The angiogenic genes predict prognosis and immune characteristics in esophageal squamous cell carcinoma: Evidence from multi-omics and experimental verification

Prognostic value of FCER1G expression and M2 macrophage infiltration in esophageal squamous cell carcinoma

BACKGROUND

FCER1G as an immune-associated protein, which belongs to the immunoglobulin superfamily and is involved in mediating and executing antibody-mediated immune responses. However, the role of FCER1G in cancers remains controversial. Our objectives were to study the association between FCER1G and tumor- infiltrating immune cells (TIICs) as well as the predictive significance of FCER1G.

METHODS

The expression of FCER1G and its prognostic value in ESCC was examined by The Cancer Genome Atlas and Gene Expression Omnibus databases. We also evaluated the relationship between FCER1G expression and 22 TIICs. Immunohistochemistry was used to detect the expression and distribution of FCER1G. Double immunofluorescence was used to detect the co-expression of FCER1G and CD163 positive cells. Kaplan-Meier survival curves and Cox regression analysis was performed to determine the prognostic significance of FCER1G and CD163.

RESULTS

The analysis revealed that FCER1G was upregulated in ESCC, which was distributed more in the intra-tumor mesenchyme than in the cancer nests. The more infiltration in intra-tumor mesenchyme the worse the overall survival (OS) for patients with ESCC. The infiltration of FCER1G+ cells was positively correlated with that of M2 macrophages and most of the CD163+ M2 macrophages expressed FCER1G. The more the infiltration of FCER1G+ M2 macrophages, the worse the OS of ESCC patients. FCER1G and TNM stage were identified as independent risk factors affecting the OS of ESCC patients.

CONCLUSIONS

FCER1G+ cells infiltration may help to predict the prognosis of ESCC. The combined detection of FCER1G and CD163 has a higher prognostic value.

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs (EIF4H, CDK2, TCEA1, SPTLC2, TMEM209, RGP1, EIF3D, and CAPZA3) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.