Uncovering the anticancer mechanism of petroleum extracts of Farfarae Flos against Lewis lung cancer by metabolomics and network pharmacology analysis

Untargeted metabolomics analysis of cerebrospinal fluid in patients with leptomeningeal metastases from non-small cell lung cancer

OBJECTIVE

To explore and analyze the diagnostic value of metabolic markers in cerebrospinal fluid (CSF) in leptomeningeal metastases (LM) of non-small cell lung cancer (NSCLC).

METHODS

Forty-six CSF samples from patients with NSCLC-LM were collected. Another 48 CSF samples from patients with nonmalignant neurological diseases were selected as control group. Metabolomic analysis of CSF was performed by high-performance liquid chromatography-mass spectrometry. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied for modeling. A multi-criteria evaluation system (variable importance value >1, multiple of change >2 and P < 0.05 for univariate analysis) was used to find differential metabolites between two groups. The subject working characteristic curves and pathway enrichment analysis were used to screen metabolites and pathways associated with NSCLC-LM.

RESULTS

The PCA model and OPLS-DA model showed good overall data quality. Thirty endogenous differential metabolites were screened, and six potential biomarkers were further identified, including tyrosine (t = 3.37, P = 0.024, AUC = 0.967), phenylalanine (t = 3.98, P < 0.001, AUC = 0.992), pyruvate (t = 4.48, P < 0.001, AUC = 0.976), tryptophan (t = -2.5, P = 0.014, AUC = 0.935), adenosine monophosphate (t = -6.13, P < 0.001, AUC = 0.932) and glucose (t = -4.00, P < 0.001, AUC = 0.993). Thirty differential metabolites screened were subjected to metabolic pathway enrichment analysis and matched to 20 relevant metabolic pathways, of which the four most likely to cause metabolite changes were as follows: glycolysis and sugar metabolism synthesis, pyruvate metabolism, phenylalanine metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis.

CONCLUSIONS

Untargeted metabolomics can effectively screen for CSF metabolites specific to NSCLC-LM patients, and six potential metabolites and their metabolic pathways might be involved in the pathogenesis of NSCLC-LM.

Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-Stage Lung Cancer Diagnosis

Background

Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis.

Methods

In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients. To screen potential diagnostic markers for early lung cancer.

Results

62.5% (30/48) of lung cancer patients had ≥1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC >0.9).

Conclusions

Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer.

Integrating hippocampal metabolomics and network pharmacology deciphers the antidepressant mechanisms of Xiaoyaosan

ETHNOPHARMACOLOGICAL RELEVANCE

Xiaoyaosan (XYS), a classic description, has a history of thousands of years for treating depression through invigorating the liver and strengthening the spleen, which have been verified both clinically and experimentally. However, explanation of its underlying mechanisms remains a great challenge.

AIM OF THE STUDY

The mechanisms of XYS in treating depression were investigated, with emphasis on the important biomarkers, targets and pathways.

MATERIALS AND METHODS

In this study, taking the targeted organ of depression, hippocampus, as the object, a combination of GC-MS based metabolomics and network pharmacology was established to illustrate the abnormality of metabolic characteristics of hippocampus of depression rats and to demonstrate the antidepressant mechanisms of XYS. Hippocampal metabolomics demonstrated potential metabolites involving in the antidepressant effects of XYS, as well as the corresponding metabolic pathways. Network pharmacology screened the potential ingredients and the targets of XYS against depression.

RESULTS

Metabolomics revealed that XYS significantly regulated the abnormal levels of lactic acid, glycerol, glutamine, glutamic acid, hypoxanthine, myo-inositol and cholesterol, which involved in the D-glutamine and D-glutamate metabolism, arginine biosynthesis and alanine, aspartate and glutamate metabolism. Network pharmacology showed that XYS exhibited anti-depression effects through paeoniflorin, quercetin, licochalcone a, naringenin, β-sitosterol, formononetin and kaempferol acting on interleukin-6 (IL6), mitogen-activated protein kinase 1 (MAPK1), signal transducer and activator of transcription 3 (STAT3) and transcription factor AP-1 (JUN).

CONCLUSION

Based on hippocampal metabolomics and network pharmacology, this study proved that the actions of XYS in treating depression depend on multi-components, multi-targets and multi-pathways, the unique characteristics of TCMs.

Uncovering the Effect of Passage Number on HT29 Cell Line Based on the Cell Metabolomic Approach

The passage number is an important factor when designing the cell line-based experiment. Although HT29 cells were widely used in the laboratory for colorectal cancer studies, the impact of cell passage number on the HT29 cells was still unknown. In this study, phenotypic assay and metabolomic approach were applied to analyze the systemic effects of passage numbers (passage 4, 10, and 16) on the HT29 cells. The results showed that the increased cell passage number affected the cell cycle distribution and also decreased the proliferation and migration ability of HT29 cells. The metabolomic analysis coupled with heatmap and hierarchical cluster analysis showed obvious metabolome difference among the cells with different passage numbers, which was related with 61 differential metabolites. Three metabolic pathways were determined as the key pathways, and arginine participated in two of them. In addition, it was found that arginine supplementation could inhibit the proliferation ability of HT29 cells in vitro, and a synergistic effect existed between arginine and cisplatin. In conclusion, this study not only revealed the influence of passage numbers on the HT29 cell but also provided an important reference that arginine has the potential role to be developed as the cisplatin therapeutic adjuvant.