<b>osteosarcoma</b>

<b>Background</b> The osteosarcoma has unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits.<b> Methods</b> The independent gene datasets, and corresponding clinical information were collected from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. The ICI contents were evaluated by ‘ESTIMATE’ and ‘CIBERSORT’. We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis (PCA) was conducted to construct ICI score based on three ICI patterns. We stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The key genes were preliminary screened by weighted gene co-expression network analysis (WGCNA) based on ICI scores. And they were further identified by construction of prognosis predicting model. The predictive ability of ICI score for metastasis and HUVOS grade were also verified in cohorts. <b>Results</b> The ICI patients with a better prognosis are characterized by the presence of CD8⁺ T cells, monocytes, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and activated mast cells. The genes identified by ICI scores and various levels included WAS, ARHGAP30, and PARVG. <b>Conclusion</b> The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in osteosarcoma, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

**背景** 骨肉瘤具有独特的分子特征与临床异质性。本研究旨在明确免疫细胞浸润（immune cell infiltration, ICI）亚型的特征，以评估患者预后与治疗获益。**方法** 本研究从治疗可及性研究以生成有效治疗方案（Therapeutically Applicable Research to Generate Effective Treatments, TARGET）数据库与基因表达综合（Gene Expression Omnibus, GEO）数据库中收集了独立的基因数据集及对应临床信息。采用「ESTIMATE」与「CIBERSORT」算法评估免疫细胞浸润丰度。通过两种计算方法解析与预后相关的免疫细胞浸润图谱，并挖掘其独特的浸润特征。随后，基于三种免疫细胞浸润模式开展主成分分析（principal component analysis, PCA），构建免疫细胞浸润评分（ICI score）。将患者划分为与预后相关的高、低免疫细胞浸润评分组（分别记为HSG与LSG）。基于免疫细胞浸润评分，通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）初步筛选关键基因，并通过构建预后预测模型进一步验证这些关键基因。此外，在队列中验证了免疫细胞浸润评分对转移情况与HUVOS分级的预测能力。**结果** 预后较好的骨肉瘤患者免疫细胞浸润特征表现为存在CD8⁺T细胞、单核细胞、M1型巨噬细胞、M2型巨噬细胞、活化树突状细胞、静息肥大细胞与活化肥大细胞。通过免疫细胞浸润评分及相关分层筛选得到的关键基因包括WAS、ARHGAP30与PARVG。**结论** 免疫细胞浸润亚型与免疫细胞浸润评分的鉴定，有助于深入解析骨肉瘤的异质性，并筛选出可能从治疗中获益的患者。免疫细胞浸润评分与关键基因可作为有效的生物标志物，用于预测患者预后与免疫治疗敏感性。