Identification of a Tumor Microenvironment-relevant Gene set-based Prognostic Signature and Related Therapy Targets in Gastric Cancer
Identification of a Tumor Microenvironment-relevant Gene set-based Prognostic Signature and Related Therapy Targets in Gastric Cancer
复制标题
胃癌中基于肿瘤微环境相关基因组的预后特征和相关治疗靶点的鉴定
DOI：
10.7150/thno.47938
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发表时间：
2020-01-01
期刊：
影响因子：
12.4
通讯作者：
Chen, Yu-Chao
中科院分区：
文献类型：
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作者：
Cai, Wang-Yu;Dong, Zi-Nan;Chen, Yu-Chao
理论基础：胃癌患者预后较差，由于遗传异质性和早期筛查困难，治疗效果有限。在这里，我们开发并验证了一种基于个体化基因集的胃癌预后标志(GPSGC)，并进一步探索了与生存相关的调控机制和治疗靶点。方法：通过实施机器学习，基于来自5个独立队列的1699例胃癌患者的基因表达数据，建立了一个预后模型，该数据集具有完整的临床注释。对来自三个独立队列的834例胃癌患者的肿瘤微环境进行了分析，包括间质和免疫亚组分、细胞类型、全免疫基因集和免疫调节基因，以探索与GPSGC相关的生存调控机制和治疗靶点。为了验证GPSGC模型和治疗靶点的稳定性和可靠性，对186例GC患者的组织芯片进行了多重荧光免疫组织化学研究。结果：通过机器学习，我们得到了一个最优的风险评估模型GPSGC，该模型比单个预后因素具有更高的预测准确率。GPSGC评分对胃癌患者生存不良的影响可能与肿瘤微环境中间质成分的重塑有关。具体地说，转化生长因子β和血管生成相关基因集与GPSGC风险评分和不良结局显著相关。根据GPSGC，免疫调节基因分析结合实验验证进一步表明，转化生长因子β1和血管内皮生长因子B可能成为预后不良的GC患者的潜在治疗靶点。此外，我们开发了基于GPSGC和其他临床变量的诺模图来预测GC患者的3年和5年总生存期，其预后准确率高于仅根据临床特征预测的准确率。结论：GPSGC模型作为一种基于肿瘤微环境相关基因集的预后标志，为评估GC患者的生存结果提供了一种有效的方法，并可能通过选择个性化的靶向治疗来延长总体生存期。
Rationale: The prognosis of gastric cancer (GC) patients is poor, and there is limited therapeutic efficacy due to genetic heterogeneity and difficulty in early-stage screening. Here, we developed and validated an individualized gene set-based prognostic signature for gastric cancer (GPSGC) and further explored survival-related regulatory mechanisms as well as therapeutic targets in GC.Methods: By implementing machine learning, a prognostic model was established based on gastric cancer gene expression datasets from 1699 patients from five independent cohorts with reported full clinical annotations. Analysis of the tumor microenvironment, including stromal and immune subcomponents, cell types, panimmune gene sets, and immunomodulatory genes, was carried out in 834 GC patients from three independent cohorts to explore regulatory survival mechanisms and therapeutic targets related to the GPSGC. To prove the stability and reliability of the GPSGC model and therapeutic targets, multiplex fluorescent immunohistochemistry was conducted with tissue microarrays representing 186 GC patients. Based on multivariate Cox analysis, a nomogram that integrated the GPSGC and other clinical risk factors was constructed with two training cohorts and was verified by two validation cohorts.Results: Through machine learning, we obtained an optimal risk assessment model, the GPSGC, which showed higher accuracy in predicting survival than individual prognostic factors. The impact of the GPSGC score on poor survival of GC patients was probably correlated with the remodeling of stromal components in the tumor microenvironment. Specifically, TGF beta and angiogenesis-related gene sets were significantly associated with the GPSGC risk score and poor outcome. Immunomodulatory gene analysis combined with experimental verification further revealed that TGF beta 1 and VEGFB may be developed as potential therapeutic targets of GC patients with poor prognosis according to the GPSGC. Furthermore, we developed a nomogram based on the GPSGC and other clinical variables to predict the 3-year and 5-year overall survival for GC patients, which showed improved prognostic accuracy than clinical characteristics only.Conclusion: As a tumor microenvironment-relevant gene set-based prognostic signature, the GPSGC model provides an effective approach to evaluate GC patient survival outcomes and may prolong overall survival by enabling the selection of individualized targeted therapy.