PDF(Pubmed)
Abstract：
UNASSIGNED: Patients with colorectal cancer commonly experience disturbances in coagulation homeostasis. Activation of the coagulation system contributes to cancer-associated thrombosis as the second risk factor for death in cancer patients. This study intended to discover coagulation-related genes and construct a risk model for colorectal cancer patients\' prognosis.
UNASSIGNED: Coagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model\'s predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients.
UNASSIGNED: Eight hundred forty-five genes were obtained by searching the theme term \"coagulation\" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction.
UNASSIGNED: The prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.
UNASSIGNED: Coagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model\'s predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients.
UNASSIGNED: Eight hundred forty-five genes were obtained by searching the theme term \"coagulation\" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction.
UNASSIGNED: The prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.
摘要：
■结直肠癌患者通常会经历凝血稳态紊乱。凝血系统的激活有助于癌症相关的血栓形成作为癌症患者死亡的第二危险因素。本研究旨在发现凝血相关基因并构建结直肠癌患者预后的风险模型。
■通过在分子特征数据库中搜索凝血相关途径来鉴定凝血相关基因。转录组数据和临床数据从癌症基因组图谱和基因表达综合数据集下载。使用单变量Cox和后向逐步回归来识别预后相关基因并构建训练队列的预测风险模型。接下来,生存分析决定了风险模型的预测能力，与临床病理特征相关，和列线图。此外,我们表征了免疫细胞浸润的差异，体细胞突变,免疫检查点分子，生物学功能,高和低评分患者之间的药物敏感性。
■在去重复后，通过搜索主题词“凝血”获得了八百四十五个基因。经过单因素回归分析，与预后相关的69个基因来自癌症基因组图谱数据集。通过向后逐步回归建立了由17个凝血相关基因组成的签名。Kaplan-Meier曲线提示高评分患者预后较差。与时间相关的受试者工作特征曲线分析在预测总生存期方面具有很高的准确性。Further,结果通过两个独立的数据集(GSE39582和GSE17536)进行验证.结合临床病理特点，风险模型被证明是预测不良病理状态和不良预后的独立预后因素.此外，高分患者的基质细胞浸润明显增高.低分患者与静息记忆CD4+T细胞高浸润有关，激活的CD4+T细胞，和T滤泡辅助细胞。低分患者表现出免疫检查点基因表达增加，这可能与他们更好的预后有关。高分患者的紫杉醇IC50值较低，雷帕霉素,替莫唑胺,环磷酰胺,等。差异信号通路主要涉及钙信号通路和神经活性配体-受体相互作用。最后,建立了一个列线图，并显示了良好的预测。
■17个凝血相关基因的预后特征对结直肠癌患者具有显著的预后价值。我们希望通过对分子特征的研究来改善治疗方式并使更多患者受益。
■通过在分子特征数据库中搜索凝血相关途径来鉴定凝血相关基因。转录组数据和临床数据从癌症基因组图谱和基因表达综合数据集下载。使用单变量Cox和后向逐步回归来识别预后相关基因并构建训练队列的预测风险模型。接下来,生存分析决定了风险模型的预测能力，与临床病理特征相关，和列线图。此外,我们表征了免疫细胞浸润的差异，体细胞突变,免疫检查点分子，生物学功能,高和低评分患者之间的药物敏感性。
■在去重复后，通过搜索主题词“凝血”获得了八百四十五个基因。经过单因素回归分析，与预后相关的69个基因来自癌症基因组图谱数据集。通过向后逐步回归建立了由17个凝血相关基因组成的签名。Kaplan-Meier曲线提示高评分患者预后较差。与时间相关的受试者工作特征曲线分析在预测总生存期方面具有很高的准确性。Further,结果通过两个独立的数据集(GSE39582和GSE17536)进行验证.结合临床病理特点，风险模型被证明是预测不良病理状态和不良预后的独立预后因素.此外，高分患者的基质细胞浸润明显增高.低分患者与静息记忆CD4+T细胞高浸润有关，激活的CD4+T细胞，和T滤泡辅助细胞。低分患者表现出免疫检查点基因表达增加，这可能与他们更好的预后有关。高分患者的紫杉醇IC50值较低，雷帕霉素,替莫唑胺,环磷酰胺,等。差异信号通路主要涉及钙信号通路和神经活性配体-受体相互作用。最后,建立了一个列线图，并显示了良好的预测。
■17个凝血相关基因的预后特征对结直肠癌患者具有显著的预后价值。我们希望通过对分子特征的研究来改善治疗方式并使更多患者受益。
