Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer
Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer
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DOI：
10.3389/fonc.2019.01212
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发表时间：
2019-11-12
影响因子：
4.7
通讯作者：
Chen, Yiming
中科院分区：
文献类型：
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作者：
Wang, Hao;Wu, Xiaosheng;Chen, Yiming
背景：越来越多的证据表明基质细胞和免疫细胞在胃癌微环境中的临床重要性。然而，基于基质和免疫成分评估的可靠预后特征尚未得到充分建立。本研究旨在开发一种基于基质免疫评分的胃癌基因标签。方法：使用ESTIMATE算法从TCGA的胃癌队列的转录组学谱估计基质和免疫评分。应用稳健的基于部分似然的考克斯比例风险回归模型来选择预后基因并构建基于基质免疫评分的基因签名。使用来自GEO的两个独立数据集进行外部验证。结果：在高基质评分（p = 0.014）和免疫评分（p = 0.045）的患者中发现了有利的总生存率。45间质免疫评分相关的差异表达基因进行了鉴定。使用稳健的基于部分似然的考克斯比例风险回归模型，鉴定含有SOX 9、LRRC32、CECR 1和MS 4A4 A的基因签名以开发风险分层模型。多变量分析显示，基质免疫风险评分是一个独立的预后因素（p = 0.018）。根据风险分层模型，将队列分为三组，产生增量生存结局（对数秩检验p = 0.0004）。整合危险分层模型和临床病理因素的列线图。校准和决策曲线显示出更好的性能和净效益诺模图。在两个独立的队列中验证了类似的结果。结论：基于基质免疫评分的基因标签是胃癌预后分层的工具。
Background: A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the gastric cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to develop a stromal-immune score-based gene signature in gastric cancer. Methods: Stromal and immune scores were estimated from transcriptomic profiles of a gastric cancer cohort from TCGA using the ESTIMATE algorithm. A robust partial likelihood-based Cox proportional hazard regression model was applied to select prognostic genes and to construct a stromal-immune score-based gene signature. Two independent datasets from GEO were used for external validation. Results: Favorable overall survivals were found in patients with high stromal score (p = 0.014) and immune score (p = 0.045). Forty-five stromal-immune score-related differentially expressed genes were identified. Using a robust partial likelihood-based Cox proportional hazard regression model, a gene signature containing SOX9, LRRC32, CECR1, and MS4A4A was identified to develop a risk stratification model. Multivariate analysis revealed that the stromal-immune risk score was an independent prognostic factor (p = 0.018). Based on the risk stratification model, the cohort was classified into three groups yielding incremental survival outcomes (log-rank test p = 0.0004). A nomogram integrating the risk stratification model and clinicopathologic factors was developed. Calibration and decision curves showed a better performance and net benefits for the nomogram. Similar findings were validated in two independent cohorts. Conclusion: The stromal-immune score-based gene signature represents a prognosis stratification tool in gastric cancer.