Abstract

Objective

   The study purpose was to explore the causal association between
   pyruvate metabolism and breast cancer (BC), as well as the molecular
   role of key metabolic genes, by using bioinformatics and Mendelian
   randomization (MR) analysis.

Methods

   We retrieved and examined diverse datasets from the GEO database to
   ascertain differentially acting genes (DAGs) in BC via differential
   expression analysis. Following this, we performed functional and
   pathway enrichment analyses to ascertain noteworthy molecular functions
   and metabolic pathways in BC. Employing MR analysis, we established a
   causal association between pyruvate metabolism and the susceptibility
   to BC. Additionally, utilizing the DGIdb database, we identified
   potential targeted medications that act on genes implicated in the
   pyruvate metabolic pathway and formulated a competing endogenous RNA
   (ceRNA) regulatory network in BC.

Results

   We collected the datasets [26]GSE54002, [27]GSE70947, and [28]GSE22820,
   and identified a total of 1127 DEGs between the BC and NC groups. GO
   and KEGG enrichment analysis showed that the molecular functions of
   these DEGs mainly included mitotic nuclear division, extracellular
   matrix, signaling receptor activator activity, etc. Metabolic pathways
   were mainly concentrated in PI3K−Akt signaling pathway,
   Cytokine−cytokine receptor binding and Pyruvate, Tyrosine, Propanoate
   and Phenylalanine metabolism, etc. In addition, MR analysis
   demonstrated a causal relationship between pyruvate metabolism and BC
   risk. Finally, we constructed a regulatory network between pathway
   genes (ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C) and targeted
   drugs, as well as a ceRNA (lncRNA-miRNA-mRNA) regulatory network for
   BC, further revealing their interactions.

Conclusions

   Our research revealed a causal association between pyruvate metabolism
   and BC risk, found that ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C
   takes place an important part in the development of BC in the molecular
   mechanisms related to pyruvate metabolism, and identified some
   potential targeted small molecule drugs.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-024-01125-7.

   Keywords: Breast cancer, Pyruvate metabolism, Pathway genes, Targeted
   drugs, Mendelian randomization

Introduction

   Breast cancer (BC) is a condition due to the unchecked growth of
   mammary epithelial cells as a result of several carcinogenic factors
   [[29]1]. Nipple discharge, Breast lumps, enlargement of axillary lymph
   node and other symptoms are common in the early stages of the disease,
   and multiple organ lesions and distant metastases of cancer cells can
   cause multiple organ lesions in the late stages, which can directly
   jeopardize patients' lives [[30]2, [31]3]. Although the precise
   etiology of BC is still unknown, scientists have discovered numerous
   risk factors that are linked to the disease's occurrence. Estrone and
   estradiol are associated directly to the occurrence of BC.
   Additionally, an increased risk factors for BC is genetic factors and
   some gene mutations can also raised the BC risks. Certain lifestyle
   choices, such as overindulging in food, becoming obese, maintaining a
   high-fat diet, binge drinking, etc., are related with a higher chance
   of developing breast cancer [[32]4–[33]7].

   Pyruvate is a product of the catabolism of sugars and most amino acids.
   It can convert sugars, fats and amino acids into each other via the
   cycles of tricarboxylic acid and acetyl CoA [[34]8]. As a result, it is
   crucial to the metabolic connections between the three main nutrients.
   High plasma pyruvate can be seen in vitamin B1 deficiency, diabetes,
   congestive heart failure, severe diarrhea and other digestive
   disorders, serious infection and liver disease can also be increased
   pyruvate, accompanied by hyperlactemia. Investigation has revealed that
   the end byproduct of the anaerobic glycolysis cycle is pyruvate, and
   large number of cancer cells use pyruvate for growth and development
   [[35]9]. Abnormal pyruvate metabolism is associated with many human
   metabolic disorders and leads to diabetes, cancer, and heart disease
   [[36]10]. Impaired function of mitochondrial pyruvate carriers may
   induce tumors with strong proliferation, migration and invasion
   capabilities [[37]11]. Coincidentally, several researches have also
   found that pyruvate metabolism affects the proliferation,
   differentiation and metastasis of BC cell lines [[38]12–[39]14].
   Reports on the link between pyruvate metabolism and BC risk, however,
   are not readily available. Some previous studies have shown that Mendel
   randomization can be used to predict and study breast cancer. For
   example, in a 2021 study, researchers assessed the potential causal
   impact of smoking on the risk of breast cancer through Mendel
   randomization analysis. The individual level data and summary
   statistical data of a total of 164 SNPs reported in lifetime smoking
   index or genome-wide association studies of daily cigarettes were used
   for MR evaluation. The researchers measured 2929 unique proteins in the
   plasma of 598 women selected from the Karolinska mammography project to
   explore the relationship between protein levels, clinical
   characteristics and gene variation, and identify the proteins that have
   causal relationships with breast cancer. They proposed 812 cis acting
   protein quantitative trait loci of 737 proteins, which were used as a
   tool for Mendelian random analysis of breast cancer risk.

