An Anoikis-Based Prognostic Signature Identifies MYC as a Key Regulator of Malignancy and Immunotherapy Resistance in Bladder Cancer

Abstract

Background:

Resistance to anoikis is a critical mechanism that enables metastatic dissemination. Abrogation of this cellular safeguard is therefore a hallmark of aggressive cancer progression. Despite the importance of anoikis, there are still few biomarkers among anoikis-related genes (ARGs) that could aid in the prognostication of bladder cancer (BC) patients and potentially serve as drug targets.

Methods:

This study leveraged bioinformatics analyses of publicly available BC datasets to evaluate the association between differentially expressed ARGs and patient prognosis. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was employed to build a novel prognostic signature model for BC based on ARGs. This model was also used to predict the response of BC to anticancer drugs. Additionally, immunohistochemistry was used to assess expression of the key gene, MYC, in BC samples obtained from patients undergoing surgery and from those receiving immune checkpoint inhibitor (ICI) therapy.

Results:

The ARG-based signature, developed and validated through the analysis of public databases, was an independent predictor of patient outcomes. Furthermore, it effectively stratified patients into two cohorts (high- and low-risk), allowing investigation of differential drug sensitivities. The risk stratification model identified 10 ARGs (IGF1, CALR, E2F1, MYC, PLK1, SATB1, FASN, ID2, RAC3, and GKN1) as potential therapeutic vulnerabilities. Notably, MYC was identified as a central hub gene within the ARG signature. Elevated MYC expression was strongly associated with worse prognosis in muscle-invasive bladder cancer (MIBC), and with a diminished response to immunotherapy.

Conclusion:

This work demonstrated significant prognostic value for the ARG-based model. Specific ARGs could function as crucial biomarkers for patient outcome, while simultaneously offering new avenues for therapeutic intervention.

Graphical abstract

Keywords

bladder cancer / anoikis / MYC / immunohistochemistry / immunotherapy

1. Introduction

Bladder cancer (BC) ranks as the fourth most common malignancy globally in males and the ninth in females [1]. As the most prevalent neoplasm of the urinary tract, it constitutes approximately 4% of all adult malignancies and presents a growing health problem due to its rising incidence worldwide [2]. While immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of advanced BC, its efficacy is limited to a subset of patients. Current biomarkers, such as Programmed Cell Death Ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and Fibroblast Growth Factor Receptor (FGFR) alterations, have shown inconsistent predictive power and/or low incidence, leaving a critical unmet need for robust biomarkers to guide patient selection and improve outcomes [3, 4]. For instance, PD-L1 expression does not reliably predict response in all patients, and high TMB is found in only a fraction of cases. This highlights the need to explore novel biological pathways that drive malignancy and treatment resistance in BC.

A fundamental process co-opted by cancer cells for metastatic dissemination is the evasion of anoikis, which is a form of programmed cell death initiated upon detachment from the extracellular matrix (ECM) [5, 6]. This cellular safeguard is orchestrated by a complex network of molecular regulators, including integrin signaling, the Phosphoinositide 3-kinase (PI3K)/AKT and Focal Adhesion Kinase (FAK) pathways, and BCL-2 family proteins [7]. Resistance to anoikis, a recognized hallmark of cancer, allows tumor cells to survive in circulation and colonize distant organs. Anoikis has been strongly implicated in the progression of numerous cancer types, including breast and prostate cancer [8, 9]. Despite its fundamental importance, the possibility of leveraging anoikis-related genes (ARGs) to create prognostic tools for BC patients has been largely overlooked. While ARG-based signatures have shown promise in other malignancies [10, 11], their application to BC, especially for predicting response to immunotherapy, remains unknown.

The aim of this study was therefore to develop and validate a novel ARG-based prognostic signature for BC, and to evaluate its predictive value for clinical outcomes and response to ICI therapy. We sought to identify key regulatory genes, with a particular focus on MYC as a potential biomarker and therapeutic target. Moreover, we leveraged clinical and pathological data from local BC cohorts (The First Affiliated Hospital of Guangxi Medical University, GXMU; and The Fourth Affiliated Hospital of Guangxi Medical University, FAH) and recent immunotherapy recipients. Our analysis confirmed that expression of the hub gene MYC in BC was significantly associated with advanced T stage, poor prognosis, and the efficacy of ICI therapy.

