Introduction

Breast cancer is one of the most common malignancies among women globally. According to statistics for 2024, there are approximately 2.46 million newly diagnosed cases of breast cancer worldwide, with an estimated 369,769 new cases in China, making it the country with the highest incidence rate. In terms of mortality, the number of breast cancer-related deaths in China is expected to reach 77,959, highlighting its significant role in cancer-related fatalities1. As societal and lifestyle changes continue, the outcomes for breast cancer patients, particularly the rates of recurrence and metastasis, have also shown an upward trend. This phenomenon underscores the urgent need for early detection and intervention strategies for breast cancer2.

Despite the variety of current treatment options, which include surgery, radiotherapy, chemotherapy, and targeted therapy, there remain notable limitations in early screening, personalized treatment plans, and prognostic assessment3. Therefore, exploring new biomarkers to enhance the predictive capabilities for breast cancer prognosis holds substantial research significance.

MYLK2 (myosin light chain kinase 2) is an essential enzyme that primarily participates in cellular contraction, migration, and signal transduction, playing a critical role in the regulation of the tumor microenvironment4,5. MYLK2 interacts with actin, modulating the dynamic changes in the cytoskeleton, which in turn influences the invasiveness and metastatic potential of cancer cells6. High expression of MYLK2 in the tumor microenvironment is often associated with malignant phenotypes of cancer cells, and it can alter the composition of the microenvironment by affecting the behavior of surrounding cells7,8. Therefore, investigating the function of MYLK2 in breast cancer is not only crucial for elucidating its role in tumor development but also provides new insights for the identification of clinical biomarkers and potential therapeutic targets.

Although the role of MYLK2 in cancer has garnered increasing attention, its specific biological mechanisms and role in breast cancer prognosis remain contentious. Current studies mainly focus on the impact of MYLK2 on cell motility and signaling pathway regulation. For instance, MYLK2 has been shown to promote the migration and invasion of tumor cells by activating the RhoA/ROCK signaling pathway6. Some researchers propose that the role of MYLK2 may not only depend on its expression levels but also be influenced by factors such as the tumor microenvironment and genetic mutation status, complicating its specific function in breast cancer research9.

This study employs bioinformatics to analyze the expression patterns of MYLK2 and its associations with clinical prognostic indicators in breast cancer, aiming to explore its potential as a biomarker for clinical applications.

Materials and methods

Comparison analysis of MYLK2 expression levels

This study was conducted using The Cancer Genome Atlas (TCGA) database(https://portal.gdc.cancer.gov/) and GEO dataset GSE22820, employing R (Version 4.2.1) programming and bioinformatics methods to analyze the expression levels of MYLK2 in breast cancer tissue samples compared to normal breast tissue. Differentially expressed genes (DEGs) between breast cancer and normal tissues were identified using the DESeq2 R package, with a significance threshold set at |log2 fold change| > 1 and an adjusted P-value (FDR) < 0.05. The infiltration levels of various immune cells were estimated using the CIBERSORT algorithm, and only samples with a CIBERSORT P-value < 0.05 were retained for subsequent analysis to ensure reliability. We determined the expression differences of MYLK2 across various cancer types and integrated these findings with clinical data to evaluate its clinical significance in breast cancer.

Prognostic outcome assessment

Kaplan-Meier survival analysis was performed, along with the Xiantao Academic Platform (https://www.xiantaozi.com/), to analyze survival outcomes between high and low expression groups of MYLK2. The analysis focused on overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) for the high versus low MYLK2 expression groups, exploring the relationship between MYLK2 expression levels and patient prognosis.

Analysis of immune cell infiltration and immunohistochemical staining

Using the Xiantao Academic Platform (https://www.xiantaozi.com/), the relationship between MYLK2 expression and immune cell infiltration in the breast cancer microenvironment was analyzed. Additionally, immunohistochemical staining analysis was performed using the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) with antibodies HPA059704 and HPA059890 to compare MYLK2 expression patterns in breast cancer and normal tissues.

