Identification of Immune-Cell-Related Prognostic Biomarkers of Esophageal Squamous Cell Carcinoma Based on Tumor Microenvironment - Cui, Yiyao; Hou, Ruiqin; Lv, Xiaoshuo; Wang, Feng; Yu, Zhaoyan; Cui, Yong (Frontiers in Oncology, 2021)
Background: ESCC is one of the most fatal cancers in the world. The five-year survival rate of ESCC is less than 30%. However, few biomarkers can accurately predict the prognosis of patients with ESCC. Our aim was to identify potential survival-associated biomarkers for ESCC to improve its poor prognosis. Methods: 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 co-expression network analysis (WGCNA) was used for selecting the candidate DEGs. LASSO Cox regression used to build the immune cell-associated prognostic model (ICPM). Kaplan Meier curve of survival analysis were performed to evaluate the efficacy of the ICPM. Results: Based on the ESTIMATE and ImmuneAI analysis, we obtained 24 immune cells’ abundance. Next, we identified six co-expression module that was associated with the abundance. Then, lasso regression models were constructed by selecting the genes in the module which is most relevant to immune cells. Two test dataset was used to testify the model and we finally obtained a 7 genes survival model that performed an excellent prognostic efficacy. Conclusion: In the current study, we filtered 7 key genes which may be potential prognostic biomarkers of ESCC, and they may use as new factors to improve the prognosis of the cancer.