Integrative Analysis of Lipid Metabolism-Associated Genes Unveils ALDH2 as a Prognostic Marker in Breast Cancer

Integrative Analysis of Lipid Metabolism-Associated Genes Unveils ALDH2 as a Prognostic Marker in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative Analysis of Lipid Metabolism-Associated Genes Unveils ALDH2 as a Prognostic Marker in Breast Cancer Yufeng Lu, Zirong Lu, Qing Gao, Yichao Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3932507/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The oncogenesis and development of breast cancer is closely related to abnormal lipid metabolism. We focused on the identification of pivotal lipid metabolism-associated genes (LMAGs) and constructed a prognostic signature (ALDH2, CYP21A2, and IL24) by LASSO and Cox regression. Survival and time-dependent ROC analysis demonstrated the predictive accuracy of our signature in both training and validation cohort. Additionally, patients in high-risk group showed an immunosuppressive phenotype with lower abundance of immune cells and down-regulation of recognized immune checkpoints. Subsequent cellular experiments indicated that the downregulaiton of ALDH2 was correlated with the aggressive behavior of breast cancer cells. These findings offer crucial insights into understanding the interplay between lipid metabolism and breast cancer progression, potentially paving the way for innovative strategies to impede breast cancer metastasis. Breast cancer Lipid metabolism ALDH2 CYP21A2 IL24 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Breast cancer represents a significant global health challenge, particularly impacting the quality of life and longevity of women around the world( 1 ). Despite notable advancements in treatment, the morbidity and mortality of breast cancer remain alarmingly high( 2 ). This underscores the urgent need for research into its molecular mechanisms and the development of innovative therapeutic strategies. Lipid metabolism is one of the crucial processes necessary for the proper function of cells in maintaining normal physiological function( 3 ). The reprogramming of lipid metabolism has been found to stimulate tumor growth, angiogenesis, and metastasis( 4 ). Certain studies have shown that short-chain fatty acids are anti-inflammatory and anti-proliferative, which is relevant for cancer prevention and treatment( 5 ). These anomalies in lipid metabolism are pivotal in the development and progression of breast cancer. Abnormal lipid metabolism can affect proliferation, migration, invasion, and resistance to therapy( 6 ). The comprehension of the metabolic pathways is critical for the development of targeted breast cancer therapy and diagnostics. However, the precise roles and mechanisms of lipid metabolism-associated genes (LMAGs) in breast cancer are still not fully understood. Aldehyde dehydrogenase 2 (ALDH2) is an important member of the acetaldehyde dehydrogenase family and plays a key role in intracellular oxidative stress, metabolic regulation and antioxidant defence( 7 ). ALDH2 has garnered significant interest in cancer research, given its potential role in modulating tumor cell behavior through redox homeostasis, metabolic pathways, and signaling cascades( 7 ). Additionally, ALDH2 is essential for maintaining stem cell traits via phospholipid metabolism and free fatty acid signaling pathways in breast cancer( 8 , 9 ). The impact of ALDH2, as a member of LMAGs, on the proliferation and migration of breast cancer cells remains to be elucidated. In this study, we investigated the potential roles of LMAGs in breast cancer. Based on the common databases, we identified several LMAGs differentially expressed in breast cancer, including ALDH2, CYP21A2, and IL24. The downregulation of ALDH2 was found to promote the invasive and migratory ability of breast cancer cells. Methods Breast cancer data acquisition and processing The TCGA database provided the RNA sequencing (RNA-seq) data of breast cancer and the corresponding clinical characteristics of 1100 cases and 112 normal samples. The Bioconductor tool was used in the R statistical environment to process the data. We collected 2043 LMAGs from the Molecular Signatures Database (MSigDB) and identified the differentially expressed genes with criteria for FDR 2. Development and verification of the gene prognostic signature related to lipid metabolism To determine the prognostic significance of LMAGs, we employed Cox regression analysis to examine the associations between each gene and clinical characteristics in the TCGA cohort. Under the benchmark for P -value < 0.005, 3 differentially expressed LMAGs were screened out for subsequent analysis. Least Absolute Shrinkage and Selection Operator regression (LASSO) is a form of penalized regression which could be used in screening variables from high dimensional data to construct risk models( 10 ). All enrollers were stochastically divided into training and validation cohorts by 6:4 ratio. The training cohort underwent LASSO regression (R package “glmnet”) to identify the most significant genes with prognostic potential in breast cancer. Optimal value of tuning parameter (λ) was determined by ten-time cross-validation using minimum criteria. These genes' prognostic values were further confirmed on the validation cohort. Multivariate cox regression was used to construct the gene signature. According to the signature, a risk score algorithm was developed as follows: RiskScore= h0(t) × exp[ \(\sum _{i}Coefficient of \left(i\right)\times Expression of gene\left(i\right)\) ] Coefficient of gene (i) was the regression coefficient of gene (i) in LASSO-Cox regression model and Expression of gene (i) was the expression value of Gene (i) for each patient. Patients were classified into high- and low-risk groups based on the median of risk score. Construction of nomogram To further assess the potential independent prognostic role of the RiskScore, we performed the univariable and multivariable Cox regression analysis. The nomogram was constructed by the R package "rms" and factors with predictive value in multivariate analysis ( P < 0.05). Calibration curves were applied to evaluate the consistency between the expected and actual survival outcome. Additionally, time-dependent ROC curves were used to evaluate the nomogram and gene risk model's accuracy in predicting. Analysis of the tumor microenvironment and immune cell infiltration ESTIMATE and CIBERSORT were employed to assess the abundance of immune cells between high- and low-risk groups using expression data from TCGA database( 11 ). Expression of immune checkpoints were further evaluated to understand immune infiltration in breast cancer ( 12 , 13 ). Weighted gene co-expression network analysis (WGCNA) WGCNA is a R software package used for module identification, network construction, gene screening, property computation, and data display( 14 ). We used the expression data of TCGA cohort to perform WGCNA. Tests were conducted on the average connection degree and scale independence of networks with varying power values (from 1 to 18). When scale independence was greater than 0.85 and the degree of connectedness was comparatively higher, the right power value was identified. Genes were then categorized into distinct gene modules based on topological overlap matrix (TOM)-based dissimilarities. The critical module was defined as the one that showed the strongest association with clinical characteristics or ESTIMATE score. Functional enrichment analysis of ALDH2-related genes Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on genes correlated with ALDH2 by R package “clusterprofiler”( 15 ). Utilizing the "ggplot2" package, GO terms and KEGG pathways were displayed under the criteria for adjusted P < 0.05 was set. Cell culture and transfection MCF-7 (ER/PR+) and MDA-MB-231 (ER/PR/HER2−) breast cancer cells were obtained from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). MCF-7 and MDA-MB-231 cells were maintained in high glucose DMEM medium (Hyclone, Thermo Scientific, Waltham, USA) supplemented with 10% fetal bovine serum (FBS, ExCell) at 37℃ with 5% CO 2 . For gene knockdown, small interfering RNA (siRNA) targeting ALDH2 (5’-UGUAAUGCCACUUUGUCCAC-3’) and negative controls (GenePharma, Shanghai, China) were transfected into MCF-7 and MDA-MD-231 cells using Lipofectamine 2000 according to the manufacturer’s instructions. Western blotting analysis Breast cancer cells were seeded in 35-mm dishes (6×10 5 cells/dish) and transfected with siRNA and negative control. After 48 hours of transfection, total proteins of breast cancer cells were collected using lysis buffer. Subsequently, SDS-polyacrylamide