The combination of novel immune checkpoints HHLA2 and ICOSLG: A new system to predict survival and immune features in esophageal squamous cell carcinoma

新型免疫检查点HHLA2和ICOSLG的组合:预测食管鳞状细胞癌生存率和免疫特征的新系统

阅读:3

Abstract

Studies on immune checkpoint inhibitors targeting B7-CD28 family pathways in esophageal squamous cell carcinoma (ESCC) have shown promising results. However, a comprehensive understanding of B7-CD28 family members in ESCC is still limited. This study aimed to construct a novel B7-CD28 family-based prognosis system to predict survival in patients with ESCC. We collected 179 cases from our previously published microarray data and 86 cases with qPCR data. Specifically, 119 microarray data (GSE53624) were used as a training set, whereas the remaining 60 microarray data (GSE53622), all 179 microarray data (GSE53625) and an independent cohort with 86 qPCR data were used for validation. The underlying mechanism and immune landscape of the system were also explored using bioinformatics and immunofluorescence. We examined 13 well-defined B7-CD28 family members and identified 2 genes (ICSOLG and HHLA2) with the greatest prognostic value. A system based on the combination HHLA2 and ICOSLG (B7-CD28 signature) was constructed to distinguish patients as high- or low-risk of an unfavorable outcome, which was further confirmed as an independent prognostic factor. As expected, the signature was well validated in the entire cohort and in the independent cohort, as well as in different clinical subgroups. The signature was found to be closely related to immune-specific biological processes and pathways. Additionally, high-risk group samples demonstrated high infiltration of Tregs and fibroblasts and distinctive immune checkpoint panels. Collectively, we built the first, practical B7-CD28 signature for ESCC that could independently identify high-risk patients. Such information may help inform immunotherapy-based treatment decisions for patients with ESCC.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。