Genome-wide association and functional investigation of M2-like tumor-associated macrophages identified hub genes for breast cancer

Genome-wide association and functional investigation of M2-like tumor-associated macrophages identified hub genes for 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide association and functional investigation of M2-like tumor-associated macrophages identified hub genes for breast cancer Guang Yang, Qian Peng, Yao Tian, Handan Xie, Binlian Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4166156/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 M2-like tumor-associated macrophages (M2-like TAMs) have great potential in promoting oncogenesis and provide the potential biomarkers for diagnosis and treatment of tumor. However, the role of M2-like TAMs in breast invasive carcinoma (BRCA) is still unclear. Based on The Cancer Genome Atlas of America (TCGA) and the Gene Expression Omnibus (GEO) databases, we compared multiple tumors and found the diametrically opposite survival of M1-like and M2-like macrophages in BRCA. And then, we systematically explored the function of M2-like TAMs in BRCA using differentially expressed analysis, weighted gene co-expression network analysis (WGCNA), GO and KEGG analysis, Nomogram, Gene Set Enrichment Analysis (GSEA), CIBERSORT algorithm, pan-cancer and mendelian randomization study. We evaluated the sensitivity and resistance to drugs targeting hub genes using the Genomics of Drug Sensitivity in Cancer (GDSC) database. A total of 85 M2-like TAM-related genes were screened out and the results of functional enrichment analysis were correlated with tight junction, Rap1 signaling pathway and PI3K-Akt signaling pathway. FOXA1 , ERBB3 , MUC1 , AGR2 were identified as hub genes by protein interaction (PPI) network, "CytoNCA" toolkit and degree algorithm. Additionally, nomogram and ROC curve indicated great prognostic performance, and the high expressing four hub genes were positively correlated with M2-like macrophages. FOXA1 and ERBB3 expressed at higher levels in BRCA than in other tumors by pan-cancer analysis. In fixed effected inverse variance weighting, we found that FOXA1 , ERBB3 , MUC1 were positively associated with BRCA risk. Finally, highly FOXA1 , ERBB3 , MUC1 expressing patients were more sensitive to Lapatinib through drug sensitivity analysis. Our studies contribute to understand the M2-like TAM-related mechanisms involved in breast cancer, which provide further insights into drug sensitivity therapy. Breast invasive carcinoma (BRCA) M2-like tumor-associated macrophages (M2-like TAMs) hub genes mendelian randomization (MR) Drug sensitivity analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer is one of the most common malignant tumors in the world, and it is seriously endangered the life and health in women [ 1 ]. Although the improvement of health awareness and early diagnosis technology are important to ameliorate the prognosis of patients, the five-year survival rate for metastatic breast cancer is less than 30% [ 2 ]. Due to the hidden onset, recurrence and metastasis, poor prognosis and lack of effective early diagnostic markers, all of these have brought great challenges to the diagnosis and treatment of breast cancer. Therefore, there is an urgent need to find new molecular biomarkers and therapeutic targets for breast cancer. Bone marrow-derived cells penetrate tumor tissues or aggregate in the solid tumor microenvironment (TME) called tumor-associated macrophages (TAMs) [ 3 ]. As an important component of the TME, TAMs affect tumor growth, agiogenesis, metastasis, and chemotherapy resistance [ 4 , 5 ]. Influenced by cytokines, macrophages differentiate into M1-like and M2-like phenotypes. M1-like macrophages are generally considered to be tumor-killing macrophages, primarily act as the role of anti-tumor and immune-promotion. However, M2-like macrophages are immunosuppressive, promoting tumor progression [ 6 ]. Therefore, it is necessary to describe the molecular characteristics combined with the M2-like macrophage, and to determine the key regulatory factors of M2-like TAMs polarization. With the progression of tumor, M1-phenotype is gradually polarized to M2-phenotype, and the increasing number of M2-like TAMs also indicates poor prognosis, including breast cancer [ 7 ]. We compared whether there were any differences in the survival of M1-like or M2-like macrophages in various tumors, and found that significant difference between M1-like and M2-like macrophages in BRCA, and BRCA patients in the low-content M2 and high-content M1 macrophage groups had longer survival. Based on the significant differences between M1 and M2 macrophages in survival, we conducted a series of studies to discover biomarkers closely related to BRCA. Two-sample Mendelian randomization (MR) is a reliable technological means that has been widely used in recent years, which uses genetic variants strongly associated with exposure factors as instrumental variables for deducing a causal relationship between exposure factors (etiology) and study outcomes (diseases). Typically, people utilize single nucleotide polymorphisms (SNPs) as instrumental variables to assess the causal relationship between exposure factors and outcomes [ 8 , 9 ]. We investigated the correlation between M2-like TAM-related hub genes and the risk of breast cancer by genome-wide association analysis (GWAS) and Mendelian randomization (MR) analysis. In this study, we firstly identified differentially expressed genes (DEGs) between normal and breast tumor tissues from TCGA-BRCA and GSE42568, respectively, and pivotal modular genes most positively related to M2-like macrophage were obtained by weighted gene co-expression network analysis (WGCNA). Next, M2-like TAM-related genes were identified by integrative differential analysis and WGCNA. Then, we identified FOXA1 , MUC1 , ERBB3 and AGR2 as hub markers, which may contribute to BRCA and further mining of the underlying mechanisms and clinical significance of BRCA-associated risk genes. Finally, the signature of four hub genes in breast tumor and the causal relationship between four hub genes and BRCA was explored through pan-cancer and MR studies. Meanwhile, the sensitivity and resistance to IC 50 of drugs targeting hub genes was evaluated by the Genomics of Drug Sensitivity in Cancer (GDSC) database. Materials and methods Source data A total of 1,226 cases with RNA-seq transcriptome profiling (HTSeq-FPKM) and clinical information from TCGA-BRCA were obtained from the Genomic Data Commons (GDC) TCGA data portal ( https://portal.gdc.cancer.gov/ ). Two GEO cohorts (GSE42568, GSE58812) were downloaded from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ). Patients without survival information and RNA sequencing data were excluded from the analysis. The FPKM values were transformed to TPM (transcripts per million) values for subsequent analyses [ 10 , 11 ]. Macrophages related survival analyses The relative content of macrophages in each sample of TCGA-BRCA or GSE58812 was calculated on CIBERSORT algorithm [ 12 ]. The Kaplan‑Meier survival analysis was generated by the ‘survival’ R packages to determine whether survival of BRCA patients was correlated with M1 or M2 macrophage. The "surv_cutpoint" function of the "survminer" package was explored to calculate the optimal cut-off value of the high- and low-content M1 or M2 macrophages group, and to investigate whether there were significant differences between M1-like and M2 macrophages of BRCA. DEGs identification Firstly, we read the data of TCGA-BRCA (including 1113 tumor tissues and 113 normal tissue samples) and GSE42568 (including 104 tumor tissues and 17 normal tissue samples) using R software (version 4.3.1) and preprocessed it for normalized batch correction. After that, we utilized the "limma" R package for DEGs analysis. After the significance analysis of gene expressing levels, the "ggplot2" and "pheatmap" R packages were used for processing to generate up-regulated and down-regulated DEGs. Acquisition pivotal genes associated with M2-like TAMs WGCNA can be conducted to find highly correlated genes in modules associated to diseases, and it can identify candidate biomarkers or therapeutic targets, which has been successfully applied in various biological fields, such as cancer [ 13 ]. After grouping BRCA samples according to differences between M1-like or M2-like macrophages, we analyzed TCGA-BRCA expressing data using “WGCNA” R package to obtain the genes most associated with M2-like TAMs. Then, the obtained modular genes related to M2-like TAMs were intersected with the up-regulated DEGs acquired from differential analysis to filter M2-like TAMs-related genes. These intersecting genes are considered as candidate pivotal genes relevant for pathogenesis of BRCA. Kyoto Encyclopedia of Genes and Genomes (KEGG) serves as a database for systematic analysis of gene metabolic pathways and functions [ 14 ]. We performed functional enrichment analysis to help us understand the potential progression and pathogenesis of BRCA by KEGG pathway and gene ontology (GO) enrichment analyses. Screening of hub genes in protein-protein interaction (PPI) network We used STRING ( https://string-db.org ) to predict and visualize PPI networks with a 0.400 medium confidence required minimum interaction score. The important genes were sequenced using "CytoNCA" toolkit and degree algorithm of Cytoscape software. The top genes were displayed, and the hub genes with the highest BRCA correlation were selected for subsequent analysis. Nomogram construction We used a nomogram scoring model to predict the risk of BRCA using the “rms” and “rmda” packages [ 15 , 16 ]. The risk of disease is predicted based on the risk score of hub genes. And then, we used the "ROC" package to construct the receiver operator characteristic (ROC) curve to verify the prophetic validity of hub genes. The area under ROC curve (AUC) was utilized to represent its accuracy. If the AUC range was 0.8 < AUC < 1, it indicated that the gene had excellent prediction accuracy. Correlation, GSEA and pan-cancer analysis The correlation between the expression of hub genes and immune cells infiltration was investigated by CIBERSORT algorithm. At the same time, we performed Gene Set Enrichment