gel electrophoresis and Western blotting analysis were performed following standard protocols. Primary antibodies used were ALDH2 (1:1500 dilution, Cat no. 15310-1-AP, ProteinTech), GAPDH (1:10000 dilution, Cat no. 10494-1-AP, ProteinTech). ALDH2 protein level were standardized to GAPDH for each sample. Wound-healing assay To check migratory capacity, breast cancer cells were seeded in 24-well plates (Costar, Corning, NY) and cultured to confluence. The monolayers of cells were wounded by removing the culture insert and washed with PBS to remove cell debris. The images were photographed at times 0 h and 12 h after migration using a Nikon optics microscope. The migratory distance was calculated by the minus of the edge of the wound closure between 0 h and 12 h. Boyden chamber assays The invasive capacity of MCF7 and MDA-MB-231 cells was further examined in the Boyden chamber (Cat no.14341, 8.0 µm, Labselect). Breast cancer cells were added to the wells where the membrane coated with matrigel. Medium with 10% FBS was supplemented to the top layer and medium with 20% FBS was supplemented to the bottom layer. MCF7 and MDA-MB-231 cells were invaded for 30 h and 22 h, respectively. The medium was then discarded, the cells were fixed and stained with 0.5% crystal violet for 30 min, the upper layer of cells was removed, and the response was assessed by counting the number of cells invading the lower layer under a light microscope. Statistical analysis Statistical analyses and figures were mainly conducted by R scripts/Bioconductor packages and GraphPad Prism 9.0. Differences between two groups were computed by Student’s t -tests, while those among more than two groups were computed using one-way ANOVA. Statistical results with P < 0.05 were considered significant. Results Expression variations of lipid metabolism-associated genes in breast cancer RNA-seq from 1100 breast cancer tissue samples and 112 non-tumor samples were downloaded from The Cancer Genome Atlas (TCGA). Given the criteria for FDR 2, we finally identified 80 up-regulated and 105 down-regulated lipid metabolism-associated genes (LMAGs) by “DESeq2” R package. The expression levels of these genes were shown in the heatmap (Fig. 1 A), where blue represented the down-regulation of gene expression and red represented the up-regulation of gene expression. Additionally, the volcano map (Fig. 1 B) was labeled with the names of differentially expressed LMAGs. Development of a prognostic signature in the training cohort A prognostic signature was constructed to apprise the prognostic value of the differentially expressed LMAGs in breast cancer. All enrollers were randomly divided into training and validation cohorts in a ratio of 6:4. We developed the model in the training cohort with 651 samples. Univariate Cox analysis was initially performed under the benchmark for P -value < 0.005. According to the forest map of the hazard ratio (Fig. 2 A), three differentially expressed LMAGs (ALDH2, CYP21A2, and IL24) were screened out for subsequent analysis. Lasso regression revealed that these 3 genes were preserved when the tuning parameter (λ) was minimized to 0.0027 and log(λ) was − 5.93 (Fig. 2 B). After the preceding univariate and lasso analysis, ALDH2, CYP21A2, and IL24 were eventually included in the prognostic signature by multivariate cox regression. RiskScore was calculated as follows: RiskScore = h0(t) × exp [(-2.231 × expression level of ALDH2) + (-1.363 × expression level of CYP21A2) + (-2.353 × expression level of IL24)]. Based on the median RiskScore, patients in the training cohort were classified into low-risk ( median number) groups. The overall survival rates of patients in the high- and the low-risk group were calculated by Kaplan-Meier, indicating that higher RiskScore corresponded with poorer survival ( P < 0.001, Fig. 2 C). In the light of model diagnosis, the areas under the curve (AUC) of time-dependent ROC curves were 0.7, 0.7, and 0.65 for 1-, 3-, and 5-year survival, respectively (Fig. 2 D). Patients in the high-risk group had high mortality and a shorter overall survival (OS) time than those in the low-risk group (Fig. 2 E). A heatmap depicting the expression pattern of the genes incorporated in the risk model suggested that patients in the high-risk group tended to express a lower level of risk genes (Fig. 2 F). Internal validation of the risk model Of the remaining breast cancer patients in the TCGA cohort, a total of 431 samples were employed as the validation set. According to the median RiskScore in the training cohort, 231 patients were encompassed in the low-risk group, while 200 patients were encompassed in the high-risk group. Kaplan-Meier analysis uncovered a significant difference in survival between the high- and low-risk groups ( P < 0.01, Fig. 3 A), with patients in a low-risk group having better survival outcomes. ROC curves verified that our risk model had fine predictive potency (AUC = 0.81 for 1-year, 0.7 for 3-year, and 0.64 for 5-year survival, Fig. 3 B). Additionally, RiskScore, survival status distributions, and the risk genes expression pattern of the patients showed the same characteristics as the training cohort (Fig. 3 C-D). Clinical application value of the prognostic signature Considering the significance of RiskScore in portending the prognosis of breast cancer patients, we further explored its value in clinical application. Univariable and multivariable Cox regression were performed to assess the potential independent prognostic role of RiskScore. Univariable Cox regression demonstrated that RiskScore was an independent predictor of poor survival in both the training and validation cohorts (HR = 1.534, 95%CI: 1.199–1.961 and HR: 1.510, 95%CI: 1.080–2.110, Fig. 4 A). After adapting for other confounders, multivariable Cox regression also revealed that RiskScore (HR = 1.683, 95%CI: 1.308–2.167 and HR: 1.765, 95%CI: 1.232–2.527, Fig. 4 B) played an independent role in predicting prognosis of breast cancer patients. A nomogram model was constructed based on the age, TNM stage, tumor stage, and RiskScore. The survival contingency of breast cancer patients for 1-, 3-, and 5 years were computed according to the total points (Fig. 4 C). A C-index of 0.781 suggested that the nomogram had an agreeable predictive value. Calibration plot for the probability of survival also showed satisfactory compliance with the prediction of 1-, 3-, and 5-year OS (Fig. 4 C). These results indicate that the risk model has a weighty hand in clinical prediction. Immune microenvironment landscape between high- and low-risk group The discrepancy in the immune microenvironment between high- and low-risk groups was evaluated by multiple immune analyses. Outcomes of the ESTIMATE algorithm demonstrated that patients in the high-risk group had lower Immune Score and ESTIMATE Score, as well as a higher level of Tumor purity (Fig. 5 A). The abundance of immune cell infiltration in high- and low-risk groups predicted by the CIBERSORT analytical tool manifested that the high-risk group possessed a significantly lower abundance of immune cells, including naïve B cells, CD8 + T cells, naïve CD4 + T cells, resting memory CD4 + T cells, activated memory CD4 + T cells and monocytes. While the infiltration of T cells follicular helper, resting NK cells, macrophages (M0), and resting mast cells was prominently up-regulated (Fig. 5 B). In addition, we investigated the expression of immune checkpoints in high- and low-risk groups, finding that CTLA4, PD1, LAG3, BTLA, and TNFRSF14 were significantly down-regulated in the high-risk group (Fig. 5 C). Weighted gene co-expression network analysis singled out the module closely related to immune infiltration To figure out the predominant module that correlated with clinical traits or immune infiltration, we performed the weighted gene co-expression network analysis (WGCNA) on the TCGA cohort. After the soft threshold power β being set at 6 (scale-free R2 = 0.90, Fig. 6 A), a total of 14 modules with unique colors were identified, each comprising genes with similar expression patterns recognized by hierarchical clustering (Fig. 6 B). And genes contained in the gray module did not belong to any other modules. Module-trait relationships analysis was carried out to discern the association between co-expression modules and immune infiltration, as well as clinical traits (Fig. 6 C). In accordance with the correlation heatmap, the MEbrown module had a strikingly positive correlation with ESTIMATE score (R = 0.81, P = 1e-211). Nevertheless, none of the modules had a strong link with clinical traits. Intriguingly, one of these genes in the risk