A novel gene panel for prediction of lymph-node metastasis and recurrence in patients with thyroid cancer

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning

BACKGROUND AND AIMS

The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC.

METHODS

A total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores.

RESULTS

The combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS.

CONCLUSION

We proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis

PURPOSE

Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets.

METHODS

ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors.

RESULTS

Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001).

CONCLUSIONS

This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Meta-analysis of the effect and clinical significance of Delphian lymph node metastasis in papillary thyroid cancer

Objective

To investigate the effect and clinical significance of Delphian lymph nodes (DLN) on the factors influencing papillary thyroid cancer (PTC) to provide individualized guidance for the surgical treatment of thyroid cancer.

Methods

Relevant studies from PubMed, Web of Science, the Cochrane Library, Embase, and China National Knowledge Infrastructure databases were searched until February 13, 2023. Stringent selection parameters were used to obtain included data and homogeneous articles. Analyses were performed using Revman 5.4 and SPSS software. A P-value of < 0.05 was considered statistically significant.

Results

Five studies were finally included in this study. The results revealed a higher risk of DLN metastasis (DLNM) in patients with tumor size >1cm, multifocality, and extrathyroidal extension (ETE) of the thyroid. The risk of central lymph node metastasis (CLNM) was 11.25 times higher in DLN-positive patients with PTC than in DLN-negative (OR = 11.25, 95% CI: 8.64-14.64, P < 0.05) patients. The risk of LLNM was 5.57 times higher in DLN-positive patients with PTC than in DLN-negative (OR = 5.57, 95% CI: 4.57-6.78, P < 0.001) patients. The risk of postoperative recurrence in DLN-positive patients with PTC was 3.49 times higher (OR = 3.49, 95% CI: 1.91-6.38, P < 0.001) than in DLN-negative patients with PTC.

Conclusion

Patients with tumor size >1 cm in diameter, multifocality, and ETE have an increased risk for DLN development. DLN-positive patients with central and lateral cervical lymph node metastasis and postoperative recurrence are at higher risk than DLN-negative patients.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

RUNX1 methylation as a cancer biomarker in differentiating papillary thyroid cancer from benign thyroid nodules

Aim: It remains a challenge to accurately identify malignancy of thyroid nodules when biopsy is indeterminate. The authors aimed to investigate the abnormal DNA methylation signatures in papillary thyroid cancer (PTC) compared with benign thyroid nodules (BTNs). Methods: The authors performed genome profiling by 850K array and RNA sequencing in early-stage PTC and BTN tissue samples. The identified gene was validated in two independent case-control studies using mass spectrometry. Results: Hypomethylation of RUNX1 in PTC was identified and verified (all odds ratios: ≥1.50). RUNX1 methylation achieved good accuracy in differentiating early-stage PTC from BTNs, especially for younger women. Conclusion: The authors disclosed a significant association between RUNX1 hypomethylation and PTC, suggesting RUNX1 methylation as a potential biomarker for companion diagnosis of malignant thyroid nodules.

Applications of machine and deep learning to thyroid cytology and histopathology: a review

This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner.

E2F1-driven histone demethylase KDM6B enhances thyroid malignancy via manipulating TFEB-dependent autophagy axis

Aberrant epigenetic modifications or events regulate autophagy to influence tumor progression, which has gained increasing attention. KDM6B is an essential histone demethylase that participates in multiple processes of tumors, but its role in thyroid carcinoma (THCA) remains to be unknown. Here, in this study, we used the MTT assay to screen and validate that KDM6B is an essential demethylase for THCA. KDM6B promotes THCA proliferation, migration, invasion in vitro and in vivo. Transcriptional factor E2F1 directly binds to the promoter region of KDM6B and regulates its mRNA levels in THCA. E2F1 partially depended on KDM6B to exert its oncogenic functions. Mechanistically, KDM6B binds to TFEB promoter region and mediates the demethylation of H3K27me3. KDM6B depended on TFEB to activate a series of lysosomal-related genes. KDM6B enhances autophagy process, as evidenced by elevated p62 and Beclin-1 proteins. KDM6B depended on TFEB-driven autophagy activity to accelerate THCA progression. Lastly, targeting autophagy with 3-MA could notably abrogate growth of KDM6Bhigh THCA, but has mild influence on KDM6Blow THCA. Together, this study identified KDM6B as an essential epigenetic regulator for THCA, functioning as an autophagy regulator. The fundamental mechanisms underlying E2F1/KDM6B/TFEB axis provided novel vulnerabilities for THCA treatment.

