CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：Unraveling the prognostic significance and molecular characteristics of tumor-infiltrating B lymphocytes in clear cell renal cell carcinoma through a comprehensive bioinformatics analysis.
Unraveling the prognostic significance and molecular characteristics of tumor-infiltrating B lymphocytes in clear cell renal cell carcinoma through a comprehensive bioinformatics analysis.
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在我们的研究中，我们阐明了 ccRCC 与 B 细胞之间的显著关联。
首先使用xCell算法预测TCGA-KIRC及其他ccRCC转录组数据集中的TIL-B，并采用Log-rank检验和Cox回归分析B细胞与ccRCC生存的关系。随后结合共识亚群分析和单细胞RNA测序数据，使用加权基因共表达网络分析（WGCNA）识别与TIL-B相关的重要模块。为筛选候选生物标志物，研究提出预后特征，进一步分析特征中的单个基因及风险评分，并探讨该特征与临床表型和药物的潜在关联。
初步发现ccRCC生存与TIL-B呈负相关，并在其他数据集中得到验证。随后识别出10个共表达模块，并进一步发现一个独特的ccRCC亚群；研究还评估了ccRCC中B细胞的转录变化，并确定了一个相关B细胞亚型。基于两个核心模块（棕色和红色），研究在训练集中构建了由10个基因（TNFSF13B、SHARPIN、B3GAT3、IL2RG、TBC1D10C、STAC3、MICB、LAG3、SMIM29、CTLA4）组成的特征，并在测试集中验证。研究进一步考察这些生物标志物的差异表达及其与免疫特征的相关性，并分析与风险评分相关的突变和通路。最后，研究建立了结合肿瘤分级的列线图，并根据药物敏感性反应发现潜在药物。讨论：本研究阐明了ccRCC与B细胞之间显著的关联，发现了若干关键基因模块，以及与TIL-B可能相关的患者亚群和B细胞亚型，并提出一种从多个角度刻画其分子特征的10基因特征。总体而言，了解TIL-B的作用可能有助于制定ccRCC免疫治疗策略，但其对患者预后和治疗的意义仍需进一步研究。
Initially, xCell algorithm was used to predict TIL-Bs in TCGA-KIRC and other ccRCC transcriptomic datasets. The Log-Rank test and Cox regression were applied to explore the relationship of B-cells with ccRCC survival. Then, we used WGCNA method to identify important modules related to TIL-Bs combining Consensus subcluster and scRNA-seq data analysis. To narrow down the prospective biomarkers, a prognostic signature was proposed. Next, we explored the feature of the signature individual genes and the risk-score. Finally, the potential associations of signature with clinical phenotypes and drugs were investigated.
Preliminary, we found ccRCC survival was negatively associated with TIL-Bs, which was confirmed by other datasets. Afterwards, ten co-expression modules were identified and a distinct ccRCC cluster was subsequently detected. Moreover, we assessed the transcriptomic alteration of B-cell in ccRCC and a relevant B-cell subtype was also pinpointed. Based on two core modules (brown, red), a 10-gene signature (TNFSF13B, SHARPIN, B3GAT3, IL2RG, TBC1D10C, STAC3, MICB, LAG3, SMIM29, CTLA4) was developed in train set and validated in test sets. These biomarkers were further investigated with regards to their differential expression and correlation with immune characteristics, along with risk-score related mutations and pathways. Lastly, we established a nomogram combined tumor grade and discovered underlying drugs according to their sensitivity response. DISCUSSION: In our research, we elucidated the remarkable association between ccRCC and B-cells. Then, we detected several key gene modules, together with close patient subcluster and B-cell subtype,which could be responsible for the TIL-Bs in ccRCC. Moreover, we proposed a 10-gene signature and investigated its molecular features from multiple perspectives. Overall, understanding the roles of TIL-Bs could aid in the immunotherapeutic approaches for ccRCC, which deserve further research to clarify the implications for patient prognosis and treatment.
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