Background: Angiosarcomas (ASs) represent a heterogeneous and highly aggressive subset of tumors that respond poorly to systemic treatments and are associated with short progression-free survival (PFS) and overall survival (OS). The aim of this study was to develop and validate an immune-related prognostic model—termed the AS score—using data from two independent sarcoma cohorts.Methods: A prognostic model was developed using a previously characterized cohort of 25 angiosarcoma samples. Candidate genes were identified via the Maxstat algorithm (Maxstat v0.7-25 for R), combined with log-rank testing. The AS score was then computed by weighing normalized gene expression levels according to Cox regression coefficients. For external validation, transcriptomic data from TCGA Sarcoma cohort (n= 253) were analyzed. The Immunoscore—which reflects the tumor immune microenvironment—was inferred using the ESTIMATE package (v1.0.13) in R. All statistical analyses were performed in RStudio (v 4.0.3).Results: Four genes—IGF1R,MAP2K1,SERPINE1, andTCF12—were ultimately selected to construct the prognostic model. The resulting AS score enabled the classification of angiosarcoma cases into two prognostically distinct groups (p= 0.00012). Cases with high AS score values, which included both cutaneous and non-cutaneous forms, exhibited significantly poorer outcomes, whereas cases with low AS scores were predominantly cutaneous. A significant association was observed between the AS score and the Immunoscore (p= 0.025), with higher Immunoscore values found in high-AS score tumors. Validation using TCGA sarcoma cohort confirmed the prognostic value of both the AS score (p= 0.0066) and the Immunoscore (p= 0.0029), with a strong correlation between their continuous values (p= 2.9 × 10−8). Further survival analysis, integrating categorized scores into four groups, demonstrated robust prognostic significance (p= 0.00021). Notably, in tumors with a low Immunoscore, AS score stratification was not prognostic. In contrast, among cases with a high Immunoscore, the AS score effectively distinguished outcomes (p< 0.0001), identifying a subgroup with poor prognosis but potential sensitivity to immunotherapy.Conclusions: This combined classification using the AS score and Immunoscore has prognostic relevance in sarcoma, suggesting that angiosarcomas with an immunologically active microenvironment (high Immunoscore) and poor prognosis (high AS score) may be prime candidates for immunotherapy and this approach warrants prospective validation.
背景：血管肉瘤（AS）是一组异质性高、侵袭性强的肿瘤亚型，对全身性治疗反应不佳，且与较短的无进展生存期（PFS）和总生存期（OS）相关。本研究旨在利用两个独立的肉瘤队列数据，开发并验证一种免疫相关预后模型——称为AS评分。 方法：使用先前已鉴定的25例血管肉瘤样本队列开发预后模型。通过Maxstat算法（R语言Maxstat v0.7-25）结合对数秩检验识别候选基因。随后，根据Cox回归系数对标准化基因表达水平进行加权计算AS评分。为进行外部验证，分析了来自TCGA肉瘤队列（n=253）的转录组数据。使用R语言中的ESTIMATE包（v1.0.13）推断反映肿瘤免疫微环境的免疫评分。所有统计分析均在RStudio（v 4.0.3）中完成。 结果：最终筛选出四个基因——IGF1R、MAP2K1、SERPINE1和TCF12——构建预后模型。由此得到的AS评分能够将血管肉瘤病例分为两个预后显著不同的组别（p=0.00012）。高AS评分病例（包括皮肤型和非皮肤型）表现出显著较差的预后，而低AS评分病例主要为皮肤型。观察到AS评分与免疫评分之间存在显著关联（p=0.025），高AS评分肿瘤中免疫评分值更高。使用TCGA肉瘤队列验证证实了AS评分（p=0.0066）和免疫评分（p=0.0029）的预后价值，且两者连续值间存在强相关性（p=2.9×10⁻⁸）。进一步将分类评分整合为四组进行生存分析，显示出稳健的预后意义（p=0.00021）。值得注意的是，在免疫评分低的肿瘤中，AS评分分层无预后价值；而在免疫评分高的病例中，AS评分能有效区分预后（p<0.0001），识别出预后不良但可能对免疫治疗敏感的亚组。 结论：这种结合AS评分和免疫评分的分类方法在肉瘤中具有预后相关性，表明具有免疫活性微环境（高免疫评分）和不良预后（高AS评分）的血管肉瘤可能是免疫治疗的优选候选者，该方法值得进行前瞻性验证。
肿瘤(癌症)患者之家