Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer

Abstract

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan–Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability

“…Using differentially expressed genes (DEGs) and phosphosites (DEPPs) expression, we predicted TF activity ( Supplementary Table 5B ) and kinase activity ( Supplementary Table 5C ) by “DoRothEA” and “PHONEMeS” packages in R. A total of 26 TFs associated with lung cancer, encompassing both NSCLC and SCLC 27 , have been identified as frequently dysregulated in lung tumoral cells. Utilizing these TFs, we conducted a comparative analysis between two sample sets, each representing different storage periods ( Figure 5B ).…”

Inferring gene regulatory networks by hypergraph variational autoencoder

“…We further input gene lists of corresponding gene clusters of bone marrow B cell learned by gene cluster within Metascape [2], protein-protein interaction enrichment analysis has been carried out with the following databases: STRING [3], BioGrid [4], OmniPath [5], and InWeb IM [6]. Only physical interactions in STRING (physical score larger than 0.132) and BioGrid are used (details).…”

“…Gene Regulatory Networks (GRNs) within single-cell RNA sequencing (scRNA-seq) datasets present a sophisticated interplay of transcription factors (TFs) and target genes, uniquely capturing the modulation of gene expression and thereby delineating the intricate cellular functions and responses within diverse cell populations [1]. GRNs illuminate core biological processes and underpin applications from disease modeling to therapeutic design [2, 3, 4], empowering researchers to interpret the mechanisms of gene interactions within cells and leverage this understanding for medical and biotechnological innovations [5, 6].…”

Crosstalk between SOX Genes and Long Non-Coding RNAs in Glioblastoma

“…Like many other genes involved in the regulation of development, SOX genes are frequently dysregulated in cancer. A large amount of RNA-seq data revealed that SOX genes are aberrantly expressed in a variety of solid tumors, including bladder carcinoma [ 18 ], prostate carcinoma [ 19 ], renal cell carcinoma [ 20 ], liver carcinoma [ 21 , 22 ], sarcoma [ 22 ], cervical carcinoma [ 23 ], breast carcinoma [ 24 ], and lung carcinoma [ 25 ].…”