Hossain Sk Md Mosaddek, Halsana Aanzil Akram, Khatun Lutfunnesa, Ray Sumanta, Mukhopadhyay Anirban
Computer Science and Engineering, Aliah University, Kolkata, 700160, India.
Computer Science and Engineering, University of Kalyani, Kalyani, 741235, India.
Sci Rep. 2021 Apr 12;11(1):7853. doi: 10.1038/s41598-021-87234-7.
Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease's progression, helping the disease's etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.
胰腺导管腺癌（PDAC）是胰腺癌中最致命的类型，检测延迟会导致治疗失败。本研究旨在确定关键调控基因及其对疾病进展的影响，以助于了解该疾病的病因，而其病因目前大多仍不清楚。我们利用了该疾病时间序列基因表达数据的标志性优势，从而识别出了能够捕捉癌症进展中基因活性模式特征的关键调控因子。我们确定了关键基因模块，并从重建的基因关联网络（GAN）中预测了顶级基因的功能。利用偏相关方法的一种变体来分析GAN，随后进行基因功能预测任务。此外，我们使用动态GENIE3（dynGENIE3）算法通过基因调控网络推断为每个靶基因确定了调控因子。分别采用狄利克雷过程高斯过程混合模型和三次样条回归模型（splineTimeR）来识别关键基因模块和差异表达基因。我们的分析展示了一组对PDAC疾病进展至关重要的关键调控因子和基因模块。
