Cui Yiyao, Hou Ruiqin, Lv Xiaoshuo, Wang Feng, Yu Zhaoyan, Cui Yong
Department of Thoracic Surgery, Beijing Friendship Hospital, Affiliated to the Capital University of Medical Sciences, Beijing, China.
Department of Blood Transfusion, Peking University People's Hospital, Beijing, China.
Front Oncol. 2021 Oct 25;11:771749. doi: 10.3389/fonc.2021.771749. eCollection 2021.
Esophageal squamous cell carcinoma (ESCC) is one of the most fatal cancers in the world. The 5-year survival rate of ESCC is <30%. However, few biomarkers can accurately predict the prognosis of patients with ESCC. We aimed to identify potential survival-associated biomarkers for ESCC to improve its poor prognosis.
ImmuneAI analysis was first used to access the immune cell abundance of ESCC. Then, ESTIMATE analysis was performed to explore the tumor microenvironment (TME), and differential analysis was used for the selection of immune-related differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was used for selecting the candidate DEGs. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to build the immune-cell-associated prognostic model (ICPM). Kaplan-Meier curve of survival analysis was performed to evaluate the efficacy of the ICPM.
Based on the ESTIMATE and ImmuneAI analysis, we obtained 24 immune cells' abundance. Next, we identified six coexpression module that was associated with the abundance. Then, LASSO regression models were constructed by selecting the genes in the module that is most relevant to immune cells. Two test dataset was used to testify the model, and we finally, obtained a seven-genes survival model that performed an excellent prognostic efficacy.
In the current study, we filtered seven key genes that may be potential prognostic biomarkers of ESCC, and they may be used as new factors to improve the prognosis of cancer.
食管鳞状细胞癌（ESCC）是世界上最致命的癌症之一。ESCC的5年生存率<30%。然而，很少有生物标志物能够准确预测ESCC患者的预后。我们旨在识别ESCC潜在的生存相关生物标志物，以改善其不良预后。
首先使用ImmuneAI分析来评估ESCC的免疫细胞丰度。然后，进行ESTIMATE分析以探索肿瘤微环境（TME），并使用差异分析来选择免疫相关差异表达基因（DEG）。加权基因共表达网络分析（WGCNA）用于选择候选DEG。使用最小绝对收缩和选择算子（LASSO）Cox回归构建免疫细胞相关预后模型（ICPM）。进行生存分析的Kaplan-Meier曲线以评估ICPM的疗效。
基于ESTIMATE和ImmuneAI分析，我们获得了24种免疫细胞的丰度。接下来，我们鉴定了六个与丰度相关的共表达模块。然后，通过选择模块中与免疫细胞最相关的基因构建LASSO回归模型。使用两个测试数据集对模型进行验证，最终我们获得了一个具有优异预后疗效的七基因生存模型。
在本研究中，我们筛选出了七个可能是ESCC潜在预后生物标志物的关键基因，它们可能作为改善癌症预后的新因素。
