| Objective: Breast cancer is the most common malignant tumor among women,which affects the physical and mental health of women all over the world.According to the gene changes of patients with breast cancer,we can make correct and quantitative predictions when we find the undetermined breast tumors in the early stage.Therefore,identification of potential biomarkers that can be used to predict poor prognosis is of great significance for breast cancer patients.In recent years,with the emergence of the The Cancer Genome Atlas(TCGA)database,people can better understand breast cancer and more accurately predict,treat and prognosis breast cancer through its unique analysis technology.More and more studies based on genomics and Proteomics have found that changes in some metabolic pathways are related to the pathogenesis and progression of breast cancer.Metabolomics provides valuable intracellular metabolic information through in-depth research on metabolites.In recent years,metabonomics technology has been widely used in breast cancer research.In cancer cells,various metabolic pathways undergo changes to meet the energy requirements for rapid proliferation of cancer cells.Among them,the increased synthesis of glutamine can also promote the growth and proliferation of cancer cells.The biosynthesis of glutamine is controlled by Phosphoribosyl pyrophosphate Aminotransferase(PPAT).It has been confirmed that PPAT gene expression increases in gastric cancer,colorectal cancer,Adenocarcinoma of the lung and thyroid cancer,and is related to the prognosis of patients.Therefore,this study obtained PPAT gene with high expression and prognostic value in breast cancer through bioinformatics technology,and explored the expression of PPAT in breast cancer and its clinicopathological characteristics.At the same time,metabonomics research method was used to detect the effect of PPAT expression level on metabolites in breast cancer cells and the metabolic pathways that may be involved,providing new ideas and basis for early clinical diagnosis and prognosis of breast cancer.Research methods:1.This study utilized the TCGA database(TCGA,http://cancergenome.nih.gov)Download Transcriptome data and all clinical data of 1222 breast cancer patient samples(1109 cancerous tissues and 113 paracancerous tissues),and use KEGG(https://www.kegg.jp/)Download 201 metabolic related genes from the website.The "Edge R" package in R software was used to process breast cancer samples and paracancerous tissue samples to screen out differentially expressed genes.The criteria for screening differentially expressed genes are | log fold change(log FC)|>1 and P value<0.05.The R language software "survival" and "glmnet" packages were used to carry out univariate Cox risk regression analysis,lasso regression analysis,and multivariate Cox risk regression analysis for the differential genes,and the independent prognostic gene PPAT of breast cancer was obtained.Use Kaplan Meier(K-M)method to plot the overall survival(OS)curve and recurrence free survival(RFS)curve of patients.2.Use the R language software "beesworm" package for PPAT m RNA expression analysis.The protein expression of PPAT between normal and breast cancer tissues was analyzed using the human protein mapping database(www.proteinatlas.org,HPA).UALCAN website was used to evaluate the relationship between PPAT expression level and various clinical characteristics and pathological parameters of breast cancer patients.3.Three sequences specifically binding to the PPAT gene were screened using the NCBI database(www.ncbi.nlm.nih.gov/)for chemical synthesis of si RNA.These three si RNAs were used to knock down the PPAT gene in MCF-7 cells and MDA-MB-231 cells,respectively.Western blot was used to detect the expression of PPAT protein in the cells,and the si RNA with the highest silencing efficiency was selected for subsequent experiments.4.Based on the non-targeted metabolomics research method,use UPLC-QTOF/MS technology to analyze MCF-7 cells,MCF-7 PPAT si RNA cells,MDA-MB-231 cells and MDA-MB-231 PPAT si RNA cells respectively These two groups of samples were tested,and the raw data collected by UPLC-QTOF/MS were imported into Progenesis QI 2.3 for peak extraction,peak matching and peak alignment.Data were normalized using total ionic strength.Then use SIMCA14.1 to conduct principal component analysis,partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis on the preprocessed data.Peaks with RSD ≤ 30% in quality control(QC)were selected for subsequent screening and identification.Differential metabolites were screened out according to VIP > 1 and t test(P < 0.05).5.Import the obtained differential metabolites into the HMDB database(www.hmdb.ca/)for identification of endogenous metabolites.The identified metabolites were imported into Metabo Analyst(www.metaboanalyst.ca/)for pathway enrichment analysis.Results:1.According to 201 metabolism related genes from KEGG database,105 differentially expressed metabolism related genes between breast cancer and normal tissues were screened through TCGA database.The 105 genes were further analyzed by Cox proportional hazards regression model,lasso regression model and Cox proportional hazards regression model.Three prognostic genes were obtained:Glutamine synthetase(GLUL),Tyrosine hydroxylase(TH)and Phosphoribosyl pyrophosphate aminotransferase(PPAT).Kaplan Meier survival curve analysis showed that the survival curve of PPAT was more significant(P<0.05).Therefore,PPAT was selected as an independent prognostic gene of breast cancer for further analysis.2.Using TCGA database,we found that PPAT m RNA level was highly expressed in breast cancer tissues.Using HPA database,we found that the protein expression level of PPAT in breast cancer tissue was higher than that in normal breast tissue.Based on the clinical pathological parameters and PPAT expression of breast cancer in the TCGA database,the Cox proportional hazard regression model was used to analyze the factors.It was found that PPAT was an independent risk factor independent of other clinical pathological parameters.Analysis using the UALCAN website found that high expression of PPAT was significantly positively correlated with tumor staging(P<0.001),tumor subtypes(P<0.001),and lymph node metastasis status(P<0.001).3.Metabonomics experiment results showed that there was abnormal metabolism of metabolites in breast cancer cells before and after PPAT knockdown.In the MDAMB-231 cell sample,a total of 58 differential metabolites were identified,mainly including amino acids,nucleotides,phospholipids,lysophospholipids,and a small amount of carnitine substances.In MCF-7 cell samples,31 differential metabolites were identified,mainly including amino acids,phospholipids,lysophospholipids,Sphingosine and a small amount of choline.After enrichment analysis of differential metabolites,the metabolic pathways of differential metabolites of the two samples obtained through enrichment analysis include alanine,Aspartic acid and glutamic acid metabolism.When PPAT is knocked down,the level of amino acid metabolites will be lowered.Conclusions: In this study,we found that PPAT was highly expressed in breast cancer through bioinformatics analysis.The high expression of PPAT was significantly related to tumor stage,molecular subtype and lymph node metastasis of breast cancer;The non targeted metabonomics research method was used to process and analyze the cell samples,and the enrichment pathway analysis was conducted for the results.It was found that the PPAT expression level would affect the alanine,Aspartic acid and glutamate metabolic pathways.The above results suggest that PPAT can up regulate the levels of alanine,Aspartic acid and glutamic acid in the metabolites of breast cancer cells,and it is expected to be a biomarker related to poor prognosis of breast cancer patients. |
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