scholarly journals A Novel Prognostic Nomogram Based on Metastasis-Related Genes Signature for Predicting the Progression-free Interval in Patients With Papillary Thyroid Carcinoma

2021 ◽  
Author(s):  
Rui Liu ◽  
Mengwei Wu ◽  
Zhen Cao ◽  
Xiaobin Li ◽  
Hongwei Yuan ◽  
...  
Keyword(s):  
High Risk ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Papillary Thyroid ◽  
High Risk Patients ◽  
Free Interval ◽  
Risk Patients

Abstract Background: The recurrence rate for papillary thyroid carcinoma (PTC) after surgery is high, which is a significant issue for patients regarding with low-grade malignancy. We built a novel predictive model with metastasis-related genes (MTGs) and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for PTC.Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a 14-gene signature using Lasso-Penalty regression. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC . We then validated the efficacy of the signature in marking off high risk patients; the nomogram's performance in predicting PFI was also evaluated with receiver operating characteristic (ROC) curve and Harrell's concordance index (C-index).Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with important oncogenic process. Consequently, we found a novel 14-gene signature. The 14-gene signature could distinguish patients with poorer prognosis and predicted PFI accurately. The signature was a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram’s efficacy was superior to the current clinical risk evaluating system in predicting the recurrence of PTC. Conclusions: The 14-gene signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

scholarly journals A Novel Metastasis-related Genes Based Signature for Predicting the Progression-free Interval of Patients With Papillary Thyroid Carcinoma

2022 ◽  
Author(s):  
Rui Liu ◽  
Zhen Cao ◽  
Meng-wei Wu ◽  
Xiao-bin Li ◽  
Hong-wei Yuan ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Cell Lines ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Papillary Thyroid ◽  
Free Interval

Abstract Background: We aimed to build a novel model with metastasis-related genes (MTGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a novel MTGs based signature. After that, we validated the signature on multiple datasets and PTC cell lines. Further, we carried out uni- and multivariate analysis to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC. Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic process. Consequently, we found a novel 10-gene signature and could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.76 and the relevant nomogram had a C-index of 0.80. Also, it was closely related to pivotal clinical characters of datasets and invasiveness of cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC’s PFI. Conclusions: The MTG signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

2021 ◽  
pp. 019459982110098
Author(s):  
Xianhua Zhuo ◽  
Jiandong Yu ◽  
Zhiping Chen ◽  
Zeyu Lin ◽  
Xiaoming Huang ◽  
...  
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Papillary Thyroid Carcinoma ◽  
Lymph Node Metastasis ◽  
Thyroid Carcinoma ◽  
Area Under The Curve ◽  
Papillary Thyroid ◽  
High Risk Patients ◽  
Node Metastasis ◽  
Risk Patients

Objective To establish a dynamic nomogram based on preoperative clinical data for prediction of lateral lymph node metastasis (LLNM) of papillary thyroid carcinoma. Study Design Retrospective study. Setting The Sixth Affiliated Hospital of Sun Yat-Sen University. Methods The data of 477 patients from 2 centers formed the training group and validation group and were retrospectively reviewed. Preoperative clinical factors influencing LLNM were identified by univariable and multivariable analysis and were to construct a predictive dynamic nomogram for LLNM. Receiver operating characteristic analysis and calibration curves were used to evaluate the predictive power of the nomogram. Results The following were identified as independent risk factors for LLNM: male sex (odds ratio [OR] = 4.6, P = .04), tumor size ≥10.5 mm (OR = 7.9, P = .008), thyroid nodules (OR = 6.1, P = .013), irregular tumor shape (OR = 24.6, P = .001), rich lymph node vascularity (OR = 9.7, P = .004), and lymph node location. The dynamic nomogram constructed with these factors is available at https://zxh1119.shinyapps.io/DynNomapp/ . The nomogram showed good performance, with an area under the curve of 0.956 (95% CI, 0.925-0.986), a sensitivity of 0.87, and a specificity of 0.91, if high-risk patients were defined as those with a predicted probability ≥0.3 or total score ≥200. The nomogram performed well in the external validation cohort (area under the curve, 0.915; 95% CI, 0.862-0.967). Conclusions The dynamic nomogram for preoperative prediction of LLNM in papillary thyroid carcinoma can help surgeons identify high-risk patients and develop individualized treatment plans.

