Identifying the Transcriptional Regulatory Network Associated With Extrathyroidal Extension in Papillary Thyroid Carcinoma by Comprehensive Bioinformatics Analysis

Long Non-Coding RNAs as Determinants of Thyroid Cancer Phenotypes: Investigating Differential Gene Expression Patterns and Novel Biomarker Discovery

Thyroid Cancer (TC) is the most common endocrine malignancy, with increasing incidence globally. Papillary thyroid cancer (PTC), a differentiated form of TC, accounts for approximately 90% of TC and occurs predominantly in women of childbearing age. Although responsive to current treatments, recurrence of PTC by middle age is common and is much more refractive to treatment. Undifferentiated TC, particularly anaplastic thyroid cancer (ATC), is the most aggressive TC subtype, characterized by it being resistant and unresponsive to all therapeutic and surgical interventions. Further, ATC is one of the most aggressive and lethal malignancies across all cancer types. Despite the differences in therapeutic needs in differentiated vs. undifferentiated TC subtypes, there is a critical unmet need for the identification of molecular biomarkers that can aid in early diagnosis, prognosis, and actionable therapeutic targets for intervention. Advances in the field of cancer genomics have enabled for the elucidation of differential gene expression patterns between tumors and healthy tissue. A novel category of molecules, known as non-coding RNAs, can themselves be differentially expressed, and extensively contribute to the up- and downregulation of protein coding genes, serving as master orchestrators of regulated and dysregulated gene expression patterns. These non-coding RNAs have been identified for their roles in driving carcinogenic patterns at various stages of tumor development and have become attractive targets for study. The identification of specific genes that are differentially expressed can give insight into mechanisms that drive carcinogenic patterns, filling the gaps of deciphering molecular and cellular processes that modulate TC subtypes, outside of well-known driver mutations.

Comprehensive Analysis of Prognostic Alternative Splicing Signature Reveals Recurrence Predictor for Papillary Thyroid Cancer

Background

Alternative splicing (AS) plays a key role in the diversity of proteins and is closely associated with tumorigenicity. The aim of this study was to systemically analyze RNA alternative splicing (AS) and identify its prognostic value for papillary thyroid cancer (PTC).

Methods

AS percent-splice-in (PSI) data of 430 patients with PTC were downloaded from the TCGA SpliceSeq database. We successfully identified recurrence-free survival (RFS)-associated AS events through univariate Cox regression, LASSO regression and multivariate regression and then constructed different types of prognostic prediction models. Gene function enrichment analysis revealed the relevant signaling pathways involved in RFS-related AS events. Simultaneously, a regulatory network diagram of AS and splicing factors (SFs) was established.

Results

We identified 1397 RFS-related AS events which could be used as the potential prognostic biomarkers for PTC. Based on these RFS-related AS events, we constructed a ten-AS event prognostic prediction signature that could distinguish high-and low-risk patients and was highly capable of predicting PTC patient prognosis. ROC curve analysis revealed the excellent predictive ability of the ten-AS events model, with an area under the curve (AUC) value of 0.889; the highest prediction intensity for one-year RFS was 0.923, indicating that the model could be used as a prognostic biomarker for PTC. In addition, the nomogram constructed by the risk score of the ten-AS model also showed high predictive efficiency for the prognosis of PTC patients. Finally, the constructed SF-AS network diagram revealed the regulatory role of SFs in PTC.

Conclusion

Through the limited analysis, AS events could be regarded as reliable prognostic biomarkers for PTC. The splicing correlation network also provided new insight into the potential molecular mechanisms of PTC.

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.