Abstract

Cancer-associated fibroblasts (CAFs) are an important component of the stroma. Studies showed that CAFs were pivotally in glioma progression which have long been considered a promising therapeutic target. Therefore, the identification of prognostic CAF markers might facilitate the development of novel diagnostic and therapeutic approaches. A total of 1333 glioma samples were obtained from the TCGA and CGGA datasets. The EPIC, MCP-counter, and xCell algorithms were used to evaluate the relative proportion of CAFs in glioma. CAF markers were identified by the single-cell RNA-seq datasets (GSE141383) from the Tumor Immune Single-Cell Hub database. Unsupervised consensus clustering was used to divide the glioma patients into different distinct subgroups. The least absolute shrinkage and selection operator regression model was utilized to establish a CAF-related signature (CRS). Finally, the prognostic CAF markers were further validated in clinical specimens by RT‒qPCR. Combined single-cell RNA-seq analysis and differential expression analysis of samples with high and low proportions of CAFs revealed 23 prognostic CAF markers. By using unsupervised consensus clustering, glioma patients were divided into two distinct subtypes. Subsequently, based on 18 differentially expressed prognostic CAF markers between the two CAF subtypes, we developed and validated a new CRS model (including PCOLCE, TIMP1, and CLIC1). The nomogram and calibration curves indicated that the CRS was an accurate prognostic marker for glioma. In addition, patients in the high-CRS score group had higher immune infiltration and tumor mutation burden levels. Moreover, the CRS score had the potential to predict the response to immune checkpoint blockade (ICB) therapy and chemotherapy. Finally, the expression profiles of three CAF markers were verified by RT‒qPCR. In general, our study classified glioma patients into distinct subgroups based on CAF markers, which will facilitate the development of individualized therapy. We also provided insights into the role of the CRS in predicting the response to ICB and chemotherapy in glioma patients.

中文翻译：

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整合单细胞和批量 RNA-seq 数据来识别胶质瘤中与癌症相关的成纤维细胞亚型和风险模型

摘要

癌症相关成纤维细胞（CAF）是基质的重要组成部分。研究表明，CAF 在神经胶质瘤的进展中发挥着关键作用，长期以来一直被认为是一个有前途的治疗靶点。因此，预后 CAF 标志物的鉴定可能有助于开发新的诊断和治疗方法。从 TCGA 和 CGGA 数据集中总共获得了 1333 个神经胶质瘤样本。 EPIC、MCP-counter 和 xCell 算法用于评估神经胶质瘤中 CAF 的相对比例。 CAF 标记物通过肿瘤免疫单细胞中心数据库的单细胞 RNA 序列数据集 (GSE141383) 进行鉴定。使用无监督共识聚类将神经胶质瘤患者分为不同的亚组。利用最小绝对收缩和选择算子回归模型来建立 CAF 相关特征 (CRS)。最后，通过 RT-qPCR 在临床标本中进一步验证预后 CAF 标志物。对具有高比例和低比例 CAF 的样本进行单细胞 RNA-seq 分析和差异表达分析相结合，揭示了 23 个预后 CAF 标记物。通过使用无监督共识聚类，神经胶质瘤患者被分为两种不同的亚型。随后，基于两种 CAF 亚型之间 18 个差异表达的预后 CAF 标志物，我们开发并验证了新的 CRS 模型（包括 PCOLCE、TIMP1 和 CLIC1）。列线图和校准曲线表明 CRS 是神经胶质瘤的准确预后标志物。此外，高CRS评分组的患者具有较高的免疫浸润和肿瘤突变负荷水平。此外，CRS 评分有可能预测对免疫检查点阻断 (ICB) 疗法和化疗的反应。最后，通过 RT-qPCR 验证了三个 CAF 标记的表达谱。总的来说，我们的研究根据 CAF 标志物将胶质瘤患者分为不同的亚组，这将有助于个体化治疗的发展。我们还深入了解了 CRS 在预测神经胶质瘤患者对 ICB 和化疗反应中的作用。