Identification of important genes related to anoikis in acute myocardial infarction.

Acute myocardial infarction (AMI) increasingly precipitates severe heart failure, with diagnoses now extending to progressively younger demographics. The focus of this study was to pinpoint critical genes linked to both AMI and anoikis, thereby unveiling potential novel biomarkers for AMI detection and intervention. Differential analysis was performed to identify significant differences in expression, and gene functionality was explored. Weighted gene coexpression network analysis (WGCNA) was used to construct gene coexpression networks. Immunoinfiltration analysis quantified immune cell abundance. Protein-protein interaction (PPI) analysis identified the proteins that interact with theanoikis. MCODE identified key functional modules. Drug enrichment analysis identified relevant compounds explored in the DsigDB. Through WGCNA, 13 key genes associated with anoikis and differentially expressed genes were identified. GO and KEGG pathway enrichment revealed the regulation of apoptotic signalling pathways and negative regulation of anoikis. PPI network analysis was also conducted, and 10 hub genes, such as IL1B, ZAP70, LCK, FASLG, CD4, LRP1, CDH2, MERTK, APOE and VTN were identified. IL1B were correlated with macrophages, mast cells, neutrophils and Tcells in MI, and the most common predicted medications were roxithromycin, NSC267099 and alsterpaullone. This study identified key genes associated with AMI and anoikis, highlighting their role in immune infiltration, diagnosis and medication prediction. These findings provide valuable insights into potential biomarkers and therapeutic targets for AMI.

The Underlying Mechanism Involved in Gefitinib Resistance and Corresponding Experiment Validation in Lung Cancer.

Background: Gefitinib resistance remains a major problem in the treatment of lung cancer. However, the underlying mechanisms involved in gefitinib resistance are largely unclear. Methods: Open-accessed data of lung cancer patients were downloaded from The Cancer Genome Atlas Program and Gene Expression Omnibus databases. CCK8, colony formation, and 5-ethynyl-2'-deoxyuridine assays were utilized to evaluate the cell proliferation ability. Transwell and wound-healing assays were utilized to evaluate the cell invasion and migration ability. Quantitative real-time PCR was utilized to detect the RNA level of specific genes. Results: Here, we obtained the expression profile data of wild and gefitinib-resistant cells. Combined with the data from the TCGA and GDSC databases, we identified six genes, RNF150, FAT3, ANKRD33, AFF3, CDH2, and BEX1, that were involved in gefitinib resistance in both cell and tissue levels. We found that most of these genes were expressed in the fibroblast of the NSCLC microenvironment. Hence, we also comprehensively investigated the role of fibroblast in the NSCLC microenvironment, including its biological effect and cell interaction. Ultimately, CDH2 was selected for further analysis for its prognosis correlation. In vitro experiments presented the cancer-promoting role of CDH2 in NSCLC. Moreover, cell viability detection showed that the inhibition of CDH2 could significantly decrease the IC50 of gefitinib in NSCLC cells. GSEA showed that CDH2 could significantly affect the pathway activity of PI3K/AKT/mTOR signaling. Conclusions: This study is aimed at investigating the underlying mechanism involved in gefitinib resistance to lung cancer. Our research has improved researchers' understanding of gefitinib resistance. Meanwhile, we found that CDH2 could lead to gefitinib resistance through PI3K/AKT/mTOR signaling.