Dataset Information

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer.

ABSTRACT:

Background

Distant metastasis (DM) is not common in differentiated thyroid cancer (DTC). However, it is associated with a significantly poor prognosis. Early detection of high-risk DTC patients is difficult, and the molecular mechanism is still unclear. Therefore, the present study aims to establish a novel predictive model based on clinicopathological parameters and DM-related gene signatures to provide guidelines for clinicians in decision making.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to discover co-expressed gene modules and hub genes associated with DM. Univariate and multivariate analyses were carried out to identify independent clinicopathological risk factors based on The Cancer Genome Atlas (TCGA) database. An integrated nomogram prediction model was established. Finally, real hub genes were validated using the GSE60542 database and various thyroid cell lines.

Results

The midnightblue module was most significantly positively correlated with DM (R=0.56, P=9e-06) by as per WGCNA. DLX5 (AUC: 0.769), COX6B2 (AUC: 0.764), and LYPD1 (AUC: 0.760) were determined to be the real hub genes that play a crucial role in predicting DM. Meanwhile, univariate and multivariate analyses demonstrated that T-stage (OR, 15.03; 95% CI, 1.75-319.40; and P=0.024), histologic subtype (OR, 0.17; 95% CI, 0.03-0.92; and P=0.042) were the independent predictors of DM. Subsequently, a nomogram model was constructed based on gene signatures and independent clinical risk factors exhibited good performance. Additionally, the mRNA expressions of real hub genes in the GSE60542 dataset were consistent with TCGA.

Conclusions

The present study has provided a reliable model to predict DM in patients with DTC. This model is likely to serve as an individual risk assessment tool in therapeutic decision-making.

SUBMITTER: Wang W

PROVIDER: S-EPMC8339873 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer.

Wang Wenlong W Shen Cong C Zhao Yunzhe Y Sun Botao B Bai Ning N Li Xinying X

Annals of translational medicine 20210701 13

<h4>Background</h4>Distant metastasis (DM) is not common in differentiated thyroid cancer (DTC). However, it is associated with a significantly poor prognosis. Early detection of high-risk DTC patients is difficult, and the molecular mechanism is still unclear. Therefore, the present study aims to establish a novel predictive model based on clinicopathological parameters and DM-related gene signatures to provide guidelines for clinicians in decision making.<h4>Methods</h4>Weighted gene co-expres ...[more]

PMID: 34422965

Similar Datasets

Project description:BackgroundDespite their excellent prognosis, children and young adults (CAYA) with differentiated thyroid cancer (DTC) tend to have more frequent occurrence of distant metastasis (DM) compared to adult DTC. Data about DM in CAYA from Middle Eastern ethnicity is limited.MethodsMedical records of 170 patients with DTC ≤18 years were retrospectively reviewed. Clinico-pathological factors associated with lung metastasis in CAYA, their clinical presentation and outcome were analyzed. Rick factors related to distant metastasis-free survival (DMFS) for the whole cohort were evaluated.ResultsDM was observed in 27 patients and all were lung metastasis. Lung metastasis was significantly associated with younger age (≤15 years), extrathyroidal extension (ETE), multifocal tumors, bilaterality, presence of lymph node (LN) disease and high post-operative stimulated thyroglobulin (sTg). Highest negative predictive values were seen with low post-operative sTg (97.9%), absence of LN disease (93.8%), absence of ETE (92.2%) and age older than 15 years (92.9%). Post-therapy whole body scan (WBS) identified most of the lung metastasis (21 of 27; 77.8%). Upon evaluating patients response according to ATA guidelines, excellent response was seen in only one patient, while biochemical persistence and structural persistence were seen in 11.1% (3/27) and 77.8% (21/27), respectively. Elevated post-operative sTg (>10ng/ml) was the only risk factor found to be significantly associated with both biochemical persistence (with or without structural persistence (p = 0.0143)) and structural persistence (p = 0.0433). Cox regression analysis identified age and post-operative sTg as independent risk factors related to DMFS. Based on these two risk factors for DMFS, patients were divided into 3 groups: low risk (no risk factors), intermediate risk (1 risk factor) and high risk (both risk factors). 20-year DMFS rates in the low-, intermediate- and high-risk groups were 100.0%, 81.3% and 23.7% respectively (p < 0.0001).ConclusionHigher suspicion for metastatic pediatric DTC should be considered in patients who are young, have LN disease, extrathyroidal extension and elevated post-operative sTg. Persistent disease, despite therapy, is very common and it appears to be related to post-operative sTg level. Hence, risk adaptive management is desirable in CAYA with DTC.

Project description:Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Dataset Information

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer.

Background

Methods

Results

Conclusions

Publications

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets