A nomogram to predict skip metastasis in papillary thyroid cancer

Prediction of bilateral thyroid carcinoma and lateral cervical lymph node metastasis in PTC patients with suspicious thyroid nodules

PURPOSE

This study aimed to evaluate the factors associated with bilateral papillary thyroid carcinoma (PTC) and lateral cervical lymph node metastasis (LLNM) in patients with suspicious unilateral PTC.

METHODS

This study analyzed patients with suspicious unilateral PTC who were enrolled in a university hospital between 2016 and 2019 in Zhejiang, China. Using logistic regression, the study examined the factors associated with bilateral PTC and LLNM in demographic data, anthropometric measurements, lifestyle factors, medical history, preoperative diagnostic tests, and histopathological factors.

RESULTS

A total of 256 patients, with a mean age of 49 years, were enrolled. Bilateral PTC was associated with multifocality (aOR: 5.069, 95% CI: 2.440-10.529, P < 0.001), and contralateral nodule in the upper (aOR: 9.073, 95% CI: 2.111-38.985, P = 0.003) and middle (aOR: 9.926, 95% CI: 2.683-36.717, P < 0.001). LLNM was positively associated with bilateral PTC (aOR, 4.283, 95% CI: 1.378-13.308, p = 0.012), male (aOR, 3.377, 95% CI: 1.205-9.461, P = 0.021), upper location of carcinoma (aOR, 3.311, 95% CI: 1.091-10.053, p = 0.035), and punctate echogenic foci (aOR, 3.309, 95% CI: 1.165-9.394, P = 0.025). Contralateral maximal nodule in the upper (aOR: 0.098, 95% CI: 0.015-0.628, p = 0.014), middle (aOR: 0.114, 95% CI: 0.033-0.522, p < 0.001), and lower (aOR, 0.028, 95% CI: 0.003-0.276, P = 0.002) location were inversely associated with LLNM.

CONCLUSION

Upper and middle location of contralateral nodule and tumor multifocality predicted the risk bilateral PTC. Bilateral PTC, male, upper tumor location, punctate echogenic foci and contralateral nodule location in the entire lobes were independent predictors for LLNM.

m6A reader IGF2BP2 promotes lymphatic metastasis by stabilizing DPP4 in papillary thyroid carcinoma

Lymph node metastasis (LNM) is a major cause of locoregional recurrence of papillary thyroid carcinoma (PTC). However, the mechanisms responsible for LNM are unclear. Aberrant N6-methyladenosine (m6A) RNA modification plays a vital role in cancer progression and metastasis, and whether m6A modification regulates LNM in PTC remains to be determined. This study showed that IGF2BP2 was upregulated in PTC and positively associated with LNM. Functionally, IGF2BP2 knockdown significantly inhibited PTC cell proliferation and invasion in vitro, and vice versa. Moreover, IGF2BP2 knockdown significantly inhibited lymphatic metastasis in vivo. Mechanistically, Human m6A epitranscriptomic microarray, MeRIP, and RIP assays demonstrated that IGF2BP2 activated the NF-KB pathway by enhancing DPP4 stability in an m6A-dependent manner. Furthermore, IGF2BP2 knockdown increased the sensitivity of PTC cells to cisplatin therapy to a certain extent, while its overexpression produced the opposite effects. Overall, this study uncovers that IGF2BP2 promotes lymphatic metastasis via stabilizing DPP4 in an m6A-dependent manner, and provides new insights for understanding the mechanism of lymphatic metastasis in PTC.

A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures

Aims

This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures.

Methods

504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses. Based on these parameters, a nomogram model was constructed and validated. The predicting performance of nomogram was evaluated by discrimination, calibration, and clinical usefulness.

Results

The predictors contained in the nomogram model included age, surgical approach, 1-day postoperative D-dimer value and admission ultrasound diagnosis of the lower limb vein. Furthermore, the area under the ROC curve (AUC) for the specific DVT risk-stratification nomogram model (0.815; 95% CI 0.746-0.884) was significantly higher than the current model (Caprini) (0.659; 95% CI 0.572-0.746, P < 0.05). According to the calibration plots, the prediction and actual observation were in good agreement. In the range of threshold probabilities of 0.2-0.8, the predictive performance of the model on DVT risk could be maximized.

Conclusions

The current predictive model could serve as a reliable tool to quantify the possibility of postoperative DVT in hip fractures patients.