Analysis (GSEA) to evaluate which functional pathways were associated with hub genes. Finally, we did pan-cancer analysis to explore whether these M2-like TAMs-associated hub genes are more highly expressed in BRCA than in other tumors. Mendelian randomization (MR) study From the genome wide association study (GWAS) data of hub genes, we extracted SNPs that were strongly associated with hub genes at the significant level of the whole genome (P-value < 5*10 − 6 ) to ensure the independence of instrumental variables and exclude the influence of linkage disequilibrium (LD) [ 17 – 19 ]. At the same time, the genetic distance was set to 10,000 kb and the LD parameter (r 2 ) threshold to 0.01. A large amount GWAS data of breast cancer was used here, including 89,677 individuals combined the 42,892 control and 46,785 case datasets from European. MR analysis was performed based on the “TwoSampleMR” package, and MR-Egger regression, Weighted median, fixed effects inverse variance weighting (IVW ), Simple mode and Weighted mode were used to assess the relationship between the levels of hub genes and the risk of breast cancer. MR–Egger was used for additional sensitivity analysis. The intercept terms in causal estimation were considered, and the MR-Egger regression was used to explain uncorrelated pleiotropy of SNPs as instrumental variables. In addition, the IVW method and MR-Egger regression were utilized to detect heterogeneity in individual genetic variation estimates. Drug Sensitivity Analysis Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/ ) database contains more than 1,000 human cancer cell line, 265 drug response data, and over 17,000 genomic markers. We collected the corresponding mRNA expression levels of hub genes and IC 50 of 265 drugs in 51 cell lines associated with BRCA, and mRNA expression data and drug susceptibility data were combined. With P-value < 0.03, Pearson correlation analysis was used to determine the correlation between mRNA expression of hub genes and drug IC 50 , and to investigate whether the high expression of hub genes had sensitization or drug resistance on the therapeutic effect. Statistical analysis Survival analysis differences between groups were discussed using Kaplan-Meier curves and log-rank tests. Correlation coefficients were calculated by Pearson correlation analyses. The statistical analysis was performed using R software 4.3.1 and values represent the mean ± standard deviation. P-value < 0.05 was considered statistically significant. Results Screening M2-like TAMs-related genes To elucidate the relationship between macrophages and prognosis of BRCA, the contents of M1 and M2 macrophages in TCGA-BRCA samples were calculated by CIBERSORT algorithm. Next, BRCA patients were divided into high-content and low-content M1 or M2 macrophages group. Kaplan-Meier survival analyses showed some difference between high-content and low-content M1 macrophage groups (Fig. 1 A, P-value = 0.032). Furthermore, significant difference in survival has shown between high-content and low-content M2 macrophages group, and the patients in the high-content M2 macrophages had worse prognosis than that of the low-content M2 macrophages (Fig. 1 B, P-value < 0.001). This result indicated that the effects of M1 and M2 macrophages on prognosis were quite opposite, and M2 macrophages had a more pivotal role in BRCA than M1 macrophages. GSE58812 datasets confirmed the above result (Fig. 1 C, D). Based on this observation, WGCNA was performed to identify the key module related to M2 macrophages. Figure 1 E demonstrates that the green module was positively and negatively correlated with M2 macrophages and M1 macrophages, respectively. And 3,127 genes in the green module were selected for downstream analyses. Then, 2,098 DEGs (935 up-regulated and 1,163 down-regulated genes) were screened out from the breast cancer tissues compared to the normal tissues in GSE42568. Meanwhile, 2,753 DEGs (1,078 up-regulated and 1,675 down-regulated genes) were identified from TCGA-BRCA. Lastly, we searched for co-expressed genes between WGCNA-derived M2-like TAMs-related genes and up-regulated DEGs associated with BRCA, and eventually screened out 85 overlapping genes, which may play an important role in the development and progression of breast cancer (Fig. 1 F). Go enrichment and KEGG pathway and PPI network analysis GO and KEGG analyses were conducted to further explore the potential roles of intersecting hub genes. GO analysis showed that the overlapping 85 genes mainly affect the cellular component of bicellular tight junction, tight junction and apical junction complex (Fig. 2 A). KEGG pathway analysis showed that these 85 genes mainly affect tight junction, Rap1 signaling pathway and PI3K-Akt signaling pathway (Fig. 2 B). It is well known that the Rap1 signaling pathway and PI3K-Akt signaling pathway are closely associated with proliferation, invasion and metastasis of cancer, while the relationship between tight junction and tumor has been rarely reported. Breast cancer is a complex and heterogeneous disease, and proper adhesion between adjacent epithelial cells of the breast is essential for the normal structure and function of the epithelial tissue. There is growing evidence that dysregulation of cell-cell adhesion is associated with many cancers. There is a study that suggests that tight junction may be involved in the development or progression of breast cancer [ 20 ]. The PPI network of candidate hub genes was constructed by using the STRING online tool. Subsequently, the top 10 up-regulated genes in breast cancer were then visually analyzed by using "CytoNCA" toolkit and degree algorithm. Briefly, FOXA1 , AGR2 , MUC1 , ERBB3 , TFF1 , EZR , GATA3 , FGFR3 , SPDEF and AGR3 were sorted out (Fig. 2 C). The deeper the red color, the higher the relevance score, and the more significant the importance in the BRCA. It is clear that FOXA1 , AGR2 , MUC1 and ERBB3 are the most important genes. Construction of nomogram and ROC diagnostic curve We constructed a nomogram model to predict the risk of BRCA. Therefore, our nomogram performs well in BRCA prediction, the higher the scores, the higher the risk of disease. As shown in Fig. 2 D, the patient has 100 total points of these four hub genes, the risk of breast cancer is up to more than 90%. Figure 2 E verifies the reliability of nomogram. Subsequently, we calculated ROC curves for the four hub genes ( FOXA1 , AGR2 , MUC1 and ERBB3 ) to assess reliability of prediction, and the AUC values of FOXA1 , MUC1 , ERBB3 and AGR2 are 0.877, 0.907, 0.930 and 0.865, respectively. It demonstrated that these four hub genes could differentiate breast cancer from normal tissues (Fig. 2 F). Functional analysis of M2-like TAMs-related hub genes According to the correlation analysis between four hub genes and M2-like macrophages, three of the four hub genes were closely related with M2-like macrophages. Compared with other genes, FOXA1 and ERBB3 were more closely associated with M2-like macrophages, among which FOXA1 and ERBB3 had the highest correlation (Fig. 3 A). In TCGA datasets, high FOXA1 expression correlated with shorter overall survival (OS) (Fig. 3 B). But the high expression of AGR2 group showed a better survival rate than the low expression of AGR2 group (Fig. 3 C). The expressed differences of four hub genes between normal tissues and breast tumor tissues were analyzed based on TCGA-BRCA, and the four hub genes were all highly expressed in breast tumor tissues compared with normal tissues (Fig. 3 D). GSEA analysis showed that FOXA1 and ERBB3 were mainly related to tumor-related pathways, such as complement and coagulation cascades [ 21 ], colorectal cancer, mtor signaling pathway and prostate cancer (Table 1 ). Complement and coagulation cascade are important components of the human immune system. Platelets in TME can promote tumor cell invasion and metastasis by activating the coagulation cascade. And then, the relationship between the expression levels of four hub genes and the tumor immune microenvironment was analyzed. CIBERSORT algorithm analysis showed that four hub genes were positively correlated with the infiltration of immune cells such as M2 macrophages, resting Mast cells, resting CD 4+ T cells, monocytes and native B cells (Fig. 3 E-H). In addition, FOXA1 , ERBB3 , AGR2 had a higher positive correlation with M2 macrophages, which was consistent with the correlation analysis results in Fig. 3 A. The expression of these four hub genes was positively correlated with M2-like macrophages infiltration in BRCA, but it was completely opposite to M1-like macrophages infiltration in BRCA. Table 1 Gene set enrichment analysis (GSEA) of four hub genes Gene KEGG Pathway Enrichment Score P-value FOXA1 GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS 0.64890 0.00064 COMPLEMENT_AND_COAGULATION_CASCADES 0.46633 0.00027 VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION 0.51269 0.00147 NUCLEOTIDE_EXCISION_REPAIR 0.49271 0.00267 PROTEIN_EXPORT 0.57175 0.00729 AGR2 COMPLEMENT_AND_COAGULATION_CASCADES 0.44045 0.00459 GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS 0.59598 0.02040 PRIMARY_IMMUNODEFICIENCY -0.60826 0.02514 SPLICEOSOME 0.32993 0.02020 ERBB3 PROSTATE_CANCER 0.42079 0.00012 MTOR_SIGNALING_PATHWAY 0.50957 0.00023 GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS 0.67080 0.00039 COLORECTAL_CANCER 0.42077 0.00204 RNA_DEGRADATION 0.44905 0.00061 MUC1 VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION 0.54677 0.00018 FATTY_ACID_METABOLISM 0.51296 0.00144 CIRCADIAN_RHYTHM_MAMMAL 0.69052 0.00805 GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS 0.51112 0.01853 GALACTOSE_METABOLISM 0.48660 0.03024 In pan-cancer analysis, four hub genes were found to be highly expressed in most kinds of tumor tissues, and FOXA1 , ERBB3 had higher expression in breast tumors than other tumors particularly (Fig. 4 A-D). Moreover, it was found that the age of patients over 65 years old were positively associated with the expression level of four hub genes from the clinical analysis of BRCA (Fig. 4 E-H). Hub genes causally associated with the risk of BRCA We assessed the causal association between four hub genes and breast cancer. The SNPs characteristics of four hub genes were strong instrumental variables (P value < 5*10 − 6 ). Using the fixed effects IVW method, we found that four hub genes were all associated with the risk of breast cancer (Table 2 ). β-value is greater than zero indicates that exposure factor is a risk factor, and the risk of morbidity increases with the increase of exposure factor. Except for AGR2 , the expressions of FOXA1 , ERBB3 and MUC1 were positively correlated with the occurrence of breast tumors (β-value > 0), this result was consistent with the prognostic analysis of AGR2 (Figs. 3 C). Obvious causal association between FOXA1 and breast cancer was observed in Figs. 5 A. The combined effect value of all SNPs is greater than zero, indicating that FOXA1 is a risk factor, and the risk of breast cancer increases with the increase of FOXA1 expression (Figs. 5 B). The weighted median and weighted mode method also confirmed the result of fixed effects IVW method. The causal effect of FOXA1 funnel plot was roughly symmetrical along the symmetry axis (Figs. 5 C), and no horizontal pleiotropy is observed at the intercept of MR Egger regression, which further indicates that pleiotropy does not bias the causal effect. In the leave-one-out plot, the MR analysis was performed again on the remaining SNPs after removing each SNP, the comprehensive effect value was consistent with the main effect value, indicating that the calculation results of all SNPs made causality significant (Figs. 5 D). Table 2 The results of mendelian randomization (MR) analysis Gene Exposure Outcome Method nSNPs β-value P-value FOXA1 prot-a-2611 Breast cancer id:ebi-a-GCST007236 MR Egger 26 0.1462 0.9402 Weighted median 26 0.9526 5.54E-11 Inverse variance weighted (fixed effects) 26 0.6474 5.44E-245 Simple mode 26 0.4266 0.1621 Weighted mode 26 0.5861 9.18E-07 MUC1 prot-a-1967 Breast cancer id:ebi-a-GCST007236 MR Egger 16 4.6470 0.0002 Weighted median 16 1.8530 4.25E-18 Inverse variance weighted (fixed effects) 16 0.7446 0 Simple mode 16 1.6230 0.0040 Weighted mode 16 1.6600 6.72E-10 AGR2 prot-a-58 Breast cancer id:ebi-a-GCST007236 MR Egger 10 -1.3710 0.6640 Weighted median 10 -0.6746 7.32E-05 Inverse variance weighted (fixed effects) 10 -0.4915 6.48E-53 Simple mode 10 -1.6350 0.0004 Weighted mode 10 -0.6215 0.0061 ERBB3 prot-c-2617_56_35 Breast cancer id:ebi-a-GCST007236 MR Egger 3 1.4770 0.9365 Weighted median 3 0.3960 2.55E-06 Inverse variance weighted (fixed effects) 3 0.03712 1.93E-78 Simple mode 3 0.3527 0.0267 Weighted mode 3 0.3025 0.0229 Drug Sensitivity Analysis To investigate the sensitization or resistance of four hub genes to drugs during chemotherapy, we integrated drug IC 50 and gene expression profile data from GDSC BRCA cell lines (Table 3 ). Through this method, the drugs with the highest degree of correlation with hub genes can be screened out. The results showed that the IC 50 of A-443654, Lapatinib, KIN001-102, CP724714 and ZSTK474 were negatively correlated with FOXA1 expression, and the IC 50 of Pyrimethamine and BX-795 were positively correlated with FOXA1 expression according by Pearson’s correlation analysis (Fig. 6 A). The IC 50 of Lapatinib, A-770041, Sorafenib, Roscovitine, A-443654, MS-275 and Paclitaxel was negatively correlated with the expression of ERBB3 , indicating that high ERBB3 expression has the sensitizing effect on these drugs (Fig. 6 B). The IC 50 of Saracatinib, Imatinib, BMS-509744 and Lapatinib was negatively correlated with MUC1 expression, and Olaparib, YK 4-279 was positively correlated with MUC1 expression (Fig. 6 C). Interestingly, the expression of FOXA1 , ERBB3 and MUC1 all increased the sensitivity of Lapatinib to BRCA patients. In 2007, FDA approved Lapatinib as a common treatment for HER2-positive advanced breast cancer. Whether the combined expression of FOXA1 , ERBB3 and MUC1 could be considered as a potential opportunity for the using of Lapatinb in clinical breast cancer patients. Moreover, the expression of AGR2 is closely related to the resistance of TAE684, XMD8-85, AZ628 and Pyrimethamine (Fig. 6 D). In other words, the expression of AGR2 is more likely to lead to drug resistance. Table 3 The results of drug sensitivity analysis Gene Drug Pearson Correlation Coefficient P-value FOXA1 A-443654 -0.502 0.00736 Lapatinib -0.488 0.00883 KIN001-102 -0.419 0.02051 CP724714 -0.414 0.02173 ZSTK474 -0.391 0.02821 Pyrimethamine 0.634 0.02205 BX-795 0.616 0.02705 ERBB3 Lapatinib -0.633 0.00420 A-770041 -0.589 0.00724 Sorafenib -0.530 0.01437 Roscovitine -0.493 0.02152 A-443654 -0.490 0.02222 MS-275 -0.474 0.02627 Paclitaxel -0.473 0.02655 MUC1 Saracatinib -0.708 2.11E-05 Imatinib -0.402 0.01027 BMS-509744 -0.347 0.02361 Lapatinib -0.334 0.02839 Olaparib 0.491 0.01859 YK 4-279 0.481 0.02153 AGR2 TAE684 0.709 0.00735 XMD8-85 0.68 0.01109 KIN001-102 -0.355 0.01299 CP724714 -0.314 0.02230 AZ628 0.625 0.02303 Pyrimethamine 0.613 0.02678 ZSTK474 -0.291 0.02974 Afatinib -0.291 0.02974 Discussion Metastatic breast cancer is less effective in immunotherapy than corresponding primary tumors, but some immune-oncology targets, macrophages, and angiogenesis characteristics show preserved expression [ 22 ]. Stage IV (metastatic) breast cancer is treatable, but not curable, the goals of treatment include extending the patient's life span and improving quality of life [ 23 ]. Immunotherapy is a potentially effective way to control tumor growth in patients with stage IV breast cancer [ 24 ]. Although encouraging immunotherapy results have been reported in some clinical trials, there are still some obstacles that need to be resolved. Therefore, personalized immunotherapy is considered as a potential complementary therapy for breast cancer in combination with chemotherapy [ 25 ]. Considering that existing studies have proved the importance of TAMs in the immunotherapy of BRCA [ 26 ], the reliability of M2-like TAMs as biomarkers for BRCA and in predicting tumorigenesis was evaluated based on the significant difference between M1-like and M2-like macrophages in the prognosis of patients with breast cancer. TAMs are one of the most abundant immune cell populations in the TME [ 27 ]. M2-like TAMs mainly are associated not only with poor prognosis in various tumors, but also with the generation of immunosuppressive [ 26 – 29 ]. M2-like macrophages release anti-inflammatory cytokines, growth factors and support angiogenesis, which results in tumor cell proliferation [ 30 – 32 ]. Therefore, TAMs can be used as a breakthrough in cancer treatment and it is urgent to find TAMs-specific markers for cancer. A TAMs-related gene signature in breast cancer was constructed, which highly enriched in aggressive breast cancer subtypes [ 33 ]. The M2 macrophages-related genes prognostic model was established in pancreatic ductal adenocarcinoma [ 34 ]. However, few studies have explored M2-like TAMs-related hub genes in breast cancer. With the aim of identifying a potential biomarker for predicting prognosis in BRCA, we took advantage of WGCNA and differential analysis to obtain M2-like TAMs-related hub genes. Firstly, we validated that the high-content of M2-like TAMs are significantly associated with poorer prognosis in TCGA-BRCA and GSE58812 cohorts. The survival rate of patients with high-content M2-like TAMs was significantly worse than that of patients with low-content M2-like TAMs, indicating that M2-like TAMs are closely related to the poor prognosis of BRCA patients. Then, 85 M2-like TAMs-related genes were obtained by intersecting M2-like macrophages modular genes and DEGs distinguished from BRCA. Next, four hub genes of breast cancer, including FOXA1 , ERBB3 , AGR2 and MUC1 were sorted out by STRING and Cytoscape platform. Consequently, the nomogram model that integrates four hub genes performed well in the prediction of breast cancer, and the ROC curves confirmed that the four hub genes can successfully differentiate breast cancer from normal tissues. These four hub genes were indeed more highly expressed in tumor than in normal tissues, and the high FOXA1 expression was significantly associated with poor prognosis, and GSEA analysis demonstrated that FOXA1 , AGR2 and ERBB3 were closely related to tumor-related pathways and functions. In pan-cancer analysis, four hub genes were highly expressed in the age of breast cancer patients over 65 years old. A study of 92 women over 65 years old was reported, of whom 77 women had malignant breast disease (83.6%) [ 35 ]. It has been reported that mutation in FOXA1 is a hallmark of estrogen receptor-positive (ER+) breast cancer [ 36 ]. Overexpression of FOXA1 may be a prognostic factor for treatment resistance. Moreover, it may be a viable target for immune and chemotherapy sensitization of estrogen receptor-positive luminal breast cancer [ 37 ]. Meanwhile, the positive correlation between FOXA1 expressed levels and M2 macrophages content further explains why patients with higher FOXA1 levels have poorer prognosis. A study has shown that MUC1 can up-regulate M2 macrophage infiltration, and MUC1 cytoplasm domain plays an important role in promoting postpartum mammary tumor, which providing a new strategy for the prevention and treatment of postpartum breast cancer [ 38 ]. And then, we explored the causal relation between the expressing levels of four hub genes ( FOXA1 , AGR2 , MUC1 and ERBB3 ) and breast cancer risk by a two-sample MR analysis based on a large scale of GWAS data of exposure factor and outcome. This MR study suggested that FOXA1 , MUC1 and ERBB3 might be causally associated with the increased risk of breast cancer. MR can reduce the systematic bias caused by traditional observational studies [ 39 ]. To ensure that SNPs were not related to any confounding factors between four hub genes and breast cancer, we only selected participants from the European populations. In order to ensure the stability of the results, MR-Egger adds the intercept term was conducted, and no evidence of directed level pleiotropy was observed [ 40 ]. Lastly, we explored whether the expression of hub genes enhance or resist the therapeutic effect of drugs by mining the relationship between drug IC 50 and hub genes expression. The results showed that the expressions of FOXA1 , MUC1 and ERBB3 all sensitized the efficacy of Lapatinib to patients of BRCA. Lapatinib is an inhibitor of human epidermal growth factor receptor-2 (HER-2) tyrosine kinase and a molecularly targeted new drug for breast cancer, it is often used in combination with capecitabine to treat advanced or metastatic breast cancer. We hope that the analysis results will play an auxiliary role in the clinical application of Lapatinib. Conclusion Our study identified M2-like TAMs-related hub genes for predicting prognosis in breast cancer. We hope that the combined expression of FOXA1 , ERBB3 and MUC1 could be as a potential means for the using of Lapatinb for breast cancer patients. Declarations Author contributions GY was the major contributor in writing the main manuscript text and analyzing the data. YT, HX and QP prepared all tables, figures and graphs. BS was responsible for review and editing. All authors have read and agreed to the published version of the manuscript. All authors reviewed and approved the final version of the manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Conflict of interes ALL authors declare that they have no conflict of interest. Ethical Approval Our raw data could be available from open databases. No human/animal studies were involved. Not applicable. 