Dataset Information

Transcriptomics data mining to uncover signature genes in head and neck squamous cell carcinoma: a bioinformatics analysis and RNA-sequencing based validation.

ABSTRACT: Due to its heterogeneous nature, head and neck squamous cell carcinoma (HNSC) had the worst prognosis. Hence, there is an urgent need to develop novel diagnostic and prognostic models for effective disease management. A multi-layer dry-lab and wet-lab methodologies were adopted in the present study to identify novel diagnostic and prognostic biomarkers of HNSC. Initially, the GSE6631 gene microarray HNSC dataset was retrieved from the Gene Expression Omnibus (GEO) database. The R language-based "limma" package was employed to identify differentially expressed genes (DEGs) between HNSC and control samples. The Cytohubba plug-in software was used to identify the top four hub genes based on the degree score method. The Cancer Genome Atlas (TCGA) datasets, Gene Expression Omnibus (GEO) datasets, clinical HNSC tissue samples, HNSC cell line (FaDu), and normal cell line (HOK) were used to validate the expressions of hub genes. Moreover, additional bioinformatics analyses were performed to further evaluate the mechanisms of hub genes in the development of HNSC. In total, 1372 reliable DEGs were screened from the GSE6631 dataset. Out of these DEGs, only based on the four up-regulated hub genes, including UBE2C (Ubiquitin-conjugating enzyme E2C), BUB1B (BUB1 Mitotic Checkpoint Serine/Threonine Kinase B), MCM4 (Minichromosome Maintenance Complex Component 4), and KIF23 (Kinesin family member 23), we developed and validated a diagnostic and prognostic model for HNSC patients. Moreover, some interesting correlations observed between hub gene expression and infiltration level of immune cells may also improve our understanding of HNSC immunotherapy. In conclusion, we developed a novel diagnostic and prognostic model consisting of the UBE2C, BUB1B, MCM4, and KIF23 genes for HNSC patients. However, the efficiency of this model needs to be verified through more experimental studies.

SUBMITTER: Yu W

PROVIDER: S-EPMC10695818 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Transcriptomics data mining to uncover signature genes in head and neck squamous cell carcinoma: a bioinformatics analysis and RNA-sequencing based validation.

Yu Wenjie W He Xiaoling X Zhang Chunming C Huangfu Hui H

American journal of cancer research 20231115 11

Due to its heterogeneous nature, head and neck squamous cell carcinoma (HNSC) had the worst prognosis. Hence, there is an urgent need to develop novel diagnostic and prognostic models for effective disease management. A multi-layer dry-lab and wet-lab methodologies were adopted in the present study to identify novel diagnostic and prognostic biomarkers of HNSC. Initially, the GSE6631 gene microarray HNSC dataset was retrieved from the Gene Expression Omnibus (GEO) database. The R language-based ...[more]

PMID: 38058845

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Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a malignancy of epithelial origin and with poor prognosis. Exploring the biomarkers and prognostic models that can contribute to early tumor detection is meaningful. A comprehensive analysis was conducted according to the stage-related signature genes of HNSCC, and a prognostic model was developed to validate their ability to predict the prognosis.MethodsThe transcriptome profiles and clinical information of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. mRNA expressions of differentially expressed genes (DEGs) were analyzed in stage I-II patients and stage III-IV patients from TCGA by R packages. A protein-protein interaction (PPI) network and core-gene network map were constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to examine pathway enrichment. Kaplan-Meier, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were applied to establish a stage-associated signature model. A Spearman analysis was conducted to examine the correlations between the characteristic genes and immune cell infiltration. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. Univariate multivariate Cox regression analyses were used to assess whether the risk score was an independent prognostic indicator for HNSCC.ResultsIn TCGA cohort, 5 genes (i.e., BRINP1, IL17A, ALB, FOXA2, and ZCCHC12) in the constructed prognostic risk model were associated with prognosis. Patients in the low-risk group had a better prognosis outcome than those in the high-risk group. The predictive power was good because all the area under the curve (AUC) of the risk score was higher than 0.6. Risk score [hazard ratio (HR) =1.985; P<0.001] was an independent risk factor for the prognosis of HNSCC. The results in the GEO cohort were consistent with those in the TCGA cohort.ConclusionsWe constructed and verified a prognostic risk model of stage-related signature genes for HNSCC based on the GEO and TCGA data. Due to the good predictive accuracy of this model, the prognosis of and the tumor immune cell infiltration with patients can be estimated.

