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| Natura: | Artículo científico |
| Lingua: | en |
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Computers in biology and medicine
2025
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| Accesso online: | https://pubmed.ncbi.nlm.nih.gov/40378566/ |
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| _version_ | 1868266203989934080 |
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| author | Zheng, Zhiming Xu, Haijiong Luo, Lianxiang |
| author_facet | Zheng, Zhiming Xu, Haijiong Luo, Lianxiang Zheng, Zhiming Xu, Haijiong Luo, Lianxiang |
| collection | PubMed - marine biology |
| contents | Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma. Zheng, Zhiming Xu, Haijiong Luo, Lianxiang Humans Carcinoma, Hepatocellular Liver Neoplasms Autophagy Sequestosome-1 Protein Prognosis Biomarkers, Tumor Gene Expression Regulation, Neoplastic Protein Interaction Maps Computational Biology Databases, Genetic The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby providing fresh insights into the diagnosis and treatment of HCC. Autophagy's role in HCC progression is recognized, but the exact mechanisms are still unclear. This study seeks to build a predictive model to offer new diagnostic and therapeutic insights for HCC. Through combining bioinformatics and experiments, we aim to clarify autophagy pathways' part in HCC and spot possible treatment targets, thus aiding future HCC research and treatment. We screened HCC-related prognostic differential genes from the TCGA dataset combined with GeneCards, constructed a prognostic risk model related to autophagy genes and verified it in the GEO dataset and ICGC dataset. We integrated machine learning with protein-protein interaction (PPI) network analysis to pinpoint core targets and performed independent prognostic assessments. Leveraging single-cell sequencing data of hepatocellular carcinoma (HCC) from published literature, we ascertained the cellular distribution of these core genes.We used drug sensitivity analysis to screen clinical drugs for core genes. We established a prognostic model using 12 differential prognostic genes, which was validated in both the GEO data set and the ICGC data set, and was more effective than the 5 collected prognostic models. Machine learning combined with the PPI network screened the core gene SQSTM1, and It can be a key factor in prognosis. Single-cell analysis showed that it is significantly distributed in Tumor-associated macrophages (TAM) where SQSTM1 is concentrated. Additionally, drug susceptibility analysis showed that patients with HCC and high SQSTM1 expression are responsive to 17-AGG. Our study proposed a new risk model for predicting HCC patients based on autophagy-related genes (ARGs). The model has good predictive performance and screened out a potential target for HCC patients, which can be used as an independent prognostic factor. SQSTM1 was significantly enriched in tumor-associated macrophages. We also screened drugs for the treatment of hepatocellular carcinoma. |
| format | Artículo científico |
| id | pubmed_40378566 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Computers in biology and medicine |
| record_format | pubmed |
| spellingShingle | Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma. Zheng, Zhiming Xu, Haijiong Luo, Lianxiang Humans Carcinoma, Hepatocellular Liver Neoplasms Autophagy Sequestosome-1 Protein Prognosis Biomarkers, Tumor Gene Expression Regulation, Neoplastic Protein Interaction Maps Computational Biology Databases, Genetic Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma. Zheng, Zhiming Xu, Haijiong Luo, Lianxiang Humans Carcinoma, Hepatocellular Liver Neoplasms Autophagy Sequestosome-1 Protein Prognosis Biomarkers, Tumor Gene Expression Regulation, Neoplastic Protein Interaction Maps Computational Biology Databases, Genetic The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby providing fresh insights into the diagnosis and treatment of HCC. Autophagy's role in HCC progression is recognized, but the exact mechanisms are still unclear. This study seeks to build a predictive model to offer new diagnostic and therapeutic insights for HCC. Through combining bioinformatics and experiments, we aim to clarify autophagy pathways' part in HCC and spot possible treatment targets, thus aiding future HCC research and treatment. We screened HCC-related prognostic differential genes from the TCGA dataset combined with GeneCards, constructed a prognostic risk model related to autophagy genes and verified it in the GEO dataset and ICGC dataset. We integrated machine learning with protein-protein interaction (PPI) network analysis to pinpoint core targets and performed independent prognostic assessments. Leveraging single-cell sequencing data of hepatocellular carcinoma (HCC) from published literature, we ascertained the cellular distribution of these core genes.We used drug sensitivity analysis to screen clinical drugs for core genes. We established a prognostic model using 12 differential prognostic genes, which was validated in both the GEO data set and the ICGC data set, and was more effective than the 5 collected prognostic models. Machine learning combined with the PPI network screened the core gene SQSTM1, and It can be a key factor in prognosis. Single-cell analysis showed that it is significantly distributed in Tumor-associated macrophages (TAM) where SQSTM1 is concentrated. Additionally, drug susceptibility analysis showed that patients with HCC and high SQSTM1 expression are responsive to 17-AGG. Our study proposed a new risk model for predicting HCC patients based on autophagy-related genes (ARGs). The model has good predictive performance and screened out a potential target for HCC patients, which can be used as an independent prognostic factor. SQSTM1 was significantly enriched in tumor-associated macrophages. We also screened drugs for the treatment of hepatocellular carcinoma. |
| title | Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma. |
| topic | Humans Carcinoma, Hepatocellular Liver Neoplasms Autophagy Sequestosome-1 Protein Prognosis Biomarkers, Tumor Gene Expression Regulation, Neoplastic Protein Interaction Maps Computational Biology Databases, Genetic |
| url | https://pubmed.ncbi.nlm.nih.gov/40378566/ |