BACKGROUND: Cancer stem cells (CSCs) induces tumor metastasis and recurrence. However, the role of CSCs in molding the tumor immune microenvironment (TIME) is largely inexplicit. This study aimed to comprehensively characterize the stemness of esophageal cancer (EC) and correlate the stemness patterns with TIME.
METHODS: A trained stemness index model was used to score EC patients based on the one-class logistic regression (OCLR) machine-learning algorithm. Gene expression-based stemness index (mRNAsi) and DNA methylation-based stemness index (mDNAsi) were calculated for integrative analyses of EC stemness in the training cohort (n = 182) and validation cohort (n = 179). Intrinsic stemness patterns were estimated to determine its association with clinical features, biological pathways, prognosis, and potential inhibitors. Additionally, the dynamic interplay between EC stemness and TIME was integrally characterized.
RESULTS: Analyses of EC stemness and clinical characteristics indicated that higher-stage and metastatic tumors featured more dedifferentiated phenotypically. Univariate and multivariate Cox regression analyses revealed that mRNAsi was significantly associated with overall survival (OS) of EC patients, whereas no relationship was observed between mDNAsi and OS. Notably, prolonged OS was observed with esophageal squamous cell carcinoma (ESCC) in low versus high mRNAsi groups, whereas the OS was equivalent between the two groups for esophageal adenocarcinoma (ESAD). The mRNAsi may thus recapitulate prognostic molecular subgroups of EC. The prognostic model comprising 14 stemness signatures was constructed using combined Cox and Lasso regression analyses which effectively distinguished individual survival of ESCC in two cohorts. Nevertheless, no significant differences in OS was observed when the same prognostic model of ESCC was applied to ESAD. Gene Set Enrichment Analysis (GSEA) of selected stemness signatures indicated that ESCC stemness is involved in immune-related pathways. Furthermore, ESCC stemness and stemness-related signatures were associated with tumor-infiltrating immune cells, immunoscore, and PD-L1 expression. Compounds specific to the selected stemness signatures were detected using the CMap database.
CONCLUSION: This study determined integrated characteristics of EC stemness. The identified mRNAsi-based signatures conferred with the predictive ability of personalized ESCC prognosis and highlighted the potential targets for CSC-mediated immunotherapy. Analyses of the interface between ESCC stemness and TIME may help in predicting the efficacy of CSC-specific immunotherapy and provide insight into combinatorial therapy by targeting ESCC stem cells and TIME.
