Abstract Breast cancer, a leading global health threat with rising incidence, demands precision medicine guided by molecular subtyping. ADGRV1, an adhesion G protein-coupled receptor gene implicated in tumorigenesis but unexplored in breast cancer, was investigated using TCGA data (1,231 cases) and a local cohort (408 cases). While ADGRV1 showed no differential expression between tumors and normal tissues (P = 0.210), it was significantly downregulated in basal-like subtypes (P < 0.001). The association between high ADGRV1 expression and poor prognosis remains significant in the overall cohort as well as within individual molecular subtype. Functional enrichment analyses linked ADGRV1 to ribosome suppression, ECM remodeling (positive ECM-receptor interaction), and immunosuppression (negative immune pathway regulation), suggesting its role in tumor metastasis. Drug sensitivity assays demonstrated ADGRV1-high tumors confer resistance to lapatinib, gemcitabine, and 5-fluorouracil, particularly in LumB subtypes. Mechanistically, copy number variations and promoter methylation (Pearson r =-0.45, P < 0.001) regulated ADGRV1 expression, with basal-like tumors showing hypermethylation-associated suppression. These findings position ADGRV1 as a prognostic biomarker and potential therapeutic target, highlighting its dual role in tumor microenvironment modulation and drug resistance. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-04695-w. Keywords: Breast cancer, ADGRV1, Methylation, Immune microenvironment, Prognosis, Drug sensitivity Subject terms: Prognostic markers, Breast cancer Introduction Breast cancer, one of the most prevalent malignancies among women globally, poses a severe public health threat due to its rising incidence and therapeutic challenges^[46]1,[47]2. The disease inflicts both physical and psychological trauma while exacerbating socioeconomic burdens, underscoring the urgent need to identify novel biomarkers and therapeutic targets^[48]3. With over 2 million new cases diagnosed annually worldwide, accounting for 24.5% of female cancers, its mortality remains disproportionately high in regions with limited healthcare resources^[49]4. Significant geographic disparities exist: high-income countries achieve early diagnosis through screening, whereas low- and middle-income nations often face delayed detection and poor outcomes^[50]5,[51]6. Genetic factors (e.g., BRCA1/2 mutations) and lifestyle influences (diet, obesity, alcohol) jointly modulate risk, while aging populations and environmental changes are expected to further escalate disease burden^[52]7. Molecular subtyping of breast cancer has revolutionized its management, transitioning from traditional histopathological classification to hormone receptor-positive, HER2-positive, and triple-negative subtypes^[53]8. These subtypes exhibit distinct clinical behaviors and treatment responses—for instance, ER-positive tumors respond to endocrine therapy, while triple-negative breast cancer lacks effective targeted therapies^[54]8. Genomic tools (e.g., Oncotype DX) have advanced precision medicine^[55]9,[56]10. However, existing clinical-pathological parameters fail to fully explain tumor heterogeneity: 30% of node-negative patients develop distant metastases, while some node-positive patients achieve long-term survival^[57]11–[58]13. This paradox highlights the necessity for novel molecular markers, particularly in the era of precision medicine, where discovering new therapeutic targets is critical for optimizing individualized strategies. The ADGRV1 gene, encoding the adhesion G protein-coupled receptor VLGR1, has emerged as a research focus for novel biomarkers due to its large structure and multifunctional domains (e.g., Calx-beta, EPTP-like domains, and seven transmembrane regions)^[59]14,[60]15. ADGRV1 is crucial in the auditory system (mutations cause Usher syndrome)^[61]16,[62]17. ADGRV1 gene is dynamically regulated in a variety of cancers, and its abnormal expression (e.g., significant downregulation in thyroid cancer^[63]18 and high expression in endometrial cancer^[64]19) or somatic mutations (e.g., de novo mutations in platinum-resistant ovarian cancer^[65]20) are related to tumor progression, drug resistance, and immune microenvironment regulation^[66]21, suggesting that the gene has the potential for bidirectional regulation of cancer promotion or tumor suppression, and may be used as a prognostic marker or therapeutic