Multi-Omics Analysis for Transcriptional Regulation of Immune-Related Targets Using Epigenetic Data: A New Research Direction

Identification of Hub Genes for Psoriasis and Cancer by Bioinformatic Analysis

Psoriasis increases the risk of developing various cancers, including colon cancer. The pathogenesis of the co-occurrence of psoriasis and cancer is not yet clear. This study is aimed at analyzing the pathogenesis of psoriasis combined with cancer by bioinformatic analysis. Skin tissue data from psoriasis (GSE117239) and intestinal tissue data from colon cancer (GSE44076) were downloaded from the GEO database. One thousand two hundred ninety-six common differentially expressed genes and 688 common shared genes for psoriasis and colon cancer were determined, respectively, using the limma R package and weighted gene coexpression network analysis (WGCNA) methods. The results of the GO and KEGG enrichment analyses were mainly related to the biological processes of the cell cycle. Thirteen hub genes were selected, including AURKA, DLGAP5, NCAPG, CCNB1, NDC80, BUB1B, TTK, CCNB2, AURKB, TOP2A, ASPM, BUB1, and KIF20A. These hub genes have high diagnostic value, and most of them are positively correlated with activated CD4 T cells. Three hub transcription factors (TFs) were also predicted: E2F1, E2F3, and BRCA1. These hub genes and hub TFs are highly expressed in various cancers. Furthermore, 251 drugs were predicted, and some of them overlap with existing therapeutic drugs for psoriasis or colon cancer. This study revealed some genetic mechanisms of psoriasis and cancer by bioinformatic analysis. These hub genes, hub TFs, and predicted drugs may provide new perspectives for further research on the mechanism and treatment.

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Multi-omics analysis for potential inflammation-related genes involved in tumour immune evasion via extended application of epigenetic data

Accumulating evidence suggests that inflammation-related genes may play key roles in tumour immune evasion. Programmed cell death ligand 1 (PD-L1) is an important immune checkpoint involved in mediating anti-tumour immunity. We performed multi-omics analysis to explore key inflammation-related genes affecting the transcriptional regulation of PD-L1 expression. The open chromatin region of the PD-L1 promoter was mapped using the assay for transposase-accessible chromatin using sequencing (ATAC-seq) profiles. Correlation analysis of epigenetic data (ATAC-seq) and transcriptome data (RNA-seq) were performed to identify inflammation-related transcription factors (TFs) whose expression levels were correlated with the chromatin accessibility of the PD-L1 promoter. Chromatin immunoprecipitation sequencing (ChIP-seq) profiles were used to confirm the physical binding of the TF STAT2 and the predicted binding regions. We also confirmed the results of the bioinformatics analysis with cell experiments. We identified chr9 : 5449463-5449962 and chr9 : 5450250-5450749 as reproducible open chromatin regions in the PD-L1 promoter. Moreover, we observed a correlation between STAT2 expression and the accessibility of the aforementioned regions. Furthermore, we confirmed its physical binding through ChIP-seq profiles and demonstrated the regulation of PD-L1 by STAT2 overexpression in vitro. Multiple databases were also used for the validation of the results. Our study identified STAT2 as a direct upstream TF regulating PD-L1 expression. The interaction of STAT2 and PD-L1 might be associated with tumour immune evasion in cancers, suggesting the potential value for tumour treatment.