Circular RNA (CircRNA) plays an important role in tumorigenesis and progression of non-small cell lung cancer (NSCLC), but the pathogenesis of NSCLC caused by circRNA has not been fully elucidated. This study aimed to investigate differentially expressed circRNAs and identify the underlying pathogenesis hub genes of NSCLC by comprehensive bioinformatics analysis. Data of gene expression microarrays (GSE101586, GSE101684, and GSE112214) were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DECs) were obtained by the “limma” package of R programs and the overlapping operation was implemented of DECs. CircBase database and Cancer-Specific CircRNA database (CSCD) were used to find miRNAs binding to DECs. Target genes of the found miRNAs were identified utilizing Perl programs based on miRDB, miRTarBase, and TargetScan databases. Functional and enrichment analyses of selected target genes were performing using the “cluster profiler” package. Protein-protein interaction (PPI) network was constructed by the Search Tool for the STRING database and module analysis of selected hub genes was performed by Cytoscape 3.7.1. Survival analysis of hub genes were performed by Gene Expression Profiling Interactive Analysis (GEPIA). Respectively, 1 DEC, 249 DECs, and 101 DECs were identified in GSE101586, GSE101684, and GSE112214. A total of eight overlapped circRNAs, 43 miRNAs and 427 target genes were identified. Gene Ontology (GO) enrichment analysis showed these target genes were enriched in biological processes of regulation of histone methylation, Ras protein signal transduction and covalent chromatin modification etc. Pathway enrichment analysis showed these target genes are mainly involved in AMPK signaling pathway, signaling pathways regulating pluripotency of stem cells and insulin signaling pathway etc. A PPI network was constructed based on 427 target genes of the 43 miRNAs. Ten hub genes were found, of which the expression of MYLIP, GAN, and CDC27 were significantly related to NSCLC patient prognosis. Our study provide a deeper understanding the circRNAs-miRNAs-target genes by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of NSCLC. MYLIP, GAN, and CDC27 genes might serve as novel biomarker for precise treatment and prognosis of NSCLC in the future.
The growing evidence suggests that circular RNAs (circRNAs) have significant associations with tumor occurrence and progression, yet the regulatory mechanism of circRNAs in lung adenocarcinoma (LUAD) remains unclear. This study clarified the potentially regulatory network and functional mechanism of circRNAs in LUAD. The expression data of circRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were obtained from the Gene Expression Omnibus (GEO) database. Relying on GSE101586, GSE101684, and GSE112214, we identified differentially expressed circRNAs (DEcircRNAs). Depending on GSE135918 and GSE32863, we screened out differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs), respectively. Then, a novel competing endogenous RNA (ceRNA) regulatory network related to LUAD was constructed. We also revealed biological processes and signal pathways regulated by these DEcircRNAs. Based on gene expression data and survival information of LUAD patients in The Cancer Genome Atlas (TCGA) and GEO, we implemented survival analysis to select DEmRNAs related to prognosis and build a novel circRNA–miRNA–mRNA hub regulatory network. Meanwhile, quantitative real-time PCR (qRT-PCR) was utilized to validate DEcircRNAs in the ceRNA hub regulatory network. As a result, a total of 8 DEcircRNAs, 19 DEmiRNAs, and 85 DEmRNAs were identified. The novel ceRNA regulatory network included 5 circRNAs, 8 miRNAs, and 22 mRNAs. The final ceRNA hub regulatory network contained two circRNAs, two miRNAs, and two mRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the five DEcircRNAs may affect LUAD onset and progression through Wnt signaling pathway and Hippo signaling pathway. All in all, this study revealed the regulatory network and functional mechanism of circRNA-related ceRNAs in LUAD.
Hyaluronic acid-mediated motility receptor (HMMR), a tumor-related gene, plays a vital role in the occurrence and progression of various cancers. This research is aimed to reveal the effect of HMMR in lung adenocarcinoma (LUAD). We first obtained the gene expression profiles and clinical data of patients with LUAD from The Cancer Genome Atlas (TCGA) database. Then, based on the TCGA cohort, the HMMR expression difference between LUAD tissues and nontumor tissues was detected and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort, and Gene Expression Omnibus (GEO) cohort. Logistic regression analysis and chi-square test were adopted to study the correlation between HMMR expression and clinicopathological parameters. The effect of HMMR expression on survival was evaluated by Kaplan–Meier survival analysis and using the Cox regression model. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to screen out signaling pathways related to LUAD and the co-expression analysis was employed to build the protein–protein interaction (PPI) network. The HMMR expression level in LUAD tissues was dramatically higher than that in nontumor tissues. Logistic regression analysis and chi-square test demonstrated that the high HMMR expression in LUAD has relation with gender, pathological stage, T classification, lymph node metastasis, and distant metastasis. The Kaplan–Meier curve suggested a poor prognosis for LUAD patients with high HMMR expression. Multivariate analysis implied that the high HMMR expression was a vital independent predictor of poor overall survival (OS). GSEA indicated that a total of 15 signaling pathways were enriched in samples with the high HMMR expression phenotype. The PPI network gave 10 genes co-expressed with HMMR. HMMR may be an oncogene in LUAD and is expected to become a potential prognostic indicator and therapeutic target for LUAD.
In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.
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