   In this study, we collected and analyzed various datasets from the GEO
   database to identify differentially acting genes (DAGs) in breast
   cancer (BC) through differential expression analysis. Subsequently, we
   conducted functional and pathway enrichment analyses to determine
   significant molecular functions and metabolic pathways in BC. Utilizing
   Mendelian randomization analysis, we established a causal relationship
   between pyruvate metabolism and the risk of BC. Furthermore, leveraging
   the DGIdb database, we identified potential targeted drugs that target
   genes involved in the pyruvate metabolic pathway and established a
   competing endogenous RNA (ceRNA) regulatory complex in BC.

Materials and methods

Data source and collation

   The GENE EXPRESSION OMNIBUS (GEO) database was found in 2000 by the
   National Center for Biotechnology Information (NCBI). It functions as a
   comprehensive repository for gene expression data provided by research
   institutions worldwide, encompassing various types of data such as gene
   chips, high-throughput sequencing data, and other pertinent information
   [[40]15]. In this study, we utilized datasets [41]GSE54002,
   [42]GSE70947, and [43]GSE22820, which are Breast cancer Database, from
   the GEO database to procure three transcriptome gene expression
   matrices. The probe ids were converted into gene symbols. Subsequently,
   the samples were segregated as two different groups, such as the BC
   group and the Normal group, according to the clinical data file.
   Lastly, the R software packages limma and sva were employed to
   amalgamate these three gene expression matrices, resulting in the
   creation of a corrected gene expression matrix [[44]16].

Principal component analysis (PCA) of different datasets

   In order to mitigate the presence of batch effects across distinct GEO
   datasets, we conducted batch correction on the amalgamated gene
   expression matrix. Subsequently, the efficacy of this correction was
   assessed through Principal component analysis (PCA) [[45]17]. PCA is a
   method of statistics that employs orthogonal transformation to change
   possibly correlated variables sets into a linearly independent
   variables sets. These modified variables are referred to as major
   components [[46]18]. To execute the PCA analysis on the expression
   matrix pre and post correction, we employed the R packages ggplot2 and
   ggpubr, utilizing the bioPCA function. The outcomes were then visually
   represented using scatter plots [[47]19].

Differentially expressed gene (DEG) analysis in BC

   In order to ascertain differentially acting genes (DAGs) among BC and
   normal groups, a DEG analysis was conducted. This analysis entailed the
   utilization of the R program to input the compiled transcriptome gene
   expression matrix. By employing the wilcox.test function, a comparison
   of gene expression levels was performed between the two groups,
   resulting in the generation of an expression matrix and difference
   result parameters for the DAGs by the R package limma. Subsequently,
   the DAGs were visually represented through a heatmap and a volcano map
   [[48]20, [49]21]. Statistical significance was found out by an adjusted
   P-value < 0.05 and an absolute log fold change (|lgFC|) of ≥ 0.585.

