Unraveling the Role of Anoikis in Non-Alcoholic Fatty Liver Disease Progression and Immune Cell Infiltration

Yuming Wang; Xinqiang Li; Jijun Shan; Ruidong Ding; Jinzhen Cai

doi:10.71321/p63ws623

Authors

Yuming Wang Organ Transplantation Center, Affiliated Hospital of Qingdao University, Qingdao, China
Xinqiang Li Organ Transplantation Center, Affiliated Hospital of Qingdao University, Qingdao, China
Jijun Shan Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
Ruidong Ding Organ Transplantation Center, Affiliated Hospital of Qingdao University, Qingdao, China
Jinzhen Cai Qingdao University

DOI:

https://doi.org/10.71321/p63ws623

Keywords:

NAFLD, Anoikis, Apoptosis, Machine learning

Abstract

Background: Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disease with complex molecular mechanisms. Anoikis, a distinct form of programmed cell death, has been implicated in disease progression, but its specific role in NAFLD remains unclear. This study aims to identify anoikis-related molecular clusters, explore their immune characteristics, and construct a predictive model for NAFLD prognosis.

Methods: Gene expression profiles of NAFLD samples were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to identify cluster-specific differentially expressed genes. Immune infiltration analysis was conducted to evaluate the association between anoikis-related clusters and immune cell composition. Machine learning was used to screen feature genes, and a predictive model was developed. The model’s performance was assessed using nomograms, calibration curves, and decision curve analysis (DCA).

Results: Two distinct anoikis-related molecular clusters were identified, each exhibiting unique immune microenvironment characteristics. Cluster 1 showed higher levels of CD8 T cells, γ-delta T cells, and macrophages (M1 and M2), while Cluster 2 had increased monocytes, activated dendritic cells, and neutrophils, reflecting inflammatory heterogeneity. Four key genes (TMEM169, THBS1, ASIP, and BRCA1) were identified through machine learning and incorporated into a predictive model. The model’s accuracy in predicting NAFLD prognosis was confirmed through nomograms, calibration curves, and DCA.

Conclusion: This study established an anoikis-related prognostic model for NAFLD and identified key genes involved in disease progression. The findings provide novel insights into the interplay between anoikis, immune responses, and NAFLD, offering potential biomarkers and therapeutic targets for personalized treatment.

Author Biographies

Xinqiang Li, Organ Transplantation Center, Affiliated Hospital of Qingdao University, Qingdao, China

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Jijun Shan, Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China

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Ruidong Ding, Organ Transplantation Center, Affiliated Hospital of Qingdao University, Qingdao, China

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