2. Materials and Methods

2.1 Bioinformatic Analysis

2.1.1 Data Acquisition and Gene Set Curation

Transcriptomic (RNA-seq) and corresponding clinical data for BC was extracted from The Cancer Genome Atlas (TCGA-BLCA cohort, comprising 412 tumor and 19 normal samples) and the Gene Expression Omnibus (GEO accession: GSE13507). A comprehensive list of ARGs was curated from the Harmonizome and GeneCards databases, retaining genes with a relevance score exceeding 0.4. To investigate the tumor immune microenvironment, immune cell infiltration data were retrieved from the Tumor Immune Estimation Resource (TIMER) [12]. Furthermore, to evaluate the predictive utility of our model for immunotherapy outcomes, we incorporated the IMvigor210 trial cohort, a real-world dataset of urothelial cancer patients treated with Atezolizumab post-chemotherapy [13].

2.1.2 Identification and Prognostic Evaluation of ARGs

Differential expression analysis was performed to identify ARGs with altered expression in BC compared to normal tissues. This utilized the TCGA-BLCA dataset and the “limma” R package (version 3.54.2; Bioconductor, www.bioconductor.org), with a false discovery rate (FDR) 0.05 and an absolute log2 fold change (|log2FC|) >1. The expression patterns of the most significantly altered genes were visualized via a heatmap and volcano plot. Subsequently, the prognostic value of each ARG was determined in both the TCGA and GEO cohorts by conducting univariate Cox regression analysis with the “survival” R package (version 3.5-5; CRAN, https://cran.r-project.org/). The interplay between these ARGs and corresponding copy number variations (CNVs), sourced from the UCSC XENA platform, was explored and depicted in network diagrams.

2.1.3 Molecular Subtyping via Consensus Clustering

To stratify BC patients into distinct molecular subtypes, we performed consensus clustering based on the expression profiles of the prognostic ARGs and the “ConsensusClusterPlus” package (version 1.54.0; Bioconductor, www.bioconductor.org; settings: 1000 reps, 80% item resampling, Pearson distance, and Ward’s linkage algorithm). Differences in overall survival (OS) among the identified subtypes were assessed using the Kaplan-Meier (KM) method. Techniques including Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-distributed Stochastic Neighbor Embedding (tSNE) were used to evaluate the clustering accuracy. Expression patterns and clinicopathological correlations were depicted in heatmaps and box plots. We also examined immune infiltration patterns and pathway enrichment differences among clusters by Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) analyses using MSigDB’s Kyoto Encyclopedia of Genes and Genomes (KEGG) and Hallmark gene sets, respectively.

2.1.4 Development of ARG-Based Prognostic Model and Nomogram

Initially, univariate Cox regression analysis (“survival” R package) was used to screen for ARGs with prognostic significance. These were further refined using the “glmnet” package to build a LASSO-Cox model (10-fold cross-validation to select the optimal lambda value), creating a prognostic signature for predicting outcomes in BC patients. This signature was expressed as a risk score equation: ARG-based risk score = \mathrm{\Sigma } (\betai \times Expi). For model development and validation, the patient cohort was randomly allocated into training and test sets. A risk score was calculated for each patient, and using the median score as a threshold, patients were stratified into high- and low-risk groups. The prognostic efficacy of this signature was evaluated using Kaplan-Meier survival analysis, while its predictive accuracy was quantified by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariate Cox regression analysis was performed to assess whether the risk signature was an independent predictor of survival. Additionally, a heatmap was constructed to illustrate the expression profiles of the signature genes across different risk strata.

To enhance clinical utility, a nomogram was developed by integrating the ARG-based risk score with clinicopathological variables. The nomogram was constructed and internally validated using the entire TCGA cohort. Its performance was rigorously assessed via the concordance index (C-index) and calibration plots, which compare predicted probabilities against observed survival rates. The LASSO model construction inherently mitigates overfitting by taking into account the number of included variables. The net clinical benefit of this predictive model was further evaluated through decision curve analysis (DCA) [14].

