Dataset Information

Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics.

ABSTRACT:

Objectives

Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC.

Materials and methods

Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate.

Results

Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients.

Conclusions

We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.

SUBMITTER: Zhang YY

PROVIDER: S-EPMC8032525 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is causing a high mortality rate due to the lack of efficient early prognosis markers and suitable therapeutic regimens. The prognostic role of genes responsible for the acquisition of radioresistance in ESCC has not been fully elucidated.AimTo establish a prognostic model by studying gene expression patterns pertinent to radioresistance in ESCC patients.MethodsDatasets were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The edgeR, a Bioconductor package, was used to analyze mRNA expression between different groups. We screened genes specifically responsible for radioresistance to estimate overall survival. Pearson correlation analysis was performed to confirm whether the expression of those genes correlated with each other. Genes contributing to radioresistance and overall survival were assessed by the multivariate Cox regression model through the calculation of βi and risk score using the following formula: .ResultsWe identified three prognostic mRNAs (cathepsin S [CTSS], cluster of differentiation 180 [CD180], and SLP adapter and CSK-interacting membrane protein [SCIMP]) indicative of radioresistance. The expression of the three identified mRNAs was related to each other (r > 0.70 and P < 0.05). As to 1-year and 3-year overall survival prediction, the area under the time-dependent receiver operating characteristic curve of the signature consisting of the three mRNAs was 0.716 and 0.841, respectively. When stratifying patients based on the risk score derived from the signature, the high-risk group exhibited a higher death risk and shorter survival time than the low-risk group (P < 0.0001). Overall survival of the low-risk patients was significantly better than that of the high-risk patients (P = 0.018).ConclusionWe have developed a novel three-gene prognostic signature consisting of CTSS, CD180, and SCIMO for ESCC, which may facilitate the prediction of early prognosis of this malignancy.