Dataset Information

Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma.

ABSTRACT: Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions. Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan-Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset. Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3, and FXYD6, and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI. Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making.

SUBMITTER: Wu M

PROVIDER: S-EPMC6872544 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma.

Wu Mengwei M Yuan Hongwei H Li Xiaobin X Liao Quan Q Liu Ziwen Z

Frontiers in endocrinology 20191115

<b>Background:</b> The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions. <b>Materials and Methods:</b> Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and c ...[more]

PMID: 31803141

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Project description:The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

Project description:BackgroundThis study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients.MethodsDifferentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center.ResultsIn total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5-7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062-8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061-4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225-9.807, p = 0.019), and five-DNA methylation risk model (HR = 3.654, 95%CI: 1.458-9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan-Meier survival curve also proved to be significantly different (p < 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01).ConclusionsBriefly, our work established and verified a five-DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.

Project description:BackgroundAlthough the prognosis of papillary thyroid cancer (PTC) is relatively good, some patients experience recurrence or distant metastasis after thyroidectomy and progress to radioactive iodine refractory stage. Therefore, accurate prediction of clinical outlook can aid to screen out the minority of patients with poorer prognosis and avoid excessive treatment in low-risk patients.MethodsThe RNA-seq and clinical data of PTC patients was downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Multivariate and Lasso Cox regression analyses were used to construct a prognostic nomogram to predict overall survival (OS). Thereafter, quantitative RT-PCR and Human Protein Atlas (HPA) database were employed to verify the expression of key genes.ResultsA four-gene risk score comprising ABI3BP, DPT, MRO, and TENM1 was exhibited strong prognostic value. Moreover, an integrated nomogram was established based on the risk score, age, AJCC (American Joint Commission on Cancer) stage, tumor size, extrathyroidal extension, and history of neoadjuvant treatment, which exhibited significantly better predictive performance than TNM stage system (P < 0.05). GSEA (Gene Set Enrichment Analysis) and GSVA (Gene Set Variation Analysis) revealed that the different tumor-associated hallmarks, biological processes, and pathways were substantially enriched in the poor-prognosis group. In addition, a ceRNA network was constructed by including the four genes (ABI3BP, DPT, MRO, and TENM1), 54 lncRNAs, and 10 miRNAs. Finally, both the relative mRNA and protein expression of ABI3BP, DPT, MRO, and TENM1 were validated.ConclusionThe present study identified a four-gene risk signature and developed a novel nomogram, which could be regarded as a reliable prognostic model for PTC patients. The findings also revealed preliminary potential mechanisms that may influence the prognosis outcome. These results can be conducive to design personalized treatment and prognosis management in affected patients.

Project description:AbstractVarious researches demonstrated that transcription factors (TFs) played a crucial role in the progression and prognosis of cancer. However, few studies indicated that TFs were independent biomarkers for the prognosis of thyroid papillary carcinoma (TPC). Our aim was to establish and validate a novel TF signature for the prediction of TPC patients' recurrence-free survival (RFS) from The Cancer Genome Atlas (TCGA) database to improve the prediction of survival in TPC patients.The genes expression data and corresponding clinical information for TPC were obtained from TCGA database. In total, 722 TFs and 545 TPC patients with eligible clinical information were determined to build a novel TF signature. All TFs were included in a univariate Cox regression model. Then, the least absolute shrinkage and selection operator Cox regression model was employed to identify candidate TFs relevant to TPC patients' RFS. Finally, multivariate Cox regression was conducted via the candidate TFs for the selection of the TF signatures in the RFS assessment of TPC patients.We identified 6 TFs that were related to TPC patients' RFS. Receiver operating characteristic analysis was performed in training, validation, and whole datasets, we verified the high capacity of the 6-TF panel for predicting TPC patients' RFS (AUC at 1, 3, and 5 years were 0.880, 0.934, and 0.868, respectively, in training dataset; 0.760, 0.737, and 0.726, respectively, in validation dataset; and 0.777, 0.776, and 0.761, respectively, in entire dataset). The result of Kaplan-Meier analysis suggested that the TPC patients with low scores had longer RFS than the TPC patients with high score (P = .003). A similar outcome was displayed in the validation dataset (P = .001) and the entire dataset (P = 2e-05). In addition, a nomogram was conducted through risk score, cancer status, C-index, receiver operating characteristic, and the calibration plots analysis implied good value and clinical utility of the nomogram.We constructed and validated a novel 6-TF signature-based nomogram for predicting the RFS of TPC patients.

Dataset Information

Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma.

Publications

Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma.

Similar Datasets

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