The Related Research Of Multi-omics To Explore The Generative Mechanism Of Gastric Cancer Breath Volatile Organic Compounds

Background Early diagnoses of gastric cancer can significantly improve its prognoses.Exhaled volatile organic compounds(VOCs)have received increasing attention as a new type of simple,non-invasive diagnostic method.Our studies previously found that exhaled VOCs had potential application value in the early diagnosis of gastric cancer,but its generative mechanism is still unclear.Most of the arguments speculated that the production of VOCs was associated with oxidative stress in cancer cells.The formation of exhaled VOCs is associated with abnormal tumor metabolism,and the production of metabolites is closely related to the transcriptional regulation of the body.Considering that both exhaled VOCs and salivary metabolites are products of abnormal gene metabolism,they have something in common.Therefore,we intended to provide clues for the generative mechanism of gastric cancer breath VOCs through the integrated analysis of transcriptomics high-throughput sequencing data on gastric cancer tissue and non-targeted metabolomics data on saliva of gastric cancer patients.Objective To establish a transcriptomics high-throughput analysis method based on gastric cancer tissue and non-targeted metabolomics analysis method on saliva of gastric cancer patients based on Ultra Performance Liquid Chromatography Quadrupole Time of Flight Mass Spectrometer(UHPLC-QTOFMS).Multi-omics integration analysis was conducted to look for oxidative stress related genes involved in the metabolites,providing clues for generative mechanism research of gastric cancer breath VOCs.Methods Postoperative cancer tissue samples from 100 patients with primary gastric cancer and corresponding 30 adjacent normal tissue samples from these patients were collected.After c DNA library construction and quality inspection,high-throughput sequencing platform was used for paired-end sequencing of the library.The standard of screening significant DEGs was q(p value corrected by multiple hypothesis test)less than 0.05 and the absolute value of foldchange greater than 2.Q<0.05 was used as a significant enrichment analysis standard,and KEGG pathway enrichment analysis was performed on DEGs using KOBAS3.0 software.Further,saliva samples were collected from 148 patients with gastric cancer,30 patients with benign gastric disease and 26 healthy patients.UHPLC-QTOFMS technique was selected for saliva non-targeted metabolomics analysis after the extraction of saliva samples.Then we took the student t test p value <0.1 and the variable importance in the projection(VIP)of the first principal component of the orthogonal projections to latent structures-discriminant analysis(OPLS-DA)model >1 as the card value standard to screen out the differential metabolites between each group after the pre-processing of original data.The secondorder mass spectrometry matching and the combination analysis of positive and negative ion groups of differential metabolites were used to get clearly matched differential metabolites.Further,metabolic pathway analysis of the obtained differential metabolites was performed using the Metabo Analyst 4.0 online tool.Finally,transcriptomics and metabolomics data were integrated and analyzed to find out the DEGs involved in the metabolomics significant enrichment pathway,and DAVID6.8 software was used for the GO function enrichment analysis to screen out the genes related to oxidative stress.Results In transcriptomics analysis,a total of 5,845 significant DEGs were found,in which 3,661 genes were significantly up-regulated and 2,184 genes were significantly down-regulated.KEGG enrichment analysis revealed 29 significantly metabolic pathways,including protein digestion and absorption,cytokine-cytokine receptor interaction,mineral absorption,pancreatic secretion,drug metabolism-cytochrome P450,et al.According to the study of saliva metabolomics,3637 and 2538 chromatographic peaks were retained in the positive and negative ion modes,respectively.Among them,the differential compounds between the cancer saliva(CS)and the benign saliva(BS)group were 631 and 859,the CS and the normal saliva(NS)group were 427 and 567,respectively,and the BS and NS group were 170 and 453,respectively.After the second-order mass spectrometry matching and combination analysis of positive and negative ion groups for these differential compounds,112,31 and 45 clearly matched differential metabolites were obtained in CS and BS group,CS and NS group,and BS and NS group,respectively.Further metabolic pathway analysis revealed that 14 metabolic pathways,including arginine and proline metabolism,aminoacyl-t RNA biosynthesis,nitrogen metabolism,pyrimidine metabolism and so on,were enriched in CS and BS group and CS and NS group.By integrating the multiomics data,we found 97 DEGs correlated with significantly enriched metabolic pathways of metabolomics.Further GO analysis,19 genes related to oxidative stress was found in the process of oxidation-reduction biology.Of which 12 genes(GLDC,ALDH3B2,NOS3,PRODH,AOC1,NOS2,PRODH2,RRM2,P4HA3,IL4I1,PIPOX,PAH)were up-regulated obviously,7 genes(ALDH4A1 MAOA PHGDH,ALDH6A1,TXNRD2 SARDH ALDH3A1)were down-regulated significantly.Conclusion Through the integrated analysis of multi-omics,we found several potential candidate genes related to the generation of exhaled VOCs,which provides clues for the study on the generative mechanism of gastric cancer breath VOCs.However,further exhalation experiments are needed to explore its specific mechanism.