Single-cell and spatial transcriptomics reveal post-translational modifications in osteosarcoma progression and tumor...

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Post-translational modifications (PTMs) play a role in osteosarcoma (OS) development, but their specific functions are not fully understood.
• Understanding PTMs in OS is crucial for developing effective diagnostic and therapeutic strategies.

Purpose of the Study:

• To investigate the role of PTMs in osteosarcoma pathogenesis using integrated multi-omics data.
• To identify novel prognostic biomarkers and therapeutic targets for osteosarcoma.
• To evaluate the potential of a new gene signature for predicting immunotherapy outcomes.

Main Methods:

• Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were used to classify and map tumor cells based on PTM scores.
• A machine learning approach developed a consensus machine learning-derived post-translational modification gene signature (CMDPTMS).
• Bulk transcriptomic data and laboratory experiments (Vimentin/VIM) were utilized for validation and functional analysis.

Main Results:

• PTM scores were elevated in OS tumor cells, identifying distinct subtypes (PTMs high/low) with varying malignant characteristics and interactions with fibroblasts.
• The CMDPTMS demonstrated strong prognostic predictive capability, with high-CMDPTMS linked to poor outcomes and reduced immunotherapy benefit.
• Seven potential therapeutic drugs were identified for high-CMDPTMS patients, and Vimentin (VIM) was confirmed to inhibit OS cell growth and migration.

Conclusions:

• The CMDPTMS serves as a potent tool for improving osteosarcoma prognosis prediction and optimizing immunotherapy strategies.
• Integrated analysis of PTMs, transcriptomics, and spatial data provides novel insights into OS heterogeneity and therapeutic vulnerabilities.
• Targeting Vimentin (VIM) may represent a potential therapeutic avenue for osteosarcoma treatment.