Comprehensive Analysis of Single-Cell and Bulk Transcriptomics Identified Regulatory T-Cell Features as Predictors of Prognosis in Diffuse Large B-Cell Lymphoma.

细胞 淋巴瘤 转录组 T细胞淋巴瘤 弥漫性大B细胞淋巴瘤 癌症研究 计算生物学 医学 生物 内科学 基因 遗传学 基因表达
作者
Yao‐Li Cui,Ge Hu,Xue Han,Wei Li,Huijuan Lv,Zhengzi Qian,Lanfang Li,Lihua Qiu,Shiyong Zhou,Huilai Zhang
出处
期刊:PubMed 卷期号:43 (2): e70050-e70050
标识
DOI:10.1002/hon.70050
摘要

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous malignancy. Advances in transcriptomic and genetic profiling have significantly enhanced our understanding of the disease's intrinsic pathogenesis, uncovering numerous potential therapeutic targets. However, the impact of tumor-infiltrating Regulatory T cells (Tregs) on the prognosis of DLBCL remains controversial. Here, we developed a Treg-associated gene signature by integrating single-cell and bulk transcriptome data to predict the prognosis of DLBCL patients receiving standard immunochemotherapy. In total, 227 Tregs feature genes were identified, six of which were selected for constructing a prognostic signature. DLBCL patients possessing high-risk scores had significantly poorer survival outcomes than those who possess low-risk scores in NCICCR and validation cohorts. Mutations in PIM1, MYD88, DTX1, CARD11, CD79B, ETV6, BCL6, and CDKN2A were predominantly observed in the high-risk group, whereas alterations in TNFRSF14 and DNMT3A were more frequently detected in the low-risk group. Immune infiltration analysis revealed that the high-risk group exhibited an immunosuppressive microenvironment, whereas the low-risk group showed a higher abundance of non-cellular components in the tumor microenvironment (TME). Finally, the Treg features TNFRSF25 and SELL can effectively predict long-term responses to Axicabtagene Ciloleucel (Axi-cel) treatment. In summary, our study developed a prognostic signature consisting of six Treg feature genes by integrating single-cell and bulk transcriptomics to predict clinical outcomes in DLBCL patients. The risk signature was significantly associated with immunological characteristics.
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