Transcriptomic and Single-Cell Analyses Reveal a Prognostic Mitochondria- and Immunity-Related Risk Signature in Osteosarcoma.
While mitochondrial dysfunction and immune cell dysregulation have been established as important contributors to the pathogenesis of osteosarcoma (OS), the prognostic value of mitochondria-related genes (MRGs) and immune-related genes (IRGs) in OS remains poorly understood.
Data were obtained from public databases. Differentially expressed genes (DEGs) and key module genes were identified viadifferential expression analysis and weighted gene co-expression network analysis, respectively. Candidate genes of interest were identified by intersecting key module genes, DEGs, MRGs, and IRGs. Then, candidate genes were screened using regression analysis and proportional hazards assumption testing to identify prognostic genes. A risk model was then constructed in the TARGET-OS dataset and validated in GSE16091. Independent prognostic, immune infiltration, and gene expression analyses were conducted. In addition, single-cell analysis was performed to identify key cell populations, and pseudotime analysis as well as cell communication network construction were carried out. Finally, the expression patterns of selected prognostic genes were additionally validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the functional role of protein kinase Cα (PRKCA) in osteosarcoma cell proliferation, migration, and invasion was evaluated in vitro.
Glutathione S-transferase Pi-1 (GSTP1), catalase (CAT), TNFSF10, and PRKCA were identified as mitochondria- and immunity-related prognostic genes in OS and were used to construct a risk model. The risk model was able to effectively predict the survival of patients with OS. A nomogram based on the identified prognostic genes additionally exhibited excellent performance when predicting OS patient survival. Significant differences in the abundance of 14 immune cell types and 8 immune checkpoint molecules were noted between the high-risk and low-risk groups. Single-cell analyses were then conducted to annotate 10 cell types, and M1 macrophages were identified as a potentially relevant cell population in the OS microenvironment. During M1 macrophage differentiation, initial reductions in CAT and PRKCA expression were noted, followed by subsequent increases and final decreases. In contrast, the expression of GSTP1 and TNFSF10 in these cells first rose and then declined. Furthermore, PRKCA was selected for further in vitro analyses, which revealed that it can enhance the invasion, migration, and proliferation of OS cells.
This study identified GSTP1, CAT, TNFSF10, and PRKCA as prognostic genes associated with mitochondrial and immune function in OS, with in vitro evidence providing direct support for the potential functional role of PRKCA in OS progression. These findings highlight a new avenue for predicting clinical prognosis in OS and may provide a basis for future studies of putative therapeutic targets.
Data were obtained from public databases. Differentially expressed genes (DEGs) and key module genes were identified viadifferential expression analysis and weighted gene co-expression network analysis, respectively. Candidate genes of interest were identified by intersecting key module genes, DEGs, MRGs, and IRGs. Then, candidate genes were screened using regression analysis and proportional hazards assumption testing to identify prognostic genes. A risk model was then constructed in the TARGET-OS dataset and validated in GSE16091. Independent prognostic, immune infiltration, and gene expression analyses were conducted. In addition, single-cell analysis was performed to identify key cell populations, and pseudotime analysis as well as cell communication network construction were carried out. Finally, the expression patterns of selected prognostic genes were additionally validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the functional role of protein kinase Cα (PRKCA) in osteosarcoma cell proliferation, migration, and invasion was evaluated in vitro.
Glutathione S-transferase Pi-1 (GSTP1), catalase (CAT), TNFSF10, and PRKCA were identified as mitochondria- and immunity-related prognostic genes in OS and were used to construct a risk model. The risk model was able to effectively predict the survival of patients with OS. A nomogram based on the identified prognostic genes additionally exhibited excellent performance when predicting OS patient survival. Significant differences in the abundance of 14 immune cell types and 8 immune checkpoint molecules were noted between the high-risk and low-risk groups. Single-cell analyses were then conducted to annotate 10 cell types, and M1 macrophages were identified as a potentially relevant cell population in the OS microenvironment. During M1 macrophage differentiation, initial reductions in CAT and PRKCA expression were noted, followed by subsequent increases and final decreases. In contrast, the expression of GSTP1 and TNFSF10 in these cells first rose and then declined. Furthermore, PRKCA was selected for further in vitro analyses, which revealed that it can enhance the invasion, migration, and proliferation of OS cells.
This study identified GSTP1, CAT, TNFSF10, and PRKCA as prognostic genes associated with mitochondrial and immune function in OS, with in vitro evidence providing direct support for the potential functional role of PRKCA in OS progression. These findings highlight a new avenue for predicting clinical prognosis in OS and may provide a basis for future studies of putative therapeutic targets.
Authors
Ai Ai, Peng Peng, Xu Xu, Yuan Yuan, Miao Miao, Yang Yang, Zhao Zhao, Cheng Cheng
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