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pmid:38720781

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation.

Hongyuan Jiang · Xuliang Zhao · Jinhui Zang · Ruijiao Wang · Jiake Gao · Jinli Chen · Tengbo Yu

Frontiers in pharmacology · 2024 doi:10.3389/fphar.2024.1399625

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Data

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title
Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation.
doi
10.3389/fphar.2024.1399625
pmid
38720781
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Hongyuan Jiang
[1]
Xuliang Zhao
[2]
Jinhui Zang
[3]
Ruijiao Wang
[4]
Jiake Gao
[5]
Jinli Chen
[6]
Tengbo Yu
journal
Frontiers in pharmacology
year
2024
abstract
Objective: To investigate the immune mechanism of osteosarcoma (OS)-specific markers to mitigate bone destruction in the aggressive OS, prone to recurrence and metastasis. Methods: Gene expression patterns from the Gene Expression Omnibus (GEO) database (GSE126209) were analyzed using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, least absolute shrinkage and selection operator (LASSO) modeling, and survival analysis to identify charged multivesicular body protein 4C (CHMP4C). Subsequently, its role in regulating the immune system and immune cell infiltration was explored. CHMP4C expression and signaling molecules in OS were assessed in osteosarcoma cell lines (MG63, U2OS, HOS) and hFOB1.19 cells using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence staining. The impact of CHMP4C upregulation and interference on OS-related signaling molecules in MG63 cells was studied. Functional validation of CHMP4C in MG63 OS cells was confirmed through cell counting Kit-8 (CCK-8), transwell, and colony formation assays. In vivo experiments were conducted using Specific Pathogen Free (SPF)-grade male BALB/C nude mice for OS xenograft studies. Results: Based on the gene expression profiles analysis of six osteosarcoma samples and six normal tissue samples, we identified 1,511 upregulated DEGs and 5,678 downregulated DEGs in normal tissue samples. A significant positive correlation between the "yellow-green" module and OS was found through WGCNA analysis. Expression levels of CHMP4C, phosphorylated Glycogen Synthase Kinase 3β (p-GSK3β), and β-catenin were notably higher in U2OS, HOS, and MG63 OS cells than in hFOB1.19 human osteoblasts. Overexpressing CHMP4C in MG63 OS cells upregulated CHMP4C, p-GSK3β, and β-catenin while downregulating GSK3β, leading to increased proliferation and migration of MG63 cells. Conversely, interrupting CHMP4C had the opposite effect. High expression of CHMP4C significantly accelerated the growth of OS in nude mice, resulting in substantial upregulation of CHMP4C, p-GSK3β, and β-catenin expression and suppression of Glycogen Synthase Kinase 3β (GSK3β) expression in OS tissues. Conclusion: CHMP4C may serve as a specific immunomodulatory gene for OS. Its activation of the Wnt/β-catenin signaling pathway, mainly by increasing the phosphorylation echelon of GSK3β, promotes the invasion and spread of OS.
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In this study, we utilized the ‘glmnet’ package in R for LASSO regression analysis to screen core genes within the ‘greenyellow’ module (Park et al., 2015). LASSO regression analysis identifies variables by detecting the optimal λ value that minimizes classification error. The optimal value of the penalty parameter λ was selected using tenfold cross-validation.
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In this study, we utilized the ‘glmnet’ package in R for LASSO regression analysis to screen core genes within the ‘greenyellow’ module (Park et al., 2015). LASSO regression analysis identifies variables by detecting the optimal λ value that minimizes classification error. The optimal value of the penalty parameter λ was selected using tenfold cross-validation.
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Methods: Gene expression patterns from the Gene Expression Omnibus (GEO) database (GSE126209) were analyzed using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, least absolute shrinkage and selection operator (LASSO) modeling, and survival analysis to identify charged multivesicular body protein 4C (CHMP4C). Subsequently, its role in regulating the immune system and immune cell infiltration was explored. CHMP4C expression and signaling molecules in OS were assessed in osteosarcoma cell lines (MG63, U2OS, HOS) and hFOB1.19 cells using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence staining. The impact of CHMP4C upregulation and interference on OS-related signaling molecules in MG63 cells was studied. Functional validation of CHMP4C in MG63 OS cells was confirmed through cell counting Kit-8 (CCK-8), transwell, and colony formation assays. In vivo experiments were conducted using Specific Pathogen Free (SPF)-grade male BALB/C nude mice for OS xenograft studies. Results: Based on the gene expression profiles analysis of six osteosarcoma samples and six normal tissue samples, we identified 1,511 upregulated DEGs and 5,678 downregulated DEGs in normal tissue samples. A significant positive correlation between the \"yellow-green\" module and OS was found through WGCNA analysis. Expression levels of CHMP4C, phosphorylated Glycogen Synthase Kinase 3β (p-GSK3β), and β-catenin were notably higher in U2OS, HOS, and MG63 OS cells than in hFOB1.19 human osteoblasts. Overexpressing CHMP4C in MG63 OS cells upregulated CHMP4C, p-GSK3β, and β-catenin while downregulating GSK3β, leading to increased proliferation and migration of MG63 cells. Conversely, interrupting CHMP4C had the opposite effect. High expression of CHMP4C significantly accelerated the growth of OS in nude mice, resulting in substantial upregulation of CHMP4C, p-GSK3β, and β-catenin expression and suppression of Glycogen Synthase Kinase 3β (GSK3β) expression in OS tissues. Conclusion: CHMP4C may serve as a specific immunomodulatory gene for OS. Its activation of the Wnt/β-catenin signaling pathway, mainly by increasing the phosphorylation echelon of GSK3β, promotes the invasion and spread of OS.", "claims": [{"id": "claim-method", "statement": "In this study, we utilized the ‘glmnet’ package in R for LASSO regression analysis to screen core genes within the ‘greenyellow’ module (Park et al., 2015). LASSO regression analysis identifies variables by detecting the optimal λ value that minimizes classification error. 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The impact", "n-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence staining. The impact", "e fully excised and weighed. Following that, RT-qPCR and immunofluorescence techniques were emplo", "ully excised and weighed. Following that, RT-qPCR and immunofluorescence techniques were emplo"], "order_basis": "paper_text_order:869", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tive polymerase chain reaction (RT-qPCR) and immunofluorescence staining. The impact of CHMP4C upregulation", "sed and weighed. 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Mice were randomly divided into four groups (OE-NC, OE", "-CHMP4C), each consisting of eight mice. The mice were acclimated in an animal facility at 25°C and", "cell suspension (0.5 mL). 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