Investigating the Expression Pattern, Prognostic and Immunological Significance of the WNT family in Breast Cancer

Investigating the Expression Pattern, Prognostic and Immunological Significance of the WNT family in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigating the Expression Pattern, Prognostic and Immunological Significance of the WNT family in Breast Cancer Fatema Tuj Johora Fariha, Muntasim Fuad, Chandra Shekhar Saha, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6001541/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Breast cancer (BRCA) is one of the most diagnosed cancers and the leading cause of cancer-related deaths among women globally. Previous studies have shown that the WNT (wingless type) family plays a role in the development of various cancers. However, comprehensive analysis of WNTs in BRCA remains largely unexplored. In this extensive study, we examined the expression patterns, clinical relevance, and survival outcomes associated with the WNT family and identified the key prognostic WNTs. We further investigated genetic alterations, DNA methylation, and drug sensitivity using the cBioPortal, SMART, and GSCA databases. Data from GEO and DepMap were used for validation. Our findings revealed that WNT2 and WNT7B were significantly upregulated, while WNT11 was downregulated, which affected the overall survival of patients with BRCA. Amplification was the most common type of alteration among the key WNTs selected for analysis, showing a significant correlation with immune cells and immune therapy-related genes. Enrichment analysis revealed the involvement of WNTs in crucial pathways responsible for cancer. Additionally, WNTs and their co-expressed genes were strongly associated with the efficacy of anticancer drugs. This study highlights the dysregulation of WNTs in BRCA progression and their correlation with patient survival, suggesting a potential immunotherapeutic target and a valuable prognostic biomarker for BRCA management and treatment. Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Biological sciences/Immunology Biological sciences/Systems biology Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Oncology WNTs breast cancer prognostic biomarkers expression patterns immunologic cancer treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Breast cancer is one of the most prevalent malignancies and the primary cause of cancer-related mortality in women globally 1,2 . In 2022, approximately 2.3 million new cases (11.6% of all cancer cases) and 666,000 deaths (6.9% of all cancer deaths) were documented in women across 157 countries for incidence and 112 countries for mortality, projected to rise to nearly 3 million by 2040 3,4 . The incidence of breast cancer is expected to increase in East and South Asian countries, with age-standardized death rates increasing by 7.0–35% from 1990 to 2030 5 . The heterogeneous nature of breast cancer, characterized by diverse genetic, epigenetic, histopathological, and clinical features, as well as frequent resistance to various therapies, and the development of recurrence and metastasis, poses significant challenges in clinical management 6 . Therefore, understanding the molecular mechanisms underlying breast carcinogenesis is crucial to develop more effective and personalized treatment approaches. The human genome contains 19 WNT genes that encode highly conserved secreted glycoproteins 7 . These proteins are hydrophobic, notoriously insoluble, and rich in cysteine, with molecular weights ranging from 39 kDa to 46 kDa, and consist of 350–400 amino acids 8 . WNT proteins are secreted via the endoplasmic reticulum (ER) and Golgi apparatus and play diverse roles in various cellular and biological processes, including cell proliferation, differentiation, polarity, migration, apoptosis, survival, embryonic development, stem cell maintenance, and tissue homeostasis 9 . WNT signaling is categorized into two main pathways: (1) the canonical (β-catenin-dependent) pathway and (2) the non-canonical (β-catenin-independent) pathway 10 . In the canonical pathway, WNT binding stabilizes β-catenin, preventing its degradation and allowing its nuclear translocation, where it regulates the transcription of genes involved in cell proliferation and survival 11 . Non-canonical pathways, independent of β-catenin, are involved in cell movement and polarity. WNT signaling is initiated when WNT binds to Frizzled (Fz) receptors along with low-density lipoprotein (LDL) receptor-related proteins (LRP) on the cell surface 12 . This binding triggers a cascade involving various intracellular proteins, such as Dishevelled (Dsh), glycogen synthase kinase-3β (GSK-3β), Axin, Adenomatous Polyposis Coli (APC), and β-catenin 13 . Abnormal WNT activity frequently contributes to cancer progression, metastasis, and treatment resistance, particularly in colorectal, breast, and liver cancers 14,15 . Both genetic alterations and epigenetic modifications, such as promoter hypermethylation of WNT inhibitors in the WNT family, have been associated with human malignancies 16 . WNT signaling promotes cell division by activating transcription factors of the TCF/LEF family of target genes, including c-MYC and cyclin D1, which are essential regulators of cell cycle progression. Additionally, WNT signaling can also inhibit cell cycle arrest by modulating CDK inhibitors, such as p21 and p27 17 . When WNT signaling is dysregulated, often due to mutations in its components, such as APC, β-catenin can also suppress tumor suppressor pathways, leading to unchecked cell proliferation 18 . Moreover, WNT signaling interacts with several other pathways, including PI3K/AKT, Hippo, Notch, MAPK/ERK, and p53, which are implicated in tumorigenesis 19 . Beyond cancer, aberrant expression of WNTs has been linked to various diseases such as osteoporosis and degenerative disorders 20,21 . Thus, identifying abnormal WNT gene expression could serve as a biomarker for early tumor detection, and targeting WNT signaling may offer a novel and promising approach for cancer treatment. In this study, we evaluated the expression of WNT in clinical samples from BRCA patients using the UCSC XENA and GEPIA Web portal. Here, we report that the expression of key WNTs was significantly deregulated in BRCA. Next, we proceeded with prognostic significance, genetic alterations, epigenetic modifications, the ratio of immune cell infiltration and immune therapy-related genes, enrichment analysis, and the responsiveness of key WNTs to drugs (Table 1 ). This analysis demonstrated the potential molecular mechanism of WNTs in BRCA progression and highlighted their roles as prognostic biomarkers. These key WNTs may be used for early detection, targeted therapy, or personalized medicine in the treatment of patients with BRCA. Table 1 List of the web-tools, databases, software and R packages used in the study Web tools/Software/ R packages Data type Analysis type Database URL UCSC XENA Gene expression Heatmap showing the expression of genes among normal breast tissue, solid normal tissue surrounding the tumor, and primary tumor. TCGA GTEx https://xenabrowser.net/ GEPIA2 Gene expression BRCA vs. normal breast tissue analysis TCGA GTEx http://gepia2.cancer-pku.cn/ UALCAN Gene expression Gene expression based on clinicopathological characteristics of BRCA TCGA http://ualcan.path.uab.edu/index.html TIMER2.0 Gene expression levels across various pan-cancers Gene expression levels across different cancer types TCGA http://timer.cistrome.org/ Kaplan-Meier Plotter Gene expression & patient prognosis data Survival Analysis TCGA https://kmplot.com/analysis/ GEOquery (version 2.74.0) R package Expression profiling by array Retrieving data from NCBI GEO GEO https://seandavi.github.io/GEOquery/ DESeq2 (v1.46.0) R Package Expression profiling by array Differential gene expression analysis GEO https://bioconductor.org/packages/DESeq2 EnhancedVolcano R package (v1.24.0) Differential gene expression data Visualization of differential expression results GEO https://bioconductor.org/packages/EnhancedVolcano DepMap Cancer cell line analysis Cell lines with gene effect scores Achilles and Sanger's SCORE projects https://depmap.org/ TCGAplot R package (v8.0.0) Gene expression Gene-gene correlation analysis TCGA https://github.com/tjhwangxiong/TCGAplot ggplot2 R Package (v3.5.1) Breast cancer cell lines and gene effect score data Barplot visualizing cancer line analysis results DepMap https://ggplot2.tidyverse.org/ SMART Promoter DNA methylation DNA methylation Analysis TCGA http://www.bioinfo-zs.com/smartapp/ cBioPortal Genetic alteration Frequency of mutation, amplification, deep deletion and multiple alterations across various BRCA studies TCGA https://www.cbioportal.org/ AlphaFold Protein structure predictions Predicting protein 3D structures from amino acid sequences AlphaFold Protein Structure Database https://alphafold.com/ TCGAplot R package (v8.0.0) Immune infiltration correlation and immune-related genes correlation Gene expression Immune cell ratio, immune score, chemokines, chemokine receptors, immune checkpoint genes (ICGs), immune inhibitors, and immune stimulators correlation analysis TCGA https://github.com/tjhwangxiong/TCGAplot ggVennDiagram (v1.5.3) R package Positively correlated immune-related genes Venn diagram visualizing common Positively correlated immune-related genes Correlation analysis results https://gaospecial.github.io/ggVennDiagram/ STRING Protein-protein interaction Protein-protein interaction network visualization Proteins and their interactions https://string-db.org/ clusterProfiler (v4.14.4) R package Genomic data (gene expression, gene sets, pathways) Functional enrichment analysis KEGG, Reactome, GO https://yulab-smu.top/biomedical-knowledge-mining-book/ Gene Set Cancer Analysis (GSCA) Drug Sensitivity Correlation between drug sensitivity and mRNA expression GDSC, CTRP https://guolab.wchscu.cn/GSCA/#/drug ggpubr (v0.6.0) R Package Correlation between drug sensitivity and mRNA expression results Visualization of Drug Sensitivity Analysis GDSC, CTRP https://rpkgs.datanovia.com/ggpubr/ Methods Analysis of the expression patterns of WNTs in breast cancer We used UCSC XENA ( https://xenabrowser.net/ ), a web-based, high-performance, interaction visualization, exploration, and analysis tool for multi-omics cancer data, to analyze the expression patterns of WNTs in breast cancer 22 . We generated a heatmap comparing the expression of WNTs among normal breast tissue, solid normal tissue surrounding the tumor, and primary tumor by following these steps: A. Select a study to explore: "TCGA target GTEx" as study, B. Select Data Type: Genomic, input all 19 genes from WNT family in 'Add gene or Position' and selected "Gene Expression" under "Basic" as Dataset, C. Select Data Type: Phenotype, and selected "Basic" under "primary_site,” then we typed "breast" to select samples and used "Keep Samples" to filter breast as the only primary site. We retrieved Gene Symbol, Gene ID, log2(Fold Change), and adjp value using “Differential Genes” from GEPIA2 23 . We used the "Expression DIY" module of GEPIA2 to obtain box plots of gene expression in BRCA and normal breast tissues. WNTs expression based on clinicopathological characteristics of breast cancer UALCAN ( http://ualcan.path.uab.edu/ ) is a comprehensive, facilitative, interaction-based online resource for analyzing cancer omics data and tumor clinical information from TCGA 24 . Using the UALCAN platform, we examined the expression levels of WNT2, WNT7B, and WNT11 across diverse clinical parameters in breast cancer. The parameters included cancer stage, tumor subtype, patient age, and menopausal status. Pan-cancer View of WNTs The TIMER2.0 ( http://timer.cistrome.org/ ) database is an excellent resource for systematically analyzing the connections between gene expression and tumor characteristics in TCGA 25 . We used the TIMER database to assess WNT2, WNT7B, and WNT11 expression levels across various pan-cancers. We used the “Gene_DE” module to compare the expression profiles of these genes across different cancer types and contrasting tumor and normal tissue samples. Survival prognosis analysis across the BRCA cohort To evaluate the survival prognostic significance of WNT2, WNT7B, and WNT11 genes in BRCA, we employed the Kaplan-Meier Plotter ( https://kmplot.com/analysis/ ), which is mainly based on Affymetrix microarray information from the TCGA database 26 . This tool contains information on approximately 54,000 genes and survival data for 21 different types of cancers. We analyzed the impact of these genes on overall survival (OS) in patients with breast cancer. Validation of prognostic WNTs using GEO and correlation analysis We queried the NCBI GEO database using “breast tumor” as a keyword, selecting original experimental studies that profiled both tumor and normal breast tissues. Our inclusion criteria for the datasets used in our study were as follows: (i) the samples in the datasets were from “Homo sapiens”; (ii) the datasets were "expression profiling by array"; (iii) the dataset submission date to GEO was within the last 12 years (i.e., 2012–2024); (iv) for each dataset, the total number of available samples was ≥ 50; (v) the samples from the studies included both tumor and healthy controls; and (vi) both raw and processed data of the datasets were available. Furthermore, we excluded abstracts, case reports, review articles, studies using cell-line-based experimental designs, and studies lacking healthy controls or using non-human samples from our query. We then downloaded the data using the GEOquery (version 2.74.0) R package and performed differential gene expression analysis using the DESeq2 (v1.46.0) R package 27 . We utilized the EnhancedVolcano (version 1.24.0) R package to visualize the most significant DEGs, with specified thresholds of log2(fold change) (log2FC) greater than |2| and a P value cut-off of 10e-6 28 . The presence of key prognostic WNTs was verified based on the list of differentially expressed genes. Next, we utilized the TCGAplot (v8.0.0) R package to perform gene-gene correlation analysis of key prognostic WNTs across TCGA-BRCA tumors and corresponding normal tissues 29 . The complete workflow for this step, including all code and documentation is available on GitHub: https://github.com/bigbiolab/WNT_BRCA . Gene effect scores for key WNTs in BRCA Cell Lines We acquired breast cancer cell lines along with gene effect score data processed by the Chronos algorithm from UALCAN, which is derived from genome-wide CRISPR knockout screens published by Broad's Achilles and Sanger's SCORE projects in DepMap 24,30 . We then visualized the processed data using the ggplot2 (version 3.5.1) R package 31 . DNA methylation analysis of WNTs in BRCA patients The Shiny Methylation Analysis Resource Tool (SMART; http://www.bioinfo-zs.com/smartapp/ ) was used to generate the tumor vs. Normal Methylation Box Plot to visualize differential methylation patterns between tumor and normal samples 32 . The "Methylation Box Plot" option was selected from the "Methylation DIY" module, and the BRCA dataset was chosen. The methylation value was configured to a beta-value, and the aggregation method was set to the median. Investigation of genetic alteration within BRCA cohorts The genetic alteration status of WNT2, WNT7B, and WNT11 in patients with BRCA across various cancer cohorts was analyzed using cBioPortal for Cancer Genomics ( https://www.cbioportal.org/ ) 33 . This investigation focused on breast cancer across 30 studies available on cBioPortal, evaluating alteration frequency, mutation types, mutations, structural variants, amplifications, deep deletions, multiple alterations, and copy number alterations (CNA). The WNT2, WNT7B, and WNT11 alteration frequencies and mutation types in TCGA tumors were examined using the "Cancer Types Summary" module of cBioPortal. The "Mutations" module was employed to obtain a detailed overview of gene mutations. We obtained the 3D structure of the proteins from AlphaFold 34 . Immune filtration assessment We investigated the correlations between WNT2, WNT7B, and WNT11 expression, and immune cell ratio and immune score utilizing the TCGAplot (version 8.0.0) ( https://github.com/tjhwangxiong/TCGAplot ) R package and visualized them using heatmaps. 35 Additionally, we sorted out the common positively correlated chemokines, chemokine receptors, immune checkpoint genes (ICGs), immune inhibitors, and immune stimulators from the heatmaps generated by the TCGAplot R package and visualized them using the ggVennDiagram (v1.5.3) R package 36 . Single-Cell analysis We used the "Gene" module of Tumor Immune Single-cell Hub 2 (TISCH2) ( http://tisch.comp-genomics.org/ ) , a scRNA-seq database with detailed cell-type annotation at the single-cell level across different cancer types, to investigate the expression of WNTs in different cell-types within the tumor microenvironment (TME) for BRCA 37 . We set the following configurations: (i) Gene: "WNT2, WNT7B, and WNT11"; (ii) Cell-type annotation: "Celltype(major-lineage)"; (ii) Cancer type: "BRCA (Breast Invasive Carcinoma)"; (iv) Lineage for calculating correlation: "All lineage"; (v) selected datasets associated with "Homo sapiens" to be used. We downloaded the log (TPM/10 + 1) expression of genes in different cell types across datasets as "CSV" and visualized the data using the ggplot2 (version 3.5.3) R package. We used the ggVennDiagram (v1.5.4) R package to visualize the common cell types across the WNTs in a Venn diagram. PPIN construction and enrichment analysis The STRING database ( https://​string-​db.​org/ ) was utilized to acquire the protein-protein interaction (PPI) networks of WNT2, WNT7B, and WNT11 38 . We input WNT2, WNT7B, and WNT11 in the Multiple proteins and set the following configurations in the "Basic Settings" of the “Settings” module: (i) Network type: full STRING network; (ii) meaning of network edges: evidence; (iii) active interaction sources: Text mining, Experiments, Databases, CO-expression, Neighborhood, Gene Fusion, Co-occurrence; (iv) minimum required interaction score: low confidence (0.150), max number of interactors to show (1st shell): no more than 50 interactors. The results were exported and visualized using the Cytoscape (v 3.10.3) software 39 . Thereafter, we used the retrieved interacting genes from STRING to perform enrichment analysis, including GO (Gene Ontology) enrichment analysis of molecular functions (MFs), cellular components (CCs), biological processes (BPs), and Kyoto Encyclopedia of Genes and Genomes (KEGG) using clusterProfiler (v4.14.4) R package and Reactome enrichment analysis through ReactomePA (v1.50.0) 40,41 . The top 15 most significant (p < 0.05) pathways from each analysis were visualized using the enrichplot (v1.26.5) R package 42 . Drug sensitivity analysis We employed the "Drug" module of Gene Set Cancer Analysis (GSCA)( https://guolab.wchscu.cn/GSCA ) to perform drug sensitivity analysis 43 . We input genes acquired from the STRING database to obtain the correlation between drug sensitivity and mRNA expression using drug sensitivity information from the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP). Furthermore, we used the ggplot2 ((v3.5.1) and ggpubr (v0.6.0) R packages to visualize the correlation between WNT2, WNT7B, and WNT11 expression and drug sensitivity using GDSC and CTRP data exported from GSCA 44 . Statistical analysis In UCSC XENA, a gene expression heatmap was generated using log2-transformed expression values, and a t-test was performed to compare expression levels between different groups. The Wilcoxon test was used to evaluate the differential expression between tumors and adjacent normal tissues. The Log-rank test and Cox regression were employed to calculate the HR and log-rank P-value in Kaplan–Meier Plotter to analyze the overall survival curves. Pearson’s correlation was used to estimate the correlations between gene expression and chemokines, chemokine receptors, immune checkpoint genes (ICGs), immune inhibitors, immune stimulators, immune cell ratio, and immune score. The Benjamini-Hochberg (BH) procedure was applied to account for the adjusted p-value in pathway enrichment analysis and the false discovery rate (FDR) in drug sensitivity analysis. Results with p-adjust < 0.05 & FDR < 0.10 were considered statistically significant. All statistical analyses were conducted using R (v4.4.2) through RStudio software (v2024.12.0 + 467) 45 . A p-value of less than 0.05 was set as the significance threshold in all statistical analyses. Results Analysis of the expression patterns of WNTs in breast cancer First, we used the UCSC XENA database to examine the expression patterns of WNTs in BRCA patients. Our study revealed substantial deregulation of WNTs in BC (Fig. 1 A). In addition, we used the GEPIA2 database, which displayed the log2 fold change of different WNTs in BRCA. WNT2 and WNT7B were upregulated, with log2 fold changes of 1.237 and 1.712, respectively. In contrast, WNT11 was downregulated, with a log2 fold change of − 2.639 and a p-value of 4.07E-74 (Table 2 ). The relative mRNA expression distribution across the TCGA-BRCA cohort was compiled using GEPIA2 and displayed as WNT2 (Fig. 1 B), and WNT7B (Fig. 1 C) expression levels were significantly upregulated; conversely, WNT11 expression was significantly downregulated in tumor samples compared to the corresponding control tissue, as shown by box-and-whisker plots (Fig. 1 D). ( Fig. 1 . Expression patterns of WNTs in Breast