Department of Medical Oncology, the First Hospital of China Medical University, 110001, Shenyang, China.
Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, the First Hospital of China Medical University, Shenyang, 110001, China.
BMC Cancer. 2021 Dec 11;21(1):1324. doi: 10.1186/s12885-021-09065-z.
Advanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.
Single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.
Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.
We identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.
由于目前缺乏有效的治疗策略,晚期胃癌(AGC)预后较差。免疫检查点阻断治疗在患者亚组中显示出有效反应,但生物标志物仍然具有挑战性。传统的胃癌(GC)分类基于基因组谱和分子特征。因此,通过免疫基因组谱分析识别免疫相关亚型和预测标志物至关重要。
在两个独立的 GEO 数据集上,使用单样本基因集富集分析(ssGSEA)和 ESTIMATE 算法来鉴定 AGC 的免疫相关亚型。应用加权基因共表达网络分析(WGCNA)和分子复合物检测(MCODE)算法鉴定免疫相关亚型的枢纽网络。通过 KMplotter 和 GEO 数据集的预后数据验证枢纽基因。通过 IMvigor210 数据集分析枢纽基因在预测免疫治疗反应中的价值。通过 MTT 检测、Transwell 迁移实验和 Western blot 实验验证枢纽基因在体外的细胞功能。
在两个独立的 GEO 数据集上鉴定出三种 AGC 的免疫相关亚型(Immunity_H、Immunity_M 和 Immunity_L)。与 Immunity_L 相比,Immunity_H 亚型表现出更高的免疫细胞浸润和免疫活性,预后较好。基于 GSE62254 数据集构建了一个加权基因共表达网络,并鉴定出一个与 Immunity_H 亚型显著相关的基因模块。基于拓扑特征和分子复合物检测(MCODE)算法提取了一个代表高免疫活性的枢纽网络。此外,在 GSE62254 和外部验证数据集中,ADAM 样脱细胞 1(ADAMDEC1)被鉴定为枢纽网络基因中的一个种子基因,与良好的预后高度相关。此外,在 IMvigor210 数据集中,ADAMDEC1 的高表达与免疫治疗反应相关。在体外,ADAMDEC1 被证实是一种潜在的调节胃癌细胞增殖和迁移的蛋白。胃癌细胞中 ADAMDEC1 的缺失也与 PD-L1 的高表达和 Jurkat T 细胞凋亡相关。
我们鉴定了 AGC 中的免疫相关亚型和关键肿瘤微环境标志物,这可能有助于开发新的免疫治疗靶点。