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Nature 475:222-5. https://doi.org/10.1038/nature10138 Cassetta L, Fragkogianni S, Sims AH, Swierczak A, Forrester LM, Zhang H, et al (2019) Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers and therapeutic targets. Cancer Cell 35:588–602.e10. https://doi.org/10.1016/j.ccell.2019.02.009 Haddad A, Zoukar O, Daldoul A, Bhiri H, Wiem K, Mhabrich H, Zaied S, Faleh R (2018) Breast diseases in women over the age of 65 in Monastir, Tunisia. Pan Afr Med J 31:67. https://doi.org/10.11604/pamj.2018.31.67.16105 Arruabarrena-Aristorena A, Maag JLV, Kittane S, Cai Y, Karthaus WR, Ladewig E, Park J, Kannan S, Ferrando L, Cocco E, Ho SY et al (2020) FOXA1 Mutations Reveal Distinct Chromatin Profiles and Influence Therapeutic Response in Breast Cancer. Cancer Cell 38:534-550.e9. https://doi.org/10.1016/j.ccell.2020.08.003 He Y, Wang L, Wei T, Xiao YT, Sheng H, Su H, Hollern DP, Zhang X, Ma J, Wen S et al (2021) FOXA1 overexpression suppresses interferon signaling and immune response in cancer. J Clin Invest 131:e147025. https://doi.org/10.1172/JCI147025 Li Y, Pang Z, Dong X, Liao X, Deng H, Liao C, Liao Y, Chen G, Huang L (2017) MUC1 induces M2 type macrophage influx during postpartum mammary gland involution and triggers breast cancer. Oncotarget 9:3446-3458. https://doi.org/10.18632/oncotarget.23316 Gala H, Tomlinson I (2020) The use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations. J Pathol 250:541-554. https://doi.org/10.1002/path.5421 Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7:e34408. https://doi.org/10.7554/eLife.34408 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-4166156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284441181,"identity":"7a730ba8-fea7-4cb1-822f-21080aa27104","order_by":0,"name":"Guang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFAD9oY0COMA0Vp4DpCsRSKBjTgtBsfPHn75tc0uTz7ywbPHvDsY5PhuJDB+LsCn5UxemrVsW3Kx4e2EdGPeMwzGkjcSmKVn4NNyIMfMWLKNOXHj7IQ0ad42hsQNNxLYmHnwaTn/BqSlPnHjzANgLfWEtdzIMX74se1w4nwJBrCWBANCWiRvvDFjZjh3PHEDT0Ka5Nw2CcOZZx42S+PTwnc+x/jjj7LqxPntZ9Ik3rbZyPMdTz74GZ8WhQMMbGAzDQ7wJAApCSBmbMCjgYFBvoGB+eMPMIP9AF6Vo2AUjIJRMHIBAPmkUMHoLqQnAAAAAElFTkSuQmCC","orcid":"","institution":"Jianghan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guang","middleName":"","lastName":"Yang","suffix":""},{"id":284441182,"identity":"cebe7b70-63b5-406d-81f6-b29d4c74f9e3","order_by":1,"name":"Qian Peng","email":"","orcid":"","institution":"Jianghan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Peng","suffix":""},{"id":284441183,"identity":"9867cbb2-e023-48b1-b2b8-fec55f711022","order_by":2,"name":"Yao Tian","email":"","orcid":"","institution":"Jianghan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Tian","suffix":""},{"id":284441184,"identity":"6961281b-fbe7-4258-a094-01902d35e2e5","order_by":3,"name":"Handan Xie","email":"","orcid":"","institution":"Jianghan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Handan","middleName":"","lastName":"Xie","suffix":""},{"id":284441185,"identity":"d3e545dd-5862-4bc1-9d94-7189e0b3ae73","order_by":4,"name":"Binlian Sun","email":"","orcid":"","institution":"Jianghan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Binlian","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-03-26 01:27:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4166156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4166156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53885353,"identity":"3f5c1867-ee60-40e7-aad5-f45222b4d131","added_by":"auto","created_at":"2024-04-01 19:10:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1365441,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis and screening of M2-like TAMs-related genes. (A) Kaplan-Meier survival curves showed some difference between high-content and low-content M1 macrophages group. (B) The prognosis was significantly worse in the high-content M2 macrophages group. (C) Kaplan–Meier survival analysis of M1 macrophages in GSE58812 cohorts. (D) Kaplan–Meier survival analysis of M2 macrophages in GSE58812 cohorts. (E) Correlation analysis of modules and traits with the green module considered to be the most relevant module for M2 macrophages. (F) Venn diagram showing the intersecting hub genes between WGCNA-derived M2-like TAMs-related genes and up-regulated DEGs. WGCNA, weighted gene co-expression network analysis.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/e24b131b6ea3831e40cc0a72.jpg"},{"id":53885358,"identity":"c168253f-b150-4063-845c-20aaff0ebd93","added_by":"auto","created_at":"2024-04-01 19:10:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1144530,"visible":true,"origin":"","legend":"\u003cp\u003eGO, KEGG and PPI network analysis and construction of nomogram and ROC. (A) GO enrichment analysis of intersecting 85 genes. (B) KEGG pathway analysis of intersecting 85 genes. (C) The top 10 genes of the interaction network were obtained by degree algorithm. (D) Nomogram model of four hub genes. (E) Calibration verifies the reliability of nomogram model. (F) ROC curves to assess the predictable efficacy of each hub gene.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/c252543e00821b2b8b0fa80f.jpg"},{"id":53885357,"identity":"912b424e-6ce4-4617-93c6-282f0574beeb","added_by":"auto","created_at":"2024-04-01 19:10:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2269287,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelated, survival and immune cells infiltration analysis of M2-like TAMs-related hub genes in BRCA. (A) Correlation plot between four hub genes and M2-like macrophages. (B) Overall survival of TCGA-BRCA patients with high- and low-\u003cem\u003eFOXA1\u003c/em\u003eexpression measured by Kaplan-Meier survival analysis. (C) Overall survival of TCGA-BRCA patients with high- and low-\u003cem\u003eAGR2\u003c/em\u003e expression measured by Kapla-Meier survival analysis. (D) Expression distributions of four hub genes between normal and breast tumor tissues. Red represents the tumor samples, blue represents the normal samples. *P\u0026lt;0.05, **P\u0026lt;0.01 and ***P\u0026lt;0.001. (E) The expression of \u003cem\u003eFOXA1\u003c/em\u003e correlated with immune cells infiltration. (F) The expression of \u003cem\u003eERBB3\u003c/em\u003e correlated with immune cells infiltration. (G) The expression of \u003cem\u003eMUC1\u003c/em\u003e correlated with immune cells infiltration. (H)\u003cem\u003e \u003c/em\u003eThe expression of \u003cem\u003eAGR2\u003c/em\u003e correlated with immune cells infiltration.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/6ef5a2311aa94c53abc4ab68.jpg"},{"id":53885356,"identity":"5efdd7f5-6efa-4101-b8d3-c193632e42ae","added_by":"auto","created_at":"2024-04-01 19:10:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2943113,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer analysis. (A-D) The high expressing levels of hub genes in most kinds of tumor tissues. (E-H) The age of patients over 65 years old correlated with the expression of four hub genes\u003cem\u003e \u003c/em\u003efrom the clinical analysis in pan-carcinoma.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/5ae19df06982df00b0367ce7.jpg"},{"id":53885354,"identity":"78ab3dd6-5023-4690-9b63-6d93b0bc17a0","added_by":"auto","created_at":"2024-04-01 19:10:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":798355,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis of \u003cem\u003eFOXA1\u003c/em\u003e. (A) Scatter plot showing the causal effect of \u003cem\u003eFOXA1\u003c/em\u003e on the risk of breast cancer. (B) Forest plot showing the causal effect of each SNP on the risk of breast cancer. (C) Funnel plots to visualize overall heterogeneity of MR estimates for the effect of \u003cem\u003eFOXA1\u003c/em\u003e on breast cancer. (D) Leave-one-out plot to visualize causal effect of \u003cem\u003eFOXA1\u003c/em\u003e on breast cancer risk when leaving one SNP out.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/2d126594cfccad0c7a3f3c4c.jpg"},{"id":53885359,"identity":"5e3bad73-a09d-4c8c-948b-40a1e9fdd2ee","added_by":"auto","created_at":"2024-04-01 19:10:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":743757,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation analysis between mRNA expression of hub genes and drug IC\u003csub\u003e50\u003c/sub\u003e. (A) \u003cem\u003eFOXA1\u003c/em\u003e (B) \u003cem\u003eERBB3\u003c/em\u003e (C)\u003cem\u003e MUC1 \u003c/em\u003e(D)\u003cem\u003e AGR2 \u003c/em\u003e(Green indicates that the IC\u003csub\u003e50\u003c/sub\u003e of drug was negatively correlated with gene expression, and red indicates that the IC\u003csub\u003e50\u003c/sub\u003e of drug was positively correlated with gene expression.)