cancer. (A) mRNA expression patterns of WNTs in breast cancer patients. Heat Map displaying the expression patterns of WNTs using UCSC XENA. Box-and-whisker plots displaying the relative mRNA expression levels of (B) WNT2, (C) WNT7B, and (D) WNT11, across TCGA-BRCA and normal samples. Grey-and red-colored box areas signify normal and tumor patient samples. * P < 0.05.) Table 2 Log2 fold change of WNTs in Breast Cancer Gene Symbol Gene ID Median (Tumor) Median (Normal) Log2 (Fold Change) adjp WNT1 ENSG00000125084.11 0.000 0.020 -0.029 5.19e-13 WNT2 ENSG00000105989.8 4.610 1.380 1.237 5.65e-25 WNT2B ENSG00000134245.17 0.520 2.100 -1.028 4.96e-87 WNT3 ENSG00000108379.9 1.970 2.110 -0.066 4.15e-1 WNT3A ENSG00000154342.5 0.020 0.030 -0.014 3.64e-1 WNT4 ENSG00000162552.14 2.720 1.370 0.650 3.42e-7 WNT5A ENSG00000114251.13 4.080 2.820 0.411 2.02e-3 WNT5B ENSG00000111186.12 2.930 4.020 -0.353 1.16e-12 WNT6 ENSG00000115596.3 0.190 1.920 -1.295 1.80e-34 WNT7B ENSG00000188064.9 5.520 0.990 1.712 2.35e-50 WNT8B ENSG00000075290.7 0.040 0.060 -0.027 3.57e-3 WNT9A ENSG00000143816.7 2.930 2.170 0.310 1.19e-7 WNT9B ENSG00000158955.10 0.060 0.180 -0.155 3.37e-24 WNT10B ENSG00000169884.13 0.210 0.560 -0.367 2.12e-28 WNT11 ENSG00000085741.12 1.320 13.450 -2.639 4.07e-74 WNT16 ENSG00000002745.12 0.030 0.040 -0.014 6.80e-1 WNTs expression based on clinicopathological characteristics of breast cancer The UALCAN database analysis revealed the expression pattern of highly deregulated WNTs among cancer stages, major subclasses, patient age, and menopausal status in BRCA. Both WNT2 and WNT7B showed elevated expression levels across individual cancer stages; in particular, WNT2 had higher expression at stage 1, whereas WNT7B had higher expression at stage 4, compared to normal tissue. Conversely, lower expression of WNT11 was observed across individual cancer stages; specifically, stage 2 displayed the lowest expression compared with that in normal tissue (Fig. 2 A). Similarly, WNT2 and WNT7B were highly upregulated, whereas WNT11 was highly downregulated in HER2-positive breast tumors compared with that in the control tissue (Fig. 2 B). WNT2 was highly upregulated in women under the age of 21–40 years, followed by WNT7B, which showed elevated expression at 81–100 years, whereas WNT11 showed lower expression at 81–100 years (Fig. 2 C). In addition, WNT2 and WNT7B were highly upregulated in the pre-menopausal and post-menopausal stages; in contrast, WNT11 was highly downregulated in the pre-menopausal stage (Fig. 2 D). ( Fig. 2 . The expression pattern of highly deregulated WNTs in BC was based on clinicopathological characteristics obtained from the UALCAN database. Expression patterns of WNT2, WNT7B, and WNT11 based on (A) cancer stages, (B) major subclasses, (C) patient age, and (D) menopausal status. * P < 0.05; ** P < 0.01; *** P < 0.001.) Pan-cancer view of the WNT family To investigate the patterns of WNT2, WNT7B, and WNT11 expression in various cancer types, we utilized the TIMER2.0 database to explore the expression levels of WNT2, WNT7B, and WNT11 between 33 cancer types and matched normal pairs from the TCGA and GTEx databases. The expression level of WNT2 was significantly lower in tumors than in the corresponding normal tissues, including CESC, HNSC-HPV positive, KIRP (P < 0.05), GBM, UCEC (P < 0.01), KIRC, LIHC, LUAD, LUSC, PRAD, THCA ( P < 0.001) than in the corresponding control tissues. In contrast, the expression level of WNT2 was significantly higher in BLCA, SKSM ( P < 0.05), BRCA, COAD, ESCA, NHSC, READ, and STAD ( P < 0.001) than in matched adjacent normal tissues (Fig. 3 A). Furthermore, the expression level of WNT7B was significantly higher than that in matched adjacent healthy tissues, including CESC (P < 0.01), BRCA, CHOL, COAD, ESCA, GBM, LUAD, LUSC, READ, SKSM, STAD, THCA, and UCEC ( P < 0.001). Interestingly, the expression level of WNT7B was significantly lower in HNSC-HPV-positive, KIHC ( P < 0.01), KIRC, LIHC, and NHSC ( P < 0.001) tissues than in the corresponding healthy tissues (Fig. 3 B). The expression level of WNT11 was significantly lower in tumors than in the corresponding normal tissues, including BRCA, HNSC-HPV-positive, KIRC, KIRP, LIHC, LUAD ( P < 0.001), LUSC, and PCPG ( P < 0.01). In contrast, the expression level of WNT11 was significantly higher in SKSM, ESCA ( P < 0.01), COAD, READ, and THCA ( P < 0.001) than in the matched normal tissues (Fig. 3 C). ( Fig. 3 . Pan-cancer analysis of highly deregulated WNTs using the TIMER2.0. (A) Expression levels of WNT2 in different tumors versus the corresponding controls; (B) expression levels of WNT7B in different tumors versus the corresponding controls; (C) expression levels of WNT11 in different tumors vs. corresponding controls. (* P < 0.05; ** P < 0.01; *** P < 0.001)) Survival prognosis analysis across the BRCA cohort The Kaplan–Meier Plotter tool was used to explore the correlation between WNTs expression levels and the prognosis of BRCA patients. According to the overall survival module, the analysis revealed that higher expression of WNT2 was significantly associated with a better prognosis than lower expression (OS: HR = 0.67, P = 0.0029) (Fig. 4 A), whereas WNT7B displayed a substantially poorer prognosis with elevated expression, indicating its oncogenic potential in BRCA progression (OS: HR = 1.34, P = 0.035) (Fig. 4 B). Interestingly, following WNT2, elevated expression of WNT11 was linked to a better survival rate than lower expression (OS: HR = 0.75, P = 0.033) (Fig. 4 C). These findings highlight that WNT2 and WNT11 may serve as protective and favorable prognostic biomarkers. Although WNT7B may have a detrimental effect, it could be a potential target for therapeutic intervention in BRCA. ( Fig. 4 . The expression of WNTs correlates with survival outcomes in patients with BC. KM plots showing the OS of (A) WNT2, (B) WNT7B, and (C) WNT11. Red and black colors signify higher and lower expression groups.) Validation of prognostic WNTs using GEO and correlation analysis Based on the defined inclusion and exclusion criteria, we selected the BRCA-associated expression profiling by array datasets GSE15852 (43 healthy controls and 43 tumor tissues) and GSE42568 (17 healthy controls and 104 tumor tissues). The analysis yielded 21056 differentially expressed genes (DEGs) from GSE15852 and 44650 DEGs from GSE42568 (Supplementary Table S1 ). Importantly, all critical prognostic WNT genes, namely, WNT2, WNT7B, and WNT11, were consistently identified within the DEG lists from both datasets, corroborating their validation in external GEO datasets. WNT2 and WNT7B were upregulated, whereas WNT11 was downregulated in the DEG lists, which aligns with the preliminary results derived from GEPIA 2. A volcano plot was used to visualize the most significant DEGs (Fig. 5 A). Scatter plots display pairwise correlations among the key prognostic WNT genes. A significant positive correlation was observed between WNT2 and WNT7B (R = 0.19, p-value = 7.9 × 10 − 11) (Fig. 5 B). and between WNT2 and WNT11 (R = 0.08, p-value = 0.0077) (Fig. 5 C). ( Fig. 5 . (A) Visualization of the most significant DEGs in the GEO datasets. Scatterplots showing Spearman pairwise correlations between (B) WNT2 and WNT7B, (C) WNT2 and WNT11.) Gene effect scores for key WNTs in BRCA Cell Lines Gene effect scores were assessed across several breast cancer cell lines to determine whether key WNTs were crucial in the progression of BRCA. A negative gene effect score indicated that the cell line was highly dependent on the gene for survival, as gene depletion reduced cell viability. Conversely, a positive score reflected minimal dependency on the gene, with a minor impact on survival upon depletion. Bar plot analysis revealed that WNT2 and WNT11 exhibited negative gene effect scores in most cell lines, whereas WNT7B exhibited a negative gene effect score in a few cell lines (Fig. 6 ). Additionally, all these genes showed a negative gene effect score in MCF7, HCC1187, CAL120, MDAMB436, JIMT1, HMC18, MDAMB468, and EVSAT breast cancer cell lines, as visualized using the Venn diagram (Supplementary Fig. 1). ( Fig. 6 . Gene effect scores for (A) WNT2, (B) WNT7B, and (C) WNT11 in various breast cancer cell lines.) DNA methylation analysis of WNTs in BRCA patients DNA methylation, a pivotal epigenetic mechanism, plays a crucial role in the onset and progression of diverse forms of cancer. We investigated 20 probes within WNT2, 28 probes within WNT7B, and 31 probes within WNT11 to evaluate the methylation levels of these specific genes. Compared with normal tissues, WNT2 and WNT7B exhibited lower methylation (Fig. 7 A, B), whereas WNT11 showed higher methylation in BRCA tumors (Fig. 7 C). The probes cg03794862, cg20539366, and cg18001524 revealed significant levels of methylation within the WNT2, WNT7B, and WNT11 genes, respectively. ( Fig. 7 . The methylation levels of (A) WNT2, (B) WNT7B, and (C) WNT11 between normal tissues and tumor tissues using the SMART database.) Investigation of genetic alteration within BRCA cohorts Investigation of the genetic alteration status of WNT2, WNT7B, and WNT11 in patients with BRCA across various cancer cohorts using cBioPortal has revealed notable insights. Among the 12,148 patients analysed, WNT2 alterations were observed in 128 individuals (1%), showing diverse alterations with frequencies ranging from 0.12–11.08% (Supplementary Fig. 2A). Similarly, WNT7B mutations were identified in 173 patients (1%) in the total cohort. Distinct copy number alterations were identified, ranging from 0.37–28.23% in frequency (Supplementary Fig. 3A). As observed, WNT11 displayed a maximum frequency of genetic alterations in 609 patients (5%), showing diverse alterations with frequencies ranging from 0.54–37.73% (Fig. 8 A). Our analysis highlighted “amplification” as a common genetic alteration across various BRCA cohorts. The highest frequency rate of “amplification” of WNT2, WNT7B, and WNT11 was recorded at 3.69%, 15.83%, and 33.25%, respectively, notably within The Metastatic Breast Cancer Project (Provisional, December 2021). Missense mutations were the main type of WNT genetic mutations, and the most frequent mutations were A145T/G (Supplementary Fig. 2B), A176T (Supplementary Fig. 3B), and V69A (Fig. 8 B) Missense mutations in WNT2, WNT7B, and WNT11. The 3D structures of the WNT11 protein were predicted using AlphaFold (Fig. 8 C). ( Fig. 8 . Genetic alterations in WNT11 across the BRCA cohort were analysed using the cBioPortal database. (A) Alteration summary of WNT11. (B) Mutation types, numbers, and sites of the WNT11 genetic alterations. (C) 3D protein structure of WNT11 from AlphaFold.) Correlation between WNTs and immune microenvironment in breast cancer We explored whether gene expression was related to the immune infiltration level in BRCA. Immune and stromal cells play essential roles in regulating the development and progression of cancers, accounting for significant components of the tumor microenvironment (TME), and their infiltration levels influence immunotherapy efficacy. WNT2 and WNT11 were significantly positively linked to the immune scores in patients with BRCA (Supplementary Fig. 4). WNT2 was negatively correlated with the maximum number of immune cells, including resting mast cells, monocytes, resting NK cells, plasma cells, activated dendritic cells, memory B cells, naïve CD4 T cells, activated NK cells, T cells CD8, follicular helper T cells, and eosinophils (Supplementary Fig. 5). Similarly, WNT7B showed a negative correlation, particularly with memory-activated CD4 T cells, naïve B cells, activated NK cells, follicular helper T cells, plasma cells, memory B cells, and naïve CD4 T cells (Supplementary Fig. 6). In contrast, WNT11 expression was positively correlated with follicular helper T cells, activated dendritic cells, and naïve B cells (Supplementary Fig. 7). The correlation between the expression of WNTs and immune-related genes was visualized using heat maps (Supplementary Fig. 8–12). All these genes were positively correlated with the immune checkpoint gene SIGLEC15 (Fig. 9 A), immune inhibitory genes VTCN1, LGALS9, TGFB1, and TGFBR1 (Fig. 9 B), and the immunostimulatory genes TNFSF9, NT5E, CD276, ENTPD1, and CXCL12 (Fig. 9 C). All these genes were positively correlated with the chemokines CXCL12, CCL22, CXCL14, CXCL8, and CCL26 (Fig. 9 D), and chemokine receptor CCR10 (Fig. 9 E). This comprehensive correlation underscores the wide-ranging influence of WNT’s expression on immunity in BRCA. ( Fig. 9 . Visualization of typical patterns of (A) immune checkpoint genes, (B) immune inhibitors, (C) immune stimulators, (D) chemokines, (E) chemokine receptors among WNT2, WNT7B, and WNT11.) Single-Cell analysis The tumor microenvironment comprises a heterogeneous collection of immune, stromal, and cancer cells. We used TISCH2, an scRNA sequencing database focusing on TME, to provide detailed cell-type annotation and gene expression at the single-cell level in BRCA. We found that WNT2 is highly expressed in myofibroblasts and fibroblasts (Fig. 10 A). WNT7B is highly expressed in malignant cells and pericytes (Fig. 10 B). WNT11 was highly expressed in the fibroblasts (Fig. 10 C). Setting the value of log (TPM/10 + 1) > 0, we found that all these genes were expressed in Mono/Macro, epithelial, malignant, myofibroblasts, endothelial cells, fibroblasts, and pericytes, as visualized by the Venn diagram (Fig. 10 D). (Fig. 10 . Expression analysis of key WNTs from the scRNA-sequencing database. (A) Expression of WNT2 in the immune cell subgroups. (B) Expression of WNT7B in the immune cell subgroups. (C) Expression of WNT11 in subgroups of immune cells. (D) Visualization of typical patterns of immune cells among WNT2, WNT7B, and WNT11.) Drug sensitivity analysis of WNTs These findings suggest the involvement of WNT genes in BRCA prognosis and immune responses. We further investigated the potential links between WNTs and 53 identified interacting genes and their sensitivity to drugs using the GDSC and CTRP databases. Our analysis revealed the strongest positive correlation between WNT7B and genes co-expressed with 19 and 24 anticancer drugs and a negative correlation with 6 and 4 drugs, respectively (Fig. 11 A, B). The correlation between the individual expression of WNT2, WNT7B, and WNT11 and drug sensitivities was visualized using the ggpubr package. Notably, the results from the GDSC database indicated that WNT2 expression was most positively correlated with QL-VIII-58 and negatively correlated with AR-42 (Fig. 11 C), WNT7B demonstrated the most positive correlation with UNC0638, in contrast to the negative correlation with lapatinib (Fig. 11 E). WNT11 showed the most positive correlations with Piperlongumine, while exhibiting negative correlations with lestaurtinib (Fig. 11 G). Furthermore, findings from the CTRP database revealed that WNT2 was negatively correlated with all anticancer drugs (Fig. 11 D). WNT7B exhibited the most positive correlation with QW-BI-011, but was negatively correlated with saracatinib (Fig. 11 F). WNT11, on the other hand, was associated with the most positive correlations with simvastatin and the most negative correlations with lapatinib (Fig. 11 H). These results suggest that WNT genes may serve as valuable biomarkers for cross-cancer drug screening, thereby facilitating the identification of effective therapeutic strategies. ( Fig. 11 . Key WNTs expression predicts drug sensitivity. Correlation between WNTs and 53 interacting genes and their drug sensitivity (A) by GDSC and (B) by CTRP. Correlation between WNT2 expression and drug sensitivity (C) by GDSC (D) by CTRP. Correlation between WNT7B expression and drug sensitivity (E) by GDSC (F) by CTRP. The correlation between WNT11 expression and the most significant drug sensitivity (G) by GDSC, (H) by CTRP.) PPIN construction and enrichment analysis We screened the proteins interacting with WNT2, WNT7B, and WNT11 using the STRING online tool to explore their molecular mechanisms in tumorigenesis. We found 53 proteins supported by experimental evidence, and the interaction network of these genes is displayed (Supplementary Fig. 13). Our PPIN consists of 53 nodes and 1234 edges. Within the PPIN, the node degrees ranged from 16 to 56, betweenness ranged from 1 to 63.966, and closeness ranged from 0 to 1. The average degree, betweenness, and closeness values for the PPIN were 46.576, 33.802, and 0.947, respectively. Topological/centrality measures for the PPIN, including node degree, betweenness, closeness, clustering coefficient, neighborhood connectivity, and average shortest path length, are presented in Supplementary Tables S2 and S3. Subsequently, we utilized the gene set to perform Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway, and Gene Ontology (GO) analysis of key prognostic WNTs. KEGG pathway analysis correlated with critical pathways, notably “Alzheimer's disease,” “Pathways of neurodegeneration,” “Breast & Gastric cancer,” “Hippo signaling,” and “mTOR signaling pathway” (Fig. 12 A). Reactome pathway enrichment analysis showed that they are primarily involved in “class B/2,” “GPCR ligand binding,” “TCF-dependent signaling,” and “PCP/CE pathway” (Fig. 12 B). Both pathways were highly enriched in the “Wnt signaling pathway.” In addition, GO analysis showed that the genes were highly enriched in pathways “cell-cell signaling” (Fig. 12 C), “endocytic vesicle membrane” (Fig. 12 D), and “frizzled binding” (Fig. 12 E) in the BP, CC, and MF. ( Fig. 12 . Enrichment analysis of genes related to WNT2, WNT7B, and WNT11. (A) KEGG pathway, (B) Reactome pathway, (C) GO-BP, (D) GO-CC, and (E) GO-MF analysis.) Discussion This study highlights the complex roles of the WNT gene family and treatment strategies in BRCA. The WNT family encodes signaling proteins crucial for regulating various cellular functions, including survival, proliferation, migration, and stem cell renewal 46 . Abnormal activation of the WNT pathway has been identified as a predisposing factor in various cancers and plays a significant role in CSC biology 47 . Notably, WNT2 activates the canonical WNT/β-catenin pathway and has been implicated in CRC and HCC 48 . In addition, WNT7B appears to promote vascularization and angiogenesis in tumors and is linked to CSCC, HNSC, LUSC, and BTCC 12 . In contrast, WNT11 operates through non-canonical WNT signaling pathways, such as the WNT/planar cell polarity (PCP) and WNT/Ca²⁺ pathways, which are critical for cell organization and metastasis 49 and are linked to prostate cancer 50 . Nearly 40 years ago, studies on mouse mammary tumor virus (MMTV) discovered the WNT gene family as a crucial player in mammary cancer 51,52 . Few studies have explored the role of WNT in cancer, and none have employed a comprehensive approach to breast cancer development and progression. Our study fills this gap by utilizing comprehensive multi-omics to reveal the molecular mechanisms driving tumorigenesis, offering new and promising avenues for targeted therapies and revolutionizing treatment strategies for BRCA patients. The study revealed the expression of the WNT family in BRCA, with WNT2 and WNT7B showing upregulation, whereas WNT11 was downregulated, which corroborates previous findings 53,54 . Potential mechanisms may drive these dysregulations: 1) genetic alteration, particularly gene amplification, which may contribute to the heightened WNT2 and WNT7B expression, supported by our cBioPortal analysis; and 2) epigenetic modifications, such as higher methylation, could account for the downregulation of WNT11. In HER2-positive breast cancer, the significant upregulation of WNT2 and WNT7B implies possible synergy with HER2-driven oncogenic signaling. These findings highlight a promising therapeutic approach targeting WNT2 and WNT7B in conjunction with HER2, which could inhibit tumor growth in HER2 + patients. Further preclinical and clinical studies are required to validate this combinatorial approach. The pan-cancer analysis emphasizes the tissue- and tumor-dependent expression pattern of WNT2, WNT7B, and WNT11, in line with previous studies 7,10,12,46,53,55 . This context-dependent regulation suggests that WNT signaling may play distinct oncogenic or tumor-suppressive roles depending on the tumor type and microenvironment. Further investigation is needed to elucidate the functional implications of these variations in different cancers. Our results showed that WNT2's elevated expression was associated with a significantly better prognosis, which contradicts its typical oncogenic role. However, previous studies have demonstrated that specific oncogenes can paradoxically activate protective mechanisms by promoting (i) Oncogene-induced Senescence (OIS) 56, which limits unchecked proliferation and induces a stable growth arrest; (ii) feedback loops Leading to Tumor Suppression 57 , where oncogene activation triggers compensatory anti-tumor pathways; iii) Non-oncogenic addiction 58 , where cancer cells become dependent on specific pathways, making them more susceptible to targeted therapies; and iv) Immunogenic Modulation of TME 59 , potentially enhancing immune recognition and anti-tumor responses. Further investigations are needed to understand these mechanisms, which may provide a predictive basis for developing novel drug combinations. In contrast, WNT7B overexpression was linked to worse prognosis, which is consistent with its suggested oncogenic role 60 . In contrast, WNT11 overexpression was linked to better survival outcomes, underscoring its potential tumor-suppressive