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10.1007/s12672-025-04022-9 http://scihub22266oqcxt.onion/10.1007/s12672-025-04022-9 41391060!?!41391060 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41391060&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Discov+Oncol 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41391060|t=2025. Neuronal inflammatory genes-based machine learning model for breast cancer: a novel perspective on clinical prognosis and tumor immunity |pdf=|usr=}}{{41391060}} gab.com Text Neuronal inflammatory genes-based machine learning model for breast cancer: a novel perspective on clinical prognosis and tumor immunity JO: Discov Oncol http://www.kidney.de/pget.php?&tw=1&pmid=41391060 #FOAvip BACKGROUND: Breast cancer heterogeneity complicates personalized treatment and prognosis. Current clinical prognostic indicators remain limited, and neuronal inflammation's crucial role in breast cancer progression is underexplored. This study identified neuronal inflammation-related biomarkers and constructed a prognostic model to improve risk evaluation and treatment. METHODS: Unsupervised clustering classified patients into subtypes according to the expression level of neuronal inflammation-related genes (NIRGs). The risk score was calculated to divide patients into different risk groups. The prognostic genes were identified by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a machine learning prognostic model. Single-cell RNA sequencing (scRNA-seq) analysis screened the key prognostic genes. Cell subtypes were manually annotated and cell communication was analyzed using the CellChat package. RESULTS: Compared to the high-risk group, patients in the low-risk group showed richer immune infiltration and more favorable prognostic outcomes. Notably, a time-saving and user-friendly web tool (http://wys.helyly.top/cox-whx/cox.html) was applied to predict patients' survival and treatment response. The scRNA-seq analysis identified VDAC1 as the most neuronally inflammation-associated gene. Cell communication analysis indicated a strong interaction among VDAC1(+) breast cancer cells, exhausted CD8(+) T cells, and M2 macrophages, potentially through the MIF pathway. CONCLUSION: The NIRGs-based prognostic model demonstrated good predictive performance. VDAC1 plays as a central role in tumor-immune cell interactions and neuronal inflammation. This study provides a novel perspective on neuronal inflammation and the prognosis and immunity of breast cancer, contributing to the identification of new therapeutic targets. The web tool facilitates clinical translation, bridging research and patient care. Twit Text FOAVip Neuronal inflammatory genes-based machine learning model for breast cancer: a novel perspective on clinical prognosis and tumor immunity ????Discov Oncol http://www.kidney.de/pget.php?&tw=1&pmid=41391060 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41391060 #FOAvip English Wikipedia <ref>{{Cite pmid|41391060}}</ref> Neuronal inflammatory genes-based machine learning model for breast cancer: a novel perspective on clinical prognosis and tumor immunity #MMPMID41391060 Wang H; Hu H; Zhang J; Hao B; Zhu H; Zhan W; Wang S; Li Z; Zhang T Discov Oncol 2025[Dec]; ? (?): ? PMID41391060show ga BACKGROUND: Breast cancer heterogeneity complicates personalized treatment and prognosis. Current clinical prognostic indicators remain limited, and neuronal inflammation's crucial role in breast cancer progression is underexplored. This study identified neuronal inflammation-related biomarkers and constructed a prognostic model to improve risk evaluation and treatment. METHODS: Unsupervised clustering classified patients into subtypes according to the expression level of neuronal inflammation-related genes (NIRGs). The risk score was calculated to divide patients into different risk groups. The prognostic genes were identified by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a machine learning prognostic model. Single-cell RNA sequencing (scRNA-seq) analysis screened the key prognostic genes. Cell subtypes were manually annotated and cell communication was analyzed using the CellChat package. RESULTS: Compared to the high-risk group, patients in the low-risk group showed richer immune infiltration and more favorable prognostic outcomes. Notably, a time-saving and user-friendly web tool (http://wys.helyly.top/cox-whx/cox.html) was applied to predict patients' survival and treatment response. The scRNA-seq analysis identified VDAC1 as the most neuronally inflammation-associated gene. Cell communication analysis indicated a strong interaction among VDAC1(+) breast cancer cells, exhausted CD8(+) T cells, and M2 macrophages, potentially through the MIF pathway. CONCLUSION: The NIRGs-based prognostic model demonstrated good predictive performance. VDAC1 plays as a central role in tumor-immune cell interactions and neuronal inflammation. This study provides a novel perspective on neuronal inflammation and the prognosis and immunity of breast cancer, contributing to the identification of new therapeutic targets. The web tool facilitates clinical translation, bridging research and patient care. ?Neuronal inflammatory genes-based machine learning model for breast ca(epmc).pdf DeepDyve Pubget Overpricing