The incidence of breast cancer increase continuously in the world and had been a serious women disease(16).Even with current significant progress in breast cancer treatment,25% to 50% of breast cancers would eventually develop metastasis, leading to poor prognosis(17).The fields of personalized prognosis and precise prediction in medicine are rapidly growing ones(18).In the era of precision medicine,it is vital to include as much prognostic and predictive information as possible for decision‐making(19).
In this study, we first performed a differential analysis of the metabolic genes in breast cancer and normal tissues in TCGA and GSE20605, and then performed enrichment analysis, and found DEGs mainly involved in various DNA, protein and fat metabolism processes.Furtherly,through Cmap database, we mainly screened 6 kinds of drugs that may treat breast cancer, among which the first 3 kinds are LY-294002, tanespimycin, apigenin. LY294002 is a PI3K inhibitor that has been used to treat breast cancer in clinical practice,LY294002 has a significant inhibitory effect on the proliferation of TNBC cells,and LY294002 is more effective on TNBC cells lacking BRCA1(20).Tanespimycin is a quinone quinone anti-tumor antibiotic, derived from the anti-tumor antibiotic geldanamycin.Tanespimycin binds to and inhibits the cytoplasmic chaperone function of heat shock protein 90 (HSP90), It has been reported in the literature that Tanespimycin has strong anti-proliferation activity against breast cancer, colorectal cancer and cervical cancer(21) and can reshape the immunosuppressive microenvironment of triple-negative breast cancer, which is conducive to checkpoint blocking immunotherapy(22).Acquired resistance to adriamycin is a major obstacle to triple negative breast cancer (TNBC) treatment,apigenin can enhance the cytotoxic effect of adriamycin on breast cancer cells(23).
It has been reported that the occurrence of tumors depends on the direct and indirect consequences of oncogenic mutations, namely the reprogramming of cell metabolism and has been proposed to be a core hallmark of cancer(24). Therefore,identifying new metabolic genes in cancer to predict the risk of cancer death has become a hot spot.
In this study, we performed LASSO regression analyses and identified a novel six metabolic prognosis-related genes signature including NT5E,PAICS,PFKL,PLA2G2D,QPRT and SHMT2 base on TCGA data set and verified its efficiency through the data downloaded from the GEO database (GSE20685).Among them, PLA2G2D was considered a protective factor while others were risk factors.It has satisfactory robustness and internal predictive ability in both data set.According to the all patients’riskscore results, the low-risk and high-risk patients are effectively stratified.The data from the GEO database further validated the efficacy, indicating that the predictive efficacy of these metabolism-related genes has strong prognostic value. Abnormal metabolic function in the tumor microenvironment may lead to various prognosis of patients,and genes related to metabolism can be used as prognostic indicators for tumors.thus,we explored the correlation between signatures and TME to expand clinical applications and provide more clues for the choice of treatment strategies.
Previous studies have reported the study of genes in the model. NT5E, 5'-nucleotidase ecto, also known as CD73, is one of the key components of tumor immunosuppressive microenvironment formation. Studies have reported that the high expression of NT5E has a poor prognosis in breast and ovarian cancer, but has a protective effect in lung and gastric cancer. (25).Buisseret L etal reported that the expression of CD73 is related to poor prognosis of human TNBC and low anti-tumor immune function. Targeting CD73 may be a promising strategy to reshape the tumor microenvironment of this subtype(26). CD73 is highly expressed in breast cancer tissues, and increases with the increase of tumor grade and lymph node metastasis. CD73 is highly expressed in more malignant cells, and CD73 overexpression promotes the proliferation of breast cancer cells in vivo and in vitro. It activates the akt/gsk-3 and β/β-catenin/cyclinD1 signaling pathways through mechanisms such as CD73 enzyme activity(27).
PAICS(phosphoribosamidoimidazole carboxylase and phosphoribosamidoimidazole succinyloxycarbonylacetamide synthetase) catalyze two basic steps in the de novo purine biosynthetic pathway and overexpressed in many cancers and may become a promising target for the development of cancer treatments(28). The depletion of PAICS largely eliminates the expansion of breast cancer, exemplifying a prognostic gene related to breast cancer activity(29). Knockdown of PAICS inhibits malignant proliferation of human breast cancer cell lines, These findings demonstrate that PAICS plays an essential role in breast cancer proliferation in vitro, which provides a new opportunity for discovering and identifying novel effective treatment strategies(30).PAICS is a therapeutic target for pancreatic cancer and can be targeted by small molecules(31).