Exploration of co-expressed genes with MYLK2

To further investigate the function of MYLK2, the LinkedOmics database (https://www.linkedomics.org/) was utilized to analyze genes co-expressed with MYLK2, identifying upregulated and downregulated genes, and generating heatmaps to visualize the expression patterns. Additionally, the STRING database (https://string-db.org/) was used to construct a protein-protein interaction network related to MYLK2, which was further visualized using Cytoscape(Version 3.10.3) software to elucidate potential molecular mechanisms. The top ten genes interacting with MYLK2 were analyzed using the Maximum Clique Centrality (MCC) method. The KEGG pathway enrichment analysis was performed using the clusterProfiler R package. The KEGG pathway map was obtained from the KEGG PATHWAY database (www.kegg.jp/kegg/kegg1.html).

Prediction of drug sensitivity

Drug sensitivity predictions were conducted based on the Connectivity Map (CMAP) database (https://clue.io/), analyzing the inhibitory effects of drugs on cells related to MYLK2 expression and calculating normalized connectivity scores (norm_cs). Furthermore, the chemical properties and biological activities of the predicted drugs were analyzed using the PubChem database (https://www.ncbi.nlm.nih.gov/pccompound/) to provide a theoretical basis for future clinical applications.

Statistical analysis

All data were obtained from the aforementioned online databases and processed using statistical analysis software. A p-value of less than 0.05 was considered statistically significant.

Results

Differential expression analysis of MYLK2

This study confirmed a significant upregulation of MYLK2 in breast cancer through TCGA data analysis. The volcano plot (Fig. 1A) indicates that the expression levels of MYLK2 in breast cancer samples are significantly higher than those in normal tissues, providing empirical support for its potential as a tumor biomarker. The Venn diagram (Fig. 1B) further reveals an intersection of 100 genes between 2,847 differentially expressed genes and 847 prognosis-related genes. This suggests that MYLK2 is not only significantly differentially expressed but may also serve as a prognostic biomarker for breast cancer.

Fig. 1
figure 1

Identification and correlation of MYLK2 expression with differentially expressed genes (DEGs) and prognostic relevance. (A) Volcano plot displaying DEGs, with significant upregulated (red) and downregulated (blue) genes indicated. MYLK2 is labeled within the plot, highlighting its expression change and significance. (B) Venn diagram illustrating the overlap between DEGs (green) and genes linked to prognosis (blue), showing 100 shared genes.

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In various cancer types, the expression levels of MYLK2 are significantly higher in tumor tissues compared to normal tissues. Analysis of data from the TCGA database reveals that MYLK2 is markedly upregulated in breast cancer tissues, with a statistically significant difference in expression levels compared to normal breast tissues (P < 0.05).(Fig. 2).

Fig. 2
figure 2

Differential expression of MYLK2 in various cancer types compared to normal tissues. (A) Boxplots illustrating the log2(TPM + 1) expression levels of MYLK2 across different cancers (Tumor: red, Normal: blue). Significant differences are indicated (***p < 0.001). (B) Comparison of MYLK2 expression levels in specific cancer types, showcasing notable variations with statistical significance (ns = not significant, ***p < 0.001). (C) Summary of MYLK2 expression in normal versus tumor tissues, highlighting significant upregulation in tumors. (D) Paired analysis of MYLK2 expression between normal and tumor samples indicating a marked increase in tumors (***p < 0.001).

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Additionally, the expression of MYLK2 is significantly higher in estrogen receptor-positive (ER-positive) and progesterone receptor-positive (PR-positive) patients compared to their negative counterparts (P < 0.001), suggesting a close correlation with hormonal status. According to different molecular subtypes, the expression of MYLK2 varies significantly among Luminal A and Luminal B types, and it increases markedly with the pathological staging of lymph nodes. These results indicate that MYLK2 may not only serve as a biomarker for breast cancer but also play a significant role in tumor progression and prognostic assessment. (Fig. 3)

Fig. 3
figure 3

Expression levels of MYLK2 based on hormone receptor and clinical parameters. (A) Boxplot indicating MYLK2 expression (log2(TPM + 1)) in estrogen receptor (ER) negative versus positive samples, with significant differences marked (***p < 0.001). (B) Similar analysis for progesterone receptor (PR) status, showing significant elevation in MYLK2 expression in positive cases (***p < 0.001). (C) MYLK2 expression comparison between HER2 negative and positive statuses, with no significant difference observed. (D) Expression stratified by PAM50 subtypes, highlighting significant variations among normal, Luminal A, Luminal B, HER2, and Basal subtypes (*p < 0.05; ***p < 0.001). (E) Comparison of MYLK2 expression across pathologic N stages (N1, N2, N3), indicating significant differences between stages (**p < 0.01).

