Association of survivin positive circulating tumor cell levels with immune escape and prognosis of osteosarcoma.

PURPOSE: Function of survivin protein (encoded by BIRC5) in circulating tumor cells (CTCs) of osteosarcoma (OS) has not been investigated. The goal of this study is to determine whether the expression of survivin protein of CTCs is associated with circulating immune cell infiltration and disease prognosis of OS. METHODS: Blood samples of 20 patients with OS were collected. CanPatrol™ CTC enrichment technology combined with in situ hybridization (ISH) was applied to enrich and test CTCs and survivin protein. Bioinformation analysis combined with data of routine blood test was used to verify the association between survivin and immune cell infiltration in circulatory system. To screen independent prognostic factors, Kaplan-Meier survival curve, univariate and multivariable Cox regression analyses were performed. RESULTS: Bioinformatics analysis showed that BIRC5 was strongly negatively related to lymphocyte, including T cell, NK cell and B cell, which released that BIRC5 played a key role in immune escape via reducing immune cell infiltration in circulatory system. Meanwhile, the number of survivin+ CTCs was significantly negatively connection with lymphocyte count (R = -0.56, p = 0.011), which was consistent with bioinformatics analysis. Kaplan-Meier curve showed that the overall survival rate in high survivin+ CTCs group was significantly lower than low group (88.9% vs 36.4%, p = 0.04). Multivariable Cox regression analyses showed that survivin+ CTCs were an independent prognostic factor (p = 0.019). CONCLUSION: These findings suggested that survivin protein played a key role in immune escape of CTCs and the presence of survivin+ CTCs might be a promising prognostic factor in OS patients.

Long non-coding RNA and circular RNA and coding RNA profiling of plasma exosomes of osteosarcoma by RNA seq.

Osteosarcoma (OS) is a primary bone tumor with high malignancy and the mechanism of hematogenous metastasis in OS is still not clear. The plasma exosomes derived from osteosarcoma play a key role in the process of tumor metastasis. Here, we established RNA-seq dataset for lncRNAs, circRNAs and mRNAs in plasma exosomes from 10 OS patients and 5 healthy donors. A total of 329.52 Gb of clean data was obtained. Besides, 1754 lincRNAs, 7096 known and 1935 new circRNA was identified. Finally, gene expression profiles and differentially expressed genes (DEGs) were analyzed among these 15 samples. There were 331 DEGs of mRNA, 132 of lincRNA and 489 of circRNA was obtained, respectively. This data set provides a significant resource for relevant researchers to excavate potential dysregulated lncRNAs, circRNAs and mRNAs of plasma exosomes in OS versus normal conditions.

High GNG4 predicts adverse prognosis for osteosarcoma: Bioinformatics prediction and experimental verification.

Background: Guanine nucleotide binding (G) protein subunit γ 4 (GNG4) is closely related to the malignant progression and poor prognosis of various tumours. However, its role and mechanism in osteosarcoma remain unclear. This study aimed to elucidate the biological role and prognostic value of GNG4 in osteosarcoma. Methods: Osteosarcoma samples in the GSE12865, GSE14359, GSE162454 and TARGET datasets were selected as the test cohorts. The expression level of GNG4 between normal and osteosarcoma was identified in GSE12865 and GSE14359. Based on the osteosarcoma single-cell RNA sequencing (scRNA-seq) dataset GSE162454, differential expression of GNG4 among cell subsets was identified at the single-cell level. As the external validation cohort, 58 osteosarcoma specimens from the First Affiliated Hospital of Guangxi Medical University were collected. Patients with osteosarcoma were divided into high- and low-GNG4 groups. The biological function of GNG4 was annotated using Gene Ontology, gene set enrichment analysis, gene expression correlation analysis and immune infiltration analysis. Kaplan-Meier survival analysis was conducted and receiver operating characteristic (ROC) curves were calculated to determine the reliability of GNG4 in predicting prognostic significance and diagnostic value. Functional in vitro experiments were performed to explore the function of GNG4 in osteosarcoma cells. Results: GNG4 was generally highly expressed in osteosarcoma. As an independent risk factor, high GNG4 was negatively correlated with both overall survival and event-free survival. Furthermore, GNG4 was a good diagnostic marker for osteosarcoma, with an area under the receiver operating characteristic curve (AUC) of more than 0.9. Functional analysis suggested that GNG4 may promote the occurrence of osteosarcoma by regulating ossification, B-cell activation, the cell cycle and the proportion of memory B cells. In in vitro experiments, silencing of GNG4 inhibited the viability, proliferation and invasion of osteosarcoma cells. Conclusion: Through bioinformatics analysis and experimental verification, high expression of GNG4 in osteosarcoma was identified as an oncogene and reliable biomarker for poor prognosis. This study helps to elucidate the significant potential of GNG4 in carcinogenesis and molecular targeted therapy for osteosarcoma.

Bioinformatics analysis of the key genes in osteosarcoma metastasis and immune invasion.

