Dataset Information

The m6A methyltransferase METTL3 drives thyroid cancer progression and lymph node metastasis by targeting LINC00894.

ABSTRACT:

Background

Long noncoding RNAs (lncRNAs) are significant contributors to various human malignancies. The aberrant expression of lncRNA LINC00894 has been reported in various human malignancies. We aimed to illustrate the role of LINC00894 and its underlying mechanism in the development of papillary thyroid carcinoma (PTC).

Methods

We performed bioinformatics analysis of differentially expressed RNAs from TCGA and GEO datasets and selected the target lncRNA LINC00894. SRAMP analysis revealed abundant M6A modification sites in LINC00894. Further analysis of StarBase, GEPIA, and TCGA datasets was performed to identify the related differentially expressed genes METTL3. Colony formation and CCK-8 assays confirmed the relationship between LINC00894, METTL3, and the proliferative capacity of PTC cells. The analysis of AnnoLnc2, Starbase datasets, and meRIP-PCR and qRT‒PCR experiments confirmed the influence of METTL3-mediated m6A modification on LINC00894. The study employed KEGG enrichment analysis as well as Western blotting to investigate the impact of LINC00894 on the expression of proteins related to the Hippo signalling pathway.

Results

LINC00894 downregulation was detected in PTC tissues and cells and was even further downregulated in PTC with lymphatic metastasis. LINC00894 inhibits the lymphangiogenesis of vascular endothelial cells and the proliferation of cancer cells. METTL3 enhances PTC progression by upregulating LINC00894 by enhancing LINC00894 mRNA stability through the m6A-YTHDC2-dependent pathway. LINC00894 may inhibit PTC malignant phenotypes through the Hippo signalling pathway.

Conclusion

The METTL3-YTHDC2 axis stabilizes LINC00894 mRNA in an m6A-dependent manner and subsequently inhibits tumour malignancy through the Hippo signalling pathway.

SUBMITTER: Zhou X

PROVIDER: S-EPMC10826051 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

The m6A methyltransferase METTL3 drives thyroid cancer progression and lymph node metastasis by targeting LINC00894.

Zhou Xiang X Chang Lisha L Liang Qiaoqiao Q Zhao Rongjie R Xiao Yong Y Xu Zheng Z Yu Leitao L

Cancer cell international 20240130 1

<h4>Background</h4>Long noncoding RNAs (lncRNAs) are significant contributors to various human malignancies. The aberrant expression of lncRNA LINC00894 has been reported in various human malignancies. We aimed to illustrate the role of LINC00894 and its underlying mechanism in the development of papillary thyroid carcinoma (PTC).<h4>Methods</h4>We performed bioinformatics analysis of differentially expressed RNAs from TCGA and GEO datasets and selected the target lncRNA LINC00894. SRAMP analysi ...[more]

PMID: 38291427

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Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.

Dataset Information

The m6A methyltransferase METTL3 drives thyroid cancer progression and lymph node metastasis by targeting LINC00894.

Background

Methods

Results

Conclusion

Publications

The m6A methyltransferase METTL3 drives thyroid cancer progression and lymph node metastasis by targeting LINC00894.

Similar Datasets

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