Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

Long non-coding RNA AC133552.2: biomarker and therapeutic target in osteosarcoma via PANoptosis gene screening

Osteosarcoma, the most common primary bone cancer in children and adolescents, presents significant challenges, particularly in metastasis and recurrence, resulting in poor survival rates. This study explores the role of PANoptosis-a complex cell death mechanism involving pyroptosis, apoptosis, and necroptosis-in osteosarcoma by identifying relevant long non-coding RNAs (lncRNAs) and their prognostic significance. Bioinformatics analyses used RNA expression data from the GEO and TARGET databases to identify differentially expressed genes (DEGs) and PANoptosis-related genes (PRGs). Co-expression analysis revealed lncRNAs linked to PRGs, forming a risk prognostic model. Five PRGs and two lncRNAs were significantly associated with prognosis, with the model showing high predictive accuracy (AUC 0.876, 0.787, and 0.794 for 1, 3, and 5 years). Notably, lncRNA AC133552.2 was downregulated in osteosarcoma tissues, correlating with poor survival and reduced immune infiltration. Silencing AC133552.2 promoted cell proliferation and migration, while overexpression inhibited tumor growth and metastasis, confirmed in xenograft models. AC133552.2 emerges as a potential biomarker and therapeutic target, with future research needed to explore its molecular mechanisms and clinical application.

The emerging landscape of small nucleolar RNA host gene 10 in cancer mechanistic insights and clinical relevance

Small nucleolar RNA host gene 10 (SNHG10) is a newly recognized long non-coding RNA (lncRNA) with significant implications in cancer biology. Abnormal expression of SNHG10 has been observed in various solid tumors and hematological malignancies. Research conducted in vivo and in vitro has revealed that SNHG10 plays a pivotal role in numerous biological processes, including cell proliferation, apoptosis, invasion and migration, drug resistance, energy metabolism, immune evasion, as well as tumor growth and metastasis. SNHG10 regulates tumor development through several mechanisms, such as competing with microRNA (miRNA) for binding sites, modulating various signaling pathways, influencing transcriptional activity, and affecting epigenetic regulation. The diverse biological functions and intricate mechanisms of SNHG10 highlight its considerable clinical relevance, positioning it as a potential pan-cancer biomarker and therapeutic target. This review aims to summarize the role of SNHG10 in tumorigenesis and cancer progression, clarify the molecular mechanisms at play, and explore its clinical significance in cancer diagnosis and prognosis prediction, along with its therapeutic potential.

Osteosarcoma biomarker analysis and drug targeting prediction based on pyroptosis-related genes

Osteosarcoma is a malignant bone tumor originating from mesenchymal tissue. Recent studies have found that the tumor inflammatory microenvironment plays an important role in promoting the malignant characteristics and metastatic potential of malignant tumors. Pyroptosis, an inflammatory programmed cell death, elicits immune responses that exhibit anti-tumor effects through released factors and contents. Therefore, improving anti-tumor immunity by targeting osteosarcoma-related pyroptosis genes and pathways may be of great significance in delaying early metastasis of osteosarcoma and improving patient survival rate. The study aimed to identify pyroptosis-related genes and biomarkers in osteosarcoma, predicting therapeutic drugs targeting these genes. Gene expression profiles of osteosarcoma were retrieved from Gene Expression Omnibus and cross-referenced with GeneCards and Comparative Toxicogenomics Database to identify differentially expressed pyroptosis-related genes. We conducted enrichment analysis on intersecting genes to identify their biological processes and signaling pathways and assessed immune cell composition in the tumor microenvironment through immune infiltration analysis. In addition, we further utilized Cytoscape software to screen out the top 10 genes with Degree values among the intersected genes as hub genes and performed GSEA analysis and drug prediction based on the hub genes. A total of 22 differentially expressed pyroptosis-related genes were identified in osteosarcoma, with 10 of them (TP53, CYCS, IL-1A, IL-1B, IL-18, CASP-3, CASP-8, IL-6, TNF, CASP-1) pinpointed as hub genes. Enrichment analysis found that the 22 intersection genes are mainly associated with pyroptosis, apoptosis, immune regulation, and related biological processes. The results of data validation targeting hub genes suggest that IL-18, CASP-1, and CASP-8 may be key genes involved in the regulation of pyroptosis in osteosarcoma. Immune infiltration analysis shows statistical differences in the distribution of immune cells like naive B cells, monocytes, M2 macrophages, and dendritic/mast cells, suggesting they play a role in the osteosarcoma tumor microenvironment. Hub gene drug targets suggest Triethyl phosphate, Plinabulin, and Siltuximab as potential osteosarcoma treatments. Our findings suggest potential mechanisms of action for 22 pyroptosis-related genes in osteosarcoma and preliminarily predicted that the occurrence of osteosarcoma is closely related to pyroptosis, apoptosis, and immune regulation. Predicted Triethyl phosphate, Plinabulin, Siltuximab as potential osteosarcoma treatments.

A novel prognostic signature related to programmed cell death in osteosarcoma

Background

Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.

Method

We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.

Result

In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.

Conclusion

The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

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.