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与结直肠癌淋巴结转移相关的T细胞受体库的特征分析

Characterization of the T-cell receptor repertoire associated with lymph node metastasis in colorectal cancer.

作者信息

Zhen Ya'nan, Wang Hong, Jiang Runze, Wang Fang, Chen Cunbao, Xu Zhongfa, Xiao Ruixue

机构信息

Department of Gastrointestinal Surgery, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China.

Jinan Biomedical Industry Academy of Shandong First Medical University, Jinan, Shandong, China.

出版信息

Front Oncol. 2024 Nov 12;14:1354533. doi: 10.3389/fonc.2024.1354533. eCollection 2024.

Abstract

PURPOSE

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with lymph node (LN) metastasis playing a pivotal role in disease progression. This study aimed to explore the T-cell receptor (TCR) repertoire among CRC patients, distinguishing those with LN metastasis from those without, in order to uncover potential biomarkers for predicting metastasis.

METHODS

We analyzed the TCR repertoire in CRC patients with and without LN metastasis. A classification model utilizing random forest analysis was developed to assess the predictive potential of the TCR repertoire.

RESULTS

The findings demonstrated a significant increase in the number of V-J combinations and immune CDR3 sequences in patients with LN metastasis compared to the control group. The classification model achieved high accuracy in differentiating patients with LN metastasis, with AUC values ranging from 0.514 to 0.794. Specific V-J combinations and CDR3 sequences were identified as significant predictors of the model's predictive accuracy.

CONCLUSION

These results suggest that the TCR repertoire is altered in CRC patients exhibiting LN metastasis, potentially influencing disease progression. This study highlights the importance of TCR repertoire analysis as a non-invasive biomarker for predicting LN metastasis in CRC patients.

摘要

目的

结直肠癌(CRC)是全球癌症相关死亡的主要原因,淋巴结(LN)转移在疾病进展中起关键作用。本研究旨在探索CRC患者的T细胞受体(TCR)库,区分有LN转移和无LN转移的患者,以发现预测转移的潜在生物标志物。

方法

我们分析了有和无LN转移的CRC患者的TCR库。开发了一种利用随机森林分析的分类模型,以评估TCR库的预测潜力。

结果

研究结果表明,与对照组相比,LN转移患者的V-J组合和免疫CDR3序列数量显著增加。分类模型在区分LN转移患者方面具有较高的准确性,AUC值范围为0.514至0.794。特定的V-J组合和CDR3序列被确定为模型预测准确性的重要预测因子。

结论

这些结果表明,在表现出LN转移的CRC患者中TCR库发生了改变,可能影响疾病进展。本研究强调了TCR库分析作为预测CRC患者LN转移的非侵入性生物标志物的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2c7/11588627/a2edfbcc12d5/fonc-14-1354533-g001.jpg

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