Investigation and validation of neurotransmitter receptor-related biomarkers for forecasting clinical outcomes and immunotherapeutic efficacy in breast cancer.

Purpose: The prognostic role of neurotransmitters and their receptors in breast cancer (BC) has not been fully investigated. The aim of this study was to construct a survival model for the prognosis of BC patients based on neurotransmitter receptor-related genes (NRRGs).

Methods: BC-related differentially expressed genes (DEGs) were screened and intersected with NRRGs. GO, KEGG and PPI analyses were performed. Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to construct prognostic models for biomarker expression levels. The model was validated using an external validation set. The receiver operating characteristic curves (ROC) for diagnostic value prediction and clinicopathologic characteristic nomogram were constructed. qRT-PCR was used for further in vitro validation experiments.

Results: Forty-five overlapping genes were obtained by intersecting BC-related DEGs with 172 NRRGs. Univariate Cox, LASSO and multivariate Cox regression analyses were used to construct prognostic models for the expression levels of biomarkers including DLG3, SLC1A1, PSCA and PRKCZ. The feasibility of the model was validated by the GEO validation set. ROC curves were established for diagnostic value prediction. Patients in the high-risk group had a worse prognosis, higher TMB score, higher probability of gene mutation, and higher immune cell infiltration. RiskScore, M, N and Age were strongly correlated with survival. The mRNA expression levels of DLG3, PSCA and PRKCZ in the BC group were significantly higher than those in the control group.

Conclusion: Risk prediction model based on DLG3, SLC1A1, PSCA and PRKCZ, which are closely related to BC prognosis, was successfully constructed.