Prognostic Signature and Therapeutic Value Based on Membrane Lipid Biosynthesis-Related Genes in Breast Cancer

There is a need to improve diagnostic and therapeutic approaches to enhance the prognosis of breast cancer, the most common malignancy worldwide. Membrane lipid biosynthesis is a hot biological pathway in current cancer research. It is unclear whether membrane lipid biosynthesis is involved in the prognosis of BRCA. With LASSO regression, a 14-gene prediction model was constructed using data from the TCGA-BRCA cohort. The prediction model includes GPAA1, PIGF, ST3GAL1, ST6GALNAC4, PLPP2, ELOVL1, HACD1, SGPP1, PRKD2, VAPB, CERS2, SGMS2, ALDH3B2, and HACD3. BRCA patients from the TCGA-BRCA cohort were divided into two risk subgroups based on the model. Kaplan–Meier survival curves showed that patients with lower risk scores had significantly improved overall survival (P=2.49e − 09). In addition, risk score, age, stage, and TNM classification were used to predict mortality in BRCA patients. In addition, the 14 genes in the risk model were analyzed for gene variation, methylation level, drug sensitivity, and immune cell infiltration, and the miRNA-mRNA network was constructed. Afterward, the THPA website then analyzed the protein expression of 14 of these risk model genes in normal and pathological BRCA tissues. In conclusion, the membrane lipid biosynthesis-related risk model and nomogram can be used to predict BRCA clinical prognosis.


Introduction
Breast cancer is the most common cancer in women and one of the leading causes of cancer death [1][2][3]. Breast cancer mainly refers to malignant tumors in breast epithelial tissue, with a high clinical cure rate early. In contrast, advanced breast cancer is highly aggressive, malignant, and has sizeable histological heterogeneity [4,5]. In the actual diagnosis and treatment process, the clinical symptoms of early breast cancer are not obvious, which is easy to cause misdiagnosis and missed diagnosis, leading to breast cancer patients missing the best time for treatment [6,7]. At the same time, there is still no effective treatment for breast cancer patients with metastases [8]. With the continuous development of molecular biology, gene therapy has become a new direction in breast cancer treatment. However, breast cancer is a complex disease, and its occurrence and development are affected by many molecular factors. erefore, studying the pathogenesis of breast cancer is of great significance to its treatment.
Cancer cells, as mutated cells, are the source of cancer. Unlike normal cells, cancer cells have three characteristics of infinite proliferation, transformation, and easy transfer [9]. ey can proliferate indefinitely and destroy normal cells and tissues. In addition to dividing out of control, cancer cells can invade surrounding normal tissues locally and even metastasize to other body parts via the circulatory system or lymphatic system. By remodeling their metabolism, cancer cells provide ATP and macromolecules necessary for cell growth, division, and survival. To meet their rapid proliferation needs, tumor cells exhibit metabolic characteristics different from normal tissue cells [10,11]. Although cancer types and etiologies are different, there are certain similarities in the changes in metabolic pathways in cancer cells. Glucose, pentose phosphate, and glutamine metabolic pathways adapt to cancer cells' rapid growth and proliferation. Recent studies have shown that membrane lipid biosynthesis is also altered in cancer cells [12][13][14]. In cancer development, changes in membrane lipid biosynthesis play an essential role in providing biomembrane macromolecules and signaling molecules for the occurrence and development of cancer [15][16][17][18]. Alterations in membrane lipid biosynthesis in tumor tissues provide macromolecular precursors necessary for their abnormal survival and growth.
As a computer science and high-throughput sequencing technology developed, bioinformatic analysis can provide researchers with more opportunities to explore the genetic risk, regulatory mechanism, and protein function of cancer. As a highly representative bioinformatics database, the TCGA database is generated by large-scale gene sequencing, which can quantitatively study and analyze the changes in gene expression during tumor occurrence and development [24,25]. erefore, in this study, we performed a series of indepth explorations of membrane lipid biosynthesis in BRCA based on the TCGA database using multiple bioinformatic analyses. Our findings suggest that targeting membrane lipid biosynthesis has excellent potential for BRCA prevention, treatment, and control.

