术前化疗 (PC) 是结直肠癌 (CRC) 治疗的重要组成部分，但其对 CRC 中成纤维细胞和上皮细胞生物学功能的影响尚不清楚。本研究利用了来自 22 个国家的大量、单细胞和空间转录组测序数据CRC 的独立队列。研究通过生物信息学分析和体外实验，探讨了PC对结直肠癌成纤维细胞和上皮细胞的影响。确定了与 PC 和 CRC 预后相关的亚群，并使用机器学习构建了预测模型。PC 显着减弱了成纤维细胞和上皮细胞中与肿瘤进展相关的途径。 NOTCH3 成纤维细胞 (NOTCH3 Fib)、TNNT1 上皮细胞 (TNNT1 Epi) 和 HSPA1A 上皮细胞 (HSPA1A Epi) 亚群在邻近空间区域被识别，并且与 CRC 不良预后相关。 PC 有效地减少了这些亚群的存在，同时抑制通路活性和细胞间串扰。使用机器学习构建了一个风险特征模型，称为术前化疗风险特征模型（PCRSM）。 PCRSM 成为 CRC 的独立预后指标，影响总生存期 (OS) 和无复发生存期 (RFS)，超过了之前发布的 89 个 CRC 风险特征的表现。此外，PCRSM 风险评分高的患者对基于氟尿嘧啶的辅助化疗 (FOLFOX) 敏感，但对单一化疗药物（如贝伐珠单抗和奥沙利铂）耐药。此外，本研究预测高 PCRSM 的患者对抗 PD1 治疗有抵抗力。 总之，本研究鉴定了与 PC 相关的三个细胞亚群（NOTCH3 Fib、TNNT1 Epi 和 HSPA1A Epi），可以有针对性地改善 PC 的预后结直肠癌患者。 PCRSM 模型在提高 CRC 患者的生存和治疗方面显示出希望。版权所有 © 2024。由 Elsevier B.V. 出版。

Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.This study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning.PC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy.In conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.Copyright © 2024. Published by Elsevier B.V.