model, ALDH2, was also located in the MEbrown module. Potential prognostic value of ALDH2 in breast cancer We explored the expression variants of ALDH2 in different TNM stages and tumor stages, noticing that ALDH2 was up-regulated in samples with lymphatic metastasis ( P < 0.01, Fig. 7 A). However, ALDH2 expression manifested no divergence in samples with T3-T4, M1 and Stage III-IV when compared with samples with T1-2, M0 and Stage I-II, respectively. Following the median expression level of ALDH2, patients were classified into two subgroups. Comparisons of the patients’ OS between the two subgroups were made by Kaplan-Meier curves, revealing that patients with high expression of ALDH2 had long-term OS (Fig. 7 B). Downregulation of ALDH2 in breast cancer impacted tumor malignancy Western blotting was conducted to assess the knockdown efficiency after the transfection of siRNA targeting ALDH2 in MCF7 and MDA-MB-231 cells (Fig. 8 A). Wound-healing assay exhibited a significant high migratory rate of ALDH2-silenced MDA-MB-231 cells, but not of MCF7 cells (Fig. 8 B). We further performed cell invasion assay to compare the invasion ability of breast cancer cells between NC and si-ALDH2 groups. The quantity of cells invading to the lower chambers were more in MCF7 cells and MDA-MB-231 cells of si-ALDH2 group than that of NC group (Fig. 8 C). These results indicate that downregulation of ALDH2 promotes the invasion and migration capacity of breast cancer cells. Next, invasive breast ductal carcinoma tissue from 30 patients pasted on tissue microarray was analyzed by immunohistochemistry (IHC). Of the 30 patients, 16 patients were diagnosed with Nottingham grade II, and the 6 patients with grade III. The expression level of ALDH2 was compared between 16 patients with grade II and 6 patients with grade III, which showed no significant difference between the two groups (Supplementary Figure S1 ). The limited number of clinical samples may be one of main reasons for no differential expression of ALDH2 in breast cancer tissues with various pathological grades. Potentially underlying mechanism of ALDH2 for modulating lipid metabolism To investigate the predicted underlying mechanism of ALDH2 for modulating lipid metabolism, we performed the correlation analysis of ALDH2 in TCGA cohort. Under the screening condition of Pearson’s r > 0.2, 62 ALDH2-related genes were further probed by GO and KEGG enrichment analysis (Table 1 ). GO enrichment analysis showed that ALDH2-related genes involved in regulation of lipid metabolic process, lipid transport, alcohol metabolic process, lipid catabolic process and fatty acid metabolic process (Fig. 9 A). KEGG pathways of ALDH2-related genes were mainly related to PPAR signaling pathway and AMPK signaling pathway (Fig. 9 B). Most of the ALDH2-related genes enriched in PPAR signaling pathway, AMPK signaling pathway and fatty acid degradation were remarkably down-regulated (Fig. 9 C). Network diagrams of the above three pathways showed the genes contained in each (Fig. 9 D). Among them, ALDH2 was enriched in fatty acid degradation pathway. We also inspected the role of ALDH2-related genes played in PPAR signaling pathway, AMPK signaling pathway and fatty acid degradation pathway. Scatter plot demonstrated the correlation between ALDH2 and the genes were enriched in these pathways, whose Pearson value was greater than 0.3 (Fig. 9 E). Table 1 The co-expressed genes of ALDH2 (Pearson’s r > 0.4) Gene Pearson correlation coefficient P -value G0S2 0.393086 5.83E-42 CEBPA 0.387443 1.03E-40 GPD1 0.371073 3.07E-37 CES1 0.370515 4.00E-37 PPARG 0.370499 4.03E-37 PLIN1 0.369284 7.17E-37 RBP4 0.364016 8.43E-36 CIDEC 0.356032 3.24E-34 CIDEA 0.353297 1.10E-33 HSD11B1 0.346122 2.60E-32 GPX3 0.341698 1.75E-31 CCL21 0.338768 6.08E-31 FABP4 0.334929 3.05E-30 ADH1B 0.334714 3.34E-30 GPBAR1 0.328521 4.28E-29 FUT7 0.327943 5.42E-29 THRSP 0.325781 1.30E-28 LIPE 0.321347 7.66E-28 GPIHBP1 0.318527 2.33E-27 ADIPOQ 0.316617 4.91E-27 CDO1 0.316036 6.16E-27 ADH1C 0.31455 1.10E-26 PCK1 0.31381 1.46E-26 LEP 0.311488 3.55E-26 ALDH1A2 0.306692 2.18E-25 CD19 0.291018 6.52E-23 ABCD2 0.290849 6.92E-23 FOXP3 0.286725 2.92E-22 ABCA9 0.285313 4.76E-22 HSD17B13 0.285249 4.86E-22 ABCA6 0.280071 2.84E-21 DPEP1 0.275443 1.33E-20 COL1A1 0.274019 2.13E-20 CXCL9 0.272996 2.98E-20 ABCA8 0.271679 4.58E-20 ABCB1 0.270824 6.05E-20 IL12B 0.261906 1.04E-18 CRHBP 0.255216 8.13E-18 AKR1C1 0.253674 1.30E-17 ZFP36 0.251817 2.26E-17 BMX 0.242248 3.73E-16 CD36 0.239409 8.37E-16 P2RY12 0.237706 1.35E-15 CAV1 0.235943 2.22E-15 F7 0.235352 2.61E-15 LPL 0.232621 5.55E-15 ENPP2 0.231905 6.75E-15 TACR1 0.226428 2.96E-14 ABCA10 0.221977 9.56E-14 KL 0.221878 9.81E-14 AKR1C2 0.21886 2.14E-13 TIMP4 0.217554 2.99E-13 KLF4 0.214531 6.43E-13 FOS 0.214395 6.66E-13 ACADL 0.211172 1.49E-12 Discussion In this study, we delved into the differential expression of LMAGs in breast cancer, culminating in a novel prognostic signature encompassing ALDH2, CYP21A2, and IL24. This advancement provides critical insights into the nexus between lipid metabolism and breast cancer progression. It has been reported that up-regulation of lipid metabolism genes in breast cancer prior to cancer diagnosis, metabolic activation of the mammary gland in the early stages of cancer development, and increased epithelial-adipose tissue interactions may create a favourable environment for eventual cell transformation, proliferation and survival( 16 ). Our comprehensive analysis of TCGA data identified key shifts in LMAGs, with 80 up-regulated genes and 105 down-regulated genes. The prognostic signature, derived from these differentially expressed LMAGs, highlighted ALDH2, CYP21A2, and IL24 as pivotal prognostic indicators. Rigorous statistical analyses underpinned their prognostic significance, and the model's robust correlation with patient survival outcomes underscored its clinical relevance. Current research has focused extensively on the relationship between lipid metabolism abnormality and tumor immune response, highlighting the correlation between lipid metabolism and immune escape from tumors, suggesting a potential role in tumor immunotherapy( 17 ). However relatively few studies have focused on how lipid metabolism influences tumor metastatic mechanisms. Our study provides some new insights in this area, highlighting the potential role of lipid metabolism in regulating the invasive capacity of tumor invasion. Based on the WGCNA analysis, we identified the MEbrown module with the highest correlation with clinical features or immune infiltration, and one of the genes in the risk model, ALDH2, was also located in the MEbrown module. ALDH2 is a gene of high interest in breast cancer( 18 ). In the TCGA cohort, high expression of ALDH2 was positively associated with lymphatic metastasis. However, Kaplan-Meier curves showed that the expression level of ALDH2 in breast cancer was closely correlated with the prognosis of patients, and patients with high expression of ALDH2 showed better survival. We therefore demonstrated that low ALDH2 expression promoted the migration and invasion of breast cancer cells. Nevertheless, based on IHC experiments, we failed to find a significant difference in ALDH2 expression between the gradeⅡ and gradeⅢ groups of patients, possibly due to small sample size. The movement of breast cancer cell is closely related to microfilament remodeling( 19 , 20 ). We speculated that ALDH2 might regulate breast cancer cell motility by affecting the microfilament dynamics. However, the immunofluorescent staining showed that ALDH2 did not co-located on microfilaments (Supplementary Figure S2). Thus, ALDH2 may mediate breast cancer metastasis through other signaling pathways. Future research should intensively explore the regulatory mechanisms of lipid metabolism in tumor metastasis. This exploration might reveal key lipid metabolic pathways and their intersections with other signaling networks, offering new avenues for therapeutic interventions to inhibit breast cancer invasion and metastasis, thereby improving patient survival and quality of life. Declarations Ethics approval and consent to participate Ethical approval and informed consent of the study was obtained by the Clinical Research Ethics Committee, Shanghai Outdo Biotech Company. Consent for publication All the authors consent for the publication of this paper. Competing interests The authors declare no conflict of interest. Author contributions YL analyzed the data and wrote the manuscript. ZL performed the cytological experiments. YZ and QG proposed the conception and revised the manuscript. All authors approved the final manuscript. 