Department of Oncology, Tumor Hospital of Shaanxi Province, Xi'an, 710061, People's Republic of China.
Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China.
BMC Cancer. 2021 Mar 1;21(1):213. doi: 10.1186/s12885-021-07911-8.
Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory.
The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues.
In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells.
In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.
肺腺癌（LUAD）是肺癌最常见的组织学亚型。近年来，免疫疗法、靶向治疗和化疗带来了一定的疗效。然而，LUAD 患者的疗效和预后存在差异，现有 LUAD 风险预测模型的效果并不理想。
下载癌症基因组图谱（TCGA）LUAD 数据集。采用 edgeR 和 DESeq2 分析差异表达免疫基因（DEIGs）。采用 COX 回归鉴定预后 DEIGs。采用 STRING 基于预后 DEIGs 推断蛋白-蛋白相互作用（PPI）网络。采用 Lasso 回归建立基于 DEIGs 的预后模型。免疫组化评估 30 例 LUAD 组织中 FERMT2、FKBP3、SMAD9、GATA2 和 ITIH4 的表达。
共鉴定出 1654 个 DEIGs，其中 436 个基因具有预后意义。基因功能富集分析表明，DEIGs 参与了炎症通路。我们使用 DEIGs 构建了 4 个模型。最后，使用 436 个 DEIGs 构建的模型 4 在预后预测中表现最佳，3 年、5 年、10 年的受试者工作特征曲线（ROC）分别为 0.824、0.838、0.834。FERMT2 高表达、FKBP3 低表达和 SMAD9、GATA2、ITIH4 低表达与 LUAD 总生存期不良相关（p<0.05）。基于 DEIGs 的预后模型反映了免疫细胞的浸润。
本研究使用 DEIGs 构建了 LUAD 的最优预后特征，并验证了 LUAD 中选定基因的表达。我们的结果表明，免疫特征可用于获得预后信息。
