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. 2024 Apr 17;25(1):379.
doi: 10.1186/s12864-024-10283-5.

Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma

Yueshu Wu  1   2 , Jun Yang  1   2 , Gang Xu  1   2 , Xiaolin Chen  3 , Xiaochen Qu  4   5
Affiliations

Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma

Yueshu Wu et al. BMC Genomics. .

Abstract

Background: Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454.

Results: Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time.

Conclusions: A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.

Keywords: Biomarkers; Immune; Lysosome-dependent cell death; Osteosarcoma; Prognosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The flowchart for this study. GSE, gene expression omnibus series; DEGs, differentially expressed genes; PPI, protein-protein interaction; LCD-RGs, lysosomal-dependent cell death-related genes; LASSO, least absolute shrinkage and selection operator; TCGA-OS, The Cancer Genome Atlas-osteosarcoma; K-M, Kaplan-Meier; ROC, receiver operating characteristic; GSEA, gene set enrichment analysis; TF, transcription factor
Fig. 2
Fig. 2
Identification and function analysis of DEGs. (A) Volcano plot (left) and heatmap (right) of DEGs1 between OS and control cohorts in GSE16088. (B) Volcano plot (left) and heatmap (right) of DEGs2 between metastasis and non-metastasis patients with OS in GSE14359. (C) Venn diagram displays the 21 intersection genes on overlapping DEGs1 from GSE16088, DEGs2 from GSE14359 and LCD-related genes. (D) The PPI network of 21 intersection genes. (E) Functional enrichment results for 21 intersection genes. DEGs, differentially expressed genes; OS, osteosarcoma; LCD, lysosomal-dependent cell death, PPI, protein-protein interaction
Fig. 3
Fig. 3
Construction of prognostic models. (A) The forest plot of univariate Cox analysis. (B) LASSO regression analysis for biomarkers. (C) PH assumption test. (D) Risk curve and heat map of gene expressions in high- and low-risk cohorts of the training set. (E) Kaplan–Meier curves for the high- and low-risk cohorts in the training set. (F) ROC curve in the training set. (G-H) The expression levels of ATP6V0D1 and HDAC6 in GSE16088 (G) and GSE14359 (H). LASSO, least absolute shrinkage and selection operator; PH, proportional hazards; ROC, receiver operating characteristic; HR, hazard ratio; lower 95%CI and upper 95%CI represent the 95% confidence intervals of the risk values
Fig. 4
Fig. 4
Validation of prognostic risk models. (A-B) Distribution of risk score and survival time, and heat map of biomarkers’ expression in the high- and low-risk cohorts in the testing (A) and GSE21257 (B) sets. (C-D) Kaplan–Meier curves in the testing (C) and GSE21257 (D) sets. (E-F) ROC curves in the testing (E) and GSE21257 (F) sets. ROC, receiver operating characteristic
Fig. 5
Fig. 5
Construction and validation for the nomogram. (A) The forest plot of univariate Cox analysis. (B) The forest plot of multivariate Cox analysis. (C) A nomogram based on independent prognostic factors. (D) Calibration curves for the nomogram. (E) ROC curves: 1-year (top), 3-year (middle) and 5-year (bottom)
Fig. 6
Fig. 6
GSEA results for the high and low-risk cohorts. (A) GO enrichment profiles. (B) KEGG pathways. GSEA, gene set enrichment analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes
Fig. 7
Fig. 7
Immune microenvironment and drug sensitivity. (A) Box plot of immune cell percentage in high- and low-risk cohorts. (B) Scatter plot of correlation of risk scores and differential immune cells. (C) Relative expression of immune checkpoints in the high- and low-risk cohorts. (D) The correlation of risk score and differential immune checkpoints. (E) Drug sensitivity results for the high- and low-risk cohorts
Fig. 8
Fig. 8
Acquisition of cell types. PCA results. (A) PCA of the dataset before correction (left), PCA of the dataset after batch correction (middle) and the standard deviation results (right). (B) High-quality cell clustering results. (C) Expression of marker genes in different cell clusters (up) and cell types (down). (D) High-quality cell annotation results. PCA, principal component analysis
Fig. 9
Fig. 9
Pseudo-temporal analysis of biomarkers in single cells. (A) Biomarker expression in different cell types. (B) The single-cell trajectory map for eight cell types. (C) Pseudo-temporal trajectories for biomarkers
Fig. 10
Fig. 10
Analysis of cellular communication and transcription factors regulation in different cell types. (A) Scatter plot of ligand-receptor and multimeric interactions inter-cells. (B) Cell-cell interactions. (C) Cell-cell interactions in single cells. (D) The expression of transcription factors with significantly different activities in different cell types
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