scholarly journals Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259534
Author(s):  
Chakit Arora ◽  
Dilraj Kaur ◽  
Leimarembi Devi Naorem ◽  
Gajendra P. S. Raghava
Keyword(s):  
High Risk ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Prediction Models ◽  
Prognostic Biomarker ◽  
Papillary Thyroid ◽  
Apoptotic Pathway ◽  
Altered Expression ◽  
Risk Of Death ◽  
Risk Patients

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10−4 (C = 0.88, logrank-p = 1.44x10-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

scholarly journals MON-438 Papillary Thyroid Carcinoma Arising in a Thyroglossal Duct Cyst: A Case Report

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Gabriela Zuniga ◽  
Gliceida Galarza Fortuna ◽  
Alejandro Guzman-Davant ◽  
Juan Paramo ◽  
Michael Pagacz
Keyword(s):  
High Risk ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Total Thyroidectomy ◽  
Neck Mass ◽  
Papillary Thyroid ◽  
Thyroglossal Duct ◽  
Risk Patients ◽  
Sistrunk Procedure ◽  
Solid Area

Abstract Introduction: Thyroglossal duct cysts (TGDCs) are uncommon benign congenital entities. Rarely, thyroid carcinoma can arise from a TGDC; the most common being papillary thyroid carcinoma (PTC). Similar to TGDC, carcinomas originating within them can present as an asymptomatic midline neck mass. Signs of malignancy include dysphagia, dysphonia, weight loss, and rapid growth. Given the rarity of TGDC carcinomas, clinical management remains controversial, particularly regarding the requirement for total thyroidectomy. Case: A 52-year-old female with history of an anterior central neck mass initially noted in 2017. A 0.3-cm left lobe mid-segment cyst and a complex thyroglossal avascular simple cyst measuring 2.4 × 1.1 × 1.8 cm was observed during ultrasound (US). She presented to the endocrinology clinic in April 2019 due to progressive enlargement of the mass. Repeat thyroid US revealed that the cystic structure had become complex with a peripheral solid component and measured 3.3 × 2.1 × 2.2 cm. FNA was performed and found to be suspicious for PTC (Bethesda category V) and positive for the BRAF V600E mutation. Patient was referred for surgical evaluation. Physical examination revealed a midline anterior 10-cm, painless, and fixed mass above the thyroid that moved with deglutition and tongue protrusion. Contrast computed tomography scan showed a large multiloculated cystic structure measuring 4.1 × 4.4 × 5.9 cm. A lobulated soft tissue mass measuring 2.2 × 2.4 × 3.0 cm was noted internally along the inferior margin of the cyst. She underwent en-block resection of the TGDC in addition to a total thyroidectomy. Histopathological examination identified a 7.5 × 5.5 × 5.0 cm cystic mass with fluctuation and a firm, solid area in the lower portion measuring 2.6 × 2.4 cm. Thyroid gland examination was otherwise unremarkable. No areas of extension of the mass into the thyroid tissue were clearly identified and no other gross lesions were observed. The solid area within TGDC contained a tumor with findings characteristic of PTC. Postoperatively, she was placed on thyroid hormone replacement therapy. Conclusion: The main difficulty encountered with cancer developing from TGDC is that the diagnosis is usually made during surgery and from definitive pathological samples. The most common surgical procedure used is the Sistrunk procedure. Some studies have suggested that this procedure alone is an adequate therapy, but others advocate the need for total thyroidectomy. The Sistrunk procedure is considered to be appropriate for low-risk patients, but high-risk patients must undergo total thyroidectomy. The decision to perform a total thyroidectomy in this patient was based on her high-risk classification due to: age, sex, cyst size, and a positive FNA for malignancy. Follow-up includes an annual physical examination, thyroglobulin levels, and an US every 6 months during the first year and annually thereafter.

