Identification of pyroptosis-related genes and long non-coding RNAs signatures in osteosarcoma

The role of programmed cell death in osteosarcoma: From pathogenesis to therapy

Osteosarcoma (OS) is a prevalent bone solid malignancy that primarily affects adolescents, particularly boys aged 14-19. This aggressive form of cancer often leads to deadly lung cancer due to its high migration ability. Experimental evidence suggests that programmed cell death (PCD) plays a crucial role in the development of osteosarcoma. Various forms of PCD, including apoptosis, ferroptosis, autophagy, necroptosis, and pyroptosis, contribute significantly to the progression of osteosarcoma. Additionally, different signaling pathways such as STAT3/c-Myc signal pathway, JNK signl pathway, PI3k/AKT/mTOR signal pathway, WNT/β-catenin signal pathway, and RhoA signal pathway can influence the development of osteosarcoma by regulating PCD in osteosarcoma cell. Therefore, targeting PCD and the associated signaling pathways could offer a promising therapeutic approach for treating osteosarcoma.

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation

Objective: To investigate the immune mechanism of osteosarcoma (OS)-specific markers to mitigate bone destruction in the aggressive OS, prone to recurrence and metastasis. Methods: Gene expression patterns from the Gene Expression Omnibus (GEO) database (GSE126209) were analyzed using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, least absolute shrinkage and selection operator (LASSO) modeling, and survival analysis to identify charged multivesicular body protein 4C (CHMP4C). Subsequently, its role in regulating the immune system and immune cell infiltration was explored. CHMP4C expression and signaling molecules in OS were assessed in osteosarcoma cell lines (MG63, U2OS, HOS) and hFOB1.19 cells using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence staining. The impact of CHMP4C upregulation and interference on OS-related signaling molecules in MG63 cells was studied. Functional validation of CHMP4C in MG63 OS cells was confirmed through cell counting Kit-8 (CCK-8), transwell, and colony formation assays. In vivo experiments were conducted using Specific Pathogen Free (SPF)-grade male BALB/C nude mice for OS xenograft studies. Results: Based on the gene expression profiles analysis of six osteosarcoma samples and six normal tissue samples, we identified 1,511 upregulated DEGs and 5,678 downregulated DEGs in normal tissue samples. A significant positive correlation between the "yellow-green" module and OS was found through WGCNA analysis. Expression levels of CHMP4C, phosphorylated Glycogen Synthase Kinase 3β (p-GSK3β), and β-catenin were notably higher in U2OS, HOS, and MG63 OS cells than in hFOB1.19 human osteoblasts. Overexpressing CHMP4C in MG63 OS cells upregulated CHMP4C, p-GSK3β, and β-catenin while downregulating GSK3β, leading to increased proliferation and migration of MG63 cells. Conversely, interrupting CHMP4C had the opposite effect. High expression of CHMP4C significantly accelerated the growth of OS in nude mice, resulting in substantial upregulation of CHMP4C, p-GSK3β, and β-catenin expression and suppression of Glycogen Synthase Kinase 3β (GSK3β) expression in OS tissues. Conclusion: CHMP4C may serve as a specific immunomodulatory gene for OS. Its activation of the Wnt/β-catenin signaling pathway, mainly by increasing the phosphorylation echelon of GSK3β, promotes the invasion and spread of OS.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Purpose

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and Methods

Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results

The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion

The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.