Analysis of scRNA-seq and bulk RNA-seq demonstrates the effects of EVI2B or CD361 on CD8+ T cells in osteosarcoma

N-terminal histone acetyltransferase NAA40 modulates osteosarcoma progression by controlling AGR2 expression

PURPOSE

Osteosarcoma (OS) is the most common primary malignant bone neoplasm in children and adolescents, characterized by high mortality and disability owing to frequent relapse and metastasis. However, N-alpha-acetyltransferase 40 (NAA40) molecular mechanisms underlying OS progression and metastasis remain unexplored.

METHODS

Bioinformatics analysis was used to evaluate NAA40 role in OS data from GEO and TARGET database. OS cell multiplication, invasion and migration were gauged in CCK8, EdU assays, and Transwell assays. RT-qPCR, ChIP-qPCR, dual luciferase reporter assay and rescue experiments were to explore NAA40 regulatory mechanism. Animal experiments further confirmed cell-based assays and NAA40 molecular mechanism.

RESULTS

Herein NAA40 expression was upregulated in OS samples and associated with shorter survival among patients. Functionally, NAA40 depletion resulted in reduced OS cell viability, decreased migration, and invasion in vitro. Mechanistically, NAA40 loss was associated with increased H4S1ph and H4R3me2a and decreased H4R3me2s.NAA40 overexpression improved the transcriptional activity in the promoter of AGR2. Histone marks, H3K4me3 and H3K27me3, at the AGR2 promoter were altered, inducing changes in AGR2 expression in NAA40-depleted OS cells. Anterior gradient 2 (AGR2) was identified as a downstream target of NAA40.AGR2 knockdown in OS cells resulted in reduced viability, decreased migration, and invasion. Ectopic overexpression of AGR2 partially rescued these phenotypic changes. In vivo experiments revealed that NAA40 depletion led to reduced AGR2 protein levels, inhibiting the proliferative and metastatic potential of OS cells.

CONCLUSION

NAA40 contributes to OS development and progression by epigenetically regulating AGR2 expression.

A diagnostic signature developed based on the necroptosis-related genes and its association with immune infiltration in osteosarcoma

Introduction

Osteosarcoma is a bone-derived malignancy that often leads to lung metastasis and death.

Material and methods

The RNA-seq data of TARGET-osteosarcoma were collected from TARGET database. GSE16088 and GSE12865 datasets of osteosarcoma x from Gene Expression Database (GEO) were donwloaded. ConsensusClusterPlus was used for molecular subtype classification. Univariate Cox and Lasso regression was employed to develop a risk model. To analyze the regulatory effects of model feature genes on the malignant phenotype of osteosarcoma cell lines, qRT-PCR, Transwell and wound healing assays were performed. The abundance of immune cell infiltration was assessed using MCP-Counter, Gene Set Enrichment Analysis (GSEA), and ESTIMATE. The Tumor Immune Dysfunction and Exclusion (TIDE) software was employed to evaluate immunotherapy and response to conventional chemotherapy drugs.

Results

Three clusters (C1, C2 and C3) were classified using 39 necroptosis score-associated genes. In general, C1 and C2 showed better prognosis outcome and lower death rate than C3. Specifically, C2 could benefit more from immunotherapy, while C3 was more sensitive to traditional medicines, and C1 had higher immune cell infiltration. Next, an 8-gene signature and a risk score model were developed, with a low risk score indicating better survival and immune cell infiltration. ROC analysis showed that 1-, 3-, and 5-year overall survival of osteosarcoma could be correctly predicted by the risk score model. Cellular experiments revealed that the model feature gene IFITM3 promoted the osteosarcoma cell migration and invasion. Furthermore, the overall survival of osteosarcoma patients from TARGET and validation datasets can be accurately evaluated using the nomogram model.

Conclusions

Our prognostic model developed using necroptosis genes could facilitate the prognostic prediction for patients suffering from osteosarcoma, offering potential osteosarcoma targets.

Identifying squalene epoxidase as a metabolic vulnerability in high-risk osteosarcoma using an artificial intelligence-derived prognostic index

BACKGROUND

Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment.

METHODS

We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.

RESULTS

The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.

CONCLUSIONS

AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.