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Background <p>Oral squamous cell carcinoma (OSCC) has a poor prognosis. PANoptosis, involving apoptosis, pyroptosis, and necroptosis, may be linked to cancer. Identifying OSCC subtypes and creating prognostic models based on PANoptosis-related genes (PRGs) can improve diagnosis and treatment.</p> Methods <p>RNA-seq and DNA methylation data from OSCC patients in The Cancer Genome Atlas (TCGA) were analyzed. Clustering algorithms identified OSCC subtypes, which were examined for differences in spatial distribution, biological pathways, drug sensitivity, prognosis, and immune infiltration. Prognostic genes were identified using Cox regression analyses. Mendelian randomization identified genes linked to OSCC. Drug sensitivity and tumor mutation load were assessed. Single-cell RNA sequencing (scRNA-seq) data explored the expression of prognostic genes. qRT-PCR verified gene expression.</p> Results <p>OSCC subtypes based on PRGs showed differences in prognosis, immunity, drug sensitivity, and pathways. Pantothenate and CoA biosynthesis pathways varied among subtypes, with immune cells highly infiltrated in CS1. The prognostic model highlighted differences in prognosis and immunotherapy response. BAK1 was causally linked to OSCC risk. Combining TMB score improved patient stratification. qRT-PCR confirmed differences in gene expression between control and OSCC groups. Monocytes were identified as high-scoring cells, with TGFb and MIF pathways important in cell communication.</p> Conclusion <p>Two OSCC subtypes and eight prognostic genes (TGFBR3, CYCS, HDAC9, PLK1, GSDMB, HSPA4, BAK1, SOD2) were identified, aiding in treatment and risk stratification. BAK1 is causally linked to OSCC risk.</p>

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Exploring the role of PANoptosis related genes in the prognosis of oral squamous cell carcinoma subtypes based on multi-omics analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Yang Liu,
  • Lingdu Wen,
  • Lijuan Yan,
  • Zifeng Cui

摘要

Background

Oral squamous cell carcinoma (OSCC) has a poor prognosis. PANoptosis, involving apoptosis, pyroptosis, and necroptosis, may be linked to cancer. Identifying OSCC subtypes and creating prognostic models based on PANoptosis-related genes (PRGs) can improve diagnosis and treatment.

Methods

RNA-seq and DNA methylation data from OSCC patients in The Cancer Genome Atlas (TCGA) were analyzed. Clustering algorithms identified OSCC subtypes, which were examined for differences in spatial distribution, biological pathways, drug sensitivity, prognosis, and immune infiltration. Prognostic genes were identified using Cox regression analyses. Mendelian randomization identified genes linked to OSCC. Drug sensitivity and tumor mutation load were assessed. Single-cell RNA sequencing (scRNA-seq) data explored the expression of prognostic genes. qRT-PCR verified gene expression.

Results

OSCC subtypes based on PRGs showed differences in prognosis, immunity, drug sensitivity, and pathways. Pantothenate and CoA biosynthesis pathways varied among subtypes, with immune cells highly infiltrated in CS1. The prognostic model highlighted differences in prognosis and immunotherapy response. BAK1 was causally linked to OSCC risk. Combining TMB score improved patient stratification. qRT-PCR confirmed differences in gene expression between control and OSCC groups. Monocytes were identified as high-scoring cells, with TGFb and MIF pathways important in cell communication.

Conclusion

Two OSCC subtypes and eight prognostic genes (TGFBR3, CYCS, HDAC9, PLK1, GSDMB, HSPA4, BAK1, SOD2) were identified, aiding in treatment and risk stratification. BAK1 is causally linked to OSCC risk.