Risk factor analysis and prediction model for papillary thyroid carcinoma with lymph node metastasis

Objective

We aimed to identify the clinical factors associated with lymph node metastasis (LNM) based on ultrasound characteristics and clinical data, and develop a nomogram for personalized clinical decision-making.

Methods

A retrospective analysis was performed on 252 patients with papillary thyroid carcinoma (PTC). The patient's information was subjected to univariate and multivariate logistic regression analyses to identify risk factors. A nomogram to predict LNM was established combining the risk factors. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve, calibration curve, cross-validation, decision curve analysis (DCA), and clinical impact curve.

Results

There are significant differences between LNM and non-LNM groups in terms of age, sex, tumor size, hypoechoic halo around the nodule, thyroid capsule invasion, lymph node microcalcification, lymph node hyperechoic area, peak intensity of contrast (PI), and area under the curve (AUC) of the time intensity curve of contrast (P<0.05). Age, sex, thyroid capsule invasion, lymph node microcalcification were independent predictors of LNM and were used to establish the predictive nomogram. The ROC was 0.800, with excellent discrimination and calibration. The predictive accuracy of 0.757 and the Kappa value was 0.508. The calibration curve, DCA and calibration curve demonstrated that the prediction model had excellent net benefits and clinical practicability.

Conclusion

Age, sex, thyroid capsule invasion, and lymph node microcalcification were identified as significant risk factors for predicting LNM in patients with PTC. The visualized nomogram model may assist clinicians in predicting the likelihood of LNM in patients with PTC prior to surgery.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram

Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p= 0.001), tumor location (p= 0.000), extrathyroidal extension (p= 0.000), and calcification (p= 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p= 0.000) and calcification (p= 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors. Evidence level: This article's evidence level is 3. Level 3 evidence is derived from non-randomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

A nomogram model to predict the acute venous thromboembolism risk after surgery in patients with glioma

INTRODUCTION

Postoperative venous thromboembolism (VTE) is a common complication for glioma patients, with an incidence rate of about 20 %. The purpose of this study was to explore the risk factors of acute VTE after glioma surgery, which may provide an essential reference for clinical guidance on the prevention of acute VTE.

MATERIALS AND METHODS

A total of 435 patients who underwent glioma surgery from 2012 to 2021 were included in this study. Duplex ultrasonography was performed routinely 3-5 days after the surgery to define VTE. Univariate and multivariate logistic regression analyses were performed to explore the independent predictor of acute VTE after glioma surgery and use these selected risk factors to construct and validate a nomogram.

RESULTS

Several risk factors for predicting acute VTE after glioma surgery were identified and used to build the nomogram: age, operation time, systemic immune-inflammation index (SII), hypertension, and diabetes mellitus. The area under the curve of the nomogram was 0.834, indicating good discrimination. Hosmer-Lemeshow of the calibration curve was 3.05 (P = 0.98), showing a high degree of agreement between the prediction and actual outcome. Decision curve analysis indicated that the nomogram model was helpful when the incidence of VTE was 5-80 %.

CONCLUSIONS

A nomogram to predict acute VTE after glioma surgery was constructed and validated. Clinicians can use this predictive model to achieve risk assessment and take different treatment measures to prevent acute postoperative VTE and improve patients' quality of life effectively.

Preoperative Cervical Lymph Node Metastasis Prediction in Papillary Thyroid Carcinoma: A Noninvasive Clinical Multimodal Radiomics (CMR) Nomogram Analysis

This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76-0.93) and 0.84 (0.71-0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74-0.90) and 0.77 (0.61-0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making.

Risk factors for skip metastasis in patients with papillary thyroid microcarcinoma

BACKGROUND

Lymph node metastasis (LNM) is prevalent in papillary thyroid microcarcinoma (PTMC) and is essential when determining tumor stage and prognosis. Positive lateral LNM with negative central LNM is defined as skip metastasis. Thyroid carcinoma's risk factors for skip metastasis remain controversial, especially in PTMC. This study aimed to determine the clinical features as well as the risk factors of skip metastasis among patients with PTMC.

METHODS

We conducted retrospective research among patients with PTMC who were subjected to treatment at our Hospital between January 2018 and December 2019 by reviewing their medical records. A database containing demographic characteristics, ultrasonography features, blood test outcomes, operation information, pathology details, and follow-up information was constructed. The link between skip metastasis and clinicopathological features of PTMC was evaluated using univariate as well as multivariate analyses.

