[1]孙 飞,黎春明.基于免疫细胞组织浸润的免疫评分模型预测前列腺癌免疫治疗效果及预后分析研究[J].现代检验医学杂志,2023,38(03):189-194.[doi:10.3969/j.issn.1671-7414.2023.03.035]
　SUN Fei,LI Chun-ming.Prediction of Immunotherapy Effect and Prognosis of Prostate Cancer Based on Immune Cell Tissue Infiltration Immune Score Model[J].Journal of Modern Laboratory Medicine,2023,38(03):189-194.[doi:10.3969/j.issn.1671-7414.2023.03.035]
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基于免疫细胞组织浸润的免疫评分模型预测前列腺癌免疫治疗效果及预后分析研究()
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《现代检验医学杂志》[ISSN:/CN:]

卷:
第38卷
期数:
2023年03期
页码:
189-194
栏目:
检验与临床
出版日期:
2023-05-15

文章信息/Info

Title:
Prediction of Immunotherapy Effect and Prognosis of Prostate Cancer Based on Immune Cell Tissue Infiltration Immune Score Model
文章编号:
1671-7414（2023）03-189-06
作者:
孙 飞; 黎春明
（海口市第三人民医院泌尿外科 ，海口 571100）
Author(s):
SUN Fei; LI Chun-ming
（Department of Urology, the Third People’s Hospital of Haikou, Haikou 571100, China）
关键词:
免疫细胞浸润; 免疫评分模型; 前列腺癌; 治疗效果; 预后分析
分类号:
R737.25；R730.43
DOI:
10.3969/j.issn.1671-7414.2023.03.035
文献标志码:
A
摘要:
目的 探讨基于免疫细胞浸润的免疫评分模型对前列腺癌（PCa）患者免疫治疗效果及预后的预测。方法 从癌症基因组图谱 (TCGA) 选取266 个PCa 患者的基因表达谱和临床随访参数；通过单样本基因集富集分析（single-samplegene set enrichment analysis，ssGSEA）量化免疫细胞在肿瘤组织中的浸润情况，得到22 个免疫细胞的浸润情况；通过“Glmnet”包的套索方法（least absolute shrinkage and selection operator, LASSO）分析筛选关键预测因子，以验证不同浸润免疫细胞是否与预后相关；将符合条件的细胞构建预后风险评分模型，将PCa 患者分为高风险组和低风险组，并制作生存曲线（Kaplan-Meier）进行验证；基于风险模型建立列线图来预测PCa 患者治疗无效和生存的概率；绘制随时间变化的受试者工作特征（ROC）曲线，以验证列线图的准确性；利用校准曲线比较基于浸润免疫细胞列线图的预测和观察结果；采用临床决策曲线分析法判断模型的预测可靠度。结果 使用ssGSEA 量化免疫细胞浸润数据，最后得到22 个浸润免疫细胞包括：幼稚B 细胞、记忆B 细胞、浆细胞、CD8+ T 细胞、幼稚CD4+ T 细胞、静息记忆CD4+ T 细胞、激活记忆CD4+ T 细胞、滤泡辅助性T 细胞、调节性T 细胞（Treg 细胞）、γδ T 细胞、静息NK 细胞、活化NK 细胞、单核细胞、M0 巨噬细胞、M1 巨噬细胞、M2 巨噬细胞、静息树突细胞、活化树突细胞、静息肥大细胞、活化的肥大细胞、嗜酸性粒细胞和嗜中性粒细胞；最终选择6 个免疫细胞作为预测因子，分别为Treg 细胞、M1 巨噬细胞、M2 巨噬细胞、激活记忆CD4+ T 细胞、静息树突细胞和嗜中性粒细胞；根据6 个浸润免疫细胞，计算样本的风险评分，随着风险评分的升高，高风险组PCa 患者增多，预后不良人数增多；利用Kaplan-Meier 法绘制生存曲线，结果显示高风险患者总生存时间（3.8 年）显著低于低风险组（6.9 年）（Log-Rank χ2=4.259，P ＜ 0.001）；预后列线图模型总分430 分，对应死亡风险为74.69%；治疗效果列线图模型总分423 分，对应治疗无效的风险为73.21%，且通过模型验证结果安全可靠。结论 通过评估PCa 中的免疫浸润以及免疫评分模型建立了列线图，揭示免疫细胞浸润可以预测PCa 患者的预后和治疗效果。
Abstract:
Objective To explore the effect and prognosis of immune scoring model based on immune cell infiltration on patients with prostate cancer (PCa). Methods Gene expression profile and clinical follow-up parameters of 266 patients with PCa were selected from cancer genome map (TCGA). The infiltration of immune cells in tumour tissue was obtained by ssGSEA to obtain the infiltration of 22 immune cells. The key predictors were screened by lasso analysis(LASSO) a to verify whether different infiltrating immune cells were related to prognosis. The eligible cells were used to construct the prognostic risk scoring model, and PCa patients were divided into high-risk and low-risk groups, and the survival curve(Kaplan-Meier) was made for verification. Based on the risk model, a nomogram was established to predict the Probability of treatment failure and survival of PCa patients. Drew the working characteristic(ROC) curve of the subject over time to verify the accuracy of the nomogram. Compared the predictied and observed results based on infiltrating immune cell historgrams with calibration curves, and used clinical decision curve analysis to determine the Predictive reliability of the model. Results Using ssGSEA to quantify immune cell infiltration data, 22 infiltrating immune cells were finally obtained, including infantile B cells, memory B cells, plasma cells, CD8+ T cells, infantile CD4+ T cells, resting memory CD4+ T cells, activation of memory CD4+ T fine Cell, follicle-assisted T cells, regulatory T cells (Treg cells), γδ T cells, resting NK cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, reactive dendritic cells, activated dendritic cells, resting mast cells, activated hypertrophy cells, eosinophils and neutrophils. Finally six immune cells were selected as predictors, namely, Treg cells, M1 macrophages, M2 macrophages, activated memory CD4+ T cells, resting dendritic cells, and neutrophils. According to six infiltrating immune cells, the sample was calculated. Risk score, with the increase of risk score, the number of PCa patients in the high-risk group had increased, and the number of adverse prognosis had increased. Using the Kaplan-Meier method to draw the survival curve, the results showed that the total survival time of high-risk patients(3.8 years) was significantly lower than that of the low-risk group(6.9 years) (Log-Rank χ2 = 4.259. P<0.001). The total score of the prognosis line chart model was 430 points, and the corresponding risk of death was 74.69%. The total score of the therapeutic effect line chart model was 423 points, and the corresponding risk of ineffective treatment was 73.21%, and the reliability of the verification results through the model was relatively high. Conclusion By evaluating the immune infiltration and immunisation score model in PCa , a line chart was established to reveal that immune cell infiltration can predict the prognosis and treatment effect of PCA patients.

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备注/Memo

备注/Memo:
作者简介：孙飞（1984-），男，大学本科，主治医师，研究方向：泌尿外科学， E-mail：sunfei888688@163.com。
通讯作者：黎春明（1975-），男，硕士，主任医师，研究方向：泌尿外科学。
更新日期/Last Update: 2023-05-15