In previous study, we reported that kaempferol ameliorates significantly lung ischemia-reperfusion injury (LIRI), and may be achieved by targeting the SIRT 1 pathway. This study further explored the anti-LIRI mechanism of kaempferol. In vitro, the rat alveolar epithelial cells L2 was cultured and subjected to anoxia/reoxygenation (A/R) insult. In vivo, SD rats were operated to establish LIRI model. The related indicators of oxidative stress and apoptosis in L2 cells and rats lung tissues were detected. Results showed that kaempferol pre-treatment significantly increased the cell viability, improved mitochondrial membrane potential, inhibited the opening of mitochondrial permeability transition pores, reduced the levels of oxidative stress and apoptosis, increased the expressions of Bcl-2 and mitochondrial cytochrome c, and decreased the expressions of Bax and cytoplasmic cytochrome c in L2 cells after A/R insult. In vivo, kaempferol improved the pathological injury, inhibited the levels of oxidative stress and apoptosis, increased the expressions of Bcl-2 and mitochondrial cytochrome c, and decreased the expressions of Bax and cytoplasmic cytochrome c in rats lung tissues after I/R. However, the aforementioned effects of kaempferol were significantly attenuated by the SIRT 1 inhibitor EX527 or the PGC-1α inhibitor SR-18292. What's more, SR-18292 has not reversed the effect of kaempferol on increasing the protein activity of SIRT 1. Above results suggest that kaempferol ameliorates LIRI by improving mitochondrial function, reducing oxidative stress and inhibiting cell apoptosis. Its molecular mechanism of action includes the SIRT 1/PGC-1α/mitochondria signaling pathway.

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality. It is urgently required to identify genes to elucidate the underlying molecular mechanisms. The aim of this study is to identify the key genes which may affect the prognosis of GC patients and be a therapeutic strategy for GC patients by bioinformatic analysis. Methods: The significant prognostic differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. The protein-protein interaction (PPI) network was established by STRING and screening key genes by MCODE and CytoNCA plug-ins in Cytoscape. Functional enrichment analysis, construction of a prognostic risk model, and nomograms verify key genes as potential therapeutic targets. Results: In total, 997 genes and 805 genes were related to the prognosis of GC in the GSE84437 and TCGA datasets, respectively. We define the 128 genes shared by the two datasets as prognostic DEGs (P-DEGs). Then, the first four genes (MYLK, MYL9, LUM, and CAV1) with great node importance in the PPI network of P-DEGs were identified as key genes. Independent prognostic risk analysis found that patients with high key gene expression had a poor prognosis, excluding their age, gender, and TNM stage. GO and KEGG enrichment analyses showed that key genes may exert influence through the PI3K-Akt pathway, in which extracellular matrix organization and focal adhesion may play important roles in key genes influencing the prognosis of GC patients. Conclusion: We found that MYLK, MYL9, LUM, and CAV1 are potential and reliable prognostic key genes that affect the invasion and migration of gastric cancer.

Background: Glioblastoma (GBM) is the most common and malignant type of brain tumor. A large number of studies have shown that the immunotherapy of tumors is effective, but the immunotherapy effect of GBM is not poor. Thus, further research on the immune-related hub genes of GBM is extremely important. Methods: The GBM highly correlated gene clusters were screened out by differential expression, mutation analysis, and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) and proportional hazards model (COX) regressions were implemented to construct prognostic risk models. Survival, receiver operating characteristic (ROC) curve, and compound difference analyses of tumor mutation burden were used to further verify the prognostic risk model. Then, we predicted GBM patient responses to immunotherapy using the ESTIMATE algorithm, GSEA, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Results: A total of 834 immune-related differentially expressed genes (DEGs) were identified. The five hub genes (STAT3, SEMA4F, GREM2, MDK, and SREBF1) were identified as the prognostic risk model (PRM) screened out by WGCNA and LASSO analysis of DEGs. In addition, the PRM has a significant positive correlation with immune cell infiltration of the tumor microenvironment (TME) and expression of critical immune checkpoints, indicating that the poor prognosis of patients is due to TIDE. Conclusion: We constructed the PRM composed of five hub genes, which provided a new strategy for developing tumor immunotherapy.