RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
基于自噬特征预测急性髓系白血病预后及其免疫微环境特征
英文原题：Prediction of acute myeloid leukemia prognosis based on autophagy features and characterization of its immune microenvironment.
查看英文原题
Prediction of acute myeloid leukemia prognosis based on autophagy features and characterization of its immune microenvironment.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
研究概要
本研究系统分析了自噬相关基因（ARGs），并开发了与 AML 患者 OS 相关的预后预测因子，从而更准确地评估 AML 患者的预后。这不仅有助于改善患者的预后评估和治疗效果，还可能为未来的研究和临床应用提供新的帮助。
研究思路结论见上方概要
背景
自噬通过清除受损的细胞器和蛋白质，保护急性髓系白血病（AML）细胞免受应激诱导的凋亡，从而促进其存活。尽管许多研究已经确定了与AML预后相关的候选自噬基因，但在预测AML患者的生存预后方面仍存在巨大挑战。因此，有必要识别更多新的自噬基因标志物，利用分子层面的信息来改善AML的预后。
方法
本研究分别利用随机森林、SVM 和 XGBoost 算法识别与预后相关的自噬基因。随后，通过 Lasso-Cox 回归分析识别出6个与患者总生存期（OS）显著相关的自噬基因（TSC2、CALCOCO2、BAG3、UBQLN4、ULK1 和 DAPK1）。接着构建了包含这些自噬基因的预测模型。此外，本研究还进行了自噬基因的免疫微环境分析。
结果
实验结果显示，该预测模型具有良好的预测能力。在调整临床病理参数后，该特征被证明是一个独立的预后预测因子，并在一个外部AML样本集中得到验证。对高风险组和低风险组患者的差异表达基因分析显示，这些基因富集于体液免疫反应、胸腺中T细胞分化和淋巴细胞分化等免疫相关通路。随后对患者中自噬基因的免疫浸润分析显示，高风险组中活化CD4+记忆T细胞、活化NK细胞和CD4+T细胞的细胞丰度显著低于低风险组。
展开英文摘要原文
BACKGROUND
Autophagy promotes the survival of acute myeloid leukemia (AML) cells by removing damaged organelles and proteins and protecting them from stress-induced apoptosis. Although many studies have identified candidate autophagy genes associated with AML prognosis, there are still great challenges in predicting the survival prognosis of AML patients. Therefore, it is necessary to identify more novel autophagy gene markers to improve the prognosis of AML by utilizing information at the molecular level.
METHODS
In this study, the Random Forest, SVM and XGBoost algorithms were utilized to identify autophagy genes linked to prognosis, respectively. Subsequently, six autophagy genes (TSC2, CALCOCO2, BAG3, UBQLN4, ULK1 and DAPK1) that were significantly associated with patients' overall survival (OS) were identified using Lasso-Cox regression analysis. A prediction model incorporating these autophagy genes was then developed. In addition, the immunological microenvironment analysis of autophagy genes was performed in this study.
RESULTS
The experimental results showed that the predictive model had good predictive ability. After adjusting for clinicopathologic parameters, this feature proved an independent prognostic predictor and was validated in an external AML sample set. Analysis of differentially expressed genes in patients in the high-risk and low-risk groups showed that these genes were enriched in immune-related pathways such as humoral immune response, T cell differentiation in thymus and lymphocyte differentiation. Then immune infiltration analysis of autophagy genes in patients showed that the cellular abundance of T cells CD4+ memory activated, NK cells activated and T cells CD4+ in the high-risk group was significantly lower than that in the low-risk group.
CONCLUSION
This study systematically analyzed autophagy-related genes (ARGs) and developed prognostic predictors related to OS for patients with AML, thus more accurately assessing the prognosis of AML patients. This not only helps to improve the prognostic assessment and therapeutic outcome of patients, but may also provide new help for future research and clinical applications.
论文信息
- 作者
- Zhu C、Feng X、Tong L、Mu P、Wang F、Quan W、Dong Y、Zhu X
- 单位
- School of Computer and Control Engineering, Yantai University, Yantai, Shandong, China.China
- 期刊
- Frontiers in immunology2024
- 原文标识
- PubMed 39650664 · DOI 10.3389/fimmu.2024.1489171
在 PubMed 查看 → 出版商原文（DOI） 全文 PDF（PMC）· 可下载 治疗专题与资料阅读指南 资料来源与翻译说明 报告译文或资料问题 →