Evidence map›Paper›PMID 40539082›Full record

ArticleIn silico pharmacology2025

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

Md Hasan Jafre Shovon, Partha Biswas, Md Imtiaz, Shirajut Mobin, Md Nazmul Hasan

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Article in In silico pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Md Hasan Jafre ShovonLaboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.
Partha BiswasLaboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.
Md ImtiazLaboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.
Shirajut MobinDepartment of Genetic Engineering and Biotechnology, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.
Md Nazmul HasanLaboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408 Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BiomarkerDEGGene ontologyLSCCPPIscRNA-seqSeurat

Identifiers

PMID40539082
PMCPMC12174029

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.