Data Acquisition and Selection of Membrane Lipid Biosynthesis-Related Genes.
is study downloaded BRCA mRNA expression and clinical information from TCGA (https://portal.gdc.cancer.gov/). A total of 1,109 tumor samples and 113 normal samples with clinical and expression data were obtained. Expression data were normalized using log 2 (TPM + 1) transformation. Transcript data and human profiles are matched and sorted by Perl to get comprehensive and accurate mRNA gene expression data using information from an integrated database. Gene IDs are converted to gene names for subsequent analysis. Membrane lipid biosynthesis-related genes were obtained from the Gene Set Enrichment Analysis (GSEA) database (https:// www.gsea-msigdb.org/gsea/index.jsp) [26,27]. e standard name is GOBP membrane lipid biosynthetic process. e systematic name is M16158. Differential expression analysis was carried out on TCGA data using the "limma" R package. Univariate Cox analysis was used to identify membrane lipid biosynthesis-related genes with prognostic values (P < 0.05). e TCGA cohort was evaluated for membrane lipid biosynthesis-related genes with predictive value.

Construction and Validation of a Prognostic Signature of Membrane Lipid Biosynthesis-Related Genes.
A predictive model was constructed in BRCA using LASSO regression analysis to minimize the use of overlapping membrane lipid biosynthesis-related gene risk values. Statistics and machine learning use LASSO regression to identify the most critical factors and improve the accuracy of statistical models. LASSO is a popular machine learning algorithm widely used in medical research [25,28,29]. In our study, risk scores were obtained for all patients and then divided into high-or low-risk subgroups based on the median risk score of the TCGA cohort. e "survival," "survminer," and "timeROC" packages in R were used to perform KM survival analysis and ROC analysis based on OS to estimate the predictive accuracy of the two sets of gene signatures.

Prognostic Independence of Prognostic Signature from
Routine Clinicopathological Features and Generation of the Corresponding Nomogram. To further evaluate the gene prognostic signature's independent predictive value, it was examined using univariate and multivariate Cox regression analysis to see if it was affected by other clinical traits such as age, gender, stage, and TNM stage. Corresponding nomograms were drawn using the R package "RMS" based on risk scores and other routine clinicopathological features.

Prognostic Value and Gene Variation of Risk Model Genes Associated with Membrane Lipid Biosynthesis in Pan-Cancer.
In this study, we first targeted the membrane lipid biosynthesis-related risk model genes GPAA1, PIGF, ST3GAL1, ST6GALNAC4, PLPP2, ELOVL1, HACD1, SGPP1, PRKD2, VAPB, CERS2, SGMS2, ALDH3B2, and HACD3 were analyzed for OS, CNV, SNV, methylation, and immune cell infiltration in pan-cancer via Gene Set Cancer Analysis (GSCA) database (https://bioinfo.life.hust.edu.cn/ GSCA/#/) [30]. We then used this database to drill down to the percentage of cancers, in which mRNA expression of membrane lipid biosynthesis-related risk model genes had a potential impact (activation or inhibition) on biological pathways. Finally, we used this database to construct a miRNA regulatory network for membrane lipid biosynthesis-related risk model genes.

Anticancer Drug Sensitivity Analysis of Risk Model Genes Associated with Membrane Lipid Biosynthesis.
is study integrated the drug sensitivity and gene expression profiling data from Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer erapeutics Response Portal (CTRP). e correlation between small molecule/drug sensitivity (IC50) and the expression of 14 membrane lipid biosynthesisrelated risk model genes was analyzed by Spearman correlation. GDSC was developed by the Sanger Institute in the United Kingdom to collect the sensitivity and response of tumor cells to drugs. Raw data from GDSC comes from 75,000 experiments describing the response of about 200 anticancer drugs in more than 1,000 tumor cells. e oncogene mutation information in this database comes from the COSMIC database, including oncogene point mutations, gene amplification and loss, tissue types, and expression profiles (https://www.cancerrxgene.org/) [31][32][33]. In addition, CTRP, a database of 70,000 cancer cell line compounds that link susceptibility to genetic or lineage characteristics, serves as a public resource for researchers worldwide (https://portals.broadinstitute.org/ctrp.v2.1/) [34][35][36]. is important resource drives the discovery of potential cancer drugs that match the patient population and maximize the likelihood that patients will benefit from them. Such data are therefore crucial for the discovery of potential tumor therapeutic targets.

