Dataset Information

A miRNA-Based Prognostic Model to Trace Thyroid Cancer Recurrence.

ABSTRACT: Papillary thyroid carcinomas (PTCs) account for most endocrine tumors; however, screening and diagnosing the recurrence of PTC remains a clinical challenge. Using microRNA sequencing (miR-seq) to explore miRNA expression profiles in PTC tissues and adjacent normal tissues, we aimed to determine which miRNAs may be associated with PTC recurrence and metastasis. Public databases such as TCGA and GEO were utilized for data sourcing and external validation, respectively, and miR-seq results were validated using quantitative real-time PCR (qRT-PCR). We found miR-145 to be significantly downregulated in tumor tissues and blood. Deregulation was significantly related to clinicopathological features of PTC patients including tumor size, lymph node metastasis, TNM stage, and recurrence. In silico data analysis showed that miR-145 can negatively regulate multiple genes in the TC signaling pathway and was associated with cell apoptosis, proliferation, stem cell differentiation, angiogenesis, and metastasis. Taken together, the current study suggests that miR-145 may be a biomarker for PTC recurrence. Further mechanistic studies are required to uncover its cellular roles in this regard.

SUBMITTER: Toraih EA

PROVIDER: S-EPMC9454675 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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A miRNA-Based Prognostic Model to Trace Thyroid Cancer Recurrence.

Toraih Eman A EA Fawzy Manal S MS Ning Bo B Zerfaoui Mourad M Errami Youssef Y Ruiz Emmanuelle M EM Hussein Mohammad H MH Haidari Muhib M Bratton Melyssa M Tortelote Giovane G GG Hilliard Sylvia S Nilubol Naris N Russell Jonathon O JO Shama Mohamed A MA El-Dahr Samir S SS Moroz Krzysztof K Hu Tony T Kandil Emad E

Cancers 20220826 17

Papillary thyroid carcinomas (PTCs) account for most endocrine tumors; however, screening and diagnosing the recurrence of PTC remains a clinical challenge. Using microRNA sequencing (miR-seq) to explore miRNA expression profiles in PTC tissues and adjacent normal tissues, we aimed to determine which miRNAs may be associated with PTC recurrence and metastasis. Public databases such as TCGA and GEO were utilized for data sourcing and external validation, respectively, and miR-seq results were val ...[more]

PMID: 36077665

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Project description:BackgroundThe prevalence of thyroid cancer (ThyC), a frequent malignant tumor of the endocrine system, has been rapidly increasing over time. The mitophagy pathway is reported to play a critical role in ThyC onset and progression in many studies. This research aims to create a mitophagy-related survival prediction model for ThyC patients.MethodsGenes connected to mitophagy were found in the GeneCards database. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided information on the expression patterns of ThyC-related genes. To identify differentially expressed genes (DEGs), R software was employed. The prognostic significance of each DEG was assessed using the prognostic K-M curve. The prognostic model was built using LASSO, ROC, univariate, and multivariate Cox regression analyses. Finally, a nomogram model was developed to predict the survival outcome of ThyC patients in the clinical setting.ResultsThrough differential analysis, functional enrichment analysis, and protein-protein interaction (PPI) network analysis, we screened 10 key genes related to mitophagy in ThyC. The risk model was eventually developed using LASSO and Cox regression analyses based on the six DEGs related to mitophagy. An altered expression level of a mitophagy-related prognostic gene, GGCT, was found to be the most significant one, according to the KM survival curve analysis. An immunohistochemical (IHC) investigation revealed that ThyC tissues expressed higher levels of GGCT than normal thyroid tissues. The ROC curve verified the satisfactory performance of the model in survival prediction. Multivariate Cox regression analysis showed that the pathological grade, residual tumor volume, and initial tumor lesion type were significantly linked to the prognosis. Finally, we created a nomogram to predict the overall survival rate of ThyC patients at 3-, 5-, and 7- year time points.ConclusionThe nomogram risk prediction model was developed to precisely predict the survival rate of ThyC patients. The model was validated based on the most significant DEG GGCT gene expression in ThyC. This model may serve as a guide for the creation of precise treatment plans for ThyC patients.

Dataset Information

A miRNA-Based Prognostic Model to Trace Thyroid Cancer Recurrence.

Publications

A miRNA-Based Prognostic Model to Trace Thyroid Cancer Recurrence.

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