Nomogram-Based New Recurrence Predicting System in Early-Stage Papillary Thyroid Cancer.
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ABSTRACT:
SUBMITTER: Ding Y
PROVIDER: S-EPMC6754965 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThyroid papillary carcinoma (TPC) is the most common type of thyroid cancer (TC). The prognosis of TPC patients with tumor-cell metastasis is poor. Therefore, this study aims to develop a model for predicting TPC patients' recurrence-free survival (RFS).MethodsWe included 546 TPC patients who were clinically and pathologically diagnosed with TPC. The methylation biomarkers that associate with RFS were explored. These 546 samples were divided into training dataset (first 70%) and validation dataset (remaining 30%) randomly. The training dataset was used to identify prognostic biomarkers and construct risk prediction model, in addition, the validation dataset was used to verify the predictive performance of the model. We used Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to identify the significant predictive biomarkers, and establish the relapse risk prediction model from the identified biomarkers.ResultsA 6-DNA methylation signature yielded a high evaluative performance for RFS. The Kaplan-Meier analysis indicated that the 6-DNA methylation signature could significantly distinguish the high- and low-risk patients in training, validation and entire sets. In addition, a nomogram was constructed based on risk score, metastasis status and residual tumor status, and C-index, receiver operating characteristic (ROC) and the calibration plots analysis which demonstrated the good performance and clinical utility of the nomogram.ConclusionsThe results suggested that the 6-DNA methylation signature is the independent prognostic marker for RFS and functioned as a significant tool for guiding the clinical treatment of TPC patients.
| S-EPMC7541134 | biostudies-literature
Project description:BACKGROUND:The AJCC/UICC classification is widely used for predicting survival in papillary thyroid cancer (PTC), but has not been evaluated as a predictor of recurrence. The hypothesis of this study was that the eighth edition of the AJCC system can be used in this novel way. METHODS:All patients in the study underwent surgery for PTC at a high-volume endocrine surgery centre in France between 1985 and 2015. The seventh and eighth editions of the AJCC/UICC staging system for PTC were employed to predict recurrence and disease-specific survival using the Kaplan-Meier and log rank tests. RESULTS:Among 4124 patients (79·7 per cent female), median age was 50 (i.q.r. 38-60)?years; 3906 patients (94·7 per cent) underwent total thyroidectomy, with lymph node dissection in 2495 (60·5 per cent). The eighth edition of the AJCC/UICC staging system placed 91·8, 7·1, 0·4 and 0·7 per cent of patients in stages I-IV respectively. After reclassifying patients from the seventh to the eighth AJCC/UICC edition, the disease was downstaged in 23·8 per cent. Over a median follow-up of 7?years, 260 patients (6·4 per cent) developed recurrent disease, including 5·2 per cent of patients with stage I, 19·6 per cent with stage II, 59 per cent with stage III and 50 per cent with stage IV disease, according to the eighth edition. The eighth edition was a better predictor of recurrence than the seventh edition. CONCLUSION:The eighth edition of the AJCC/UICC staging system appears to be a novel tool for predicting PTC recurrence, which is a meaningful outcome for this indolent disease. The eighth edition can be used to risk-stratify patients, keeping in mind that other molecular and pathological predictive factors must be integrated into the assessment of recurrence risk.
| S-EPMC6825520 | biostudies-literature
Project description:BackgroundPapillary thyroid cancer (PTC) is an indolent disease with a favorable prognosis but characterized by a high recurrence rate. We aimed to improve precise stratification of recurrence risk in PTC patients with early stage using multi-gene signatures.Patients and methodsThe present study was performed using data from The Cancer Genome Atlas (TCGA) and multi-center datasets. Unsupervised consensus clustering was used to obtain the optimal molecular subtypes and least absolute shrinkage and selection operator (LASSO) analysis was performed to identify potential genes for the construction of recurrence signature. Kaplan-Meier survival analysis and the log-rank test was used to detect survival differences. Harrells concordance index (C-index) was used to assess the performance of the DNA damage repair (DDR) recurrence signature.ResultsThrough screening 8 candidate gene sets, the entire cohort was successfully stratified into two recurrence-related molecular subtypes based on DDR genes: DDR-high subtype and DDR-low subtype. The recurrence rate of DDR-high subtype was significantly lower than DDR-low subtype [HR = 0.288 (95%CI, 0.084-0.986), P = 0.047]. Further, a two-gene DDR recurrence signature was constructed, including PER1 and EME2. The high-risk group showed a significantly worse recurrence-free survival (RFS) than the low-risk group [HR = 10.647 (95%CI, 1.363-83.197), P = 0.024]. The multi-center data demonstrated that proportion of patients with low expression of PER1 and EME2 was higher in the recurrence group than those in the non-recurrence group.ConclusionsThese findings could help accurately and reliably identify PTC patients with high risk of recurrence so that they could receive more radical and aggressive treatment strategies and more rigorous surveillance practices.