Gene Set Enrichment Analysis and Protein Expression Analysis of Membrane Lipid Biosynthesis-Related Risk Model
Genes in BRCA. Gene Set Enrichment Analysis (GSEA) is a method for enrichment analysis of target genes, which can be used to test the correlation between target genes and gene sets of known functions. is study used the GTBA database to explore the relationship between membrane lipid biosynthesis-related risk model genes and the HALLMARK pathway (https://guotosky.vip:13838/GTBA/). e Human Protein Atlas (THPA) database is based on proteomics, transcriptomics, and systems biology data, which can map tissues, cells, organs, etc.
is database includes protein expression in tumors and normal tissues (https://www. proteinatlas.org/) [37,38]. erefore, in this study, we used this database to explore the expression of risk model genes related to membrane lipid synthesis in breast cancer and normal breast tissue.

Tumor Immune Estimation Resource (TIMER) Database.
A comprehensive database of immune infiltration in various cancer types, the TIMER database offers multiple methods of assessing immune infiltration levels, providing researchers with the ability to generate high-quality graphs online to explore immunomics, clinical manifestations, and tumor genomes. is study used this database to assess the association between membrane lipid biosynthesis-related risk model genes and immune cell infiltration in BRCA.

Statistical
Analysis. Statistical analysis was performed using R 4.1.2 and Strawberry Perl software. e nonparametric rank test (Wilcox test) was used to analyze the expression differences of membrane lipid biosynthesisrelated genes in breast cancer and normal breast tissue samples. e Kaplan-Meier analysis of gene expression was used to analyze the relationship between breast cancer survival and gene expression, and the significance of the breast cancer risk model was assessed using univariate and multivariate Cox regression analysis. e relationship between gene expression and clinicopathological characteristics was analyzed by χ 2 test.

Gene Expression and Protein Interaction of Membrane
Lipid Biosynthesis-Related Molecules in BRCA. It is well known that the process of carcinogenesis is often accompanied by abnormal expression of gene mRNA. ese abnormally expressed genes are of more interest to cancer researchers than those that are not [9,39]. To explore whether there are differences in the expression of membrane lipid biosynthesis-related genes in BRCA, we extracted the mRNA expression data of membrane lipid biosynthesisrelated genes from the TCGA database. We used the R language to draw a corresponding heatmap (Figure 1(a)). We can intuitively see that the mRNA expression of the vast majority of membrane lipid biosynthesis-related genes is significantly different in BRCA.
en, to explore the interaction of the protein molecules encoded by membrane lipid biosynthesis-related genes, we used the String website to draw the corresponding PPI network (Figure 1(b)). e PPI network revealed extensive interactions of membrane lipid biosynthesis-related molecules. To further explore the relationship, we used the MCODE algorithm of the Metascape website to perform cluster analysis on the PPI network to identify subnetworks, that is, potential protein complexes (Figures 1(c) and 1(d)) (Supplementary Material Table S1).

Construction of Risk Models Associated with Membrane
Lipid Biosynthesis in BRCA. To fully explore the prognostic value of membrane lipid biosynthesis-related genes in BRCA, we used LASSO regression curve analysis to establish a prognostic risk model in BRCA, which included GPAA1, PIGF, ST3GAL1, ST6GALNAC4, PLPP2, ELOVL1, HACD1, SGPP1, PRKD2, VAPB, CERS2, SGMS2, ALDH3B2, and HACD3 (Figures 2(a) and 2(b)). LASSO regression curve analysis is often used in medical modeling [28,29]. Based on the calculation formula of the risk model related to membrane lipid biosynthesis, we can divide breast cancer patients into high-risk and low-risk groups. e drawn survival curve shows that the survival rate of breast cancer patients in the high-risk group is significantly lower than that in the low-risk group. cancer patients (P � 2.49e − 09) (Figure 2(c)). To test the predictive accuracy of this model of membrane lipid biosynthesis-related risk, we performed a ROC curve analysis, which showed that the five-year AUC was equal to 0.69, the seven-year AUC was equal to 0.722, and the ten-year AUC was equal to 0.729 (Figures 2(d)-2(f )). is indicates that this membrane lipid biosynthesis-related risk model has good predictive accuracy. e formula for calculating the risk model is as follows.

