1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model

Effect of Astragalus polysaccharide combined with cisplatin on exhaled volatile organic compounds as biomarkers for lung cancer and its anticancer mechanism

Cisplatin (DDP) is widely used to fight lung cancer, but there is a risk of immune damage. Astragalus polysaccharides (APS) is the main active component of Astragalus membranaceus Bunge. It has demonstrated anticancer properties across a range of cancer types as well as to be effective against cisplatin induced immune damage. However, its therapeutic mechanism has not been fully explored. This study aimed to explore the antitumor mechanisms of APS and elucidate the relationship between APS and volatile organic compounds (VOCs) in exhaled breath of Lewis lung cancer (LLC) mice. Gas chromatography-mass spectrometry (GC-MS) was utilized to analyze the exhaled VOCs in LLC mice. A specific group of VOCs was identified as potential biomarkers for monitoring tumor progression. Furthermore, the effects of combined treatment with APS and DDP on the concentration of exhaled VOCs in LLC mice was evaluated. Stoichiometric analysis revealed that the levels of 12 VOCs exhibited substantial recovery following APS treatment. And a high concentration of APS (400 mg/kg), when combined with DDP, exhibited enhanced antitumor efficacy. The metabolic pathways involved in the action of APS include 12 pathways. Our methodology elucidated both the effects and mechanisms of APS on lung cancer, as well as the pharmacological enhancement of cisplatin by APS. These findings facilitate real-time monitoring of lung cancer treatments and contribute to the future development of anticancer therapies.

Study of the mechanism underlying the anti-inflammatory effect of Miao medicine comprising raw and processed Radix Wikstroemia indica using the "sweat soaking method"

ETHNOPHARMACOLOGICAL RELEVANCE

To explore the differences in the anti-inflammatory efficacy and mechanisms of the Miao medicine, both raw and after processing, using the "sweat soaking method" of Radix Wikstroemia indica (RWI).

AIM OF THE STUDY

The purpose of this study was to explore the differences in the anti-inflammatory efficacy and mechanism of action before and after the processing of the Miao medicine (RWI) using the "sweat soaking method."

MATERIALS AND METHODS

Network pharmacology technology was used to construct the "drug-component target-pathway-disease" network, and the main anti-inflammatory pathways of RWI were identified. Rat models of collagen-induced arthritis were established. The changes in body weight, swelling rate of the foot pad and ankle joint, arthritis index, thymus index, spleen index, pathological changes of the ankle joint, and the content of inflammatory cytokines (IL-1β, IL-2, IL-6, IL-10, TNF-α, and NO) were used as indices to evaluate the effect of RWI on rats with collagen-induced arthritis before and after its processing. Plasma and urine samples were collected from the rats, and the potential biomarkers of, and metabolic pathways underlying the anti-inflammatory effects of RWI before and after processing were identified using 1H-Nuclear magnetic resonance metabolomics combined with a multivariate statistical analysis.

RESULTS

Eleven key anti-inflammatory targets of IL6, IL-1β, TNF, ALB, AKT1, IFNG, INS, STAT3, EGFR, TP53, and SRC were identified by network pharmacology. The PI3K-Akt signaling pathway, steroid hormone biosynthesis, arginine biosynthesis, arginine and proline metabolism, tryptophan metabolism, and other pathways were mainly involved in these effects. Pharmacodynamic studies found that both raw and processed RWI products downregulated inflammatory factors in rats with collagen-induced arthritis and alleviated the pathological changes. A total of 41 potential pathways for the anti-inflammatory effects of raw RWI products and 36 potential pathways for the anti-inflammatory effects of processed RWI products were identified by plasma and urine metabolomics. The common pathways of network pharmacology and metabolomics were steroid hormone biosynthesis, arginine biosynthesis, arginine and proline metabolism, and tryptophan metabolism.

CONCLUSIONS

The anti-inflammatory effect of RWI was mainly related to the regulation of steroid hormone biosynthesis, arginine biosynthesis, arginine and proline metabolism, and tryptophan metabolism. Finally, the "sweat soaking method" enhanced the anti-inflammatory effect of RWI.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.