\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/6cdaf88d0dedb09ab679661e.jpg"},{"id":53952379,"identity":"ee67c7ee-13e8-470d-a5dd-186db8cd2a8c","added_by":"auto","created_at":"2024-04-02 16:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1045414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4166156/v1/1ab2fd32-cf48-428d-b868-acf90d3ada73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association and functional investigation of M2-like tumor-associated macrophages identified hub genes for breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is one of the most common malignant tumors in the world, and it is seriously endangered the life and health in women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the improvement of health awareness and early diagnosis technology are important to ameliorate the prognosis of patients, the five-year survival rate for metastatic breast cancer is less than 30% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to the hidden onset, recurrence and metastasis, poor prognosis and lack of effective early diagnostic markers, all of these have brought great challenges to the diagnosis and treatment of breast cancer. Therefore, there is an urgent need to find new molecular biomarkers and therapeutic targets for breast cancer.\u003c/p\u003e \u003cp\u003eBone marrow-derived cells penetrate tumor tissues or aggregate in the solid tumor microenvironment (TME) called tumor-associated macrophages (TAMs) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As an important component of the TME, TAMs affect tumor growth, agiogenesis, metastasis, and chemotherapy resistance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Influenced by cytokines, macrophages differentiate into M1-like and M2-like phenotypes. M1-like macrophages are generally considered to be tumor-killing macrophages, primarily act as the role of anti-tumor and immune-promotion. However, M2-like macrophages are immunosuppressive, promoting tumor progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, it is necessary to describe the molecular characteristics combined with the M2-like macrophage, and to determine the key regulatory factors of M2-like TAMs polarization. With the progression of tumor, M1-phenotype is gradually polarized to M2-phenotype, and the increasing number of M2-like TAMs also indicates poor prognosis, including breast cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe compared whether there were any differences in the survival of M1-like or M2-like macrophages in various tumors, and found that significant difference between M1-like and M2-like macrophages in BRCA, and BRCA patients in the low-content M2 and high-content M1 macrophage groups had longer survival. Based on the significant differences between M1 and M2 macrophages in survival, we conducted a series of studies to discover biomarkers closely related to BRCA.\u003c/p\u003e \u003cp\u003eTwo-sample Mendelian randomization (MR) is a reliable technological means that has been widely used in recent years, which uses genetic variants strongly associated with exposure factors as instrumental variables for deducing a causal relationship between exposure factors (etiology) and study outcomes (diseases). Typically, people utilize single nucleotide polymorphisms (SNPs) as instrumental variables to assess the causal relationship between exposure factors and outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. We investigated the correlation between M2-like TAM-related hub genes and the risk of breast cancer by genome-wide association analysis (GWAS) and Mendelian randomization (MR) analysis.\u003c/p\u003e \u003cp\u003eIn this study, we firstly identified differentially expressed genes (DEGs) between normal and breast tumor tissues from TCGA-BRCA and GSE42568, respectively, and pivotal modular genes most positively related to M2-like macrophage were obtained by weighted gene co-expression network analysis (WGCNA). Next, M2-like TAM-related genes were identified by integrative differential analysis and WGCNA. Then, we identified \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e and \u003cem\u003eAGR2\u003c/em\u003e as hub markers, which may contribute to BRCA and further mining of the underlying mechanisms and clinical significance of BRCA-associated risk genes. Finally, the signature of four hub genes in breast tumor and the causal relationship between four hub genes and BRCA was explored through pan-cancer and MR studies. Meanwhile, the sensitivity and resistance to IC\u003csub\u003e50\u003c/sub\u003e of drugs targeting hub genes was evaluated by the Genomics of Drug Sensitivity in Cancer (GDSC) database.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eSource data\u003c/p\u003e \u003cp\u003eA total of 1,226 cases with RNA-seq transcriptome profiling (HTSeq-FPKM) and clinical information from TCGA-BRCA were obtained from the Genomic Data Commons (GDC) TCGA data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Two GEO cohorts (GSE42568, GSE58812) were downloaded from the Gene Expression Omnibus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Patients without survival information and RNA sequencing data were excluded from the analysis. The FPKM values were transformed to TPM (transcripts per million) values for subsequent analyses [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMacrophages related survival analyses\u003c/p\u003e \u003cp\u003eThe relative content of macrophages in each sample of TCGA-BRCA or GSE58812 was calculated on CIBERSORT algorithm [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Kaplan‑Meier survival analysis was generated by the \u0026lsquo;survival\u0026rsquo; R packages to determine whether survival of BRCA patients was correlated with M1 or M2 macrophage. The \"surv_cutpoint\" function of the \"survminer\" package was explored to calculate the optimal cut-off value of the high- and low-content M1 or M2 macrophages group, and to investigate whether there were significant differences between M1-like and M2 macrophages of BRCA.\u003c/p\u003e \u003cp\u003eDEGs identification\u003c/p\u003e \u003cp\u003eFirstly, we read the data of TCGA-BRCA (including 1113 tumor tissues and 113 normal tissue samples) and GSE42568 (including 104 tumor tissues and 17 normal tissue samples) using R software (version 4.3.1) and preprocessed it for normalized batch correction. After that, we utilized the \"limma\" R package for DEGs analysis. After the significance analysis of gene expressing levels, the \"ggplot2\" and \"pheatmap\" R packages were used for processing to generate up-regulated and down-regulated DEGs.\u003c/p\u003e \u003cp\u003eAcquisition pivotal genes associated with M2-like TAMs\u003c/p\u003e \u003cp\u003eWGCNA can be conducted to find highly correlated genes in modules associated to diseases, and it can identify candidate biomarkers or therapeutic targets, which has been successfully applied in various biological fields, such as cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. After grouping BRCA samples according to differences between M1-like or M2-like macrophages, we analyzed TCGA-BRCA expressing data using \u0026ldquo;WGCNA\u0026rdquo; R package to obtain the genes most associated with M2-like TAMs. Then, the obtained modular genes related to M2-like TAMs were intersected with the up-regulated DEGs acquired from differential analysis to filter M2-like TAMs-related genes. These intersecting genes are considered as candidate pivotal genes relevant for pathogenesis of BRCA. Kyoto Encyclopedia of Genes and Genomes (KEGG) serves as a database for systematic analysis of gene metabolic pathways and functions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We performed functional enrichment analysis to help us understand the potential progression and pathogenesis of BRCA by KEGG pathway and gene ontology (GO) enrichment analyses.\u003c/p\u003e \u003cp\u003eScreening of hub genes in protein-protein interaction (PPI) network\u003c/p\u003e \u003cp\u003eWe used STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict and visualize PPI networks with a 0.400 medium confidence required minimum interaction score. The important genes were sequenced using \"CytoNCA\" toolkit and degree algorithm of Cytoscape software. The top genes were displayed, and the hub genes with the highest BRCA correlation were selected for subsequent analysis.\u003c/p\u003e \u003cp\u003eNomogram construction\u003c/p\u003e \u003cp\u003eWe used a nomogram scoring model to predict the risk of BRCA using the \u0026ldquo;rms\u0026rdquo; and \u0026ldquo;rmda\u0026rdquo; packages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The risk of disease is predicted based on the risk score of hub genes. And then, we used the \"ROC\" package to construct the receiver operator characteristic (ROC) curve to verify the prophetic validity of hub genes. The area under ROC curve (AUC) was utilized to represent its accuracy. If the AUC range was 0.8\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;1, it indicated that the gene had excellent prediction accuracy.\u003c/p\u003e \u003cp\u003eCorrelation, GSEA and pan-cancer analysis\u003c/p\u003e \u003cp\u003eThe correlation between the expression of hub genes and immune cells infiltration was investigated by CIBERSORT algorithm. At the same time, we performed Gene Set Enrichment Analysis (GSEA) to evaluate which functional pathways were associated with hub genes. Finally, we did pan-cancer analysis to explore whether these M2-like TAMs-associated hub genes are more highly expressed in BRCA than in other tumors.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) study\u003c/p\u003e \u003cp\u003eFrom the genome wide association study (GWAS) data of hub genes, we extracted SNPs that were strongly associated with hub genes at the significant level of the whole genome (P-value\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) to ensure the independence of instrumental variables and exclude the influence of linkage disequilibrium (LD) [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. At the same time, the genetic distance was set to 10,000 kb and the LD parameter (r\u003csup\u003e2\u003c/sup\u003e) threshold to 0.01. A large amount GWAS data of breast cancer was used here, including 89,677 individuals combined the 42,892 control and 46,785 case datasets from European. MR analysis was performed based on the \u0026ldquo;TwoSampleMR\u0026rdquo; package, and MR-Egger regression, Weighted median, fixed effects inverse variance weighting (IVW ), Simple mode and Weighted mode were used to assess the relationship between the levels of hub genes and the risk of breast cancer. MR\u0026ndash;Egger was used for additional sensitivity analysis. The intercept terms in causal estimation were considered, and the MR-Egger regression was used to explain uncorrelated pleiotropy of SNPs as instrumental variables. In addition, the IVW method and MR-Egger regression were utilized to detect heterogeneity in individual genetic variation estimates.