ArticleFrontiers in immunology2023
Immunogenic landscape and risk score prediction based on unfolded protein response (UPR)-related molecular subtypes in hepatocellular carcinoma.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 3 citations in OpenAlex.
- Recent Advances in the Treatment of Thyroid Pathologies with Selenium-Containing Compounds, Nanoparticles and Nanocomplexes Based on Selenium and Selenoproteins.Biological trace element research · 2026Review
- Machine learning-driven development of a novel unfolded protein response-related gene signature for predicting lung adenocarcinoma patient prognosis.BMC cancer · 2026Article
- Endoplasmic Reticulum Stress in Cancer.MedComm · 2025Review
- Quantitative proteomic analysis unveils a critical role of VARS1 in hepatocellular carcinoma aggressiveness through the modulation of MAGI1 expression.Molecular cancer · 2025Article
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9 authors at 1 institution in 1 country.
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Abstract
Background: Hepatocellular carcinoma (HCC) is the most common type of cancer and causes a significant number of cancer-related deaths worldwide. The molecular mechanisms underlying the development of HCC are complex, and the heterogeneity of HCC has led to a lack of effective prognostic indicators and drug targets for clinical treatment of HCC. Previous studies have indicated that the unfolded protein response (UPR), a fundamental pathway for maintaining endoplasmic reticulum homeostasis, is involved in the formation of malignant characteristics such as tumor cell invasiveness and treatment resistance. The aims of our study are to identify new prognostic indicators and provide drug treatment targets for HCC in clinical treatment based on UPR-related genes (URGs). Methods: Gene expression profiles and clinical information were downloaded from the TCGA, ICGC and GEO databases. Consensus cluster analysis was performed to classify the molecular subtypes of URGs in HCC patients. Univariate Cox regression and machine learning LASSO algorithm were used to establish a risk prognosis model. Kaplan-Meier and ROC analyses were used to evaluate the clinical prognosis of URGs. TIMER and XCell algorithms were applied to analyze the relationships between URGs and immune cell infiltration. Real time-PCR was performed to analyze the effect of sorafenib on the expression levels of four URGs. Results: Most URGs were upregulated in HCC samples. According to the expression pattern of URGs, HCC patients were divided into two independent clusters. Cluster 1 had a higher expression level, worse prognosis, and higher expression of immunosuppressive factors than cluster 2. Patients in cluster 1 were more prone to immune escape during immunotherapy, and were more sensitive to chemotherapeutic drugs. Four key UPR genes (ATF4, GOSR2, PDIA6 and SRPRB) were established in the prognostic model and HCC patients with high risk score had a worse clinical prognosis. Additionally, patients with high expression of four URGs are more sensitive to sorafenib. Moreover, ATF4 was upregulated, while GOSR2, PDIA6 and SRPRB were downregulated in sorafenib-treated HCC cells. Conclusion: The UPR-related prognostic signature containing four URGs exhibits high potential application value and performs well in the evaluation of effects of chemotherapy/immunotherapy and clinical prognosis.
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