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基于转移的结直肠癌 14 基因特征预后模型的建立。

Development of 14-gene signature prognostic model based on metastasis for colorectal cancer.

机构信息

Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.

Department of Laboratory Medicine, Wusong Branch, Zhongshan Hospital, Fudan University, Shanghai, China.

出版信息

J Clin Lab Anal. 2023 Jan;37(1):e24800. doi: 10.1002/jcla.24800. Epub 2022 Dec 16.

Abstract

BACKGROUND

Metastasis is the main cause of death of colorectal tumors, in our study a prognosis model was built by analyzing the differentially expressed genes between metastatic and non-metastatic colorectal cancer (CRC). We used this feature to predict CRC patient prognosis and explore the causes of colorectal tumor metastasis by characterizing the immune status alteration.

METHODS

CRC patient data were obtained from TCGA and GEO databases. We constructed a risk prognostic model by using Cox regression and the least absolute shrinkage and selection operator (LASSO) based on CRC metastasis-related genes. We also obtained a nomogram to predict the prognosis of CRC patients. Finally, we explored the underlying mechanism of these metastasis-related genes and CRC prognosis using immune infiltration analysis and experimental verification.

RESULTS

According to our prognostic model, in TCGA, the area under the curve (AUC) values of the training and test sets were 0.72 and 0.76, respectively, and 0.68 for the GEO external data set. This suggested that the treatment and prognosis of patients could be effectively determined. At the same time, we found that the B and T cells in both tissues and peripheral blood of high MR-risk score patients were mostly in immune static or inactivated states compared with those of low MR-risk score patients.

CONCLUSIONS

MR-risk score has a direct correlation with CRC patient prognosis. It is useful for predicting the prognosis and patient immune status for these patients.

摘要

背景

转移是结直肠癌(CRC)死亡的主要原因,在本研究中,我们通过分析转移性和非转移性 CRC 之间差异表达的基因,构建了一个预后模型。我们利用该特征预测 CRC 患者的预后,并通过表征免疫状态改变来探索 CRC 肿瘤转移的原因。

方法

从 TCGA 和 GEO 数据库中获取 CRC 患者数据。我们使用 Cox 回归和最小绝对值收缩和选择算子(LASSO)基于 CRC 转移相关基因构建风险预后模型。我们还获得了一个列线图来预测 CRC 患者的预后。最后,我们通过免疫浸润分析和实验验证来探索这些转移相关基因和 CRC 预后的潜在机制。

结果

根据我们的预后模型,在 TCGA 中,训练集和测试集的曲线下面积(AUC)值分别为 0.72 和 0.76,GEO 外部数据集为 0.68。这表明可以有效地确定患者的治疗和预后。同时,我们发现高 MR 风险评分患者的组织和外周血中的 B 和 T 细胞与低 MR 风险评分患者相比,大多处于免疫静止或失活状态。

结论

MR 风险评分与 CRC 患者的预后直接相关,有助于预测这些患者的预后和患者的免疫状态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9b/9833974/40db21f34327/JCLA-37-e24800-g008.jpg

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