Identification of an Autophagy-Related Signature Predicting Overall Survival for Papillary Thyroid Carcinoma

Background: Papillary thyroid carcinoma usually shows an excellent prognosis. However, its recurrence or persistence rate is high. In this study, we used bioinformatics to identify autophagy-related genes (ARGs) and establish a novel scoring system for papillary thyroid carcinoma. Methods: We collected ARGs sequencing data of patients with papillary thyroid carcinoma from The Cancer Genome Atlas database. Differentially expressed ARGs were identified by the “Limma” package in R language. After univariate and multivariate Cox regression analysis, an ARG signature was developed. The established prognostic signature was evaluated by Kaplan-Meier curve and time-dependent receiver operating characteristic. Results: A sum of 26 differentially expressed ARGs were identified. Gene set enrichment analysis revealed that several significantly oncological signatures were enriched, such as autophagy, p53 signaling pathway, apoptosis, human cytomegalovirus infection, and platinum drug resistance. After univariate and multivariate analysis, 3 ARGs ( ITPR1, CCL2, and CDKN2A) were selected to develop autophagy-related signature. Patients with high risk had significantly shorter overall survival than those with low risk. The areas under the curve indicated that the signature showed good accuracy of prediction. Conclusions: We established a novel scoring system based on 3 ARGs, which provides a promising tool for the development of personalized therapy.

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Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

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Progression Risk Assessment of Post-Surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

10.21203/rs.3.rs-47878/v1 ◽

2020 ◽

Author(s):

Mengwei Wu ◽

Rui Liu ◽

Hongwei Yuan ◽

Xiequn Xu ◽

Xiaobin Li ◽

...

Keyword(s):

Risk Assessment ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Assessment Tool ◽

Cox Regression ◽

Circular Rna ◽

Differentially Expressed ◽

Papillary Thyroid ◽

Progression Risk ◽

Stratification System

Abstract BackgroundAccurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circRNAs (DE-circRNAs) via the ceRNA mechanism could help establish a novel assessment tool. MethodsceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. LASSO-Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC.ResultsSix hub DE-mRNAs, namely CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the ATA risk stratification system and MACIS score AJCC staging system.ConclusionsBased on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

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Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma

Cancer Cell International ◽

10.1186/s12935-021-02066-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Rujia Qin ◽

Chunyan Li ◽

Xuemin Wang ◽

Zhaoming Zhong ◽

Chuanzheng Sun

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cox Regression ◽

Expression Profiles ◽

Tumor Therapy ◽

Differentially Expressed ◽

Expression Level ◽

Papillary Thyroid ◽

Ppi Network ◽

Immune Activity

Abstract Background Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. The effect of traditional anti-tumor therapy is not ideal for the patients with recurrence, metastasis and radioiodine resistance. The abnormal expression of immune-related genes (IRGs) has critical roles in the etiology of PTC. However, the effect of IRGs on PTC prognosis remains unclear. Methods Based on The Cancer Genome Atlas (TCGA) and ImmPort databases, we integrated IRG expression profiles and progression-free intervals (PFIs) of PTC patients. First, we identified the differentially expressed IRGs and transcription factors (TFs) in PTC. Subsequently, an IRG model that can predict the PFI was constructed by using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses of the differentially expressed IRGs in the TCGA. Additionally, a protein–protein interaction (PPI) network showed the interactions between the differentially expressed genes (DEGs), and the top 30 genes with the highest degree were extracted from the network. Then, the key IRG was identified by the intersection analysis of the PPI network and univariate Cox regression, which was verified the differential expression of by western blotting and immunohistochemistry (IHC). ssGSEA was performed to understand the correlation between the key IRG expression level and immune activity. Results A total of 355 differentially expressed IRGs and 43 differentially expressed TFs were identified in PTC patients. Then, eight IRGs were finally utilized to construct an IRG model. The respective areas under the curve (AUCs) of the IRG model reached 0.948, 0.820, and 0.831 at 1, 3 and 5 years in the training set. In addition, lactotransferrin (LTF) was determined as the key IRG related to prognosis. The expression level of LTF in tumor tissues was significantly lower than that in normal tissues. And the results of ssGSEA showed the expression level of LTF is closely related to immune activity. Conclusions These findings show that the prognostic model and key IRG may become promising molecular markers for the prognosis of PTC patients.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Identification of Biomarkers of Hepatocellular Carcinoma Gene Prognosis Based on Immune-Related lncRNA Signature of Transcriptome Data

