Department of Geriatrics, Peking University First Hospital, Beijing 100034, China.
Dis Markers. 2020 Jun 9;2020:7291586. doi: 10.1155/2020/7291586. eCollection 2020.
PD1/PDL1 inhibitors have been adopted for the treatment of advanced non-small-cell lung cancer, and PDL1 expression has been investigated as a predictive biomarker for PD1/PDL1 inhibitor therapy. However, PDL1 lacks diagnostic accuracy in differentiating patients who are likely or unlikely to benefit. So, it is urgent and clinically significant to identify other associated predictive biomarkers for PD1/PDL1 inhibitor therapy. Our work was to identify PDL1-related biomarkers that could improve the patient selection for PD1/PDL1 inhibitor treatment. We obtained 500 genes coexpressed with PDL1 in lung adenocarcinoma from the TCGA database. Then, we identified 125 out of 500 genes differentially expressed in lung adenocarcinoma. A total of 39 genes were distinguished with prognostic value and associated with overall survival. Median survival time analysis based on gene expression level, protein-protein interaction analysis, GO and KEGG enrichment analyses, and significant GO and KEGG function consistency analyses were conducted to screen candidate biomarkers. Three candidate genes, BRCA1, BRIP1, and EREG, were identified to be functionally significantly coexpressed with PDL1. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response and cell activation. Additionally, to find potential adjuvant therapeutic targets in PD1/PDL1 inhibitor treatment, we performed transcription factor prediction analysis. A group of negative differential expression but PDL1-related biomarkers has been identified, which might help to assess the clinical management of lung cancer patients. A combination of potential biomarkers and adjuvant therapeutic targets with PDL1 will predict the response to PD1/PDL1 inhibitors more accurately and help with the patient selection for more personalized immune checkpoint inhibitor treatment.
PD1/PDL1 抑制剂已被用于治疗晚期非小细胞肺癌，并且已经研究了 PDL1 表达作为 PD1/PDL1 抑制剂治疗的预测生物标志物。然而，PDL1 在区分可能受益或不太可能受益的患者方面缺乏诊断准确性。因此，迫切需要并具有临床意义的是确定其他与 PD1/PDL1 抑制剂治疗相关的预测生物标志物。我们的工作是确定可以改善 PD1/PDL1 抑制剂治疗患者选择的 PDL1 相关生物标志物。我们从 TCGA 数据库中获得了肺腺癌中与 PDL1 共表达的 500 个基因。然后，我们鉴定了肺腺癌中 500 个基因中有 125 个差异表达。共有 39 个基因具有预后价值并与总生存期相关。根据基因表达水平、蛋白质-蛋白质相互作用分析、GO 和 KEGG 富集分析以及显著的 GO 和 KEGG 功能一致性分析进行中位生存时间分析，以筛选候选生物标志物。鉴定了三个候选基因，BRCA1、BRIP1 和 EREG，它们与 PDL1 功能显著共表达。功能富集分析和蛋白质-蛋白质相互作用网络进一步表明，这些基因主要参与免疫反应和细胞激活。此外，为了在 PD1/PDL1 抑制剂治疗中找到潜在的辅助治疗靶点，我们进行了转录因子预测分析。已经确定了一组负差异表达但与 PDL1 相关的生物标志物，这可能有助于评估肺癌患者的临床管理。PDL1 与潜在生物标志物和辅助治疗靶点的组合将更准确地预测对 PD1/PDL1 抑制剂的反应，并有助于为更个性化的免疫检查点抑制剂治疗选择患者。
