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Differential Gene Expression Analysis Reveals Global LMTK2 Regulatory Network and Its Role in TGF-β1 Signaling.

ABSTRACT: Lemur tyrosine kinase 2 (LMTK2) is a transmembrane Ser/Thr kinase whose role has been increasingly recognized; however, when compared to other kinases, understanding of the LMTK2 networks and biological functions is still limited. Recent data have shown that transforming growth factor (TGF)-β1 plays a role in modulating LMTK2 function by controlling its endocytic trafficking in human bronchial epithelial cells. Here, we aimed to unveil the LMTK2 regulatory network and elucidate how it affects cellular functions and disease pathways in either TGF-β1 dependent or independent manner. To understand how the LMTK2 and TGF-β1 pathways interconnect, we knocked down (KD) LMTK2 using small(si)RNA-mediated silencing in human bronchial epithelial CFBE41o- cells, treated cells with TGF-β1 or vehicle control, and performed differential gene expression analysis by RNA sequencing (RNAseq). In vehicle-treated cells, LMTK2 KD affected expression of 2,506 genes while it affected 4,162 genes after TGF-β1 stimulation. Bioinformatics analysis shows that LMTK2 is involved in diverse cellular functions and disease pathways, such as cell death and survival, cellular development, and cancer susceptibility. In summary, our study increases current knowledge about the LMTK2 network and its intersection with the TGF-β1 signaling pathway. These findings will serve as basis for future exploration of the predicted LMTK2 interactions and signaling pathways.

SUBMITTER: Cruz DF

PROVIDER: S-EPMC8013980 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing disease-related genes on the dataset we considered. We propose this extendable differential networking framework as a promising way to gain insights into gene regulatory mechanisms underlying cancer progression and other phenotypic changes.

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Differential Gene Expression Analysis Reveals Global LMTK2 Regulatory Network and Its Role in TGF-β1 Signaling.

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