Department of Thoracic Surgery, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Heping District Shenyang, Liaoning, 110004, People's Republic of China.
Mol Med. 2020 May 20;26(1):48. doi: 10.1186/s10020-020-00166-2.
Molecular mechanism of lung squamous cell carcinoma (LUSC) remains poorly understood, hampering effective targeted therapies or precision diagnosis about LUSC. We devised an integrative framework to investigate on the molecular patterns of LUSC by systematically mining the genomic, transcriptional and clinical information.
We utilized the genomics and transcriptomics data for the LUSC cohorts in The Cancer Genome Atlas.. Both kinds of omics data for 33 types of cancers were downloaded from The NCI's Genomic Data Commons (GDC) (https://gdc.cancer.gov/about-data/publications/pancanatlas). The genomics data were processed in mutation annotation format (maf), and the transcriptomics data were determined by RNA-seq method. Mutation significance was estimated by MutSigCV. Prognosis analysis was based on the cox proportional hazards regression (Coxph) model.
Significant somatic mutated genes (SMGs) like NFE2L2, RASA1 and COL11A1 and their potential down-stream pathways were recognized. Furthermore, two LUSC-specific and prognosis-meaningful subtypes were identified. Interestingly, the good prognosis subtype was enriched with mutations in CUL3/KEAP1/NRF2 pathway and with markedly suppressed expressions of multiple down-stream pathways like epithelial mesenchymal transition. The subtypes were verified by the other two cohorts. Additionally, primarily regulated down-stream elements of different SMGs were also estimated. NFE2L2, KEAP1 and RASA1 mutations showed remarkable effects on the subtype-determinant gene expressions, especially for the inflammatory relevant genes.
This study supplies valuable references on potential down-stream processes of SMGs and an alternative way to classify LUSC.
肺鳞状细胞癌(LUSC)的分子机制仍不清楚,这阻碍了针对 LUSC 的有效靶向治疗或精准诊断。我们设计了一种综合框架,通过系统挖掘基因组、转录组和临床信息来研究 LUSC 的分子模式。
我们利用癌症基因组图谱中的 LUSC 队列的基因组学和转录组学数据。从 NCI 的基因组数据共享(GDC)(https://gdc.cancer.gov/about-data/publications/pancanatlas)下载了 33 种癌症的这两种类型的组学数据。基因组学数据以突变注释格式(maf)处理,转录组学数据通过 RNA-seq 方法确定。突变显著性通过 MutSigCV 估计。预后分析基于 Cox 比例风险回归(Coxph)模型。
识别到具有显著体细胞突变基因(SMGs)的基因,如 NFE2L2、RASA1 和 COL11A1 及其潜在的下游途径。此外,还鉴定出两种具有 LUSC 特异性和预后意义的亚型。有趣的是,预后良好的亚型富含 CUL3/KEAP1/NRF2 途径中的突变,并且多个下游途径(如上皮间质转化)的表达明显受到抑制。这两种亚型在另外两个队列中得到了验证。此外,还估计了不同 SMGs 的主要调节下游元素。NFE2L2、KEAP1 和 RASA1 的突变对亚型决定基因的表达有显著影响,尤其是对炎症相关基因。
本研究为 SMGs 的潜在下游过程和 LUSC 的分类提供了有价值的参考。