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External Validation Confirms the Prognostic Significance of MYLK2.

To independently validate our findings, we analyzed the GEO dataset GSE22820. The results successfully corroborated the core discoveries from the TCGA database: MYLK2 expression was significantly upregulated in breast cancer tissues compared to normal breast tissues (P < 0.05, log2FC = 1.96). This cross-database consistency powerfully establishes MYLK2 as a robust prognostic biomarker in breast cancer. (Fig. 4)

Fig. 4
figure 4

Differentially expressed genes between breast cancer and normal tissues in GEO dataset GSE22820. Genes are colored based on significance: Up (red) = log2FC > 1, p < 0.05; Down (blue) = log2FC < −1, p < 0.05; Not sig (gray).

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Relationship between MYLK2 expression and prognostic outcomes

Subsequent Kaplan-Meier survival analysis, utilizing data from the TCGA database, determined the correlation between MYLK2 expression levels and prognosis in breast cancer patients. The results revealed that the overall survival rate in the high MYLK2 expression group was significantly lower than that in the low expression group (P = 0.043), with a hazard ratio (HR) of 1.39, suggesting that high MYLK2 expression may be associated with poor prognosis (Fig. 5A). In the analysis of progression-free survival, the survival duration in the high MYLK2 expression group was also significantly shorter than that in the low expression group (P = 0.033), with an HR of 1.43, supporting the importance of MYLK2 as a potential prognostic indicator (Fig. 5B). Furthermore, the analysis of disease-specific mortality further validated this finding, showing that the disease-specific survival rate was significantly reduced in the high expression group (P = 0.009), with an HR of 1.80, indicating that high MYLK2 expression increases the risk of mortality for patients (Fig. 5C).

Fig. 5
figure 5

Prognostic significance of MYLK2 expression on survival outcomes. (A) Kaplan-Meier survival curves illustrating overall survival, with patients grouped by low (blue) and high (red) MYLK2 expression levels. Hazard ratio (HR) is indicated (HR = 1.39; 95% CI: 1.01–1.92; P = 0.043). (B) Progression-free interval survival analysis showing significant differences between low and high MYLK2 expression (HR = 1.43; 95% CI: 1.03–1.99; P = 0.033). (C) Disease-specific survival analysis further demonstrating worse outcomes correlated with high MYLK2 expression (HR = 1.80; 95% CI: 1.16–2.80; P = 0.009).

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The subtypes Kaplan-Meier survival curves indicate that while MYLK2 expression does not significantly affect survival in the Luminal A, Luminal B, and HER2-enriched subtypes, it notably influences outcomes in the Basal-like subtype.In the Basal subtype, high MYLK2 expression correlates with a significantly poorer survival prognosis, as evidenced by the high hazard ratio and statistically significant p-value. (Fig. 6)

Fig. 6
figure 6

Survival Analysis of MYLK2 Expression Across PAM50 Subtypes. Kaplan-Meier survival curves illustrate the association between MYLK2 expression levels (low vs. high) and overall survival across different PAM50 molecular subtypes of breast cancer: Luminal A (A), Luminal B (B), HER2-enriched (C), and Basal-like (D).

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Role of MYLK2 as an independent prognostic factor in breast cancer

Using univariate and multivariate Cox regression analyses, we explored the status of MYLK2 as an independent prognostic factor. The results indicated (Fig. 7A) that in the univariate analysis, the risk ratio associated with high MYLK2 expression was 1.394 (95% CI: 1.010–1.924, P = 0.043), while in the multivariate analysis(Fig. 7B), its significance was further enhanced (HR = 1.574, P = 0.033), demonstrating that MYLK2 has an important indicative role in the prognosis of breast cancer patients.