role. The consistent identification of WNT2, WNT7B, and WNT11 in two independent BRCA-associated GEO datasets highlights their robustness as biomarkers of breast cancer. This finding is particularly significant, as it aligns with their established roles in breast cancer development and progression. DNA methylation is a crucial epigenetic modification for enhancing the stability of transcriptional repression associated with cancer 61 . Compared with the corresponding normal tissues, the methylation level of WNT2/7B in tumor tissues was significantly reduced, leading to decreased transcriptional repression stability and subsequent overexpression, possibly contributing to tumor progression. Conversely, WNT11 exhibited significantly increased methylation in tumor tissues, resulting in stable transcriptional repression, reduced expression, and potential impairment of tumor-suppressive functions. Notably, genomic alterations of key WNTs revealed that WNT11 had the most changes (5%), with amplification being the most common type of alteration. This is an unusual association between amplification and tumor suppressor gene expression. Previous studies have shown that tumor suppressor amplification is rare and may signal genomic instability rather than active tumor suppression 62,63 . Further investigation is warranted to determine whether WNT11 amplification represents a compensatory response or a byproduct of genomic instability during tumor evolution. WNT2 and WNT11, which are positively correlated with immune scores in BRCA, are key players in TME modulation and their potential impact on tumor progression and immune evasion. Immune cell correlation revealed that WNT2 and WNT7B are immunosuppressive drivers and WNT11 is an immune activator. These findings suggest that WNTs are dual modulators of BRCA immunity and are correlated with immune cell infiltration and immunological functions in the TME. Moreover, positive correlations between WNTs and immune-related genes play a critical role in shaping the immune landscape in BRCA, influencing both immune activation and suppression. The association between WNT expression and chemokines suggests that WNT signaling may regulate immune cell trafficking and positioning within the tumor. Single-cell RNA sequencing analysis from the TISCH2 database further confirmed the high expression of WNT genes in multiple immune cell subpopulations, including Mono/Macro, epithelial, malignant, myofibroblasts, endothelial cells, fibroblasts, and pericytes, underscoring their potential influence on immune modulation. Macrophages in the TME can polarize into two distinct phenotypes: M1 (pro-inflammatory, anti-tumorigenic) and M2 (immunosuppressive, tumor-promoting). M2 tumor-associated macrophages (TAMs) promote immune evasion by secreting immunosuppressive cytokines, such as IL-10 and TGF-β, which inhibit T-cell activation and promote regulatory T cells (Tregs). Shifting macrophage polarization from M2 to M1 can enhance cytotoxic T-cell responses and improve the efficacy of immune checkpoint inhibitors (ICIs), facilitating better immune responses 64,65 . Tumor epithelial cells often undergo epithelial-to-mesenchymal transition (EMT), which reduces immune recognition and increases the metastatic potential. EMT downregulates MHC-I expression, making tumor cells less visible to cytotoxic T cells and promoting resistance to apoptosis. Inhibiting EMT can restore immune cell recognition, improving the efficacy of immunotherapies by preventing tumor cell dissemination and enhancing T-cell infiltration 66,67 . Malignant cells evade immune surveillance by upregulating immune checkpoint ligands such as PD-L1, which binds to PD-1 on T cells, leading to T-cell exhaustion and impaired anti-tumor responses. These cells also produce immunosuppressive cytokines, such as TGF-β. Combining immune checkpoint inhibitors (anti-PD-1/PD-L1) with WNT pathway inhibitors can potentially reverse immune suppression in malignant cells, enhancing T cell-mediated tumor clearance 68–70 . Endothelial cells contribute to angiogenesis and regulate immune cell trafficking. Aberrant endothelial cell function can create a blood-tumor barrier that limits immune cell infiltration into tumors 71,72 . Fibroblasts in the TME secrete cytokines and extracellular matrix proteins that support tumor growth and suppress immune function by creating a physical barrier 73,74 . Pericytes help stabilize the tumor vasculature, but their presence can contribute to the formation of dense blood vessels, limiting immune cell entry into the tumor 75 . Targeting WNT pathways may be a promising strategy for enhancing antitumor immunity and overcoming immune evasion mechanisms in BRCA. The differential drug sensitivity of WNT2, WNT7B, and WNT11 suggests that these genes may serve as predictive biomarkers for therapeutic responses in breast cancer. The negative correlation of WNT2 with HDAC inhibitors (AR-42) and BET inhibitors (I-BET-762) implies that WNT2-expressing BRCA cells might resist epigenetic therapies, warranting combination therapeutic approaches. The negative correlation between WNT7B and Lapatinib and Afatinib (HER2-targeted therapies) suggests that WNT7B-overexpressing BRCA tumors may resist HER2-targeted therapies. Potential mechanisms underlying this resistance include: 1) β-catenin Signaling and HER2 crosstalk 76 , 2) PI3K/AKT and MAPK/ERK activation 77 , and 3) EMT induction 78 . Given these findings, targeting WNT7B-driven pathways in combination with HER2 inhibitors could be a potential strategy to overcome resistance in patients with HER2-positive BRCA. The positive correlation of WNT11 with Piperlongumine, an oxidative stress inducer, suggests that targeting redox balance in BRCA cells overexpressing WNT11 may be a promising therapeutic avenue. The positive correlation between WNT11 and Simvastatin, and fluvastatin suggests that these lipophilic statins may have a role in targeting WNT11-driven breast cancer. Statins are known to disrupt lipid metabolism and mevalonate pathways, which are crucial for WNT signaling, making them a potential adjuvant therapy for WNT-driven cancers 79,80 . The enrichment of WNT2, WNT7B, and WNT11 proteins in pathways like “Wnt signaling” 7,53 , “Hippo signaling” 81 , “mTOR signaling pathway” 82 and “PCP/CE pathways” 83 underscores their central role in tumorigenesis. These pathways regulate cell proliferation, differentiation, migration and apoptosis. Dysregulation of these pathways can lead to uncontrolled cellular growth and metastasis, as has been observed in various cancers 53,81–83 . The identification of “Alzheimer's disease” 84 and “Pathways of neurodegeneration” 85 in KEGG analysis is intriguing, as it suggests potential shared mechanisms between neurodegeneration and tumorigenesis. Understanding these shared molecular mechanisms could provide novel insights into the dual modulation of the Wnt pathway for therapeutic benefits. Enriching “frizzled binding” in GO analysis highlights the role of frizzled receptors, which are critical mediators of WNT signaling. Targeting frizzled receptors has shown promise in preclinical models for the inhibition of metastasis and angiogenesis 86 . The significant enrichment of genes associated with the “endocytic vesicle membrane” suggested a role in the intracellular trafficking of receptors and ligands. Dysregulation of endocytosis is often linked to drug resistance in cancers, as it can alter the internalization and degradation of therapeutic targets, such as tyrosine kinase receptors 87,88 . Enrichment in “GPCR ligand binding” supports GPCR modulation to influence the TME, immune response, and angiogenesis 89,90 . This study had several limitations. First, the RNA expression levels in the present study could not be verified using protein levels. Transcriptomic data (mRNA expression) do not always correlate with protein expression because of post-transcriptional modifications and microRNA (miRNA) regulation. Experimental techniques, such as western blotting, immunohistochemistry (IHC), mass spectrometry, and flow cytometry, are required to confirm protein-level changes. Second, while the study integrates GEO datasets to ensure robust and consistent findings, enhancing result reliability, dataset-specific biases, and batch effects may still influence the results. Third, this study utilized breast cancer cell line data from DepMap, which provides high-throughput functional genomic data enabling drug sensitivity analysis and gene dependency mapping in a controlled environment. However, cell lines lack tumor microenvironment interactions, immune components, and heterogeneity in patient tumors, limiting their physiological relevance. Fourth, we performed single-cell analysis using TISCH2, which reveals cell-type-specific expression and avoids bulk RNA-seq averaging effects. However, limitations include the absence of spatial transcriptomic integration, may not fully represent BRCA heterogeneity across all subtypes, and a lack of functional validation. Further validation using direct in vitro and in vivo studies are required. These findings will establish a basis for future studies to investigate the molecular mechanisms of WNTs relevant to the development and progression of BRCA. Conclusions In conclusion, our comprehensive study revealed a significant role of the WNT family in breast cancer, highlighting their diverse roles in tumor progression, immune modulation, and therapeutic response. We found that the mRNA expression levels of WNT2/7B were significantly upregulated, indicating a potential oncogenic driver, while WNT11 was downregulated, exhibiting tumor-suppressive properties in BRCA. Dysregulation of these genes appears to be influenced by genetic alterations and epigenetic modifications, with potential implications for targeted therapy. Clinically, the overexpression of WNT2 and WNT7B in HER2-positive breast cancer suggests a possible synergy between WNT signaling and HER2-driven oncogenic pathways, possibly contributing to resistance to HER2-targeted therapies. Future clinical trials should explore the feasibility of combining WNT inhibitors with standard-of-care treatments in patients with HER2 + BRCA. WNT signaling influences tumor-immune interactions, including immune suppression and macrophage polarization, suggesting that WNT inhibition could enhance immune checkpoint blockade therapies. Given the correlation between WNT expression and immune cell infiltration, further investigation of WNT-targeted immunotherapies is warranted, particularly in combination with anti-PD-1/PD-L1 therapies. To establish WNTs as therapeutic targets, in vitro and in vivo studies are necessary to validate the functional roles of WNT2, WNT7B, and WNT11 in BRCA progression. Future research should focus on integrating proteomic validation, spatial transcriptomics, preclinical drug testing, and functional assays to fully elucidate the mechanistic underpinnings of WNT signaling and its therapeutic implications in breast cancer. These findings pave the way for novel therapeutic strategies to modulate WNT signaling to improve patient outcomes in BRCA. Declarations Consent for publication The authors declare that they have no competing financial interests or personal relationships that could influence the publication of this study. Funding The authors did not receive any funding for this study. Declaration of competing interests The authors declare that they have no conflict of interest. Data availability The datasets we generated and/or analyzed during the current study are freely available in The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/tcga), UCSC XENA (https://xenabrowser.net/), GEPIA2 database (http://gepia2.cancer-pku.cn), Timer 2.0 database (http://timer.comp-genomics.org), UALCAN (http://ualcan.path.uab.edu/), Kaplan-Meier Plotter database (https://kmplot.com/analysis/), DepMap (https://depmap.org/), Shiny Methylation Analysis Resource Tool (SMART) App (http://www.bioinfo-zs.com/smartapp/), cBioPortal web database (https://www.cbioportal.org/), STRING database (https://string-db.org/), GDSC database (https://www.cancerrxgene.org/), and CTRP database (https://clinicaltrialsapi.cancer.gov/). The expression profile datasets GSE15852 and GSE42568 are available from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/). All data produced within this manuscript were attached as ‘Supplementary Materials’ file. The scripts used to perform the analysis in this are available in the following GitHub repository: https://github.com/bigbiolab/WNT_BRCA. Author contributions Fatema Tuj Johora Fariha: Conceptualization, Data curation, Methodology, Formal analysis and Result interpretation, Investigation, Writing—original draft, Writing—review and editing. Muntasim Fuad: Conceptualization, Data Curation, Methodology, Software, Formal analysis and interpretation of results, Investigation, writing —original draft, writing —review, and editing. Chandra Shekhar Saha: Data curation, Investigation Methodology, Writing—original draft, writing —review, and editing. Sajjad Hossen: Data curation, Investigation, Methodology, writing —original draft, writing —review, and editing. Md. Jubayer Hossain: Conceptualization, Formal analysis and result interpretation, Investigation, Software, Resources, Supervision, Writing—original draft, writing —review and editing, and project administration. Acknowledgement We would like to express our sincere gratitude to Dr. Syeda Tasneem Towhid for her invaluable guidance, support, and expertise in CHIRAL Bangladesh. We also extend our appreciation to CHIRAL Bangladesh for their assistance in facilitating various aspects of this study. Their contributions were instrumental in ensuring the success of this study. References Smolarz, B., Nowak, A. Z. & Romanowicz, H. Breast Cancer—Epidemiology, Classification, Pathogenesis and Treatment (Review of Literature). Cancers 14 , 2569 (2022). Bhushan, A., Gonsalves, A. & Menon, J. U. Current State of Breast Cancer Diagnosis, Treatment, and Theranostics. Pharmaceutics 13 , 723 (2021). Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 74 , 229–263 (2024). Sedeta, E. T., Jobre, B. & Avezbakiyev, B. Breast cancer: Global patterns of incidence, mortality, and trends. J. Clin. Oncol. 41 , 10528–10528 (2023). Mubarik, S. et al. Breast Cancer Mortality Trends and Predictions to 2030 and Its Attributable Risk Factors in East and South Asian Countries. Front. Nutr. 9 , (2022). Current State of Breast Cancer Diagnosis, Treatment, and Theranostics. https://www.mdpi.com/1999-4923/13/5/723. Werner, J., Boonekamp, K. E., Zhan, T. & Boutros, M. The Roles of Secreted Wnt Ligands in Cancer. Int. J. Mol. Sci. 24 , 5349 (2023). Miller, J. R. The Wnts. Genome Biol. 3 , REVIEWS3001 (2002). Grainger, S. & Willert, K. Mechanisms of Wnt signaling and control. WIREs Syst. Biol. Med. 10 , e1422 (2018). El-Sahli, S., Xie, Y., Wang, L. & Liu, S. Wnt Signaling in Cancer Metabolism and Immunity. Cancers 11 , 904 (2019). Bugter, J. M., Fenderico, N. & Maurice, M. M. Mutations and mechanisms of WNT pathway tumour suppressors in cancer. Nat. Rev. Cancer 21 , 5–21 (2021). Chen, C. et al. Tumor specificity of WNT ligands and receptors reveals universal squamous cell carcinoma oncogenes. BMC Cancer 22 , 790 (2022). Patel, S., Alam, A., Pant, R. & Chattopadhyay, S. Wnt Signaling and Its Significance Within the Tumor Microenvironment: Novel Therapeutic Insights. Front. Immunol. 10 , 2872 (2019). Zhao, H. et al. Wnt signaling in colorectal cancer: pathogenic role and therapeutic target. Mol. Cancer 21 , 144 (2022). Xu, X., Zhang, M., Xu, F. & Jiang, S. Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities. Mol. Cancer 19 , 165 (2020). Yin, P. et al. Wnt signaling in human and mouse breast cancer: Focusing on Wnt ligands, receptors and antagonists. Cancer Sci. 109 , 3368–3375 (2018). Martin-Orozco, E., Sanchez-Fernandez, A., Ortiz-Parra, I. & Ayala-San Nicolas, M. WNT Signaling in Tumors: The Way to Evade Drugs and Immunity. Front. Immunol. 10 , 2854 (2019). Mukherjee, N. & Panda, C. K. Wnt/β-Catenin Signaling Pathway as Chemotherapeutic Target in Breast Cancer: An Update on Pros and Cons. Clin. Breast Cancer 20 , 361–370 (2020). El Sabeh, M., Saha, S. K., Afrin, S., Islam, M. S. & Borahay, M. A. Wnt/β-catenin signaling pathway in uterine leiomyoma: role in tumor biology and targeting opportunities. Mol. Cell. Biochem. 476 , 3513–3536 (2021). Progress of Wnt Signaling Pathway in Osteoporosis. https://www.mdpi.com/2218-273X/13/3/483. Faraji, N., Ebadpour, N., Abavisani, M. & Gorji, A. Unlocking Hope: Therapeutic Advances and Approaches in Modulating the Wnt Pathway for Neurodegenerative Diseases. Mol. Neurobiol. 62 , 3630–3652 (2025). Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38 , 675–678 (2020). Tang, Z., Kang, B., Li, C., Chen, T. & Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47 , W556–W560 (2019). Chandrashekar, D. S. et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25 , 18–27 (2022). Li, T. et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48 , W509–W514 (2020). Lánczky, A. & Győrffy, B. Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J. Med. Internet Res. 23 , e27633 (2021). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550 (2014). Blighe, K. EnhancedVolcano. Bioconductor https://doi.org/10.18129/B9.BIOC.ENHANCEDVOLCANO (2018). Liao, C. & Wang, X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. BMC Bioinformatics 24 , 483 (2023). The present and future of the Cancer Dependency Map | Nature Reviews Cancer. https://www.nature.com/articles/s41568-024-00763-x. Wickham, H. ggplot2. WIREs Comput. Stat. 3 , 180–185 (2011). Li, Y., Ge, D. & Lu, C. The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization. Epigenetics Chromatin 12 , 71 (2019). de Bruijn, I. et al. Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res. 83 , 3861–3867 (2023). Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583–589 (2021). Liao, C. & Wang, X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. BMC Bioinformatics 24 , 483 (2023). Gao, C. et al. ggVennDiagram: Intuitive Venn diagram software extended. iMeta 3 , e177 (2024). TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment | Nucleic Acids Research | Oxford Academic. https://academic.oup.com/nar/article/51/D1/D1425/6793806. Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51 , D638–D646 (2023). Shannon, P. et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13 , 2498–2504 (2003). Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation 2 , 100141 (2021). Yu, G. & He, Q.-Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12 , 477–479 (2016). Guangchuang Yu. enrichplot. Bioconductor https://doi.org/10.18129/B9.BIOC.ENRICHPLOT (2018). Liu, C.-J. et al. GSCALite: a web server for gene set cancer analysis. Bioinformatics 34 , 3771–3772 (2018). Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2023). R: A Language for Data Analysis and Graphics: Journal of Computational and Graphical Statistics: Vol 5, No 3. https://www.tandfonline.com/doi/abs/10.1080/10618600.1996.10474713. Hayat, R., Manzoor, M. & Hussain, A. Wnt signaling pathway: A comprehensive review. Cell Biol. Int. 46 , 863–877 (2022). Bao, B. et al. The biological kinship of hypoxia with CSC and EMT and their relationship with deregulated expression of miRNAs and tumor aggressiveness. Biochim. Biophys. Acta BBA - Rev. Cancer 1826 , 272–296 (2012). Selvaggi, F., Catalano, T., Cotellese, R. & Aceto, G. M. Targeting Wnt/β-Catenin Pathways in Primary Liver Tumours: From Microenvironment Signaling to Therapeutic Agents. Cancers 14 , 1912 (2022). Chae, W.-J. & Bothwell, A. L. M. Canonical and Non-Canonical Wnt Signaling in Immune Cells. Trends Immunol. 39 , 830–847 (2018). Zheng, D. et al. Role of WNT7B-induced Noncanonical Pathway in Advanced Prostate Cancer. Mol. Cancer Res. 11 , 482–493 (2013). Taneja, P. et al. MMTV mouse models and the diagnostic values of MMTV-like sequences in human breast cancer. Expert Rev. Mol. Diagn. 9 , 423–440 (2009). Yin, P. et al. Wnt signaling in human and mouse breast cancer: Focusing on Wnt ligands, receptors and antagonists. Cancer Sci. 109 , 3368–3375 (2018). Zhan, T., Rindtorff, N. & Boutros, M. Wnt signaling in cancer. Oncogene 36 , 1461–1473 (2017). Xu, X., Zhang, M., Xu, F. & Jiang, S. Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities. Mol. Cancer 19 , 165 (2020). Jiang, S. et al. Activation of WNT7b autocrine eases metastasis of colorectal cancer via epithelial to mesenchymal transition and predicts poor prognosis. BMC Cancer 21 , 180 (2021). Chandeck, C. & Mooi, W. J. Oncogene-induced Cellular Senescence. Adv. Anat. Pathol. 17 , 42–48 (2010). Rückert, F., Sticht, C. & Niedergethmann, M. Molecular mechanism of the ‘feedback loop’ model of carcinogenesis. Commun. Integr. Biol. 5 , 506–507 (2012). Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136 , 823–837 (2009). Wang, Q. et al. Role of tumor microenvironment in cancer progression and therapeutic strategy. Cancer Med. 12 , 11149–11165 (2023). Chen, J. et al. Up-regulation of Wnt7b rather than Wnt1, Wnt7a, and Wnt9a indicates poor prognosis in breast cancer. Int. J. Clin. Exp. Pathol. 