Risk factors and prognostic nomogram for patients with second primary cancers after lung cancer using classical statistics and machine learning.

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 overall survival (OS) prediction nomogram for patients with SPC after lung cancer. Data of patients with SPC after lung cancer, covering 2000 to 2016, were gathered from the SEER database. The incidence of SPC after lung cancer was calculated by Standardized incidence ratios (SIRs). Cox proportional hazards regression, machine learning (ML), Kaplan-Meier (KM) methods, and log-rank tests were conducted to identify the important prognostic factors for predicting OS. These significant prognostic factors were used for the development of an OS prediction nomogram. Totally, 10,487 SPC samples were randomly divided into training and validation cohorts (model construction and internal validation) from the SEER database. In the random forest (RF) and extreme gradient boosting (XGBoost) feature importance ranking models, age was the most important variable which was also reflected in the nomogram. And, the models that combined machine learning with cox proportional hazards had a better predictive performance than the model that only used cox proportional hazards (AUC = 0.762 in RF, AUC = 0.737 in XGBoost, AUC = 0.722 in COX). Calibration curves and decision curve analysis (DCA) curves also revealed that our nomogram has excellent clinical utility. The web-based dynamic nomogram calculator was accessible on https://httseer.shinyapps.io/DynNomapp/ . The prognosis characteristics of SPC following lung cancer were systematically reviewed. The dynamic nomogram we constructed can provide survival predictions to assist clinicians in making individualized decisions.

Genome Mining and Metabolic Profiling Reveal Cytotoxic Cyclodipeptides in Streptomyces hygrospinosus var. Beijingensis.

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.

Exploration in the mechanism of fucosterol for the treatment of non-small cell lung cancer based on network pharmacology and molecular docking.

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.