Fucosterol, a sterol isolated from brown algae, has been demonstrated to have anti-cancer properties. However, the effects and underlying molecular mechanism of fucosterol on non-small cell lung cancer remain to be elucidated. In this study, the corresponding targets of fucosterol were obtained from PharmMapper, and NSCLC related targets were gathered from the GeneCards database, and the candidate targets of fucosterol-treated NSCLC were predicted. The mechanism of fucosterol against NSCLC was identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein–protein interaction data were collected from STRING database. The hub gene GRB2 was further screened out and verified by molecular docking. Moreover, the relationship of GRB2 expression and immune infiltrates were analyzed by the TIMER database. The results of network pharmacology suggest that fucosterol acts against candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8, and SRC, which regulate biological processes including negative regulation of the apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf/MEK/ERK signaling pathway initiated by GRB2 showed to be significant in treating NSCLC. In conclusion, our study indicates that fucosterol may suppress NSCLC progression by targeting GRB2 activated the Raf/MEK/ERK signaling pathway, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs.

Background: It has been demonstrated that fucosterol induces a therapeutic effect on cancer. However, the molecular mechanisms underlying the effects of fucosterol in the treatment of non-small cell lung cancer are still unclear.Methods: In this study, pharmMapper and GeneCards databases were utilized to gather the prediction of fucosterol targets and NSCLC-related targets. The mechanisms of fucosterol against NSCLC were identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein-protein interaction data was obtained from Sting Database. Molecular docking was used to predict the docking of GRB2. Moreover, the relationship of GRB2 expression and immune infiltrates was analyzed by TIMER database.Results: The results suggest that fucosterol acts against by candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8 and SRC, which regulate biological processes including negative regulation of apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf / MEK / ERK signaling pathway initiated by GRB2 maybe the most significant pathway for fucosterol to treat NSCLC.Conclusions: These results show that GRB2 is the key target for fucosterol in the treatment of NSCLC, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs.

Background: Previous studies have revealed an increased risk of secondary primary cancers (SPC) after lung cancer. The prognostic prediction models for SPC patients after lung cancer are particularly needed to guide screening. Therefore, we study retrospectively analyzed the Surveillance, Epidemiology, and End Results (SEER) database using classical statistics and machine learning to explore the risk factors and construct a novel OS prediction nomogram for patients with SPC after lung cancer.Methods: Data of patients with SPC after lung cancer, covering 2000 to 2016, were gathered from the Surveillance, Epidemiology, and End Results (SEER) database. The incidence of SPC after lung cancer was calculated by Standardized incidence ratios (SIRs). Cox proportional hazards regression, Kaplan-Meier methods and log-rank tests were conducted to identify the independent prognostic factors for predicting OS. These signi cant prognostic factors were used for the development of an OS prediction nomogram.Results: Totally, 10487 SPC samples gathered from the SEER database were divided into training cohort and validation cohort (score construction and internal validation) at random. Multivariate Cox regression analysis showed that age, sex, race, grade, marital status, CT (chemotherapy), TNM stage, radiation, lung cancer surgery and SPC surgery were independent risk factors for OS in SPC patients. The prognostic nomogram we constructed was also for OS. The concordance index (C-index) in the training cohort and validation cohort were 0.715(95%CI:0.710-0.718) and 0.716(95%CI:0.712-0.720). Additionally, calibration curves and decision curve analysis (DCA) curves revealed that the nomogram has excellent clinical utility.In the random forest (RF) model, SPC site and T stage were the two most important variables, which was consistent with the nomogram. And, the random forest (RF) model had slightly better prediction performance than the Cox risk regression model (AUC: 0.748 vs. AUC: 0.719).Conclusions: The prognosis characteristics of SPC following the lung cancer was systematically reviewed. The nomogram we constructed can provide survival predictions to assist clinicians in making individualized decisions for appropriate treatment.

Two new cyclodipeptide (CDP) derivatives (1–2) and another seven known cyclodipeptides (3–9) were isolated from Streptomyces 26D9-414 by the genome mining approach combined with genetic dereplication and the “one strain many compounds” (OSMAC) strategy. The structures of the new CDPs were established on the basis of 1D- and 2D-NMR and comparative electronic circular dichroism (ECD) spectra analysis. The biosynthetic gene clusters (BGCs) for these CDPs were identified through antiSMASH analysis. The relevance between this cdp cluster and the identified nine CDPs was established by genetic interruption manipulation. The newly discovered natural compound 2 displayed comparable cytotoxicity against MDA-MB-231 and SW480 with that of cisplatin, a widely used chemotherapeutic agent for the treatment of various cancers.

Background: Previous studies have revealed an increased risk of secondary primary cancers (SPC) after lung cancer. The prognostic prediction models for SPC patients after lung cancer are particularly needed to guide screening. Therefore, we study retrospectively analyzed the Surveillance, Epidemiology, and End Results (SEER) database using classical statistics and machine learning to explore the risk factors and construct a novel OS prediction nomogram for patients with SPC after lung cancer.Methods: Data of patients with SPC after lung cancer, covering 2000 to 2016, were gathered from the Surveillance, Epidemiology, and End Results (SEER) database. The incidence of SPC after lung cancer was calculated by Standardized incidence ratios (SIRs). Cox proportional hazards regression, Kaplan-Meier methods and log-rank tests were conducted to identify the independent prognostic factors for predicting OS. These significant prognostic factors were used for the development of an OS prediction nomogram. Results: Totally, 10487 SPC samples gathered from the SEER database were divided into training cohort and validation cohort (score construction and internal validation) at random. Multivariate Cox regression analysis showed that age, sex, race, grade, marital status, CT (chemotherapy), TNM stage, radiation, lung cancer surgery and SPC surgery were independent risk factors for OS in SPC patients. The prognostic nomogram we constructed was also for OS. The concordance index (C-index) in the training cohort and validation cohort were 0.715(95%CI:0.710-0.718) and 0.716(95%CI:0.712-0.720). Additionally, calibration curves and decision curve analysis (DCA) curves revealed that the nomogram has excellent clinical utility. In the random forest (RF) model, SPC site and T stage were the two most important variables, which was consistent with the nomogram. And, the random forest (RF) model had slightly better prediction performance than the Cox risk regression model (AUC: 0.748 vs. AUC: 0.719).Conclusions: The prognosis characteristics of SPC following the lung cancer was systematically reviewed. The nomogram we constructed can provide survival predictions to assist clinicians in making individualized decisions for appropriate treatment.