Additionally, by constructing a prognostic nomogram (Fig. 7C), we integrated multiple clinical characteristics, including pathological stage and MYLK2 expression, which can provide accurate predictions of survival probabilities for individual patients. This tool not only enhances the precision of prognostic assessments but also provides a theoretical basis for personalized treatment strategies.

Finally, the receiver operating characteristic (ROC) curve revealed MYLK2’s exceptional predictive performance (Fig. 7D), with an area under the curve (AUC) of 0.892 (95% CI: 0.857–0.927), marking the discovery of a promising novel biomarker in breast cancer prognostication. And the calibration curve (Fig. 7E) demonstrated a high level of consistency between the predicted survival probabilities and the observed data, indicating that the inclusion of MYLK2 expression levels improved the reliability of predictions and the clinical applicability of the model. These results further support the important role of MYLK2 as a potential biomarker in the prognostic evaluation of breast cancer.

Fig. 7
figure 7figure 7

Prognostic analysis of MYLK2 using Cox regression models. (A) Univariate Cox regression analysis presenting hazard ratios (HR) for various clinical characteristics, including pathologic T and N stages, and MYLK2 expression. Significant results are marked (e.g., MYLK2 high expression: HR = 1.394; 95% CI: 1.010–1.924; P = 0.043). (B) Multivariate Cox regression analysis highlighting the adjusted HR for clinical factors influencing survival outcomes, emphasizing the significance of MYLK2 along with pathologic N stage and age (> 60 years: HR = 2.774; P < 0.001). (C) Nomogram predicting 1-, 3-, and 5-year survival probabilities based on clinical characteristics and MYLK2 expression, providing a linear predictor for survival estimation. (D) ROC curve analysis of MYLK2, showing sensitivity and specificity, with an AUC of 0.892, indicating good predictive performance of the model. (E) Calibration plot comparing the predicted survival probabilities from the nomogram with observed outcomes, demonstrating model accuracy for 1-, 3-, and 5-year predictions.

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The improved predictive power of the newly established model(C-index: 0.749 VS 0.678) suggests a more robust framework for assessing patient prognosis, facilitating tailored treatment strategies and potentially leading to better clinical outcomes. (Fig. 8)

Fig. 8
figure 8

Prognostic Nomogram for Breast Cancer Based on Pathologic Staging. The model incorporates three key components: Pathologic T Stage: Points are assigned based on the size and extent of the primary tumor (T1 to T4). Each stage contributes to the total points used for survival predictions. Pathologic N Stage: Points reflect the involvement of regional lymph nodes, categorized as N0 to N3, impacting the overall prognosis. Pathologic M Stage: This factor considers the presence or absence of distant metastasis (M0 and M1) in the patient’s profile.

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Relationship between MYLK2 and immune cell infiltration

Comparative analysis of immune cell enrichment scores between low and high MYLK2 expression groups unveiled significant heterogeneity across multiple immune cell subsets (Fig. 9).

Fig. 9
figure 9

Immune cell enrichment scores associated with MYLK2 expression levels. Boxplots display enrichment scores for various immune cell types based on MYLK2 expression categorized as low (blue) and high (red). Statistical significance is indicated by asterisks, with significant differences observed in several cell types including T cells (CD8, CD4 activated, and others) and macrophages (M1, M2), where higher MYLK2 expression correlates with increased enrichment scores (e.g., ***p < 0.001, **p < 0.01, *p < 0.05).

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Our comprehensive correlation analysis revealed nuanced insights into MYLK2’s relationship with diverse immune cell populations (Fig. 10A). The correlation heatmap demonstrated a complex immunological landscape, with statistically significant correlations spanning multiple immune cell types.

Notably, MYLK2 exhibited the strongest positive correlations with Th2 cells (R = 0.190), Treg cells (R = 0.111), and aDC (R = 0.102), suggesting potential immunomodulatory implications. Conversely, significant negative correlations were observed with Tcm (R = −0.277), Th17 cells (R = −0.229), and Mast cells (R = −0.215), indicating a potentially complex regulatory mechanism in immune cell dynamics.