| S-EPMC10983671 | biostudies-literature
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.
| S-EPMC8500572 | biostudies-literature
Project description:PurposeTo investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then to construct a prediction model based on Lasso-Cox regression and visualize the model built.MethodsClinical data were collected from 547 patients that received minimally invasive treatment in our hospital from January 1, 2012, to December 31, 2016. Lasso regression was used to screen risk factors for recurrence. Then we established Cox proportional hazard regression model and random survival forest model including several parameters screened by Lasso regression. An optimal model was selected by comparing the values of C-index, then the model was visualized and the nomogram was finally plotted.ResultsThe variables screened by Lasso regression including age, gender, cirrhosis, tumor number, tumor size, platelet-albumin-bilirubin index (PALBI), and viral load were incorporated in the Cox model and random survival forest model (P<0.05). The C-index of these two models in the training sets was 0.729 and 0.708, and was 0.726 and 0.700 in the validation sets, respectively. So we finally chose Lasso-Cox regression model, and the calibration curve in the validation set performed well, indicating that the model built has a better predictive ability. And then a nomogram was plotted based on the model chosen to visualize the results.ConclusionsThe present study established a nomogram for predicting recurrence in patients with early-stage HCC based on the Lasso-Cox regression model. This nomogram was of some guiding significance for screening populations at high risk of recurrence after treatment, by which doctors can formulate individualized follow-up strategies or treatment protocols according to the predicted risk of relapse for patients to improve the long-term prognosis.
| S-EPMC9726717 | biostudies-literature
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.
| S-EPMC9526076 | biostudies-literature
Project description:BackgroundThe biological behavior of early recurrence is more invasive and the prognosis is worse in gastric cancer (GC). The risk of early recurrence (ER) for GC in stage II/III has not been reported of which the majority of GC patients are in China. Therefore, it is necessary to analyze the ER of gastric cancer in stage II/III.MethodsThe medical records of 1511 consecutive stage II/III GC patients who received resections were retrospectively reviewed. They were randomly classified into either a development or validation group at a ratio of 7:3. The nomogram was constructed based on prognostic factors using logistic regression analysis and was validated by bootstrap resampling and validation dataset, respectively. Concordance index (C-index) values and calibration curves were used to evaluate the predictive accuracy and discriminatory capability.ResultsThree hundred eleven patients experienced ER, accounting for 20.58% of the GC patients investigated. Multivariate logistic regression analysis identified tumors located at upper, middle third, or mixed, a positive lymph node ratio ≥ 0.335, pTNM stage III, lymphocyte count < 1.5 × 109/L, postoperative infection complications and adjuvant chemotherapy < 6 cycles were all independent predictors for ER after curative resection of stage II/III GC. The C-index value obtained for the model was 0.780 (95% CI, 0.747-0.813), and the calibration curves of validation group yielded a C-index value of 0.739 (95% CI, 0.684-0.794), suggesting the practicability of the model.ConclusionsThe nomogram which was developed for predicting ER of stage II/III GC after surgery had good accuracy and was verified through both internal and external validation. The nomogram established can assist clinicians in determining the optimal therapy strategies in counseling, adjuvant treatments, and subsequent follow-up planning.