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Reprogramming lipid metabolism prevents effector T cell senescence and enhances tumor immunotherapy. Sci Transl Med. 2021;13:587. Park B, Kim JH, Lee ES, Jung SY, Lee SY, Kang HS, et al. Role of aldehyde dehydrogenases, alcohol dehydrogenase 1B genotype, alcohol consumption, and their combination in breast cancer in East-Asian women. Sci Rep. 2020;10(1):6564. Yu X, Xu T, Su B, Zhou J, Xu B, Zhang Y, et al. The novel role of etoposide in inhibiting the migration and proliferation of small cell lung cancer and breast cancer via targeting Daam1. Biochem Pharmacol. 2023;210:115468. Mei J, Liu Y, Yu X, Hao L, Ma T, Zhan Q, et al. YWHAZ interacts with DAAM1 to promote cell migration in breast cancer. Cell Death Discov. 2021;7(1):221. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3932507","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279999323,"identity":"c22af2f1-de49-45a9-ba0b-8ea359f144cd","order_by":0,"name":"Yufeng Lu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Lu","suffix":""},{"id":279999324,"identity":"f01d8bd9-cba5-4b2b-a9b1-15f81087b0ce","order_by":1,"name":"Zirong Lu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zirong","middleName":"","lastName":"Lu","suffix":""},{"id":279999325,"identity":"3e89844d-fe8e-496b-b0b4-36a45a982a6d","order_by":2,"name":"Qing Gao","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Gao","suffix":""},{"id":279999326,"identity":"a08b26b9-94e8-4f1a-a11d-9a1023255bc6","order_by":3,"name":"Yichao Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCSBOYDjAwMDewGAAFjlAtBaeA6RoASuTSICKENIiP7vHTOJBzR15fsm3B4putjHI8d1IYPxcgEcL45wzZhIJx54Zzpydl2Cc28ZgLHkjgVl6Bh4tzBI5ZhKJDYcZN9zOMQBpSdxwI4GNmQePFjaoFvsNN8+AtdQT1MID1QI0nAesJcGAkBYJibRii4Rjh5Nn9gAdlnNOwnDmmYfN0vi0yM9I3njzR81h2372M2bGOWU28nzHkw9+xqcFCFgkYP4ygEQTYwN+DcBA+wBjPCCkdBSMglEwCkYmAABswkvhdR6+OwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yichao","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-02-06 02:44:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3932507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3932507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53033525,"identity":"02de1085-b82f-4ab4-8cf2-28fe656a8eae","added_by":"auto","created_at":"2024-03-19 20:28:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143283,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed genes related to lipid metabolism in breast cancer.\u003c/p\u003e\n\u003cp\u003eHeatmap (A) and volcano plot (B) depicted differentially expressed genes between breast cancer and paracancerous tissues. FDR\u0026lt;0.05 and [log2 (fold change)]\u0026gt;2 was considered significant.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/9da963f475468775f2c898bf.png"},{"id":53032978,"identity":"273ce74a-6ece-4c8d-9729-4504c367c5a6","added_by":"auto","created_at":"2024-03-19 20:20:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85319,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of prognostic signature based on lipid metabolism associated genes in training cohort.\u003c/p\u003e\n\u003cp\u003e(A) Univariate cox regression analysis revealed potential prognostic signature of ALDH2, CYP21A2, and IL24. (B) Lasso regression was performed to select genes with more prognostic value. The left panel depicted the coefficient trajectories of ALDH2 (black), CYP21A2 (red) and IL24 (green). And the cross-validation curves for lasso regression (right panel) suggested that the best model fit was achieved when lambda = 0.0027. (C) Patients were classified into high- and low- risk groups based on the median RiskScore. Kaplan-Meier curves showed that patients in high-risk group had a shorter overall survival in training and validation cohort. (D) Time-dependent ROC curve verified prognostic significance of lipid metabolism based prognostic indicators. In training cohort, the 1-, 3-, 5-year AUCs were \u0026gt;0.65. While in validation cohort, the 1-, 3-, 5-year AUCs were \u0026gt;0.64. (E) Distribution of survival status and RiskScore in training and validation cohort. (F) The lower level of ALDH2, CYP21A2, and IL24 were expressed in patients of high-risk group.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/ae82188406f44b79423f15e1.png"},{"id":53032980,"identity":"92363e84-b2c6-4872-8bcb-cf2378f2a5bd","added_by":"auto","created_at":"2024-03-19 20:20:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59533,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmation of the prognostic signature in the validation cohort.\u003c/p\u003e\n\u003cp\u003e(A) Kaplan-Meier curves for comparison of the overall survival (OS) between high- and low-risk groups in validation cohort. (B) Time-dependent ROC curves showed that 1-, 3-, 5-year AUCs were 0.81, 0.7, 0.64, respectively. (C) Distribution of patients in the validation cohort based on the median risk score in the training cohort. (D) Heat map of differences in risk gene expression between the high- and low-risk groups.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/de99e7bedb7772759338400e.png"},{"id":53032981,"identity":"4fb467c1-d01e-49b2-97b9-b29f138c08dc","added_by":"auto","created_at":"2024-03-19 20:20:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129951,"visible":true,"origin":"","legend":"\u003cp\u003eClinical application value of the risk model consisting of ALDH2, CYP21A2, and IL24 in breast cancer.\u003c/p\u003e\n\u003cp\u003eUnivariate cox regression analysis (A) and multivariate cox regression analysis (B) suggested the risk model was an independent prognostic index in training and validation cohort. (C) Nomogram predicted 1-, 3-, and 5-years overall survival (OS) for breast cancer patients in combined TCGA cohort. Calibration curves of nomograms in terms of the agreement between predicted and observed 1-, 3-, and 5-years of outcomes were performed.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/b904d3e9da1ed9465fd0c1d2.png"},{"id":53032984,"identity":"d29b6da3-138b-4924-ba87-2347fafb49cd","added_by":"auto","created_at":"2024-03-19 20:20:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":172620,"visible":true,"origin":"","legend":"\u003cp\u003eImmune microenvironment variations in breast cancer.\u003c/p\u003e\n\u003cp\u003e(A) ESTIMATE algorithm showed that patients in high-risk group had lower Immune Score and ESTIMATE Score. (B) CIBERSORT immune-infiltration analysis depicted variable lymphocytic infiltration in high- and low-risk group. (C) Expression of recognized immune checkpoints (CTLA4, PDCD1, LAG3, BTLA, and TNFRSF14) were significantly down-regulated in high-risk group.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/1da82e89b1cee7d43c85c49f.png"},{"id":53032986,"identity":"65348fa9-17ac-4308-8f6a-e5d9f7ec57ba","added_by":"auto","created_at":"2024-03-19 20:20:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76759,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) in breast cancer.\u003c/p\u003e\n\u003cp\u003e(A) Selection of soft threshold power value. Left panel displayed scale-free model fit index of different power values. Right panel indicated the mean connectivity of these values. (B) The cluster dendrogram of co-expression genes in breast cancer. (C) Correlation heatmap between the results of ESTIMATE algorithm and modules demonstrated brown module had the most correlation with lymphocytic infiltration.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/fca57ae27cd5b13d890b9d8f.png"},{"id":53032982,"identity":"b5141a15-9cda-4cdd-a06c-12869103cd49","added_by":"auto","created_at":"2024-03-19 20:20:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":61771,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of ALDH2 in breast cancer.\u003c/p\u003e\n\u003cp\u003e(A) Boxplot suggested that the high expression of ALDH2 was positively associated with lymphatic metastasis in TCGA cohort. (B) Kaplan-Meier curves revealed that patients with high expression of ALDH2 had long survival.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/1116c729a691e4b4362332b8.png"},{"id":53032988,"identity":"f230e4a4-669d-40cf-8dbd-606ab0bff373","added_by":"auto","created_at":"2024-03-19 20:20:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":376953,"visible":true,"origin":"","legend":"\u003cp\u003eThe downregulation of ALDH2 promotes the invasion and migration of breast cancer cells.\u003c/p\u003e\n\u003cp\u003e(A) The transfection efficiency of siRNA targeting ALDH2 (si-ALDH2) was determined by Western blotting in MCF7 and MDA-MB-231 cells. NC, negative control. (B) The migration of MCF7 and MDA-MB-231 cells transfected with NC or si-ALDH2 were investigated by wound-healing assay. (C) Cell invasion assays were performed in Boydem chambers. Both MCF7 cells and MDA-MB-231 cells in si-ALDH2 group showed more cells invading to the lower chambers compared to NC group. **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, ****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01. ns, not significant.