AUTHOR=Wang Wei , Xu Shi-wen , Teng Ya , Zhu Min , Guo Qun-yi , Wang Yuan-wen , Mao Xin-Li , Li Shao-wei , Luo Wen-da 

TITLE=The Dark Side of Pyroptosis of Diffuse Large B-Cell Lymphoma in B-Cell Non-Hodgkin Lymphoma: Mediating the Specific Inflammatory Microenvironment

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.779123

DOI=10.3389/fcell.2021.779123

ISSN=2296-634X

ABSTRACT=Background: Diffuse large B-cell lymphoma (DLBCL) is an aggressive B-cell Non-Hodgkin Lymphoma (B-NHL) for which the combined chemotherapy has improved outcomes but remains a highly detrimental disease. Pyroptosis, an inflammatory programmed cell death, is considered to have both tumor-promoting and tumor-suppressing effects. The role of pyroptosis in DLBCL has been gradually appreciated, but its value needs further investigation.
Methods: We analyzed mutations and copy number variation (CNV) alterations of pyroptosis-related genes(PRGs) from TCGA (The Cancer Genome Atlas) cohort and contrasting expression differences in normal B cells and DLBCL patients in two GEO(Gene Expression Omnibus) datasets[GSE12195 and GSE56315]. Based on the expression of the 52 PRGs, we divided the 421 DLBCL patients from the GSE31312 dataset into distinct clusters using consensus clustering. Kaplan-Meier method was used to analyze differences in prognosis among the three clusters, and GSVA was used to explore differences in the biological functions. ESTIMATE and single-sample Gene-Set Enrichment Analysis(ssGSEA) were used to analyze the tumor immune microenvironment(TME) between different clusters. A risk score signature was developed using univariate survival analysis and multivariate regression analysis, and the reliability and validity of the signature were verified. Combining the signature with clinical factors, a nomogram was established to predict the prognosis of DLBCL patients. The Alluvial diagram and correlation matrix were used to explore the relationship between pyroptosis risk score, clinical features and TME.
Results: A large proportion of PRGs are dysregulated in DLBCL and associated with prognosis. We found three distinct pyroptosis-related clusters (cluster A, B, and C), which were significantly different in prognosis, biological process, clinical characteristics, chemotherapeutic drug sensitivity, and TME. Meanwhile, we developed a risk score signature that effectively differentiates between high- and low-risk patients. The nomogram, which combines this signature with several clinical indicators, showed an excellent ability to predict the prognosis of DCBCL patients.
Conclusions: This work demonstrates that pyroptosis plays an important role in the diversity and complexity of the TME in DLBCL. The risk signature of pyroptosis is a promising predictive tool. A correct and comprehensive assessment of the mode of action of pyroptosis in individuals is beneficial to guide more effective treatment.