Identification and validation of m6A RNA methylation regulators with clinical prognostic value in Papillary thyroid cancer

Wang, Xinyi; Fu, Xiaorui; Zhang, Junjia; Cai, Xiong; Zhang, Shuyong; Lv, Yali

doi:10.1186/s12935-020-01283-y

Cited by 42 publications

(32 citation statements)

References 35 publications

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“…WGCNA is a powerful genetic analysis tool that effectively investigates the regulatory mechanisms behind a certain characteristic and a functional interpretation for systems biology (22,23). However, little is known about the application of WGCNA in patients with DTC (24). To the best of our knowledge, this study is the first to apply the WGCNA to uncover DM-specific gene co-expression modules and identify novel hub genes.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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 coexpressed 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Given the evidence that m6A contributes to CRC, 19,20 the relationship between candidate SNPs in 20 m6A regulators and the risk of CRC was also investigated. 18 However, a broader analysis method, integrating independent GWAS summary statistics with eQTL data to identify potential functional genetic variants of CRC-associated m6A-SNPs, has more research value. 21,22 Therefore, we used this method combining raw data and summary statistics of genome-wide association studies with eQTL and differential expression data to screen potential candidate CRCassociated m6A-SNPs.…”

Section: Discussionmentioning

confidence: 99%

“…The corresponding genes of the identified eQTL m6A-SNPs were further evaluated according to differential expression among CRC patients and controls. 18 Hence, three microarray gene expression CRC datasets (GSE89076, GSE21510, and GSE32323) publicly available in the Gene Expression Omnibus database (http:// www.ncbi.nlm.nih.gov/geo) were used. Then, we used GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/ geo2r/) to examine whether the CRC-associated genes (m6A-SNP with cis-eQTL) are differentially expressed between CRC and controls.…”

Section: Differential Expression Analysismentioning

confidence: 99%

Genome-Wide Identification of m6A-Associated Single-Nucleotide Polymorphisms in Colorectal Cancer

Zhao¹,

Jiang²,

Wang³

et al. 2021

PGPM

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Background: N6-methyladenosine (m6A)-associated single-nucleotide polymorphisms (SNPs) play important roles in cancers, with previous research suggesting potential associations between m6A-SNPs and cancer. However, the relationship between the genetic determinants of m6A modification and colorectal cancer (CRC) remains unclear. Methods: An integrative method combining raw data and summary statistics of genomewide association studies with expression quantitative trait loci (eQTL) and differential expression data was applied to screen potential candidate CRC-associated m6A-SNPs. Results: A total of 402 m6A-SNPs were identified as being associated with CRC (p < 0.001), with 98 showing eQTL signals. In particular, three genes were found to harbor CRCassociated m6A-SNPs: rs178184 in NOVA1, rs35782901 in HTR4, and rs60571683 in SLCO1B3. These genes were differentially expressed in at least one publicly available dataset (p < 0.05), with NOVA1 (p = 3.41×10 −11 ) and HTR4 (p = 5.56×10 −7 ) being significantly downregulated in CRC (dataset: GSE89076), and SLCO1B3 was significantly overexpressed (datasets: GSE32323 [p = 3.27×10 −5 ], GSE21510 [p = 1.09×10 −6 ], and GSE89076 [p = 7.63×10 −6 ]). Conclusion:This study identified three m6A-SNPs (rs178184, rs35782901, and rs60571683) that may be associated with CRC. However, the lack of analysis of primary CRC samples in order to further elucidate the underlying pathogenesis is a major limitation of this study. Future investigations are needed to validate these CRC-associated m6A-SNPs and explore the m6A-mediated pathogenic mechanism in CRC.

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“…They established different immune-related signatures to predict the prognosis of patients with TC. The remaining thyroid prediction models mainly focused on the study of characteristic gene sets are mainly m6A-related signature 80 , 81 and autophagy-related signature. 82 These results indicate that the characteristic signature not only has a good prediction effect on the prognosis of thyroid cancer patients but is more conducive to the comprehensive interpretation of the occurrence and development of TC and provides a theoretical basis for the use of drugs that act on this characteristic.…”

Section: Discussionmentioning

confidence: 99%

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

Li¹,

Jiang²,

Feng³

et al. 2021

OTT

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Background:The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied. Methods: Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes. Results: A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups. Conclusion: In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

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Identification and validation of m6A RNA methylation regulators with clinical prognostic value in Papillary thyroid cancer

Cited by 42 publications

References 35 publications

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

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

Genome-Wide Identification of m6A-Associated Single-Nucleotide Polymorphisms in Colorectal Cancer

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

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