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/42c6c3af0699b76d06010c8c.png"},{"id":53032983,"identity":"95093722-3028-4882-92a1-e1c71bec1fa2","added_by":"auto","created_at":"2024-03-19 20:20:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":186404,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis based on co-expressed genes of ALDH2.\u003c/p\u003e\n\u003cp\u003e(A) The functions of genes significantly associated with ALDH2 were predicted by the analysis of gene ontology (GO), including biological processes, cellular components, and molecular functions. (B) KEGG pathway enrichment analysis of ALDH2 co-expressed genes indicated that PPAR signaling pathway and AMPK signaling pathway were significantly enriched. Network plot (C) and heatmap (D) depicted the expression fold change of the co-expressed genes enriched in PPAR signaling pathway, AMPK signaling pathway and fatty acid degradation. (E) Scatter diagram revealed the tight correlation between ALDH2 and the hub genes enriched in the pathways mentioned above.\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/09dab29bb2bef0eb305ea40d.png"},{"id":75863101,"identity":"3289ab08-64a3-469d-92e7-3dec0fc34ea9","added_by":"auto","created_at":"2025-02-10 05:38:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2916174,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/b111c5e9-ac2a-4d28-9a5a-3b781ad3d42b.pdf"},{"id":53033526,"identity":"eb08029a-0eb6-4cf2-9607-097d89841101","added_by":"auto","created_at":"2024-03-19 20:28:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5955404,"visible":true,"origin":"","legend":"","description":"","filename":"0129.docx","url":"https://assets-eu.researchsquare.com/files/rs-3932507/v1/1985f09dc9c0581e5afaf0c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Analysis of Lipid Metabolism-Associated Genes Unveils ALDH2 as a Prognostic Marker in Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer represents a significant global health challenge, particularly impacting the quality of life and longevity of women around the world(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite notable advancements in treatment, the morbidity and mortality of breast cancer remain alarmingly high(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This underscores the urgent need for research into its molecular mechanisms and the development of innovative therapeutic strategies.\u003c/p\u003e \u003cp\u003eLipid metabolism is one of the crucial processes necessary for the proper function of cells in maintaining normal physiological function(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The reprogramming of lipid metabolism has been found to stimulate tumor growth, angiogenesis, and metastasis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Certain studies have shown that short-chain fatty acids are anti-inflammatory and anti-proliferative, which is relevant for cancer prevention and treatment(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These anomalies in lipid metabolism are pivotal in the development and progression of breast cancer. Abnormal lipid metabolism can affect proliferation, migration, invasion, and resistance to therapy(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The comprehension of the metabolic pathways is critical for the development of targeted breast cancer therapy and diagnostics. However, the precise roles and mechanisms of lipid metabolism-associated genes (LMAGs) in breast cancer are still not fully understood.\u003c/p\u003e \u003cp\u003eAldehyde dehydrogenase 2 (ALDH2) is an important member of the acetaldehyde dehydrogenase family and plays a key role in intracellular oxidative stress, metabolic regulation and antioxidant defence(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). ALDH2 has garnered significant interest in cancer research, given its potential role in modulating tumor cell behavior through redox homeostasis, metabolic pathways, and signaling cascades(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, ALDH2 is essential for maintaining stem cell traits via phospholipid metabolism and free fatty acid signaling pathways in breast cancer(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The impact of ALDH2, as a member of LMAGs, on the proliferation and migration of breast cancer cells remains to be elucidated.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the potential roles of LMAGs in breast cancer. Based on the common databases, we identified several LMAGs differentially expressed in breast cancer, including ALDH2, CYP21A2, and IL24. The downregulation of ALDH2 was found to promote the invasive and migratory ability of breast cancer cells.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer data acquisition and processing\u003c/h2\u003e \u003cp\u003eThe TCGA database provided the RNA sequencing (RNA-seq) data of breast cancer and the corresponding clinical characteristics of 1100 cases and 112 normal samples. The Bioconductor tool was used in the R statistical environment to process the data. We collected 2043 LMAGs from the Molecular Signatures Database (MSigDB) and identified the differentially expressed genes with criteria for FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and | log2 (fold change) |\u0026gt;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and verification of the gene prognostic signature related to lipid metabolism\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo determine the prognostic significance of LMAGs, we employed Cox regression analysis to examine the associations between each gene and clinical characteristics in the TCGA cohort. Under the benchmark for \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005, 3 differentially expressed LMAGs were screened out for subsequent analysis. Least Absolute Shrinkage and Selection Operator regression (LASSO) is a form of penalized regression which could be used in screening variables from high dimensional data to construct risk models(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). All enrollers were stochastically divided into training and validation cohorts by 6:4 ratio. The training cohort underwent LASSO regression (R package \u0026ldquo;glmnet\u0026rdquo;) to identify the most significant genes with prognostic potential in breast cancer. Optimal value of tuning parameter (λ) was determined by ten-time cross-validation using minimum criteria. These genes' prognostic values were further confirmed on the validation cohort. Multivariate cox regression was used to construct the gene signature. According to the signature, a risk score algorithm was developed as follows:\u003c/p\u003e \u003cp\u003eRiskScore= h0(t) \u0026times; exp[ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i}Coefficient of \\left(i\\right)\\times Expression of gene\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cem\u003eCoefficient of gene (i)\u003c/em\u003e was the regression coefficient of gene (i) in LASSO-Cox regression model and \u003cem\u003eExpression of gene (i)\u003c/em\u003e was the expression value of Gene (i) for each patient. Patients were classified into high- and low-risk groups based on the median of risk score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of nomogram\u003c/h2\u003e \u003cp\u003eTo further assess the potential independent prognostic role of the RiskScore, we performed the univariable and multivariable Cox regression analysis. The nomogram was constructed by the R package \"rms\" and factors with predictive value in multivariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Calibration curves were applied to evaluate the consistency between the expected and actual survival outcome. Additionally, time-dependent ROC curves were used to evaluate the nomogram and gene risk model's accuracy in predicting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the tumor microenvironment and immune cell infiltration\u003c/h2\u003e \u003cp\u003eESTIMATE and CIBERSORT were employed to assess the abundance of immune cells between high- and low-risk groups using expression data from TCGA database(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Expression of immune checkpoints were further evaluated to understand immune infiltration in breast cancer (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWGCNA is a R software package used for module identification, network construction, gene