub genes were found, of which the expression of DTL and RRM2 was significantly related to NSCLC patient prognosis. Significant methylation changes and immune cell infiltration correlations with DTL and RRM2 were also detected.Conclusionshsa_circ_0001947/hsa-miR-637/RRM2 and hsa_circ_0072305/hsa-miR-127-5p/DTL networks were constructed, and identified molecules may be involved in the occurrence and development of NSCLC.
| S-EPMC7294496 | biostudies-literature
Project description:BackgroundWe investigated the relationship between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) based on micro ribonucleic acid (miRNA) and messenger (m)RNA expression profiles.MethodsUtilizing the differentially expressed mRNAs and the targeting miRNAs, the mRNA-miRNA network for the two cancers was constructed. By integrating the miRNA expression profile, drug, and drug targets, miRNA-drug target-drug networks were established and the mechanisms in drug therapy efficacy were compared between SCLC and NSCLC.ResultsDrug targets of different expressed miRNAs of SCLC are mainly located in the organelle, act in the electron carrier activity, and consist of the synapse; while drug targets of NSCLC are the membrane-enclosed lumen, mainly distributed in the extracellular region and synapse, and function in the binding. Drug targets of miRNA expressed commonly in the two cancers are involved in the reproduction multi-organism process. In SCLC, the miR-16 in the miRNA-drug target-drug network is significant and follows the result of the mRNA-miRNA network. The pigmentation and rhythmic process of SCLC is different from NSCLC, while the process of cellular component biogenesis and cellular component organization are important for the occurrence of NSCLC. miR-16 in the miRNA-mRNA-drug network of SCLC is significant and we acquired 11 potential drugs, such as dexamethasone and budesonide. The miR-124 for NSCLC is important in the network and 17 potential drugs were screened, including dexamethasone and budesonide.ConclusionsThese findings suggest that miR-16 and miR-124 might be novel diagnostic and prognostics markers for SCLC and NSCLC, respectively.
| S-EPMC4448473 | biostudies-literature
Project description:Background:Recent evidence has indicated that long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to modulate mRNAs expression by sponging microRNAs (miRNAs). However, the specific mechanism and function of lncRNA-miRNA-mRNA regulatory network in non-small cell lung cancer (NSCLC) remains unclear. Materials and Methods:We constructed a lung cancer related lncRNA-mRNA network (LCLMN) by integrating differentially expressed genes (DEGs) with miRNA-target interactions. We further performed topological feature analysis and random walk with restart (RWR) analysis of LCLMN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate the target DEGs in LCLMN. The expression levels of significant lncRNAs in NSCLC were validated by quantitative real-time PCR (RT-qPCR). The prognostic value of the potential lncRNA was evaluated by Kaplan-Meier analysis. Results:A total of 33 lncRNA nodes, 580 mRNA nodes and 2105 edges were identified from LCLMN. Based on functional enrichment analysis and co-expression analysis, lncRNA EPB41L4A-AS1 was demonstrated to be correlated with the tumorigenesis of NSCLC. RT-qPCR results confirmed that the expression levels of lncRNA EPB41L4A-AS1 in NSCLC tissues were downregulated compared with adjacent non-cancerous tissues. Kaplan-Meier analysis showed that high expression of lncRNA EPB41L4A-AS1 was associated with better overall survival (OS) in NSCLC patients. Further investigation identified that high expression levels of COL4A3BP, CDS2, PURA, PDCD6IP, and TMEM245 were also correlated with better OS in NSCLC patients. Conclusion:In this study, we constructed a lncRNA-miRNA-mRNA ceRNA network to investigate potential prognostic biomarkers for NSCLC. We found that lncRNA EPB41L4A-AS1 could function as a regulator in the pathogenesis of NSCLC.
| S-EPMC7530329 | biostudies-literature
Project description:The present study aimed to explore specific molecular targets for the diagnosis and treatment of non?small cell lung cancer (NSCLC). The expression profiles of microRNAs (miRNAs) and mRNAs were downloaded from the GEO (GSE102286 and GSE101929) and TCGA databases. After data preprocessing, differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in cancer and normal tissues were selected and used to construct a DEM?DEG regulatory network and a protein?protein interaction (PPI) network. The genes and miRNAs in these networks were subjected to functional enrichment and survival analyses. Several key DEMs and DEGs were verified using RT?qPCR, and the results were statistically interpreted using a multivariate logistic regression analysis. In this study, 25 DEMs and 789 DEGs common to all datasets were identified, which were then used for the construction of a DEM?DEG regulatory network and a PPI network module. Survival analyses of 19 DEMs in the DEM?DEG regulatory network and 36 DEGs in the PPI network module revealed that 34 DEGs (including TOP2A, CCNB1, BIRC5, and TTK) and two miRNAs (miR?21?5p and miR?31?5p) were significantly associated with NSCLC prognosis. Moreover, RT?qPCR analysis identified three DEGs and five DEMs that had changes in expression consistent with those observed in the bioinformatic analysis. Finally, a multivariate logistic regression analysis of the data showed that TOP2A, CCNB1, BIRC5, miR?21?5p, miR?193b?3p, miR?210?3p and miR?31?5p could be combined for the diagnosis of NSCLC. In conclusion, TOP2A, CCNB1, BIRC5, miR?21?5p, miR?193b?3p, miR?210?3p and miR?31?5p may therefore serve as important biomarkers and diagnostic targets for NSCLC.
