Department of Agricultural Sciences, Università Degli Studi di Napoli Federico II, 80055 Naples, Italy.
Department of RIMAR, Stazione Zoologica "Anton Dohrn", 80122 Naples, Italy.
Int J Mol Sci. 2020 Dec 15;21(24):9560. doi: 10.3390/ijms21249560.
The main hallmarks of cancer diseases are the evasion of programmed cell death, uncontrolled cell division, and the ability to invade adjacent tissues. The explosion of omics technologies offers challenging opportunities to identify molecular agents and processes that may play relevant roles in cancer. They can support comparative investigations, in one or multiple experiments, exploiting evidence from one or multiple species. Here, we analyzed gene expression data from induction of programmed cell death and stress response in and compared the results with gene expression during the response to cell death. The aim was to identify conserved candidate genes associated with cell death, favored by crosslinks based on orthology relationships between the two species. We identified differentially-expressed genes, pathways that are significantly dysregulated across treatments, and characterized genes among those involved in induced cell death. We investigated on co-expression patterns and identified novel genes that were not expected to be associated with death pathways, that have a conserved pattern of expression between the two species. Finally, we analyzed the resulting list by HumanNet and identified new genes predicted to be involved in cancer. The data integration and the comparative approach between distantly-related reference species that were here exploited pave the way to novel discoveries in cancer therapy and also contribute to detect conserved genes potentially involved in programmed cell death.
癌症的主要特征是逃避程序性细胞死亡、不受控制的细胞分裂以及侵袭相邻组织的能力。组学技术的爆炸式发展为鉴定可能在癌症中发挥相关作用的分子代理和过程提供了具有挑战性的机会。它们可以支持在一个或多个实验中进行比较研究，利用来自一个或多个物种的证据。在这里，我们分析了程序性细胞死亡和应激反应诱导的 和 基因表达数据，并将结果与细胞死亡反应过程中的 基因表达进行了比较。目的是鉴定与 细胞死亡相关的保守候选基因，这些基因通过两个物种之间的同源关系进行基于交联的优先选择。我们鉴定了差异表达基因、在处理过程中显著失调的途径，以及参与诱导细胞死亡的基因。我们研究了共表达模式，并鉴定了那些原本不被认为与死亡途径相关的新基因，它们在两个物种之间具有保守的表达模式。最后，我们通过 HumanNet 分析了最终的基因列表，并鉴定了新的基因，这些基因被预测与癌症有关。在这里利用的远距离相关参考物种之间的数据集成和比较方法为癌症治疗的新发现铺平了道路，也有助于检测可能参与程序性细胞死亡的保守基因。