Multiple Metastases of Parathyroid and Papillary Thyroid Carcinoma in a Female Patient Treated with Long-Term Hemodialysis

Parathyroid cancer is a rare, clinically aggressive malignancy with a prevalence of approximately 0.005% relative to all carcinoma cases and 1-5% among patients with primary hyperparathyroidism. Prognosis largely depends on the extent of the primary surgery. Non-radical surgical treatment increases the risk of local and distant metastases of the parathyroid cancer associated with limited treatment options. The combination of thyroid and parathyroid disorders has been described rather well for the general population; however, cases of parathyroid and thyroid carcinoma in the same patient are extremely rare (1 case per 3000 patients with parathyroid disorders). We present a rare clinical case of combination of parathyroid and thyroid cancers with metastases of both tumors to the neck lymph nodes in a woman with a mutation in the MEN1 gene (NM_130799.2): c.658T > C p.Trp220Arg (W220R), who has been exposed to radiation for 20 years before diagnosis of thyroid cancer and received renal replacement therapy with long-term hemodialysis before the diagnosis of parathyroid cancer. The patient underwent several surgeries because of metastases of the parathyroid cancer in the neck lymph nodes. Surgeons used intraoperative navigation methods (single-channel gamma detection probe, Gamma Probe 2, and fluorescence angiography with indocyanine green (ICG)) to clarify the volume of surgery. Currently, the patient is still in laboratory remission, despite the structural recurrence of tumors.

Integrated gene profiling of fine-needle aspiration sample improves lymph node metastasis risk stratification for thyroid cancer

BACKGROUND

Lymph node metastasis risk stratification is crucial for the surgical decision-making of thyroid cancer. This study investigated whether the integrated gene profiling (combining expression, SNV, fusion) of Fine-Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer.

METHODS

In this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included. Multi-omics data of FNA samples were assessed by an integrated array. To predict lymph node metastasis, we built models using gene expressions or mutations (SNV and fusion) only and an Integrated Risk Stratification (IRS) model combining genetic and clinical information. Blinded histopathology served as the reference standard. ROC curve and decision curve analysis was applied to evaluate the predictive models.

RESULTS

One hundred and thirty two patients with pathologically confirmed papillary thyroid cancer were included between 2016-2017. The IRS model demonstrated greater performance [AUC = 0.87 (0.80-0.94)] than either expression classifier [AUC = 0.67 (0.61-0.74)], mutation classifier [AUC = 0.61 (0.55-0.67)] or TIRADS score [AUC = 0.68 (0.62-0.74)] with statistical significance (p < 0.001), and the IRS model had similar predictive performance in large nodule [>1 cm, AUC = 0.88 (0.79-0.97)] and small nodule [≤1 cm, AUC = 0.84 (0.74-0.93)] subgroups. The genetic risk factor showed independent predictive value (OR = 10.3, 95% CI:1.1-105.3) of lymph node metastasis in addition to the preoperative clinical information, including TIRADS grade, age, and nodule size.

CONCLUSION

The integrated gene profiling of FNA samples and the IRS model developed by the machine-learning method significantly improve the risk stratification of thyroid cancer, thus helping make wise decisions and reducing unnecessary extensive surgeries.

Case Report: A papillary thyroid microcarcinoma patient with skip lymph node metastasis and multiple distant metastasis

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Papillary thyroid microcarcinoma (PTMC) is defined as PTC with a diameter less than 1 centimeter. Most lymph nodes of PTC patients have metastasized to the central neck, and a few lymph nodes have metastasized to the lateral neck. Skip lymph node metastasis, that is, lateral cervical lymph node metastasis without central lymph node metastasis, is even less common. Additionally, distant metastasis of PTMC is also rare, mainly occurring in the lung and bone. Here, we reported a case of PTMC patient with skip lymph node metastasis and multiple distant metastasis. The patient presented with a huge shoulder mass and the primary tumor was found to originate from the thyroid. However, the patient only suffered with PTMC via postoperative pathological results, and interestingly, the patient only had skip lymph node metastasis. Thus, we should focus on PTMC patients with lateral cervical lymph nodes metastasis, especially those with skip metastasis. In addition, this case provides a new perspective for us to understand of skip lymph metastasis and distant metastasis of PTMC.