2.1.5 Characterization of the Tumor Microenvironment and Therapeutic Response

The tumor immune landscape was characterized by estimating immune cell infiltration levels with the CIBERSORT and single-cell gene expression clustering analysis (ssGSEA) algorithms [15], and subsequently compared between distinct risk groups. To gain deeper insights into immune heterogeneity at a higher resolution, single-cell RNA sequencing data was leveraged from the Tumor Immune Single-Cell Hub (TISCH) [16]. Potential sensitivity to various anticancer agents was inferred by querying the Genomics of Drug Sensitivity in Cancer (GDSC) database [17], with all analyses executed in the R environment. Additionally, we performed correlation analysis between MYC and CD274 (the gene encoding PD-L1) expression using the TCGA-BLCA cohort.

2.1.6 Hub Gene Identification via Network Analysis

To visualize the relationships among differentially expressed ARGs, a protein-protein interaction (PPI) network was generated using the STRING database (confidence score >0.4). Subsequent topological analysis of this network with Cytoscape (version 3.9.1; Cytoscape Consortium, San Diego, CA, USA; https://cytoscape.org/) software (using the cytoHubba plugin to calculate degree centrality) enabled identification of the central hub gene. The significance of this hub gene was validated against TCGA and GEO datasets, and its correlation with clinicopathological features and immunotherapy response was also examined.

2.2 Clinical Cohorts and Immunohistochemistry Validation

Immunohistochemistry (IHC)

We retrospectively enrolled a primary cohort of 81 BC patients who underwent either radical cystectomy (RC) or transurethral resection of bladder tumor (TURBT) at The First Affiliated Hospital of Guangxi Medical University (GXMU) between October 2012 and December 2021. These patients had not been treated with any form of adjuvant therapy prior to their surgery. This study received ethical approval from the institutional review board of The First Affiliated Hospital of Guangxi Medical University (Approval No. 2024-K054-01). The study was carried out in accordance with the guidelines of the Declaration of Helsinki. Clinical data was collected from each participant following the acquisition of written informed consent. The clinical information for these patients is presented in Table 1.

Furthermore, to assess the predictive value for immunotherapy, two separate validation cohorts were established, comprising 43 and 38 BC patients treated at our institution between November 2019 and August 2021. The cohort of patients received treatment regimens centered on ICIs, which were administered either as stand-alone therapy or combined with platinum-based chemotherapy. The effectiveness of treatment was assessed according to the immune Response Evaluation Criteria in Solid Tumors (iRECIST). This classifies patient outcomes into four categories: complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Key clinical information and treatment outcomes for these cohorts are summarized in Tables 2,3.

For the immunohistochemical analysis, tissue samples were prepared and stained using a c-MYC monoclonal antibody (Proteintech, Cat ID: 67447-1-Ig), followed by a standardized staining procedure. Human colorectal cancer tissues served as controls. Staining intensity and coverage were quantified, and these metrics were combined into a composite score to determine positivity using the H-score method: H-score = \mathrm{\Sigma } (intensity \times percentage of positive cells), where intensity was scored as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong). An H-score \ge8 was considered positive/high expression. Two experienced pathologists independently reviewed and scored the stained samples.

3. Results

3.1 Screening for Prognostically Significant ARGs in Bladder Cancer

The overall analytical workflow for this study is depicted in Fig. 1. A total of 516 ARGs were retrieved from the Genecards and Harmonizome databases (Supplementary Table 1). Subsequent differential expression analysis identified 136 ARGs that were significantly dysregulated between BC and normal tissues (Supplementary Table 2). A heatmap illustrates the expression patterns of the top 50 up- and down-regulated genes (Fig. 2A), while a volcano plot visualizes the complete set of differentially expressed ARGs (Fig. 2B). Genes that were significantly upregulated included IGF1, a potent survival factor, and FASN (Fatty Acid Synthase), which is crucial for metabolic reprogramming. This is consistent with their known roles in promoting cancer cell survival and resistance to anoikis [18, 19]. Conversely, genes like GKN1, a putative tumor suppressor, were downregulated.

