Otálora-Otálora Beatriz Andrea, Osuna-Garzón Daniel Alejandro, Carvajal-Parra Michael Steven, Cañas Alejandra, Montecino Martín, López-Kleine Liliana, Rojas Adriana
Facultad de Medicina, Universidad Nacional de Colombia, Bogotá 11001, Colombia.
Departamento de Estadística, Universidad Nacional de Colombia, Bogotá 11001, Colombia.
Biology (Basel). 2022 Jul 20;11(7):1082. doi: 10.3390/biology11071082.
The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID's tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan-Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.
我们研究实验室之前开发的生物信息学流程用于识别与肿瘤疾病的发生和发展相关的潜在通用和特定失调肿瘤基因及转录因子,目前将肺癌与其他两种癌症进行比较。选择并分别分析了20个微阵列数据集,以识别核心差异表达基因,并进行比较以确定三种癌症之间共有的所有失调基因和转录因子以及肺癌特有的基因和转录因子。通过获胜差异表达基因分析,可识别出在大多数微阵列数据集中失调的大量转录因子,这些转录因子可能成为通用肿瘤和肺癌特异性的关键生物标志物。根据DAVID工具富集分析,为每个与肺癌相关的失调基因数据集构建了共表达网络,并根据oPOSSUM工具构建了能够调控这些基因的转录因子网络。网络中几个基因和转录因子共表达,表明它们可能与任何组织尤其是肺组织中肿瘤病理的发生或发展有关。通过比较肺癌和其他类型癌症的共表达网络,可识别出与重要肿瘤过程和信号通路相关的失调基因和转录因子的共同连接模式,这些过程和通路尚未通过实验验证其在肺癌中的作用。Kaplan-Meier估计器确定了13个失调的顶级获胜转录因子与肺癌患者生存率的关联。共调控分析确定了两个与肺癌和乳腺癌基因表达调控控制相关的顶级获胜转录因子网络。我们的转录组分析表明,癌症具有与获得癌症特征相关的重要转录因子共调控网络。此外,肺癌有一组肺组织特有的基因和转录因子,它们在肿瘤发生过程中共表达,必须通过实验研究以充分了解它们在其非常复杂的转录组情况下在发病机制中的作用。因此,所开发的下游生物信息学分析能够在考虑肿瘤过程在细胞和群体水平上的巨大异质性的情况下,识别出通用的和肺癌特异性的癌症共调控元框架。