Shovon Md Hasan Jafre, Biswas Partha, Imtiaz Md, Mobin Shirajut, Hasan Md Nazmul
Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.
Department of Genetic Engineering and Biotechnology, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.
In Silico Pharmacol. 2025 Jun 17;13(2):89. doi: 10.1007/s40203-025-00382-w. eCollection 2025.
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.
喉鳞状细胞癌（LSCC）是一种由基因突变驱动的复杂癌症，对检测和治疗构成重大挑战。单细胞RNA测序（scRNA-seq）已成为一种有前景的工具，用于揭示癌症中的细胞异质性并识别新的治疗靶点。在本研究中，我们使用scRNA-seq数据（GSE252490）来探索用于LSCC诊断和治疗的分子生物标志物。在对数据进行处理和标准化后，我们进行主成分分析以识别高变基因。细胞聚类揭示了12个具有独特分子特征的不同簇。差异基因表达分析确定了6434个差异表达基因（DEG），并使用基因本体富集进一步分析以探索LSCC进展中涉及的生物学过程。蛋白质-蛋白质相互作用（PPI）网络分析揭示了与关键癌症途径相关的20个核心基因。通过KEGG进行的途径富集分析突出了这些基因在各种癌症相关途径中的参与。值得注意的是，CCL3、EPCAM和IL8等表达升高的基因与LSCC的生存结果相关。这项全面分析为LSCC的分子格局提供了有价值的见解，识别出潜在的生物标志物和治疗靶点，以改善诊断和治疗。