Function and pathway enrichment analysis of DAGs

   By employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) enrichment analyses were obtained a more comprehensive
   comprehension of the biological processes, molecular functions, and
   metabolic pathways of DEGs [[50]22]. Initially, the R packages
   clusterProfiler and org.Hs.eg.db were utilized to conduct GO enrichment
   analysis of DEGs, encompassing the examination of biological processes
   (BP), cellular components (CC), and molecular functions (MF).
   Subsequently, KEGG enrichment analysis was conducted using the
   enrichKEGG function to identify significant biological pathways based
   on DEGs. Finally, the outcomes of the enrichment analyses were visually
   represented through bubble diagrams [[51]23]. Significance was found
   out by adjusted P-value of < 0.05.

Mendelian randomization analysis

   A two-sample Mendelian randomization (MR) approach was utilized to
   investigate the causal association among biological pathways and the
   risk of BC. Single nucleotide polymorphism (SNP) was defined as the
   instrumental variable (IV). Exposure and outcome data were obtained
   from a publicly available Genome-Wide Association Study (GWAS) database
   ([52]https://gwas.mrcieu.ac.uk/) [[53]24]. Subsequently, the
   significant pathway Pyruvate metabolism (ID: ebi-a-GCST90092942) and
   the disease BC (ID: ebi-a-GCST007236) were chosen for the MR analysis.
   The MR analysis was conducted using the "TwoSampleMR" package, with the
   inverse variance weighted (IVW) method employed to examine the
   association among the activity level of Pyruvate metabolism and the
   risk of BC. We specified a threshold of P < 1 × 10^−5 to select SNP
   instruments and LD r^2< 0.1 threshold for clumping analysis to get
   independent genetic variants for MR analysis. Then, an unweighted
   polygenic risk score (PRS) was calculated for each individual using
   independent genetic variants from GWAS data. Each SNP was recoded as 0,
   1 and 2, depending on the number of trait-specific risk increasing
   alleles carried by an individual. We performed Instrumental variable
   (IV) analyses employing two-stage least square regression (TSLS)
   method. In the first stage, for each exposure trait, association
   between the GRS and observational phenotype value was assessed using
   linear regression and predicted fitted values based on the instrument
   were obtained. In the second stage, linear regression was performed
   with outcome trait and genetically predicted exposure level from the
   first stage. In both stages, analyses were adjusted for age, gender and
   top four principal components of population structure. For each trait,
   TSLS was performed using ‘ivreg’ command from the AER package in R.
   Additionally, a sensitivity analysis was performed using MR-Egger
   [[54]25].

Analysis of the binding between pathway genes and targeted drugs

   The Drug-Gene Interaction database (DGIdb)
   ([55]https://dgidb.genome.wustl.edu/), serving as a comprehensive
   repository of Drug-Gene Interactions, offers genetic information
   pertaining to established and potential drugs [[56]26]. In this
   investigation, the search field was initially populated with pathway
   genes, followed by the application of filtering conditions to specify
   the drug type as Approved, Antineoplastic and Immunotherapies. The
   default selections for database source, gene category, and interaction
   type were retained. Subsequently, the resulting gene-drug interaction
   file was downloaded. Ultimately, the software Cytoscape was employed to
   visualize the interaction between the pathway genes and the targeted
   drugs [[57]27].

Establishment of a ceRNA (lncRNA–miRNA–mRNA) regulatory complex

   Initially, we employed miRanda, miRDB, miRWalk, and TargetScan
   databases to forecast the miRNAs that are bound by pathway genes.
   Subsequently, if all four databases concur on a specific miRNA as a
   potential target, it is retained as the designated targeted miRNA
   [[58]28]. Additionally, we utilized the spongeScan database to
   anticipate the lncRNAs that target the binding of miRNAs and assigned
   names to the identified targeted lncRNAs [[59]29]. Lastly, to depict
   the regulatory network involving circRNA-miRNA-mRNA, we employed
   Cytoscape software for visualization purposes.

Results

Gene expression matrix of BC

   Initially, we compiled the datasets [60]GSE54002 (consisting of 417 BC
   samples and 16 Normal samples), [61]GSE70947 (comprising of 148 BC
   samples and 148 Normal samples), and [62]GSE22820 (comprising of 176 BC
   samples and 10 NC samples). Subsequently, we acquired expression
   matrices containing 21,654, 27,440, and 19,593 genes, respectively
   (refer to supplementary files Matrix_GSE54002.xls, Matrix_GSE70947.xls,
   and Matrix_GSE22820.xls). Following this, we merged the three matrices
   to generate a refined expression matrix comprising 15,895 genes
   (comprising 741 BC samples and 174 Normal samples) (refer to
   supplemental file Matrix_merge.xls).