Independent Predictive Value of Risk Models Associated with Membrane Lipid Biosynthesis and Construction of Corresponding Predictive Survival Nomogram in BRCA.
To gain an in-depth understanding of the correlation between membrane lipid biosynthesis-related risk models and clinical clinicopathological characteristics, we obtained the corresponding BRCA clinical data through the TCGA official website and performed a correlation analysis. e results are presented in the form of a heatmap. e results showed a significant correlation between this membrane lipid biosynthesis-related risk model and stage, age, and fustat in breast cancer patients (Figure 3(a)). We subsequently performed univariate and multivariate Cox regression analyses to explore the role of this membrane lipid biosynthesis-related risk model and clinicopathological features in breast cancer progression. Univariate Cox regression analysis showed that age, stage, T, M, N, and risk score were risk factors for the  DPM2  UGCG  DPM1  B3GALT1  SIRT3  ENPP7  UGT8  PIGK  SGPL1   ORMDL2  PPM1L ST6GALNAC5  FA2H  SPTLC1  A3GALT2  LARGE  PPAP2B  CERS2  PIGC  SPHK1   SPTLC2  PRKCD  COL4A3BP  PIGT  TMEM246  DPM3  ST3GAL5  ELOVL5  B4GALT3  ST8SIA5 ST6GALNAC3   PIGQ  PIGH  PIGM  SAMD8  ASAH1  ST3GAL4  B4GALT5  ALDH3B1  PTPLA  PIGV  GAL3ST1   SPTSSA  AGK  PIGP  ELOVL4  SGMS1  ALOXE3  ASAH2B  PIGW  KDSR  SCCPDH  ALOX12B   DEGS2  GBA  PYURF  ELOVL6  VAPA  ST6GALNAC4 PNPLA1  PEMT  PPAP2A  PIGA  ST8SIA1   ELOVL7  ORMDL3  DEGS1  PIGS  PTPLAD2  ACER1  B3GNT5  PGAP2  SMPD2  B3GALT2  CYP4F22 ASAH2  Multivariate Cox regression analysis showed that age and risk score were independent risk factors for the malignant progression of breast cancer (Figure 3(c)). Finally, based on the risk model associated with membrane lipid biosynthesis, we drew corresponding nomograms to predict breast cancer patients 5-, 7-, and 10-year survival ( Figure 3(d)). e above results indicate that this membrane lipid biosynthesis-related risk model has solid clinical relevance and clinical application value in BRCA.