Univariate Cox regression analysis was performed on the 136 ARGs to evaluate their prognostic significance. This identified 54 genes that were significantly correlated with survival outcomes in the BC cohort (p 0.05), as shown in a forest plot (Fig. 2C) and listed in Supplementary Table 3. Furthermore, we constructed a network diagram to visualize co-expression relationships among the most prominent prognosis-related ARGs (Fig. 2D). We also investigated the genomic alterations of these 54 prognostic ARGs, specifically examining their copy number variation (CNV) frequencies and chromosomal locations (Fig. 3A,B).

3.2 Consistent Clustering of BC Molecular Subgroups Using the 54 ARGs

To stratify patients based on the expression profiles of the 54 prognostic ARGs, consensus clustering was performed using the “ConsensusClusterPlus” R package. This analysis optimally partitioned the BC cohort into two distinct molecular subtypes (k = 2), hereafter referred to as ARGcluster A and B (Fig. 3C; Supplementary Table 4). Kaplan-Meier analysis revealed a significant difference in OS between these groups (p 0.001), with ARGcluster B exhibiting a markedly unfavorable prognosis (Fig. 3D). The robustness and distinctness of this classification were further validated by Principal Component Analysis (PCA), t-SNE, and UMAP, all of which demonstrated clear separation between the two subtypes (Fig. 3E–G).

We next explored the clinicopathological and molecular characteristics of these ARG-defined subtypes. A heatmap illustrates the differential expression of the 54 ARGs across the two clusters, alongside associated clinical features (Fig. 4A). Boxplots further visualize these expression differences (Fig. 4B). Moreover, analysis of the tumor microenvironment indicated different immune infiltration patterns between the subtypes (Fig. 4C).

To elucidate the underlying biological functions driving these differences, we conducted Gene Set Variation Analysis (GSVA) for KEGG pathways. This revealed that ARGcluster B, the group with worse outcome, was significantly enriched in pathways critical for tumor invasion and metastasis, such as “focal adhesion”, “regulation of actin cytoskeleton”, and “ECM receptor interaction” (Fig. 4D). These pathways are central to cell-matrix adhesion and migration, and their upregulation strongly suggests that cells in this subtype have enhanced capabilities for resisting anoikis and invading surrounding tissues. GSEA further corroborated these findings, identifying “CHEMOKINE_SIGNALING_PATHWAY” and “CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION” among the top five enriched pathways in ARGcluster B (Fig. 4E; Supplementary Table 5). This molecular profile provides a strong mechanistic rationale for the poor prognosis observed in this patient subgroup.

3.3 Development and Validation of an ARG-Based Prognostic Signature

To construct a robust prognostic model, the entire patient cohort was randomly partitioned into a training set and a test set. LASSO Cox regression analysis of the training dataset identified the most effective prognostic genes. This process yielded a 10-gene signature, which we named the ARGmodel, consisting of IGF1, CALR, E2F1, MYC, PLK1, SATB1, FASN, ID2, RAC3, and GKN1 (Fig. 5A,B). For each patient, a risk score was computed using the expression levels of these signature genes weighted by their corresponding coefficients (Supplementary Table 6).

We then rigorously validated the prognostic performance of the ARGmodel. Kaplan-Meier analysis revealed the model successfully stratified patients into high- and low-risk categories with markedly different OS. This significant stratification was observed in the training set (p 0.001), and subsequently confirmed in both the test set (p 0.001) and the entire cohort (p 0.001) (Fig. 5C–E). As expected, individuals in the high-risk group demonstrated significantly worse survival. Furthermore, ROC curve analysis yielded high Area Under the Curve (AUC) values for 1-, 3-, and 5-year OS predictions, confirming the superior predictive accuracy of the model (Fig. 5F–H). Crucially, multivariate Cox regression analysis established that the ARGmodel risk score was an independent prognostic factor for BC (p = 0.0032), distinct from other clinical variables (Fig. 5I).

We then visualized the characteristics of this signature. A heatmap illustrates the expression profiles of the 10 hub ARGs across patients sorted by risk score (Fig. 6A). The risk scores were also significantly different between the previously identified molecular subtypes (Fig. 6B). Finally, an alluvial diagram was generated to visualize the dynamic relationships between the molecular clusters, the ARGmodel risk stratification, and patient survival status (Fig. 6C).