Background: This study sought to identify potential key genes for osteosarcoma metastasis and analyze their immune infiltration patterns using bioinformatic methods. Methods: We obtained transcriptomic data related to osteosarcoma and osteosarcoma with metastasis from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) and The Gene Expression Omnibus (GEO) databases and identified the differentially expressed genes (DEGs). We also identified potential key genes for osteosarcoma metastasis by a protein-protein interaction network analysis, and we conducted a Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify the core genes for prognosis, immune cell infiltration, and drug sensitivity, and the risk prediction and prognosis models of metastasis were constructed. Results: By comparing the transcriptome data of osteosarcomas without metastasis and those with metastasis, a total of 19 core DEGs were identified, and the GO and KEGG analyses revealed an association between these DEGs and the regulation of cell division, secretory granule lumen, the Ras-associated protein 1 (Rap1) signaling pathway, and the mitogen-activated protein kinase (MAPK) signaling pathway. Compared with other immune cells, macrophage infiltration was predominant in osteosarcoma samples with metastatic osteosarcoma, and insulin-like growth factors-1 (IGF1) and myelocytomatosis protein 2 (MYC2) genes were predicted to more than 50 targeted therapeutic agents. A metastasis prediction model with 5 genes [i.e., ecotropic viral integration site 2B (EVI2B), CCAAT/enhancer binding protein (CEBPA), lymphocyte cytosolic protein 2 (LCP2), selectin L (SELL), and Niemann-Pick disease, type C2A (NPC2A)], and a prognostic model with 4 genes [i.e., insulin-like growth factors-2 (IGF2), cathepsin O (CTSO), Niemann-Pick disease, type C2 (NPC2), and amyloid beta (A4) precursor protein-binding, family B, member 1 interacting protein (APBB1IP)] were developed. Conclusions: We constructed a metastasis prediction model with 5 genes (i.e., EVI2B, CEBPA, LCP2, SELL, and NPC2A), and a prognostic model with 4 genes (i.e., IGF2, CTSO, NPC2, and APBB1IP) that may be potential biomarkers for osteosarcoma metastasis. Macrophages are the predominant immune infiltrating cells in osteosarcoma metastasis and may provide a new direction for the treatment of osteosarcoma.

Constructing a competing endogenous RNA network for osteoarthritis.

Background: Osteoarthritis (OA) is one of the most common diseases in elderly people; however, the correlation between molecular alterations and the occurrence and progression of OA are still not well understood. We conducted this study to investigate the molecular changes in OA via the competing endogenous ribonucleic acid (ceRNA) network. Methods: We downloaded the messenger RNA (mRNA) data set, GSE48556, the microRNA (miRNA) data set, GSE105027, and the long non-coding (lncRNA) data set, GSE126963 from the Gene Expression Omnibus (GEO) database, and examined the differentially expressed genes (DEGs) in these data sets. Further, we constructed a ceRNA network of the differentially expressed miRNAs, mRNAs, and lncRNAs. To determine the biological functions of the ceRNA network, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Finally, we conducted an immune cell infiltration analysisusing single-sample gene set enrichment analysis to examine the abundance of immune cells in healthy and OA patients, and compared the infiltration of 28 immune cells between the healthy and OA samples. We also analyzed the relationship between the abundance of immune cells and mRNA expression levels in the ceRNA network. Results: Ultimately hsa-mir-425-3p, dual specificity phosphatase 1, and 24 lncRNAs were identified in the ceRNA network. The functional enrichment analyses showed that these lncRNAs, miRNAs, and mRNAs are involved in various significant biological process, such as the regulation of leukocyte migration, Mitogen-Activated Protein (MAP) kinase tyrosine/serine/threonine phosphatase activity, the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and osteoclast differentiation, and can also have a strong effect on immune cell infiltration. Conclusions: The dual-specificity phosphatase 1-specific ceRNA network can be used as a diagnostic tool to assess the progression of OA patients.

Significance of the neutrophil-to-lymphocyte ratio in predicting the response to neoadjuvant chemotherapy in extremity osteosarcoma: a multicentre retrospective study.

BACKGROUND: At present, no predictive factor has been validated for the early efficacy of neoadjuvant chemotherapy (NACT) in osteosarcoma. The purpose of this study was to investigate the significance of the neutrophil-to-lymphocyte ratio (NLR) in predicting the response to NACT in extremity osteosarcoma. METHODS: Pathological complete response (pCR) was used to assess the efficacy of NACT. Receiver operating characteristic (ROC) curves and the Youden index (sensitivity + specificity-1) were used to determine the optimal cut-off values of the NLR. Univariate and multivariate analyses using logistic regression models were conducted to confirm the independent factors affecting the efficacy of NACT. RESULTS: The optimal NLR cut-off value was 2.36 (sensitivity, 80.0%; specificity, 71.3%). Univariate analysis revealed that patients with a smaller tumour volume, lower stage, lower NLR and lower PLR were more likely to achieve pCR. Multivariate analyses confirmed that the NLR before treatment was an independent risk factor for pCR. Compared to patients with a high NLR, those with a low NLR showed a more than 2-fold higher likelihood of achieving pCR (OR 2.82, 95% CI 1.36-5.17, p = 0.02). CONCLUSION: The NLR is a novel and effective predictive factor for the response to NACT in extremity osteosarcoma patients. Patients with a higher NLR showed a lower percentage of pCR after NACT.