11 , 4552–4561 (2018). Kulis, M. & Esteller, M. DNA Methylation and Cancer. in Advances in Genetics vol. 70 27–56 (Elsevier, 2010). Matsui, A., Ihara, T., Suda, H., Mikami, H. & Semba, K. Gene amplification: mechanisms and involvement in cancer. Biomol. Concepts 4 , 567–582 (2013). Motoyama, N. & Naka, K. DNA damage tumor suppressor genes and genomic instability. Curr. Opin. Genet. Dev. 14 , 11–16 (2004). Wei, B. et al. Polarization of Tumor-Associated Macrophages by Nanoparticle-Loaded Escherichia coli Combined with Immunogenic Cell Death for Cancer Immunotherapy. Nano Lett. 21 , 4231–4240 (2021). Choo, Y. W. et al. M1 Macrophage-Derived Nanovesicles Potentiate the Anticancer Efficacy of Immune Checkpoint Inhibitors. ACS Nano 12 , 8977–8993 (2018). Taki, M. et al. Tumor Immune Microenvironment during Epithelial–Mesenchymal Transition. Clin. Cancer Res. 27 , 4669–4679 (2021). Mullins, R. D. Z., Pal, A., Barrett, T. F., Heft Neal, M. E. & Puram, S. V. Epithelial–Mesenchymal Plasticity in Tumor Immune Evasion. Cancer Res. 82 , 2329–2343 (2022). DeVito, N. C. et al. Pharmacological Wnt ligand inhibition overcomes key tumor-mediated resistance pathways to anti-PD-1 immunotherapy. Cell Rep. 35 , 109071 (2021). Ross, J. et al. Abstract 1733: Wnt antagonists synergize with immune checkpoint inhibitors to enhance anti-tumor responses. Cancer Res. 78 , 1733–1733 (2018). Krishnan, S. et al. Abstract B015: Wnt inhibition improves the efficacy of anti-PD-1 therapy in glioblastoma. Cancer Immunol. Res. 12 , B015–B015 (2024). Leone, P. et al. Endothelial cells in tumor microenvironment: insights and perspectives. Front. Immunol. 15 , 1367875 (2024). Solimando, A. G., Summa, S. D., Vacca, A. & Ribatti, D. Cancer-Associated Angiogenesis: The Endothelial Cell as a Checkpoint for Immunological Patrolling. Cancers 12 , 3380 (2020). Liao, D., Luo, Y., Markowitz, D., Xiang, R. & Reisfeld, R. A. Cancer Associated Fibroblasts Promote Tumor Growth and Metastasis by Modulating the Tumor Immune Microenvironment in a 4T1 Murine Breast Cancer Model. PLoS ONE 4 , e7965 (2009). Monteran, L. & Erez, N. The Dark Side of Fibroblasts: Cancer-Associated Fibroblasts as Mediators of Immunosuppression in the Tumor Microenvironment. Front. Immunol. 10 , 1835 (2019). Sun, R., Kong, X., Qiu, X., Huang, C. & Wong, P.-P. The Emerging Roles of Pericytes in Modulating Tumor Microenvironment. Front. Cell Dev. Biol. 9 , 676342 (2021). Hao, X., Zheng, J., Yu, X., Li, Z. & Ren, G. β-catenin promotes resistance to trastuzumab in breast cance. Acta Biochim. Pol. (2023) doi:10.18388/abp.2020_6357. Wilks, S. T. Potential of overcoming resistance to HER2-targeted therapies through the PI3K/Akt/mTOR pathway. The Breast 24 , 548–555 (2015). Oliveras-Ferraros, C. et al. Epithelial-to-mesenchymal transition (EMT) confers primary resistance to trastuzumab (Herceptin). Cell Cycle 11 , 4020–4032 (2012). Gong, L. et al. The mevalonate coordinates energy input and cell proliferation. Cell Death Dis. 10 , 327 (2019). Juarez, D. & Fruman, D. A. Targeting the Mevalonate Pathway in Cancer. Trends Cancer 7 , 525–540 (2021). Xiao, Y. & Dong, J. The Hippo Signaling Pathway in Cancer: A Cell Cycle Perspective. Cancers 13 , 6214 (2021). Pópulo, H., Lopes, J. M. & Soares, P. The mTOR Signalling Pathway in Human Cancer. Int. J. Mol. Sci. 13 , 1886–1918 (2012). Dworkin, S., Jane, S. M. & Darido, C. The planar cell polarity pathway in vertebrate epidermal development, homeostasis and repair. Organogenesis 7 , 202–208 (2011). Zhang, C., Wang, J. & Wang, W. Wnt signaling in synaptogenesis of Alzheimer’s disease. Ibrain 9 , 316–325 (2023). Anand, A. A., Khan, M., V, M. & Kar, D. The Molecular Basis of Wnt/β-Catenin Signaling Pathways in Neurodegenerative Diseases. Int. J. Cell Biol. 2023 , 1–13 (2023). Sun, Y., Wang, W. & Zhao, C. Frizzled Receptors in Tumors, Focusing on Signaling, Roles, Modulation Mechanisms, and Targeted Therapies. Oncol. Res. 28 , 661–674 (2021). Banushi, B., Joseph, S. R., Lum, B., Lee, J. J. & Simpson, F. Endocytosis in cancer and cancer therapy. Nat. Rev. Cancer 23 , 450–473 (2023). Mellman, I. & Yarden, Y. Endocytosis and cancer. Cold Spring Harb. Perspect. Biol. 5 , a016949 (2013). Lappano, R. & Maggiolini, M. GPCRs and cancer. Acta Pharmacol. Sin. 33 , 351–362 (2012). Lämmermann, T. & Kastenmüller, W. Concepts of GPCR ‐controlled navigation in the immune system. Immunol. Rev. 289 , 205–231 (2019). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.pdf SupplementaryTablesS1.xlsx SupplementaryTablesS2.xlsx SupplementaryTablesS3.xlsx Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 17 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviews received at journal 14 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 01 Apr, 2025 Editor invited by journal 13 Feb, 2025 Submission checks completed at journal 12 Feb, 2025 First submitted to journal 10 Feb, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6001541","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":415355259,"identity":"2afc37c8-eb35-4fca-b596-7cafa8f63b41","order_by":0,"name":"Fatema Tuj Johora Fariha","email":"","orcid":"","institution":"Big Bioinformatics Lab, Center for Health Innovation, Research, Action, and Learning—Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Fatema","middleName":"Tuj Johora","lastName":"Fariha","suffix":""},{"id":415355260,"identity":"10e431ed-135d-4edb-918a-032d0232e6cf","order_by":1,"name":"Muntasim Fuad","email":"","orcid":"","institution":"Big Bioinformatics Lab, Center for Health Innovation, Research, Action, and Learning—Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Muntasim","middleName":"","lastName":"Fuad","suffix":""},{"id":415355261,"identity":"7a65ce9e-31d7-444d-a550-08ddc2bb0d4a","order_by":2,"name":"Chandra Shekhar Saha","email":"","orcid":"","institution":"Big Bioinformatics Lab, Center for Health Innovation, Research, Action, and Learning—Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Chandra","middleName":"Shekhar","lastName":"Saha","suffix":""},{"id":415355262,"identity":"644e51ed-7601-4181-9e7f-88e852e50264","order_by":3,"name":"Sajjad Hossen","email":"","orcid":"","institution":"Big Bioinformatics Lab, Center for Health Innovation, Research, Action, and Learning—Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Sajjad","middleName":"","lastName":"Hossen","suffix":""},{"id":415355263,"identity":"c66f4fba-7258-4450-9d60-38ef5b4c747c","order_by":4,"name":"Md. Jubayer Hossain","email":"data:image/png;base64,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","orcid":"","institution":"Center for Health Innovation, Research, Action, and Learning—Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Jubayer","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2025-02-10 18:38:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6001541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6001541/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-13315-6","type":"published","date":"2025-10-03T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76299093,"identity":"6fcc557d-9702-4da8-9fa9-1300f5278e38","added_by":"auto","created_at":"2025-02-14 13:41:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3968705,"visible":true,"origin":"","legend":"\u003cp\u003eExpression pattern of WNTs in Breast cancer. (A) mRNA expression pattern of WNTs in breast cancer patients. Heat Map displaying the expression patterns of WNTs using UCSC XENA. Box-and-whisker plots displaying the relative mRNA expression levels of (B) WNT2, (C) WNT7B, (D) WNT11, across TCGA-BRCA and normal samples. Grey-and red-colored box areas signify normal and tumor patient samples. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.)\u003c/p\u003e","description":"","filename":"Figure01.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/5fc6a7cbbd3083591545019c.jpg"},{"id":76298107,"identity":"7baffb48-c711-4076-a443-cd93c6ef5ee6","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2621879,"visible":true,"origin":"","legend":"\u003cp\u003eExpression pattern of highly deregulated WNTs in BC based on clinicopathological characteristics by UALCAN database. Expression pattern of WNT2, WNT7B, WNT11 based on (A) cancer stages, (B) major subclasses, (C) patient age, (D) menopause status. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/66b7db836c5bda813467e7d2.jpg"},{"id":76298111,"identity":"7efa6a36-39e6-451a-8cf4-9952a6f708bd","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2244382,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer analysis of highly deregulated WNTs utilizing TIMER2.0. A. Expression levels of WNT2 in different tumors vs corresponding controls. B. Expression levels of WNT7B in different tumors vs corresponding controls. C. Expression levels of WNT11 in different tumors vs corresponding controls. (* P \u0026lt; 0.05; ** P \u0026lt; 0.01; *** P \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Figure03.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/b8170ae61a819c17b6766b7c.jpg"},{"id":76298116,"identity":"a5f1bd1d-bd23-41cb-8981-20d3af619739","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":727354,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of WNTs correlates with survival outcomes in BC patient. KM plots showing the OS of (A) WNT2, (B) WNT7B, (C) WNT11. Red and black colors signify higher and lower expression groups.\u003c/p\u003e","description":"","filename":"Figure04.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/ce73738632de25cb75483796.jpg"},{"id":76299096,"identity":"10c7922b-4807-4f06-bd33-22567ae6b136","added_by":"auto","created_at":"2025-02-14 13:41:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":993190,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Visualization of the most significant DEGs in GEO datasets. Scatterplots showing Spearman pairwise correlations between (B) WNT2 and WNT7B, (C) WNT2 and WNT11.\u003c/p\u003e","description":"","filename":"Figure05.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/19a5eab78af93db9a62f2b80.jpg"},{"id":76298118,"identity":"1603330a-90f5-4d17-a1d4-158b7cc22902","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2591617,"visible":true,"origin":"","legend":"\u003cp\u003eGene effect scores for (A) WNT2, (B) WNT7B, and (C) WNT11 in various breast cancer cell lines.\u003c/p\u003e","description":"","filename":"Figure06.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/b62b33ab3f3c5663adb100e0.jpg"},{"id":76298114,"identity":"03d4eeaf-f045-494b-9bf8-b2bd14b6d10c","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":666654,"visible":true,"origin":"","legend":"\u003cp\u003eThe methylation levels of (A) WNT2, (B) WNT7B, and (C) WNT11 between normal tissues and tumor tissues using the SMART database.\u003c/p\u003e","description":"","filename":"Figure07.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/f0b08eb4b818af2203480d55.jpg"},{"id":76298124,"identity":"3d856b48-0d90-4a90-914d-a811e525f1d0","added_by":"auto","created_at":"2025-02-14 13:33:09","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1959280,"visible":true,"origin":"","legend":"\u003cp\u003eThe genetic alterations of WNT11 across the BRCA cohort were analysed by the cBioPortal database. (A) Alterations summary of WNT11 (B) The mutation types, number, and sites of the WNT11 genetic alterations. (C) 3D protein structure of WNT11 from AlphaFold.\u003c/p\u003e","description":"","filename":"Figure08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/85ee8048991c1875e513be9f.jpg"},{"id":76298129,"identity":"1ecd4df7-75c5-4824-8f6a-1d8aaf3c05aa","added_by":"auto","created_at":"2025-02-14 13:33:09","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1744254,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of typical patterns of (A) immune checkpoint genes, (B) immune inhibitors, (C) immune stimulators, (D) chemokines, (E) chemokine receptors among WNT2, WNT7B, and WNT11.\u003c/p\u003e","description":"","filename":"Figure09.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/561bf7fb2e612f32e00d0b03.jpg"},{"id":76298105,"identity":"3689cb6d-b588-4749-b7f4-f564c7a02f4a","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1015748,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of key WNTs from scRNA sequencing database. (A) expression of WNT2 in subgroups of immune cells. (B) expression of WNT7B in subgroups of immune cells. (C) expression of WNT11 in subgroups of immune cells. (D) Visualization of typical patterns of immune cells among WNT2, WNT7B, and WNT11.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/367f5e3bc79431b4b17dc108.jpg"},{"id":76298138,"identity":"e17969b9-1af7-4149-a633-1ad60df7e200","added_by":"auto","created_at":"2025-02-14 13:33:09","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":4857535,"visible":true,"origin":"","legend":"\u003cp\u003eKey WNTs expression predicts drug sensitivity. The correlation between WNTs and 53 identified interacting genes and their drug sensitivity (A) by GDSC, (B) by CTRP. \u0026nbsp;The correlation between WNT2 expression and the most significant drug sensitivity (C) by GDSC, (D) by CTRP. The correlation between WNT7B expression and the most significant drug sensitivity (E) by GDSC, (F) by CTRP. The correlation between WNT11 expression and the most significant drug sensitivity (G) by GDSC, (H) by CTRP.\u003c/p\u003e","description":"","filename":"Figure12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/417eab8316953a8f1c2219d4.jpg"},{"id":76298117,"identity":"24a9b36a-8f66-42b7-8812-b631d992ff0b","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":2443111,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of the related genes of WNT2, WNT7B, and WNT11. (A) KEGG pathway, (B) Reactome pathway, (C) GO-BP, (D) GO-CC, and (E) GO-MF analysis.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/eeb5db742a1629ceae99ea19.jpg"},{"id":92884446,"identity":"0dce8546-7c2c-443e-9bf1-996405193168","added_by":"auto","created_at":"2025-10-06 16:12:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27462821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/ddb58d44-959c-4f1c-954f-44692bc5fb6b.pdf"},{"id":76298126,"identity":"d967ebd3-1865-4d5d-a6f3-b3cb408868ca","added_by":"auto","created_at":"2025-02-14 13:33:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3519186,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/651ee6ea38a44be3b5899b48.pdf"},{"id":76299095,"identity":"886ffba4-488f-446e-96fa-0cfb041d4436","added_by":"auto","created_at":"2025-02-14 13:41:08","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5998290,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/8819a7ce41c0439ab31aff27.xlsx"},{"id":76298106,"identity":"1dc6e0df-b569-46cf-af31-4405860d71dd","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19882,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/579034dda3037e43083ca684.xlsx"},{"id":76298112,"identity":"78aa0e21-7e51-4238-8551-c539d35423b9","added_by":"auto","created_at":"2025-02-14 13:33:08","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":124209,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6001541/v1/12fd45d5320e13efc3568d34.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Expression Pattern, Prognostic and Immunological Significance of the WNT family in Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is one of the most prevalent malignancies and the primary cause of cancer-related mortality in women globally\u003csup\u003e1,2\u003c/sup\u003e. In 2022, approximately 2.3\u0026nbsp;million new cases (11.6% of all cancer cases) and 666,000 deaths (6.9% of all cancer deaths) were documented in women across 157 countries for incidence and 112 countries for mortality, projected to rise to nearly 3\u0026nbsp;million by 2040\u003csup\u003e3,4\u003c/sup\u003e. The incidence of breast cancer is expected to increase in East and South Asian countries, with age-standardized death rates increasing by 7.0\u0026ndash;35% from 1990 to 2030\u003csup\u003e5\u003c/sup\u003e. The heterogeneous nature of breast cancer, characterized by diverse genetic, epigenetic, histopathological, and clinical features, as well as frequent resistance to various therapies, and the development of recurrence and metastasis, poses significant challenges in clinical management\u003csup\u003e6\u003c/sup\u003e. Therefore, understanding the molecular mechanisms underlying breast carcinogenesis is crucial to develop more effective and personalized treatment approaches.\u003c/p\u003e \u003cp\u003eThe human genome contains 19 WNT genes that encode highly conserved secreted glycoproteins\u003csup\u003e7\u003c/sup\u003e. These proteins are hydrophobic, notoriously insoluble, and rich in cysteine, with molecular weights ranging from 39 kDa to 46 kDa, and consist of 350\u0026ndash;400 amino acids\u003csup\u003e8\u003c/sup\u003e. WNT proteins are secreted via the endoplasmic reticulum (ER) and Golgi apparatus and play diverse roles in various cellular and biological processes, including cell proliferation, differentiation, polarity, migration, apoptosis, survival, embryonic development, stem cell maintenance, and tissue homeostasis\u003csup\u003e9\u003c/sup\u003e. WNT signaling is categorized into two main pathways: (1) the canonical (β-catenin-dependent) pathway and (2) the non-canonical (β-catenin-independent) pathway\u003csup\u003e10\u003c/sup\u003e. In the canonical pathway, WNT binding stabilizes β-catenin, preventing its degradation and allowing its nuclear translocation, where it regulates the transcription of genes involved in cell proliferation and survival\u003csup\u003e11\u003c/sup\u003e. Non-canonical pathways, independent of β-catenin, are involved in cell movement and polarity. WNT signaling is initiated when WNT binds to Frizzled (Fz) receptors along with low-density lipoprotein (LDL) receptor-related proteins (LRP) on the cell surface\u003csup\u003e12\u003c/sup\u003e. This binding triggers a cascade involving various intracellular proteins, such as Dishevelled (Dsh), glycogen synthase kinase-3β (GSK-3β), Axin, Adenomatous Polyposis Coli (APC), and β-catenin\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAbnormal WNT activity frequently contributes to cancer progression, metastasis, and treatment resistance, particularly in colorectal, breast, and liver cancers\u003csup\u003e14,15\u003c/sup\u003e. Both genetic alterations and epigenetic modifications, such as promoter hypermethylation of WNT inhibitors in the WNT family, have been associated with human malignancies\u003csup\u003e16\u003c/sup\u003e. WNT signaling promotes cell division by activating transcription factors of the TCF/LEF family of target genes, including c-MYC and cyclin D1, which are essential regulators of cell cycle progression. Additionally, WNT signaling can also inhibit cell cycle arrest by modulating CDK inhibitors, such as p21 and p27\u003csup\u003e17\u003c/sup\u003e. When WNT signaling is dysregulated, often due to mutations in its components, such as APC, β-catenin can also suppress tumor suppressor pathways, leading to unchecked cell proliferation\u003csup\u003e18\u003c/sup\u003e. Moreover, WNT signaling interacts with several other pathways, including PI3K/AKT, Hippo, Notch, MAPK/ERK, and p53, which are implicated in tumorigenesis\u003csup\u003e19\u003c/sup\u003e. Beyond cancer, aberrant expression of WNTs has been linked to various diseases such as osteoporosis and degenerative disorders\u003csup\u003e20,21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThus, identifying abnormal WNT gene expression could serve as a biomarker for early tumor detection, and targeting WNT signaling may offer a novel and promising approach for cancer treatment. In this study, we evaluated the expression of WNT in clinical samples from BRCA patients using the UCSC XENA and GEPIA Web portal. Here, we report that the expression of key WNTs was significantly deregulated in BRCA. Next, we proceeded with prognostic significance, genetic alterations, epigenetic modifications, the ratio of immune cell infiltration and immune therapy-related genes, enrichment analysis, and the responsiveness of key WNTs to drugs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This analysis demonstrated the potential molecular mechanism of WNTs in BRCA progression and highlighted their roles as prognostic biomarkers. These key WNTs may be used for early detection, targeted therapy, or personalized medicine in the treatment of patients with BRCA.\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\u003eList of the web-tools, databases, software and R packages used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeb tools/Software/ R packages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnalysis type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eURL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCSC XENA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeatmap showing the expression of genes among normal breast tissue, solid normal tissue surrounding the tumor, and primary tumor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCGA GTEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEPIA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRCA vs. normal breast tissue analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCGA GTEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eggplot2 R Package (v3.5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer cell lines and gene effect score data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBarplot visualizing\u003c/p\u003e \u003cp\u003ecancer line analysis results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepMap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ggplot2.tidyverse.org/\u003c/span\u003e\u003cspan address=\"https://ggplot2.tidyverse.