RESUMO
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.
Assuntos
Neoplasias da Mama , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Células Matadoras Naturais , Aprendizado de Máquina , Análise de Célula Única , Transcriptoma , Microambiente Tumoral , Humanos , Neoplasias da Mama/genética , Neoplasias da Mama/imunologia , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico , Células Matadoras Naturais/imunologia , Células Matadoras Naturais/metabolismo , Feminino , Prognóstico , Transcriptoma/genética , Análise de Célula Única/métodos , Microambiente Tumoral/genética , Microambiente Tumoral/imunologia , Biomarcadores Tumorais/genética , NomogramasRESUMO
The management of idiopathic granulomatous mastitis (IGM) poses a significant challenge because of its ambiguous etiology. This study aimed to investigate the efficacy of traditional Chinese medicine (TCM) combined with mammotome-assisted minimally invasive surgery (MAMIS) for the treatment of IGM. This retrospective cohort study included patients with IGM who underwent treatment at our hospital between January 2017 and June 2022. Patients treated with Shugan Sanjie decoction alone and preoperative Shugan Sanjie decoction combined with MAMIS were included in Groups A and B, respectively. We focused on the demographics, clinical characteristics, and outcomes of the patients in the 2 groups. A total of 124 female patients with an average age of 33.9 ± 3.6 years were included in the study. The demographic and clinical characteristics of patients in Groups A (n = 55) and B (n = 69) were similar (P > .05). However, there were significant differences between the 2 groups in terms of treatment duration, 1-year complete remission (CR), and recurrence. Group B showed shorter treatment time (11.7 ± 5.1 vs 15.3 ± 6.4 months, P = .001), higher 1-year CR (72.5% vs 45.5%, P = .002), and lower recurrence (7.2% vs 21.8%, P = .019) in comparison to Group A. Shugan Sanjie decoction promoted the shrinkage of breast lesions in patients with IGM. Combined with MAMIS, this treatment regimen shortened the treatment duration, accelerated the recovery process, and reduced the recurrence rate.