Project description:PurposeHead and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer globally, and radiotherapy plays a crucial part in its treatment. This study was designed to identify potential genes related to radiation resistance in HNSCC.MethodWe first used text mining to obtain common genes related to radiotherapy resistance and HNSCC in published articles. Functional enrichment analyses were conducted to identify the significantly enriched pathways and genes. Protein and protein interactions were performed, and the most significant gene modules were determined; then, genes in the gene modules were validated at transcriptional levels and overall survival. Gene set variation analysis (GSVA) score was calculated, and the association between GSVA score and survival/pathway was estimated. Immune cell infiltration, methylation, and genetic alteration analysis of these genes was conducted in HNSCC patients. Finally, potential sensitive anticancer drugs related to target genes were obtained.ResultWe identified 583 common genes through text mining. After further validation, a four-gene signature (EPHB2, SPP1, SERPINE1, and VEGFC) was constructed. The patients with higher GSVA scores have a worse prognosis than those with lower GSVA scores. Differences in methylation of these four genes in HNSCC tumor tissue and normal tissue were compared, with higher methylation levels of EBPH2 and SPP1 in normal tissue and higher methylation levels of SERPINE1 in the tumor. Immune cell infiltration revealed that the increased expression of these genes was closely related to the infiltration level of CD4+ T cell, neutrophil, macrophage, and dendritic cell. Thirty drugs, including 22 positively and eight negatively correlated drugs that most correlated with related genes, were available for treating HNSCC.ConclusionIn this study, we identified four potential genes as well as corresponding drugs that might be related to radioresistance in HNSCC patients. These candidate genes may provide a promising avenue to further elevate radiotherapy efficacy.

Project description:Head and neck squamous cell carcinoma (HNSC) is the 6th most common cancer around the globe; its underlying molecular mechanisms and accurate molecular markers are still lacking. In this study, we explored hub genes and their potential signaling pathways through which these genes participate in the development of HNSC. The GSE23036 gene microarray dataset was attained from the GEO (Gene Expression Omnibus) database. Hub genes were identified via the Cytohubba plug-in application of the Cytoscape. The Cancer Genome Atlas (TCGA) datasets and cell lines (HOK and FuDu) were used to evaluate expression variations in the hub genes. Moreover, promoter methylation, genetic alteration, gene enrichment, miRNA network, and immunocyte infiltration analysis were also performed to confirm the oncogenic role and biomarker potential of the hub genes in HNSC patients. Based on the hub gene analysis results, four hub genes, including KNTC1 (Kinetochore Associated 1), CEP55 (Centrosomal protein of 55 kDa), AURKA (Aurora A Kinase), and ECT2 (Epithelial Cell Transforming 2), with the highest degree scores were denoted as hub genes. All these four genes were significantly up-regulated in HNSC clinical samples and cell lines relative to their counterparts. Overexpression of KNTC1, CEP55, AURKA, and ECT2 was also associated with poor survival and various clinical parameters of the HNSC patients. Methylation analysis through targeted bisulfite sequencing of HOK and FuDu cell lines revealed that the overexpression of KNTC1, CEP55, AURKA, and ECT2 hub genes was due to their promoter hypomethylation. Moreover, higher expressions of KNTC1, CEP55, AURKA, and ECT2 were positively correlated with the abundance of the CD4+ T cells and macrophage while with the reduction of CD8+ T cells in HNSC samples. Finally, gene enrichment analysis showed that all hub genes are involved in "nucleoplasm, centrosome, mitotic spindle, and cytosol" pathways. In conclusion, the KNTC1, CEP55, AURKA, and ECT2 genes could be potential biomarkers for HNSC patients and provide a novel insight into the diagnosis and treatment of the disease.

Dataset Information

Transcriptomics data mining to uncover signature genes in head and neck squamous cell carcinoma: a bioinformatics analysis and RNA-sequencing based validation.

Publications

Transcriptomics data mining to uncover signature genes in head and neck squamous cell carcinoma: a bioinformatics analysis and RNA-sequencing based validation.

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