RESULTS

Overall, 293 patients diagnosed with PTMC and lateral LNM were included. There were 91 men (31.1%) and 202 women (68.9%). The median age was 38 (31-47) years. Fifty patients were diagnosed with skip metastases. Levels III and II + III were the most prevalent in single-level and two-level metastasis, correspondingly. Univariate and multivariate analyses detected two independent factors linked to skip metastasis in PTMC: female sex (odds ratio = 2.609, 95% confidence interval (CI): 1.135-6.000; p = 0.024) and location of the tumor (upper portion) (odds ratio = 2.959, 95% CI: 1.552-5.639; p = 0.001).

CONCLUSIONS

Skip metastasis is prevalent in thyroid carcinoma. Female sex and tumor location (upper portion) are independently linked to skip metastasis in PTMC. Patients who have these two risk factors should undergo a meticulous preoperative and intraoperative evaluation of lymph node status.

Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning

OBJECTIVES

To explore the molecular mechanisms underlying aggressive progression of papillary thyroid microcarcinoma and identify potential biomarkers.

METHODS

Samples were collected and sequenced using tandem mass tag-labeled liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were identified and further analyzed using Mfuzz and protein-protein interaction analysis (PPI). Parallel reaction monitoring (PRM) and immunohistochemistry (IHC) were performed to validate the DEPs.

RESULTS

Five thousand, two hundred and three DEPs were identified and quantified from the tumor/normal comparison group or the N1/N0 comparison group. Mfuzz analysis showed that clusters of DEPs were enriched according to progressive status, followed by normal tissue, tumors without lymphatic metastases, and tumors with lymphatic metastases. Analysis of PPI revealed that DEPs interacted with and were enriched in the following metabolic pathways: apoptosis, tricarboxylic acid cycle, PI3K-Akt pathway, cholesterol metabolism, pyruvate metabolism, and thyroid hormone synthesis. In addition, 18 of the 20 target proteins were successfully validated with PRM and IHC in another 20 paired validation samples. Based on machine learning, the five proteins that showed the best performance in discriminating between tumor and normal nodules were PDLIM4, ANXA1, PKM, NPC2, and LMNA. FN1 performed well in discriminating between patients with lymph node metastases (N1) and N0 with an AUC of 0.690. Finally, five validated DEPs showed a potential prognostic role after examining The Cancer Genome Atlas database: FN1, IDH2, VDAC1, FABP4, and TG. Accordingly, a nomogram was constructed whose concordance index was 0.685 (confidence interval: 0.645-0.726).

CONCLUSIONS

PDLIM4, ANXA1, PKM, NPC2, LMNA, and FN1 are potential diagnostic biomarkers. The five-protein nomogram could be a prognostic biomarker.

A Potential Four-Gene Signature and Nomogram for Predicting the Overall Survival of Papillary Thyroid Cancer

Background. Although 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. Methods. The 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. Results. A 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. Conclusion. The 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.

Nomogram individually predicts the risk for distant metastasis and prognosis value in female differentiated thyroid cancer patients: A SEER-based study

Objective

Distant metastasis (DM) is an important prognostic factor in differentiated thyroid cancer (DTC) and determines the course of treatment. This study aimed to establish a predictive nomogram model that could individually estimate the risk of DM and analyze the prognosis of female DTC patients (FDTCs).

Materials and methods

A total of 26,998 FDTCs were retrospectively searched from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 and randomly divided into validation and training cohorts. Univariate and multivariate analyses were performed to screen for prognostic factors and construct a prediction nomogram. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and a calibration curve. The overall survival (OS) and cancer-specific survival (CSS) were evaluated by Kaplan–Meier (K-M) analysis.

Results

A total of 263 (0.97%) FDTCs were reported to have DM. K-M analysis showed the association of multiple-organ metastases and brain involvement with lower survival rates (P &lt; 0.001) in patients. Tumor size, age at diagnosis, thyroidectomy, N1 stage, T3–4 stage, and pathological type were independent predictive factors of DM in FDTCs (all P &lt; 0.001). Similarly, age at diagnosis, Black, DM, T3–4 stage, thyroidectomy, and lung metastasis were determined as independent prognostic factors for FDTCs (all P &lt; 0.001). Several predictive nomograms were established based on the above factors. The C-index, AUC, and calibration curves demonstrated a good performance of these nomogram models.

Conclusion

Our study was successful in establishing and validating nomograms that could predict DM, as well as CSS and OS in individual patients with FDTC based on a large study cohort. These nomograms could enable surgeons to perform individualized survival evaluation and risk stratification for FDTCs.

