Edward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA, 24060, USA.
Fralin Biomedical Research Institute at VTC, 2 Riverside Circle, Roanoke, VA, 24016, USA.
Sci Rep. 2020 Dec 4;10(1):21246. doi: 10.1038/s41598-020-78126-3.
Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.
目前的癌症生物标志物在其预测能力方面存在差异，并表现出有限的临床疗效，这可能是由于生物标志物基因与癌症进展缺乏功能相关性。为了解决这一挑战，开发了一个生物标志物发现管道，将来自癌症基因组图谱的基因表达谱和来自癌症依赖图谱的基本生存基因数据集整合在一起，后者列出了推动癌症进展的基因。通过将该管道应用于肺腺癌、肺鳞状细胞癌和胶质母细胞瘤，鉴定出与癌症进展高度相关的基因，并将其指定为进展基因特征 (PGS)。接收者操作特征曲线下面积的分析表明，PGS 比以前确定的癌症生物标志物更能准确预测患者的生存情况。此外，PGS 将预后结果较差、癌症进展频率增加和对化疗反应不良的进展性疾病风险较高的患者分层。这些 PGS 在来自基因表达综合数据库的四个独立微阵列数据集中得到了很好的重现，并在来自胶质母细胞瘤患者的六个新解剖的肿瘤中得到了进一步验证。我们的结果证明了综合癌症生物标志物发现方法的强大功能，以及将 PGS 纳入临床生物标志物测试的可能性。
