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Updated: Sep 20, 2025
Evaluating Autophagy Levels in Two Different Pancreatic Cell Models Using LC3 Immunofluorescence
Published on: April 28, 2023
Clustering analysis revealed the autophagy classification and potential autophagy regulators' sensitivity of
Yonghao Chen1, Jialin Meng2,3,4, Xiaofan Lu5
1Department of Gastroenterology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.
Background:
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy and is unresponsive to conventional therapeutic modalities due to its high heterogeneity, expounding the necessity, and priority of searching for effective biomarkers and drugs. Autophagy, as an evolutionarily conserved biological process, is upregulated in PDAC and its regulation is linked to a poor prognosis. Increased autophagy sequestered MHC-I on PDAC cells and weaken the antigen presentation and antitumor immune response, indicating the potential therapeutic strategies of autophagy inhibitors.
Methods:
By performing 10 state-of-the-art multi-omics clustering algorithms, we constructed a robust PDAC classification model to reveal the autophagy-related genes among different subgroups.
Outcomes:
After building a more comprehensive regulating network for potential autophagy regulators exploration, we concluded the top 20 autophagy-related hub genes (GAPDH, MAPK3, RHEB, SQSTM1, EIF2S1, RAB5A, CTSD, MAP1LC3B, RAB7A, RAB11A, FADD, CFKN2A, HSP90AB1, VEGFA, RELA, DDIT3, HSPA5, BCL2L1, BAG3, and ERBB2), six miRNAs, five transcription factors, and five immune infiltrated cells as biomarkers. The drug sensitivity database was screened based on the biomarkers to predict possible drug-targeting signal pathways, hoping to yield novel insights, and promote the progress of the anticancer therapeutic strategy.
Conclusion:
We succefully constructed an autophagy-related mRNA/miRNA/TF/Immune cells network based on a 10 state-of art algorithm multi-omics analysis, and screened the drug sensitivity dataset for detecting potential signal pathway which might be possible autophagy modulators' targets.
Insights
Pancreatic cancer (PDAC) shows high heterogeneity, making it resistant to treatments. Researchers identified key autophagy-related genes, miRNAs, and immune cells as biomarkers to guide new drug development for this lethal malignancy.
Area of Science:
- Oncology
- Molecular Biology
- Immunology
Background:
- Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with poor response to current therapies.
- Autophagy is upregulated in PDAC, correlating with worse prognosis and impaired anti-tumor immunity.
- Targeting autophagy presents a potential therapeutic strategy for PDAC.
Purpose of the Study:
- To classify PDAC subtypes based on autophagy-related genes using advanced multi-omics analysis.
- To identify novel biomarkers including genes, miRNAs, transcription factors, and immune cells for PDAC.
- To explore potential drug targets and signaling pathways for novel anti-PDAC therapeutic strategies.
Main Methods:
- Utilized 10 state-of-the-art multi-omics clustering algorithms for PDAC classification.
- Constructed a comprehensive regulatory network of autophagy-related genes, miRNAs, and transcription factors.
- Screened drug sensitivity databases using identified biomarkers to predict therapeutic targets.
Main Results:
- Developed a robust PDAC classification model revealing distinct autophagy-related gene subgroups.
- Identified the top 20 autophagy-related hub genes, six miRNAs, five transcription factors, and five immune cells as potential biomarkers.
- Predicted potential drug-targeting signal pathways for autophagy modulators.
Conclusions:
- Successfully constructed an integrated autophagy-related network (mRNA/miRNA/TF/Immune cells) using multi-omics data.
- Screened drug sensitivity data to identify potential therapeutic targets and pathways for autophagy modulation in PDAC.
- This study provides novel insights for advancing anti-PDAC therapeutic strategies.
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