Sun Zhenqing, Xia Wei, Lyu Yali, Song Yanan, Wang Min, Zhang Ruirui, Sui Guode, Li Zhenlu, Song Li, Wu Changliang, Liew Choong-Chin, Yu Lei, Cheng Guang, Cheng Changming
Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266000, P.R. China.
Department of Nuclear Medicine, The Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200137, P.R. China.
Oncol Lett. 2021 Jul;22(1):543. doi: 10.3892/ol.2021.12804. Epub 2021 May 20.
The immune system is crucial in regulating colorectal cancer (CRC) tumorigenesis. Identification of immune-related transcriptomic signatures derived from the peripheral blood of patients with CRC would provide insights into CRC pathogenesis, and suggest novel clues to potential immunotherapy strategies for the disease. The present study collected blood samples from 59 patients with CRC and 62 healthy control patients and performed whole blood gene expression profiling using microarray hybridization. Immune-related gene expression signatures for CRC were identified from immune gene datasets, and an algorithmic predictive model was constructed for distinguishing CRC from controls. Model performance was characterized using an area under the receiver operating characteristic curve (ROC AUC). Functional categories for CRC-specific gene expression signatures were determined using gene set enrichment analyses. A Kaplan-Meier plotter survival analysis was also performed for CRC-specific immune genes in order to characterize the association between gene expression and CRC prognosis. The present study identified five CRC-specific immune genes [protein phosphatase 3 regulatory subunit Bα (), amyloid β precursor protein, cathepsin H, proteasome activator subunit 4 and DEAD-Box Helicase 3 X-Linked]. A predictive model based on this five-gene panel showed good discriminatory power (independent test set sensitivity, 83.3%; specificity, 94.7%, accuracy, 89.2%; ROC AUC, 0.96). The candidate genes were involved in pathways associated with 'adaptive immune responses', 'innate immune responses' and 'cytokine signaling'. The survival analysis found that a high level of expression was associated with a poor CRC prognosis. The present study identified five CRC-specific immune genes that were potential diagnostic biomarkers for CRC. The biological function analysis indicated a close association between CRC pathogenesis and the immune system, and may reveal more information about the immunogenic and pathogenic mechanisms driving CRC in the future. Overall, the association between expression and survival of patients with CRC revealed potential new targets for CRC immunotherapy.
免疫系统在调节结直肠癌（CRC）肿瘤发生过程中至关重要。识别源自CRC患者外周血的免疫相关转录组特征将为CRC发病机制提供见解，并为该疾病潜在的免疫治疗策略提示新线索。本研究收集了59例CRC患者和62例健康对照患者的血样，并使用微阵列杂交进行全血基因表达谱分析。从免疫基因数据集中识别出CRC的免疫相关基因表达特征，并构建了一种算法预测模型以区分CRC与对照。使用受试者操作特征曲线下面积（ROC AUC）来表征模型性能。使用基因集富集分析确定CRC特异性基因表达特征的功能类别。还对CRC特异性免疫基因进行了Kaplan-Meier绘图仪生存分析，以表征基因表达与CRC预后之间的关联。本研究识别出五个CRC特异性免疫基因[蛋白磷酸酶3调节亚基Bα（）、淀粉样β前体蛋白、组织蛋白酶H、蛋白酶体激活亚基4和X连锁的DEAD盒解旋酶3]。基于这个五基因组合的预测模型显示出良好的鉴别能力（独立测试集敏感性为83.3%；特异性为94.7%，准确性为89.2%；ROC AUC为0.96）。候选基因参与了与“适应性免疫反应”、“固有免疫反应”和“细胞因子信号传导”相关的途径。生存分析发现高水平的表达与CRC预后不良相关。本研究识别出五个CRC特异性免疫基因，它们是CRC潜在的诊断生物标志物。生物学功能分析表明CRC发病机制与免疫系统之间密切相关，并且未来可能揭示更多关于驱动CRC的免疫原性和致病机制的信息。总体而言，CRC患者中表达与生存之间的关联揭示了CRC免疫治疗的潜在新靶点。