screening, property computation, and data display(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). We used the expression data of TCGA cohort to perform WGCNA. Tests were conducted on the average connection degree and scale independence of networks with varying power values (from 1 to 18). When scale independence was greater than 0.85 and the degree of connectedness was comparatively higher, the right power value was identified. Genes were then categorized into distinct gene modules based on topological overlap matrix (TOM)-based dissimilarities. The critical module was defined as the one that showed the strongest association with clinical characteristics or ESTIMATE score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of ALDH2-related genes\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on genes correlated with ALDH2 by R package \u0026ldquo;clusterprofiler\u0026rdquo;(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Utilizing the \"ggplot2\" package, GO terms and KEGG pathways were displayed under the criteria for adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003e MCF-7 (ER/PR+) and MDA-MB-231 (ER/PR/HER2\u0026minus;) breast cancer cells were obtained from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). MCF-7 and MDA-MB-231 cells were maintained in high glucose DMEM medium (Hyclone, Thermo Scientific, Waltham, USA) supplemented with 10% fetal bovine serum (FBS, ExCell) at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e. For gene knockdown, small interfering RNA (siRNA) targeting ALDH2 (5\u0026rsquo;-UGUAAUGCCACUUUGUCCAC-3\u0026rsquo;) and negative controls (GenePharma, Shanghai, China) were transfected into MCF-7 and MDA-MD-231 cells using Lipofectamine 2000 according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting analysis\u003c/h2\u003e \u003cp\u003eBreast cancer cells were seeded in 35-mm dishes (6\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/dish) and transfected with siRNA and negative control. After 48 hours of transfection, total proteins of breast cancer cells were collected using lysis buffer. Subsequently, SDS-polyacrylamide gel electrophoresis and Western blotting analysis were performed following standard protocols. Primary antibodies used were ALDH2 (1:1500 dilution, Cat no. 15310-1-AP, ProteinTech), GAPDH (1:10000 dilution, Cat no. 10494-1-AP, ProteinTech). ALDH2 protein level were standardized to GAPDH for each sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWound-healing assay\u003c/h2\u003e \u003cp\u003eTo check migratory capacity, breast cancer cells were seeded in 24-well plates (Costar, Corning, NY) and cultured to confluence. The monolayers of cells were wounded by removing the culture insert and washed with PBS to remove cell debris. The images were photographed at times 0 h and 12 h after migration using a Nikon optics microscope. The migratory distance was calculated by the minus of the edge of the wound closure between 0 h and 12 h.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBoyden chamber assays\u003c/h2\u003e \u003cp\u003eThe invasive capacity of MCF7 and MDA-MB-231 cells was further examined in the Boyden chamber (Cat no.14341, 8.0 \u0026micro;m, Labselect). Breast cancer cells were added to the wells where the membrane coated with matrigel. Medium with 10% FBS was supplemented to the top layer and medium with 20% FBS was supplemented to the bottom layer. MCF7 and MDA-MB-231 cells were invaded for 30 h and 22 h, respectively. The medium was then discarded, the cells were fixed and stained with 0.5% crystal violet for 30 min, the upper layer of cells was removed, and the response was assessed by counting the number of cells invading the lower layer under a light microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses and figures were mainly conducted by R scripts/Bioconductor packages and GraphPad Prism 9.0. Differences between two groups were computed by Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, while those among more than two groups were computed using one-way ANOVA. Statistical results with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eExpression variations of lipid metabolism-associated genes in breast cancer\u003c/h2\u003e \u003cp\u003eRNA-seq from 1100 breast cancer tissue samples and 112 non-tumor samples were downloaded from The Cancer Genome Atlas (TCGA). Given the criteria for FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and | log2 (fold change) |\u0026gt; 2, we finally identified 80 up-regulated and 105 down-regulated lipid metabolism-associated genes (LMAGs) by \u0026ldquo;DESeq2\u0026rdquo; R package. The expression levels of these genes were shown in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), where blue represented the down-regulation of gene expression and red represented the up-regulation of gene expression. Additionally, the volcano map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) was labeled with the names of differentially expressed LMAGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a prognostic signature in the training cohort\u003c/h2\u003e \u003cp\u003eA prognostic signature was constructed to apprise the prognostic value of the differentially expressed LMAGs in breast cancer. All enrollers were randomly divided into training and validation cohorts in a ratio of 6:4. We developed the model in the training cohort with 651 samples. Univariate Cox analysis was initially performed under the benchmark for \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005. According to the forest map of the hazard ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), three differentially expressed LMAGs (ALDH2, CYP21A2, and IL24) were screened out for subsequent analysis. Lasso regression revealed that these 3 genes were preserved when the tuning parameter (λ) was minimized to 0.0027 and log(λ) was \u0026minus;\u0026thinsp;5.93 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter the preceding univariate and lasso analysis, ALDH2, CYP21A2, and IL24 were eventually included in the prognostic signature by multivariate cox regression. RiskScore was calculated as follows: RiskScore\u0026thinsp;=\u0026thinsp;h0(t) \u0026times; exp [(-2.231 \u0026times; expression level of ALDH2) + (-1.363 \u0026times; expression level of CYP21A2) + (-2.353 \u0026times; expression level of IL24)]. Based on the median RiskScore, patients in the training cohort were classified into low-risk (\u0026lt;\u0026thinsp;median number) and high-risk (\u0026gt;\u0026thinsp;median number) groups. The overall survival rates of patients in the high- and the low-risk group were calculated by Kaplan-Meier, indicating that higher RiskScore corresponded with poorer survival (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In the light of model diagnosis, the areas under the curve (AUC) of time-dependent ROC curves were 0.7, 0.7, and 0.65 for 1-, 3-, and 5-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Patients in the high-risk group had high mortality and a shorter overall survival (OS) time than those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). A heatmap depicting the expression pattern of the genes incorporated in the risk model suggested that patients in the high-risk group tended to express a lower level of risk genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInternal validation of the risk model\u003c/h2\u003e \u003cp\u003eOf the remaining breast cancer patients in the TCGA cohort, a total of 431 samples were employed as the validation set. According to the median RiskScore in the training cohort, 231 patients were encompassed in the low-risk group, while 200 patients were encompassed in the high-risk group. Kaplan-Meier analysis uncovered a significant difference in survival between the high- and low-risk groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), with patients in a low-risk group having better survival outcomes. ROC curves verified that our risk model had fine predictive potency (AUC\u0026thinsp;=\u0026thinsp;0.81 for 1-year, 0.7 for 3-year, and 0.64 for 5-year survival, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Additionally, RiskScore, survival status distributions, and the risk genes expression pattern of the patients showed the same characteristics as the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical application value of the prognostic signature\u003c/h2\u003e \u003cp\u003eConsidering the significance of RiskScore in portending the prognosis of breast cancer patients, we further explored its value in clinical application. Univariable and multivariable Cox regression were performed to assess the potential independent prognostic role of RiskScore. Univariable