10.21203/rs.3.rs-57303/v1 ◽

2020 ◽

Author(s):

Andi Ma ◽

Yukai Sun ◽

Racheal O. Ogbodu ◽

Ling Xiao ◽

Haibing Deng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

Abstract Background: It is well known that long non-coding RNAs (lncRNAs) play a vital role in cancer. We aimed to explore the prognostic value of potential immune-related lncRNAs in hepatocellular carcinoma (HCC). Methods: Validated the established lncRNA signature of 343 patients with HCC from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Immune-related lncRNAs for HCC prognosis were evaluated using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. LASSO analysis was performed to calculate a risk score formula to explore the difference in overall survival between high- and low-risk groups in TCGA, which was verified using GEO, Gene Ontology (GO), and pathway-enrichment analysis. These analyses were used to identify the function of screened genes and construct a co-expression network of these genes. Results: Using computational difference algorithms and lasso Cox regression analysis, the differentially expressed and survival-related immune-related genes (IRGs) among patients with HCC were established as five novel immune-related lncRNA signatures (AC099850.3, AL031985.3, PRRT3-AS1, AC023157.3, MSC-AS1). Patients in the low‐risk group showed significantly better survival than patients in the high‐risk group ( P = 3.033e−05). The signature identified can be an effective prognostic factor to predict patient survival. The nomogram showed some clinical net benefits predicted by overall survival. In order to explore its underlying mechanism, several methods of enrichment were elucidated using Gene Set Enrichment Analysis. Conclusion: Identifying five immune-related lncRNA signatures has important clinical implications for predicting patient outcome and guiding tailored therapy for patients with HCC with further prospective validation.

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Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

Cancer Cell International ◽

10.1186/s12935-019-1001-0 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 12

Author(s):

Zi-Hao Wang ◽

Yun-Zheng Zhang ◽

Yu-Shan Wang ◽

Xiao-Xin Ma

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

Mrna Expression ◽

Cox Regression ◽

Single Gene ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Cox Regression Analysis

Abstract Background Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC. Methods mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log‐rank test were used to validate the significance of risk parameters for prognosis prediction. Result Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors. Conclusion A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

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Identification of aberrantly methylated differentially expressed genes in papillary thyroid carcinoma using integrated bioinformatic analysis

10.21203/rs.3.rs-410194/v1 ◽

2021 ◽

Author(s):

Qingxian Li ◽

Weiyi Cai ◽

Jianhong Chen ◽

Jie Ning

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Regression Analysis ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Integrative Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Tumor Biomarker ◽