The heat map visualization (Fig. 10B) further elucidates the differential immune landscape between low and high MYLK2 expression groups. This intricate representation reveals subtle shifts in immune cell proportions, with particularly notable variations in B cells, T cell subsets, and macrophage populations.

The stratified analysis suggests that MYLK2 expression levels may serve as a potential marker of immune system heterogeneity, potentially influencing immune response and cellular interactions in the context of breast cancer. These findings provide a sophisticated molecular perspective on the immunological significance of MYLK2, opening new avenues for understanding its role in cancer immunobiology.

Fig. 10
figure 10

Correlation of MYLK2 expression with immune cell proportions. (A) Correlation analysis displaying the relationship between MYLK2 expression and various immune cell types. The correlation coefficient (R) and significance levels are provided, with significant positive correlations indicated (e.g., Th2 cells: R = 0.190, ***p < 0.001). Negative correlations are also noted, particularly for Th17 cells (R = −0.229, ***p < 0.001). Non-significant correlations are marked as “ns.” (B) Stacked bar plots depicting the proportion of different immune cell types categorized by MYLK2 expression levels, shown for low (left) and high (right) MYLK2 groups. The varied color coding represents different immune cell populations, highlighting shifts in composition relative to MYLK2 expression.

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Immunohistochemical analysis using the HPA database (Fig. 11) demonstrated that MYLK2 expression in normal breast tissue was nearly undetectable, whereas its expression was significantly upregulated in tumor tissues.

Fig. 11
figure 11

Immunohistochemical staining illustrating the expression levels of MYLK2 in tumor tissue samples. The images depict three distinct sections: (left) absent MYLK2 expression, (middle) weak MYLK2 expression, and (right) moderate MYLK2 expression.

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Co-expression analysis of MYLK2-related genes

Co-expression analysis of MYLK2 was performed using the LinkedOmics database, and a protein-protein interaction network was constructed. Heatmaps (Figs. 12A and B) display the top 50 upregulated and downregulated genes that co-express with MYLK2. The Z-scores of these related genes exhibited distinct expression patterns in the samples, supporting the potential important role of MYLK2 in breast cancer and providing new directions for functional research on MYLK2 in this context.

The interaction network constructed using the STRING database (Fig. 13A) revealed the interactions between MYLK2 and several key proteins, suggesting that these interactions may be involved in the regulation of cellular signaling pathways and physiological processes. Further, the top 10 genes(Table 1) interacting with MYLK2, identified using the Maximum Clique Centrality (MCC) method from Fig. 10A (Fig. 13B), demonstrated a close connection between MYLK2 and proteins associated with muscle cell functions, indicating its key role in cellular function.

Moreover, functional enrichment analysis (Fig. 14) indicated that MYLK2 is associated with various biological processes and signaling pathways, particularly in muscle contraction mechanisms, such as the sarcomere and contractile fiber pathways. These results further reaffirm the importance of MYLK2 in breast cancer progression and provide a theoretical basis for its clinical applications.

Fig. 12
figure 12

Heatmap illustrating gene expression patterns associated with MYLK2 across different groups. (A) The heatmap showcases genes related to MYLK2, highlighting differences in expression between high (red) and low (blue) expression groups, with the Z-Scores indicating the strength of gene expression. (B) A similar heatmap presenting additional genes, using the same Z-Score framework. The color intensity visually represents the expression levels, emphasizing the distribution of genes correlated with MYLK2 expression levels.

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Fig. 13
figure 13

Protein-protein interaction (PPI) network of MYLK2 and its interacting partners. (A) The comprehensive PPI network visualized using Cytoscape, showcasing various proteins associated with MYLK2. (B) : The top 10 interacting genes selected using the Maximally Connected Components (MCC) method in Cytoscape, highlighting their strength of association with MYLK2. The depth of red color indicates a stronger correlation, signifying the significance of these interactions in the biological context.

