ArticleHereditas2021
Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis.
Article in Hereditas, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 30 citations in OpenAlex.
- Identification of potential biomarkers for oral squamous cell carcinoma through multi-cohort bioinformatic analysis.Journal of the Egyptian National Cancer Institute · 2026Article
- Multi-omic discovery of SCG5 as a shared driver and prognostic biomarker in head-neck and non-small cell lung cancers.Discover oncology · 2026Article
- Biological significance, molecular mechanisms and clinical potential of EI24 in cancer.Chinese medical journal · 2026Review
- Development of a cancer metastasis-associated risk model via multi-machine-learning algorithms for prognostic risk evaluation and clinical application in oral squamous cell carcinoma.Journal of translational medicine · 2025Article
- Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis.NPJ precision oncology · 2025Article
- Exploring Metabolic and Immunological Biomarkers for Oral Squamous Cell Carcinoma: Potential Targets for Precision Therapy.Biology · 2025Review
- SP110 Could be Used as a Potential Predictive and Therapeutic Biomarker for Oral Cancer.Molecular biotechnology · 2025Article
- SKA1 promotes oncogenic properties in oral dysplasia and oral squamous cell carcinoma, and augments resistance to radiotherapy.Molecular oncology · 2025Article
- Knockdown of HOXD13 in Oral Squamous Cell Carcinoma Inhibited its Proliferation, Migration, and Influenced Fatty Acid Metabolism.Journal of Cancer · 2025Article
- Biological biomarkers of oral cancer.Periodontology 2000 · 2024Review
- Strategic approaches in oral squamous cell carcinoma diagnostics using liquid biopsy.Periodontology 2000 · 2024Review
- Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma.NPJ precision oncology · 2024Article
- Downregulation of salivary miR-3928 as a potential biomarker in patients with oral squamous cell carcinoma and oral lichen planus.Clinical and experimental dental research · 2024Article
- Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.Frontiers in immunology · 2024Article
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- Integrative Metatranscriptomic Analysis Reveals Disease-specific Microbiome-host Interactions in Oral Squamous Cell Carcinoma.Cancer research communications · 2023Article
- Desmoglein-2 as a cancer modulator: friend or foe?Frontiers in oncology · 2023Review
- Using CADD tools to inhibit the overexpressed genes FAP, FN1, and MMP1 by repurposing ginsenoside C and Rg1 as a treatment for oral cancer.Frontiers in molecular biosciences · 2023Article
- Osmanthus-Loaded PVP/PVA Hydrogel Inhibits the Proliferation and Migration of Oral Squamous Cell Carcinoma Cells CAL-27.Polymers · 2022Article
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundOral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC.
methodsFour Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature.
resultsTotally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively.
conclusionThese findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.
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