target to participate in the molecular mechanism of different cancer types. In breast cancer, although overall expression shows no difference from normal tissue, intratumoral overexpression associates with reduced survival. Basal-like subtypes exhibit specific downregulation, while abnormal upregulation correlates with advanced stages and unfavorable outcomes. Given breast cancer’s heterogeneity and clinical demands, ADGRV1’s dynamic expression across molecular subtypes positions it as both a prognostic biomarker and therapeutic target. Further exploration of its mechanisms—via cell adhesion, signal transduction, and related pathways—is essential to advance individualized therapies. This study investigates ADGRV1’s expression patterns, prognostic relevance, and epigenetic regulation across breast cancer subtypes, with a focus on its therapeutic implications. Results Expression of ADGRV1 in breast cancer ADGRV1 mRNA expression showed no significant difference between normal adjacent tissues (ANT, n = 113) and primary breast tumors (n = 1111, P = 0.210; Fig. [67]1A). Paired ANT (n = 113) and tumor tissues (n = 113) also exhibited comparable expression levels (P = 0.810; Fig. [68]1B). However, stratification by molecular subtypes revealed that Basal-like tumor (n = 196, P < 0.001) had significantly lower ADGRV1 mRNA compared to ANT (LumA: n = 407; LumB: n = 329; Her2: n = 108; Fig. [69]1C). Fig. 1. [70]Fig. 1 [71]Open in a new tab Correlation between ADGRV1 gene expression and breast cancer. A: Box-scatter plot comparing mRNA expression of ADGRV1 between adjacent normal tissues (ANT, blue, n = 113) and all breast cancer tissues (Tumor, red, n = 1111). B: Differential mRNA expression of ADGRV1 in paired adjacent normal tissues (ANT, blue, n = 113) and breast cancer tissues (Tumor, red, n = 113). C: Distinct mRNA expression patterns of ADGRV1 in adjacent normal tissues (ANT, blue, n = 113) and four molecular subtypes of breast cancer: Luminal A (LumA, green, n = 408), Luminal B (LumB, orange, n = 329), HER2-enriched (Her2, purple, n = 108), and Basal-like (cyan, n = 196). Association between ADGR V1 expression and clinicopathological features To investigate the relationship between ADGRV1 expression and breast cancer, we analyzed 408 malignant cases, of which 407 had valid prognostic data, and 34 were metastatic samples. All samples were obtained from Jiangmen Central Hospital and Jiangmen Maternity and Child Health Care Hospital (Supplemental Tables 1 and 2). More details about the cohort can be found in the supplemental Table 2. Immunohistochemical (IHC) staining index was utilized to generate Kaplan-Meier (KM) survival curves. IHC analysis of 460 samples (benign: n = 52; LumA: n = 105; LumB: n = 107; Her2: n = 86; Basal-like: n = 110) demonstrated higher ADGRV1 staining intensity in LumA/LumB subtypes (Fig. [72]2A). ADGRV1 expression levels were significantly elevated in metastatic lesions of stage IV breast cancer (Fig. [73]2B). The Basal-like tumors showed significantly reduced IHC index versus benign tissues (P < 0.050), while other subtypes lacked statistical significance (Fig. [74]2C). Frequency distribution analysis revealed marginal intergroup differences (P = 0.071; Fig. [75]2D). In supplemental Table [76]1, ADGRV1 expression analysis demonstrated significantly lower expression in primary tumors (low: 68.38% vs. high: 31.62%). Notably, metastatic samples exhibited a markedly elevated prevalence of ADGRV1 high expression (91.18%), suggesting a strong correlation between ADGRV1 upregulation and tumor progression (P < 0.001). Fig. 2. [77]Fig. 2 [78]Open in a new tab Expression of ADGRV1 protein in breast cancer tissues. (A) H&E staining and IHC results (10× and 40× magnification) of benign tumors, LumA, LumB, Her2, and Basal-like subtypes. (B) Box plot of ADGRV1 IHC staining index in benign tumors and different molecular subtypes of malignant tumors. (C) Frequency distribution histogram of ADGRV1 IHC staining index. Table [79]1 shows the ADGRV1 expression patterns in 408 breast cancer patients (median age 52 years, range 28–116) across key clinicopathological parameters. Statistically significant associations were identified between ADGRV1 expression and metastatic status (M classification: P = 0.006) as well as estrogen receptor (ER) status (P = 0.015). Metastatic (M1) tumors demonstrated substantially higher ADGRV1 expression (61.1%) compared to non-metastatic (M0) cases (30.2%). ER-positive tumors showed a 36.8% high-expression rate versus 25.5% in ER-negative tumors. No significant correlations were observed with molecular subtypes (luminal A/B, Her2, basal-like; P = 0.057), pathological classification (P = 0.354), TNM staging (P = 0.066), tumor grade (P = 0.195), T classification (P = 0.269), N classification (P = 0.737), or progesterone receptor status (P = 0.082). Notable non-significant trends included elevated high-expression frequency in stage IV tumors (50.0%), grade 1 lesions (57.1%), and advanced T3-T4 tumors (40.7%), contrasting with lower rates in early-stage (T1-T2: 29.7%) and basal-like subtypes (22.7%). The cohort predominantly consisted of invasive carcinomas (91.2%), with 8.8% missing grade data and minor incomplete staging/T/N/M documentation (0.7–3.9%). These findings may implicate ADGRV1 in metastatic progression and ER-positive disease biology. Table 1. Correlation between ADGRV1 gene expression and clinical characteristics. Parameters Cases (n) ADGRV1 expression P values Low High Molecular subtyping Luminal A 105 67 38 0.0571 Luminal B 107 66 41 Her2 86 61 25 Basal-like 110 85 25 Pathological classification Non-invasive cancer 17 14 3 0.3536 Early invasive cancer 19 14 5 Invasive cancer 372 249 123 Stage I 119 90 29 0.0658 II 170 116 54 III 98 62 37 IV 18 9 9 NA 3 2 1 Grade G1 7 3 4 0.1945 G2 260 168 92 G3 105 75 30 NA 36 30 6 T classification T1 157 115 42 0.2692 T2 187 127 60 T3 28 16 12 T4 26 16 10 NA 10 5 5 N classification N0 220 153 67 0.7370 N1-3 178 121 57 NA 10 5 5 M classification M0 374 261 113 0.0059^* M1 18 7 11 NA 16 7 9 ER Negative 188 140 48 0.0145^* Positive 220 139 81 PR Negative 234 165 69 0.0824 Positive 174 136 38 [80]Open in a new tab Prognostic relevance of ADGRV1 expression To investigate the relationship between ADGRV1 expression and breast cancer prognosis, we analyzed TCGA data stratified into five cohorts: All (n = 1070), LumA (n = 390), LumB (n = 319), Her2 (n = 105), and Basal-like (n = 186). ADGRV1 mRNA levels were compared across tumor stages (I–IV) and adjacent normal tissues (ANT) (Fig. [81]3A-E). In Stage IV tumors, ADGRV1 expression was slightly elevated in the All, Her2, and LumA cohorts, but significantly upregulated in LumB subtype (P < 0.050). Notably, Basal-like tumors exhibited consistently lower ADGRV1 expression compared to ANT across all stages. Fig. 3. [82]Fig. 3 [83]Open in a new tab High expression of ADGRV1 mRNA in breast cancer tissues from TCGA data is associated with poor patient prognosis. A: Stage box plot of all breast cancer patients and adjacent normal tissues in the TCGA database. B: Stage box plot of Luminal A (LumA) breast cancer patients and adjacent normal tissues. C: Stage box plot of Luminal B (LumB) breast cancer patients and adjacent normal tissues. D: Stage box plot of HER2-enriched (Her2) breast cancer patients and adjacent normal tissues. E: Stage box plot of Basal-like breast cancer patients and adjacent normal tissues. F: Progression-free survival (PFS) curve for all TCGA breast cancer patients stratified by high/low ADGRV1 expression, with the x-axis in months. G: PFS curve for LumA breast cancer patients stratified by high/low ADGRV1 expression. H: PFS curve for LumB breast cancer patients stratified by high/low ADGRV1 expression. I: PFS curve for Her2 breast cancer patients stratified by high/low ADGRV1 expression. J: PFS curve for Basal-like breast cancer patients stratified by high/low ADGRV1 expression. For progression-free survival (PFS) analysis, patients were stratified using ADGRV1 expression thresholds (All: 0.4451; Basal-like: 0.1294; Her2: 0.1058; LumA: 0.0358; LumB: 1.0361) derived via R packages survival and survminer. High ADGRV1 expression (red) correlated with worse PFS across all subtypes (Fig. [84]3F–J, log-rank P < 0.050). Validation using 407 patients from Jiangmen Maternity and Child Health Care Hospital and Jiangmen Central Hospital confirmed TCGA findings: high ADGRV1 levels (assessed by immunohistochemistry) predicted poor prognosis in all subtypes (Fig. [85]4A-E, P < 0.010). Importantly, ADGRV1 immunostaining index provided a cost-effective alternative to RNA-seq (FPKM) for prognosis assessment, with results consistent across datasets. These findings underscore ADGRV1’s role as a prognostic marker, particularly in advanced-stage LumB and Basal-like tumors. Fig. 4. [86]Fig. 4 [87]Open in a new tab Survival prognosis plots of 407 breast cancer