3.4 Development of a Clinically Applicable Prognostic Nomogram

We constructed a nomogram that incorporates the ARGmodel risk score, along with significant clinicopathological factors, to improve the clinical applicability of our prognostic signature (Fig. 6D). Patients stratified by the nomogram-derived risk score (nomoRisk) showed distinct outcomes, with the high-risk group exhibiting a significantly higher cumulative hazard (Fig. 6E). The performance of the nomogram was rigorously evaluated. Calibration plots for 1-, 3-, and 5-year survival demonstrated excellent agreement between the nomogram-predicted probabilities and the actual observed outcomes, indicating high predictive accuracy (Fig. 6F). Furthermore, we performed Decision Curve Analysis (DCA) to assess its clinical value. For the prediction of 1-, 3-, and 5-year survival, the nomogram provided a greater net benefit across a wide range of threshold probabilities compared to treating all or no patients, confirming its potential as a valuable tool for clinical decision-making (Fig. 6G–I).

3.5 Characterization of the Tumor Immune Microenvironment in Different Risk Groups

To elucidate the biological mechanisms underlying the prognostic significance of our ARG-based signature, we investigated the tumor immune microenvironment (TME). Deconvolution of immune cell fractions revealed significant differences between the high- and low-risk groups. Notably, the high-risk group exhibited significantly lower infiltration of CD8+ T cells (p = 0.043), alongside higher infiltration of M0 macrophages (p = 0.011) and neutrophils (p = 0.039) (Supplementary Fig. 1A). The depletion of cytotoxic CD8+ T cells in high-risk patients suggests a suppressed anti-tumor immune status, potentially leading to poorer response to immunotherapy. Direct correlation analysis further confirmed that the riskScore was positively associated with abundance of M0 macrophages (p = 0.0017) and neutrophils (p = 0.036) (Supplementary Fig. 1D,E). Notably, the high-risk group, which corresponds to high MYC expression, exhibited a distinct immune profile. Significant positive correlations were observed with pro-inflammatory cells such as neutrophils, activated memory CD4+ T cells, and activated mast cells (Supplementary Fig. 1B,C). This suggests the presence of an active, yet potentially ineffective, inflammatory response. Intriguingly, the high-risk group showed significant negative correlations with regulatory T cells (Tregs) and naive B cells (Supplementary Fig. 1B,C). The reduction in Tregs is counterintuitive to typical immunosuppressive models, but may indicate a state of immune chaos or exhaustion where standard regulatory feedback loops are disrupted. Differences in ESTIMATE scores also supported these findings (Supplementary Fig. 1F). A correlation analysis between MYC and CD274 (the gene encoding PD-L1) expression in the TCGA-BLCA cohort revealed a significant positive correlation (Supplementary Fig. 1G) (Pearson correlation, R = 0.3, p = 8.5 \times 10^-10).

3.6 Single-Cell Resolution of ARG Expression Within the TME

To pinpoint the cellular origins of the signature ARGs, we leveraged the public single-cell RNA-sequencing dataset BLCA_GSE145281 from the TISCH database. This dataset comprises 17 distinct cell clusters within the BC TME (Supplementary Fig. 2A). At single-cell resolution, the expression levels of 7 of the 10 signature genes were successfully mapped. This analysis revealed highly specific expression patterns. For instance, CALR was predominantly expressed in B cells and NK cells, while MYC expression was concentrated in B cells and CD4+ T cells (Supplementary Fig. 2B,C). These findings provide granular detail on which cell types contribute to the ARG signature. The expression of MYC within CD4+ T cells is particularly intriguing, as it may drive their differentiation towards pro-tumorigenic subsets like Tregs or Th17 cells, further contributing to the immunosuppressive microenvironment [20].