PCA analysis of three datasets

   PCA was employed to visually depict the presence of batch effects
   across distinct datasets both prior to and subsequent to batch
   correction. The obtained results indicated a notable batch effect in
   the samples originating from three separate datasets prior to any batch
   correction (Fig. [63]1a). Conversely, following the application of
   batch correction, no significant batch effect was observed within the
   samples derived from the aforementioned three datasets (Fig. [64]1b).

Fig. 1.

   [65]Fig. 1
   [66]Open in a new tab

   Scatter plots of principal component analysis before (a) and after (b)
   batch correction. PC1, principal component 1; PC2, principal component
   2. Different colored dots indicate samples from different datasets

Identification of DEGs between BC and Normal groups

   By utilizing differential expression analysis, overall 1127 DEGs were
   recognized among the BC and NC groups. Among these DEGs, 523 showed
   up-regulation, while 604 exhibited down-regulation, as visually
   depicted in Fig. [67]2a. Furthermore, Fig. [68]2b presented a heat map
   illustrating the top 50 DEGs that displayed both up-regulation and
   down-regulation.

Fig. 2.

   [69]Fig. 2
   [70]Open in a new tab

   The volcano plot (a) and heatmap (b) of DAGs expressed between the BC
   and Normal groups. Red dots or squares indicate up controlled DAGs;
   Blue dots or squares indicate down controlled DAGs

GO and KEGG enrichment of 1127 DAGs in BC

   Through the examination of GO and KEGG enrichment, we have identified
   the biological processes, molecular functions, and metabolic pathways
   of DAGs. As depicted in Fig. [71]3a, the GO enrichment analysis
   revealed that the functions of BP primarily encompass nuclear division,
   ossification, mitotic nuclear division, organization of extracellular
   matrix and segregation of sister chromatid. The functions of CC were
   found to be closely linked to collagen-containing extracellular matrix,
   mitotic spindle, apical plasma membrane, and centromeric region. The
   functions of MF were predominantly enriched in signaling receptor
   activator activity, glycosaminoglycan, microtubule, heparin, growth
   factor and integrin binding, extracellular matrix structural
   constituent. In the KEGG enrichment analysis, the pathways identified
   by DEGs were primarily focused on PI3K−Akt signaling pathway,
   Cytokine−cytokine receptor binding, Human papillomavirus infection,
   AMPK signaling pathway, Regulation of lipolysis in adipocytes, Insulin
   resistance, PPAR signaling pathway, ECM-receptor interaction, Fatty
   acid degradation, Pyruvate, Tyrosine, Propanoate and Phenylalanine
   metabolism, among others (Fig. [72]3b).

Fig. 3.

   [73]Fig. 3
   [74]Open in a new tab

   Bubble plots for GO (a) and KEGG (b) enrichment analyses. Different GO
   and KEGG terms are displayed on the ordinate, and gene enriched
   ratiosaredisplayed on the abscissa

Pyruvate metabolism was causally correlated with the BC risks

   The Supplementary file SNP.xls provided an overview of the SNP
   characteristics related to Pyruvate metabolism and BC. None of the SNPs
   were deemed weak instrumental variables. Figure [75]4a and b depicted
   the causal genetic difference effect on BC. By employing the IVW
   method, were observed a remarkable correlation between the activity
   level of Pyruvate metabolism and the risk of BC, with an odds ratio
   (OR) of 0.00048 (95% confidence interval (CI) = 1.556e−06 to 0.150,
   p = 0.009). Additionally, Simple mode, the Weighted median and Weighted
   mode methods also yielded statistically significant findings (see
   Table [76]1).The symmetrical characteristics exhibited by the funnel
   plot indicate the presence of a causal effect (Fig. [77]4c).
   Furthermore, the absence of horizontal pleiotropy in the MR Egger
   regression (p = 0.975) provides additional evidence of the lack of bias
   in the causal effect. After removing each individual SNP, we ran MR
   analysis on the remaining SNPs to confirm the validity of our results,
   as shown in Fig. [78]4d. The consistent outcomes obtained from this
   analysis suggest that all SNPs make significant contributions to the
   observed causality. This implies that there is no singular dominant SNP
   exerting influence on Pyruvate metabolism and the risk of BC.

