DataSheet_2_Establishment of a prognosis Prediction Model Based on Pyroptosis-Related Signatures Associated With the Immune Microenvironment and Molec.pdf (2.75 MB)
Download file

DataSheet_2_Establishment of a prognosis Prediction Model Based on Pyroptosis-Related Signatures Associated With the Immune Microenvironment and Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma.pdf

Download (2.75 MB)
dataset
posted on 05.11.2021, 09:35 authored by Aimin Jiang, Jialin Meng, Yewei Bao, Anbang Wang, Wenliang Gong, Xinxin Gan, Jie Wang, Yi Bao, Zhenjie Wu, Juan Lu, Bing Liu, Linhui Wang
Background

Pyroptosis is essential for tumorigenesis and progression of neoplasm. However, the heterogeneity of pyroptosis and its relationship with the tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remain unclear. The purpose of the present study was to identify pyroptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures.

Methods

First, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model established by LASSO regression was verified by the ICGC database. Then, the clinical significance, functional status, immune infiltration, cell–cell communication, genomic alteration, and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model.

Results

Two heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in the C2 subgroup was lower than that in the C1 subgroup. Pyroptosis-related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via the pyroptosis-related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while the characteristics of C2 were alterations in SPEN and ROS1 mutation. Finally, a robust and promising pyroptosis-related prediction model for ccRCC was constructed and validated.

Conclusion

Two heterogeneous pyroptosis subtypes were identified and compared in multiple omics levels, and five pyroptosis-related signatures were applied to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

History

References