Dataset Information

Transcription factor-target gene regulatory network analysis in human lung adenocarcinoma.

ABSTRACT:

Background

Transcription factors (TFs) play a crucial role in the occurrence and progression of lung adenocarcinoma (LUAD), and targeting TFs is an important direction for treating LUAD. However, targeting a single TF often fails to achieve satisfactory therapeutic outcomes. Furthermore, the regulatory TF-target gene networks involved in the development of LUAD is complex and not yet fully understood.

Methods

In this study, we performed RNA sequencing (RNA-seq) to analyze the transcriptome profile of human LUAD tissues and matched adjacent nontumor tissues. We selected the differentially expressed TFs, performed enrichment analysis and survival curve analysis, and predicted the regulatory networks of the top differential TFs with their target genes. Finally, alternative splicing analyses were also performed.

Results

We found that TFs GRHL3, SIX1, SIX2, SPDEF, and ETV4 were upregulated, while TAL1, EPAS1, SOX17, NR4A1, and EGR3 were significantly downregulated in LUAD tissues compared to normal tissues. We propose a potential GRHL3-CDH15-Wnt-β-catenin pro-oncogenic signaling axis and a potential TAL1-ADAMTS1-vascular antioncogenic signaling axis. In addition, we found that alternative splicing of intron retention (IR), approximate IR (XIR), multi-IR (MIR), approximate MIR (XMIR), and approximate alternative exon ends (XAE) showed abnormally increased frequencies in LUAD tissues.

Conclusions

These findings revealed a novel TF-target gene regulatory axis related to tumorigenesis and provided potential therapeutic targets and mechanisms for LUAD.

SUBMITTER: Huang F

PROVIDER: S-EPMC10797383 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Transcription factor-target gene regulatory network analysis in human lung adenocarcinoma.

Huang Fang F Xue Fangsu F Wang Qing Q Huang Yuchen Y Wan Zixin Z Cao Xiaowen X Zhong Lou L

Journal of thoracic disease 20231226 12

<h4>Background</h4>Transcription factors (TFs) play a crucial role in the occurrence and progression of lung adenocarcinoma (LUAD), and targeting TFs is an important direction for treating LUAD. However, targeting a single TF often fails to achieve satisfactory therapeutic outcomes. Furthermore, the regulatory TF-target gene networks involved in the development of LUAD is complex and not yet fully understood.<h4>Methods</h4>In this study, we performed RNA sequencing (RNA-seq) to analyze the tran ...[more]

PMID: 38249888

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Project description:BackgroundGene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types.ResultsThe NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY???MYB17, AG???CRC, AP2???RD20, AGL15???RAV2 and HY5???HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs?-?LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15.ConclusionsFor the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF activity. However, since NCA relies on documented connectivity information about the underlying TF-GRN, it is currently limited in its application to larger plant networks because of the lack of documented connectivities. In the future, the identification of interactions between plant TFs and their target genes on a genome scale would allow the use of NCA to provide quantitative regulatory information about plant TF-GRNs, leading to improved insights on cellular regulatory programs.

Project description:Lung cancer is the second most commonly diagnosed carcinoma and is the leading cause of cancer death. Although significant progress has been made towards its understanding and treatment, unraveling the complexities of lung cancer is still hampered by a lack of comprehensive knowledge on the mechanisms underlying the disease. High-throughput and multidimensional genomic data have shed new light on cancer biology. In this study, we developed a network-based approach integrating somatic mutations, the transcriptome, DNA methylation, and protein-DNA interactions to reveal the key regulators in lung adenocarcinoma (LUAD). By combining Bayesian network analysis with tissue-specific transcription factor (TF) and targeted gene interactions, we inferred 15 disease-related core regulatory networks in co-expression gene modules associated with LUAD. Through target gene set enrichment analysis, we identified a set of key TFs, including known cancer genes that potentially regulate the disease networks. These TFs were significantly enriched in multiple cancer-related pathways. Specifically, our results suggest that hepatitis viruses may contribute to lung carcinogenesis, highlighting the need for further investigations into the roles that viruses play in treating lung cancer. Additionally, 13 putative regulatory long non-coding RNAs (lncRNAs), including three that are known to be associated with lung cancer, and nine novel lncRNAs were revealed by our study. These lncRNAs and their target genes exhibited high interaction potentials and demonstrated significant expression correlations between normal lung and LUAD tissues. We further extended our study to include 16 solid-tissue tumor types and determined that the majority of these lncRNAs have putative regulatory roles in multiple cancers, with a few showing lung-cancer specific regulations. Our study provides a comprehensive investigation of transcription factor and lncRNA regulation in the context of LUAD regulatory networks and yields new insights into the regulatory mechanisms underlying LUAD. The novel key regulatory elements discovered by our research offer new targets for rational drug design and accompanying therapeutic strategies.

Dataset Information

Transcription factor-target gene regulatory network analysis in human lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Transcription factor-target gene regulatory network analysis in human lung adenocarcinoma.

Similar Datasets

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