RESUMO
This study aims to elucidate the mechanisms by which the effective components of Scleromitrion diffusum (Willd.) (SDW) treat lung cancer, using network pharmacology, in vitro cell experiments, and molecular docking methods. Network pharmacology techniques were employed to construct a network of SDW components, lung cancer targets, and signaling pathways. A proteinprotein interaction (P P I) network was built for target genes, identifying core gene targets. Signaling pathway and biological process analyses were conducted. MT T assays measured cell viability, and Western blot analysis assessed the impact of core protein targets and key pathway proteins on the stemness of three lung cancer cell lines. Molecular docking was performed to link SDW components with core proteins and key pathway targets related to lung cancer. SDW was found to target 88 genes and 5 active components (2-methoxy-3-methyl-9-10-anthraquinone, stigmasterol, beta-sitosterol, quercetin, and poriferasterol) relevant to lung cancer treatment. The P I3K/Akt and MEK/ERK pathways were identified as major signaling pathways. Extracts from SDW roots significantly inhibited the proliferation of three lung cancer cell lines (A549, HCC827, and NCIH-1395), primarily via P I3K/Akt and MEK/ERK pathways, significantly reducing the expression of p-Akt and p-Erk1/2 and slightly inhibiting caspase-9, p-P I3K, and EGFR expression. Molecular docking confirmed the strong binding activities of SDW components with lung cancer-related core proteins and key pathway targets. SDW may regulate apoptosis and proliferation in lung cancer treatment through P I3K-Akt and MAP K/ERK signaling pathways. The combination of network pharmacology, molecular docking, and experimental validation provides valuable insights into the molecular mechanisms of SDW in lung cancer therapy.
RESUMO
Simultaneous perturbation stochastic approximation (SPSA) algorithm, an algorithm without secondary path modeling, has been applied to active noise control by some researchers. Some extended versions of this algorithm have been also developed to improve its performance. However, these existing algorithms are mostly dedicated to controlling the periodic noise instead of the broadband noise. In particular, background noise is not taken into account when SPSA algorithms are applied to control broadband noise. In this paper, an algorithm combining the cost function with the SPSA algorithm to control broadband noise has been proposed. The suggested cost function is an inner product of the estimated cross-correlation function between a reference vector and the error signal. The elements of the reference vector are composed of the reference signals at different times. Moreover, the algorithm analysis is performed and the numerical simulations are carried out to demonstrate the validity of the proposed algorithm. The results illustrate that the proposed algorithm can effectively reduce broadband noise when interference noise exists in the control system. Furthermore, the proposed algorithm has better convergence performance than other SPSA algorithms.
RESUMO
Nasopharyngeal carcinoma (NPC) is a head and neck cancer with high incidence in South China and East Asia. To provide a theoretical basis for NPC risk screening and early prevention, we conducted a meta-analysis of relevant literature on the association of single nucleotide polymorphisms (SNP)s with NPC susceptibility. Further, expression of 15 candidate SNPs identified in the meta-analysis was evaluated in a cohort of NPC patients and healthy volunteers using next-generation sequencing technology. Among the 15 SNPs detected in the meta-analysis, miR-146a (rs2910164, C>G), HCG9 (rs3869062, A>G), HCG9 (rs16896923, T>C), MMP2 (rs243865, C>T), GABBR1 (rs2076483, T>C), and TP53 (rs1042522, C>G) were associated with decreased susceptibility to NPC, while GSTM1 (+/DEL), IL-10 (rs1800896, A>G), MDM2 (rs2279744, T>G), MDS1-EVI1 (rs6774494, G>A), XPC (rs2228000, C>T), HLA-F (rs3129055, T>C), SPLUNC1 (rs2752903, T>C; and rs750064, A>G), and GABBR1 (rs29232, G>A) were associated with increased susceptibility to NPC. In our case-control study, an association with increased risk for NPC was found for the AG vs AA genotype in HCG9 (rs3869062, A>G). In addition, heterozygous deletion of the GSTM1 allele was associated with increased susceptibility to NPC, while an SNP in GABBR1 (rs29232, G>A) was associated with decreased risk, and might thus have a protective role on NPC carcinogenesis. This work provides the first comprehensive assessment of SNP expression and its relationship to NPC risk. It suggests the need for well-designed, larger confirmatory studies to validate its findings.
RESUMO
p53 and glutathione S-transferase M1 (GSTM1) are the most popular suppressor genes. Several previous studies demonstrated positive associations of these gene polymorphisms with numerous cancer types, including hepatocellular cancer, while the association between p53/GSTM1 polymorphisms and the nasopharyngeal carcinoma (NPC) risk was inconsistent and underpowered. However, no studies investigating the combinational effect of these two genes on NPC risk were performed. To confirm the effects of p53 and GSTM1 polymorphisms on the risk of NPC, a meta-analysis of all the available previous studies associating p53 and GSTM1 with the risk of NPC was performed. A comprehensive search of PubMed, Web of Science and SD database until November 2014 was performed to identify the relevant studies. The data were extracted by two independent authors and pooled odds ratio (OR) with 95% confidence interval (CI) was calculated. Meta-regression and subgroup analyses were performed to identify the source of heterogeneity. Finally, five studies with 1,419 cases and 1,707 controls were included for the p53 polymorphism and three studies with 837 cases and 1,299 controls were included for the GSTM1 polymorphism. Regarding p53, a significantly increased NPC risk was observed in the overall population (C vs. G, OR, 1.245; 95% CI, 1.045-1.483; P=0.014; additive models: CC vs. GG, OR, 1.579; 95% CI, 1.100-2.265; P=0.013 and CG vs. GG, OR, 1.230; 95% CI, 1.039-1.456; P=0.016; dominant model, OR, 1.321; 95% CI, 1.127-1.549; P=0.001; recessive model, OR, 1.429; 95% CI, 1.017-2.009; P=0.040). Concerning GSTM1, a significantly increased NPC risk was observed in the overall population (null versus non-null, OR, 1.282; 95% CI, 1.075-1.530; P=0.006). In the subgroup analyses stratified by the source of controls, a significant association of p53 with NPC risk was also demonstrated, while no association with GSTM1 was observed. Therefore, the p53 G72C polymorphism may have a susceptible role in the carcinogenesis of NPC, while genetic deletion of GSTM1 may contribute to increased susceptibility to NPC. Further large and well-designed studies are required to confirm this association.