patients from Jiangmen Maternity and Child Health Care Hospital and Jiangmen Central Hospital, stratified into ADGRV1 high/low expression groups based on IHC staining index. (A) Prognosis of all breast cancer patients. (B) Prognosis of LumA subtype. (C) Prognosis of LumB subtype. (D) Prognosis of Her2 subtype. (E) Prognosis of Basal-like subtype. ADGRV1 expression and pathway activation From the GO functional enrichment results (Fig. [88]5A), high expression of ADGRV1 is significantly negatively correlated with ribosome structural components (structural constituent of ribosome) and ribosomal subunit pathways (with negative NES values), suggesting that this gene may influence the protein translation capacity of tumor cells by inhibiting ribosome generation. ADGRV1 is positively correlated with the immunoglobulin complex and homophilic cell adhesion via plasma membrane adhesion molecules pathways (with positive NES values), indicating its potential involvement in regulating immune responses and intercellular interactions within the tumor microenvironment. This dual regulatory feature may be closely related to its complex role in tumor invasion and metastasis. Fig. 5. [89]Fig. 5 [90]Open in a new tab Gene Set Enrichment Analysis (GSEA) results of ADGRV1 in different breast cancer subtypes. (A) Heatmap of GSEA-GO analysis. (B) Heatmap of GSEA-KEGG analysis. NES: Normalized Enrichment Score. The heatmap illustrates the magnitude of normalized enrichment scores, with black circles representing the absolute values of the normalized enrichment scores. Further KEGG^[91]22 pathway analysis (Fig. [92]5B) revealed that ADGRV1 is significantly positively enriched in the ECM-receptor interaction pathway (with positive NES values), which complements the GO analysis results regarding homophilic cell adhesion. This suggests that ADGRV1 may promote cell-matrix interactions via integrin-mediated signaling, thereby influencing the invasion and metastasis of breast cancer. Notably, this gene exhibits negative regulation in immune-related pathways such as graft-versus-host disease, allograft rejection, and autoimmune thyroid disease (with negative NES values), indicating a systemic immunosuppressive feature that may be linked to its role in reshaping the tumor immune microenvironment by regulating immune checkpoint molecules or cytokine networks. In addition, the negative correlation of ADGRV1 with the viral protein-cytokine receptor interaction pathway (with negative NES values) suggests that it may participate in tumor immune evasion by interfering with virus-related inflammatory response pathways. ADGRV1 expression and drug sensitivity prediction In the drug sensitivity testing of 17 agents (Fig. [93]6A), ADGRV1-high cancer cells exhibited increased tolerance to Lapatinib, Gemcitabine, Epirubicin, Docetaxel, and 5-Fluorouracil. Only Gemcitabine demonstrated tolerance in the ADGRV1-high group of the HER2 subtype (Fig. [94]6B). In the LumB subtype, all five drugs showed resistance in the ADGRV1-high group (Fig. [95]6B-F). Fig. 6. [96]Fig. 6 [97]Open in a new tab Drug sensitivity profiles associated with high ADGRV1 expression in breast cancer tissues. (A) Heatmap of chemotherapy drug sensitivity scores. The heatmap represents log2-transformed fold changes in drug sensitivity between high/low ADGRV1 expression groups. Circles indicate three categories of p-value significance. (B) Box plot of gemcitabine sensitivity in TCGA data across all subtypes (All), HER2-enriched, and Luminal B (LumB) subtypes. (C) Box plot of 5-fluorouracil sensitivity in TCGA data across all subtypes (All) and LumB subtypes. (D) Box plot of epirubicin sensitivity in TCGA data across all subtypes (All) and LumB subtypes. (E) Box plot of lapatinib sensitivity in TCGA data across all subtypes (All) and LumB subtypes. (F) Box plot of docetaxel sensitivity in TCGA data across all subtypes (All) and LumB subtypes. Mechanisms underlying aberrant ADGRV1 expression In the somatic mutation dataset of Breast Invasive Carcinoma (TCGA, PanCancer Atlas) from cBioPortal (Fig. [98]7A), ADGRV1 gene mutations were predominantly missense mutations, accounting for 69.1% of all mutations, followed by truncating mutations at 27.3%, while fusion mutations and splice mutations were relatively rare, each constituting 1.8%. Among 1,066 patients in cBioPortal, ADGRV1 gene exhibited 55 somatic mutations, indicating that somatic mutations are not the primary cause