PDF(Pubmed)
Abstract：
UNASSIGNED: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response.
UNASSIGNED: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified.
UNASSIGNED: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients.
UNASSIGNED: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.
UNASSIGNED: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified.
UNASSIGNED: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients.
UNASSIGNED: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.
摘要：
■结肠直肠癌（CRC）对公共卫生构成挑战，其特征是发病率高。本研究探讨了CRC患者肿瘤微环境（TME）中铁死亡与脂肪酸代谢之间的关系，以确定这些相互作用如何影响免疫治疗的预后和有效性。关注患者预后和预测治疗反应的潜力。
■使用来自多个队列的数据集，包括癌症基因组图谱（TCGA）和基因表达综合（GEO），我们进行了一项深入的多组学研究,以揭示铁凋亡调节因子与CRC脂肪酸代谢之间的关系.通过无监督聚类，我们发现了将铁死亡和脂肪酸代谢联系起来的独特模式，并在免疫细胞浸润和途径分析的背景下进一步研究了它们。我们开发了FeFAMscore,使用机器学习算法的组合创建的预后模型，并评估其对患者预后和治疗反应性的预测能力。使用RT-PCR确认FeFAMscore签名表达水平，ACAA2在癌症中的进展得到进一步证实。
■这项研究揭示了铁凋亡调节因子和脂肪酸代谢相关基因在肿瘤进展方面的显著相关性。确定了具有不同预后和免疫细胞浸润的三个不同患者群。FeFAMscore显示出比现有模型更高的预后准确性，训练队列的C指数为0.689，四个独立验证队列的值范围为0.648至0.720。它也对免疫疗法和化疗有反应，表明特殊疗法的敏感反应(例如，抗PD-1，抗CTLA4，奥希替尼）在高FeFAM评分患者中。
■铁凋亡调节因子和脂肪酸代谢相关基因不仅增强免疫激活，但也有助于免疫逃逸。因此，FeFAMscore,一种新的预后工具，对于预测CRC患者的免疫治疗策略的预后和疗效是有希望的。
■使用来自多个队列的数据集，包括癌症基因组图谱（TCGA）和基因表达综合（GEO），我们进行了一项深入的多组学研究,以揭示铁凋亡调节因子与CRC脂肪酸代谢之间的关系.通过无监督聚类，我们发现了将铁死亡和脂肪酸代谢联系起来的独特模式，并在免疫细胞浸润和途径分析的背景下进一步研究了它们。我们开发了FeFAMscore,使用机器学习算法的组合创建的预后模型，并评估其对患者预后和治疗反应性的预测能力。使用RT-PCR确认FeFAMscore签名表达水平，ACAA2在癌症中的进展得到进一步证实。
■这项研究揭示了铁凋亡调节因子和脂肪酸代谢相关基因在肿瘤进展方面的显著相关性。确定了具有不同预后和免疫细胞浸润的三个不同患者群。FeFAMscore显示出比现有模型更高的预后准确性，训练队列的C指数为0.689，四个独立验证队列的值范围为0.648至0.720。它也对免疫疗法和化疗有反应，表明特殊疗法的敏感反应(例如，抗PD-1，抗CTLA4，奥希替尼）在高FeFAM评分患者中。
■铁凋亡调节因子和脂肪酸代谢相关基因不仅增强免疫激活，但也有助于免疫逃逸。因此，FeFAMscore,一种新的预后工具，对于预测CRC患者的免疫治疗策略的预后和疗效是有希望的。
