免疫治疗变得越来越重要,但在胃癌(GC)的治疗中,总体反应率相对较低。肿瘤突变负荷(TMB)在GC患者免疫治疗疗效预测中的应用有限且存在争议。强调优化基于TMB的患者选择的重要性。通过结合TMB和主要组织相容性复合体(MHC)相关hub基因,我们建立了一个新的TM-Score。与TMB相比,该评分显示出免疫治疗选择的优异性能(AUC=0.808),MSI状态,EBV状态。此外,预测GC患者的预后。随后,通过TM-Score调整的机器学习模型进一步提高了生存预测的准确性(AUC>0.8).同时,我们发现低TM评分的GC患者有更高的突变频率,HLA基因和免疫检查点基因的高表达,和更高的CD8+T细胞浸润,CD4+辅助性T细胞,和M1巨噬细胞。这表明TM-Score与肿瘤免疫原性和肿瘤免疫环境显著相关。值得注意的是,基于RNA-seq和scRNA-seq,研究发现,AKAP5是TM-Score的关键组成基因,通过促进CD4+T细胞的浸润参与抗肿瘤免疫,NK细胞,和骨髓细胞。此外,siAKAP5显著降低GC细胞系中MHC-IImRNA的表达。此外,我们的免疫组织化学检测证实AKAP5与人类白细胞抗原(HLA)表达呈正相关.此外,在生存期较长的患者和对GC免疫疗法有反应的患者中,AKAP5水平较高,表明其在预测预后和免疫治疗结果方面的潜在价值。总之,TM-Score,作为TMB的优化,是一种更精确的生物标志物,用于预测GC人群的免疫疗法疗效。此外,AKAP5显示出有望作为GC的治疗靶标。
Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The application of tumor mutational burden (TMB) in predicting immunotherapy efficacy in GC patients is limited and controversial, emphasizing the importance of optimizing TMB-based patient selection. By combining TMB and major histocompatibility complex (MHC) related hub genes, we established a novel TM-Score. This score showed superior performance for immunotherapeutic selection (AUC = 0.808) compared to TMB, MSI status, and EBV status. Additionally, it predicted the prognosis of GC patients. Subsequently, a machine learning model adjusted by the TM-Score further improved the accuracy of survival prediction (AUC > 0.8). Meanwhile, we found that GC patients with low TM-Score had a higher mutation frequency, higher expression of HLA genes and immune checkpoint genes, and higher infiltration of CD8+ T cells, CD4+ helper T cells, and M1 macrophages. This suggests that TM-Score is significantly associated with tumor immunogenicity and tumor immune environment. Notably, based on the RNA-seq and scRNA-seq, it was found that AKAP5, a key component gene of TM-Score, is involved in anti-tumor immunity by promoting the infiltration of CD4+ T cells, NK cells, and myeloid cells. Additionally, siAKAP5 significantly reduced MHC-II mRNA expression in the GC cell line. In addition, our immunohistochemistry assays confirmed a positive correlation between AKAP5 and human leukocyte antigen (HLA) expression. Furthermore, AKAP5 levels were higher in patients with longer survival and those who responded to immunotherapy in GC, indicating its potential value in predicting prognosis and immunotherapy outcomes. In conclusion, TM-Score, as an optimization of TMB, is a more precise biomarker for predicting the immunotherapy efficacy of the GC population. Additionally, AKAP5 shows promise as a therapeutic target for GC.