Gene Expression and Prognostic Value of Risk Model Genes Associated with Membrane Lipid Biosynthesis in Pan-Cancer.
To further understand the significance of membrane lipid biosynthesis-related risk model genes in pan-cancer, first, we explored the mRNA expression differences of these fourteen risk model genes in pan-cancer and presented them in the form of a heat map. e results showed that these fourteen risk model genes were widely differentially expressed in LUSC, LUAD, BRCA, LIHC, and THCA (Figure 4(a)). Subsequently, we performed a biological pathway correlation analysis for these fourteen risk model genes. e results showed that its mRNA expression was negatively correlated with the inhibition of DNA Damage, EMT, and Hormone AR pathways. In contrast, its mRNA expression was positively associated with activation of Apoptosis, Cell Cycle, and EMT pathways (Figure 4(b)). Furthermore, to closely link the clinic, we explored the predictive value of risk model genes associated with membrane lipid biosynthesis in pan-cancer. e results showed that these fourteen risk model genes were significantly associated with DSS, OS, and PFS in patients with multiple types of cancer (Figure 4(c)). We then performed a Gene set variation analysis (GSVA) on these fourteen risk model genes to explore the differences in multiple tumors of the membrane lipid biosynthesis-related risk model genes. GSVA is a non-parametric, unsupervised algorithm that transforms gene expression data, from an expression matrix featuring a single gene to an expression matrix featuring a specific set of genes. GSVA quantified gene enrichment results, making the subsequent statistical analysis more convenient. Boxplots show that the GSVA scores of these fourteen risk model genes tended to increase in various tumors, including BLCA, BRCA, ESCA, LUAD, and THCA ( Figure 4(d)). e biological pathway correlation analysis showed a negative correlation between GSVA score and DNA Damage (Figure 4(e)). e above results suggest that this membrane lipid biosynthesis-related risk model gene may play a similar biological function in pan-cancer and provide meaningful clues for future cancer research.   biosynthesis in pan-cancer. In the SNV variant analysis results, we found that PRKD2, SGPP1, and GPAA1 have widespread high CNVs in UCEC, SKCM, COAD, and STAD. e results also showed that the top 10 SNV variants were PRKD2, ALDH3B2, GPAA1, ST3GAL1, SGPP1, CERS2, SGMS2, HACD1, PLPP2, and HACD3. Among them, PRKD2 accounted for as high as 22% of SNVs in pancancer ( Figures 5(a)-5(d)). Since CNVs are an essential part of genomic variation, we then explored the CNVs of membrane lipid biosynthesis-related risk model genes in pan-cancer. e results showed that CERS2, VAPB, GPAA1, and ST3GAL1 were widespread CNVs in pan-cancer ( Figure 5(e)). In LUAD, LUSC, OV, BRCA, and UCS, CNVs of GPAA1, VAPB, ERS2, PRKD2, and ELOVL1 showed a positive correlation with their mRNA expression ( Figure 5(f )).

Methylation and Drug Sensitivity of Risk Model Genes Associated with Membrane Lipid Biosynthesis in Pan-Cancer.
Epigenetic studies are the characteristics of heritable changes outside the DNA sequence. All cells of a living individual have essentially the same DNA. Still, different organs and tissues have different functions and maintain their specific cellular identity over multiple cell divisions, which are primarily thought to be determined by the appearance of mediated by genetic information [9,40]. Since methylation is essential to epigenetic research, we explored the methylation differences of membrane lipid biosynthesis-related genes in various tumors [41,42]. We presented them in the form of heat maps. e results showed that ALDH3B2, ST3GAL1, and SGMS2 genes had noticeable methylation differences in BRCA, KIRC, and LUSC (Figures 6(a)   efficacy of clinical treatments, responses to anticancer drugs vary widely across different therapeutic targets. erefore, we used data from GDSC and CTRP databases to explore the sensitivity of membrane lipid biosynthesis-related genes to various anticancer drugs. e results showed that SGMS2, PLPP2, HACD1, ELOVL1, ALDH3B2, and VAPB significantly correlated with the sensitivity of various anticancer drugs. Meanwhile, there were significant negative correlations between PRKD2, ST6GALNAC4, and HACD1 sensitivities to multiple anticancer drugs (Figures 6(c) and 6(d)). We believe our study provides valuable data for future drug target development.

Immune Cell Infiltration of Membrane Lipid Biosynthesis-Related Risk Model Genes in BRCA.
We all know that tumor tissue is not simply composed of tumor cells, it is composed of various types of cells, including stromal cells, fibroblasts, immune cells, etc. ese cells constitute the tumor microenvironment [43,44]. In recent years, we have paid more attention to the role of immune cells in this microenvironment [45][46][47]. Immune cells include many kinds, such as B cells and T cells. Different immune cells play different roles in the process of tumorigenesis, and the composition of immune cells of various tumors also has its characteristics. Because of the importance of immune cell infiltration in tumor progression, based on the Timer database, we examined six types of immune cell infiltration and this membrane lipid biosynthesis-related gene in BRCA. e results showed a significant positive correlation between ST3GAL1, SGPP1, VAPB, and SGMS2 and CD8+ T Cell infiltration (Figures 7(a)-7(n)).

