Zhang Minghui, Zhu Kaibin, Pu Haihong, Wang Zhuozhong, Zhao Hongli, Zhang Jinfeng, Wang Yan
Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
Front Oncol. 2019 Dec 10;9:1314. doi: 10.3389/fonc.2019.01314. eCollection 2019.
We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (, and ) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.
我们研究了肺腺癌的局部免疫状态及其预后价值。总共收集并分析了来自TCGA和ImmPort数据库的513份肺腺癌样本。使用R包coxph通过单变量和多变量分析挖掘作为显著预后指标的免疫相关基因。然后使用R软件包glmnet进行Lasso Cox回归分析，并构建肺腺癌的预后预测模型；选择clusterProfiler进行功能基因注释和KEGG富集分析。最后，建立风险评分与临床特征或信号通路之间的相关性。64个免疫相关基因与患者预后显著相关并被进一步应用。样本被分层聚类为两个亚组。因此，采用LASSO回归算法筛选出14个与患者预后最具代表性的免疫相关基因（ ，以及 ）。然后，构建肺腺癌患者的预后预测模型（即风险评分方程），并纳入训练集样本以评估该模型预测和分类患者预后的效率。随后，基于功能注释和KEGG通路分析，这14个免疫相关基因主要富集在与肺腺癌及其免疫微环境密切相关的通路中，如细胞因子 - 细胞因子受体相互作用和人类T细胞白血病病毒1感染。此外，还证明了训练集样本的风险评分与临床特征和信号通路（如p53、细胞周期和DNA修复）之间的相关性。最后，使用测试集样本数据对模型进行独立测试和验证。我们基于14个免疫相关基因的表达谱建立了一个预后预测风险评分模型，该模型在识别免疫特征方面显示出高预测准确性和稳定性。这可为不同免疫表型的诊断和预后提供临床指导，并为基于亚型特异性免疫分子的精准晚期肺腺癌治疗提示多个靶点。
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