Department of Otorhinolaryngology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China.
Sci Rep. 2024 Apr 30;14(1):9914. doi: 10.1038/s41598-024-60516-6.
Macrophages are immune cells in the TME that can not only inhibit angiogenesis, extracellular matrix remodeling, cancer cell proliferation, and metastasis but also mediate the phagocytosis and killing of cancer cells after activation, making them key targets in anti-tumor immunotherapy. However, there is little research on macrophages and their relation to disease prognosis in HNSCC. Initially, we collected scRNA-seq, bulk RNA-seq, and clinical data. Subsequently, we identified macrophages and distinguished MRGs. Using the K-means algorithm, we performed consensus unsupervised clustering. Next, we used ssGSEA analysis to assess immune cell infiltration in MRG clusters. A risk model was established using multivariate Cox analysis. Then, Kaplan-Meier, ROC curves, univariate and multivariate COX analyses, and C-index was used to validate the predictive power of the signature. The TIDE method was applied to assess the response to immunotherapy in patients diagnosed with HNSCC. In addition, drug susceptibility predictions were made for the GDSC database using the calcPhenotype function. We found that 8 MRGs had prognostic potential. Patients in the MRG group A had a higher probability of survival, and MRG clusters A and B had different characteristics. Cluster A had a higher degree of expression and infiltration in MRG, indicating a closer relationship with MRG. The accuracy of the signature was validated using univariate and multivariate Cox analysis, C-index, and nomogram. Immune landscape analysis found that various immune functions were highly expressed in the low-risk group, indicating an improved response to immunotherapy. Finally, drugs with high sensitivity to HNSCC (such as 5-Fluorouracil, Temozolomide, Carmustine, and EPZ5676) were explored and analyze the malignant characteristics of HNSCC. We constructed a prognostic model using multivariate Cox analysis, consisting of 8 MRGs (TGM2, STC1, SH2D3C, PIK3R3, MAP3K8, ITGA5, ARHGAP4, and AQP1). Patients in the low-risk group may have a higher response to immunotherapy. The more prominent drugs for drug selection are 5-fluorouracil, temozolomide and so on. Malignant features associated with HNSCC include angiogenesis, EMT, and the cell cycle. This study has opened up new prospects for the prognosis, prediction, and clinical treatment strategy of HNSCC.
巨噬细胞是 TME 中的免疫细胞，不仅可以抑制血管生成、细胞外基质重塑、癌细胞增殖和转移，还可以在激活后介导对癌细胞的吞噬和杀伤，使其成为抗肿瘤免疫治疗的关键靶点。然而，关于巨噬细胞及其与 HNSCC 疾病预后的关系的研究甚少。最初，我们收集了 scRNA-seq、批量 RNA-seq 和临床数据。随后，我们鉴定了巨噬细胞并区分了 MRGs。使用 K-means 算法进行共识无监督聚类。接下来，我们使用 ssGSEA 分析评估了 MRG 簇中的免疫细胞浸润。使用多变量 Cox 分析建立风险模型。然后，使用 Kaplan-Meier、ROC 曲线、单变量和多变量 COX 分析以及 C 指数验证了该签名的预测能力。使用 TIDE 方法评估了诊断为 HNSCC 的患者对免疫治疗的反应。此外，使用 calcPhenotype 功能对 GDSC 数据库进行药物敏感性预测。我们发现 8 个 MRGs 具有预后潜力。MRG 组 A 的患者具有更高的生存概率，MRG 簇 A 和 B 具有不同的特征。簇 A 在 MRG 中具有更高的表达和浸润程度，表明与 MRG 的关系更密切。使用单变量和多变量 Cox 分析、C 指数和列线图验证了该签名的准确性。免疫景观分析发现，低风险组中各种免疫功能高度表达，表明对免疫治疗的反应更好。最后，探索了对 HNSCC 具有高敏感性的药物（如 5-氟尿嘧啶、替莫唑胺、卡莫司汀和 EPZ5676），并分析了 HNSCC 的恶性特征。我们使用多变量 Cox 分析构建了一个包含 8 个 MRGs（TGM2、STC1、SH2D3C、PIK3R3、MAP3K8、ITGA5、ARHGAP4 和 AQP1）的预后模型。低风险组的患者可能对免疫治疗有更高的反应。用于药物选择的更突出的药物是 5-氟尿嘧啶、替莫唑胺等。与 HNSCC 相关的恶性特征包括血管生成、EMT 和细胞周期。这项研究为 HNSCC 的预后、预测和临床治疗策略开辟了新的前景。
