Methylation Assessment of Two DKK2 and DKK4 Genes in Oral Squamous Cell Carcinoma Patients

DKK2 promotes the progression of oral squamous cell carcinoma through the PI3K/AKT signaling pathway

OBJECTIVE

This study aimed to investigate the impact of Dickkopf 2 (DKK2) on the progression of oral squamous cell carcinoma (OSCC) and explore its role in the PI3K/AKT signaling transduction pathway.

MATERIALS AND METHODS

The study initially examined the expression of the DKK2 gene in OSCC tissues and normal tissues. Simultaneously, the expression of DKK2 in HOK cells and OSCC cells was verified, and changes in DKK2 expression under hypoxic conditions were detected. DKK2 overexpression and knockdown were performed in SCC-15 and CAL-27 cells. Subsequently, the effects of DKK2 on the proliferation, migration and invasion of OSCC were detected. Western blotting was employed to detect the expression of key proteins in the DKK2/PI3K/AKT signaling axis before and after transfection, and further explore the relevant molecular mechanisms.

RESULTS

Compared to normal tissues, DKK2 expression was elevated in OSCC tissues. The expression of DKK2 in the SCC-15 and CAL-27 cell lines was higher than that in HOK cells, and hypoxic conditions could promote DKK2 expression. DKK2 overexpression promoted cell proliferation, migration, and invasion, while DKK2 knockdown inhibited these processes. DKK2 overexpression activated the PI3K/AKT pathway, while DKK2 knockdown suppressed this pathway.

CONCLUSION

This study suggests that hypoxic conditions enhance the expression of DKK2 in OSCC. DKK2 regulates the proliferation, migration, and invasion of OSCC through the PI3K/AKT signaling pathway.

Oral cancers preceded by proliferative verrucous leukoplakia exhibit distinctive molecular features

OBJECTIVE

Proliferative verrucous leukoplakia (PVL) has high rates of malignant transformation into oral squamous cell carcinoma (OSCC), but the clinical and evolutionary pattern of OSCC from PVL (PVL-OSCC) is more favorable than that of OSCC not preceded by PVL (OSCC). Here, we aimed to explore the pathophysiologic differences between PVL-OSCC and OSCC through transcriptomic and DNA methylation analyses.

MATERIALS AND METHODS

In this case-control study, oral biopsies from 8 PVL-OSCC and 10 OSCC patients were obtained for global sequencing using RNAseq and a genome-wide DNA methylation analysis via the Infinium EPIC Platform (graphical abstract).

RESULTS

One hundred and thirty-three differentially expressed genes (DEGs) were detected, 94 of them upregulated in OSCC. Most of these genes were previously described in cancer and associated with prognosis. The integrative analysis revealed 26 DEGs, corresponding to 37 CpGs, whose promoters were regulated by DNA methylation. Twenty-nine of the CpGs were found as hypermethylated in PVL-OSCC. Only 5 of the genes that were aberrantly methylated and differentially expressed were upregulated in PVL-OSCC patients, whereas 21 were underexpressed.

CONCLUSIONS

PVL-OSCC patients presented lower expression of cancer-related genes. Hypermethylation of the promoter region of many genes was also noticed, indicating that DNA methylation could be a regulatory mechanism.

Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P < 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P < 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.