Cox regression demonstrated that RiskScore was an independent predictor of poor survival in both the training and validation cohorts (HR\u0026thinsp;=\u0026thinsp;1.534, 95%CI: 1.199\u0026ndash;1.961 and HR: 1.510, 95%CI: 1.080\u0026ndash;2.110, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). After adapting for other confounders, multivariable Cox regression also revealed that RiskScore (HR\u0026thinsp;=\u0026thinsp;1.683, 95%CI: 1.308\u0026ndash;2.167 and HR: 1.765, 95%CI: 1.232\u0026ndash;2.527, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) played an independent role in predicting prognosis of breast cancer patients. A nomogram model was constructed based on the age, TNM stage, tumor stage, and RiskScore. The survival contingency of breast cancer patients for 1-, 3-, and 5 years were computed according to the total points (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). A C-index of 0.781 suggested that the nomogram had an agreeable predictive value. Calibration plot for the probability of survival also showed satisfactory compliance with the prediction of 1-, 3-, and 5-year OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These results indicate that the risk model has a weighty hand in clinical prediction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmune microenvironment landscape between high- and low-risk group\u003c/h2\u003e \u003cp\u003eThe discrepancy in the immune microenvironment between high- and low-risk groups was evaluated by multiple immune analyses. Outcomes of the ESTIMATE algorithm demonstrated that patients in the high-risk group had lower Immune Score and ESTIMATE Score, as well as a higher level of Tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The abundance of immune cell infiltration in high- and low-risk groups predicted by the CIBERSORT analytical tool manifested that the high-risk group possessed a significantly lower abundance of immune cells, including na\u0026iuml;ve B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells, resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, activated memory CD4\u0026thinsp;+\u0026thinsp;T cells and monocytes. While the infiltration of T cells follicular helper, resting NK cells, macrophages (M0), and resting mast cells was prominently up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In addition, we investigated the expression of immune checkpoints in high- and low-risk groups, finding that CTLA4, PD1, LAG3, BTLA, and TNFRSF14 were significantly down-regulated in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis singled out the module closely related to immune infiltration\u003c/h2\u003e \u003cp\u003eTo figure out the predominant module that correlated with clinical traits or immune infiltration, we performed the weighted gene co-expression network analysis (WGCNA) on the TCGA cohort. After the soft threshold power β being set at 6 (scale-free R2\u0026thinsp;=\u0026thinsp;0.90, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), a total of 14 modules with unique colors were identified, each comprising genes with similar expression patterns recognized by hierarchical clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). And genes contained in the gray module did not belong to any other modules. Module-trait relationships analysis was carried out to discern the association between co-expression modules and immune infiltration, as well as clinical traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In accordance with the correlation heatmap, the MEbrown module had a strikingly positive correlation with ESTIMATE score (R\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1e-211). Nevertheless, none of the modules had a strong link with clinical traits. Intriguingly, one of these genes in the risk model, ALDH2, was also located in the MEbrown module.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePotential prognostic value of ALDH2 in breast cancer\u003c/h2\u003e \u003cp\u003eWe explored the expression variants of ALDH2 in different TNM stages and tumor stages, noticing that ALDH2 was up-regulated in samples with lymphatic metastasis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). However, ALDH2 expression manifested no divergence in samples with T3-T4, M1 and Stage III-IV when compared with samples with T1-2, M0 and Stage I-II, respectively. Following the median expression level of ALDH2, patients were classified into two subgroups. Comparisons of the patients\u0026rsquo; OS between the two subgroups were made by Kaplan-Meier curves, revealing that patients with high expression of ALDH2 had long-term OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDownregulation of ALDH2 in breast cancer impacted tumor malignancy\u003c/h2\u003e \u003cp\u003eWestern blotting was conducted to assess the knockdown efficiency after the transfection of siRNA targeting ALDH2 in MCF7 and MDA-MB-231 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Wound-healing assay exhibited a significant high migratory rate of ALDH2-silenced MDA-MB-231 cells, but not of MCF7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). We further performed cell invasion assay to compare the invasion ability of breast cancer cells between NC and si-ALDH2 groups. The quantity of cells invading to the lower chambers were more in MCF7 cells and MDA-MB-231 cells of si-ALDH2 group than that of NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). These results indicate that downregulation of ALDH2 promotes the invasion and migration capacity of breast cancer cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, invasive breast ductal carcinoma tissue from 30 patients pasted on tissue microarray was analyzed by immunohistochemistry (IHC). Of the 30 patients, 16 patients were diagnosed with Nottingham grade II, and the 6 patients with grade III. The expression level of ALDH2 was compared between 16 patients with grade II and 6 patients with grade III, which showed no significant difference between the two groups (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The limited number of clinical samples may be one of main reasons for no differential expression of ALDH2 in breast cancer tissues with various pathological grades.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePotentially underlying mechanism of ALDH2 for modulating lipid metabolism\u003c/h2\u003e \u003cp\u003eTo investigate the predicted underlying mechanism of ALDH2 for modulating lipid metabolism, we performed the correlation analysis of ALDH2 in TCGA cohort. Under the screening condition of Pearson\u0026rsquo;s r\u0026thinsp;\u0026gt;\u0026thinsp;0.2, 62 ALDH2-related genes were further probed by GO and KEGG enrichment analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). GO enrichment analysis showed that ALDH2-related genes involved in regulation of lipid metabolic process, lipid transport, alcohol metabolic process, lipid catabolic process and fatty acid metabolic process (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). KEGG pathways of ALDH2-related genes were mainly related to PPAR signaling pathway and AMPK signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Most of the ALDH2-related genes enriched in PPAR signaling pathway, AMPK signaling pathway and fatty acid degradation were remarkably down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Network diagrams of the above three pathways showed the genes contained in each (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Among them, ALDH2 was enriched in fatty acid degradation pathway. We also inspected the role of ALDH2-related genes played in PPAR signaling pathway, AMPK signaling pathway and fatty acid degradation pathway. Scatter plot demonstrated the correlation between ALDH2 and the genes were enriched in these pathways, whose Pearson value was greater than 0.