Lai Xiulan, Friedman Avner
Institute for Mathematical Sciences, Renmin University of China, Beijing, P. R. China.
Mathematical Bioscience Institute & Department of Mathematics, Ohio State University, Columbus, OH, United States of America.
PLoS One. 2016 Dec 21;11(12):e0167706. doi: 10.1371/journal.pone.0167706. eCollection 2016.
Lung cancer, primarily non-small-cell lung cancer (NSCLC), is the leading cause of cancer deaths in the United States and worldwide. While early detection significantly improves five-year survival, there are no reliable diagnostic tools for early detection. Several exosomal microRNAs (miRs) are overexpressed in NSCLC, and have been suggested as potential biomarkers for early detection. The present paper develops a mathematical model for early stage of NSCLC with emphasis on the role of the three highest overexpressed miRs, namely miR-21, miR-205 and miR-155. Simulations of the model provide quantitative relationships between the tumor volume and the total mass of each of the above miRs in the tumor. Because of the positive correlation between these miRs in the tumor tissue and in the blood, the results of the paper may be viewed as a first step toward establishing a combination of miRs 21, 205, 155 and possibly other miRs as serum biomarkers for early detection of NSCLC.
肺癌,主要是非小细胞肺癌(NSCLC),是美国和全球癌症死亡的主要原因。虽然早期检测能显著提高五年生存率,但目前尚无可靠的早期检测诊断工具。几种外泌体微小RNA(miRs)在NSCLC中过表达,并被认为是早期检测的潜在生物标志物。本文建立了一个NSCLC早期阶段的数学模型,重点关注三种过表达程度最高的miRs,即miR-21、miR-205和miR-155的作用。该模型的模拟结果给出了肿瘤体积与肿瘤中上述每种miR的总质量之间的定量关系。由于这些miRs在肿瘤组织和血液中的正相关性,本文的结果可被视为朝着建立miR-21、miR-205、miR-155以及可能的其他miRs组合作为NSCLC早期检测血清生物标志物迈出的第一步。