scholarly journals Prognostic Biomarkers for Predicting Papillary Thyroid Carcinoma Patients at High Risk Using Nine Genes of Apoptotic Pathway

2020 ◽  
Author(s):  
Chakit Arora ◽  
Dilraj Kaur ◽  
G.P.S Raghava
Keyword(s):  
High Risk ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Prediction Models ◽  
Prognostic Biomarker ◽  
Papillary Thyroid ◽  
Apoptotic Pathway ◽  
Aberrant Expression ◽  
Altered Expression ◽  
Risk Patients

AbstractObjectivesAberrant expression of apoptotic genes has been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood.Materials and methodsIn this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes.ResultsIt was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models. Our voting-based model achieved highest performance (HR=41.59, p=3.36×10−4, C=0.84, logrank-p=3.8×10−8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR=57.04 with p=10−4 (C=0.88, logrank-p=1.44×10−9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Since these genes can also be used as potential therapeutic targets in PTC, we identified potential drug molecules which could modulate their expression profile.ConclusionThis study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

Papillary Thyroid Carcinoma: How Radical Lymphadenectomy Should be Performed?

Swiss Surgery ◽  
2003 ◽  
Vol 9 (2) ◽  
pp. 63-68
Author(s):  
Schweizer ◽  
Seifert ◽  
Gemsenjäger
Keyword(s):  
High Risk ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Low Risk ◽  
Papillary Thyroid ◽  
Sentinel Nodes ◽  
Fand Sich ◽  
Radical Lymphadenectomy ◽  
N Status

Fragestellung: Die Bedeutung von Lymphknotenbefall bei papillärem Schilddrüsenkarzinom und die optimale Lymphknotenchirurgie werden kontrovers beurteilt. Methodik: Retrospektive Langzeitstudie eines Operateurs (n = 159), prospektive Dokumentation, Nachkontrolle 1-27 (x = 8) Jahre, Untersuchung mit Bezug auf Lymphknotenbefall. Resultate: Staging. Bei 42 Patienten wurde wegen makroskopischem Lymphknotenbefall (cN1) eine therapeutische Lymphadenektomie durchgeführt, mit pN1 Status bei 41 (98%) Patienten. Unter 117 Patienten ohne Anhalt für Lymphknotenbefall (cN0) fand sich okkulter Befall bei 5/29 (17%) Patienten mit elektiver (prophylaktischer) Lymphadenektomie, und bei 2/88 (2.3%) Patienten ohne Lymphadenektomie (metachroner Befall) (p < 0.005). Lymphknotenrezidive traten (1-5 Jahre nach kurativer Primärtherapie) bei 5/42 (12%) pN1 und bei 3/114 (2.6%) cN0, pN0 Tumoren auf (p = 0009). Das 20-Jahres-Überleben war bei TNM I + II (low risk) Patienten 100%, d.h. unabhängig vom N Status; pN1 vs. pN0, cN0 beeinflusste das Überleben ungünstig bei high risk (>= 45-jährige) Patienten (50% vs. 86%; p = 0.03). Diskussion: Der makroskopische intraoperative Lymphknotenbefund (cN) hat Bedeutung: - Befall ist meistens richtig positiv (pN1) und erfordert eine ausreichend radikale, d.h. systematische, kompartiment-orientierte Lymphadenektomie (Mikrodissektion) zur Verhütung von - kurablem oder gefährlichem - Rezidiv. - Okkulter Befall bei unauffälligen Lymphknoten führt selten zum klinischen Rezidiv und beeinflusst das Überleben nicht. Wir empfehlen eine weniger radikale (sampling), nur zentrale prophylaktische Lymphadenektomie, ohne Risiko von chirurgischer Morbidität. Ein empfindlicherer Nachweis von okkultem Befund (Immunhistochemie, Schnellschnitt von sampling Gewebe oder sentinel nodes) erscheint nicht rational. Bei pN0, cN0 Befund kommen Verzicht auf 131I Prophylaxe und eine weniger intensive Nachsorge in Frage.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

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