Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan–Meier curves indicated that patients’ survival in the low-risk group was better than that in the high-risk group ( P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.

BackgroundHedysarum Multijugum Maxim–Curcumae Rhizoma (HMMCR), a well-known herb pair in traditional Chinese medicine (TCM), has been widely used for the treatment of various cancers. However, the active components of HMMCR and the underlying mechanism of HMMCR for non-small-cell lung carcinoma (NSCLC) remain unclear.MethodsActive ingredients of HMMCR were detected by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). On this basis, potential targets of HMMCR were obtained from SwissTargetPrediction database. NSCLC-related targets were collected from four public databases (GeneCards, OMIM, TTD, and PharmGkb). The drug ingredients–disease targets network was visualized. The hub targets between HMMCR and NSCLC were further analyzed by protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Subsequently, the results predicted by network pharmacology were further validated via in vitro experiments.ResultsA total of 181 compounds were identified from the aqueous extract of HMMCR. Through network analysis, a compound–target network including 153 active ingredients of HMMCR and 756 HMMCR-NSCLC co-targets was conducted; 6 crucial compounds and 62 hub targets were further identified. The results of KEGG enrichment analysis showed that PI3K/Akt signaling pathway may be the critical pathway of HMMCR in the treatment of NSCLC. The in vitro experiments indicated that HMMCR inhibits the proliferation and migration of NSCLC cells via inactivation of the PI3K/Akt signaling pathway, consistent with the results predicted by network pharmacology.ConclusionIntegrating LC-ESI-MS/MS, network pharmacology approach, and in vitro experiments, this study shows that HMMCR has vital therapeutic effect on NSCLC through multi-compound, multi-target, and multi-pathway, which provides a rationale for using HMMCR for the treatment of NSCLC.