Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Sk Md Mosaddek Hossain,Sumanta Ray,Lutfunnesa Khatun,Anirban Mukhopadhyay,Aanzil Akram Halsana

doi:10.1038/s41598-021-87234-7

Sk Md Mosaddek Hossain, Sumanta Ray + Show 3 more

Open Access

https://doi.org/10.1038/s41598-021-87234-7

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Journal: Scientific Reports	Publication Date: Apr 12, 2021
Citations: 12	License type: open-access

Affiliation: Aliah University, University of Kalyani

Abstract

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.

Highlights

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure
We retrieved 47 DNA-binding RNA polymerase II transcription factors (TFs) (DbTFs) among the 66 key transcriptional regulators (TR)’s. Among these DbTFs, we have discovered 10 proteins, viz., ‘FOXO1’, ‘SOX9’, ‘GATA6’, ‘SMAD3’, ‘NFKB1’, ‘KLF6’, ‘TBX3’, ‘SREBF1’, ‘NR4A2’, ‘TCF3’ that are directly associated with PDAC using DisGeNET
We have discovered that the squared exponential kernel with maximum a posteriori (MAP) clustering and Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) hyperprior optimization technique, concentration parameter ( α = 1.0 ), shape and rate parameters for the inverse gamma prior on the cluster noise variance produces best clustering solution with 10 gene modules which yield the highest silhouette width [Fig. 4 and Supplementary Table 3(A– F)]

Summary

Introduction

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. 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. Time-series gene expression experiments are widely used to monitor biological processes in a time-series p aradigm[8] Analyzing these time-series gene expression data helps identify transient transcriptional changes, temporal patterns, and causal effects of the genes. Some DE tools, like ImpulseDE2 and splineTimeR, based on impulse and spline regression models between two groups, respectively, are used on short time-series d ata[13]. Statistical clustering techniques have been widely used like k-means, hierarchical c lustering[16,17] and self-organizing maps[18] to produce modules from time-series gene expression profiles. T imeClust[21] uses temporal gene expression profiles to produce clusters. TMixClust[22], Dirichlet process Gaussian process mixture model[14] are some of the significant non-parametric model-based clustering methods

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Conclusion