Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules

Mechanism of polygonum capitatum intervention in pulmonary nodule based on network pharmacology and molecular docking technology

The present study utilizes network pharmacology and molecular docking methodologies to investigate the mechanism of action behind the intervention of Polygonum capitatum Buch.-Ham.ex D. Don (THL) in treating pulmonary nodules (PN). This research aims to provide a theoretical foundation for broadening the clinical application of THL. Active components of THL were identified and screened through an extensive literature review and the PharmMapper database, followed by an analysis of their target interactions. Relevant targets associated with PN were selected using databases such as OMIM and GeneCards, with an intersection of the two sets being determined. STRING11.5 facilitated the acquisition of protein-protein interaction data, which was then imported into Cytoscape 3.7.2 to establish a protein interaction network topology. This enabled the identification of pivotal targets affected by THL intervention in PN. The study further employed the Metascape database to conduct GO and KEGG bioinformatics enrichment analyses, which illuminated core pathways involved in THL's therapeutic effects on PN. A comprehensive component-target-pathway diagram was constructed utilizing Cytoscape 3.7.2 software, with molecular docking validations carried out via Maestro software. A total of 49 active THL ingredients were discerned, implicating 67 PN-relevant targets. Subsequent software analysis pinpointed 10 key targets, including ALB, EGFR, and SRC. Molecular docking studies indicated strong binding affinities for most protein-compound pairs, with 44 out of 60 docking results exhibiting binding energies below -5 kcal/mol. Enrichment analysis highlights that key targets are mainly involved in pathways such as cancer, lipid metabolism and atherosclerosis, estrogen signaling, IL-17 signaling, complement and coagulation cascades, and chemical carcinogenesis through receptor activation. Through comprehensive network pharmacological approaches, this research delineates the synergy of THL's multiple components, targets, and pathways in mitigating PN. It posits that primary active ingredients of THL - quercetin, salidroside, and oleanolic acid - may exert effects on targets like ALB, EGFR, SRC, potentially modulating pathways associated with cancer, lipid and atherosclerosis, and IL-17 signaling in the context of PN intervention.

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

BACKGROUND

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

METHODS

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.

RESULTS

Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.

CONCLUSION

We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules

With the widespread application of low-dose computed tomography (LDCT) technology, pulmonary nodules have aroused more attention. Significant alteration in plasma metabolite levels, mainly amino acid and lipid, have been observed in patients of PNs. However, evidence on the association between central carbon metabolism and PNs are largely unknown. The aim of this study was to investigate the underlying association of PNs and plasma central carbon metabolites. We measured the levels of 16 plasma central carbon metabolites in 1954 participants who gained LDCT screening in MALSC cohort. The inverse probability weighting (IPW) technique was used to control for bias due to self-selection for LDCT in the assessed high-risk population. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to deal with the problem of multicollinearity among metabolites and the combined association of central carbon metabolites with PNs was estimated by using quantile g-computation (QgC) models. A quartile increase in 3-hydroxybutyric acid, gluconic acid, succinic acid and hippuric acid was positively associated with the PNs risk, whereas a quartile increase in 2-oxadipic acid and fumaric acid was negatively associated with the risk of PNs in multiple-metabolite models. A positive but insignificant joint associations of the mixture of 16 metabolites with PNs was observed by using QgC models analyses. Further studies are warranted to clarify the association between circulating metabolites and PNs and the biological mechanisms.