[Risk factors of skip lateral cervical lymph node metastasis in papillary thyroid carcinoma]

Objective:To investigate the incidence and risk factors of skip lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma（PTC）. Methods:The clinical and pathological data of 85 patients with PTC who underwent total thyroidectomy plus central and lateral neck dissection in the Department of Otolaryngology Head and Neck Surgery, the First Affiliated Hospital of Hebei North University from January 2018 to January 2022 were analyzed retrospectively. SPSS 26.0 software was used to process the data, and univariate and multivariate analysis were performed to assess the relationships between skip lateral cervical lymph node metastasis and clinicopathological characteristics. Results:There were 31 cases（36.5%） of skipped lateral cervical lymph node metastasis. Univariate analysis showed that the largest tumor diameter ≤5 mm（P=0.006） and the tumor located in the upper pole of the thyroid（P=0.002） were associated with the occurrence of skip lateral cervical lymph node metastasis in patients with PTC. Most of the skip metastases involved a single area（18/31, 58.1%）, of which area Ⅲ was most likely to be involved（10/31, 32.3%）, followed by area Ⅱ（5/31, 16.1%）. The results of binary logistic analysis showed that tumor diameter less than 5 mm（OR 7.800, 95%CI 1.710-21.394, P=0.005） and tumor at the upper pole of the gland（OR 4.060, 95%CI 1.468-11.235, P=0.007） were independent risk factors of skip lateral cervical lymph node metastasis in PTC patients. Conclusion:PTC patients with tumor diameter ≤5 mm and tumor located in the upper pole of the gland are more prone to skip lateral cervical lymph node metastasis. When the diameter of the tumor is less than 5 mm and the tumor is located at the upper pole of the gland, careful evaluation should be made before operation, even in the absence of central lymph node metastasis, attention should be paid to the possibility of lateral cervical lymph node metastasis.

The Prognosis of Skip Metastasis in Papillary Thyroid Microcarcinoma Is Better Than That of Continuous Metastasis

CONTEXT

A few papillary thyroid microcarcinomas (PTMCs) may have skip metastasis (SLNM), but the risk factors remain controversial and the prognosis is unclear.

OBJECTIVES

To investigate the incidence, lymph node metastasis (LNM) patterns, risk factors, and prognosis of SLNM in PTMCs.

METHODS

We reviewed the medical records of PTMC patients who underwent thyroid surgery in our institution. Analyses of risk factors were performed for SLNM. Recurrence-free survival (RFS) of SLNM, central lymph node metastasis (CLNM), and continuous metastasis (CLNM and lateral lymph node metastasis [CLNM + LLNM]) were compared after propensity score matching (PSM).

RESULTS

SLNM was detected in 1.7% (50/3923) and frequently involved level III (66.7%). Compared with CLNM + LLNM, SLNM had more LNM at a single level (P < 0.01) and less LNM at 2 levels (P < 0.05). A tumor size of 0.5 to 1 cm (odds ratio [OR], 2.26; 95% CI, 1.27-4.00) and location in the upper pole (OR, 3.30; 95% CI, 2.02-5.40) were independent risk factors for SLNM. A total of 910 (23.2%) PTMCs with LNM were included in the prognostic analysis. At a median follow-up of 60 months, the RFS of SLNM did not differ from that of CLNM (P = 0.10) but was significantly higher than that of CLNM + LLNM (P < 0.01) after using PSM.

CONCLUSIONS

When the tumor size is 0.5 to 1 cm or its location is in the upper pole, we must remain vigilant to SLNM in PTMC. Because its prognosis is comparable to that of only CLNM and better than that of CLNM + LLNM, less intensive treatment should be considered.

A seven-autophagy-related gene signature for predicting the prognosis of differentiated thyroid carcinoma

Background

Numerous studies have implicated autophagy in the pathogenesis of thyroid carcinoma. This investigation aimed to establish an autophagy-related gene model and nomogram that can help predict the overall survival (OS) of patients with differentiated thyroid carcinoma (DTHCA).

