BACKGROUND:Osteosarcoma is a highly malignant bone tumor that exhibits rapid growth and early metastasis. Hypoxia plays a pivotal role in promoting the proliferation and metastasis of osteosarcoma through a series of molecular events, which are partially mediated and regulated by HIF-1α. However, the regulatory network associated with HIF-1α in osteosarcoma remains limited. Therefore, the objective of this study was to identify critical hypoxia-associated genes and investigate their effects and molecular mechanisms in osteosarcoma cells. METHODS:Through bioinformatics analysis, matrilin-4 (MATN4) was identified as a crucial gene associated with hypoxia. The expression of MATN4 and HIF-1α was assessed using immunohistochemistry, RT-qPCR, and western blotting. The proliferative capacity of osteosarcoma cells was assessed through the utilization of CCK-8, EDU staining, and colony formation assays. The effects of MATN4 on the mobility of OS cells were evaluated using wound-healing assays and transwell assays. The interaction between MATN4 and HIF-1α was detected through chromatin immunoprecipitation. RESULTS:MATN4 is overexpressed in osteosarcoma tissue and cells, particularly in osteosarcoma cells with high metastatic potential. Knockdown of MATN4 inhibits the proliferation, migration, and invasion abilities of osteosarcoma cells and reverses the promoting effects of hypoxia on these functions. Additionally, HIF-1α binds to MATN4 and upregulates its expression. Interestingly, knockdown of HIF-1α reduces the stimulatory effects of MATN4 overexpression on the proliferation, migration, and invasion of osteosarcoma cells under hypoxic conditions. CONCLUSIONS:Taken together, our results suggest that MATN4 is regulated by HIF-1α and confers a more aggressive phenotype on OS cells. This evidence suggests that MATN4 may act as a potential target for OS diagnosis and treatment.

Tumour-associated macrophages (TAMs), encompassing M1 and M2 subtypes, exert significant effects on osteosarcoma (OS) progression and immunosuppression. However, the impacts of TAM-derived biomarkers on the progression of OS remains limited. The GSE162454 profile was subjected to single-cell RNA (scRNA) sequencing analysis to identify crucial mediators between TAMs and OS cells. The clinical features, effects and mechanisms of these mediators on OS cells and tumour microenvironment were evaluated via biological function experiments and molecular biology experiments. Phosphodiesterase 4C (PDE4C) was identified as a pivotal mediator in the communication between M2 macrophages and OS cells. Elevated levels of PDE4C were detected in OS tissues, concomitant with M2 macrophage level, unfavourable prognosis and metastasis. The expression of PDE4C was observed to increase during the conversion process of THP-1 cells to M2 macrophages, which transferred the PDE4C mRNA to OS cells through exosome approach. PDE4C increased OS cell proliferation and mobility via upregulating the expression of collagens. Furthermore, a positive correlation was observed between elevated levels of PDE4C and increased TIDE score, decreased response rate following immune checkpoint therapy, reduced TMB and diminished PDL1 expression. Collectively, PDE4C derived from M2 macrophages has the potential to enhance the proliferation and mobility of OS cells by augmenting collagen expression. PDE4C may serve as a valuable biomarker for prognosticating patient outcomes and response rates following immunotherapy.

Isocitrate dehydrogenase (IDH1) is frequently mutated in glioma tissues, and this mutation mediates specific tumor-promoting mechanisms in glioma cells. We aimed to identify specific immune biomarkers for IDH1-mutation (IDH1mt) glioma. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were used to obtain RNA sequencing data and clinical characteristics of glioma tissues, while the stromal and immune scores of TCGA glioma tissues were determined using the ESTIMATE algorithm. Differentially expressed genes (DEGs), the protein–protein interaction(PPI) network, and least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to select hub genes associated with stroma and immune scores and the prognoses of patients and to construct the risk model. The practicability and specificity of the risk model in both IDH1mt and IDH1-wildtype (wtIDH1) gliomas in TCGA and CGGA were evaluated. Molecular mechanisms, immunological characteristics and benefits of immune checkpoint blockade therapy in glioma tissues with IDH1mt were analyzed using GSEA, immunohistochemical staining, CIBERSORT, and T-cell dysfunction and exclusion (TIDE) analysis. The overall survival rate for IDH1mt-glioma patients with high stroma/immune scores was lower than that for those with low stroma/immune scores. A total of 222 DEGs were identified in IDH1mt glioma tissues with high stroma/immune scores. Among them, 72 genes had interactions in the PPI network, while three genes, HLA-DQA2 , HOXA3 , and SAA2 , were selected as hub genes and used to construct risk models classifying patients into high- and low-risk score groups, followed by LASSO and Cox regression analyses. This risk model showed prognostic value in IDH1mt glioma in both TCGA and CCGA; nevertheless, the model was not suitable for wtIDH1 glioma. The risk model may act as an independent prognostic factor for IDH1mt glioma. IDH1mt glioma tissues from patients with high-risk scores showed more infiltration of M1 and CD8 T cells than those from patients with low-risk scores. Moreover, TIDE analysis showed that immune checkpoint blockade(ICB) therapy was highly beneficial for IDH1mt patients with high-risk scores. The risk model showed specific potential to predict the prognosis of IDH1mt-glioma patients, as well as guide ICB, contributing to the diagnosis and therapy of IDH1mt-glioma patients.

Dysregulation of immune cell infiltration in the tumor microenvironment contributes to the progression of osteosarcoma (OS). In the present study, we explored genes related to immune cell infiltration and constructed a risk model to predict the prognosis of and guide therapeutic strategies for OS. The gene expression profile of OS was obtained from TARGET and Gene Expression Omnibus, which were set as the discovery and verification cohorts. CIBERSORT and Kaplan survival analyses were used to analyze the effects of immune cells on the overall survival rates of OS in the discovery cohort. Differentially expressed gene (DEG) analysis and protein–protein interaction (PPI) networks were used to analyze genes associated with immune cell infiltration. Cox regression analysis was used to select key genes to construct a risk model that classified OS tissues into high- and low-risk groups. The prognostic value of the risk model for survival and metastasis was analyzed by Kaplan–Meier survival analyses, receiver operating characteristic curves, and immunohistochemical experiments. Immunological characteristics and response effects of immune checkpoint blockade (ICB) therapy in OS tissues were analyzed using the ESTIMATE and Tumor Immune Dysfunction and Exclusion algorithms, while sensitivity for both targeted and chemotherapy drugs was analyzed using the OncoPredict algorithm. It was demonstrated that the high infiltration of resting dendritic cells in OS tissues was associated with poor prognosis. A total of 225 DEGs were found between the high- and low-infiltration groups of OS tissues, while 94 genes interacted with others. Through COX analyses, among these 94 genes, four genes (including AOC3, CDK6, COL22A1, and RNASE6) were used to construct a risk model. This risk model showed a remarkable prognostic value for survival rates and metastasis in both the discovery and verification cohorts. Even though a high microsatellite instability score was observed in the high-risk group, the ICB response in the high-risk group was poor. Furthermore, using OncoPredict, we found that the high-risk group OS tissues were resistant to seven drugs and sensitive to 25 drugs. Therefore, our study indicates that the resting dendritic cell signature constructed by AOC3, CDK6, COL22A1, and RNASE6 may contribute to predicting osteosarcoma prognosis and thus therapy guidance.