Scholarlink

<p>Laryngeal squamous cell carcinoma (LSCC), a complex cancer driven by genetic mutations, poses significant challenges for detection and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as a promising tool to uncover the cellular heterogeneity in cancer and identify novel therapeutic targets. In this study, we used scRNA-seq data (GSE252490) to explore molecular biomarkers for LSCC diagnosis and treatment. After processing and standardizing the data, we performed principal component analysis to identify highly variable genes. Cell clustering revealed 12 distinct clusters with unique molecular features. Differential gene expression analysis identified 6434 differentially expressed genes (DEGs), which were further analyzed using gene ontology enrichment to explore biological processes involved in LSCC progression. Protein–protein interaction (PPI) network analysis revealed 20 central genes associated with key cancer pathways. Pathway enrichment analysis through KEGG highlighted the involvement of these genes in various cancer-related pathways. Notably, genes such as CCL3, EPCAM, and IL8, with elevated expression, were linked to survival outcomes in LSCC. This comprehensive analysis provides valuable insights into the molecular landscape of LSCC, identifying potential biomarkers and therapeutic targets for improved diagnosis and treatment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for laryngeal squamous cell carcinoma (LSCC): an in-silico approach

  • 期刊论文

  • 发表日期：

    2025年6月17日

查看全文
  • Md. Hasan Jafre Shovon,
  • Partha Biswas,
  • Md. Imtiaz,
  • Shirajut Mobin,
  • Md. Nazmul Hasan

摘要

Laryngeal squamous cell carcinoma (LSCC), a complex cancer driven by genetic mutations, poses significant challenges for detection and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as a promising tool to uncover the cellular heterogeneity in cancer and identify novel therapeutic targets. In this study, we used scRNA-seq data (GSE252490) to explore molecular biomarkers for LSCC diagnosis and treatment. After processing and standardizing the data, we performed principal component analysis to identify highly variable genes. Cell clustering revealed 12 distinct clusters with unique molecular features. Differential gene expression analysis identified 6434 differentially expressed genes (DEGs), which were further analyzed using gene ontology enrichment to explore biological processes involved in LSCC progression. Protein–protein interaction (PPI) network analysis revealed 20 central genes associated with key cancer pathways. Pathway enrichment analysis through KEGG highlighted the involvement of these genes in various cancer-related pathways. Notably, genes such as CCL3, EPCAM, and IL8, with elevated expression, were linked to survival outcomes in LSCC. This comprehensive analysis provides valuable insights into the molecular landscape of LSCC, identifying potential biomarkers and therapeutic targets for improved diagnosis and treatment.