Methods

Clinical characteristics and RNA-seq expression data from TCGA (The Cancer Genome Atlas) were used in the study. We also downloaded autophagy-related genes (ARGs) from the Gene Set Enrichment Analysis website and the Human Autophagy Database. First, we assigned patients into training and testing groups. R software was applied to identify differentially expressed ARGs for further construction of a protein-protein interaction (PPI) network for gene functional analyses. A risk score-based prognostic risk model was subsequently developed using univariate Cox regression and LASSO-penalized Cox regression analyses. The model’s performance was verified using Kaplan-Meier (KM) survival analysis and ROC curve. Finally, a nomogram was constructed for clinical application in evaluating the patients with DTHCA. Finally, a 7-gene prognostic risk model was developed based on gene set enrichment analysis.

Results

Overall, we identified 54 differentially expressed ARGs in patients with DTHCA. A new gene risk model based on 7-ARGs (CDKN2A, FGF7, CTSB, HAP1, DAPK2, DNAJB1, and ITPR1) was developed in the training group and validated in the testing group. The predictive accuracy of the model was reflected by the area under the ROC curve (AUC) values. Univariate and multivariate Cox regression analysis indicated that the model could independently predict the prognosis of patients with THCA. The constrained nomogram derived from the risk score and age also showed high prediction accuracy.

Conclusions

Here, we developed a 7-ARG prognostic risk model and nomogram for differentiated thyroid carcinoma patients that can guide clinical decisions and individualized therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02590-6.

Clinicopathologic predictors of central lymph node metastases in clinical node-negative papillary thyroid microcarcinoma: a systematic review and meta-analysis

Background

The presence of central lymph node metastases (CLNM) has been suggested as a risk factor for poorer prognosis and recurrence in papillary thyroid microcarcinoma (PTMC). However, the clinicopathologic factors for CLNM in clinical node-negative (CN0) PTMC were not well defined. This study aimed to perform a systematic review and meta-analysis to investigate the significant clinicopathologic predictors of CLNM in CN0 PTMC.

Methods

A systematic literature search was performed in PubMed, Embase, Cochrane Library, and Web of Science. Case-control studies on the association of clinicopathologic risk factors with CLNM in CN0 PTMC were included.

Results

Thirteen eligible studies involving 6068 patients with CN0 PTMC were included. From the pooled analyses, male (odds ratio [OR]: 2.07, 95% CI: 1.49–2.87, P < 0.001), multifocality (OR: 1.88, 95% CI: 1.54–2.29, P < 0.001), tumor size > 5 mm (OR: 1.84, 95% CI: 1.55–2.18, P < 0.001), and extrathyroidal extension (OR: 1.96, 95% CI: 1.30–2.95, P = 0.001) are significantly associated with increased risk of CLNM in CN0 PTMC. A sample size with a cutoff point of 200 was identified as the source of heterogeneity for sex according to meta-regression (t = 3.18, P = 0.033). Then, the subgroup analysis of male was performed, which illustrated that male increased the risk of CLNM in the small sample group (SG) and the large sample group (LG) by 6.11-folds and 2.01-folds, respectively (SG: OR, 6.11, 95% CI, 3.16–11.81, P < 0.001; LG: OR, 2.01, 95% CI, 1.65–2.46, P < 0.001).

Conclusions

Male, multifocality, tumor size > 5 mm, and extrathyroidal extension may be reliable clinical predictors of CLNM in CN0 PTMC. Moreover, prophylactic central lymph node dissection should be considered in surgical decision-making for CN0 PTMC patients, who are male, multifocal, with tumor size > 5 mm, and with extrathyroidal extension.

Trial registration

CRD42021242211 (PROSPERO)

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02573-7.

The Role of m6A RNA Methylation-Related lncRNAs in the Prognosis and Tumor Immune Microenvironment of Papillary Thyroid Carcinoma

Emerging evidence has indicated that N6-methylandenosine (m6A) RNA methylation plays a critical role in cancer development. However, the function of m6A RNA methylation-related long noncoding RNAs (m6A-lncRNAs) in papillary thyroid carcinoma (PTC) has never been reported. This study aimed to investigate the role of m6A-lncRNAs in the prognosis and tumor microenvironment (TME) of PTC. Three subgroups (clusters 1, 2, and 3) were identified by consensus clustering of 19 prognosis-related m6A-lncRNA regulators, of which cluster 1 is preferentially related to unfavorable prognosis, lower immune scores, and distinct immune infiltrate level. A risk-score model was established based on 8 prognosis-related m6A-lncRNAs. Patients with a high-risk score showed a worse prognosis, and the ROC indicated a reliable prediction performance for patients with PTC (AUC = 0.802). As expected, the immune scores, the infiltration levels of immune cells, and ESTIMATE scores in the low-risk subgroups were notably higher (p < 0.001) when compared with those in high-risk subgroups. Furthermore, GSEA analysis revealed that tumor associated pathways, hallmarks, and biological processes were remarkably enriched in the high-risk subgroup. Further analysis indicated that the risk score and age were independent prognostic factors for PTC. An integrated nomogram was constructed that accurately predicted the survival status (AUC = 0.963). Moreover, a lncRNA-miRNA-mRNA regulated network was established based on seven prognosis-related m6A-lncRNAs. In addition, 30 clinical samples and different PTC cells were validated. This is the first study to reveal that m6A-lncRNAs plays a vital role in the prognosis and TME of PTC. To a certain degree, m6A-lncRNAs can be considered as new, promising prognostic biomarkers and treatment targets.

