利用三级淋巴结构基因特征评估结直肠癌的肿瘤微环境浸润和免疫疗法反应

Zhu Xu, Qin Wang, Yiyao Zhang, Xiaolan Li, Mei Wang, Yuhong Zhang, Yaxin Pei, Kezhen Li, Man Yang, Liping Luo, Chuan Wu, Weidong Wang
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引用次数: 0

摘要

三级淋巴结构(TLS)是肿瘤微环境(TME)的一个特殊组成部分。然而,它在结直肠癌(CRC)中的生物学机制还不清楚。我们希望揭示 CRC 中的 TLS 基因特征,并评估其在预后和免疫治疗反应中的作用。数据来源于癌症基因组图谱(TCGA)和基因表达总库(GEO)数据库。数据来源于癌症基因组图谱(TCGA)和基因表达全集(GEO)数据库。基于TLS相关基因(TRGs),通过无监督聚类确定了TLS相关亚群。通过 CIBERSORT 和 xCell 对子簇之间的 TME 进行了评估。随后,建立风险模型并进行外部验证。整合风险评分和临床特征,创建综合提名图。利用随机森林分析、富集和变异分析以及竞争性内源性 RNA(ceRNA)网络分析,进一步分析筛选 TLS 相关的枢纽基因,探索枢纽基因、TME 和生物过程之间的关系。采用多重免疫荧光(mIF)和免疫组织化学(IHC)分析了TLS的存在和枢纽基因的表达。这两个亚群具有不同的预后、临床特征和肿瘤免疫浸润。我们建立了一个包括14个基因的TLS相关预后风险模型,并在两个外部数据集中验证了其预测能力。该模型对1年、3年和5年总生存期(OS)的AUC值分别为0.704、0.737和0.746。低风险组的生存率更高,免疫细胞浸润更丰富,肿瘤免疫功能障碍和排斥(TIDE)评分更低,免疫治疗效果更好。此外,我们还选出了模型中最重要的特征:VSIG4、SELL 和 PRRX1。富集分析表明,这些枢纽基因对与 TLS 和肿瘤进展相关的信号通路有显著影响。ceRNA 网络:发现了PRRX1-miRNA(hsa-miR-20a-5p、hsa-miR-485-5p)-lncRNA网络。最后,IHC 和 mIF 结果证实,在 TLS- CRC 组中,PRRX1 的表达水平明显升高。TLS相关风险模型适用于预后预测和免疫疗法疗效评估。TLS枢纽基因PRRX1有可能作为TLS的免疫调节因子,成为CRC的治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting tertiary lymphoid structures gene signature to evaluate tumor microenvironment infiltration and immunotherapy response in colorectal cancer
Tertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response.The data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene.Two subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model’s AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: VSIG4, SELL and PRRX1. Enrichment analysis showed that the hub genes significantly affected signaling pathways related to TLS and tumor progression. The ceRNA network: PRRX1-miRNA (hsa-miR-20a-5p, hsa-miR-485–5p) -lncRNA has been discovered. Finally, IHC and mIF results confirmed that the expression level of PRRX1 was markedly elevated in the TLS- CRC group.We conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene PRRX1, which had the potential to function as an immunomodulatory factor of TLS, could be a therapeutic target for CRC.
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