Papillary Thyroid

Abstract Background: DNA methylation has been reported as one of the most critical epigenetic aberrations during the tumorigenesis and development of papillary thyroid carcinoma (PTC). Although PTC has been explored by gene expression and DNA methylation studies, the regulatory mechanisms of the methylation on the gene expression was poorly clarified.Results: In this study, the comparisons between PTC and NT revealed 4995 methylated probes and 1446 differentially expressed transcripts cross-validated by The Cancer Genome Atlas (TCGA) database. The integrative analysis between DNA methylation and gene expression revealed 123 and 29 genes with hypomethylation/overexpression and hypermethylation/downexpression correlation, respectively. The DNA methylation pattern of seven selected CpGs (A: UNC80-cg04507925; B: TPO-cg09757588; C: LHX8-cg11842415; D: DLG2-cg16986720; E: FOXJ1-cg20373432; F: PALM2-cg21204870; G: IPCEF1-cg24635109, of which the candidate promoter CpG sites were preliminarily identified with the least absolute shrinkage and selection operator (LASSO) regression analysis. Then, the risk prognosis model was constructed by stepwise regression analysis. Furthermore, the receiver operating characteristic (ROC) and nomogram based on the verified independent prognostic factors was established for the prognostic prediction showed that it was able to predict 3-, 5-, and 7-year survival accurately. Kaplan-Meier survival estimate demonstrated that low DLG2 expression and DLG2-cg16986720 hypermethylation were independent biomarkers for OS. From the comprehensive meta-analysis, the combined Standardised Mean Difference (SMD) of DLG2 was 0.94 with 95% CI of (0.46,1.43), indicating that less DLG2 was expressed in the PTC tissue than in the normal tissue (P<0.05). Bisulfite sequencing PCR also showed that DLG2 methylation was higher in tumor group than in normal group. Components of immune microenvironment were analyzed using TIMER, and the correlation between immune cells and DLG2 was found to be distinct across cancer types. Based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, DLG2 was implicated in pathways involved in immunity, metabolism, cancer, and infectious diseases. PCT patients with DLG2-cg16986720 hypermethylation showed significantly short survival rates in progression- free survival concomitant with reduced infiltration of myeloid dendritic cells.Conclusions: The current study validated that DLG2 was lowly expressed in PTC. More importantly, DLG2 hypermethylation might function as a latent tumor biomarker in the prognosis prediction for PTC. The results of bioinformatics analyses may present a new method for investigating the pathogenesis of PTC. DNA methylation loss in non-promoter, poor CGI and enhancer-enriched regions was a significant event in PTC. In addition to the promoter region, gene body and 3’UTR methylation have also the potential to influence the gene expression levels (both, repressing and inducing). The integrative analysis revealed genes potentially regulated by DNA methylation pointing out potential drivers and biomarkers related to PTC development.

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Identification of Potential lncRNAs and miRNAs as Diagnostic Biomarkers for Papillary Thyroid Carcinoma Based on Machine Learning

International Journal of Endocrinology ◽

10.1155/2021/3984463 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Fei Yang ◽

Jie Zhang ◽

Baokun Li ◽

Zhijun Zhao ◽

Yan Liu ◽

...

Keyword(s):

Functional Analysis ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cell Lines ◽

Epigenetic Modifications ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Endothelial Cell Migration ◽

Papillary Thyroid ◽

Diagnostic Biomarkers

Background. Papillary thyroid carcinoma (PTC) accounts for most of the proportion of thyroid cancer (TC). The objective of this study was to identify diagnostic, differentially expressed long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), contributing to understanding the epigenetics mechanism of PTC. Methods. The data of lncRNA, miRNA, and mRNA were downloaded from the Cancer Genome Atlas (TCGA) dataset, followed by functional analysis of differentially expressed mRNAs. Optimal diagnostic lncRNA and miRNA biomarkers were identified via random forest. The regulatory network between optimal diagnostic lncRNA and mRNAs and optimal diagnostic miRNA and mRNAs was identified, followed by the construction of ceRNA network of lncRNA-mRNA-miRNA. Expression validation and diagnostic analysis of lncRNAs, miRNAs, and mRNAs were performed. Overexpression of ADD3-AS1 was performed in PTC-UC3 cell lines, and cell proliferation and invasion assay were used for investigating the role of ADD3-AS1 in PTC. Results. A total of 107 differentially expressed lncRNAs, 81 differentially expressed miRNAs, and 515 differentially expressed mRNAs were identified. 11 lncRNAs and 6 miRNAs were regarded as the optimal diagnostic biomarkers for PTC. The epigenetic modifications via the above diagnostic lncRNAs and miRNAs were identified, including MIR181A2HG-FOXP2-hsa-miR-146b-3p, BLACAT1/ST7-AS1-RPS6KA5-hsa-miR-34a-5p, LBX2-AS1/MIR100HG-CDHR3-hsa-miR-34a-5p, ADD3-AS1-PTPRE-hsa-miR-9-5p, ADD3-AS1-TGFBR1-hsa-miR-214-3p, LINC00506-MMRN1-hsa-miR-4709-3p, and LOC339059-STK32A-hsa-miR-199b-5p. In the functional analysis, MMRN1 and TGFBR1 were involved in cell adhesion and endothelial cell migration, respectively. Overexpression of ADD3-AS1 inhibited cell growth and invasion in PTC cell lines. Conclusion. The identified lncRNAs/miRNAs/mRNA were differentially expressed between normal and cancerous tissues. In addition, identified altered lncRNAs and miRNAs may be potential diagnostic biomarkers for PTC. Additionally, epigenetic modifications via the above lncRNAs and miRNAs may be involved in tumorigenesis of PTC.

