Construction of a novel prognostic model in skin cutaneous melanoma based on chemokines-related gene signature

基于趋化因子相关基因特征构建皮肤黑色素瘤新型预后模型

阅读:1

Abstract

Skin cutaneous melanoma, SKCM, is one of the most aggressive treatment-resistant tumours. Despite the fact that the BRAF oncogene and immunological checkpoints such as PD-1/PD-L1 and CTLA-4 have enhanced the therapeutic efficacy of SKCM, the subsequent resistance mechanisms and remedies have raised concerns. Chemokines have a significant role in the immunological milieu of tumor, which may increase the efficacy of checkpoint blockade and serve as a possible therapeutic intervention route. However, there is still no chemokine-based typing and risk model to provide a prognosis and therapeutic efficacy assessment for SKCM patients. In this study, we verified the distinct differences of prognostic stratification as well as immune characteristics between two chemokine-related clusters in SKCM patients. Two clusters of DEGs were discovered to be primarily enriched in B and T cell receptor signaling pathways as well as TNF signaling via NF-kappa-B. Based on 14 prognosis-related DEGs from aforementioned two clusters (CCL8, GBP2, GBP4, SRNG, HLA-DMB, RARRES3, HLA-DQA1, PARP12, APOL3, IRF1, HLA-DRA, UBE2L6, IL2RA and CD38), a chemokine-related 14-gene prognostic model was established. At the same time, researchers explored differences between the low-risk and high-risk groups in clinical traits, the proportion of infiltration of 22 different types of immune cells, and how well medications worked. The risk score model's immunotherapy and prognostic predictions were also confirmed in testing groups. Based on the finding, we can claim that there is a clear link between chemokines and TME in SKCM. The risk score may perform as a trustworthy prediction model, giving therapeutic benefits for both chemotherapy and immunotherapy, as well as being beneficial for clinical decision making in SKCM patients.

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。