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMART\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromoter DNA methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA methylation\u003c/p\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinfo-zs.com/smartapp/\u003c/span\u003e\u003cspan address=\"http://www.bioinfo-zs.com/smartapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecBioPortal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenetic alteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency of mutation,\u003c/p\u003e \u003cp\u003eamplification, deep deletion and\u003c/p\u003e \u003cp\u003emultiple alterations across various\u003c/p\u003e \u003cp\u003eBRCA studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlphaFold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein structure predictions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicting protein 3D structures from amino acid sequences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlphaFold Protein Structure Database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.com/\u003c/span\u003e\u003cspan address=\"https://alphafold.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGAplot R package (v8.0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune infiltration correlation\u003c/p\u003e \u003cp\u003eand immune-related genes correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene expression Immune cell ratio, immune score, chemokines, chemokine receptors, immune checkpoint genes (ICGs), immune inhibitors, and immune stimulators correlation analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tjhwangxiong/TCGAplot\u003c/span\u003e\u003cspan address=\"https://github.com/tjhwangxiong/TCGAplot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eggVennDiagram (v1.5.3) R package\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositively correlated immune-related genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVenn diagram visualizing common Positively correlated immune-related genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation analysis results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gaospecial.github.io/ggVennDiagram/\u003c/span\u003e\u003cspan address=\"https://gaospecial.github.io/ggVennDiagram/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTRING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein-protein interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein-protein interaction network visualization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProteins and their interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\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\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclusterProfiler (v4.14.4) R package\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenomic data (gene expression, gene sets, pathways)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional enrichment analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKEGG, Reactome, GO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yulab-smu.top/biomedical-knowledge-mining-book/\u003c/span\u003e\u003cspan address=\"https://yulab-smu.top/biomedical-knowledge-mining-book/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Set Cancer Analysis (GSCA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrug Sensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrelation between drug sensitivity and mRNA expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDSC, CTRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/#/drug\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/#/drug\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eggpubr (v0.6.0) R Package\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation between drug sensitivity and mRNA expression results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisualization of Drug Sensitivity Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDSC, CTRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rpkgs.datanovia.com/ggpubr/\u003c/span\u003e\u003cspan address=\"https://rpkgs.datanovia.com/ggpubr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the expression patterns of WNTs in breast cancer\u003c/h2\u003e \u003cp\u003eWe used UCSC XENA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a web-based, high-performance, interaction visualization, exploration, and analysis tool for multi-omics cancer data, to analyze the expression patterns of WNTs in breast cancer\u003csup\u003e22\u003c/sup\u003e. We generated a heatmap comparing the expression of WNTs among normal breast tissue, solid normal tissue surrounding the tumor, and primary tumor by following these steps: A. Select a study to explore: \"TCGA target GTEx\" as study, B. Select Data Type: Genomic, input all 19 genes from WNT family in 'Add gene or Position' and selected \"Gene Expression\" under \"Basic\" as Dataset, C. Select Data Type: Phenotype, and selected \"Basic\" under \"primary_site,\u0026rdquo; then we typed \"breast\" to select samples and used \"Keep Samples\" to filter breast as the only primary site. We retrieved Gene Symbol, Gene ID, log2(Fold Change), and adjp value using \u0026ldquo;Differential Genes\u0026rdquo; from GEPIA2\u003csup\u003e23\u003c/sup\u003e. We used the \"Expression DIY\" module of GEPIA2 to obtain box plots of gene expression in BRCA and normal breast tissues.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWNTs expression based on clinicopathological characteristics of breast cancer\u003c/h3\u003e\n\u003cp\u003eUALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a comprehensive, facilitative, interaction-based online resource for analyzing cancer omics data and tumor clinical information from TCGA\u003csup\u003e24\u003c/sup\u003e. Using the UALCAN platform, we examined the expression levels of WNT2, WNT7B, and WNT11 across diverse clinical parameters in breast cancer. The parameters included cancer stage, tumor subtype, patient age, and menopausal status.\u003c/p\u003e\n\u003ch3\u003ePan-cancer View of WNTs\u003c/h3\u003e\n\u003cp\u003eThe TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database is an excellent resource for systematically analyzing the connections between gene expression and tumor characteristics in TCGA\u003csup\u003e25\u003c/sup\u003e. We used the TIMER database to assess WNT2, WNT7B, and WNT11 expression levels across various pan-cancers. We used the \u0026ldquo;Gene_DE\u0026rdquo; module to compare the expression profiles of these genes across different cancer types and contrasting tumor and normal tissue samples.\u003c/p\u003e\n\u003ch3\u003eSurvival prognosis analysis across the BRCA cohort\u003c/h3\u003e\n\u003cp\u003eTo evaluate the survival prognostic significance of WNT2, WNT7B, and WNT11 genes in BRCA, we employed the Kaplan-Meier Plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is mainly based on Affymetrix microarray information from the TCGA database\u003csup\u003e26\u003c/sup\u003e. This tool contains information on approximately 54,000 genes and survival data for 21 different types of cancers. We analyzed the impact of these genes on overall survival (OS) in patients with breast cancer.\u003c/p\u003e\n\u003ch3\u003eValidation of prognostic WNTs using GEO and correlation analysis\u003c/h3\u003e\n\u003cp\u003eWe queried the NCBI GEO database using \u0026ldquo;breast tumor\u0026rdquo; as a keyword, selecting original experimental studies that profiled both tumor and normal breast tissues. Our inclusion criteria for the datasets used in our study were as follows: (i) the samples in the datasets were from \u0026ldquo;Homo sapiens\u0026rdquo;; (ii) the datasets were \"expression profiling by array\"; (iii) the dataset submission date to GEO was within the last 12 years (i.e., 2012\u0026ndash;2024); (iv) for each dataset, the total number of available samples was \u0026ge;\u0026thinsp;50; (v) the samples from the studies included both tumor and healthy controls; and (vi) both raw and processed data of the datasets were available. Furthermore, we excluded abstracts, case reports, review articles, studies using cell-line-based experimental designs, and studies lacking healthy controls or using non-human samples from our query. We then downloaded the data using the GEOquery (version 2.74.0) R package and performed differential gene expression analysis using the DESeq2 (v1.46.0) R package \u003csup\u003e27\u003c/sup\u003e. We utilized the EnhancedVolcano (version 1.24.0) R package to visualize the most significant DEGs, with specified thresholds of log2(fold change) (log2FC) greater than |2| and a P value cut-off of 10e-6\u003csup\u003e28\u003c/sup\u003e. The presence of key prognostic WNTs was verified based on the list of differentially expressed genes. Next, we utilized the TCGAplot (v8.0.0) R package to perform gene-gene correlation analysis of key prognostic WNTs across TCGA-BRCA tumors and corresponding normal tissues\u003csup\u003e29\u003c/sup\u003e. The complete workflow for this step, including all code and documentation is available on GitHub: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/bigbiolab/WNT_BRCA\u003c/span\u003e\u003cspan address=\"https://github.com/bigbiolab/WNT_BRCA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene effect scores for key WNTs in BRCA Cell Lines\u003c/h2\u003e \u003cp\u003eWe acquired breast cancer cell lines along with gene effect score data processed by the Chronos algorithm from UALCAN, which is derived from genome-wide CRISPR knockout screens published by Broad's Achilles and Sanger's SCORE projects in DepMap \u003csup\u003e24,30\u003c/sup\u003e. We then visualized the processed data using the ggplot2 (version 3.5.1) R package \u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA methylation analysis of WNTs in BRCA patients\u003c/h3\u003e\n\u003cp\u003eThe Shiny Methylation Analysis Resource Tool (SMART; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinfo-zs.com/smartapp/\u003c/span\u003e\u003cspan address=\"http://www.bioinfo-zs.com/smartapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to generate the tumor vs. Normal Methylation Box Plot to visualize differential methylation patterns between tumor and normal samples\u003csup\u003e32\u003c/sup\u003e. The \"Methylation Box Plot\" option was selected from the \"Methylation DIY\" module, and the BRCA dataset was chosen. The methylation value was configured to a beta-value, and the aggregation method was set to the median.\u003c/p\u003e\n\u003ch3\u003eInvestigation of genetic alteration within BRCA cohorts\u003c/h3\u003e\n\u003cp\u003eThe genetic alteration status of WNT2, WNT7B, and WNT11 in patients with BRCA across various cancer cohorts was analyzed using cBioPortal for Cancer Genomics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e33\u003c/sup\u003e. This investigation focused on breast cancer across 30 studies available on cBioPortal, evaluating alteration frequency, mutation types, mutations, structural variants, amplifications, deep deletions, multiple alterations, and copy number alterations (CNA). The WNT2, WNT7B, and WNT11 alteration frequencies and mutation types in TCGA tumors were examined using the \"Cancer Types Summary\" module of cBioPortal. The \"Mutations\" module was employed to obtain a detailed overview of gene mutations. We obtained the 3D structure of the proteins from AlphaFold\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmune filtration assessment\u003c/h2\u003e \u003cp\u003eWe investigated the correlations between WNT2, WNT7B, and WNT11 expression, and immune cell ratio and immune score utilizing the TCGAplot (version 8.0.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tjhwangxiong/TCGAplot\u003c/span\u003e\u003cspan address=\"https://github.com/tjhwangxiong/TCGAplot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) R package and visualized them using heatmaps.\u003csup\u003e35\u003c/sup\u003e Additionally, we sorted out the common positively correlated chemokines, chemokine receptors, immune checkpoint genes (ICGs), immune inhibitors, and immune stimulators from the heatmaps generated by the TCGAplot R package and visualized them using the ggVennDiagram (v1.5.3) R package\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell analysis\u003c/h2\u003e \u003cp\u003eWe used the \"Gene\" module of Tumor Immune Single-cell Hub 2 (TISCH2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, a scRNA-seq database with detailed cell-type annotation at the single-cell level across different cancer types, to investigate the expression of WNTs in different cell-types within the tumor microenvironment (TME) for BRCA\u003csup\u003e37\u003c/sup\u003e. We set the following configurations: (i) Gene: \"WNT2, WNT7B, and WNT11\"; (ii) Cell-type annotation: \"Celltype(major-lineage)\"; (ii) Cancer type: \"BRCA (Breast Invasive Carcinoma)\"; (iv) Lineage for calculating correlation: \"All lineage\"; (v) selected datasets associated with \"Homo sapiens\" to be used. We downloaded the log (TPM/10\u0026thinsp;+\u0026thinsp;1) expression of genes in different cell types across datasets as \"CSV\" and visualized the data using the ggplot2 (version 3.5.3) R package. We used the ggVennDiagram (v1.5.4) R package to visualize the common cell types across the WNTs in a Venn diagram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePPIN construction and enrichment analysis\u003c/h2\u003e \u003cp\u003eThe STRING database (\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) was utilized to acquire the protein-protein interaction (PPI) networks of WNT2, WNT7B, and WNT11\u003csup\u003e38\u003c/sup\u003e. We input WNT2, WNT7B, and WNT11 in the Multiple proteins and set the following configurations in the \"Basic Settings\" of the \u0026ldquo;Settings\u0026rdquo; module: (i) Network type: full STRING network; (ii) meaning of network edges: evidence; (iii) active interaction sources: Text mining, Experiments, Databases, CO-expression, Neighborhood, Gene Fusion, Co-occurrence; (iv) minimum required interaction score: low confidence (0.150), max number of interactors to show (1st shell): no more than 50 interactors. The results were exported and visualized using the Cytoscape (v 3.10.3) software\u003csup\u003e39\u003c/sup\u003e. Thereafter, we used the retrieved interacting genes from STRING to perform enrichment analysis, including GO (Gene Ontology) enrichment analysis of molecular functions (MFs), cellular components (CCs), biological processes (BPs), and Kyoto Encyclopedia of Genes and Genomes (KEGG) using clusterProfiler (v4.14.4) R package and Reactome enrichment analysis through ReactomePA (v1.50.0)\u003csup\u003e40,41\u003c/sup\u003e. The top 15 most significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) pathways from each analysis were visualized using the enrichplot (v1.26.5) R package\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe employed the \"Drug\" module of Gene Set Cancer Analysis (GSCA)( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform drug sensitivity analysis\u003csup\u003e43\u003c/sup\u003e. We input genes acquired from the STRING database to obtain the correlation between drug sensitivity and mRNA expression using drug sensitivity information from the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP). Furthermore, we used the ggplot2 ((v3.5.1) and ggpubr (v0.6.0) R packages to visualize the correlation between WNT2, WNT7B, and WNT11 expression and drug sensitivity using GDSC and CTRP data exported from GSCA\u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn UCSC XENA, a gene expression heatmap was generated using log2-transformed expression values, and a t-test was performed to compare expression levels between different groups. The Wilcoxon test was used to evaluate the differential expression between tumors and adjacent normal tissues. The Log-rank test and Cox regression were employed to calculate the HR and log-rank P-value in Kaplan\u0026ndash;Meier Plotter to analyze the overall survival curves. Pearson\u0026rsquo;s correlation was used to estimate the correlations between gene expression and chemokines, chemokine receptors, immune checkpoint genes (ICGs), immune inhibitors, immune stimulators, immune cell ratio, and immune score. The Benjamini-Hochberg (BH) procedure was applied to account for the adjusted p-value in pathway enrichment analysis and the false discovery rate (FDR) in drug sensitivity analysis. Results with p-adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were considered statistically significant. All statistical analyses were conducted using R (v4.4.2) through RStudio software (v2024.12.0\u0026thinsp;+\u0026thinsp;467)\u003csup\u003e45\u003c/sup\u003e. A p-value of less than 0.05 was set as the significance threshold in all statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the expression patterns of WNTs in breast cancer\u003c/h2\u003e \u003cp\u003eFirst, we used the UCSC XENA database to examine the expression patterns of WNTs in BRCA patients. Our study revealed substantial deregulation of WNTs in BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In addition, we used the GEPIA2 database, which displayed the log2 fold change of different WNTs in BRCA. WNT2 and WNT7B were upregulated, with log2 fold changes of 1.237 and 1.712, respectively. In contrast, WNT11 was downregulated, with a log2 fold change of \u0026minus;\u0026thinsp;2.639 and a p-value of 4.07E-74 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The relative mRNA expression distribution across the TCGA-BRCA cohort was compiled using GEPIA2 and displayed as WNT2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and WNT7B (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) expression levels were significantly upregulated; conversely, WNT11 expression was significantly downregulated in tumor samples compared to the corresponding control tissue, as shown by box-and-whisker plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Expression patterns of WNTs in Breast cancer. (A) mRNA expression patterns of WNTs in breast cancer patients. Heat Map displaying the expression patterns of WNTs using UCSC XENA. Box-and-whisker plots displaying the relative mRNA expression levels of (B) WNT2, (C) WNT7B, and (D) WNT11, across TCGA-BRCA and normal samples. Grey-and red-colored box areas signify normal and tumor patient samples. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.)\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\u003eLog2 fold change of WNTs in Breast Cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (Tumor)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (Normal)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog2 (Fold Change)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eadjp\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000125084.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.19e-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWNT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eENSG00000105989.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.610\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.380\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.237\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.65e-25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000134245.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.96e-87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000108379.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.15e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000154342.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.64e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000162552.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42e-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000114251.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.02e-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000111186.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16e-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000115596.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80e-34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWNT7B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eENSG00000188064.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.520\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.712\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.35e-50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT8B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000075290.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.57e-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT9A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000143816.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19e-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT9B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000158955.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.37e-24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT10B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000169884.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.12e-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWNT11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eENSG00000085741.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.320\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.450\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.639\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.07e-74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNT16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000002745.