3.7 Prediction of Therapeutic Sensitivity and Potential Drug Targets

Finally, we explored the potential of the ARGmodel to predict therapeutic response in BC. By estimating the sensitivity to a panel of clinical drugs, distinct response patterns were observed between the high- and low-risk groups (Fig. 7). This analysis indicates the risk stratification system based on our 10-ARG signature (IGF1, CALR, E2F1, MYC, PLK1, SATB1, FASN, ID2, RAC3, and GKN1) could potentially guide treatment selection. The pathways regulated by these genes appear to be linked to differential drug sensitivity, marking them as promising targets for the development of personalized therapeutic strategies in BC. We posit that a subset of ARGs may act as valuable biomarkers for OS in BC, and as actionable targets for therapeutic intervention.

3.8 MYC Expression Correlates With Tumor Stage, Prognosis, and Immunotherapy Efficacy in BC

We next employed a multi-step approach to identify a key driver in our prognostic signature. First, a PPI network was constructed using the STRING database to investigate functional connections among the 54 differentially expressed ARGs with prognostic significance. Subsequent analysis with Cytoscape identified MYC as the principal hub gene within this network based on its high degree of connectivity (Fig. 8A). This network centrality suggests a pivotal regulatory role for MYC. The top 10 hub genes ranked by score are detailed in Supplementary Table 7.

We proceeded to validate the clinical and prognostic relevance of MYC using the TCGA-BLCA and GSE13507 cohorts, confirming its unique position among the signature genes. MYC emerged as a pivotal factor among the identified hub genes. In the TCGA cohort, MYC was significantly upregulated in BC tissue compared to normal controls (p = 0.016, Fig. 8B), and its elevated expression was strongly predictive of unfavorable OS (p = 1.76 \times 10^-4, Fig. 8C). The prognostic value of MYC was further validated in our independent GXMU-BC cohort, which confirmed its negative impact on OS (p = 0.032, Fig. 8D).

Crucially, MYC was linked to key features of clinical aggressiveness, with high MYC expression being associated with advanced tumor stage and MIBC (Fig. 8E,F). Furthermore, analysis of the IMvigor210 immunotherapy cohort revealed that high MYC expression correlated with a significantly poorer response to immune checkpoint blockade (p = 0.03), suggesting a role in immunotherapy resistance (Fig. 8G). This combination of network centrality, robust prognostic power, and strong association with both tumor invasion and immunotherapy failure provided a compelling rationale for focusing on MYC as a key regulator.

At the protein level, IHC analysis of our 81 GXMU-BC patient samples confirmed aberrant MYC expression in 74% (60/81) of BC tissues (Fig. 8H–K). To further assess MYC as a predictive biomarker, we investigated its association with outcomes from ICI therapy. In two separate cohorts of 43 and 38 ICI-treated BC patients, a significant link was observed between MYC expression and clinical response. Specifically, patients with high MYC expression were significantly less likely to achieve a clinical response (Complete/Partial Response) compared to those with low MYC expression (p = 0.044 and p = 0.036 for the two cohorts, Fisher’s exact test). This finding indicates that MYC may be a potential biomarker for identifying patients who are resistant to ICI therapy (Tables 2,3).

4. Discussion

Therapies targeting the PD-1/PD-L1 axis have become a pivotal treatment modality for advanced BC, addressing the historically poor prognosis and high recurrence rates associated with this disease [21, 22, 23, 24]. Immunotherapies offer significant survival advantages, potentially transforming the therapeutic landscape for patients with advanced or metastatic BC. However, immunotherapy for BC still has shortcomings such as low treatment response rate and a lack of relevant biomarkers [25]. Therefore, it is very important to find relevant biomarkers in BC that could help to diagnose this tumor type and predict metastasis, recurrence, and response to immunotherapy.

The ECM constitutes a dynamic microenvironment whose components provide crucial signals that regulate tumor growth and metastasis [26, 27]. A key homeostatic mechanism, anoikis, is a form of programmed cell death that is triggered when cells lose their anchorage to the ECM, thereby preventing the survival and colonization of aberrant cells [28, 29]. Consequently, the acquisition of resistance to anoikis is a hallmark of metastatic cancer cells, permitting their survival during transit in the circulatory system [30]. To achieve this, detached tumor cells employ survival strategies, such as autocrine and paracrine signaling, which sustain them until they can re-establish adhesion and invade distant tissues [31, 32]. While anoikis has been associated with gastric cancer, prostate cancer, and hepatocellular carcinoma [33, 34, 35], its role in BC has not yet been evaluated. In the current research, we constructed a novel ARG-based prognostic prediction signature for BC patients. Our findings underscore the prognostic relevance of our ARG-based model in BC, which effectively captures anoikis resistance patterns across diverse patient profiles.