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Table 1 Top 10 in STRING network ranked by MCC method.
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Fig. 14
figure 14

KEGG pathway analysis of the top 10 interacting genes associated with MYLK2. The bar plot illustrates the significance of various biological processes (BP), cellular components (CC), molecular functions (MF), and KEGG pathways related to muscle contraction. The -log10(p.adjust) values highlight the significance of each category, with key terms such as “contractile fiber” and “sarcomere” showing strong associations. Color coding reflects different ontologies: biological processes (light blue), cellular components (red), molecular functions (purple), and KEGG pathways (yellow).

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Prediction of drug sensitivity

Drug sensitivity prediction(Table 2) was conducted using the CMAP database, and potential drug targets were evaluated. The results indicated that Pitavastatin (IC50 = 1.11 µM, norm_cs = −2.1217) exhibited the strongest cytotoxic effect, significantly impacting tumor cells compared to other drugs.

Table 2 Top 6 compounds associated with MYLK2 sensitivity based on CMAP analysis.
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Further analysis of the molecular structures using the PubChem database revealed the chemical characteristics and potential biological activities of these drugs. Below are the.

molecular formulas of the top six predicted compounds.(Fig. 15).

Fig. 15
figure 15

Molecular structures of compounds that are sensitive to MYLK2. These structures, identified from the PubChem database, illustrate various chemical configurations associated with MYLK2 activity. (A) pitavastatin. (B) BRD-K90805324. (C) BVD-523. (D) selumetinib. (E) FCCP. (F) sunitinib.

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Discussion

Breast cancer, as one of the most common malignancies, poses a significant threat to women’s health, with over 2 million new cases reported annually10. Despite existing treatment options—such as surgery, radiotherapy, and chemotherapy—limitations remain in prognostic assessment and recurrence prediction3.

This study investigated the expression of MYLK2 in breast cancer and its correlation with patient prognosis. Previous research indicates that MYLK2 plays a significant role in various cancers, particularly regarding the regulation of tumor cell proliferation and migration7. Our analysis demonstrates that MYLK2 expression is markedly elevated in tumor tissues compared to normal breast tissues, with a pronounced upregulation in estrogen receptor-positive and progesterone receptor-positive patients. These findings underline a strong connection with hormonal status, which is well-documented in the literature11. The correlation between high MYLK2 expression and detrimental prognostic outcomes further consolidates its potential as a vital prognostic indicator in clinical practice.

Moreover, we have identified a noteworthy relationship between MYLK2 expression and immune cell infiltration. A higher incidence of tumor-infiltrating lymphocytes (TILs) was observed in the high MYLK2 expression group, suggesting that MYLK2 may modulate the tumor immune microenvironment significantly. This aligns with research linking MYLK2 to immune evasion mechanisms in various cancers9. Intriguingly, lower infiltration levels of T cells and M1 macrophages in this group highlight potential immune escape pathways12.

Our co-expression analysis and protein-protein interaction (PPI) network analysis indicate that MYLK2 could play an instrumental role in the tumor microenvironment and cellular signaling pathways, potentially affecting processes like tumor cell motility, metastasis, and adhesion13. Through RNA sequencing, it has been discovered that cells secreting large amounts of Periostin are enriched in genes associated with focal adhesions, including MYLK2. This enrichment can regulate multiple biological aspects of tumor cells, including proliferation, invasion, survival, angiogenesis, metastasis, and resistance to chemotherapy14. Our integrated bioinformatics analysis offers clues to the potential mechanisms by which MYLK2 influences breast cancer progression. The PPI network reveals its strong association with key regulators of cytoskeletal dynamics, such as MYL9 and CALML3, suggesting a role in promoting cancer cell migration and invasion, possibly through the RhoA/ROCK signaling pathway. Furthermore, the significant correlation between high MYLK2 expression and altered immune cell infiltration (e.g., increased Tregs and Th2 cells, decreased cytotoxic T cells) suggests a novel role for MYLK2 in shaping an immunosuppressive tumor microenvironment. This dual function in both cell-intrinsic motility and cell-extrinsic immune modulation positions MYLK2 as a critical node in the breast cancer ecosystem. Based on these findings, future research could explore the possibility of combining MYLK2 with other therapeutic targets, as well as examine its role across different cancer types. Further analysis of the mechanisms by which MYLK2 is related to tumor immune evasion may contribute to the development of new treatment strategies for breast cancer.

