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Published on: October 27, 2020
Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
Qiaofeng Zhang1,2,3, Furong Liu1,2,3, Lu Qin4
1Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, 1095 Jiefang Avenue, Wuhan, 430030, China.
Background:
Gastrointestinal adenocarcinoma (GIAD) has caused a serious disease burden globally. Targeted therapy for the transforming growth factor beta (TGF-β) signaling pathway is becoming a reality. However, the molecular characterization of TGF-β associated signatures in GIAD requires further exploration.
Methods:
Multi-omics data were collected from TCGA and GEO database. A pivotal unsupervised clustering for TGF-β level was performed by distinguish status of TGF-β associated genes. We analyzed differential mRNAs, miRNAs, proteins gene mutations and copy number variations in both clusters for comparison. Enrichment of pathways and gene sets were identified in each type of GIAD. Then we performed differential mRNA related drug response by collecting data from GDSC. At last, a summarized deep neural network for TGF-β status and GIADs was constracted.
Results:
The TGF-βhigh group had a worse prognosis in overall GIAD patients, and had a worse prognosis trend in gastric cancer and colon cancer specifically. Signatures (including mRNA and proteins) of the TGF-βhigh group is highly correlated with EMT. According to miRNA analysis, miR-215-3p, miR-378a-5p, and miR-194-3p may block the effect of TGF-β. Further genomic analysis showed that TGF-βlow group had more genomic changes in gastric cancer, such as TP53 mutation, EGFR amplification, and SMAD4 deletion. And drug response dataset revealed tumor-sensitive or tumor-resistant drugs corresponding to TGF-β associated mRNAs. Finally, the DNN model showed an excellent predictive effect in predicting TGF-β status in different GIAD datasets.
Conclusions:
We provide molecular signatures associated with different levels of TGF-β to deepen the understanding of the role of TGF-β in GIAD and provide potential drug possibilities for therapeutic targets in different levels of TGF-β in GIAD.
Insights
High transforming growth factor beta (TGF-β) levels correlate with poor prognosis in gastrointestinal adenocarcinoma (GIAD). This study identifies molecular signatures and potential drug targets for different TGF-β levels in GIAD.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Gastrointestinal adenocarcinoma (GIAD) presents a significant global health challenge.
- Targeted therapies for the transforming growth factor beta (TGF-β) pathway are emerging.
- Further molecular characterization of TGF-β signaling in GIAD is needed.
Purpose of the Study:
- To explore TGF-β associated molecular signatures in GIAD.
- To identify potential therapeutic targets based on TGF-β levels.
- To deepen the understanding of TGF-β's role in GIAD progression.
Main Methods:
- Utilized multi-omics data from TCGA and GEO databases.
- Performed unsupervised clustering based on TGF-β gene expression.
- Analyzed differential mRNAs, miRNAs, proteins, gene mutations, and copy number variations.
- Conducted pathway enrichment analysis and drug response prediction.
- Developed a deep neural network (DNN) model for TGF-β status prediction.
Main Results:
- The TGF-β high group exhibited a worse prognosis in overall GIAD, gastric cancer, and colon cancer.
- TGF-β high signatures were strongly associated with epithelial-mesenchymal transition (EMT).
- Specific miRNAs (miR-215-3p, miR-378a-5p, miR-194-3p) may inhibit TGF-β.
- The TGF-β low group showed increased genomic alterations in gastric cancer.
- Identified potential tumor-sensitive and resistant drugs based on TGF-β associated mRNAs.
- The DNN model demonstrated excellent predictive performance for TGF-β status.
Conclusions:
- This study provides molecular signatures linked to varying TGF-β levels in GIAD.
- Offers insights into potential drug strategies for different TGF-β strata in GIAD.
- Enhances the understanding of TGF-β's multifaceted role in GIAD pathogenesis.
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