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe co-expressed genes of ALDH2 (Pearson\u0026rsquo;s r\u0026thinsp;\u0026gt;\u0026thinsp;0.4)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson correlation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG0S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.393086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.83E-42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEBPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.387443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03E-40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.371073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07E-37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCES1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.370515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00E-37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPARG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.370499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.03E-37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLIN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.369284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.17E-37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.43E-36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIDEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.356032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24E-34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIDEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.353297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10E-33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSD11B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.346122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60E-32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.341698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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breast cancer, culminating in a novel prognostic signature encompassing ALDH2, CYP21A2, and IL24. This advancement provides critical insights into the nexus between lipid metabolism and breast cancer progression.\u003c/p\u003e \u003cp\u003eIt has been reported that up-regulation of lipid metabolism genes in breast cancer prior to cancer diagnosis, metabolic activation of the mammary gland in the early stages of cancer development, and increased epithelial-adipose tissue interactions may create a favourable environment for eventual cell transformation, proliferation and survival(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our comprehensive analysis of TCGA data identified key shifts in LMAGs, with 80 up-regulated genes and 105 down-regulated genes. The prognostic signature, derived from these differentially expressed LMAGs, highlighted ALDH2, CYP21A2, and IL24 as pivotal prognostic indicators. Rigorous statistical analyses underpinned their prognostic significance, and the model's robust correlation with patient survival outcomes underscored its clinical relevance. Current research has focused extensively on the relationship between lipid metabolism abnormality and tumor immune response, highlighting the correlation between lipid metabolism and immune escape from tumors, suggesting a potential role in tumor immunotherapy(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However relatively few studies have focused on how lipid metabolism influences tumor metastatic mechanisms. Our study provides some new insights in this area, highlighting the potential role of lipid metabolism in regulating the invasive capacity of tumor invasion.\u003c/p\u003e \u003cp\u003eBased on the WGCNA analysis, we identified the MEbrown module with the highest correlation with clinical features or immune infiltration, and one of the genes in the risk model, ALDH2, was also located in the MEbrown module. ALDH2 is a gene of high interest in breast cancer(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In the TCGA cohort, high expression of ALDH2 was positively associated with lymphatic metastasis. However, Kaplan-Meier curves showed that the expression level of ALDH2 in breast cancer was closely correlated with the prognosis of patients, and patients with high expression of ALDH2 showed better survival. We therefore demonstrated that low ALDH2 expression promoted the migration and invasion of breast cancer cells. Nevertheless, based on IHC experiments, we failed to find a significant difference in ALDH2 expression between the gradeⅡ and gradeⅢ groups of patients, possibly due to small sample size. The movement of breast cancer cell is closely related to microfilament remodeling(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). We speculated that ALDH2 might regulate breast cancer cell motility by affecting the microfilament dynamics. However, the immunofluorescent staining showed that ALDH2 did not co-located on microfilaments (Supplementary Figure S2). Thus, ALDH2 may mediate breast cancer metastasis through other signaling pathways.\u003c/p\u003e \u003cp\u003eFuture research should intensively explore the regulatory mechanisms of lipid metabolism in tumor metastasis. This exploration might reveal key lipid metabolic pathways and their intersections with other signaling networks, offering new avenues for therapeutic interventions to inhibit breast cancer invasion and metastasis, thereby improving patient survival and quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eEthical approval and informed consent of the study was obtained by the Clinical Research Ethics Committee, Shanghai Outdo Biotech Company.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eAll the authors consent for the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e YL analyzed the data and wrote the manuscript. ZL performed the cytological experiments. YZ and QG proposed the conception and revised the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, et al. Breast Cancer Stat 2022 CA Cancer J Clin. 2022;72(6):524\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurstein HJ, Curigliano G, Th\u0026uuml;rlimann B, Weber WP, Poortmans P, Regan MM, et al. Customizing local and systemic therapies for women with early breast cancer: the St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021. Ann Oncol. 2021;32(10):1216\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon H, Shaw JL, Haigis MC, Greka A. Lipid metabolism in sickness and in health: Emerging regulators of lipotoxicity. Mol Cell. 2021;81(18):3708\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBian X, Liu R, Meng Y, Xing D, Xu D, Lu Z. Lipid metabolism and cancer. J Exp Med. 2021;218(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Bosch C, Zunszain PA, Mann GE. Control of Redox Homeostasis by Short-Chain Fatty Acids: Implications for the Prevention and Treatment of Breast Cancer. Pathogens. 2023;12(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZipinotti Dos Santos D, de Souza JC, Pimenta TM, da Silva Martins B, Junior RSR, Butzene SMS, et al. The impact of lipid metabolism on breast cancer: a review about its role in tumorigenesis and immune escape. 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Sci Rep. 2020;10(1):6564.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Xu T, Su B, Zhou J, Xu B, Zhang Y, et al. The novel role of etoposide in inhibiting the migration and proliferation of small cell lung cancer and breast cancer via targeting Daam1. Biochem Pharmacol. 2023;210:115468.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMei J, Liu Y, Yu X, Hao L, Ma T, Zhan Q, et al. YWHAZ interacts with DAAM1 to promote cell migration in breast cancer. Cell Death Discov. 2021;7(1):221.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Lipid metabolism, ALDH2, CYP21A2, IL24","lastPublishedDoi":"10.21203/rs.3.rs-3932507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3932507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe oncogenesis and development of breast cancer is closely related to abnormal lipid metabolism. We focused on the identification of pivotal lipid metabolism-associated genes (LMAGs) and constructed a prognostic signature (ALDH2, CYP21A2, and IL24) by LASSO and Cox regression. Survival and time-dependent ROC analysis demonstrated the predictive accuracy of our signature in both training and validation cohort. Additionally, patients in high-risk group showed an immunosuppressive phenotype with lower abundance of immune cells and down-regulation of recognized immune checkpoints. Subsequent cellular experiments indicated that the downregulaiton of ALDH2 was correlated with the aggressive behavior of breast cancer cells. These findings offer crucial insights into understanding the interplay between lipid metabolism and breast cancer progression, potentially paving the way for innovative strategies to impede breast cancer metastasis.\u003c/p\u003e","manuscriptTitle":"Integrative Analysis of Lipid Metabolism-Associated Genes Unveils ALDH2 as a Prognostic Marker in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 20:20:05","doi":"10.21203/rs.3.rs-3932507/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c414b313-f8d3-41cb-a9b3-2dc1e4db2478","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T05:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 20:20:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3932507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3932507","identity":"rs-3932507","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}