Aouiche Chaima, Chen Bolin, Shang Xuequn
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Xi'an, China.
Front Genet. 2020 Feb 26;11:160. doi: 10.3389/fgene.2020.00160. eCollection 2020.
Exploring the evolution process of cancers and its related complex molecular mechanisms at the genomic level through pathological staging angle is particularly important for providing novel therapeutic strategies most relevant to every cancer patient diagnosed at each stage. This is because the genomic level involving copy number variation (CNV) has been recognized as a critical genetic variation, which has a large influence on the progression of a variety of complex diseases. Great efforts have been devoted to the identification of recurrent aberrations, single genes and individual static pathways related to cancer progression. However, we still have little knowledge about the most important aberrant genes related to the pathology stages and their interconnected pathways from genomic profiles. In this study, we propose an identification framework that allows determining cancer-stages specific patterns dynamically. Firstly, a two-stage GAIA method is employed to identify stage-specific aberrant copy number variants segments. Secondly, stage-specific cancer genes fully located within the aberrant segments are then identified according to the reference annotation dataset. Thirdly, a pathway evolution network is constructed based on the impacted pathways functions and their overlapped genes. The involved significant functions and evolution paths uncovered by this network enabled investigation of the real progression of cancers, and thus facilitated the determination of appropriate clinical settings that will help to assess risk in cancer patients. Those findings at individual levels can be integrated to identify robust biomarkers in cancer progressions.
从病理分期角度在基因组水平探索癌症的演变过程及其相关复杂分子机制，对于为每个处于不同阶段被诊断出的癌症患者提供最相关的新型治疗策略尤为重要。这是因为涉及拷贝数变异（CNV）的基因组水平已被公认为一种关键的遗传变异，对多种复杂疾病的进展有很大影响。人们已投入大量精力来识别与癌症进展相关的复发性畸变、单个基因和个体静态通路。然而，我们对与病理阶段相关的最重要异常基因及其从基因组图谱中相互关联的通路仍知之甚少。在本研究中，我们提出了一个能够动态确定癌症阶段特异性模式的识别框架。首先，采用两阶段GAIA方法来识别阶段特异性异常拷贝数变异片段。其次，根据参考注释数据集识别完全位于异常片段内的阶段特异性癌症基因。第三，基于受影响的通路功能及其重叠基因构建通路进化网络。该网络揭示的相关重要功能和进化路径有助于研究癌症的真实进展，从而有助于确定合适的临床情况，以帮助评估癌症患者的风险。个体水平的这些发现可以整合起来，以识别癌症进展中的可靠生物标志物。
