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Abstract：
BACKGROUND: Cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, the impact of CAF subpopulation trajectory differentiation on CRC remains unclear.
METHODS: In this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data.
RESULTS: Based on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data.
CONCLUSIONS: In conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.
METHODS: In this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data.
RESULTS: Based on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data.
CONCLUSIONS: In conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.
摘要：
背景：癌相关成纤维细胞（CAF）在结直肠癌（CRC）的进展中起着至关重要的作用。然而,CAF亚群轨迹分化对CRC的影响尚不清楚.
方法：在本研究中，我们首先使用批量和整合的单细胞测序数据探索CAFs亚群的轨迹差异，然后基于CAFs亚群的轨迹差异基因对CRC样本进行一致性聚类。随后，我们利用生物信息学分析了CRC亚型的异质性.最后,我们使用机器学习构建了相关的预后特征,并使用空间转录组数据进行了验证.
结果：基于CAFs亚群轨迹分化的差异基因，在这项研究中,我们确定了两种CRC亚型(C1和C2).与C1相比，C2的预后较差，较高的免疫逃避微环境和高CAF特性。C1主要与代谢有关，而C2主要与细胞转移和免疫调节有关。通过结合10种机器学习算法的101种组合，我们开发了基于C2特征基因的高CAF风险特征(HCAFRS).HCAFRS是CRC的独立预后因素,当结合临床参数时，可显著预测CRC患者的总生存期。HCAFRS与上皮间质转化密切相关,血管生成,和缺氧。此外，HCAFRS的风险评分主要来自CAFs,并在空间转录组数据中得到验证.
结论：结论：HCAFRS有可能作为CRC的一个有希望的预后指标，改善CRC患者的生活质量。
方法：在本研究中，我们首先使用批量和整合的单细胞测序数据探索CAFs亚群的轨迹差异，然后基于CAFs亚群的轨迹差异基因对CRC样本进行一致性聚类。随后，我们利用生物信息学分析了CRC亚型的异质性.最后,我们使用机器学习构建了相关的预后特征,并使用空间转录组数据进行了验证.
结果：基于CAFs亚群轨迹分化的差异基因，在这项研究中,我们确定了两种CRC亚型(C1和C2).与C1相比，C2的预后较差，较高的免疫逃避微环境和高CAF特性。C1主要与代谢有关，而C2主要与细胞转移和免疫调节有关。通过结合10种机器学习算法的101种组合，我们开发了基于C2特征基因的高CAF风险特征(HCAFRS).HCAFRS是CRC的独立预后因素,当结合临床参数时，可显著预测CRC患者的总生存期。HCAFRS与上皮间质转化密切相关,血管生成,和缺氧。此外，HCAFRS的风险评分主要来自CAFs,并在空间转录组数据中得到验证.
结论：结论：HCAFRS有可能作为CRC的一个有希望的预后指标，改善CRC患者的生活质量。
