Integrated analysis identifies DUSP5 as a novel prognostic indicator for thyroid follicular carcinoma

Prognosis and influencing factors of follicular thyroid cancer

OBJECTIVES

Follicular thyroid cancer (FTC) is prone to distant metastasis, and patients with distant metastasis often have poor prognosis. In this study, the impact of metastasis and other relevant factors on the prognosis of follicular thyroid carcinoma was examined.

METHODS

This was a retrospective study. Data were obtained from Zhejiang Cancer Hospital, Sun Yat-sen University Cancer Center and Hangzhou First People's Hospital affiliated with Zhejiang University School of Medicine, from January 2009 to June 2021 for 153 FTC patients. The patients were assigned into three groups according to their distant metastasis: distant metastasis at initial diagnosis (M1), distant metastasis during follow-up (M2), and no evidence of distant metastasis over the course of the study (M0). Data were collected and summarized on clinical data, laboratory parameters, imaging features, postoperative pathologic subtypes, and metastases. The Cox proportional hazard model was used to perform the univariate and multivariate analysis. Kaplan-Meier curves were used to evaluate cancer-specific survival (CSS).

RESULTS

Based on metastasis, the patients were assigned into three groups, including 31 in the M1 group, 15 in the M2 group, and 107 in the M0 group. These individuals were followed up for an average of 5.9 years, and the group included 46 patients with distant metastasis (31 confirmed at diagnosis and 15 found during follow-up). Univariate Cox regression analysis showed that age, Hashimoto's thyroiditis (HT), surgery method, postoperative adjuvant therapy, histologic subtype, nodule size, calcification, TSH, and distant metastasis all impacted prognosis. Multivariate Cox regression analysis suggested that histologic subtype (widely invasive; HR: 7.440; 95% CI: 3.083, 17.954; p < 0.001), nodule size (≥40 mm; HR: 8.622; 95% CI: 3.181, 23.369; p < 0.001) and distant metastasis (positive; HR: 6.727; 95% CI: 2.488, 18.186; p < 0.001) were independent risk factors affecting the prognosis of follicular thyroid cancer.

CONCLUSIONS

Histologic subtype, nodule size, and distant metastasis are important risk factors for the prognosis of follicular thyroid cancer. Patients with metastatic follicular thyroid cancer have a poor prognosis, especially with metastasis at the time of initial diagnosis. As a result, this group of patients requires individualized treatment and closer follow-up.

METTL3-mediated N6-methyladenosine modification of DUSP5 mRNA promotes gallbladder-cancer progression

N⁶-methyladenosine (m⁶A) RNA methylation and its associated methyltransferase METTL3 play an important role in tumorigenesis of a series of tumors. However, dysregulation of METTL3 in gallbladder cancer (GBC) remains obscure. Here, we showed that upregulated METTL3 level predicted poor prognosis and correlated with increased lymphatic metastasis and high TNM stage. Functionally, we found that METTL3 could promote cell proliferation, invasion, and migration of GBC-SD and NOZ cells. Mechanistically, we revealed the METTL3-mediated m⁶A-modification profile in GBC cells and identified DUSP5 as the downstream gene of METTL3. METTL3 promoted the degradation of DUSP5 mRNA in a YTHDF2-dependent manner. Rescue assays showed that downregulation of DUSP5 could attenuate the knockdown METTL3-mediated inhibition of cell proliferation, invasion, and migration of GBC-SD and NOZ cells. Thus, our finding shows that elevated METTL3 expression contributes to tumor aggression in GBC, suggesting that METTL3 is a possible prognostic predictor and therapeutic target against GBC.

Machine learning analysis of DNA methylation in a hypoxia-immune model of oral squamous cell carcinoma

BACKGROUND

Hypoxia status and immunity are related with the development and prognosis of oral squamous cell carcinoma (OSCC). Here, we constructed a hypoxia-immune model to explore its upstream mechanism and identify potential CpG sites.

METHODS

The hypoxia-immune model was developed and validated by the iCluster algorithm. The LASSO, SVM-RFE and GA-ANN were performed to screen CpG sites correlated to the hypoxia-immune microenvironment.

RESULTS

We found seven hypoxia-immune related CpG sites. Lasso had the best classification performance among three machine learning algorithms.

CONCLUSION

We explored the clinical significance of the hypoxia-immune model and found seven hypoxia-immune related CpG sites by multiple machine learning algorithms. This model and candidate CpG sites may have clinical applications to predict the hypoxia-immune microenvironment.