of ADGRV1’s influence on breast cancer prognosis. Figure [99]7B shows that these mutation sites are distributed across different functional domains of ADGRV1, particularly concentrated in critical regions such as Laminin_G_3 and EPTP, which may impact gene function. Fig. 7. [100]Fig. 7 [101]Open in a new tab Genetic and epigenetic alterations of ADGRV1 in breast cancer. (A) Mutation types of ADGRV1 in the TCGA database. (B) Mutation sites of ADGRV1 in the TCGA database. (C) Box plot comparing ADGRV1 mRNA expression levels and methylation status. (D) Heatmap of ADGRV1 methylation Beta values across molecular subtypes. (E) Correlation plot between ADGRV1 mRNA expression levels and methylation status. In the analysis of mRNA expression across different copy number variation (CNV) types, Fig. [102]7C reveals that ADGRV1 CNVs primarily manifest as “Gain” (third-copy amplification), with increased ADGRV1 expression observed in samples with third-copy gains and reduced expression in samples with deep deletions, suggesting that ADGRV1 expression may be directly influenced by CNVs. The methylation status heatmap in Fig. [103]7D demonstrates no significant differences in methylation levels among LumA, LumB, and Her2 subtypes, whereas Basal-like subtypes and normal tissues exhibited significantly elevated methylation levels at probes cg20938802, cg13003311, and cg21998208. In Fig. [104]7E, a negative correlation between methylation levels and ADGRV1 mRNA expression was confirmed (Pearson r = -0.45, P < 0.001), indicating that elevated methylation leads to reduced ADGRV1 expression, which implies that methylation plays a critical role in regulating ADGRV1 expression. Discussion In this study, we explored the expression of ADGRV1 in breast cancer tissues and its association with patients’ clinicopathological features and prognosis, as well as the underlying molecular mechanisms. Analysis of data from the TCGA public database revealed that there was no significant difference in ADGRV1 mRNA expression between breast cancer tissues and adjacent normal tissues, suggesting that ADGRV1 may not serve as a primary driving factor in the development of breast cancer. However, further analysis based on molecular subtyping demonstrated characteristic differences in ADGRV1 expression among various breast cancer subtypes. While basal-like and triple-negative breast cancers are distinct molecular classifications, their clinical differentiation via standard immunohistochemistry remains challenging. Due to the overlapping phenotypic characteristics in routine diagnostic practice, we pragmatically merged these two categories for analytical purposes in this study^[105]23. In particular, in basal-like breast cancer, the expression level of ADGRV1 was significantly lower than that in adjacent normal tissues, indicating the possibility of a subtype-specific suppression mechanism. Additional clinical data analysis, which incorporated information from both the TCGA database and patient records from Jiangmen Maternity and Child Health Care Hospital and Jiangmen Central Hospital, showed that ADGRV1 expression levels were closely associated with patients’ clinicopathological characteristics. Overall, patients with high ADGRV1 expression tend to have a poor prognosis. This is consistent with the previous findings of Cowan and Guda, whose research also indicated that ADGRV1 (GPR98) is associated with shorter survival and a higher risk of metastasis in patients with breast cancer (BC)^[106]24. Notably, in LumB and basal-like breast cancer patients, high ADGRV1 expression was markedly upregulated in advanced (Stage IV) tumors, suggesting a role for ADGRV1 in tumor progression. Primary tumor specimens from stage IV breast cancer patients are typically limited to biopsy samples in clinical practice. Due to institutional restrictions on using these minimally invasive specimens for research purposes, our study exclusively utilized metastatic lesions (brain, lung, and bone metastases) from stage IV breast cancer patients. Histological subtyping of metastatic specimens is complicated by their dissociation from the mammary microenvironment. Our IHC analysis revealed enhanced staining intensity in metastatic lesions compared to primary tumors (Fig. [107]2B), demonstrating upregulated ADGRV1 expression in metastatic sites. This observation supports our hypothesis that elevated ADGRV1 expression is associated with poorer clinical prognosis. In