A novel fatty acid metabolism-related signature identifies MUC4 as a novel therapy target for esophageal squamous cell carcinoma
- Shanshan Li 1, Zhengcao Liu 2, Qingqing Chen 2, Yuetong Chen 2, Shengjun Ji 3
- Shanshan Li 1, Zhengcao Liu 2, Qingqing Chen 2
- 1Department of Operating Room, Weifang Traditional Chinese Hospital, Weifang, China.
- 2Department of Radiotherapy & Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, No.16 Baita Road, Suzhou, 215001, China.
- 3Department of Radiotherapy & Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, No.16 Baita Road, Suzhou, 215001, China. drshengjunji@163.com.
- 0Department of Operating Room, Weifang Traditional Chinese Hospital, Weifang, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies a fatty acid metabolism-related gene signature for esophageal squamous cell carcinoma (ESCC) prognosis. The developed risk model and molecular subtypes can predict patient survival and guide treatment strategies.
Area Of Science
- Oncology
- Molecular Biology
- Cancer Genomics
Background
- Fatty acid metabolism is a key hallmark of cancer, influencing prognosis.
- The role of fatty acid metabolism-related genes (FMGs) in esophageal squamous cell carcinoma (ESCC) prognosis is not well understood.
Purpose Of The Study
- To identify a reliable FMGs signature for ESCC prognosis and treatment decision support.
- To investigate the association between FMGs molecular subtypes and clinical outcomes in ESCC.
Main Methods
- Consensus clustering analysis of 259 ESCC samples from TCGA and GEO databases.
- Development and validation of an eight-gene FMGs risk model using Kaplan-Meier and ROC analyses.
- Nomogram construction for predicting patient survival rates.
- In vitro assays (CCK-8, wound healing, Transwell) to assess FMGs' role in ESCC tumorigenesis.
Main Results
- Two distinct FMGs molecular subtypes were identified; cluster 2 correlated with poor prognosis and lower immune infiltration.
- An eight-gene FMGs risk model was established, with high-risk patients exhibiting worse survival and higher immune/stromal scores.
- The nomogram demonstrated good predictive accuracy for ESCC patient outcomes.
- In vitro experiments showed MUC4 silencing inhibited ESCC cell proliferation and invasion.
Conclusions
- FMGs signatures and molecular subtypes are valuable for predicting ESCC patient prognosis.
- The identified FMGs risk model and subtypes offer potential therapeutic targets for ESCC.
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