Integrating cell interaction with transcription factors to obtain a robust gene panel for prognostic prediction and therapies in cholangiocarcinoma

整合细胞相互作用与转录因子以获得用于胆管癌预后预测和治疗的强大基因组

阅读:7
作者:Tingjie Wang, Chuanrui Xu, Dan Xu, Xiaofei Yang, Yaxin Liu, Xiujuan Li, Zihang Li, Ningxin Dang, Yi Lv, Zhijing Zhang, Lei Li, Kai Ye

Conclusion

Cell communication-related genes can be used as important markers for predicting patient prognosis and immunotherapy responses. The TMRS panel is a reliable tool for prognostic prediction and chemotherapeutic decision-making in CCA.

Methods

We constructed empirical Bayes and Markov random field models eLBP to determine transcription factors, interacting genes, and associated signaling pathways involved in cell-cell communication using single-cell RNAseq data. We then analyzed the mechanism of immune exhaustion during CCA progression.

Objective

The efficacy of immunotherapy for cholangiocarcinoma (CCA) is blocked by a high degree of tumor heterogeneity. Cell communication contributes to heterogeneity in the tumor microenvironment. This study aimed to explore critical cell signaling and biomarkers induced via cell communication during immune exhaustion in CCA.

Results

We found that VEGFA-positive macrophages with high levels of LGALS9 could interact with HAVCR2 to promote the exhaustion of CD8+ T cells in CCA. Transcription factors SPI1 and IRF1 can upregulate the expression of LGALS9 in VEGFA-positive macrophages. Subsequently, we obtained a panel containing 54 genes through the model, which identified subtype S2 with high expression of immune checkpoint genes that are suitable for immunotherapy. Moreover, we found that patients with subtype S2 with a higher mutation ratio of MUC16 had immune-exhausted genes, such as HAVCR2 and TIGIT. Finally, we constructed a nine-gene eLBP-LASSO-COX risk model, which was designated the tumor microenvironment risk score (TMRS).

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。