Immunoseroproteomic profiling in autoantibody to ENO1 as potential biomarker in immunodiagnosis of osteosarcoma by serological proteome analysis (SERPA) approach

Role of ENO1 and its targeted therapy in tumors

ENO1, also called 2-phospho-D-glycerate hydrolase in cellular glycolysis, is an enzyme that converts 2-phosphoglycerate to phosphoenolpyruvate and plays an important role in the Warburg effect. In various tumors, ENO1 overexpression correlates with poor prognosis. ENO1 is a multifunctional oncoprotein that, when located on the cell surface, acts as a "moonlighting protein" to promote tumor invasion and metastasis. When located intracellularly, ENO1 facilitates glycolysis to dysregulate cellular energy and sustain tumor proliferation. Additionally, it promotes tumor progression by activating oncogenic signaling pathways. ENO1 is a tumor biomarker and represents a promising target for tumor therapy. This review summarizes recent advances from 2020 to 2024 in understanding the relationship between ENO1 and tumors and explores the latest targeted therapeutic strategies involving ENO1.

A 3D-printed scaffold-based osteosarcoma model allows to investigate tumor phenotypes and pathogenesis in an in vitro bone-mimicking niche

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Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Background: The aim of this study was to identify a panel of candidate autoantibodies against tumor-associated antigens in the detection of osteosarcoma (OS) so as to provide a theoretical basis for constructing a non-invasive serological diagnosis method in early immunodiagnosis of OS. Methods: The serological proteome analysis (SERPA) approach was used to select candidate anti-TAA autoantibodies. Then, indirect enzyme-linked immunosorbent assay (ELISA) was used to verify the expression levels of eight candidate autoantibodies in the serum of 51 OS cases, 28 osteochondroma (OC), and 51 normal human sera (NHS). The rank-sum test was used to compare the content of eight autoantibodies in the sera of three groups. The diagnostic value of each indicator for OS was analyzed by an ROC curve. Differential autoantibodies between OS and NHS were screened. Then, a binary logistic regression model was used to establish a prediction logistical regression model. Results: Through ELISA, the expression levels of seven autoantibodies (ENO1, GAPDH, HSP27, HSP60, PDLIM1, STMN1, and TPI1) in OS patients were identified higher than those in healthy patients (p < 0.05). By establishing a binary logistic regression predictive model, the optimal panel including three anti-TAAs (ENO1, GAPDH, and TPI1) autoantibodies was screened out. The sensitivity, specificity, Youden index, accuracy, and AUC of diagnosis of OS were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively. Conclusion: The results proved that through establishing a predictive model, an optimal panel of autoantibodies could help detect OS from OC or NHS at an early stage, which could be used as a promising and powerful tool in clinical practice.