| S-EPMC6923869 | biostudies-literature
Project description:IntroductionRecently, the rise in the incidences of thyroid cancer worldwide renders it to be the sixth most common cancer among women. Commonly, Fine Needle Aspiration biopsy predominantly facilitates the diagnosis of the nature of thyroid nodules. However, it is inconsiderable in determining the tumor's state, i.e., benign or malignant. This study aims to identify the key RNA transcripts that can segregate the early and late-stage samples of Thyroid Carcinoma (THCA) using RNA expression profiles.Materials and methodsIn this study, we used the THCA RNA-Seq dataset of The Cancer Genome Atlas, consisting of 500 cancer and 58 normal (adjacent non-tumorous) samples obtained from the Genomics Data Commons (GDC) data portal. This dataset was dissected to identify key RNA expression features using various feature selection techniques. Subsequently, samples were classified based on selected features employing different machine learning algorithms.ResultsSingle gene ranking based on the Area Under the Receiver Operating Characteristics (AUROC) curve identified the DCN transcript that can classify the early-stage samples from late-stage samples with 0.66 AUROC. To further improve the performance, we identified a panel of 36 RNA transcripts that achieved F1 score of 0.75 with 0.73 AUROC (95% CI: 0.62-0.84) on the validation dataset. Moreover, prediction models based on 18-features from this panel correctly predicted 75% of the samples of the external validation dataset. In addition, the multiclass model classified normal, early, and late-stage samples with AUROC of 0.95 (95% CI: 0.84-1), 0.76 (95% CI: 0.66-0.85) and 0.72 (95% CI: 0.61-0.83) on the validation dataset. Besides, a five protein-coding transcripts panel was also recognized, which segregated cancer and normal samples in the validation dataset with F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91-1).ConclusionWe identified 36 important RNA transcripts whose expression segregated early and late-stage samples with reasonable accuracy. The models and dataset used in this study are available from the webserver CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).
| S-EPMC7179925 | biostudies-literature
Project description:Central lymph node (CLN) status is considered to be an important risk factor in patients with papillary thyroid carcinoma (PTC). The aim of the present study was to identify risk factors associated with CLN metastasis (CLNM) for patients with PTC based on preoperative clinical, ultrasound (US) and contrast-enhanced computed tomography (CT) characteristics, and establish a prediction model for treatment plans. A total of 786 patients with a confirmed pathological diagnosis of PTC between January 2021 to December 2022 were included in the present retrospective study, with 550 patients included in the training group and 236 patients enrolled in the validation group (ratio of 7:3). Based on the preoperative clinical, US and contrast-enhanced CT features, univariate and multivariate logistic regression analyses were used to determine the independent predictive factors of CLNM, and a personalized nomogram was constructed. Calibration curve, receiver operating characteristic (ROC) curve and decision curve analyses were used to assess discrimination, calibration and clinical application of the prediction model. As a result, 38.9% (306/786) of patients with PTC and CLNM(-) status before surgery had confirmed CLNM using postoperative pathology. In multivariate analysis, a young age (≤45 years), the male sex, no presence of Hashimoto thyroiditis, isthmic location, microcalcification, inhomogeneous enhancement and capsule invasion were independent predictors of CLNM in patients with PTC. The nomogram integrating these 7 factors exhibited strong discrimination in both the training group [Area under the curve (AUC)=0.826] and the validation group (AUC=0.818). Furthermore, the area under the ROC curve for predicting CLNM based on clinical, US and contrast-enhanced CT features was higher than that without contrast-enhanced CT features (AUC=0.818 and AUC=0.712, respectively). In addition, the calibration curve was appropriately fitted and decision curve analysis confirmed the clinical utility of the nomogram. In conclusion, the present study developed a novel nomogram for preoperative prediction of CLNM, which could provide a basis for prophylactic central lymph node dissection in patients with PTC.
| S-EPMC11332582 | biostudies-literature
Project description:BackgroundThe Memorial Sloan Kettering Cancer Center (MSK) colon cancer recurrence nomogram is a risk calculator that provides patients and clinicians with individualized prediction of recurrence following curative resection of colon cancer. Although validated on multiple separate cohorts, the nomogram requires periodic updating as patient care changes over time. The aim of this study was to evaluate the nomogram's accuracy in a contemporary cohort and modify the tool to reflect improvements in outcome related to advances in colon cancer therapy.MethodsA contemporary patient cohort was compiled, including consecutive colon cancer patients undergoing curative resection for stage I-III colon adenocarcinoma at MSK from 2007 to 2014. The nomogram's predictive accuracy was assessed by concordance index and calibration plots of predicted vs actual freedom from recurrence at 5 years after surgery.ResultsData from a total of 999 eligible patients with complete records were used for validation. Median follow-up among survivors was 37 months. The concordance index was 0.756 (95% confidence interval = 0.707 to 0.805), indicating continued discriminating power, but the calibration plot revealed that the nomogram overestimated recurrence risk. Recalibration of the nomogram by estimating a new baseline freedom-from-recurrence function restored the nomogram's accuracy.ConclusionThe updated nomogram retains the original nomogram's variables but includes a lower baseline estimation of recurrence risk, reflecting improvements in outcomes for all stages of colon cancer, likely resulting from advances in imaging and integration of multiple treatment modalities.
| S-EPMC6512350 | biostudies-literature