A critical analysis of the current TNM classification for differentiated thyroid carcinoma in young patients: Time for a change?

BACKGROUND

The current TNM classification that simply classifies differentiated thyroid carcinoma (DTC) patients younger than 55 years into stage I and stage II based on the presence or absence of distant metastases has been questioned. In this study, we reexamined the impact of T status and N status on prognosis and then developed a new prediction model to improve the predictability of cancer-specific survival (CSS) in young patients.

MATERIALS AND METHODS

Kaplan-Meier method was applied to calculate the CSS. Multivariable Cox proportional hazards models were used to assess the impact of T status and N status on CSS after adjustment for known covariates. The area under the receiver operating characteristic curve (AUC), C-index, Bayesian information criterion (BIC), and Akaike information criterion (AIC) were calculated to compare model performance.

RESULTS

A total of 9,242 DTC patients younger than 55 years were enrolled in the study. After adjusting for gender, age at diagnosis, race, pathology subtype, N stage, and M stage, T3 disease [hazard ratio (HR): 3.78, P = 0.006] and T4 disease (HR: 7.96, P < 0.001) remain independent predictors of CSS. Similarly, the 10-year CSS rate of N1b disease (HR: 3.78, P < 0.001) was significantly higher than that of N0 disease after adjustment. Moreover, Kaplan-Meier survival analysis showed that the 10-year CSS of stage II disease in younger patients with DTC showed a sharp decrease compared with that in older patients with DTC (74.47% vs. 98.43%, P < 0.001). Furthermore, a modified TNM staging system based on significantly prognostic T stage and N stage was established, which showed better performance than the current TNM staging system (P < 0.05). The new prediction model is also applicable to papillary thyroid carcinoma patients and follicular thyroid carcinoma patients.

CONCLUSIONS

This is the first study to question the rationality of the current TNM staging system for patients younger than 55 years and successfully develop a new prognostic model, which improves prognostic stratification and guides individualized management.

Sex disparities in thyroid cancer: a SEER population study

BACKGROUND

The incidence and mortality of thyroid cancer vary based on race as well as gender. Both gender thyroid cancer patients give variable clinical characteristics, such as tumor size and distant metastasis. However, sex differences in the prognosis of thyroid cancer remain controversial. Therefore, the present study explored the relationship between gender and prognosis of patients with thyroid cancer for conducive personalized treatment.

METHODS

A retrospective analysis was carried out on patients with pathologically proven thyroid cancer from the Surveillance, Epidemiology, and End Results (SEER) database. The gender disparities in the prognosis of different cohorts, derived by propensity score matching were investigated using Cox proportional hazards models and Kaplan-Meier curves.

RESULTS

Among the studied 41,270 female and 13,188 males with thyroid cancer, gender was an independent prognostic factor for overall (OS) and cancer-specific (CSS) survival (HR =1.632, 95% CI: 1.499-1.777, P<0.001; HR =1.473, 95% CI: 1.245-1.741, P<0.001). Though, male patients had a larger tumor size (17.4 vs. 23.5 cm) and a larger proportion of metastasis [lymph nodes (LNs): 33.2% vs. 21.0%; distant: 2.3% vs. 0.9%], female had a higher incidence and earlier age diagnosis with thyroid cancer (48.0 vs. 52.5 years old). Survival Time (in months) of male patients was also significantly lower than female patients (72.4 vs. 76.8 months). In the Kaplan-Meier curves of cohorts derived by propensity score matching, OS and CSS declined much sharply for male (P<0.001). The mean number (2.0 vs. 4.0) and mean ratio (0.192 vs. 0.297) of positive nodes supported worse prognosis for male patients. Whereas factors including race, age, surgery, histology recodes, T, N, M stage and combined summary stage affected the CSS of male and female patients, however plus median income had an extra impact on male population (≥$55,000 vs. <$55,000: HR =0.739, 95% CI: 0.574-0.953, P=0.020).

