State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiamen University Research Center of Retroperitoneal Tumor Committee of Oncology Society of Chinese Medical Association, College of Medicine, Xiamen University, Xiamen, China.
Elife. 2023 May 9;12:e82959. doi: 10.7554/eLife.82959.
The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with nine proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellently in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of five proteins was used to build an IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.
淋巴结转移（LNM）的存在影响 T1NxM0 结直肠癌（CRC）的治疗策略决策，但目前使用的临床病理风险分层不能准确预测 LNM。在这项研究中，我们检测了 143 例 LNM 阴性和 78 例 LNM 阳性 T1 CRC 患者福尔马林固定石蜡包埋（FFPE）肿瘤样本中的蛋白质，通过无标记液相色谱串联质谱（LC-MS/MS）揭示了分子和生物学途径的变化，并建立了用于预测 T1 CRC 中 LNM 的分类器。通过机器学习建立了一个有效的 55 种蛋白质预测模型，并在训练队列（N=132）和两个验证队列（VC1，N=42；VC2，N=47）中进行了验证，在训练队列中获得了 1.00 的出色 AUC，在 VC1 和 VC2 中分别为 0.96 和 0.93。我们进一步构建了一个包含 9 种蛋白质的简化分类器，AUC 为 0.824。该简化分类器在两个外部验证队列中表现出色。通过免疫组织化学验证了 13 种蛋白质的表达模式，并用 5 种蛋白质的 IHC 评分构建了一个 AUC 为 0.825 的 IHC 预测模型。沉默 RHOT2 显著增强了结肠癌细胞的迁移和侵袭能力。我们的研究探讨了 T1 CRC 转移的机制，并可用于促进 T1 CRC 患者 LNM 的个体化预测，这可能为 T1 CRC 的临床实践提供指导。
