Identification of core biomarkers associated with pathogenesis and prognostic outcomes of laryngeal squamous-cell cancer using bioinformatics analysis

Chen, Wei; Liao, Lianming; Lai, Haichun; Yi, Xuehan; Wang, Desheng

doi:10.1007/s00405-020-05856-5

Cited by 10 publications

(2 citation statements)

References 46 publications

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“…And a recent study demonstrated that a high TYMS level predicted a good prognosis in TNBC patients [ 72 ]. EXO1 is a core gene of DNA metabolism [ 73 ]. Its polymorphism is related to clinical outcome and susceptibility of multiple tumors [ 74 , 75 ].…”

Section: Discussionmentioning

confidence: 99%

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Liu

Teng

et al. 2021

BMC Cancer

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Background Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management. Methods We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF). Results A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in ‘cell cycle’ and ‘oocyte meiosis’ related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients. Conclusions The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients’ prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

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Section: Discussionmentioning

confidence: 99%

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Liu

Teng

et al. 2021

BMC Cancer

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“…Bioinformatics methods, such as DEG identification, screening of hub genes based on coexpression networks, and survival analysis, have been extensively used to screen potential biomarkers related to LSCC. For example, Chen et al [ 24 ] combined bioinformatics methods such as DEG identification, pathway enrichment analysis, PPI network construction, survival analysis, and TCGA dataset validation to identify potential biomarkers and analyze their predictions. Li [ 25 ] and Zhang et al [ 26 ] combined DEG screening, WGCNA, pathway enrichment analysis, and PPI network construction to identify biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Cell Cycle‐Related Gene SPC24: A Novel Potential Diagnostic and Prognostic Biomarker for Laryngeal Squamous Cell Cancer

Chen

Luo

et al. 2023

BioMed Research International

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Laryngeal squamous cell cancer (LSCC) is a common malignant tumor with a high degree of malignancy, and its etiology remains unclear. Therefore, screening potential biomarkers is necessary to facilitate the treatment and diagnosis of LSCC. Robust rank aggregation (RRA) analysis was used to integrate two gene expression datasets of LSCC patients from the Gene Expression Omnibus (GEO) database and identify differentially expressed genes (DEGs) between LSCC and nonneoplastic tissues. A gene coexpression network was constructed using weighted gene correlation network analysis (WGCNA) to explore potential associations between the module genes and clinical features of LSCC. Combining differential gene expression analysis and survival analysis, we screened potential hub genes, including CDK1, SPC24, HOXB7, and SELENBP1. Subsequently, western blotting and immunohistochemistry were used to test the protein levels in clinical specimens to verify our findings. Finally, four candidate diagnostic and prognostic biomarkers (CDK1, SPC24, HOXB7, and SELENBP1) were identified. We propose, for the first time, that SPC24 is a gene that may associate with LSCC malignancy and is a novel therapeutic target. These findings may provide important mechanistic insight of LSCC.

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Prognostic nomogram based on immune scores for laryngeal squamous cell cancer

Liao

Chen

Lai

et al. 2020

Eur Arch Otorhinolaryngol

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Identification of core biomarkers associated with pathogenesis and prognostic outcomes of laryngeal squamous-cell cancer using bioinformatics analysis

Cited by 10 publications

References 46 publications

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Cell Cycle‐Related Gene SPC24: A Novel Potential Diagnostic and Prognostic Biomarker for Laryngeal Squamous Cell Cancer

Prognostic nomogram based on immune scores for laryngeal squamous cell cancer

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