由于高度异质性，肝细胞癌（HCC）死亡率比其他癌症类型持续增长得更快，这限制了诊断和治疗。病理和分子分型已确定预后不良的 HCC 肿瘤的特点是瘤内胶原堆积。然而，对结果至关重要的肿瘤胶原蛋白的翻译和翻译后调节仍然很大程度上未知。在这里，我们研究空间细胞外蛋白质组，以了解与 HCC 肿瘤相关的差异，该差异由 Hoshida 转录组亚型定义的不良结果（亚型 1；S1；n = 12）和更好的结果（亚型 3；S3；n = 24）显示差异基质调节途径。使用由非靶向和靶向 LC-MS/MS 构建的具有相同组织参考库的胶原靶向质谱成像 (MSI)，从临床特征、福尔马林固定、石蜡包埋的组织切片中空间定义细胞外微环境。盘状蛋白结构域受体和整合素结合的胶原 α-1(I) 链结构域在肿瘤微环境中显示出独特的空间分布。来自纤维状胶原蛋白三螺旋区域的含有羟基化脯氨酸 (HYP) 的肽将 S1 肿瘤与 S3 肿瘤区分开来。对从肿瘤区域提取的多个肽进行探索性机器学习可以区分 S1 和 S3 肿瘤（受试者工作曲线下面积≥0.98；95% 置信区间在 0.976 和 1.00 之间；准确度高于 94%）。总体发现是细胞外微环境非常有可能预测 HCC 的临床相关结果。

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.