\u003c/p\u003e \u003cp\u003eDrug Sensitivity Analysis\u003c/p\u003e \u003cp\u003eGenomics of Drug Sensitivity in Cancer (GDSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database contains more than 1,000 human cancer cell line, 265 drug response data, and over 17,000 genomic markers. We collected the corresponding mRNA expression levels of hub genes and IC\u003csub\u003e50\u003c/sub\u003e of 265 drugs in 51 cell lines associated with BRCA, and mRNA expression data and drug susceptibility data were combined. With P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.03, Pearson correlation analysis was used to determine the correlation between mRNA expression of hub genes and drug IC\u003csub\u003e50\u003c/sub\u003e, and to investigate whether the high expression of hub genes had sensitization or drug resistance on the therapeutic effect.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSurvival analysis differences between groups were discussed using Kaplan-Meier curves and log-rank tests. Correlation coefficients were calculated by Pearson correlation analyses. The statistical analysis was performed using R software 4.3.1 and values represent the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eScreening M2-like TAMs-related genes\u003c/p\u003e \u003cp\u003eTo elucidate the relationship between macrophages and prognosis of BRCA, the contents of M1 and M2 macrophages in TCGA-BRCA samples were calculated by CIBERSORT algorithm. Next, BRCA patients were divided into high-content and low-content M1 or M2 macrophages group. Kaplan-Meier survival analyses showed some difference between high-content and low-content M1 macrophage groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, P-value\u0026thinsp;=\u0026thinsp;0.032). Furthermore, significant difference in survival has shown between high-content and low-content M2 macrophages group, and the patients in the high-content M2 macrophages had worse prognosis than that of the low-content M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result indicated that the effects of M1 and M2 macrophages on prognosis were quite opposite, and M2 macrophages had a more pivotal role in BRCA than M1 macrophages. GSE58812 datasets confirmed the above result (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). Based on this observation, WGCNA was performed to identify the key module related to M2 macrophages. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE demonstrates that the green module was positively and negatively correlated with M2 macrophages and M1 macrophages, respectively. And 3,127 genes in the green module were selected for downstream analyses. Then, 2,098 DEGs (935 up-regulated and 1,163 down-regulated genes) were screened out from the breast cancer tissues compared to the normal tissues in GSE42568. Meanwhile, 2,753 DEGs (1,078 up-regulated and 1,675 down-regulated genes) were identified from TCGA-BRCA. Lastly, we searched for co-expressed genes between WGCNA-derived M2-like TAMs-related genes and up-regulated DEGs associated with BRCA, and eventually screened out 85 overlapping genes, which may play an important role in the development and progression of breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGo enrichment and KEGG pathway and PPI network analysis\u003c/p\u003e \u003cp\u003eGO and KEGG analyses were conducted to further explore the potential roles of intersecting hub genes. GO analysis showed that the overlapping 85 genes mainly affect the cellular component of bicellular tight junction, tight junction and apical junction complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). KEGG pathway analysis showed that these 85 genes mainly affect tight junction, Rap1 signaling pathway and PI3K-Akt signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). It is well known that the Rap1 signaling pathway and PI3K-Akt signaling pathway are closely associated with proliferation, invasion and metastasis of cancer, while the relationship between tight junction and tumor has been rarely reported. Breast cancer is a complex and heterogeneous disease, and proper adhesion between adjacent epithelial cells of the breast is essential for the normal structure and function of the epithelial tissue. There is growing evidence that dysregulation of cell-cell adhesion is associated with many cancers. There is a study that suggests that tight junction may be involved in the development or progression of breast cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The PPI network of candidate hub genes was constructed by using the STRING online tool. Subsequently, the top 10 up-regulated genes in breast cancer were then visually analyzed by using \"CytoNCA\" toolkit and degree algorithm. Briefly, \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, \u003cem\u003eTFF1\u003c/em\u003e, \u003cem\u003eEZR\u003c/em\u003e, \u003cem\u003eGATA3\u003c/em\u003e, \u003cem\u003eFGFR3\u003c/em\u003e, \u003cem\u003eSPDEF\u003c/em\u003e and \u003cem\u003eAGR3\u003c/em\u003e were sorted out (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The deeper the red color, the higher the relevance score, and the more significant the importance in the BRCA. It is clear that \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e are the most important genes.\u003c/p\u003e \u003cp\u003eConstruction of nomogram and ROC diagnostic curve\u003c/p\u003e \u003cp\u003eWe constructed a nomogram model to predict the risk of BRCA. Therefore, our nomogram performs well in BRCA prediction, the higher the scores, the higher the risk of disease. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, the patient has 100 total points of these four hub genes, the risk of breast cancer is up to more than 90%. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE verifies the reliability of nomogram. Subsequently, we calculated ROC curves for the four hub genes (\u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e) to assess reliability of prediction, and the AUC values of \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e and \u003cem\u003eAGR2\u003c/em\u003e are 0.877, 0.907, 0.930 and 0.865, respectively. It demonstrated that these four hub genes could differentiate breast cancer from normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional analysis of M2-like TAMs-related hub genes\u003c/p\u003e \u003cp\u003eAccording to the correlation analysis between four hub genes and M2-like macrophages, three of the four hub genes were closely related with M2-like macrophages. Compared with other genes, \u003cem\u003eFOXA1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e were more closely associated with M2-like macrophages, among which \u003cem\u003eFOXA1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e had the highest correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In TCGA datasets, high \u003cem\u003eFOXA1\u003c/em\u003e expression correlated with shorter overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). But the high expression of \u003cem\u003eAGR2\u003c/em\u003e group showed a better survival rate than the low expression of \u003cem\u003eAGR2\u003c/em\u003e group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The expressed differences of four hub genes between normal tissues and breast tumor tissues were analyzed based on TCGA-BRCA, and the four hub genes were all highly expressed in breast tumor tissues compared with normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). GSEA analysis showed that \u003cem\u003eFOXA1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e were mainly related to tumor-related pathways, such as complement and coagulation cascades [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], colorectal cancer, mtor signaling pathway and prostate cancer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Complement and coagulation cascade are important components of the human immune system. Platelets in TME can promote tumor cell invasion and metastasis by activating the coagulation cascade. And then, the relationship between the expression levels of four hub genes and the tumor immune microenvironment was analyzed. CIBERSORT algorithm analysis showed that four hub genes were positively correlated with the infiltration of immune cells such as M2 macrophages, resting Mast cells, resting CD\u003csup\u003e4+\u003c/sup\u003e T cells, monocytes and native B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-H). In addition, \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e had a higher positive correlation with M2 macrophages, which was consistent with the correlation analysis results in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. The expression of these four hub genes was positively correlated with M2-like macrophages infiltration in BRCA, but it was completely opposite to M1-like macrophages infiltration in BRCA.