contrast to findings in other cancer types—for instance, Claire Briet’s study on thyroid follicular carcinoma reported low ADGRV1 expression^[108]18, whereas Dao-Lai Zhang observed high expression in endometrial carcinoma^[109]19—these results indicate that the regulatory mechanisms of ADGRV1 differ substantially across various tumors. Integrating TCGA data, IHC results, and KM survival analyses across breast cancer subtypes, we propose that the high-expression threshold of ADGRV1 for prognostic prediction is not universally defined relative to adjacent normal tissue (ANT), but rather subtype-specific. Although overall ADGRV1 levels were low in basal-like subtype, basal-like patients with relatively higher ADGRV1 expression exhibited worse clinical outcomes compared to those with lower expression. Similar patterns were observed in other subtypes, highlighting the need for distinct ADGRV1 expression thresholds in each molecular subgroup. As is well-established, the pathogenesis of basal-like tumors fundamentally differs from hormone receptor-positive or HER2-amplified subtypes. This raises a critical question: Why is ADGRV1 expression inherently reduced in basal-like tumors? We hypothesize that epigenetic mechanisms, such as ADGRV1 promoter hypermethylation or diminished activity of transcription factors and enhancers specific to this subtype, may drive this phenomenon. To address this, we conducted exploratory analyses of ADGRV1 methylation patterns and CNV in subsequent sections. In investigating the mechanisms behind the aberrant expression of ADGRV1, our study examined both genetic and epigenetic factors. Although the mutation rate of ADGRV1 was low (approximately 3%) and not significantly related to prognosis, its copy number variation (CNV) was positively correlated with mRNA expression levels, suggesting that gene amplification may lead to abnormal overexpression in certain patients. Moreover, promoter methylation levels were negatively correlated with ADGRV1 expression, with higher methylation status—especially in the basal-like subtype—potentially accounting for its reduced expression. These genetic and epigenetic regulatory mechanisms may influence cell adhesion, signal transduction, and the immune microenvironment, ultimately contributing to the poorer prognosis observed in patients with high ADGRV1 expression. ADGRV1 may influence breast cancer progression through coordinated mechanisms involving suppression of ribosome function, remodeling of the extracellular matrix (ECM), and regulation of the immune microenvironment. Specifically, its positive modulation of ECM receptor signaling and broad inhibition of immune-related pathways may collectively establish a microenvironment that promotes tumor metastasis. These multi-pathway interactions provide novel insights into the molecular mechanisms underlying ADGRV1’s potential role as a prognostic biomarker in breast cancer, though its precise regulatory network requires further experimental validation. Drug sensitivity analysis showed that high ADGRV1 expression was significantly correlated with resistance to multiple anticancer drugs. In LumB breast cancer, cells with high ADGRV1 expression exhibited increased resistance to agents such as lapatinib, gemcitabine, and 5-fluorouracil, while showing relatively lower IC50 values for epirubicin and docetaxel. These findings not only align with the treatment recommendations outlined in the CSCO Breast Cancer Guidelines 2023 but also help explain the association between high ADGRV1 expression and poorer patient outcomes. Despite these comprehensive findings, the study has several limitations. First, some analyses relied on public databases and local samples, and the sample size may be insufficient to fully capture the true expression patterns of ADGRV1 across different breast cancer subtypes. Second, the current analysis is primarily based on bioinformatic data and statistical correlations, lacking in-depth functional validation through in vitro and in vivo experiments. Finally, given that breast cancer is a complex disease regulated by multiple factors and pathways, the role of a single gene like ADGRV1 might be influenced by interactions with other molecules and signaling pathways, necessitating further investigation into these interrelationships. Future research will focus on expanding the sample size and validating the findings through multicenter studies, conducting functional experiments