Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.
Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.
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发表时间：
2020-04
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通讯作者：
J. Carreras;R. Hamoudi;N. Nakamura
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作者：
J. Carreras;R. Hamoudi;N. Nakamura
目的：我们旨在利用深度学习技术在弥漫性大B细胞淋巴瘤（DLBCL）中识别新的生物标志物。
方法与结果：在GSE10846系列中进行了多层感知器（MLP）分析，该系列分为发现集（n = 100）和验证集（n = 414）。从总共54614个基因探针中，根据其对结果预测（死亡/存活）的归一化重要性选择了前25个基因探针。通过基因集富集分析（GSEA），与不良预后的关联得到了证实。在验证集中，通过单变量Cox回归分析，ARHGAP19、MESD、WDCP、DIP2A、CACNA1B、TNFAIP8、POLR3H、ENO3、SERPINB8、SZRD1、KIF23和GGA3的高表达与不良预后相关，而SFTPC、ZSCAN12、LPXN和METTL21A的高表达与良好预后相关。多变量分析证实MESD、TNFAIP8和ENO3为危险因素，ZSCAN12和LPXN为保护因素。使用风险评分公式，这25个基因确定了两组具有不同生存率的患者，且该生存率与细胞起源分子分类无关（5年总生存率，低风险组与高风险组）：分别为65%和24%（风险比 = 3.2，P < 0.000001）。最后，与已知的DLBCL标志物的相关性表明，MYC、BCL2和ENO3的高表达均与最差的预后相关。
结论：通过人工智能，我们确定了一组具有预后相关性的基因。
OBJECTIVE We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique. METHODS AND RESULTS The multilayer perceptron (MLP) analysis was performed in the GSE10846 series, divided into discovery (n = 100) and validation (n = 414) sets. The top 25 gene-probes from a total of 54,614 were selected based on their normalized importance for outcome prediction (dead/alive). By Gene Set Enrichment Analysis (GSEA) the association to unfavorable prognosis was confirmed. In the validation set, by univariate Cox regression analysis, high expression of ARHGAP19, MESD, WDCP, DIP2A, CACNA1B, TNFAIP8, POLR3H, ENO3, SERPINB8, SZRD1, KIF23 and GGA3 associated to poor, and high SFTPC, ZSCAN12, LPXN and METTL21A to favorable outcome. A multivariate analysis confirmed MESD, TNFAIP8 and ENO3 as risk factors and ZSCAN12 and LPXN as protective factors. Using a risk score formula, the 25 genes identified two groups of patients with different survival that was independent to the cell-of-origin molecular classification (5-year OS, low vs. high risk): 65% vs. 24%, respectively (Hazard Risk = 3.2, P < 0.000001). Finally, correlation with known DLBCL markers showed that high expression of all MYC, BCL2 and ENO3 associated to the worst outcome. CONCLUSION By artificial intelligence we identified a set of genes with prognostic relevance.