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A stroma‐related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early‐stage colon cancer

机译:与早期结肠癌患者预测复发和佐剂化疗的基质相关的LNCrNA面板

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The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a s troma‐related l ncRNA s ignature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.
机译:具有相似临床特征的结肠癌患者的预称性和化学治疗反应的异质性强调了新的生物标志物的必要性,这有助于改善生存预测和裁缝疗法更合理且精确地裁缝。在本研究中,我们建立了基于52克朗的型与NCRNA的令人难以置词(SLS)全面预测临床结果。 SLS模型不仅可以通过单变量分析区分患者,并通过单变量分析区分不同的复发性和死亡风险,而且还担任无复发和整体存活的独立因素。与传统使用的TNM阶段系统相比,SLS模型明显具有更高的预测精度。此外,SLS模型还有效地筛查了化疗响应患者,因为只有低SLS组的患者才能受益于佐剂化疗。以下细胞浸润和竞争内源性RNA(Cerna)网络功能分析进一步证实了SLS模型与基质活化相关的生物过程之间的关联。此外,本研究还确定了三种表型不同的结肠癌亚型,可在临床结果和化疗益处中变化。总之,我们的SLS模型可能是结肠癌中生存和化学治疗决策的重要决定因素,并且可能具有强烈的临床转化价值。

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