The strong correlation between the risk score from our model and clinical outcomes indicates these ARGs are strongly implicated in the pathogenesis of BC [36, 37]. A PPI network was constructed to elucidate the functional interplay among the prognostic set of ARGs. This analysis identified MYC, an established oncogene, as a key regulatory hub. Our rationale for highlighting MYC is multifaceted. Not only was it the top-ranked hub gene in the network analysis, it was also a core component of our validated 10-gene signature, with its expression consistently correlated with poor prognosis, advanced tumor stage, and immunotherapy resistance across multiple cohorts. This convergence of evidence strongly suggests that MYC is a central driver of the malignant phenotype captured by our model.

A critical question is how MYC functionally mediates the anoikis-resistant and aggressive phenotype observed in high-risk BC. While MYC is a well-established oncogene, its specific role in the anoikis-BC axis is still an emerging area. MYC is a master transcriptional regulator that can orchestrate cellular programs conducive to the evasion of anoikis. For instance, MYC is known to drive metabolic reprogramming, particularly by increasing glutaminolysis, which provides detached cancer cells with the necessary energy and biosynthetic precursors to survive in anchorage-independent conditions [38, 39]. Furthermore, our GSVA results highlighted the enrichment of pathways like “focal adhesion” and “ECM receptor interaction” in the high-risk group. MYC can directly influence these pathways by regulating the expression of integrins and other adhesion molecules, thereby altering the cell’s interaction with its matrix and lowering the threshold for triggering anoikis [40, 41, 42]. Therefore, we hypothesize that MYC overexpression in BC rewires cellular metabolism and adhesion properties, conferring a potent survival advantage upon cell detachment and facilitating metastasis.

Beyond its role in anoikis, our findings linking high MYC expression to immunotherapy resistance warrant further investigation. MYC can profoundly shape the tumor immune microenvironment, often fostering an immunosuppressive, or “cold,” tumor landscape [43]. Overexpression of MYC has been shown to directly upregulate the expression of immune checkpoint ligands such as PD-L1 and the signal CD47, which help tumor cells evade T-cell and macrophage-mediated killing, respectively [44]. Moreover, MYC can orchestrate the secretion of cytokines like IL-23 and CCL2, which promote the polarization of tumor-associated macrophages towards an immunosuppressive M2 phenotype [45]. This aligns with our observation of higher M0 macrophage infiltration in the high-risk group, which may represent a precursor pool for M2 differentiation. Thus, MYC likely contributes to immunotherapy resistance in BC through a dual mechanism involving the direct arming of tumor cells with immune-evasive molecules, and the promotion of a non-permissive immune microenvironment.

Several limitations of the present study merit consideration. The retrospective nature of our analysis introduces the possibility of inherent selection bias and confounding variables. Furthermore, the cohort size was modest, particularly the patient subgroup that received immunotherapy, thus limiting the statistical power and generalizability of our findings regarding ICI response. Finally, our investigation was primarily focused on the expression and prognostic significance of MYC. The underlying molecular mechanisms by which MYC contributes to BC progression, metastasis, and the modulation of ICI response—initially suggested by our bioinformatics analysis—necessitate validation through future functional experiments.

5. Conclusion

In conclusion, our findings demonstrate the robust prognostic value and clinical utility of the novel ARG-based signature in BC. We posit that a subset of ARGs may be valuable biomarkers for predicting overall survival, as well as actionable targets for therapeutic intervention.

Availability of Data and Materials

The TCGA-BLCA cohort dataset is available at The Cancer Genome Atlas (TCGA) website (https://xena.ucsc.edu/). The GSE13507 cohort dataset for this study is available at Gene Expression Omnibus (GEO) with accession number: GSE13507 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507). The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

Guangxi Zhuang Autonomous Region Health Commission scientific research project(Z20210478)