CONCLUSIONS

Our study demonstrated that male patients had a prognostic factor for poorer OS and CSS. Other factors including race, age, income, histological type, surgery, T, N, M stage influenced OS of male and female thyroid cancer patients. Interestingly, race had no impact on CSS of thyroid cancer patients, whereas median income affected only the male patients CSS.

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis

Objectives

The aim of this study was to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting peritoneal metastasis (PM) of gastric cancer (GC).

Methods

In this study, a total of 355 patients (109PM+, 246PM-) who underwent preoperative fluorine-18-fludeoxyglucose (¹⁸F-FDG) PET images were retrospectively analyzed. According to a 7:3 ratio, patients were randomly divided into a training set and a validation set. Radiomics features and metabolic parameters data were extracted from PET images. The radiomics features were selected by logistic regression after using maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator (LASSO) method. The radiomics models were based on the rest of these features. The performance of the models was determined by their discrimination, calibration, and clinical usefulness in the training and validation sets.

Results

After dimensionality reduction, 12 radiomics feature parameters were obtained to construct radiomics signatures. According to the results of the multivariate logistic regression analysis, only carbohydrate antigen 125 (CA125), maximum standardized uptake value (SUVmax), and the radiomics signature showed statistically significant differences between patients (P<0.05). A radiomics model was developed based on the logistic analyses with an AUC of 0.86 in the training cohort and 0.87 in the validation cohort. The clinical prediction model based on CA125 and SUVmax was 0.76 in the training set and 0.69 in the validation set. The comprehensive model, which contained a rad-score and the clinical factor (CA125) as well as the metabolic parameter (SUVmax), showed promising performance with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort, respectively. The calibration curve showed the actual rate of the nomogram-predicted probability of peritoneal metastasis. Decision curve analysis (DCA) also demonstrated the good clinical utility of the radiomics nomogram.

Conclusions

The comprehensive model based on the rad-score and other factors (SUVmax, CA125) can provide a novel tool for predicting peritoneal metastasis of gastric cancer patients preoperatively.

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer

Background

Distant metastasis (DM) is not common in differentiated thyroid cancer (DTC). However, it is associated with a significantly poor prognosis. Early detection of high-risk DTC patients is difficult, and the molecular mechanism is still unclear. Therefore, the present study aims to establish a novel predictive model based on clinicopathological parameters and DM-related gene signatures to provide guidelines for clinicians in decision making.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to discover co-expressed gene modules and hub genes associated with DM. Univariate and multivariate analyses were carried out to identify independent clinicopathological risk factors based on The Cancer Genome Atlas (TCGA) database. An integrated nomogram prediction model was established. Finally, real hub genes were validated using the GSE60542 database and various thyroid cell lines.

Results

The midnightblue module was most significantly positively correlated with DM (R=0.56, P=9e-06) by as per WGCNA. DLX5 (AUC: 0.769), COX6B2 (AUC: 0.764), and LYPD1 (AUC: 0.760) were determined to be the real hub genes that play a crucial role in predicting DM. Meanwhile, univariate and multivariate analyses demonstrated that T-stage (OR, 15.03; 95% CI, 1.75-319.40; and P=0.024), histologic subtype (OR, 0.17; 95% CI, 0.03-0.92; and P=0.042) were the independent predictors of DM. Subsequently, a nomogram model was constructed based on gene signatures and independent clinical risk factors exhibited good performance. Additionally, the mRNA expressions of real hub genes in the GSE60542 dataset were consistent with TCGA.

Conclusions

The present study has provided a reliable model to predict DM in patients with DTC. This model is likely to serve as an individual risk assessment tool in therapeutic decision-making.

Predicting factors of central lymph node metastasis and BRAFV600E mutation in Chinese population with papillary thyroid carcinoma

Objective

The aim of this study was to evaluate the predictive factors of central lymph node metastasis (CLNM) and BRAF^V600E mutation in Chinese patients with papillary thyroid carcinoma (PTC).

Methods

A total of 943 PTC patients who underwent thyroidectomy from 2014 to 2016 at our hospital were enrolled. Those patients were divided into PTC > 10 mm and papillary thyroid microcarcinoma (PTMC) groups by tumor size. The BRAF^V600E mutation was examined by quantitative real-time PCR. Univariate and multivariate analyses were used to examine risk factors associated with CLNM and the BRAF^V600E mutation.