Biological Pathway Enrichment Analysis and Protein Expression of Membrane Lipid Biosynthesis-Related Risk
Model Genes in BRCA. In this study, we performed GSEA on the HALLMARK gene set for membrane lipid biosynthesisrelated risk model genes in BRCA (Figures 8(a) and 8(b)) [26,48]. GSEA results showed that these risk model genes    Journal of Oncology were associated with abnormal activation of PI3K AKT MTOR SIGNALING, PROTEIN SECRETION, and MTORC1 SIGNALING, HEME METABOLISM, and OX-IDATIVE PHOSPHORYLATION. Subsequently, we used immunohistochemical data from the THPA database to validate our previous findings to explore the expression of risk model genes associated with membrane lipid biosynthesis in mammary and normal breast tissues. Since ST6GALNAC4 and HACD1 have not been included in the THPA database, we provide the immunohistochemical results for ALDH3B2, CERS2, ELOVL1, GPAA1, HACD3, PIGF, PLPP2, PRKD2, SGMS2, SGPP1, ST3GAL1, and VAPB. e results showed that the protein expression levels confirmed our previous findings at the mRNA level, confirming our findings were correct (Figures 8(c)-8(n)). ese data may provide a solid basis for further research into these risk model genes and pave the way for future interventions.

Construction of a Risk Model Genes-Related miRNA Interaction Network for Membrane Lipid Biosynthesis.
MicroRNAs (miRNAs) are a class of endogenous noncoding RNAs with a length of about 22 nt, which regulate gene expression by complementary binding to target gene transcripts [49,50]. In recent years, studies have found that miRNAs are closely related to the occurrence of cancer, and miRNAs are involved in regulating multiple aspects of tumors, including transcription, cell cycle regulation, apoptosis, angiogenesis, tumor invasion, and invasion metastasis. miRNAs can directly act as oncogenes or tumor suppressor genes to affect the occurrence and growth of tumors. To further explore the regulatory relationship between membrane lipid biosynthesisrelated risk genes and miRNAs, we used the GSCALite database to map the miRNA-mRNA regulation network associated with membrane lipid biosynthesis-related risk genes ( Figure 9). Among them, we can find a regulatory relationship between a variety of miRNAs and the SGMS2 gene, which suggests that we can target these miRNAs to regulate SGMS2 in the future, thereby affecting the progression of BRCA.

Discussion
Globally, cancer is the leading cause of death and a significant barrier to extending life [51]. With the aging population and the rapid development of society, the cancer burden in countries worldwide is overgrowing [52,53]. It is worth   noting that in 2020, breast cancer in women surpassed lung cancer as the most common cancer worldwide for the first time, with an estimated 2,261,419 new cases. Meanwhile, breast cancer is the fifth leading cause of cancer death globally, with 684,996 deaths. In recent years, the morbidity and mortality of female breast cancer worldwide have increased sharply, and the disease burden has also increased, becoming a significant global public health problem [54]. In recent years, the biological role of membrane lipid biosynthesis-related genes in cancer has attracted more and more attention from cancer researchers. erefore, in this study, we used "glmnet" and "survival" extension packages to perform LASSO regression curve analysis. We utilized genes related to membrane lipid biosynthesis to construct a prognosis-related risk model in BRCA. e risk model contains fourteen genes, namely, GPAA1, PIGF, ST3GAL1, ST6GALNAC4, PLPP2, ELOVL1, HACD1, SGPP1, PRKD2, VAPB, CERS2, SGMS2, ALDH3B2, and HACD3.

