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Integrating Expression Data-Based Deep Neural Network Models with Biological Networks to Identify Regulatory Modules
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China.
Abstract:
Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. First, the mRNAs, lncRNAs and miRNAs with significant differences in the expression levels between tumor and non-tumor tissues were obtained. MRNA DNN models were established and optimized to mine candidate mRNAs that significantly contributed to the DNN models and were in the center of an interaction network. Another DNN model was then constructed and potential ceRNAs were screened out based on the contribution of each RNA to the model. Finally, three modules comprised of miRNAs and their regulated mRNAs and lncRNAs with the same regulation direction were identified as regulatory modules that regulated the initiation of lung adenocarcinoma through ceRNAs relationships. They were validated by literature and functional enrichment analysis. The effectiveness of these regulatory modules was evaluated in an independent lung adenocarcinoma dataset. Regulatory modules for lung adenocarcinoma identified in this study provided a reference for regulatory mechanisms during carcinogenesis.
Insights
Researchers identified key regulatory modules in lung adenocarcinoma using deep neural networks. These findings offer insights into cancer development mechanisms and potential therapeutic targets.
Area of Science:
- Bioinformatics
- Genomics
- Oncology
Background:
- Lung adenocarcinoma is the most common lung cancer subtype.
- Regulatory mechanisms driving lung adenocarcinoma initiation are not fully understood.
- Identifying these mechanisms is a key area in bioinformatics research.
Purpose of the Study:
- To identify regulatory modules involved in lung adenocarcinoma carcinogenesis.
- To elucidate the roles of mRNAs, lncRNAs, and miRNAs in lung adenocarcinoma.
- To leverage deep neural networks for uncovering complex regulatory relationships.
Main Methods:
- Differential gene expression analysis of tumor versus non-tumor tissues.
- Construction and optimization of deep neural network (DNN) models.
- Identification of key RNAs and competing endogenous RNA (ceRNA) networks.
Main Results:
- Three distinct regulatory modules involving miRNAs, mRNAs, and lncRNAs were identified.
- These modules demonstrated consistent regulatory directions in lung adenocarcinoma.
- Validation in an independent dataset confirmed the modules' effectiveness.
Conclusions:
- The identified regulatory modules provide a reference for understanding lung adenocarcinoma carcinogenesis.
- This study highlights the potential of DNNs in identifying cancer-related regulatory networks.
- The findings contribute to the comprehension of ceRNA crosstalk in lung cancer.