Additionally, our drug sensitivity predictions indicated that Pitavastatin, a statin associated with MYLK2 sensitivity, exhibits significant cytotoxic effects on tumor cells, supporting the hypothesis that MYLK2 mediates cytoskeletal dynamics through RhoA signaling pathways15,16,17. This connection warrants further investigation, particularly regarding the therapeutic potential of combining MYLK2 modulation with existing treatment strategies.

In this study, we demonstrate the potential clinical value of MYLK2 in the management of breast cancer, particularly in prognosis assessment and personalized treatment. Unlike existing literature, this article uniquely integrates comprehensive analyses of immune cell infiltration and protein-protein interactions (PPI), revealing the role of MYLK2 in the tumor microenvironment and providing a new perspective for research. This integrated approach not only enhances the credibility of MYLK2 as a biomarker but may also offer new insights for the formulation of cancer treatment strategies. From a translational perspective, the detection of MYLK2 protein by immunohistochemistry (IHC) could be readily integrated into routine pathological assessment. Future studies should evaluate whether MYLK2 expression provides prognostic information independent of and additive to established markers like ER, PR, HER2, and Ki-67. If validated, MYLK2 IHC could identify a subset of patients with high-risk disease who might benefit from more intensive monitoring or novel therapeutic approaches targeting cytoskeletal dynamics or the immune microenvironment. Based on these findings, we recommend considering MYLK2 as a prognostic indicator for breast cancer patients in clinical practice to promote the implementation of personalized treatment.

The limitations of this study mainly stem from the lack of clinical validation analyses and support from multicenter data. This may lead to insufficient generalizability of the results, limiting the widespread application of MYLK2 in breast cancer prognosis assessment. It is important to note that this study is primarily bioinformatic in nature. While our analysis provides strong associative evidence for the role of MYLK2 in breast cancer prognosis, it lacks experimental validation. Future work is essential to confirm these findings. This will include: (1) Validating MYLK2 protein expression levels using immunohistochemistry on a cohort of clinical breast cancer samples with follow-up data; (2) Investigating the functional role of MYLK2 in breast cancer cell proliferation, migration, and invasion through in vitro gain- and loss-of-function experiments; (3) Exploring the impact of MYLK2 inhibition on tumor growth and metastasis in animal models.

In conclusion, our study underscores the clinical significance of MYLK2 as a prognostic biomarker in breast cancer, advocating for its routine evaluation in clinical settings. The integration of MYLK2 expression levels with established prognostic markers could refine the identification of high-risk patients, ultimately guiding more targeted and effective treatment paradigms. Future research should focus on mechanistic explorations and the clinical validations necessary to harness MYLK2’s potential in breast cancer management.

Conclusion

Our comprehensive investigation reveals MYLK2 as a critical molecular determinant in breast cancer pathogenesis, offering unprecedented insights into its prognostic and therapeutic potential. We demonstrate that MYLK2 is not merely a passive biomarker, but an active modulator of tumor progression and immune microenvironment dynamics. The observed correlations between MYLK2 expression and immune cell infiltration, particularly in dendritic cells, M2 macrophages, and T cell subsets, suggest its crucial role in tumor immune evasion strategies. These molecular insights provide a rational foundation for developing targeted immunotherapeutic approaches. The significant associations with disease progression, survival outcomes, and immune cell population dynamics position MYLK2 as a promising candidate for future clinical interventions. While our study presents compelling evidence, further validation through multicenter prospective clinical trials is necessary to fully elucidate MYLK2’s clinical utility. Future research should prioritize mechanistic investigations, therapeutic strategy development, and exploration of personalized treatment paradigms based on MYLK2 expression profiles. In conclusion, MYLK2 emerges as a potential game-changing molecular target, offering new horizons in understanding and managing breast cancer progression and treatment.