Fig. 4.

   [79]Fig. 4
   [80]Open in a new tab

   a Scatter plot illustrating the causal effect of Pyruvate metabolism on
   the risk of BC. b Forest plot displaying the causal effect of each SNP
   on BC risk. c Funnel plots used to visualize overall heterogeneity of
   MR estimates for the effect of Pyruvate metabolism on BC. d
   Leave-one-out plot allowing for the visualization of the causal effect
   of Pyruvate metabolism on BC risk when leaving one SNP out

Table 1.

   Statistical results of different Mendelian randomization methods
   Method Nsnp B SE P-value Lo_ci Up_ci OR OR_lci95 OR_uci95
   MR Egger 21 − 7.8002 5.9906 0.2085 − 19.5418 3.9414 0.0004 0.0000
   51.4924
   Weighted median 21 − 6.0890 0.3658 0.0000 − 6.8060 − 5.3721 0.0023
   0.0011 0.0046
   Inverse variance weighted 21 − 7.6365 2.9271 0.0091 − 13.3736 − 1.8994
   0.0005 0.0000 0.1497
   Simple mode 21 − 20.5532 2.1320 0.0000 − 24.7320 − 16.3744 0.0000
   0.0000 0.0000
   Weighted mode 21 − 5.5964 0.3427 0.0000 − 6.2680 − 4.9247 0.0037 0.0019
   0.0073
   [81]Open in a new tab

Regulatory complex of pathway genes and targeted drugs

   The DGIdb database was utilized to acquire a comprehensive table
   encompassing pathway genes and their corresponding targeted drug
   interactions, as described in the supplementary file (Drug-gene.xls).
   Subsequently, a regulatory complex was established by Cytoscape to
   visually represent the connections between pathway genes and targeted
   drugs. The graphical representation in Fig. [82]5 depicted pathway
   genes as red oval nodes and targeted drugs as green diamond nodes, with
   the lines connecting these nodes representing their interactions. This
   drug regulatory network encompassed a total of 6 pathway genes (ADH1B,
   ACSS2, ACACB, ADH1A, ALDH2, and ADH1C), 14 molecular drugs, and 25
   pairs of interacting relationships.

Fig. 5.

   [83]Fig. 5
   [84]Open in a new tab

   Regulatory complex between pathway genes and targeted drugs. The red
   nodes represent pathway genes, the green nodes represent targeted
   drugs, and the lines between them represent interaction pairs

The ceRNA (lncRNA–miRNA–mRNA) regulatory complex of BC

   Overall 47 pairs of mRNAs and miRNAs, as well as 98 pairs of lncRNAs
   and miRNAs, were acquired from various databases (refer to
   Supplementary filesmRNA-miRNA.xls and miRNA-lncRNA.xls). The ceRNA
   (lncRNA-miRNA-mRNA) regulatory complex (Fig. [85]6) was employed to
   visualize the interactions among these entities. This regulatory
   network comprised a total of 7 mRNAs, 15 miRNAs, 88 lncRNAs, and 113
   pairs of interacting relationships.

Fig. 6.

   [86]Fig. 6
   [87]Open in a new tab

   The ceRNA (lncRNA-miRNA-mRNA) regulatory complex of BC. Red square
   nodes indicate mRNAs, green elliptic nodes indicate miRNAs, and blue
   diamond nodes indicate lncRNAs. The lines between them represent
   interactions between different types of RNAs

