Integrated single-cell and machine learning analysis identifies an autophagy- and immune-related prognostic signature in adrenocortical carcinoma
- 1Department of Urology, the Third Affiliated Hospital of Sun Yat-sen University
- 2Department of Urology, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangdong Provincial Engineering Technology Research Institute of Traditional Chinese Medicine, The Fifth Clinical College of Guangzhou University of Chinese Medicine
Abstract
Background Adrenocortical carcinoma (ACC) is an uncommon but highly aggressive endocrine malignancy characterized by poor survival outcomes and remarkable molecular heterogeneity. Emerging evidence suggests that autophagy participates in tumor progression and immune regulation; however, its comprehensive molecular landscape and clinical implications in ACC remain largely undefined. Methods Multi-omics datasets from TCGA-ACC, E-GEOD-19,776, and GSE252112 were integrated to identify core autophagy- and immunity-associated genes. A prognostic signature was established through machine-learning-based model selection. Survival analysis, ROC curves, and nomogram construction were performed to evaluate predictive performance. Immune characteristics were explored using ssGSEA, CIBERSORT, TIP, ESTIMATE, and TIDE algorithms. Somatic mutation profiling, pathway enrichment analysis, and oncoPredict-based drug sensitivity assessment were further conducted. Results A three-gene signature composed of BIRC5, CASP3, and MAPK8 was developed and showed generally consistent prognostic stratification across the available cohorts. Individuals classified as high risk exhibited significantly shorter overall survival, increased tumor mutation burden, frequent TP53 alterations, and distinct immune-context features inferred from bulk transcriptomic deconvolution analyses. Functional analyses indicated enrichment of cell-cycle regulation, epithelial–mesenchymal transition, and DNA replication pathways. Moreover, predicted IC50 values indicated potential differential sensitivity to several targeted agents in the high-risk subgroup. Conclusion This autophagy- and immune-related signature may provide a useful tool for prognostic assessment in ACC and may reflect potential associations with genomic instability, immune-context variation, and therapeutic vulnerability, which require further experimental and clinical validation.
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BibTeX
@article{chen2026integrated,
title = {{Integrated single-cell and machine learning analysis identifies an autophagy- and immune-related prognostic signature in adrenocortical carcinoma}},
author = {Yuezhou Chen and Shengjie Lai and Sijian Fang and Baihao Liao and Xin Fang and Xiaorong Chen and Zhouzhou Xie and Youqiang Fang},
journal = {Discover Oncology},
year = {2026},
month = jul,
doi = {10.1007/s12672-026-05630-9},
url = {https://doi.org/10.1007/s12672-026-05630-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrated single-cell and machine learning analysis identifies an autophagy- and immune-related prognostic signature in adrenocortical carcinoma
AU - Yuezhou Chen
AU - Shengjie Lai
AU - Sijian Fang
AU - Baihao Liao
AU - Xin Fang
AU - Xiaorong Chen
AU - Zhouzhou Xie
AU - Youqiang Fang
PY - 2026
DA - 2026/07/01
JO - Discover Oncology
DO - 10.1007/s12672-026-05630-9
UR - https://doi.org/10.1007/s12672-026-05630-9
AB - Background Adrenocortical carcinoma (ACC) is an uncommon but highly aggressive endocrine malignancy characterized by poor survival outcomes and remarkable molecular heterogeneity. Emerging evidence suggests that autophagy participates in tumor progression and immune regulation; however, its comprehensive molecular landscape and clinical implications in ACC remain largely undefined. Methods Multi-omics datasets from TCGA-ACC, E-GEOD-19,776, and GSE252112 were integrated to identify core autophagy- and immunity-associated genes. A prognostic signature was established through machine-learning-based model selection. Survival analysis, ROC curves, and nomogram construction were performed to evaluate predictive performance. Immune characteristics were explored using ssGSEA, CIBERSORT, TIP, ESTIMATE, and TIDE algorithms. Somatic mutation profiling, pathway enrichment analysis, and oncoPredict-based drug sensitivity assessment were further conducted. Results A three-gene signature composed of BIRC5, CASP3, and MAPK8 was developed and showed generally consistent prognostic stratification across the available cohorts. Individuals classified as high risk exhibited significantly shorter overall survival, increased tumor mutation burden, frequent TP53 alterations, and distinct immune-context features inferred from bulk transcriptomic deconvolution analyses. Functional analyses indicated enrichment of cell-cycle regulation, epithelial–mesenchymal transition, and DNA replication pathways. Moreover, predicted IC50 values indicated potential differential sensitivity to several targeted agents in the high-risk subgroup. Conclusion This autophagy- and immune-related signature may provide a useful tool for prognostic assessment in ACC and may reflect potential associations with genomic instability, immune-context variation, and therapeutic vulnerability, which require further experimental and clinical validation.
SN - 2730-6011
ER -