Dataset Information

Characterization of Cancer Stem Cell Characteristics and Development of a Prognostic Stemness Index Cell-Related Signature in Oral Squamous Cell Carcinoma.

ABSTRACT:

Objective

Cancer stem cells (CSCs) with self-renewal and plasticity contribute to tumor initiation and progression. This study developed an mRNA expression-based stemness index- (mRNAsi-) associated signature and validated biological functions of stem cell-related genes in oral squamous cell carcinoma (OSCC).

Methods

Here, mRNAsi was measured for OSCC samples from TCGA cohort, and prognosis and tumor microenvironment (stromal/immune scores, tumor purity) in high- and low-mRNAsi samples were evaluated with survival analyses and ESTIMATE algorithm. Based on prognostic mRNAsi-related genes, a risk score model was constructed by the LASSO method. The predictive accuracy was evaluated by uni- and multivariate Cox analyses and ROC curves. Among the genes in the model, the functions of H2AFZ on proliferation, apoptosis, invasion, and EMT were investigated in OSCC cells.

Results

High mRNAsi was distinctly associated with undesirable prognosis, increased stromal and immune scores, and lowered tumor purity. The mRNAsi-associated signature containing 11 genes was developed, and high-risk score was distinctly related to poor survival outcomes. Moreover, this signature was an independent and robust risk factor. H2AFZ upregulation significantly enhanced proliferative and invasive capacities and facilitated EMT as well as lowered apoptotic levels in Cal-27 and HSC-3 cells.

Conclusion

Our study characterized cancer stem cell characteristics that were closely related to tumor microenvironment and developed a stemness index cell-related signature that could assist prognosis prediction and risk stratification for OSCC. H2AFZ could become a potential therapeutic target against OSCC.

SUBMITTER: Feng J

PROVIDER: S-EPMC8617564 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA).MethodsExpression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual's overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed.ResultsThree mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (-0.38602 × expression level of CLEC3B) + (-0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis.ConclusionA 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.