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Impact of Multifocality on the Recurrence of Papillary Thyroid Carcinoma

Journal of Clinical Medicine ◽

10.3390/jcm10215144 ◽

2021 ◽

Vol 10 (21) ◽

pp. 5144

Author(s):

Joohyun Woo ◽

Hyeonkyeong Kim ◽

Hyungju Kwon

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cox Regression ◽

Medical Center ◽

Disease Free Survival ◽

Prognostic Impact ◽

Papillary Thyroid ◽

Free Survival ◽

Cox Regression Analysis

The incidence of thyroid cancer has dramatically increased over the last few decades, and up to 60% of patients have multifocal tumors. However, the prognostic impact of multifocality in patients with papillary thyroid carcinoma (PTC) remains unestablished and controversial. We evaluate whether multifocality can predict the recurrence of PTC. A total of 1249 patients who underwent total thyroidectomy for PTC at the Ewha Medical Center between March 2012 and December 2019 were reviewed. In this study, multifocality was found in 487 patients (39.0%) and the mean follow-up period was 5.5 ± 2.7 years. Multifocality was associated with high-risk features for recurrence, including extrathyroidal extension, lymph node metastasis, and margin involvement. After adjustment of those clinicopathological features, 10-year disease-free survival was 93.3% in patients with multifocal tumors, whereas those with unifocal disease showed 97.6% (p = 0.011). Multivariate Cox regression analysis indicated that male sex (HR 2.185, 95% CI 1.047–4.559), tumor size (HR 1.806, 95% CI 1.337–2.441), N1b LN metastasis (HR 3.603, 95% CI 1.207–10.757), and multifocality (HR 1.986, 95% CI 1.015–3.888) were independent predictors of recurrence. In conclusion, multifocality increased the risk of recurrence in patients with PTC. Patients with multifocal PTCs may need judicious treatment and follow-up approaches.

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Progression Risk Assessment of Post-surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.606327 ◽

2021 ◽

Vol 8 ◽

Author(s):

Mengwei Wu ◽

Shuo Li ◽

Jiashu Han ◽

Rui Liu ◽

Hongwei Yuan ◽

...

Keyword(s):

Risk Assessment ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cox Regression ◽

Differentially Expressed ◽

Circular Rnas ◽

American Thyroid Association ◽

Papillary Thyroid ◽

Cerna Network ◽

Progression Risk

Background: Accurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circular RNAs (circRNAs) via the ceRNA mechanism could help establish a novel assessment tool.Methods: ceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. least absolute shrinkage and selection operator (LASSO)–Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC.Results: Six hub DE-mRNAs, namely, CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC, and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the American Thyroid Association risk stratification system and distant Metastasis, patient Age, Completeness of resection, local Invasion, and tumor Size (MACIS) score American Joint Committee on Cancer staging system.Conclusions: Based on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

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