Discussion

   One of the most prevalent cancerous tumors in women is BC. Based on
   statistical data, 7–10% of all malignant tumors in the body are
   accounted for by this incidence rate. An occurrence is often associated
   with heredity and the rate of occurrence is greater in women between 40
   and 60 years old and before and after menopause [[88]30, [89]31].
   Pyruvate is a tricarboxylic acid produced in the body, which is the end
   product of the glycolytic pathway. For energy, it is either oxidized
   into acetyl CoA in mitochondria or reduced to lactate in the cytoplasm.
   After entering the tricarboxylic acid cycle, it undergoes oxidation to
   produce carbon dioxide and water, consummating the energy supply
   process of aerobic oxidation of glucose [[90]32]. Through the acetyl
   CoA and tricarboxylic acid cycles, pyruvate can also accomplish mutual
   conversion between sugars, lipids, and amino acids in the body, and it
   plays a crucial part in the metabolic relationships of the three main
   nutrients [[91]33]. This article identified a total of 1127 DEGs in the
   BC and NC groups through differential expression analysis.
   Subsequently, the biological processes, molecular functions, and
   metabolic pathways of DEGs were determined through GO and KEGG
   enrichment analysis. Furthermore, through Mendelian randomization
   analysis, it was demonstrated that there is a causal relationship
   between pyruvate metabolism and the risk of BC. Finally, we constructed
   a regulatory network between pathway genes and targeted drugs, as well
   as a ceRNA (lncRNA miRNA mRNA) regulatory network for BC, further
   revealing their interactions.

   Our study not only revealed a causal relationship between pyruvate
   metabolism and BC risk, but also found that ADH1B, ACSS2, ACACB, ADH1A,
   ALDH2, and ADH1C takes place a major part in the development of BC in
   the molecular mechanisms related to pyruvate metabolism, and identified
   some potential targeted small molecule drugs. Our comprehensive
   analysis links GWAS and transcriptomics together.

   In our study, we indirectly demonstrated a causal association among
   pyruvate metabolism and the BC risks. As the activity of the pyruvate
   metabolism pathway increases, the risk of developing BC decreases
   (OR < 1 and B < 0). Therefore, the pyruvate metabolism pathway may have
   a protective causal relationship in BC. By comparing ^13C tracer
   analyses, Christen S et al. [[92]14] found that lung metastatic stoves
   had higher pyruvate carboxylase (PC)-dependent repair to adapt to the
   lung microenvironment compared to primary BC. The Elia I team [[93]13]
   found that in order to stimulate collagen-based remodeling of the
   extracellular matrix in the lung metastatic niche, BC cells need the
   nutrient pyruvate. Nonetheless, in many animal models, it was
   sufficient to restrict pyruvate metabolism in order to hinder collagen
   hydroxylation and, consequently, the formation of lung metastases
   originating from BC.

   The gene ADH1A (Alcohol dehydrogenase 1A (class I), alpha polypeptide),
   ADH1C (Alcohol dehydrogenase 1C (class I), gamma polypeptide) and ADH1B
   (Alcohol dehydrogenase 1B (class I), beta polypeptide) encode proteins
   that belong to the alcohol dehydrogenase family, which are known for
   its capacity to metabolize a diverse range of substrates such as
   retinol, ethanol, hydroxysteroids, aliphatic alcohols and lipid
   peroxidation products. These proteins, composed of various homo- and
   heterodimers of alpha, beta and gamma subunits, demonstrate significant
   effect in ethanol oxidation and are crucial in the process of ethanol
   catabolism [[94]34–[95]36]. Jiang C et al. [[96]37] determined that
   ADH1B may suppress the invasion, proliferation and migration of BC
   cells by suppressing mitogen-activated protein kinase (MAPK) signaling
   pathway, so it may be a potential clinical therapeutic target for BC.
   Coutelle C et al. [[97]38] found a significant increase in the allele
   frequency of ADH1C*1 in the moderate drinking group with BC compared to
   the normal control group who drank alcohol (62% vs. 41.9%, p = 0.0035).
   Women with the ADH1C*1 genotype had a 1.8 times greater risk of
   developing BC than women with other genotypes (95% CI 1.431–2.330,
   p < 0.001).

