RESUMO
Background: The value of plasma threonine, cysteine, and piperonamide as diagnostic biomarkers for non-small cell lung cancer (NSCLC) has been rarely explored. The lack of a validation set containing confounders is common to most previous metabolomics studies. The purpose of this study was to explore and validate the value of plasma amino acids and piperonamide as diagnostic biomarkers for NSCLC using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Methods: A total of 250 participants were included in this study, including 167 patients with pathologically confirmed NSCLC and 83 healthy controls (HCs). These participants were divided into training set, validation set 1, and validation set 2 in chronological order and in a certain proportion. The plasma levels of 22 amino acids and 1 piperonamide in these pre-treatment NSCLC patients and HCs were measured by LC-MS/MS. Metabolic biomarkers were identified after multivariate analysis, univariate analysis, receiver operating characteristic (ROC) analysis. Furthermore, these biomarkers and transcriptomic data were subjected to joint pathway analysis. Results: The area under the ROC curve (AUC) values for threonine, piperonamide, arginine, alanine, cysteine, methionine, and histidine in the integrated data set were 0.911, 0.848, 0.909, 0.869, 0.786, 0.597 and 0.637, respectively. This panel composed of these 7 metabolites showed good diagnostic capability for NSCLC (the AUC of this diagnostic panel in each data set was greater than 0.9). The specificity of this diagnostic panel in validation set 2, which included confounders, was 0.970, similar to that of the other datasets. The presence of confounding factors had little effect on the diagnostic accuracy of this panel. The ROC analysis of this diagnostic panel between all stage I NSCLC patients and HCs showed AUC, sensitivity, and specificity of 1.000, 1.000, and 0.988, respectively. Moreover, PSAT1, SHMT2, AOC3, and MAOB were found to be involved in the metabolism of threonine and cysteine. Conclusions: Plasma amino acids and piperonamide have potential as diagnostic biomarkers in NSCLC. This metabolic biomarker panel appears useful for the diagnosis and screening of NSCLC. In addition, metabolomic and transcriptomic integration pathway analysis may help elucidate the mechanism of NSCLC occurrence and development and even reveal new treatment vulnerabilities.
RESUMO
BACKGROUND: Gasdermins (GSDMs) are a class of proteins related to pyrolysis and in humans, consist of GSDMA, GSDMB, GSDMC, GSDMD, DFNA5, and DFNB59. The inflammatory factors and cell contents released during pyrolysis can recruit immune cells and change the microenvironment. However, to date, there is a paucity of studies examining the relationship between GSDMs and the immune microenvironment in tumors. Therefore, this current report analyzed the expression of GSDM genes in tumors and their relationship with the immune microenvironment. METHODS: Apply GSCALite and GEPIA2 online analysis tools to analyze the gene expression levels and the Single nucleotide variant (SNV), copy number variation (CNV), and methylation characteristics of GSDM genes respectively. Use R software or TISIDB online analysis tool to carry out the correlation analysis required in the article. Furthermore, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to examine the role of these GSDM genes in various cancers. RESULTS: The results demonstrated that CNV can cause an increase in GSDM gene expression, and methylation can inhibit GSDM gene expression. The elevated expression of GSDMA, GSDMB, GSDMC, GSDMD, and DFNA5 in some or most tumors was often accompanied by elevated immune scores, increased immune cell infiltration, and high expression of major histocompatibility complex (MHC) molecules, chemokines and their receptors, and immune checkpoint-related genes. However, DFNB59 was often negatively correlated with these indicators in tumors. GSDMD was the most highly expressed GSDM protein in various normal tissues and tumors, and showed the strongest correlation with immune microenvironment-related genes. Moreover, the methylation of GSDMD was accompanied by low immune cell infiltration, low expression of MHC molecule-related genes, low expression of chemokines and receptor-related genes, and low expression of immune checkpoint-related genes. CONCLUSIONS: Therefore, the expression of GSDM-related genes is associated with the tumor immune microenvironment. The GSDM genes, especially GSDMD, may be used as therapeutic targets to predict or change the tumor microenvironment and as biomarkers to predict the therapeutic efficacy of immune checkpoint inhibitors.