\u003c/p\u003e \u003cp\u003e \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\u003eGene set enrichment analysis (GSEA) of four hub genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eKEGG Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnrichment Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eFOXA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOMPLEMENT_AND_COAGULATION_CASCADES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNUCLEOTIDE_EXCISION_REPAIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROTEIN_EXPORT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eAGR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOMPLEMENT_AND_COAGULATION_CASCADES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRIMARY_IMMUNODEFICIENCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.60826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPLICEOSOME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROSTATE_CANCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTOR_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOLORECTAL_CANCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA_DEGRADATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eMUC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFATTY_ACID_METABOLISM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCIRCADIAN_RHYTHM_MAMMAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGALACTOSE_METABOLISM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn pan-cancer analysis, four hub genes were found to be highly expressed in most kinds of tumor tissues, and \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e had higher expression in breast tumors than other tumors particularly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). Moreover, it was found that the age of patients over 65 years old were positively associated with the expression level of four hub genes from the clinical analysis of BRCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHub genes causally associated with the risk of BRCA\u003c/p\u003e \u003cp\u003eWe assessed the causal association between four hub genes and breast cancer. The SNPs characteristics of four hub genes were strong instrumental variables (P value\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Using the fixed effects IVW method, we found that four hub genes were all associated with the risk of breast cancer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). β-value is greater than zero indicates that exposure factor is a risk factor, and the risk of morbidity increases with the increase of exposure factor. Except for \u003cem\u003eAGR2\u003c/em\u003e, the expressions of \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e and \u003cem\u003eMUC1\u003c/em\u003e were positively correlated with the occurrence of breast tumors (β-value\u0026thinsp;\u0026gt;\u0026thinsp;0), this result was consistent with the prognostic analysis of \u003cem\u003eAGR2\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Obvious causal association between \u003cem\u003eFOXA1\u003c/em\u003e and breast cancer was observed in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. The combined effect value of all SNPs is greater than zero, indicating that \u003cem\u003eFOXA1\u003c/em\u003e is a risk factor, and the risk of breast cancer increases with the increase of \u003cem\u003eFOXA1\u003c/em\u003e expression (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The weighted median and weighted mode method also confirmed the result of fixed effects IVW method. The causal effect of \u003cem\u003eFOXA1\u003c/em\u003e funnel plot was roughly symmetrical along the symmetry axis (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and no horizontal pleiotropy is observed at the intercept of MR Egger regression, which further indicates that pleiotropy does not bias the causal effect. In the leave-one-out plot, the MR analysis was performed again on the remaining SNPs after removing each SNP, the comprehensive effect value was consistent with the main effect value, indicating that the calculation results of all SNPs made causality significant (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of mendelian randomization (MR) analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003enSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eFOXA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eprot-a-2611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBreast cancer id:ebi-a-GCST007236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.54E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.44E-245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.18E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eMUC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eprot-a-1967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBreast cancer id:ebi-a-GCST007236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.6470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25E-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.72E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eAGR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eprot-a-58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBreast cancer id:ebi-a-GCST007236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.3710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.6746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.32E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.4915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.48E-53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.6350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.6215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eprot-c-2617_56_35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBreast cancer id:ebi-a-GCST007236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.55E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.93E-78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDrug Sensitivity Analysis\u003c/p\u003e \u003cp\u003eTo investigate the sensitization or resistance of four hub genes to drugs during chemotherapy, we integrated drug IC\u003csub\u003e50\u003c/sub\u003e and gene expression profile data from GDSC BRCA cell lines (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Through this method, the drugs with the highest degree of correlation with hub genes can be screened out. The results showed that the IC\u003csub\u003e50\u003c/sub\u003e of A-443654, Lapatinib, KIN001-102, CP724714 and ZSTK474 were negatively correlated with \u003cem\u003eFOXA1\u003c/em\u003e expression, and the IC\u003csub\u003e50\u003c/sub\u003e of Pyrimethamine and BX-795 were positively correlated with \u003cem\u003eFOXA1\u003c/em\u003e expression according by Pearson\u0026rsquo;s correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The IC\u003csub\u003e50\u003c/sub\u003e of Lapatinib, A-770041, Sorafenib, Roscovitine, A-443654, MS-275 and Paclitaxel was negatively correlated with the expression of \u003cem\u003eERBB3\u003c/em\u003e, indicating that high \u003cem\u003eERBB3\u003c/em\u003e expression has the sensitizing effect on these drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The IC\u003csub\u003e50\u003c/sub\u003e of Saracatinib, Imatinib, BMS-509744 and Lapatinib was negatively correlated with \u003cem\u003eMUC1\u003c/em\u003e expression, and Olaparib, YK 4-279 was positively correlated with \u003cem\u003eMUC1\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Interestingly, the expression of \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e and \u003cem\u003eMUC1\u003c/em\u003e all increased the sensitivity of Lapatinib to BRCA patients. In 2007, FDA approved Lapatinib as a common treatment for HER2-positive advanced breast cancer. Whether the combined expression of \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e and \u003cem\u003eMUC1\u003c/em\u003e could be considered as a potential opportunity for the using of Lapatinb in clinical breast cancer patients. Moreover, the expression of \u003cem\u003eAGR2\u003c/em\u003e is closely related to the resistance of TAE684, XMD8-85, AZ628 and Pyrimethamine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In other words, the expression of \u003cem\u003eAGR2\u003c/em\u003e is more likely to lead to drug resistance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of drug sensitivity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePearson Correlation Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cem\u003eFOXA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA-443654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLapatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIN001-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP724714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZSTK474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyrimethamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBX-795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLapatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA-770041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoscovitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA-443654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS-275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cem\u003eMUC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaracatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMS-509744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLapatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlaparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYK 4-279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eAGR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAE684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXMD8-85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIN001-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP724714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAZ628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePyrimethamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZSTK474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetastatic breast cancer is less effective in immunotherapy than corresponding primary tumors, but some immune-oncology targets, macrophages, and angiogenesis characteristics show preserved expression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Stage IV (metastatic) breast cancer is treatable, but not curable, the goals of treatment include extending the patient's life span and improving quality of life [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Immunotherapy is a potentially effective way to control tumor growth in patients with stage IV breast cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although encouraging immunotherapy results have been reported in some clinical trials, there are still some obstacles that need to be resolved. Therefore, personalized immunotherapy is considered as a potential complementary therapy for breast cancer in combination with chemotherapy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Considering that existing studies have proved the importance of TAMs in the immunotherapy of BRCA [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the reliability of M2-like TAMs as biomarkers for BRCA and in predicting tumorigenesis was evaluated based on the significant difference between M1-like and M2-like macrophages in the prognosis of patients with breast cancer.