using cellular and animal models to explore the specific mechanisms by which ADGRV1 affects tumorigenesis, progression, and drug resistance, and further dissecting the interactions between ADGRV1, cellular signaling pathways, and epigenetic regulation. Additionally, we plan to investigate personalized treatment strategies based on ADGRV1 expression levels and related molecular mechanisms, including the potential for combination chemotherapy or targeted therapy to provide more precise treatment options for breast cancer patients. In summary, our study comprehensively examined the expression and prognostic significance of ADGRV1 in breast cancer, as well as its underlying molecular mechanisms, revealing that high ADGRV1 expression is associated with poorer prognosis and drug resistance. These findings underscore the potential of ADGRV1 as a prognostic biomarker and pave the way for the development of personalized therapeutic strategies in breast cancer. Conclusion This study reveals that CNV amplification and promoter demethylation of ADGRV1 can lead to its aberrantly high expression in tissues. High expression of ADGRV1 predicts poor prognosis in patients across various breast cancer subtypes and is associated with tumor cell resistance to multiple chemotherapy drugs and abnormal immune infiltration patterns. ADGRV1 holds potential as a biomarker and therapeutic target in breast cancer. Methods Downloading and processing public data for ADGRV1 Data were obtained breast cancer-related data from The Cancer Genome Atlas (TCGA) through the Genomic Data Commons (GDC) website ([110]https://portal.gdc.cancer.gov). The Project ID and Program ID for these data were TCGA-BRCA and TCGA, respectively. The datasets included RNA-Seq data (updated on April 7, 2024), methylation data (updated on August 27, 2015), and clinical data (updated on May 9, 2016). The RNA-Seq dataset comprised 1,231 samples, including 1,111 primary tumor cases (1,095 unique tumors), 7 metastatic breast cancer cases, and 113 solid tissue normal samples paired with 113 clinical breast cancer samples. To analyze ADGRV1 expression across molecular subtypes, samples were classified into five groups: all subtypes (including 70 normal-like cases), Luminal A (LumA), Luminal B (LumB), HER2-enriched (Her2), and Basal-like (BasL). Custom scripts were developed to visualize ADGRV1 expression patterns across these subtypes. Identification of differentially expressed genes Based on the optimal cutoff value derived from survival analysis, samples were stratified into high- and low-expression groups according to the expression levels of the ADGRV1 gene (measured as FPKM-UQ). For data preprocessing, raw count data from TCGA were normalized using the edgeR package (version 4.2.0). Specifically, the calcNormFactors function in edgeR was applied to adjust for sequencing depth variations across samples, ensuring the accuracy of subsequent differential expression analysis. Following normalization, differential expression analysis was conducted using the limma package (version 3.60.3). Linear models were fitted to the preprocessed data using the lmFit function, and empirical Bayes moderation was performed via the eBayes function to calculate log2 fold change (log2FC) and p-values for each gene. Significantly differentially expressed genes were identified using thresholds of |log2FC| > 1 and adjusted p-value < 0.05. These genes were subsequently used for gene pathway enrichment analysis. Immunohistochemical staining for ADGRV1 protein Paraffin-embedded tissue sections were processed for immunohistochemistry (IHC) following standard protocols, including dewaxing, antigen retrieval, and incubation with primary and secondary antibodies. Positive staining was visualized using DAB, followed by nuclear counterstaining with Mayer’s hematoxylin. Sections were dehydrated, cleared, and mounted with neutral resin. Immunohistochemical staining index The Staining Index (SI) was calculated by averaging the scores determined by two independent, experienced researchers following established methodology^[111]25. The proportion of tumor cells was scored according to the following criteria: “0” for no positive tumor cells; “1” for < 10% positive tumor cells; “2” for 10–35% positive tumor cells; “3” for 35–70% positive tumor cells; and “4” for > 70% positive tumor cells. Staining intensity was graded as: “0” for no staining; “1” for weak staining (pale yellow); “2” for