| S-EPMC6908938 | biostudies-literature
Project description:Small‑cell lung cancer (SCLC) is a type of lung cancer with early metastasis, and high recurrence and mortality rates. The molecular mechanism is still unclear and further research is required. The aim of the present study was to examine the pathogenesis and potential molecular markers of SCLC by comparing the differential expression of mRNA and microRNA (miRNA) between SCLC tissue and normal lung tissue. A transcriptome sequencing dataset (GSE6044) and a non‑coding RNA sequence dataset (GSE19945) were downloaded from the Gene Expression Omnibus (GEO) database. In total, 451 differentially expressed genes (DEGs) and 134 differentially expressed miRNAs (DEMs) were identified using the R limma software package and the GEO2R tool of the GEO, respectively. The Gene Ontology function was significantly enriched for 28 terms, and the Kyoto Encyclopedia of Genes and Genomes database had 19 enrichment pathways, mainly related to 'cell cycle', 'DNA replication' and 'oocyte meiosis mismatch repair'. The protein‑protein interaction network was constructed using Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities in SCLC. The 1,402 miRNA‑gene pairs encompassed 602 target genes of the DEMs using miRNAWalk, which is a bioinformatics platform that predicts DEM target genes and miRNA‑gene pairs. There were 19 overlapping genes regulated by 32 miRNAs between target genes of the DEMs and DEGs. Bioinformatics analysis may help to better understand the role of DEGs, DEMs and miRNA‑gene pairs in cell proliferation and signal transduction. The related hub genes may be used as biomarkers for the diagnosis and prognosis of SCLC, and as potential drug targets.
| S-EPMC6691276 | biostudies-literature
Project description:BackgroundThe underlying circular RNAs (circRNAs)-related competitive endogenous RNA (ceRNA) mechanisms of pathogenesis and prognosis in non-small cell lung cancer (NSCLC) remain unclear.MethodsDifferentially expressed circRNAs (DECs) in two Gene Expression Omnibus datasets (GSE101684 and GSE112214) were identified by utilizing R package (Limma). Circinteractome and StarBase databases were used to predict circRNA associated-miRNAs and mRNAs, respectively. Then, protein-protein interaction (PPI) network of hub genes and ceRNA network were constructed by STRING and Cytoscape. Also, analyses of functional enrichment, genomic mutation and diagnostic ROC were performed. TIMER database was used to analyze the association between immune infiltration and target genes. Kaplan-Meier analysis, cox regression and the nomogram prediction model were used to evaluate the prognostic value of target genes. Finally, the expression of potential circRNAs and target genes was validated in cell lines and tissues by quantitative real-time PCR (qRT-PCR) and Human Protein Atlas (HPA) database.ResultsIn this study, 15 DECs were identified between NSCLC tissues and adjacent-normal tissues in two GEO datasets. Following the qRT-PCR corroboration, 7 DECs (hsa_circ_0002017, hsa_circ_0069244, hsa_circ_026337, hsa_circ_0002346, hsa_circ_0007386, hsa_circ_0008234, hsa_circ_0006857) were dramatically downregulated in A549 and SK-MES-1 compared with HFL-1 cells. Then, 12 circRNA-sponged miRNAs were screened by Circinteractome and StarBase, especially, hsa-miR-767-3p and hsa-miR-767-5p were significantly up-regulated and relevant to the prognosis. Utilizing the miRDB and Cytoscape, 12 miRNA-target genes were found. Functional enrichment, genomic mutation and diagnostic analyses were also performed. Among them, FNBP1, AKT3, HERC1, COL4A1, TOLLIP, ARRB1, FZD4 and PIK3R1 were related to the immune infiltration via TIMER database. The expression of ARRB1, FNBP1, FZD4, and HERC1 was correlated with poor overall survival (OS) in NSCLC patients by cox regression and nomogram. Furthermore, the hub-mRNAs were validated in cell lines and tissues.ConclusionWe constructed the circRNA-miRNA-mRNA network that might provide novel insights into the pathogenesis of NSCLC and reveal promising immune infiltration and prognostic biomarkers.
| S-EPMC8605517 | biostudies-literature