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.80e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWNTs expression based on clinicopathological characteristics of breast cancer\u003c/h2\u003e \u003cp\u003eThe UALCAN database analysis revealed the expression pattern of highly deregulated WNTs among cancer stages, major subclasses, patient age, and menopausal status in BRCA. Both WNT2 and WNT7B showed elevated expression levels across individual cancer stages; in particular, WNT2 had higher expression at stage 1, whereas WNT7B had higher expression at stage 4, compared to normal tissue. Conversely, lower expression of WNT11 was observed across individual cancer stages; specifically, stage 2 displayed the lowest expression compared with that in normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similarly, WNT2 and WNT7B were highly upregulated, whereas WNT11 was highly downregulated in HER2-positive breast tumors compared with that in the control tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). WNT2 was highly upregulated in women under the age of 21\u0026ndash;40 years, followed by WNT7B, which showed elevated expression at 81\u0026ndash;100 years, whereas WNT11 showed lower expression at 81\u0026ndash;100 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In addition, WNT2 and WNT7B were highly upregulated in the pre-menopausal and post-menopausal stages; in contrast, WNT11 was highly downregulated in the pre-menopausal stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The expression pattern of highly deregulated WNTs in BC was based on clinicopathological characteristics obtained from the UALCAN database. Expression patterns of WNT2, WNT7B, and WNT11 based on (A) cancer stages, (B) major subclasses, (C) patient age, and (D) menopausal status. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePan-cancer view of the WNT family\u003c/h2\u003e \u003cp\u003eTo investigate the patterns of WNT2, WNT7B, and WNT11 expression in various cancer types, we utilized the TIMER2.0 database to explore the expression levels of WNT2, WNT7B, and WNT11 between 33 cancer types and matched normal pairs from the TCGA and GTEx databases. The expression level of WNT2 was significantly lower in tumors than in the corresponding normal tissues, including CESC, HNSC-HPV positive, KIRP \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), GBM, UCEC \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), KIRC, LIHC, LUAD, LUSC, PRAD, THCA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than in the corresponding control tissues. In contrast, the expression level of WNT2 was significantly higher in BLCA, SKSM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), BRCA, COAD, ESCA, NHSC, READ, and STAD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than in matched adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, the expression level of WNT7B was significantly higher than that in matched adjacent healthy tissues, including CESC \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), BRCA, CHOL, COAD, ESCA, GBM, LUAD, LUSC, READ, SKSM, STAD, THCA, and UCEC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, the expression level of WNT7B was significantly lower in HNSC-HPV-positive, KIHC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), KIRC, LIHC, and NHSC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) tissues than in the corresponding healthy tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The expression level of WNT11 was significantly lower in tumors than in the corresponding normal tissues, including BRCA, HNSC-HPV-positive, KIRC, KIRP, LIHC, LUAD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LUSC, and PCPG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, the expression level of WNT11 was significantly higher in SKSM, ESCA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), COAD, READ, and THCA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than in the matched normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Pan-cancer analysis of highly deregulated WNTs using the TIMER2.0. (A) Expression levels of WNT2 in different tumors versus the corresponding controls; (B) expression levels of WNT7B in different tumors versus the corresponding controls; (C) expression levels of WNT11 in different tumors vs. corresponding controls. (* \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001))\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSurvival prognosis analysis across the BRCA cohort\u003c/h2\u003e \u003cp\u003eThe Kaplan\u0026ndash;Meier Plotter tool was used to explore the correlation between WNTs expression levels and the prognosis of BRCA patients. According to the overall survival module, the analysis revealed that higher expression of WNT2 was significantly associated with a better prognosis than lower expression (OS: HR\u0026thinsp;=\u0026thinsp;0.67, P\u0026thinsp;=\u0026thinsp;0.0029) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), whereas WNT7B displayed a substantially poorer prognosis with elevated expression, indicating its oncogenic potential in BRCA progression (OS: HR\u0026thinsp;=\u0026thinsp;1.34, P\u0026thinsp;=\u0026thinsp;0.035) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Interestingly, following WNT2, elevated expression of WNT11 was linked to a better survival rate than lower expression (OS: HR\u0026thinsp;=\u0026thinsp;0.75, P\u0026thinsp;=\u0026thinsp;0.033) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These findings highlight that WNT2 and WNT11 may serve as protective and favorable prognostic biomarkers. Although WNT7B may have a detrimental effect, it could be a potential target for therapeutic intervention in BRCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The expression of WNTs correlates with survival outcomes in patients with BC. KM plots showing the OS of (A) WNT2, (B) WNT7B, and (C) WNT11. Red and black colors signify higher and lower expression groups.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eValidation of prognostic WNTs using GEO and correlation analysis\u003c/h2\u003e \u003cp\u003eBased on the defined inclusion and exclusion criteria, we selected the BRCA-associated expression profiling by array datasets GSE15852 (43 healthy controls and 43 tumor tissues) and GSE42568 (17 healthy controls and 104 tumor tissues). The analysis yielded 21056 differentially expressed genes (DEGs) from GSE15852 and 44650 DEGs from GSE42568 (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Importantly, all critical prognostic WNT genes, namely, WNT2, WNT7B, and WNT11, were consistently identified within the DEG lists from both datasets, corroborating their validation in external GEO datasets. WNT2 and WNT7B were upregulated, whereas WNT11 was downregulated in the DEG lists, which aligns with the preliminary results derived from GEPIA 2. A volcano plot was used to visualize the most significant DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Scatter plots display pairwise correlations among the key prognostic WNT genes. A significant positive correlation was observed between WNT2 and WNT7B (R\u0026thinsp;=\u0026thinsp;0.19, p-value\u0026thinsp;=\u0026thinsp;7.9 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). and between WNT2 and WNT11 (R\u0026thinsp;=\u0026thinsp;0.08, p-value\u0026thinsp;=\u0026thinsp;0.0077) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. (A) Visualization of the most significant DEGs in the GEO datasets. Scatterplots showing Spearman pairwise correlations between (B) WNT2 and WNT7B, (C) WNT2 and WNT11.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGene effect scores for key WNTs in BRCA Cell Lines\u003c/h2\u003e \u003cp\u003eGene effect scores were assessed across several breast cancer cell lines to determine whether key WNTs were crucial in the progression of BRCA. A negative gene effect score indicated that the cell line was highly dependent on the gene for survival, as gene depletion reduced cell viability. Conversely, a positive score reflected minimal dependency on the gene, with a minor impact on survival upon depletion. Bar plot analysis revealed that WNT2 and WNT11 exhibited negative gene effect scores in most cell lines, whereas WNT7B exhibited a negative gene effect score in a few cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Additionally, all these genes showed a negative gene effect score in MCF7, HCC1187, CAL120, MDAMB436, JIMT1, HMC18, MDAMB468, and EVSAT breast cancer cell lines, as visualized using the Venn diagram (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Gene effect scores for (A) WNT2, (B) WNT7B, and (C) WNT11 in various breast cancer cell lines.)\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDNA methylation analysis of WNTs in BRCA patients\u003c/h2\u003e \u003cp\u003eDNA methylation, a pivotal epigenetic mechanism, plays a crucial role in the onset and progression of diverse forms of cancer. We investigated 20 probes within WNT2, 28 probes within WNT7B, and 31 probes within WNT11 to evaluate the methylation levels of these specific genes. Compared with normal tissues, WNT2 and WNT7B exhibited lower methylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B), whereas WNT11 showed higher methylation in BRCA tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The probes cg03794862, cg20539366, and cg18001524 revealed significant levels of methylation within the WNT2, WNT7B, and WNT11 genes, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The methylation levels of (A) WNT2, (B) WNT7B, and (C) WNT11 between normal tissues and tumor tissues using the SMART database.)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInvestigation of genetic alteration within BRCA cohorts\u003c/h2\u003e \u003cp\u003eInvestigation of the genetic alteration status of WNT2, WNT7B, and WNT11 in patients with BRCA across various cancer cohorts using cBioPortal has revealed notable insights. Among the 12,148 patients analysed, WNT2 alterations were observed in 128 individuals (1%), showing diverse alterations with frequencies ranging from 0.12\u0026ndash;11.08% (Supplementary Fig.\u0026nbsp;2A). Similarly, WNT7B mutations were identified in 173 patients (1%) in the total cohort. Distinct copy number alterations were identified, ranging from 0.37\u0026ndash;28.23% in frequency (Supplementary Fig.\u0026nbsp;3A). As observed, WNT11 displayed a maximum frequency of genetic alterations in 609 patients (5%), showing diverse alterations with frequencies ranging from 0.54\u0026ndash;37.73% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Our analysis highlighted \u0026ldquo;amplification\u0026rdquo; as a common genetic alteration across various BRCA cohorts. The highest frequency rate of \u0026ldquo;amplification\u0026rdquo; of WNT2, WNT7B, and WNT11 was recorded at 3.69%, 15.83%, and 33.25%, respectively, notably within The Metastatic Breast Cancer Project (Provisional, December 2021). Missense mutations were the main type of WNT genetic mutations, and the most frequent mutations were A145T/G (Supplementary Fig.\u0026nbsp;2B), A176T (Supplementary Fig.\u0026nbsp;3B), and V69A (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) Missense mutations in WNT2, WNT7B, and WNT11. The 3D structures of the WNT11 protein were predicted using AlphaFold (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Genetic alterations in WNT11 across the BRCA cohort were analysed using the cBioPortal database. (A) Alteration summary of WNT11. (B) Mutation types, numbers, and sites of the WNT11 genetic alterations. (C) 3D protein structure of WNT11 from AlphaFold.)\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation between WNTs and immune microenvironment in breast cancer\u003c/h2\u003e \u003cp\u003eWe explored whether gene expression was related to the immune infiltration level in BRCA. Immune and stromal cells play essential roles in regulating the development and progression of cancers, accounting for significant components of the tumor microenvironment (TME), and their infiltration levels influence immunotherapy efficacy. WNT2 and WNT11 were significantly positively linked to the immune scores in patients with BRCA (Supplementary Fig.\u0026nbsp;4). WNT2 was negatively correlated with the maximum number of immune cells, including resting mast cells, monocytes, resting NK cells, plasma cells, activated dendritic cells, memory B cells, na\u0026iuml;ve CD4 T cells, activated NK cells, T cells CD8, follicular helper T cells, and eosinophils (Supplementary Fig.\u0026nbsp;5). Similarly, WNT7B showed a negative correlation, particularly with memory-activated CD4 T cells, na\u0026iuml;ve B cells, activated NK cells, follicular helper T cells, plasma cells, memory B cells, and na\u0026iuml;ve CD4 T cells (Supplementary Fig.\u0026nbsp;6). In contrast, WNT11 expression was positively correlated with follicular helper T cells, activated dendritic cells, and na\u0026iuml;ve B cells (Supplementary Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eThe correlation between the expression of WNTs and immune-related genes was visualized using heat maps (Supplementary Fig.\u0026nbsp;8\u0026ndash;12). All these genes were positively correlated with the immune checkpoint gene SIGLEC15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), immune inhibitory genes VTCN1, LGALS9, TGFB1, and TGFBR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), and the immunostimulatory genes TNFSF9, NT5E, CD276, ENTPD1, and CXCL12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). All these genes were positively correlated with the chemokines CXCL12, CCL22, CXCL14, CXCL8, and CCL26 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD), and chemokine receptor CCR10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). This comprehensive correlation underscores the wide-ranging influence of WNT\u0026rsquo;s expression on immunity in BRCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Visualization of typical patterns of (A) immune checkpoint genes, (B) immune inhibitors, (C) immune stimulators, (D) chemokines, (E) chemokine receptors among WNT2, WNT7B, and WNT11.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eSingle-Cell analysis\u003c/h2\u003e \u003cp\u003eThe tumor microenvironment comprises a heterogeneous collection of immune, stromal, and cancer cells. We used TISCH2, an scRNA sequencing database focusing on TME, to provide detailed cell-type annotation and gene expression at the single-cell level in BRCA. We found that WNT2 is highly expressed in myofibroblasts and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). WNT7B is highly expressed in malignant cells and pericytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). WNT11 was highly expressed in the fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Setting the value of log (TPM/10\u0026thinsp;+\u0026thinsp;1)\u0026thinsp;\u0026gt;\u0026thinsp;0, we found that all these genes were expressed in Mono/Macro, epithelial, malignant, myofibroblasts, endothelial cells, fibroblasts, and pericytes, as visualized by the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Expression analysis of key WNTs from the scRNA-sequencing database. (A) Expression of WNT2 in the immune cell subgroups. (B) Expression of WNT7B in the immune cell subgroups. (C) Expression of WNT11 in subgroups of immune cells. (D) Visualization of typical patterns of immune cells among WNT2, WNT7B, and WNT11.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eDrug sensitivity analysis of WNTs\u003c/h2\u003e \u003cp\u003eThese findings suggest the involvement of WNT genes in BRCA prognosis and immune responses. We further investigated the potential links between WNTs and 53 identified interacting genes and their sensitivity to drugs using the GDSC and CTRP databases. Our analysis revealed the strongest positive correlation between WNT7B and genes co-expressed with 19 and 24 anticancer drugs and a negative correlation with 6 and 4 drugs, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA, B). The correlation between the individual expression of WNT2, WNT7B, and WNT11 and drug sensitivities was visualized using the ggpubr package. Notably, the results from the GDSC database indicated that WNT2 expression was most positively correlated with QL-VIII-58 and negatively correlated with AR-42 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC), WNT7B demonstrated the most positive correlation with UNC0638, in contrast to the negative correlation with lapatinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE). WNT11 showed the most positive correlations with Piperlongumine, while exhibiting negative correlations with lestaurtinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, findings from the CTRP database revealed that WNT2 was negatively correlated with all anticancer drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). WNT7B exhibited the most positive correlation with QW-BI-011, but was negatively correlated with saracatinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eF). WNT11, on the other hand, was associated with the most positive correlations with simvastatin and the most negative correlations with lapatinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eH). These results suggest that WNT genes may serve as valuable biomarkers for cross-cancer drug screening, thereby facilitating the identification of effective therapeutic strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Key WNTs expression predicts drug sensitivity. Correlation between WNTs and 53 interacting genes and their drug sensitivity (A) by GDSC and (B) by CTRP. Correlation between WNT2 expression and drug sensitivity (C) by GDSC (D) by CTRP. Correlation between WNT7B expression and drug sensitivity (E) by GDSC (F) by CTRP. The correlation between WNT11 expression and the most significant drug sensitivity (G) by GDSC, (H) by CTRP.)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003ePPIN construction and enrichment analysis\u003c/h2\u003e \u003cp\u003eWe screened the proteins interacting with WNT2, WNT7B, and WNT11 using the STRING online tool to explore their molecular mechanisms in tumorigenesis. We found 53 proteins supported by experimental evidence, and the interaction network of these genes is displayed (Supplementary Fig.\u0026nbsp;13). Our PPIN consists of 53 nodes and 1234 edges. Within the PPIN, the node degrees ranged from 16 to 56, betweenness ranged from 1 to 63.966, and closeness ranged from 0 to 1. The average degree, betweenness, and closeness values for the PPIN were 46.576, 33.802, and 0.947, respectively. Topological/centrality measures for the PPIN, including node degree, betweenness, closeness, clustering coefficient, neighborhood connectivity, and average shortest path length, are presented in Supplementary Tables S2 and S3. Subsequently, we utilized the gene set to perform Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway, and Gene Ontology (GO) analysis of key prognostic WNTs. KEGG pathway analysis correlated with critical pathways, notably \u0026ldquo;Alzheimer's disease,\u0026rdquo; \u0026ldquo;Pathways of neurodegeneration,\u0026rdquo; \u0026ldquo;Breast \u0026amp; Gastric cancer,\u0026rdquo; \u0026ldquo;Hippo signaling,\u0026rdquo; and \u0026ldquo;mTOR signaling pathway\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). Reactome pathway enrichment analysis showed that they are primarily involved in \u0026ldquo;class B/2,\u0026rdquo; \u0026ldquo;GPCR ligand binding,\u0026rdquo; \u0026ldquo;TCF-dependent signaling,\u0026rdquo; and \u0026ldquo;PCP/CE pathway\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). Both pathways were highly enriched in the \u0026ldquo;Wnt signaling pathway.\u0026rdquo; In addition, GO analysis showed that the genes were highly enriched in pathways \u0026ldquo;cell-cell signaling\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC), \u0026ldquo;endocytic vesicle membrane\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD), and \u0026ldquo;frizzled binding\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eE) in the BP, CC, and MF.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Enrichment analysis of genes related to WNT2, WNT7B, and WNT11. (A) KEGG pathway, (B) Reactome pathway, (C) GO-BP, (D) GO-CC, and (E) GO-MF analysis.)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the complex roles of the WNT gene family and treatment strategies in BRCA. The WNT family encodes signaling proteins crucial for regulating various cellular functions, including survival, proliferation, migration, and stem cell renewal\u003csup\u003e46\u003c/sup\u003e. Abnormal activation of the WNT pathway has been identified as a predisposing factor in various cancers and plays a significant role in CSC biology\u003csup\u003e47\u003c/sup\u003e. Notably, WNT2 activates the canonical WNT/β-catenin pathway and has been implicated in CRC and HCC\u003csup\u003e48\u003c/sup\u003e. In addition, WNT7B appears to promote vascularization and angiogenesis in tumors and is linked to CSCC, HNSC, LUSC, and BTCC\u003csup\u003e12\u003c/sup\u003e. In contrast, WNT11 operates through non-canonical WNT signaling pathways, such as the WNT/planar cell polarity (PCP) and WNT/Ca\u0026sup2;⁺ pathways, which are critical for cell organization and metastasis\u003csup\u003e49\u003c/sup\u003e and are linked to prostate cancer\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNearly 40 years ago, studies on mouse mammary tumor virus (MMTV) discovered the WNT gene family as a crucial player in mammary cancer\u003csup\u003e51,52\u003c/sup\u003e. Few studies have explored the role of WNT in cancer, and none have employed a comprehensive approach to breast cancer development and progression. Our study fills this gap by utilizing comprehensive multi-omics to reveal the molecular mechanisms driving tumorigenesis, offering new and promising avenues for targeted therapies and revolutionizing treatment strategies for BRCA patients.