Phosphofructokinase(PFK) is a key regulator of glycolysis and a key control point for glycolytic flux by catalyzing the phosphorylation of fructose 6 phosphate to fructose 1,6 bisphosphate. PFK is a tetramer composed of three subunits M (muscle), L (liver) and P (platelets), which are respectively encoded by three different genes PFKM, PFKL and PFKP (32).PFKL rs2073436C>G can predict the efficacy of NSCLC chemotherapy(33).Study reported A20 is an E3 ubiquitin ligase that interacts with the liver-type phosphofructokinase (PFKL) in liver cancer A20 to promote the degradation of PFKL, thereby inhibiting glycolysis of liver cancer cell lines(34). The PFKL/miR-128 axis regulates glycolysis by inhibiting AKT phosphorylation and predicts the low survival rate of lung cancer patients(35). PFKs are key genes responsible for glycolysis, and their expression is up-regulated in bladder cancer.Targeting this pathway can inhibit the growth of bladder cancer cells(36).
PLA2G2D, a secreted PLA subtype ,Quantitative PCR analysis of bovine PLA2G2D transcripts showed that their expression levels in breast samples were different between the dry period and lactation period, and their expression in liver tissue was polymorphic(37). Recent studies have shown that secreted phospholipase A2 (sPLA2s) represents an attractive potential tumor biomarker and a therapeutic target for various cancers, and the expression of PLA2G2D is reduced by up to 23 times. May be an ideal candidate for new biomarkers that influence colon cancer(38). In the context of cancer, the lack of Pl2g2d leads to a significant reduction in the occurrence of skin cancer, which may be due to enhanced anti-tumor immunity. In summary, these results emphasize the general role of sPLA2-IID as an immunosuppressive sPLA2, which allows the lipid balance of the microenvironment to enter an anti-inflammatory state, and exert beneficial or harmful effects according to different pathophysiological backgrounds in inflammation and cancer(39).
Quinolinate phosphoribosyltransferase (QPRT) is a key enzyme in the de novo synthesis of NAD.Compared with normal tissues, DCTPP1 and QPRT are highly expressed in BC. The overexpression of DCTPP1 and QPRT is related to the slow progress of BC, which promotes the growth, migration and invasion of MCF7 and T47D cells, but inhibits cell apoptosis,DSCAM-AS1 gene knockout reduced the expression of DCTPP1 and QPRT, and inhibited the growth, migration and invasion of estrogen receptor-positive BC(40). Phosphoribosyl transferase (QPRT) is the terminal rate-limiting enzyme in KP(Kynurenine pathway),and its expression is down-regulated, Widespread dysregulation of KP in renal cell carcinoma is common and may lead to tumor immune escape, which is of great significance for effective targeted therapy of this key pathway(41).
Serine hydroxymethyltransferase 2 (SHMT2) is a protein-coding gene that regulates the production of glycine in mitochondria, which is an important intermediate in purine biosynthesis, It is a valuable marker in a variety of cancers, including intrahepatic cholangiocarcinoma, large B-cell lymphoma,Kidney Cancer and breast cancer(42).Studies have shown that SHMT2 expression is an independent prognostic factor for breast cancer(43).
GSEA analysis revealed that the difference in enrichment pathways between high-risk and low-risk groups.The results show that the two risk groups have significantly different metabolic characteristics.The pathway in the high-risk group is mainly related to DNA synthesis metabolism, and the pathway in the low-risk group is related to lipid metabolism.
Changes in tumor cell metabolism have caused a very large impact on the tumor microenvironment, causing corresponding changes in the pH value and metabolites of the microenvironment.In this study,we have not only clarified the metabolic characteristics and mechanisms under the conditions of different risk stratifications, but also predicted the metabolic microenvironment and prognostic characteristics through different immune scoring methods. Correlation analysis shows that our metabolic signature is statistically correlated with approximately half of immune cells,therefore,we can monitor the infiltration composition of immune cells and the degree of immune response through metabolic characteristics.Our metabolic signature can reflect the changes in TME from different aspects,and may provide clues for explaining immunological resistance and immunotherapyapplications.
Our study has several limitations.Firstly,the included metabolic genes were from the TCGA and GSE20685 databases, and some metabolic genes may be missed.Secondly,although our signatures had been validated in multiple databases,the experiments are still needed to further confirm its clinical application value.Thirdly,our data was based on transcriptomics data, it was difficult to reflect the overall landscape of tumor metabolism.