Osteosarcoma immune prognostic index can indicate the nature of indeterminate pulmonary nodules and predict the metachronous metastasis in osteosarcoma patients

He, Xuanhong; Lu, Minxun; Hu, Xin; Li, Longqing; Zou, Chang; Luo, Yi; Zhou, Yong; Li, Min; Tu, Chongqi

doi:10.3389/fonc.2022.952228

Cited by 6 publications

(4 citation statements)

References 66 publications

(59 reference statements)

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“…Current standard treatments for OS include neoadjuvant chemotherapy, surgical resection of the primary tumor, and adjuvant chemotherapy. However, the prognosis for metastatic or recurrent OS has not significantly improved ( He et al, 2022 ). Traditional clinical markers have limited predictive capabilities because the molecular mechanisms underlying OS are unclear.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation

Jiang,

Zhao,

Zang

et al. 2024

Front. Pharmacol.

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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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Section: Discussionmentioning

confidence: 99%

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation

Jiang,

Zhao,

Zang

et al. 2024

Front. Pharmacol.

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“…He et al demonstrated creativity by developing OIPI, a predictor speci cally designed for bone tumors. They combined the role of LIPI in lung cancer to create OIPI, which plays a crucial role in predicting the prognosis and metastasis of bone tumors 20,21 . To date, there has been no reports on the prognostic effect of LIPI in patients with ICC.…”

Section: Discussionmentioning

confidence: 99%

A novel model for predicting the prognosis of postoperative intrahepatic cholangiocarcinoma patients

Lv,

Liu,

et al. 2023

Preprint

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Objective: Intrahepatic cholangiocarcinoma (ICC) accounts for 20% of liver malignancies with a 5-year survival rate of 35% at best with limited prognostic predictors. Lung Immune Prognostic Index (LIPI) is a novel prognostic factor in pulmonary cancers. In this study, we developed a modified prognostic model from LIPI called intrahepatic immune prognostic index (IIPI) for ICC. Patients and method: A retrospectively study was conducted at Liver Transplant Center of West China Hospital between January 2015 and January 2023. Hematological factors and clinical features of ICC patients were collected and analyzed. The area under curve (AUC) and optimal cuff-off of each single hematological factor was calculated. Results: In this study, derived neurtrophil to lymphocyte ratio (dNLR), arbohydrate antigen199 (CA199) and carcinoembryonic antigen (CEA) have higher AUC values. LIPI was composed of dNLR and was further modified by combing CA199 and CEA, forming the IIPI. The IIPI consists of four grades which are None, Light, Moderate and Severe. Compared to other prognostic factors, IIPI exhibited better ability to predict overall survival. The multivariate analysis indicated that cirrhosis, differentiation, hilar invasion and IIPI were independent prognostic factors for ICC patients. An IIPI-based nomogram was also established and could predict the overall survival. In addition, the subgroup analyses based on clinical prognostic factors showed that the IIPI exhibited excellent prognostic influence. Conclusion: IIPI model is suitable for predicting the prognosis of postoperative ICC patients. Further research is needed to explore the relationship between postoperative recurrence and metastasis of ICC patients and IIPI.

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“…Consequently, creating a prognostic model to effectively estimate the survival of individuals with ependymomas is essential. A nomogram, which is a precise and user-friendly tool, can evaluate risk factors and predict tumor prognosis (5). The Surveillance,Epidemiology,and End Results (SEER) database in the United States collects comprehensive patient information, clinicopathological characteristics, and treatment data for about a third of the U.S. population, making it a valuable resource for studying cancer outcomes.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation a Nomogram for Predicting Cancer-Specific Survival for patients with Ependymoma: An Analysis of the Surveillance, Epidemiology, and End Results Database

Liu,

Chen,

Yan

et al. 2024

Preprint

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Background The purpose of this study was to Construct and validate a novel nomogram to predict cancer-specific survival (CSS) in patients with ependymoma. Methods Clinical data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018 were used for the analysis. Data were randomly categorized into development and validation groups in a 7:3 ratio. Univariate and multivariate Cox regression analyses and LASSO regression were conducted to identify independent risk factors for CSS. Predictive models were evaluated using calibration plots, concordance index (C-index), and area under the receiver operating characteristic curve (AUC). Additionally, decision curve analysis and clinical impact curves were conducted. Results The final sample comprised 2,340 patients. Multivariate analysis identified race, age, histological type, surgery, and tumor site as independent predictors of CSS. A nomogram incorporating these risk factors was developed to predict CSS in patients with ependymomas. The calibration plot shows strong agreement between predicted and actual values. The C-index for the training cohort was 0.746 (95% CI: 0.715–0.777), and for the validation cohort, it was 0.743 (95% CI: 0.698–0.788). Additionally, both AUC and decision curve analysis analyses indicated robust performance and clinical significance benefits. Kaplan–Meier curves further demonstrated the nomogram’s strong ability to predict patient outcomes. Conclusions Our nomogram may be of great value in predicting the prognosis of patients with ependymoma. This predictive model will assist doctors and patients in devising effective clinical strategies.

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Osteosarcoma immune prognostic index can indicate the nature of indeterminate pulmonary nodules and predict the metachronous metastasis in osteosarcoma patients

Cited by 6 publications

References 66 publications

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation

A novel model for predicting the prognosis of postoperative intrahepatic cholangiocarcinoma patients

Construction and validation a Nomogram for Predicting Cancer-Specific Survival for patients with Ependymoma: An Analysis of the Surveillance, Epidemiology, and End Results Database

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