\u003c/p\u003e \u003cp\u003eThe study revealed the expression of the WNT family in BRCA, with WNT2 and WNT7B showing upregulation, whereas WNT11 was downregulated, which corroborates previous findings\u003csup\u003e53,54\u003c/sup\u003e. Potential mechanisms may drive these dysregulations: 1) genetic alteration, particularly gene amplification, which may contribute to the heightened WNT2 and WNT7B expression, supported by our cBioPortal analysis; and 2) epigenetic modifications, such as higher methylation, could account for the downregulation of WNT11. In HER2-positive breast cancer, the significant upregulation of WNT2 and WNT7B implies possible synergy with HER2-driven oncogenic signaling. These findings highlight a promising therapeutic approach targeting WNT2 and WNT7B in conjunction with HER2, which could inhibit tumor growth in HER2\u0026thinsp;+\u0026thinsp;patients. Further preclinical and clinical studies are required to validate this combinatorial approach. The pan-cancer analysis emphasizes the tissue- and tumor-dependent expression pattern of WNT2, WNT7B, and WNT11, in line with previous studies\u003csup\u003e7,10,12,46,53,55\u003c/sup\u003e. This context-dependent regulation suggests that WNT signaling may play distinct oncogenic or tumor-suppressive roles depending on the tumor type and microenvironment. Further investigation is needed to elucidate the functional implications of these variations in different cancers.\u003c/p\u003e \u003cp\u003eOur results showed that WNT2's elevated expression was associated with a significantly better prognosis, which contradicts its typical oncogenic role. However, previous studies have demonstrated that specific oncogenes can paradoxically activate protective mechanisms by promoting (i) Oncogene-induced Senescence (OIS)\u003csup\u003e56,\u003c/sup\u003e which limits unchecked proliferation and induces a stable growth arrest; (ii) feedback loops Leading to Tumor Suppression\u003csup\u003e57\u003c/sup\u003e, where oncogene activation triggers compensatory anti-tumor pathways; iii) Non-oncogenic addiction\u003csup\u003e58\u003c/sup\u003e, where cancer cells become dependent on specific pathways, making them more susceptible to targeted therapies; and iv) Immunogenic Modulation of TME\u003csup\u003e59\u003c/sup\u003e, potentially enhancing immune recognition and anti-tumor responses. Further investigations are needed to understand these mechanisms, which may provide a predictive basis for developing novel drug combinations. In contrast, WNT7B overexpression was linked to worse prognosis, which is consistent with its suggested oncogenic role\u003csup\u003e60\u003c/sup\u003e. In contrast, WNT11 overexpression was linked to better survival outcomes, underscoring its potential tumor-suppressive role.\u003c/p\u003e \u003cp\u003eThe consistent identification of WNT2, WNT7B, and WNT11 in two independent BRCA-associated GEO datasets highlights their robustness as biomarkers of breast cancer. This finding is particularly significant, as it aligns with their established roles in breast cancer development and progression.\u003c/p\u003e \u003cp\u003eDNA methylation is a crucial epigenetic modification for enhancing the stability of transcriptional repression associated with cancer\u003csup\u003e61\u003c/sup\u003e. Compared with the corresponding normal tissues, the methylation level of WNT2/7B in tumor tissues was significantly reduced, leading to decreased transcriptional repression stability and subsequent overexpression, possibly contributing to tumor progression. Conversely, WNT11 exhibited significantly increased methylation in tumor tissues, resulting in stable transcriptional repression, reduced expression, and potential impairment of tumor-suppressive functions. Notably, genomic alterations of key WNTs revealed that WNT11 had the most changes (5%), with amplification being the most common type of alteration. This is an unusual association between amplification and tumor suppressor gene expression. Previous studies have shown that tumor suppressor amplification is rare and may signal genomic instability rather than active tumor suppression\u003csup\u003e62,63\u003c/sup\u003e. Further investigation is warranted to determine whether WNT11 amplification represents a compensatory response or a byproduct of genomic instability during tumor evolution.\u003c/p\u003e \u003cp\u003eWNT2 and WNT11, which are positively correlated with immune scores in BRCA, are key players in TME modulation and their potential impact on tumor progression and immune evasion. Immune cell correlation revealed that WNT2 and WNT7B are immunosuppressive drivers and WNT11 is an immune activator. These findings suggest that WNTs are dual modulators of BRCA immunity and are correlated with immune cell infiltration and immunological functions in the TME. Moreover, positive correlations between WNTs and immune-related genes play a critical role in shaping the immune landscape in BRCA, influencing both immune activation and suppression. The association between WNT expression and chemokines suggests that WNT signaling may regulate immune cell trafficking and positioning within the tumor. Single-cell RNA sequencing analysis from the TISCH2 database further confirmed the high expression of WNT genes in multiple immune cell subpopulations, including Mono/Macro, epithelial, malignant, myofibroblasts, endothelial cells, fibroblasts, and pericytes, underscoring their potential influence on immune modulation. Macrophages in the TME can polarize into two distinct phenotypes: M1 (pro-inflammatory, anti-tumorigenic) and M2 (immunosuppressive, tumor-promoting). M2 tumor-associated macrophages (TAMs) promote immune evasion by secreting immunosuppressive cytokines, such as IL-10 and TGF-β, which inhibit T-cell activation and promote regulatory T cells (Tregs). Shifting macrophage polarization from M2 to M1 can enhance cytotoxic T-cell responses and improve the efficacy of immune checkpoint inhibitors (ICIs), facilitating better immune responses\u003csup\u003e64,65\u003c/sup\u003e. Tumor epithelial cells often undergo epithelial-to-mesenchymal transition (EMT), which reduces immune recognition and increases the metastatic potential. EMT downregulates MHC-I expression, making tumor cells less visible to cytotoxic T cells and promoting resistance to apoptosis. Inhibiting EMT can restore immune cell recognition, improving the efficacy of immunotherapies by preventing tumor cell dissemination and enhancing T-cell infiltration\u003csup\u003e66,67\u003c/sup\u003e. Malignant cells evade immune surveillance by upregulating immune checkpoint ligands such as PD-L1, which binds to PD-1 on T cells, leading to T-cell exhaustion and impaired anti-tumor responses. These cells also produce immunosuppressive cytokines, such as TGF-β. Combining immune checkpoint inhibitors (anti-PD-1/PD-L1) with WNT pathway inhibitors can potentially reverse immune suppression in malignant cells, enhancing T cell-mediated tumor clearance\u003csup\u003e68\u0026ndash;70\u003c/sup\u003e. Endothelial cells contribute to angiogenesis and regulate immune cell trafficking. Aberrant endothelial cell function can create a blood-tumor barrier that limits immune cell infiltration into tumors\u003csup\u003e71,72\u003c/sup\u003e. Fibroblasts in the TME secrete cytokines and extracellular matrix proteins that support tumor growth and suppress immune function by creating a physical barrier\u003csup\u003e73,74\u003c/sup\u003e. Pericytes help stabilize the tumor vasculature, but their presence can contribute to the formation of dense blood vessels, limiting immune cell entry into the tumor\u003csup\u003e75\u003c/sup\u003e. Targeting WNT pathways may be a promising strategy for enhancing antitumor immunity and overcoming immune evasion mechanisms in BRCA.\u003c/p\u003e \u003cp\u003eThe differential drug sensitivity of WNT2, WNT7B, and WNT11 suggests that these genes may serve as predictive biomarkers for therapeutic responses in breast cancer. The negative correlation of WNT2 with HDAC inhibitors (AR-42) and BET inhibitors (I-BET-762) implies that WNT2-expressing BRCA cells might resist epigenetic therapies, warranting combination therapeutic approaches. The negative correlation between WNT7B and Lapatinib and Afatinib (HER2-targeted therapies) suggests that WNT7B-overexpressing BRCA tumors may resist HER2-targeted therapies. Potential mechanisms underlying this resistance include: 1) β-catenin Signaling and HER2 crosstalk\u003csup\u003e76\u003c/sup\u003e, 2) PI3K/AKT and MAPK/ERK activation\u003csup\u003e77\u003c/sup\u003e, and 3) EMT induction\u003csup\u003e78\u003c/sup\u003e. Given these findings, targeting WNT7B-driven pathways in combination with HER2 inhibitors could be a potential strategy to overcome resistance in patients with HER2-positive BRCA. The positive correlation of WNT11 with Piperlongumine, an oxidative stress inducer, suggests that targeting redox balance in BRCA cells overexpressing WNT11 may be a promising therapeutic avenue. The positive correlation between WNT11 and Simvastatin, and fluvastatin suggests that these lipophilic statins may have a role in targeting WNT11-driven breast cancer. Statins are known to disrupt lipid metabolism and mevalonate pathways, which are crucial for WNT signaling, making them a potential adjuvant therapy for WNT-driven cancers\u003csup\u003e79,80\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe enrichment of WNT2, WNT7B, and WNT11 proteins in pathways like \u0026ldquo;Wnt signaling\u0026rdquo;\u003csup\u003e7,53\u003c/sup\u003e, \u0026ldquo;Hippo signaling\u0026rdquo;\u003csup\u003e81\u003c/sup\u003e, \u0026ldquo;mTOR signaling pathway\u0026rdquo;\u003csup\u003e82\u003c/sup\u003e and \u0026ldquo;PCP/CE pathways\u0026rdquo;\u003csup\u003e83\u003c/sup\u003e underscores their central role in tumorigenesis. These pathways regulate cell proliferation, differentiation, migration and apoptosis. Dysregulation of these pathways can lead to uncontrolled cellular growth and metastasis, as has been observed in various cancers\u003csup\u003e53,81\u0026ndash;83\u003c/sup\u003e. The identification of \u0026ldquo;Alzheimer's disease\u0026rdquo;\u003csup\u003e84\u003c/sup\u003e and \u0026ldquo;Pathways of neurodegeneration\u0026rdquo;\u003csup\u003e85\u003c/sup\u003e in KEGG analysis is intriguing, as it suggests potential shared mechanisms between neurodegeneration and tumorigenesis. Understanding these shared molecular mechanisms could provide novel insights into the dual modulation of the Wnt pathway for therapeutic benefits. Enriching \u0026ldquo;frizzled binding\u0026rdquo; in GO analysis highlights the role of frizzled receptors, which are critical mediators of WNT signaling. Targeting frizzled receptors has shown promise in preclinical models for the inhibition of metastasis and angiogenesis\u003csup\u003e86\u003c/sup\u003e. The significant enrichment of genes associated with the \u0026ldquo;endocytic vesicle membrane\u0026rdquo; suggested a role in the intracellular trafficking of receptors and ligands. Dysregulation of endocytosis is often linked to drug resistance in cancers, as it can alter the internalization and degradation of therapeutic targets, such as tyrosine kinase receptors\u003csup\u003e87,88\u003c/sup\u003e. Enrichment in \u0026ldquo;GPCR ligand binding\u0026rdquo; supports GPCR modulation to influence the TME, immune response, and angiogenesis\u003csup\u003e89,90\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the RNA expression levels in the present study could not be verified using protein levels. Transcriptomic data (mRNA expression) do not always correlate with protein expression because of post-transcriptional modifications and microRNA (miRNA) regulation. Experimental techniques, such as western blotting, immunohistochemistry (IHC), mass spectrometry, and flow cytometry, are required to confirm protein-level changes. Second, while the study integrates GEO datasets to ensure robust and consistent findings, enhancing result reliability, dataset-specific biases, and batch effects may still influence the results. Third, this study utilized breast cancer cell line data from DepMap, which provides high-throughput functional genomic data enabling drug sensitivity analysis and gene dependency mapping in a controlled environment. However, cell lines lack tumor microenvironment interactions, immune components, and heterogeneity in patient tumors, limiting their physiological relevance. Fourth, we performed single-cell analysis using TISCH2, which reveals cell-type-specific expression and avoids bulk RNA-seq averaging effects. However, limitations include the absence of spatial transcriptomic integration, may not fully represent BRCA heterogeneity across all subtypes, and a lack of functional validation. Further validation using direct in vitro and in vivo studies are required. These findings will establish a basis for future studies to investigate the molecular mechanisms of WNTs relevant to the development and progression of BRCA.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our comprehensive study revealed a significant role of the WNT family in breast cancer, highlighting their diverse roles in tumor progression, immune modulation, and therapeutic response. We found that the mRNA expression levels of WNT2/7B were significantly upregulated, indicating a potential oncogenic driver, while WNT11 was downregulated, exhibiting tumor-suppressive properties in BRCA. Dysregulation of these genes appears to be influenced by genetic alterations and epigenetic modifications, with potential implications for targeted therapy. Clinically, the overexpression of WNT2 and WNT7B in HER2-positive breast cancer suggests a possible synergy between WNT signaling and HER2-driven oncogenic pathways, possibly contributing to resistance to HER2-targeted therapies. Future clinical trials should explore the feasibility of combining WNT inhibitors with standard-of-care treatments in patients with HER2\u0026thinsp;+\u0026thinsp;BRCA. WNT signaling influences tumor-immune interactions, including immune suppression and macrophage polarization, suggesting that WNT inhibition could enhance immune checkpoint blockade therapies. Given the correlation between WNT expression and immune cell infiltration, further investigation of WNT-targeted immunotherapies is warranted, particularly in combination with anti-PD-1/PD-L1 therapies. To establish WNTs as therapeutic targets, in vitro and in vivo studies are necessary to validate the functional roles of WNT2, WNT7B, and WNT11 in BRCA progression. Future research should focus on integrating proteomic validation, spatial transcriptomics, preclinical drug testing, and functional assays to fully elucidate the mechanistic underpinnings of WNT signaling and its therapeutic implications in breast cancer. These findings pave the way for novel therapeutic strategies to modulate WNT signaling to improve patient outcomes in BRCA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could influence the publication of this study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any funding for this study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets we generated and/or analyzed during the current study are freely available in The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/tcga), UCSC XENA (https://xenabrowser.net/), GEPIA2 database (http://gepia2.cancer-pku.cn), Timer 2.0 database (http://timer.comp-genomics.org), UALCAN (http://ualcan.path.uab.edu/), Kaplan-Meier Plotter database (https://kmplot.com/analysis/), DepMap (https://depmap.org/), Shiny Methylation Analysis Resource Tool (SMART) App (http://www.bioinfo-zs.com/smartapp/), cBioPortal web database (https://www.cbioportal.org/), STRING database (https://string-db.org/), GDSC database (https://www.cancerrxgene.org/), and CTRP database (https://clinicaltrialsapi.cancer.gov/). The expression profile datasets GSE15852 and GSE42568 are available from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/). All data produced within this manuscript were attached as \u0026lsquo;Supplementary Materials\u0026rsquo; file. The scripts used to perform the analysis in this are available in the following GitHub repository: https://github.com/bigbiolab/WNT_BRCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatema Tuj Johora Fariha: \u003c/strong\u003eConceptualization, Data curation, Methodology, Formal analysis and Result interpretation, Investigation, Writing\u0026mdash;original draft, Writing\u0026mdash;review and editing. \u003cstrong\u003eMuntasim Fuad:\u003c/strong\u003e Conceptualization, Data Curation, Methodology, Software, Formal analysis and interpretation of results, Investigation, writing \u0026mdash;original draft, writing \u0026mdash;review, and editing. \u003cstrong\u003eChandra Shekhar Saha:\u003c/strong\u003e Data curation, Investigation Methodology, Writing\u0026mdash;original draft, writing \u0026mdash;review, and editing. \u003cstrong\u003eSajjad Hossen: \u003c/strong\u003eData curation, Investigation, Methodology, writing \u0026mdash;original draft, writing \u0026mdash;review, and editing. \u003cstrong\u003eMd. Jubayer Hossain:\u003c/strong\u003e Conceptualization, Formal analysis and result interpretation, Investigation, Software, Resources, Supervision, Writing\u0026mdash;original draft, writing \u0026mdash;review and editing, and project administration. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Dr. Syeda Tasneem Towhid for her invaluable guidance, support, and expertise in CHIRAL Bangladesh. We also extend our appreciation to CHIRAL Bangladesh for their assistance in facilitating various aspects of this study. Their contributions were instrumental in ensuring the success of this study. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmolarz, B., Nowak, A. Z. \u0026amp; Romanowicz, H. Breast Cancer\u0026mdash;Epidemiology, Classification, Pathogenesis and Treatment (Review of Literature). \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2569 (2022).\u003c/li\u003e\n\u003cli\u003eBhushan, A., Gonsalves, A. \u0026amp; Menon, J. U. Current State of Breast Cancer Diagnosis, Treatment, and Theranostics. \u003cem\u003ePharmaceutics\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 723 (2021).\u003c/li\u003e\n\u003cli\u003eBray, F. \u003cem\u003eet al.\u003c/em\u003e Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA. Cancer J. Clin.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 229\u0026ndash;263 (2024).\u003c/li\u003e\n\u003cli\u003eSedeta, E. T., Jobre, B. \u0026amp; Avezbakiyev, B. Breast cancer: Global patterns of incidence, mortality, and trends. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 10528\u0026ndash;10528 (2023).\u003c/li\u003e\n\u003cli\u003eMubarik, S. \u003cem\u003eet al.\u003c/em\u003e Breast Cancer Mortality Trends and Predictions to 2030 and Its Attributable Risk Factors in East and South Asian Countries. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eCurrent State of Breast Cancer Diagnosis, Treatment, and Theranostics. https://www.mdpi.com/1999-4923/13/5/723.\u003c/li\u003e\n\u003cli\u003eWerner, J., Boonekamp, K. E., Zhan, T. \u0026amp; Boutros, M. The Roles of Secreted Wnt Ligands in Cancer. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 5349 (2023).\u003c/li\u003e\n\u003cli\u003eMiller, J. R. The Wnts. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, REVIEWS3001 (2002).\u003c/li\u003e\n\u003cli\u003eGrainger, S. \u0026amp; Willert, K. Mechanisms of Wnt signaling and control. \u003cem\u003eWIREs Syst. Biol. Med.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e1422 (2018).\u003c/li\u003e\n\u003cli\u003eEl-Sahli, S., Xie, Y., Wang, L. \u0026amp; Liu, S. Wnt Signaling in Cancer Metabolism and Immunity. \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 904 (2019).\u003c/li\u003e\n\u003cli\u003eBugter, J. M., Fenderico, N. \u0026amp; Maurice, M. M. Mutations and mechanisms of WNT pathway tumour suppressors in cancer. \u003cem\u003eNat. Rev. Cancer\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 5\u0026ndash;21 (2021).\u003c/li\u003e\n\u003cli\u003eChen, C. \u003cem\u003eet al.\u003c/em\u003e Tumor specificity of WNT ligands and receptors reveals universal squamous cell carcinoma oncogenes. \u003cem\u003eBMC Cancer\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 790 (2022).\u003c/li\u003e\n\u003cli\u003ePatel, S., Alam, A., Pant, R. \u0026amp; Chattopadhyay, S. Wnt Signaling and Its Significance Within the Tumor Microenvironment: Novel Therapeutic Insights. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2872 (2019).\u003c/li\u003e\n\u003cli\u003eZhao, H. \u003cem\u003eet al.\u003c/em\u003e Wnt signaling in colorectal cancer: pathogenic role and therapeutic target. \u003cem\u003eMol. Cancer\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 144 (2022).\u003c/li\u003e\n\u003cli\u003eXu, X., Zhang, M., Xu, F. \u0026amp; Jiang, S. Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities. \u003cem\u003eMol. Cancer\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 165 (2020).\u003c/li\u003e\n\u003cli\u003eYin, P. \u003cem\u003eet al.\u003c/em\u003e Wnt signaling in human and mouse breast cancer: Focusing on Wnt ligands, receptors and antagonists. \u003cem\u003eCancer Sci.\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 3368\u0026ndash;3375 (2018).\u003c/li\u003e\n\u003cli\u003eMartin-Orozco, E., Sanchez-Fernandez, A., Ortiz-Parra, I. \u0026amp; Ayala-San Nicolas, M. WNT Signaling in Tumors: The Way to Evade Drugs and Immunity. