Background: We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS.Methods: Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication.Results: OS was classified into three subtypes based on lipid metabolic pathways.Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2.In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells.Conclusions: Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Although the incidence of osteosarcoma (OS) is relatively low compared with other cancer types, the overall survival of metastatic OS was less than 30%. This study aimed to reveal the role of pyroptosis in osteosarcoma and develop a prognostic model related to pyroptosis. Weighted correlation network analysis (WGCNA) was applied to identify key gene modules related to pyroptosis. Univariate Cox regression analysis was used to screen prognostic genes related to pyroptosis. The least absolute shrinkage and selection operator (LASSO) and stepwise Akaike information criterion (stepAIC) were employed to optimize and construct a prognostic model. Five prognostic genes (COL13A1, TNFRSF1A, LILRA6, CTNNBIP1, and CD180) related to pyroptosis were identified. According to the 5-gene signature, OS samples were divided into high- and low-PPRS groups with differential prognosis. Immune-related pathways were more activated in the low-PPRS group. The 5-gene signature was effective and robust to predict OS prognosis. These five prognostic genes were involved in OS development and may serve as new targets for developing therapeutic drugs.

Background Targeting cancer stem cells (CSC) may represent a future therapeutic direction for osteosarcoma (OS), which mainly relies on the identification of CSC markers. This study aimed to classify OS based on messenger ribonucleic acid (mRNA) stemness indices (mRNAsi) and construct a mRNAsi-related risk model to predict the prognosis of OS. Methods The one-class logistic regression (OCLR) algorithm was applied to the RNA- sequencing (seq) data of human embryonic stem cells (hESC) and induced pluripotent stem cell (iPSC) lines to calculate mRNAsi. Weighted gene co-expression network analysis (WGCNA) was performed on data obtained from the TARGET database to screen the mRNAsi-related genes. Univariate Cox regression analysis was implemented to screen mRNAsi-related genes with prognostic significance for consensus clustering of OS. The least absolute shrinkage and selection operator (LASSO) and COX regression analysis were conducted to construct a risk model based on mRNAsi-related genes. Results Six gene modules were identified in the TARGET database. The yellow module showed the strongest negative correlation with mRNAsi and the strongest significant positive correlation with the immune score and stromal score. OS was divided into three molecular subtypes with significant survival differences based on 73 mRNAsi-related genes with prognostic value for OS. The survival rate was ranked as C3 < C1 < C2 from low to high. The levels of immune components in C2 was significantly higher than those in C1 and C3. HSD11B2, GBP1, RNF130, APBB1IP, and NPC2 in the yellow module were used as variables for building the mRNAsi-related risk model. The survival rate of the high-risk group (as defined by this model) was significantly higher than that of the low-risk group, and it had significant survival prediction ability in 28 types of cancer. In addition, the mRNAsi-related risk model was superior to the Tumor Immune Dysfunction and Exclusion (TIDE) model in predicting the prognosis and immunotherapy response in all three immunotherapy cohorts. Conclusions This study classified OS and constructed a mRNAsi-related risk model based on mRNAsi-related genes, which provides a potential tool for more accurate risk stratification of OS and prediction of immunotherapy response.