Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer

Gut microbial metabolites in lung cancer development and immunotherapy: Novel insights into gut-lung axis

Metabolic derivatives of numerous microorganisms inhabiting the human gut can participate in regulating physiological activities and immune status of the lungs through the gut-lung axis. The current well-established microbial metabolites include short-chain fatty acids (SCFAs), tryptophan and its derivatives, polyamines (PAs), secondary bile acids (SBAs), etc. As the study continues to deepen, the critical function of microbial metabolites in the occurrence and treatment of lung cancer has gradually been revealed. Microbial derivates can enter the circulation system to modulate the immune microenvironment of lung cancer. Mechanistically, oncometabolites damage host DNA and promote the occurrence of lung cancer, while tumor-suppresive metabolites directly affect the immune system to combat the malignant properties of cancer cells and even show considerable application potential in improving the efficacy of lung cancer immunotherapy. Considering the crosstalk along the gut-lung axis, in-depth exploration of microbial metabolites in patients' feces or serum will provide novel guidance for lung cancer diagnosis and treatment selection strategies. In addition, targeted therapeutics on microbial metabolites are expected to overcome the bottleneck of lung cancer immunotherapy and alleviate adverse reactions, including fecal microbiota transplantation, microecological preparations, metabolite synthesis and drugs targeting metabolic pathways. In summary, this review provides novel insights and explanations on the intricate interplay between gut microbial metabolites and lung cancer development, and immunotherapy through the lens of the gut-lung axis, which further confirms the possible translational potential of the microbiome metabolome in lung cancer treatment.

Evaluating for Correlations between Specific Metabolites in Patients Receiving First-Line or Second-Line Immunotherapy for Metastatic or Recurrent NSCLC: An Exploratory Study Based on Two Cohorts

Immune checkpoint inhibitors (ICI) have displayed impressive clinical efficacy in the context of non-small cell lung cancer (NSCLC). However, most patients do not achieve long-term survival. Minimally invasive collected samples are attracting significant interest as new fields of biomarker study, and metabolomics is one of these growing fields. We concentrated on the augmented value of the metabolomic profile in differentiating long-term survival from short-term survival in patients with NSCLC subjected to ICIs. We prospectively recruited 97 patients with stage IV NSCLC who were treated with anti-PD-1 inhibitor, including patients treated with monoimmunotherapy as second-line treatment (Cohort 1), and patients treated with combination immunotherapy as first-line treatment (Cohort 2). Each cohort was divided into long-term and short-term survival groups. All blood samples were collected before beginning immunotherapy. Serum metabolomic profiling was performed by UHPLC-Q-TOF MS analysis. Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis were performed. In Cohort 1, the mPFS and mOS of long-survival patients are 27.05 and NR months, respectively, and those of short-survival patients are 2.79 and 10.59 months. In Cohort 2, the mPFS and mOS of long-survival patients are 27.35 and NR months, respectively, and those of short-survival patients are 3.77 and 12.17 months. A total of 41 unique metabolites in Cohort 1 and 47 in Cohort 2 were screened. In Cohorts 1 and 2, there are 6 differential metabolites each that are significantly associated with both progression-free survival and overall survival. The AUC values for all groups ranged from 0.73 to 0.95. In cohort 1, the top 3 enriched KEGG pathways, as determined through significant different metabolic pathway analysis, were primary bile acid biosynthesis, African trypanosomiasis, and choline metabolism in cancer. In Cohort 2, the top 3 enriched KEGG pathways were the citrate cycle (TCA cycle), PPAR signaling pathway, and primary bile acid biosynthesis. The primary bile acid synthesis pathway had significant differences in the long-term and short-term survival groups in both Cohorts 1 and 2. Our study suggests that peripheral blood metabolomic analysis is critical for identifying metabolic biomarkers and pathways responsible for the patients with NSCLC treated with ICIs.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

A novel preliminary metabolomic panel for IHD diagnostics and pathogenesis

Cardiovascular disease (CVD) represents one of the main causes of mortality worldwide and nearly a half of it is related to ischemic heart disease (IHD). The article represents a comprehensive study on the diagnostics of IHD through the targeted metabolomic profiling and machine learning techniques. A total of 112 subjects were enrolled in the study, consisting of 76 IHD patients and 36 non-CVD subjects. Metabolomic profiling was conducted, involving the quantitative analysis of 87 endogenous metabolites in plasma. A novel regression method of age-adjustment correction of metabolomics data was developed. We identified 36 significantly changed metabolites which included increased cystathionine and dimethylglycine and the decreased ADMA and arginine. Tryptophan catabolism pathways showed significant alterations with increased levels of serotonin, intermediates of the kynurenine pathway and decreased intermediates of indole pathway. Amino acid profiles indicated elevated branched-chain amino acids and increased amino acid ratios. Short-chain acylcarnitines were reduced, while long-chain acylcarnitines were elevated. Based on these metabolites data, machine learning algorithms: logistic regression, support vector machine, decision trees, random forest, and gradient boosting, were used for IHD diagnostic models. Random forest demonstrated the highest accuracy with an AUC of 0.98. The metabolites Norepinephrine; Xanthurenic acid; Anthranilic acid; Serotonin; C6-DC; C14-OH; C16; C16-OH; GSG; Phenylalanine and Methionine were found to be significant and may serve as a novel preliminary panel for IHD diagnostics. Further studies are needed to confirm these findings.