Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer

Non-Specific Elevated Serum Free Fatty Acids in Lung Cancer Patients: Nutritional or Pathological?

IMPORTANCE

The reprogramming of lipid metabolism is a significant feature of tumors, yet the circulating levels of fatty acids in lung cancer patients remain to be explored. Moreover, the association between fatty acid levels and related factors, including nutritional intake, tumor metabolism, and tumor immunity, has been rarely discussed.

OBJECTIVES

To explore the differences in serum free fatty acids between lung cancer patients and healthy controls, and investigate the factors associated with this phenomenon.

DESIGN AND PARTICIPANTS

A case-control study enrolled 430 primary lung cancer patients and 430 healthy controls. The whole population had a medium [Q1, Q3] age of 48.0 [37.0, 58.9] years, with females comprising 56% of the participants. The absolute quantification of 27 serum free fatty acids (FFAs) was measured using a liquid chromatography-mass spectrometry (LC-MS/MS) detection. Data, including dietary intake, blood indicators, and gene expression of lung tissues, were obtained from questionnaires, blood tests, and RNA-sequencing. Statistical differences in FFA levels between lung cancer patients and healthy controls were investigated, and related contributing factors were explored.

RESULTS

Levels of 22 FFAs were significantly higher in lung cancer patients compared to those in healthy controls, with fold changes ranging from 1.14 to 1.69. Lung cancer diagnosis models built with clinical and FFA features yielded an area under the receiver operating characteristic curve (AUROC) of 0.830 (0.780-0.880). Total fatty acids (TFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) showed no significant dietary-serum associations, indicating that the elevations might not be attributed to an excessive intake of relevant fatty acids from the diet. For RNA-sequencing of lung tissues, among the 68 lipid metabolism genes, 26 genes showed significant upregulation (FDR < 0.05), while 33 genes exhibited significant downregulation, indicating the involvement of the fatty acids in the tumor metabolism. Through joint analysis with immune cells and inflammatory factors in the blood, fatty acids might exert suppressing effects on tumor immunity.

CONCLUSIONS

Lung cancer patients had elevated levels of serum free fatty acids compared to healthy individuals. The elevations might not be attributed to an excessive intake of relevant fatty acids from the diet but related to pathological factors of tumor metabolism and immunity. These findings will complement research on fatty acid metabolism of lung cancer and provide insights into potential intervention targets.

The integrated analysis of gut microbiota and metabolome revealed steroid hormone biosynthesis is a critical pathway in liver regeneration after 2/3 partial hepatectomy

Introduction: The liver is the only organ capable of full regeneration in mammals. However, the exact mechanism of gut microbiota and metabolites derived from them relating to liver regeneration has not been fully elucidated. Methods: To demonstrate how the gut-liver axis contributes to liver regeneration, using an LC-QTOF/MS-based metabolomics technique, we examine the gut microbiota-derived metabolites in the gut content of C57BL/6J mice at various points after 2/3 partial hepatectomy (PHx). Compound identification, multivariate/univariate data analysis and pathway analysis were performed subsequently. The diversity of the bacterial communities in the gastrointestinal content was measured using 16S rRNA gene sequencing. Then, the integration analysis of gut microbiota and metabolome was performed. Results: After 2/3 PHx, the residual liver proliferated quickly in the first 3 days and had about 90% of its initial weight by the seventh day. The results of PLS-DA showed that a significant metabolic shift occurred at 6 h and 36 h after 2/3 PHx that was reversed at the late phase of liver regeneration. The α and β-diversity of the gut microbiota significantly changed at the early stage of liver regeneration. Specifically, Escherichia Shigella, Lactobacillus, Akkermansia, and Muribaculaceae were the bacteria that changed the most considerably during liver regeneration. Further pathway analysis found the most influenced co-metabolized pathways between the host and gut bacteria including glycolysis, the TCA cycle, arginine metabolism, glutathione metabolism, tryptophan metabolism, and purine and pyrimidine metabolism. Specifically, steroid hormone biosynthesis is the most significant pathway of the host during liver regeneration. Discussion: These findings revealed that during liver regeneration, there was a broad modification of gut microbiota and systemic metabolism and they were strongly correlated. Targeting specific gut bacterial strains, especially increasing the abundance of Akkermansia and decreasing the abundance of Enterobacteriaceae, may be a promising beneficial strategy to modulate systemic metabolism such as amino acid and nucleotide metabolism and promote liver regeneration.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.