moderate staining (yellow-brown); “3” for strong staining (brown); and “4” for super strong staining (brown-black). The SI was derived by multiplying the staining intensity score by the proportion score of positive tumor cells. This scoring system was applied to evaluate ADGRV1 expression in lung tumor specimens, with possible SI values of 0, 1, 2, 3, 4, 6, 8, 9, 12, or 16. ADGRV1 regulatory protein expression levels were stratified as follows: SI < 6 indicated low expression, while SI ≥ 6 denoted high expression in tumor tissues.Whole-slide imaging of breast cancer tissue sections was performed using an M8 digital microscope (PreciPoint, Freising, Bavaria, Germany) with 10× and 40× objective lenses. Association between ADGRV1 expression and signaling pathway activation status This study employed Gene Set Enrichment Analysis (GSEA) to identify gene functions and signaling pathways associated with breast cancer and its molecular subtypes. Publicly available unstranded RNA-seq count data from The Cancer Genome Atlas (TCGA) breast cancer cohort were stratified into five subgroups: all subtypes combined (All), Luminal A (LumA), Luminal B (LumB), HER2-enriched (Her2), and Basal-like (BasL). For each subgroup, genes were ranked based on log2 fold change (LogFC) values to generate ordered gene lists. Functional and pathway analyses were performed using the “gseGO” and “gseKEGG” functions from the R package ClusterProfiler (v4.10.1), which calculated the Normalized Enrichment Score (NES), gene ratio, and adjusted p-value for each gene set. Finally, customized R scripts were implemented to visualize shared or subtype-specific biological functions and pathways that were significantly upregulated or downregulated in tumor tissues compared to normal controls. ADGRV1 expression and drug sensitivity prediction ADGRV1 FPKM data from TCGA breast cancer samples were divided into five subtypes: All, LumA, LumB, Her2, and BasL. The R packages survival (version 3.6-4) and survminer (version 0.4.9) were used to analyze drug sensitivity in high and low ADGRV1 expression groups by determining optimal cutoff values for gene expression, progression-free survival time, and status. Samples were classified into high or low expression groups based on these cutoffs. The intersection of FDA-approved breast cancer drugs and OncoPredict’s GDSC2 cancer drug database yielded 17 common drugs. Drug sensitivity for these 17 drugs was calculated using OncoPredict. Results with p-values < 0.05 were visualized using ggplot2 (version 3.5.1). ADGRV1 abnormal expression mechanism exploration To explore the methylation mechanism in the promoter region, the following steps were taken: The whole-genome methylation data of breast cancer (BRCA) samples from the TCGA database were retrieved. Methylation probes associated with the ADGRV1 promoter region were selected, and their Beta values were extracted as quantitative indicators of methylation levels. Beta values range from 0 to 1, representing unmethylated and fully methylated states, respectively. Beta values greater than or equal to 0.6 mean complete methylation, between 0.2 and 0.6 partial methylation, and less than or equal to 0.2 no methylation at all. To analyze the changes in ADGRV1 promoter methylation across different tissue types, the samples were grouped into different breast cancer subtypes and adjacent normal tissues (e.g., BasL, Her2, LumA, LumB, NorL, Normal). By comparing the Beta values of ADGRV1-related methylation probes across these groups, the variations in methylation levels in cancerous and normal tissues were assessed. To visualize these differences, a heatmap was generated using the Pheatmap package (version 1.0.12), categorizing the samples by subtype to observe the distribution of ADGRV1 promoter methylation in different breast cancer subtypes. Next, the correlation between the methylation region and mRNA expression was analyzed. The cBioPortal website was used for the correlation analysis using the Breast Invasive Carcinoma (TCGA, Cell 2015) dataset. RSEM method was used to obtain mRNA expression data, which was then correlated with the ADGRV1 gene methylation data from the HM450 platform to assess the impact of methylation on ADGRV1 mRNA expression levels. The results were displayed in a scatter plot. Electronic supplementary material Below is the link to the electronic supplementary material. [112]Supplementary Material 1^ (17.5KB, docx) Acknowledgements