\u003c/p\u003e \u003cp\u003eTAMs are one of the most abundant immune cell populations in the TME [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. M2-like TAMs mainly are associated not only with poor prognosis in various tumors, but also with the generation of immunosuppressive [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. M2-like macrophages release anti-inflammatory cytokines, growth factors and support angiogenesis, which results in tumor cell proliferation [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, TAMs can be used as a breakthrough in cancer treatment and it is urgent to find TAMs-specific markers for cancer. A TAMs-related gene signature in breast cancer was constructed, which highly enriched in aggressive breast cancer subtypes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The M2 macrophages-related genes prognostic model was established in pancreatic ductal adenocarcinoma [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, few studies have explored M2-like TAMs-related hub genes in breast cancer.\u003c/p\u003e \u003cp\u003eWith the aim of identifying a potential biomarker for predicting prognosis in BRCA, we took advantage of WGCNA and differential analysis to obtain M2-like TAMs-related hub genes. Firstly, we validated that the high-content of M2-like TAMs are significantly associated with poorer prognosis in TCGA-BRCA and GSE58812 cohorts. The survival rate of patients with high-content M2-like TAMs was significantly worse than that of patients with low-content M2-like TAMs, indicating that M2-like TAMs are closely related to the poor prognosis of BRCA patients. Then, 85 M2-like TAMs-related genes were obtained by intersecting M2-like macrophages modular genes and DEGs distinguished from BRCA. Next, four hub genes of breast cancer, including \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e and \u003cem\u003eMUC1\u003c/em\u003e were sorted out by STRING and Cytoscape platform. Consequently, the nomogram model that integrates four hub genes performed well in the prediction of breast cancer, and the ROC curves confirmed that the four hub genes can successfully differentiate breast cancer from normal tissues.\u003c/p\u003e \u003cp\u003eThese four hub genes were indeed more highly expressed in tumor than in normal tissues, and the high \u003cem\u003eFOXA1\u003c/em\u003e expression was significantly associated with poor prognosis, and GSEA analysis demonstrated that \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e were closely related to tumor-related pathways and functions. In pan-cancer analysis, four hub genes were highly expressed in the age of breast cancer patients over 65 years old. A study of 92 women over 65 years old was reported, of whom 77 women had malignant breast disease (83.6%) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It has been reported that mutation in \u003cem\u003eFOXA1\u003c/em\u003e is a hallmark of estrogen receptor-positive (ER+) breast cancer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Overexpression of \u003cem\u003eFOXA1\u003c/em\u003e may be a prognostic factor for treatment resistance. Moreover, it may be a viable target for immune and chemotherapy sensitization of estrogen receptor-positive luminal breast cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Meanwhile, the positive correlation between \u003cem\u003eFOXA1\u003c/em\u003e expressed levels and M2 macrophages content further explains why patients with higher \u003cem\u003eFOXA1\u003c/em\u003e levels have poorer prognosis. A study has shown that \u003cem\u003eMUC1\u003c/em\u003e can up-regulate M2 macrophage infiltration, and \u003cem\u003eMUC1\u003c/em\u003e cytoplasm domain plays an important role in promoting postpartum mammary tumor, which providing a new strategy for the prevention and treatment of postpartum breast cancer [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. And then, we explored the causal relation between the expressing levels of four hub genes (\u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e) and breast cancer risk by a two-sample MR analysis based on a large scale of GWAS data of exposure factor and outcome. This MR study suggested that \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e might be causally associated with the increased risk of breast cancer. MR can reduce the systematic bias caused by traditional observational studies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To ensure that SNPs were not related to any confounding factors between four hub genes and breast cancer, we only selected participants from the European populations. In order to ensure the stability of the results, MR-Egger adds the intercept term was conducted, and no evidence of directed level pleiotropy was observed [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Lastly, we explored whether the expression of hub genes enhance or resist the therapeutic effect of drugs by mining the relationship between drug IC\u003csub\u003e50\u003c/sub\u003e and hub genes expression. The results showed that the expressions of \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e all sensitized the efficacy of Lapatinib to patients of BRCA. Lapatinib is an inhibitor of human epidermal growth factor receptor-2 (HER-2) tyrosine kinase and a molecularly targeted new drug for breast cancer, it is often used in combination with capecitabine to treat advanced or metastatic breast cancer. We hope that the analysis results will play an auxiliary role in the clinical application of Lapatinib.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study identified M2-like TAMs-related hub genes for predicting prognosis in breast cancer. We hope that the combined expression of \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e and \u003cem\u003eMUC1\u003c/em\u003e could be as a potential means for the using of Lapatinb for breast cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGY was the major contributor in writing the main manuscript text and analyzing the data. YT, HX and QP prepared all tables, figures and graphs. BS was responsible for review and editing. All authors have read and agreed to the published version of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\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\n\u003cp\u003e\u003cstrong\u003eConflict of interes\u0026nbsp;\u003c/strong\u003eALL authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e Our raw data could be available from open databases. No human/animal studies were involved. Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge patients and researchers involved in TCGA, GEO, and GDSC for their data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-249. https://doi.org/10.3322/caac.21660\u003c/li\u003e\n\u003cli\u003eHudson BI, Lippman ME (2023) Comment on \u0026quot;The lingering mysteries of metastatic recurrence in breast cancer\u0026quot;. Br J Cancer 128:484-485. https://doi.org/10.1038/s41416-022-02012-0\u003c/li\u003e\n\u003cli\u003ePathria P, Louis TL, Varner JA (2019) Targeting Tumor-Associated Macrophages in Cancer. 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Elife 7:e34408. https://doi.org/10.7554/eLife.34408\u003c/li\u003e\n\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 invasive carcinoma (BRCA), M2-like tumor-associated macrophages (M2-like TAMs), hub genes, mendelian randomization (MR), Drug sensitivity analysis","lastPublishedDoi":"10.21203/rs.3.rs-4166156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4166156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eM2-like tumor-associated macrophages (M2-like TAMs) have great potential in promoting oncogenesis and provide the potential biomarkers for diagnosis and treatment of tumor. However, the role of M2-like TAMs in breast invasive carcinoma (BRCA) is still unclear. Based on The Cancer Genome Atlas of America (TCGA) and the Gene Expression Omnibus (GEO) databases, we compared multiple tumors and found the diametrically opposite survival of M1-like and M2-like macrophages in BRCA. And then, we systematically explored the function of M2-like TAMs in BRCA using differentially expressed analysis, weighted gene co-expression network analysis (WGCNA), GO and KEGG analysis, Nomogram, Gene Set Enrichment Analysis (GSEA), CIBERSORT algorithm, pan-cancer and mendelian randomization study. We evaluated the sensitivity and resistance to drugs targeting hub genes using the Genomics of Drug Sensitivity in Cancer (GDSC) database. A total of 85 M2-like TAM-related genes were screened out and the results of functional enrichment analysis were correlated with tight junction, Rap1 signaling pathway and PI3K-Akt signaling pathway. \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e, \u003cem\u003eAGR2\u003c/em\u003e were identified as hub genes by protein interaction (PPI) network, \"CytoNCA\" toolkit and degree algorithm. Additionally, nomogram and ROC curve indicated great prognostic performance, and the high expressing four hub genes were positively correlated with M2-like macrophages. \u003cem\u003eFOXA1\u003c/em\u003e and \u003cem\u003eERBB3\u003c/em\u003e expressed at higher levels in BRCA than in other tumors by pan-cancer analysis. In fixed effected inverse variance weighting, we found that \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e were positively associated with BRCA risk. Finally, highly \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eERBB3\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e expressing patients were more sensitive to Lapatinib through drug sensitivity analysis. Our studies contribute to understand the M2-like TAM-related mechanisms involved in breast cancer, which provide further insights into drug sensitivity therapy.\u003c/p\u003e","manuscriptTitle":"Genome-wide association and functional investigation of M2-like tumor-associated macrophages identified hub genes for breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 19:10:17","doi":"10.21203/rs.3.rs-4166156/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":"de23e340-6580-427e-9407-de31a464a746","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-02T16:03:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 19:10:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4166156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4166156","identity":"rs-4166156","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}