ArticleFrontiers in pharmacology2022
Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 9 citations in OpenAlex.
- Identification of tumor-associated antigens with multi-cancer therapeutic potential.Frontiers in immunology · 2026Article
- Promoter Hypomethylation Unleashes HMGA1 to Orchestrate Immune Evasion and Therapy Resistance Across Cancers.Biology · 2025Article
- Multi-omics prognostic marker discovery and survival modelling: a case study on multi-cancer survival analysis of women's specific tumours.Scientific reports · 2025Article
- Current innovations in head and neck cancer: From diagnostics to therapeutics.Oncology research · 2025Review
- Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.Human genomics · 2024Article
- Identification of prognostic biomarkers in neuroblastoma using WGCNA and multi-omics analysis.Discover oncology · 2024Article
- Upregulation of TTK expression is associated with poor prognosis and immune infiltration in endometrial cancer patients.Cancer cell international · 2024Article
- Review
- Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel.Annals of medicine · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is one of the leading causes of death worldwide, bringing a significant burden to human health and society. Accurate cancer diagnosis and biomarkers that can be used as robust therapeutic targets are of great importance as they facilitate early and effective therapies. Shared etiology among cancers suggests the existence of pan-cancer biomarkers, performance of which could benefit from the large sample size and the heterogeneity of the studied patients. In this study, we conducted a systematic RNA-seq study of 9,213 tumors and 723 para-cancerous tissue samples of 28 solid tumors from the Cancer Genome Atlas (TCGA) database, and 7,008 normal tissue samples from the Genotype-Tissue Expression (GTEx) database. By differential gene expression analysis, we identified 214 up-regulated and 186 downregulated differentially expressed genes (DEGs) in more than 80% of the studied tumors, respectively, and obtained 20 highly linked up- and downregulated hub genes from them. These markers have rarely been reported in multiple tumors simultaneously. We further constructed pan-cancer diagnostic models to classify tumors and para-cancerous tissues using 10 up-regulated hub genes with an AUC of 0.894. Survival analysis revealed that these hub genes were significantly associated with the overall survival of cancer patients. In addition, drug sensitivity predictions for these hub genes in a variety of tumors obtained several broad-spectrum anti-cancer drugs targeting pan-cancer. Furthermore, we predicted immunotherapy sensitivity for cancers based on tumor mutational burden (TMB) and the expression of immune checkpoint genes (ICGs), providing a theoretical basis for the treatment of tumors. In summary, we identified a set of biomarkers that were differentially expressed in multiple types of cancers, and these biomarkers can be potentially used for diagnosis and used as therapeutic targets.
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