Results

The frequency of CLNM was 53% (505/943). Both univariate and multivariate analyses suggested that the risk factors for CLNM in PTC patients were male, younger age, and larger tumor size (P < 0.05). Coexistent Hashimoto thyroiditis (HT) was an independent protective factor against CLNM when the tumor was > 10 mm (P = 0.006). Stratified analysis revealed that male, age ≤ 30 years, and tumor size > 5 mm were independent risk factors for CLNM. The BRAF^V600E mutation rate was 85%. Multivariate logistic regression analysis revealed that age (P < 0.001) and coexistent HT (P = 0.005) were independent predictive factors of BRAF^V600E mutation in PTC patients. Only age was a risk factor for the BRAF^V600E mutation when the tumor was > 10 mm (P = 0.004). In the PTMC group, the BRAF^V600E mutation was significantly correlated with tumor size (P < 0.001) and coexistent HT (P = 0.03). Stratified analysis revealed that age > 30 years and tumor size > 5 mm were independent predictive factors of BRAF^V600E mutation. Furthermore, the incidence of CLNM was significantly higher in BRAF^V600E mutation-positive patients (P = 0.009) when the tumor was ≤ 5 mm.

Conclusion

The factors male, younger age (≤ 30 years), large tumor size (> 5 mm), and coexistent HT are independent predicative factors for CLNM. The BRAF^V600E mutation is associated with both large size and without HT in PTMC patients, age > 30 years in the PTC > 10 mm group. The BRAF^V600E mutation was an independent risk factor for CLNM when the tumor was ≤ 5 mm. For optimal management, these features should be comprehensively evaluated to determine the initial surgical approach for PTC patients.

The influence of nodule size on the aggressiveness of thyroid carcinoma varies with patient's age

Background

Thyroid nodule size is one of the key parameters that determines the operative approach for thyroid carcinoma. It is necessary to evaluate the influence of nodule size on the aggressiveness of thyroid carcinoma. The eighth edition of staging system has updated the prognostic age cutoff from 45 to 55 years old. It is needed to re-evaluate the difference in aggressiveness of thyroid carcinoma between younger (<55 years old) and older (≥55 years old) patients. Importantly, whether the influence of nodule size on the aggressiveness of thyroid carcinoma varies according to the new age stratification remains to be explored.

Methods

Medical records from patients were retrospectively reviewed. Patients with a documented thyroid ultrasonography (US), US-guided fine needle aspiration (FNA) and histopathology were included. The risks of unfavorable events such as central-compartment neck lymph node (CLN) metastasis, lateral-compartment neck lymph node (LLN) metastasis and gross extrathyroidal extension (ETE) were analyzed in four subsets of patients according to size and age.

Results

Large nodule size (≥10 mm) significantly increased the frequencies of CLN metastasis, LLN metastasis and gross ETE (P<0.05). The frequency of CLN metastasis was significantly higher in younger patients compared with that in older ones. Logistic regression analysis recognized large nodule size as an independent risk factor for all CLN metastasis (OR: 3.304, 95% CI: 2.473-4.415), LLN metastasis (OR: 9.673, 95% CI: 4.542-20.597), and gross ETE (OR: 2.430, 95% CI: 1.508-3.916). Secondly, in younger patients, frequencies of all CLN metastasis, LLN metastasis and gross ETE were significantly higher in nodules ≥10 mm than in nodules <10 mm (P<0.001). However, in older patients, no significant difference was found in the frequencies of LLN metastasis or gross ETE between nodules <10 mm and ≥10 mm. Logistic regression analysis showed, in younger patients, large nodule size was an independent risk factor for all CLN metastasis (OR: 3.241, 95% CI: 2.393-4.389), LLN metastasis (OR: 12.495, 95% CI: 5.281-29.562), and gross ETE (OR: 2.591, 95% CI: 1.519-4.419), while in older patients large nodule size was recognized as an independent risk factor for CLN metastasis (OR: 3.924, 95% CI: 1.413-10.899) but not for LLN metastasis or gross ETE.

Conclusions

Large nodule size is significantly related to high aggressiveness of thyroid carcinoma. The correlation between large nodule size and high aggressiveness varies according to patient's age, indicating that the presence of unfavorable events has different clinical significance for patients of varied ages. These findings contribute to accurately assessing the prognosis of individual patient and developing a better management strategy.