   This geneACSS2 (Acyl-CoA synthetase short chain family member 2)
   encodes a cytosolic enzyme responsible for catalyzing the acetate
   activation to be utilized in synthesis of lipids and production of
   energy. This enzyme functions as a monomer and facilitates the
   conversion of acetate to acetyl-CoA in an ATP-dependent reaction
   [[98]39, [99]40]. Miller KD et al. [[100]41] synthesized a small
   molecule inhibitor as a transition state mimetic to block ACSS2
   activity in vitro and in vivo, while ACSS2 acts as an individual drug
   to inhibit BC cell growth. This indicates that targeting ACSS2 may be
   an effective method for treating BC patients.This geneACACB (Acetyl-CoA
   carboxylase beta) is believed to regulate fatty acid oxidation through
   the inhibitory effect of malonyl-CoA on carnitine-palmitoyl-CoA
   transferase I, the crucial step in mitochondrial fatty acid uptake and
   oxidation [[101]42]. Yang JH et al. [[102]43] observed that combined
   inhibition of the pentose phosphate pathway and fatty acid oxidation
   (FAO) with clinically available drugs effectively restored SNAI1, a
   basic transcriptional suppressor of epithelial-mesenchymal
   transformation, mediates reprogramming of metabolism and inhibits the
   in vivo metastasis progression in BC cells. This protein encoded by
   gene ALDH2(Aldehyde dehydrogenase 2 family member) is classified within
   the aldehyde dehydrogenase protein family, which plays a key role as
   the second enzyme in the primary oxidative pathway of alcohol
   metabolism [[103]44].Ugai T et al. [[104]45] found that functional
   ALDH2 polymorphisms are correlated to the BC risks, and Lys/Lys
   genotypes confer susceptibility to BC risk in Asian women, especially
   for ER-positive, PR-positive, and HER2-negative tumor types. These
   previous studies are similar to our results.

   The main advantage of this study is that it fully incorporates various
   metabolic processes and strict MR design. The data related to BC comes
   from the GEO database to ensure the richness and credibility of the
   data. The causal relationship between the pyruvate metabolism pathways
   of BC was determined through MR analysis, and further confirmed through
   meta-analysis and MVMR methods. Conduct in-depth research on causal
   relationships using mediation MR analysis. Therefore, we believe that
   our research findings are convincing and provide reliable causal
   explanations for the impact. These findings may provide valuable
   insights for prospective treatment of BC by further investigating the
   mechanisms associated with BC. However, our investigation has some
   limitations. Firstly, we cannot determine the applicability of our
   population analysis based on this study to other populations. Although
   our research findings have demonstrated a causal relationship between
   the pyruvate metabolism pathways of BC and identified potential
   mediators, further research is needed to explore their potential
   mechanisms in order to develop effective and feasible treatment methods
   for BC due to data limitations. Besides, our study is limited by the
   content and workload of the study, and further verification on protein
   levels and large sample clinical trials is needed in the future. In
   addition, we ultimately did not use BC's pyruvate metabolism pathway
   genes and potential drugs in animal studies to verify its effects.
   These will be the subjects of our upcoming studies.

   The pyruvate metabolism pathway of BC is an important health issue
   today, although there seems to be a causal relationship between them,
   it is currently unclear. This study was the first to use the MR method
   to test this relationship. By using instrumental variables, we
   evaluated the causal relationship between the pyruvate metabolism
   pathways of BC. In addition, mediating MR helps us understand the role
   of the pyruvate metabolism pathway in this relationship. These findings
   lay the foundation for promoting the understanding and treatment of BC,
   with the ultimate goal of reducing incidence rate, mortality and the
   burden of the global health system.

Conclusions

   Our study demonstrated a causal association among pyruvate metabolism
   and the risk of BC, and found that ADH1B, ACSS2, ACACB, ADH1A, ALDH2,
   and ADH1C play a major part in the molecular mechanisms related to
   pyruvate metabolism in the occurrence of BC, and identified some
   potential targeted small molecule drugs. In addition, the regulatory
   network of ceRNA (lncRNA-miRNA-mRNA) further revealed the interactions
   between mRNAs related to this pathway.

Supplementary Information

   [105]Additional file 1.^ (1.8KB, xls)
   [106]Additional file 2.^ (60.8MB, xls)

Acknowledgements