Ding Yuan C, Wu Huiqing, Davicioni Elai, Karnes R Jeffrey, Klein Eric A, Den Robert B, Steele Linda, Neuhausen Susan L
Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California, CA 91010, USA.
Department of Pathology, City of Hope Medical Center, Duarte, California, CA 91010, USA.
J Transl Genet Genom. 2021;5:50-61. doi: 10.20517/jtgg.2021.01. Epub 2021 Mar 9.
Several genomic signatures are available to predict Prostate Cancer (CaP) outcomes based on gene expression in prostate tissue. However, no signature was tailored to predict aggressive CaP in younger men. We attempted to develop a gene signature to predict the development of metastatic CaP in young men.
We measured genome-wide gene expression for 119 tumor and matched benign tissues from prostatectomies of men diagnosed at ≤ 50 years and > 70 years and identified age-related differentially expressed genes (DEGs) for tissue type and Gleason score. Age-related DEGs were selected using the improved Prediction Analysis of Microarray method (iPAM) to construct and validate a classifier to predict metastasis using gene expression data from 1,232 prostatectomies. Accuracy in predicting early metastasis was quantified by the area under the curve (AUC) of receiver operating characteristic (ROC), and abundance of immune cells in the tissue microenvironment was estimated using gene expression data.
Thirty-six age-related DEGs were selected for the iPAM classifier. The AUC of five-year survival ROC for the iPAM classifier was 0.87 (95%CI: 0.78-0.94) in young (≤ 55 years), 0.82 (95%CI: 0.76-0.88) in middle-aged (56-70 years), and 0.69 (95%CI: 0.55-0.69) in old (> 70 years) patients. Metastasis-associated immune responses in the tumor microenvironment were more pronounced in young and middle-aged patients than in old ones, potentially explaining the difference in accuracy of prediction among the groups.
We developed a genomic classifier with high precision to predict early metastasis for younger CaP patients and identified age-related differences in immune response to metastasis development.
有几种基因组特征可根据前列腺组织中的基因表达来预测前列腺癌（CaP）的预后。然而，尚无专门针对预测年轻男性侵袭性CaP的特征。我们试图开发一种基因特征来预测年轻男性转移性CaP的发生。
我们测量了119例年龄≤50岁和＞70岁男性前列腺切除术中肿瘤组织及配对的良性组织的全基因组基因表达，并确定了与年龄相关的组织类型和 Gleason 评分差异表达基因（DEG）。使用改进的微阵列预测分析方法（iPAM）选择与年龄相关的DEG，以构建和验证一个使用来自1232例前列腺切除术的基因表达数据预测转移的分类器。通过受试者操作特征（ROC）曲线下面积（AUC）来量化预测早期转移的准确性，并使用基因表达数据估计组织微环境中免疫细胞的丰度。
为iPAM分类器选择了36个与年龄相关的DEG。iPAM分类器的五年生存ROC的AUC在年轻（≤55岁）患者中为0.87（95%CI：0.78-0.94），中年（56-70岁）患者中为0.82（95%CI：0.76-0.88），老年（＞70岁）患者中为0.69（95%CI：0.55-0.69）。肿瘤微环境中与转移相关的免疫反应在年轻和中年患者中比老年患者更明显，这可能解释了各组预测准确性的差异。
我们开发了一种高精度的基因组分类器来预测年轻CaP患者的早期转移，并确定了对转移发生的免疫反应中与年龄相关的差异。
