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Background: HER2-positive breast cancer (BC) is a subtype of breast cancer. Increased ERBB3 expression has been implicated as a potential cause of resistance to other HER-targeted therapies. Our study aimed to screen and validate prognostic markers associated with ERBB3 expression by bioinformatics and affecting the prognosis of HER2 staging., Methods: Analyzing differences in ERBB3-related groups. ERBB3 expression-related differentially expressed genes (DEGs) were identified and intersected with survival status-related DEGs to obtain intersected genes. Three algorithms, LASSO, RandomForest and XGBoost were combined to identify the signature genes. we construct risk models and generate ROC curves for prediction. Furthermore, we delve into the immunological traits, correlations, and expression patterns of signature genes by conducting a comprehensive analysis that encompasses immune infiltration analysis, correlation analysis, and differential expression analysis., Results: Significant variability in ERBB3 expression and prognosis in high and low ERBB3 expression groups. Twenty-five candidate DEGs were identified by intersecting ERBB3-related DEGs with survival-related DEGs. Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. In addition, the risk model had better prognostic and predictive effects, and the immune infiltration analysis showed that IGHM, CXCL13 might affect the proliferation of BC cells through immune cells. Functional studies demonstrated that interference with PBX1 inhibited the proliferation, migration, and epithelial-mesenchymal transition process of HER2-positive BC cells., Conclusion: PBX1, IGHM and CXCL13 are associated with the expression level of the ERBB3 and are prognostic markers for HER2-positive in BC, which may play an important role in the development and progression of BC., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: All the authors consent for the manuscript to be published. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: The study aimed to investigate the expression of OVOLs in breast cancer (BRCA) tissues and their value in prognosis., Methods: ONCOMINE was used to analyze the expressions of OVOL1, OVOL2, and OVOL3 mRNA between BRCA tissues and normal breast tissues. The Wilcoxon rank sum test and t -test were used to assess the expression of OVOLs between BRCA tissues and unpaired/paired normal breast tissues. GEPIA and ROC curves were used to analyze the relationship between OVOLs expression and clinical pathological stage. Kaplan-Meier plotter was used to analyze prognosis. cBioPortal was used to analyze the mutation of OVOLs. GEPIA was used to analyze the co-expression of OVOLs. GO and KEGG analyses were performed by the DAVID software to predict the function of OVOLs co-expression genes., Results: The expression of OVOL1/2 was significantly higher in BRCA tissues than in normal breast tissues. The OVOL3 expression correlated with tumor stage. The AUC of OVOLs was 0.757, 0.754, and 0.537, respectively. OVOL1 high expression was associated with shorter overall survival (HR: 1.48; 95% CI: 1.07-2.04; P=0.018). The OVOLs were associated with pathways including axon guidance, thyroid hormone signaling pathway, and ubiquinone and other terpenoid-quinone biosynthesis., Conclusion: OVOL1 is a new potential marker of prognosis in BRCA, and OVOL1/2 are potential therapeutic targets in BRCA., Competing Interests: The authors declared that they have no conflicts of interest., (© 2021 Chen et al.)