Purpose This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment. Methods The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the “least absolute shrinkage and selection operator” in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset. Results A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4. Conclusion The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.
The morbidity of lung cancer ranks first among all cancers. Lung adenocarcinoma (LUAD) is a classification of lung cancer, and cell invasion and migration of LUAD are the main causes for its high mortality. Therefore, further exploring the potential mechanism of LUAD metastasis may provide bases for following targeted drug development and treatment of LUAD. In this study, clinical data as well as gene expression profiles were obtained from TCGA-LUAD and GEO to analyze CTHRC1 expression. The result found that CTHRC1 was significantly high in LUAD. Similar results were also discovered in 4 cancer cell lines. Moreover, overexpressed/knock-down CTHRC1 cell lines were constructed. It was uncovered that overexpressing CTHRC1 promoted LUAD cell migration and invasion, and inhibited cell adhesion, while knocked down CTHRC1 had the opposite effect. Afterward, the upstream miRNAs that regulated CTHRC1 were predicted by several bioinformatics websites. It was testified by dual-luciferase method that CTHRC1 was negatively mediated by miR-30a-5p. Overexpressed miR-30a-5p suppressed cell invasion/migration, and increased cell adhesion, while overexpressing CTHRC1 as well reversed such impacts. In conclusion, it was disclosed in this study that CTHRC1 worked as a cancer promoter in LUAD, and miR-30a-5p could target and downregulate CTHRC1 to regulate cell adhesion, and inhibited LUAD cell invasion and migration. These results elucidated at cellular level that upregulated CTHRC1 may be a marker protein for LUAD metastasis.
Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues, fine-grained parallel computing techniques are widely used in tasks such as sensitive feature filtering, medical image classification, and dangerous goods detection. In this study, we adopt a fine-grained parallel computing programming approach and propose a multiobjective matrix regular optimization algorithm that can simultaneously perform the joint square root, low-rank, and sparse regular optimization for bilinear visual features, which is used to stabilize the higher-order semantic information in bilinear features, improve the generalization ability of features, and apply it to the construction of mental health education models for college students to promote the construction of mental health education bases, improve mental health education network platform, and strengthen the construction of mental health education data platform. A new practical aspect has been added to the abstract. The saliency-guided data augmentation method in this study is an improvement on random data augmentation but reduces the randomness in the data augmentation process and significantly improves the results. The best result belongs to SCutMix data augmentation, which improves by 1.9% compared to the baseline network.
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