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2854 (2019).\u003c/li\u003e\n\u003cli\u003eMukherjee, N. \u0026amp; Panda, C. K. Wnt/\u0026beta;-Catenin Signaling Pathway as Chemotherapeutic Target in Breast Cancer: An Update on Pros and Cons. \u003cem\u003eClin. Breast Cancer\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 361\u0026ndash;370 (2020).\u003c/li\u003e\n\u003cli\u003eEl Sabeh, M., Saha, S. K., Afrin, S., Islam, M. S. \u0026amp; Borahay, M. A. Wnt/\u0026beta;-catenin signaling pathway in uterine leiomyoma: role in tumor biology and targeting opportunities. \u003cem\u003eMol. Cell. Biochem.\u003c/em\u003e \u003cstrong\u003e476\u003c/strong\u003e, 3513\u0026ndash;3536 (2021).\u003c/li\u003e\n\u003cli\u003eProgress of Wnt Signaling Pathway in Osteoporosis. https://www.mdpi.com/2218-273X/13/3/483.\u003c/li\u003e\n\u003cli\u003eFaraji, N., Ebadpour, N., Abavisani, M. \u0026amp; Gorji, A. Unlocking Hope: Therapeutic Advances and Approaches in Modulating the Wnt Pathway for Neurodegenerative Diseases. \u003cem\u003eMol. Neurobiol.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 3630\u0026ndash;3652 (2025).\u003c/li\u003e\n\u003cli\u003eGoldman, M. J. \u003cem\u003eet al.\u003c/em\u003e Visualizing and interpreting cancer genomics data via the Xena platform. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 675\u0026ndash;678 (2020).\u003c/li\u003e\n\u003cli\u003eTang, Z., Kang, B., Li, C., Chen, T. \u0026amp; Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, W556\u0026ndash;W560 (2019).\u003c/li\u003e\n\u003cli\u003eChandrashekar, D. S. \u003cem\u003eet al.\u003c/em\u003e UALCAN: An update to the integrated cancer data analysis platform. \u003cem\u003eNeoplasia\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 18\u0026ndash;27 (2022).\u003c/li\u003e\n\u003cli\u003eLi, T. \u003cem\u003eet al.\u003c/em\u003e TIMER2.0 for analysis of tumor-infiltrating immune cells. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, W509\u0026ndash;W514 (2020).\u003c/li\u003e\n\u003cli\u003eL\u0026aacute;nczky, A. \u0026amp; Győrffy, B. Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. \u003cem\u003eJ. Med. Internet Res.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e27633 (2021).\u003c/li\u003e\n\u003cli\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 550 (2014).\u003c/li\u003e\n\u003cli\u003eBlighe, K. EnhancedVolcano. Bioconductor https://doi.org/10.18129/B9.BIOC.ENHANCEDVOLCANO (2018).\u003c/li\u003e\n\u003cli\u003eLiao, C. \u0026amp; Wang, X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 483 (2023).\u003c/li\u003e\n\u003cli\u003eThe present and future of the Cancer Dependency Map | Nature Reviews Cancer. https://www.nature.com/articles/s41568-024-00763-x.\u003c/li\u003e\n\u003cli\u003eWickham, H. ggplot2. \u003cem\u003eWIREs Comput. Stat.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 180\u0026ndash;185 (2011).\u003c/li\u003e\n\u003cli\u003eLi, Y., Ge, D. \u0026amp; Lu, C. The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization. \u003cem\u003eEpigenetics Chromatin\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 71 (2019).\u003c/li\u003e\n\u003cli\u003ede Bruijn, I. \u003cem\u003eet al.\u003c/em\u003e Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 3861\u0026ndash;3867 (2023).\u003c/li\u003e\n\u003cli\u003eJumper, J. \u003cem\u003eet al.\u003c/em\u003e Highly accurate protein structure prediction with AlphaFold. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e596\u003c/strong\u003e, 583\u0026ndash;589 (2021).\u003c/li\u003e\n\u003cli\u003eLiao, C. \u0026amp; Wang, X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 483 (2023).\u003c/li\u003e\n\u003cli\u003eGao, C. \u003cem\u003eet al.\u003c/em\u003e ggVennDiagram: Intuitive Venn diagram software extended. \u003cem\u003eiMeta\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e177 (2024).\u003c/li\u003e\n\u003cli\u003eTISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment | Nucleic Acids Research | Oxford Academic. https://academic.oup.com/nar/article/51/D1/D1425/6793806.\u003c/li\u003e\n\u003cli\u003eSzklarczyk, D. \u003cem\u003eet al.\u003c/em\u003e The STRING database in 2023: protein\u0026ndash;protein association networks and functional enrichment analyses for any sequenced genome of interest. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, D638\u0026ndash;D646 (2023).\u003c/li\u003e\n\u003cli\u003eShannon, P. \u003cem\u003eet al.\u003c/em\u003e Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 2498\u0026ndash;2504 (2003).\u003c/li\u003e\n\u003cli\u003eWu, T. \u003cem\u003eet al.\u003c/em\u003e clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. \u003cem\u003eThe Innovation\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 100141 (2021).\u003c/li\u003e\n\u003cli\u003eYu, G. \u0026amp; He, Q.-Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. \u003cem\u003eMol. Biosyst.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 477\u0026ndash;479 (2016).\u003c/li\u003e\n\u003cli\u003eGuangchuang Yu. enrichplot. Bioconductor https://doi.org/10.18129/B9.BIOC.ENRICHPLOT (2018).\u003c/li\u003e\n\u003cli\u003eLiu, C.-J. \u003cem\u003eet al.\u003c/em\u003e GSCALite: a web server for gene set cancer analysis. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 3771\u0026ndash;3772 (2018).\u003c/li\u003e\n\u003cli\u003eKassambara, A. ggpubr: \u0026lsquo;ggplot2\u0026rsquo; Based Publication Ready Plots. (2023).\u003c/li\u003e\n\u003cli\u003eR: A Language for Data Analysis and Graphics: Journal of Computational and Graphical Statistics: Vol 5, No 3. https://www.tandfonline.com/doi/abs/10.1080/10618600.1996.10474713.\u003c/li\u003e\n\u003cli\u003eHayat, R., Manzoor, M. \u0026amp; Hussain, A. Wnt signaling pathway: A comprehensive review. \u003cem\u003eCell Biol. Int.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 863\u0026ndash;877 (2022).\u003c/li\u003e\n\u003cli\u003eBao, B. \u003cem\u003eet al.\u003c/em\u003e The biological kinship of hypoxia with CSC and EMT and their relationship with deregulated expression of miRNAs and tumor aggressiveness. \u003cem\u003eBiochim. Biophys. Acta BBA - Rev. Cancer\u003c/em\u003e \u003cstrong\u003e1826\u003c/strong\u003e, 272\u0026ndash;296 (2012).\u003c/li\u003e\n\u003cli\u003eSelvaggi, F., Catalano, T., Cotellese, R. \u0026amp; Aceto, G. M. Targeting Wnt/\u0026beta;-Catenin Pathways in Primary Liver Tumours: From Microenvironment Signaling to Therapeutic Agents. \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1912 (2022).\u003c/li\u003e\n\u003cli\u003eChae, W.-J. \u0026amp; Bothwell, A. L. M. Canonical and Non-Canonical Wnt Signaling in Immune Cells. \u003cem\u003eTrends Immunol.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 830\u0026ndash;847 (2018).\u003c/li\u003e\n\u003cli\u003eZheng, D. \u003cem\u003eet al.\u003c/em\u003e Role of WNT7B-induced Noncanonical Pathway in Advanced Prostate Cancer. \u003cem\u003eMol. Cancer Res.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 482\u0026ndash;493 (2013).\u003c/li\u003e\n\u003cli\u003eTaneja, P. \u003cem\u003eet al.\u003c/em\u003e MMTV mouse models and the diagnostic values of MMTV-like sequences in human breast cancer. \u003cem\u003eExpert Rev. Mol. Diagn.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 423\u0026ndash;440 (2009).\u003c/li\u003e\n\u003cli\u003eYin, P. \u003cem\u003eet al.\u003c/em\u003e Wnt signaling in human and mouse breast cancer: Focusing on Wnt ligands, receptors and antagonists. \u003cem\u003eCancer Sci.\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 3368\u0026ndash;3375 (2018).\u003c/li\u003e\n\u003cli\u003eZhan, T., Rindtorff, N. \u0026amp; Boutros, M. Wnt signaling in cancer. \u003cem\u003eOncogene\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 1461\u0026ndash;1473 (2017).\u003c/li\u003e\n\u003cli\u003eXu, X., Zhang, M., Xu, F. \u0026amp; Jiang, S. Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities. \u003cem\u003eMol. Cancer\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 165 (2020).\u003c/li\u003e\n\u003cli\u003eJiang, S. \u003cem\u003eet al.\u003c/em\u003e Activation of WNT7b autocrine eases metastasis of colorectal cancer via epithelial to mesenchymal transition and predicts poor prognosis. \u003cem\u003eBMC Cancer\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 180 (2021).\u003c/li\u003e\n\u003cli\u003eChandeck, C. \u0026amp; Mooi, W. J. Oncogene-induced Cellular Senescence. \u003cem\u003eAdv. Anat. Pathol.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 42\u0026ndash;48 (2010).\u003c/li\u003e\n\u003cli\u003eR\u0026uuml;ckert, F., Sticht, C. \u0026amp; Niedergethmann, M. Molecular mechanism of the \u0026lsquo;feedback loop\u0026rsquo; model of carcinogenesis. \u003cem\u003eCommun. Integr. Biol.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 506\u0026ndash;507 (2012).\u003c/li\u003e\n\u003cli\u003eLuo, J., Solimini, N. L. \u0026amp; Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e136\u003c/strong\u003e, 823\u0026ndash;837 (2009).\u003c/li\u003e\n\u003cli\u003eWang, Q. \u003cem\u003eet al.\u003c/em\u003e Role of tumor microenvironment in cancer progression and therapeutic strategy. \u003cem\u003eCancer Med.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 11149\u0026ndash;11165 (2023).\u003c/li\u003e\n\u003cli\u003eChen, J. \u003cem\u003eet al.\u003c/em\u003e Up-regulation of Wnt7b rather than Wnt1, Wnt7a, and Wnt9a indicates poor prognosis in breast cancer. \u003cem\u003eInt. J. Clin. Exp. Pathol.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 4552\u0026ndash;4561 (2018).\u003c/li\u003e\n\u003cli\u003eKulis, M. \u0026amp; Esteller, M. DNA Methylation and Cancer. in \u003cem\u003eAdvances in Genetics\u003c/em\u003e vol. 70 27\u0026ndash;56 (Elsevier, 2010).\u003c/li\u003e\n\u003cli\u003eMatsui, A., Ihara, T., Suda, H., Mikami, H. \u0026amp; Semba, K. Gene amplification: mechanisms and involvement in cancer. \u003cem\u003eBiomol. Concepts\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 567\u0026ndash;582 (2013).\u003c/li\u003e\n\u003cli\u003eMotoyama, N. \u0026amp; Naka, K. DNA damage tumor suppressor genes and genomic instability. \u003cem\u003eCurr. Opin. Genet. Dev.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 11\u0026ndash;16 (2004).\u003c/li\u003e\n\u003cli\u003eWei, B. \u003cem\u003eet al.\u003c/em\u003e Polarization of Tumor-Associated Macrophages by Nanoparticle-Loaded \u003cem\u003eEscherichia coli\u003c/em\u003e Combined with Immunogenic Cell Death for Cancer Immunotherapy. \u003cem\u003eNano Lett.\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 4231\u0026ndash;4240 (2021).\u003c/li\u003e\n\u003cli\u003eChoo, Y. W. \u003cem\u003eet al.\u003c/em\u003e M1 Macrophage-Derived Nanovesicles Potentiate the Anticancer Efficacy of Immune Checkpoint Inhibitors. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 8977\u0026ndash;8993 (2018).\u003c/li\u003e\n\u003cli\u003eTaki, M. \u003cem\u003eet al.\u003c/em\u003e Tumor Immune Microenvironment during Epithelial\u0026ndash;Mesenchymal Transition. \u003cem\u003eClin. Cancer Res.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 4669\u0026ndash;4679 (2021).\u003c/li\u003e\n\u003cli\u003eMullins, R. D. Z., Pal, A., Barrett, T. F., Heft Neal, M. E. \u0026amp; Puram, S. V. Epithelial\u0026ndash;Mesenchymal Plasticity in Tumor Immune Evasion. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 2329\u0026ndash;2343 (2022).\u003c/li\u003e\n\u003cli\u003eDeVito, N. C. \u003cem\u003eet al.\u003c/em\u003e Pharmacological Wnt ligand inhibition overcomes key tumor-mediated resistance pathways to anti-PD-1 immunotherapy. \u003cem\u003eCell Rep.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 109071 (2021).\u003c/li\u003e\n\u003cli\u003eRoss, J. \u003cem\u003eet al.\u003c/em\u003e Abstract 1733: Wnt antagonists synergize with immune checkpoint inhibitors to enhance anti-tumor responses. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 1733\u0026ndash;1733 (2018).\u003c/li\u003e\n\u003cli\u003eKrishnan, S. \u003cem\u003eet al.\u003c/em\u003e Abstract B015: Wnt inhibition improves the efficacy of anti-PD-1 therapy in glioblastoma. \u003cem\u003eCancer Immunol. Res.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, B015\u0026ndash;B015 (2024).\u003c/li\u003e\n\u003cli\u003eLeone, P. \u003cem\u003eet al.\u003c/em\u003e Endothelial cells in tumor microenvironment: insights and perspectives. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1367875 (2024).\u003c/li\u003e\n\u003cli\u003eSolimando, A. G., Summa, S. D., Vacca, A. \u0026amp; Ribatti, D. Cancer-Associated Angiogenesis: The Endothelial Cell as a Checkpoint for Immunological Patrolling. \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3380 (2020).\u003c/li\u003e\n\u003cli\u003eLiao, D., Luo, Y., Markowitz, D., Xiang, R. \u0026amp; Reisfeld, R. A. Cancer Associated Fibroblasts Promote Tumor Growth and Metastasis by Modulating the Tumor Immune Microenvironment in a 4T1 Murine Breast Cancer Model. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e7965 (2009).\u003c/li\u003e\n\u003cli\u003eMonteran, L. \u0026amp; Erez, N. The Dark Side of Fibroblasts: Cancer-Associated Fibroblasts as Mediators of Immunosuppression in the Tumor Microenvironment. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1835 (2019).\u003c/li\u003e\n\u003cli\u003eSun, R., Kong, X., Qiu, X., Huang, C. \u0026amp; Wong, P.-P. The Emerging Roles of Pericytes in Modulating Tumor Microenvironment. \u003cem\u003eFront. Cell Dev. Biol.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 676342 (2021).\u003c/li\u003e\n\u003cli\u003eHao, X., Zheng, J., Yu, X., Li, Z. \u0026amp; Ren, G. \u0026beta;-catenin promotes resistance to trastuzumab in breast cance. \u003cem\u003eActa Biochim. Pol.\u003c/em\u003e (2023) doi:10.18388/abp.2020_6357.\u003c/li\u003e\n\u003cli\u003eWilks, S. T. Potential of overcoming resistance to HER2-targeted therapies through the PI3K/Akt/mTOR pathway. \u003cem\u003eThe Breast\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 548\u0026ndash;555 (2015).\u003c/li\u003e\n\u003cli\u003eOliveras-Ferraros, C. \u003cem\u003eet al.\u003c/em\u003e Epithelial-to-mesenchymal transition (EMT) confers primary resistance to trastuzumab (Herceptin). \u003cem\u003eCell Cycle\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 4020\u0026ndash;4032 (2012).\u003c/li\u003e\n\u003cli\u003eGong, L. \u003cem\u003eet al.\u003c/em\u003e The mevalonate coordinates energy input and cell proliferation. \u003cem\u003eCell Death Dis.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 327 (2019).\u003c/li\u003e\n\u003cli\u003eJuarez, D. \u0026amp; Fruman, D. A. Targeting the Mevalonate Pathway in Cancer. \u003cem\u003eTrends Cancer\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 525\u0026ndash;540 (2021).\u003c/li\u003e\n\u003cli\u003eXiao, Y. \u0026amp; Dong, J. The Hippo Signaling Pathway in Cancer: A Cell Cycle Perspective. \u003cem\u003eCancers\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 6214 (2021).\u003c/li\u003e\n\u003cli\u003eP\u0026oacute;pulo, H., Lopes, J. M. \u0026amp; Soares, P. The mTOR Signalling Pathway in Human Cancer. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1886\u0026ndash;1918 (2012).\u003c/li\u003e\n\u003cli\u003eDworkin, S., Jane, S. M. \u0026amp; Darido, C. The planar cell polarity pathway in vertebrate epidermal development, homeostasis and repair. \u003cem\u003eOrganogenesis\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 202\u0026ndash;208 (2011).\u003c/li\u003e\n\u003cli\u003eZhang, C., Wang, J. \u0026amp; Wang, W. Wnt signaling in synaptogenesis of Alzheimer\u0026rsquo;s disease. \u003cem\u003eIbrain\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 316\u0026ndash;325 (2023).\u003c/li\u003e\n\u003cli\u003eAnand, A. A., Khan, M., V, M. \u0026amp; Kar, D. The Molecular Basis of Wnt/\u0026beta;-Catenin Signaling Pathways in Neurodegenerative Diseases. \u003cem\u003eInt. J. Cell Biol.\u003c/em\u003e \u003cstrong\u003e2023\u003c/strong\u003e, 1\u0026ndash;13 (2023).\u003c/li\u003e\n\u003cli\u003eSun, Y., Wang, W. \u0026amp; Zhao, C. Frizzled Receptors in Tumors, Focusing on Signaling, Roles, Modulation Mechanisms, and Targeted Therapies. \u003cem\u003eOncol. Res.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 661\u0026ndash;674 (2021).\u003c/li\u003e\n\u003cli\u003eBanushi, B., Joseph, S. R., Lum, B., Lee, J. J. \u0026amp; Simpson, F. Endocytosis in cancer and cancer therapy. \u003cem\u003eNat. Rev. Cancer\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 450\u0026ndash;473 (2023).\u003c/li\u003e\n\u003cli\u003eMellman, I. \u0026amp; Yarden, Y. Endocytosis and cancer. \u003cem\u003eCold Spring Harb. Perspect. Biol.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, a016949 (2013).\u003c/li\u003e\n\u003cli\u003eLappano, R. \u0026amp; Maggiolini, M. GPCRs and cancer. \u003cem\u003eActa Pharmacol. Sin.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 351\u0026ndash;362 (2012).\u003c/li\u003e\n\u003cli\u003eL\u0026auml;mmermann, T. \u0026amp; Kastenm\u0026uuml;ller, W. Concepts of GPCR ‐controlled navigation in the immune system. \u003cem\u003eImmunol. Rev.\u003c/em\u003e \u003cstrong\u003e289\u003c/strong\u003e, 205\u0026ndash;231 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"WNTs, breast cancer, prognostic biomarkers, expression patterns, immunologic, cancer treatment","lastPublishedDoi":"10.21203/rs.3.rs-6001541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6001541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer (BRCA) is one of the most diagnosed cancers and the leading cause of cancer-related deaths among women globally. Previous studies have shown that the WNT (wingless type) family plays a role in the development of various cancers. However, comprehensive analysis of WNTs in BRCA remains largely unexplored. In this extensive study, we examined the expression patterns, clinical relevance, and survival outcomes associated with the WNT family and identified the key prognostic WNTs. We further investigated genetic alterations, DNA methylation, and drug sensitivity using the cBioPortal, SMART, and GSCA databases. Data from GEO and DepMap were used for validation. Our findings revealed that WNT2 and WNT7B were significantly upregulated, while WNT11 was downregulated, which affected the overall survival of patients with BRCA. Amplification was the most common type of alteration among the key WNTs selected for analysis, showing a significant correlation with immune cells and immune therapy-related genes. Enrichment analysis revealed the involvement of WNTs in crucial pathways responsible for cancer. Additionally, WNTs and their co-expressed genes were strongly associated with the efficacy of anticancer drugs. This study highlights the dysregulation of WNTs in BRCA progression and their correlation with patient survival, suggesting a potential immunotherapeutic target and a valuable prognostic biomarker for BRCA management and treatment.\u003c/p\u003e","manuscriptTitle":"Investigating the Expression Pattern, Prognostic and Immunological Significance of the WNT family in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 13:33:02","doi":"10.21203/rs.3.rs-6001541/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T05:54:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-17T17:04:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151970213929409535074435516345808350637","date":"2025-05-07T03:59:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T10:48:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T22:17:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54228239342676718992043544392355393821","date":"2025-04-01T07:05:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34674495066752846515005671051202907124","date":"2025-04-01T06:54:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331891930087049578577600682489420517697","date":"2025-04-01T06:54:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T06:49:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T06:41:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-02-13T13:46:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-12T13:56:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-10T18:33:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16864653-8ee4-4278-8c88-d9e40610be7b","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44291535,"name":"Biological sciences/Cancer"},{"id":44291536,"name":"Biological sciences/Cell biology"},{"id":44291537,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":44291538,"name":"Biological sciences/Genetics"},{"id":44291539,"name":"Biological sciences/Immunology"},{"id":44291540,"name":"Biological sciences/Systems biology"},{"id":44291541,"name":"Health sciences/Biomarkers"},{"id":44291542,"name":"Health sciences/Diseases"},{"id":44291543,"name":"Health sciences/Health care"},{"id":44291544,"name":"Health sciences/Medical research"},{"id":44291545,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-10-06T16:08:50+00:00","versionOfRecord":{"articleIdentity":"rs-6001541","link":"https://doi.org/10.1038/s41598-025-13315-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-03 15:57:21","publishedOnDateReadable":"October 3rd, 2025"},"versionCreatedAt":"2025-02-14 13:33:02","video":"","vorDoi":"10.1038/s41598-025-13315-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-13315-6","workflowStages":[]},"version":"v1","identity":"rs-6001541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6001541","identity":"rs-6001541","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}