GPAA1
is a crucial subunit of glycosylphosphatidylinositol transferase, responsible for binding GPI anchors to precursor proteins, but not involved in GPI anchor synthesis. Previous studies have shown that GPAA1 is abnormally expressed and amplified in various malignant tumors, such as liver, breast, gastric, and colorectal [55][56][57][58]. Since GPAA1 is an enzyme, its chemical activity is vulnerable to the intervention of various physicochemical factors or inhibitors, so the development of its inhibitors is less complex and more drug-producing. erefore, we believe that targeting GPAA1-mediated synthesis of GPIanchored proteins is a potential tumor-targeted therapeutic option [55]. Furthermore, PIGF is a homodimeric glycoprotein belonging to the VEGF subfamily. It exists in two isoforms, PIGF-1 and PIGF-2, the latter having a heparinbinding domain. Like VEGF, it is a potent angiogenic factor. Tumor growth promotes the proliferation of tumor blood vessels by interacting with its receptors, thereby affecting    tumor growth, infiltration, and metastasis [59,60]. And ST3GAL1 regulates cancer progression by affecting the activity of the TGF-β signaling pathway in glioblastoma and breast cancer [61,62].
Additionally, SGPP1 controls multiple cellular functions, including differentiation, proliferation, metastasis, cytoskeleton remodeling, senescence, and apoptosis [63]. SGPP1 is a tumor suppressor gene that plays a crucial role in cancer invasion and metastasis [64][65][66]. In gastric cancer tissues, the expression of SGPP1 was down-regulated compared with adjacent tissues and cancer-free tissues. It was confirmed that the low expression of SGPP1 was positively correlated with the distant metastasis of lymph nodes and gastric cancer. SGPP1 downregulation can promote cell migration to epidermal growth factor (EGF), while SGPP1 overexpression can decrease chemotaxis to EGF [67]. ere is evidence that PRKD2 regulates fundamental biological processes, including signal transduction, membrane trafficking, cell survival, migration, differentiation, and proliferation [68][69][70][71]. PRKD2 affects drug resistance in various tumors such as breast cancer and leukemia by regulating tumor cell proliferation, apoptosis, metastasis, and invasion [72]. As in glioblastoma, PRKD2 can promote CDKN1A gene overexpression through p53-dependent or independent pathways and integrated extracellular signal regulation, promoting cellular senescence and affecting cell sensitivity [73]. Furthermore, SGMS2 is a key regulator involved in ceramide and sphingomyelin homeostasis. A previous study showed that high expression of SGMS2 is associated with breast cancer metastasis. SGMS2 promotes cancer cell invasion by enhancing TGF-β/smad signaling to initiate epithelial-mesenchymal transition [74].
Meanwhile, several other risk model genes were identified in this study (ST6GALNAC4, PLPP2, ELOVL1, HACD1, VAPB, CERS2, ALDH3B2, and HACD3) have not yet been explored in depth in breast cancer. In the future, we should focus on these genes' biological functions and roles in the malignant progression of breast cancer. is study still has some limitations, mainly divided into the following three points. First, the data of BRCA patients used in this study were extracted from TCGA, and data from more databases are still needed for miRNA Gene Figure 9: miRNA regulatory networks associated with risk model genes related to membrane lipid biosynthesis. Among them, blue represents miRNA and yellow represents genes. validation; second, although the newly proposed risk model has apparent clinical significance, its underlying mechanisms are still not precise. erefore, we plan to explore further the role of this risk model in single-center or multicenter clinical samples in the future. ird, due to experimental conditions and time constraints, we have not yet validated the biological functions of risk model genes in BRCA. erefore, we hope to plan to explore the biological processes of these risk models in BRCA through in vivo and in vitro experiments in the future.

Conclusions
is study deeply explored the potential biological functions and application values of membrane lipid biosynthesis-related genes in various tumors, including breast cancer. Surprisingly, we successfully constructed a fourteen-gene risk model in BRCA using these lipid biosynthesis-related genes. Based on this risk model, we can easily divide BRCA patients into high-risk and lowrisk groups. In addition, to facilitate future practical applications, we also draw a nomogram corresponding to this risk. erefore, we believe that this study guides the precision treatment of breast cancer by interpreting genomic data and lays the foundation for future scientific research and molecular typing of BRCA.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Ethical Approval
Ethical approval was not required.

Consent
Consent is not applicable.

Conflicts of Interest
e authors declare that they have no conflicts of interest.

Authors' Contributions
Shengchun Liu and Yingkun Xu designed the research methods and analyzed the data. Yingkun Xu, Yudi Jin, and Shun Gao participated in data collection. Yuan Wang, Chi Qu, and Yinan Wu drafted the manuscript. Nan Ding, Yuran Dai, and Linshan Jiang revised the manuscript. All authors approved the release version and agreed to be responsible for all aspects of the work.