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.
BACKGROUND Huayan Capsules (HYCA) has adjuvant therapeutic effect to the patients with OS in China, which is based on our hospital long-term clinical practice. Network pharmacology is a theory based on systems biology that helps to reveal the underlying mechanisms of action between drugs and disease development. OBJECTIVE Osteosarcoma (OS) is the most frequent primary bone sarcomas. It is commonly found in the long bones of the limbs of the human body. The purpose of study was to explore the active ingredients, targets and mechanism of HYCA in the treatment of OS through network pharmacology and molecular docking technology. METHODS TCMSP and TCMID database were used to obtain the active ingredients and targets of HYCA. Targets related to OS were obtained by GeneCards, TTD and OMIM. The related target protein network was constructed and analyzed for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The compound was molecularly docked with proteins associated with OS by using AutoDock Vina. RESULTS In this study, we searched that HYCA has 239 active compounds, 1703 targets and of HYCA. Meanwhile, there was 220 intersection targets of OS and HYCA. Construction of protein-protein interactions (PPI) network analysis showed that there were 25 key targets of HYCA in the treatment of OS including TP53, AKT1, etc. GO enrichment analysis mainly including cell response to hormones and other compounds. KEGG enrichment analysis obtained 196 signaling pathways, which mainly including pathways in cancer, PI3K-Aktsignaling pathway, MAPK signaling pathway. Molecular docking showed that quercetin, kamanol and luteolin have strong binding ability with AKT1, TP53. CONCLUSIONS HYCA may treat OS by regulating the pathways in cancer, PI3K-Akt and MAPK signaling pathway. We will demonstrate the relationship among the active compounds of HYCA, TCM classification of OS and signal pathway, so as to provide a systematic TCM treatment plan for the clinical treatment of OS.
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