Aberrant kinesin family member 2A signifies tumor size and invasion, and may help predict prognosis of patients with papillary thyroid carcinoma

Kinesin family member 2A (KIF2A) has been reported as an oncogene and potential biomarker for the progression of numerous cancer types; however, its role in papillary thyroid carcinoma (PTC) has remained elusive. The present study aimed to assess KIF2A expression in patients with PTC and explore the potential association between KIF2A, clinicopathological features and the prognosis of PTC. A total of 200 patients with PTC who received surgical resection were retrospectively reviewed. KIF2A expression was detected using immunohistochemistry (IHC) in 200 pairs of carcinoma/para-carcinoma tissues and using reverse transcription-quantitative PCR in 91 pairs of carcinoma/para-carcinoma tissues. Clinical and pathological data, disease-free survival (DFS) and overall survival (OS) rates of all patients were obtained. The results of the present study demonstrated that KIF2A protein and mRNA expression were both elevated in carcinoma tissues compared with those in para-carcinoma tissues. KIF2A protein expression in carcinoma tissues was positively associated with increased tumor size and a higher pathologic tumor-nodes-metastasis (pTNM) stage. However, KIF2A mRNA expression in carcinoma tissues was only associated with an increased pTNM stage and not with any other clinicopathological features. In addition, high levels of KIF2A protein expression in carcinoma tissues led to a poor predicted DFS, but were not associated with OS. Following adjustments using a multivariate Cox regression model, high KIF2A protein expression levels were indicated to be independently associated with a decreased DFS. In conclusion, aberrant KIF2A signifies tumor size and invasion, and may help to predict prognosis in patients with PTC.

Intramedullary Spinal Cord Metastases from Differentiated Thyroid Cancer, a Case Report

Intramedullary spinal cord metastases (ISCM) are uncommon metastases of the spinal cord. Magnetic resonance (MR) plays an important role in surgical planning when ISCM is suspected in the differential diagnosis. The incidence of ISCM is expected to increase due to the longer survival of cancer patients as well as the widespread use of MR in the diagnosis of neurological syndromes. The management of these patients is controversial because of the multiple clinical presentations and lack of controlled studies on the efficacy of different therapeutic approaches. Increased awareness of this rare entity may lead to an earlier diagnosis with novel imaging approaches at a stage when neurological deficits are reversible. A case of ISCM in a 49-year-old patient with differentiated thyroid cancer is reported.

A novel prognostic model for papillary thyroid cancer based on epithelial-mesenchymal transition-related genes

BACKGROUND

The frequent incidence of postsurgical recurrence issues in papillary thyroid cancer (PTC) patients is a primary concern considering the low cancer-related mortality. Previous studies have demonstrated that epithelial-mesenchymal transition (EMT) activation is closely related to PTC progression and invasion. In this study, we aimed to develop a novel EMT signature and ancillary nomogram to improve personalized prediction of progression-free interval (PFI).

METHODS

First, we carried out a differential analysis of PTC samples and pairwise normal thyroid samples to explore the differentially expressed genes (DEGs). The intersection of the DEGs with EMT-related genes (ERGs) were identified as differentially expressed EMT-related genes (DE-ERGs). We determined PFI-related DE-ERGs by Cox regression analysis and then established a novel gene classifier by LASSO regression analysis. We validated the signature in external datasets and in multiple cell lines. Further, we used uni- and multivariate analyses to identify independent prognostic characters.

RESULTS

We identified 244 prognosis-related DE-ERGs. The 244 DE-ERGs were associated with several pivotal oncogenic processes. We also constructed a novel 10-gene signature and relevant prognostic model for recurrence prediction of PTC. The 10-gene signature had a C-index of 0.723 and the relevant nomogram had a C-index of 0.776. The efficacy of the signature and nomogram was satisfying and closely correlated with relevant clinical parameters. Furthermore, the signature also had a unique potential in differentiating anaplastic thyroid cancer (ATC) samples.

CONCLUSIONS

The novel EMT signature and nomogram are useful and convenient for personalized management for thyroid cancer.

Multi-channel convolutional neural network architectures for thyroid cancer detection

Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians' adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians' trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.

Serum Linkage-Specific Sialylation Changes Are Potential Biomarkers for Monitoring and Predicting the Recurrence of Papillary Thyroid Cancer Following Thyroidectomy

BACKGROUND

Although papillary thyroid cancer (PTC) could remain indolent, the recurrence rates after thyroidectomy are approximately 20%. There are currently no accurate serum biomarkers that can monitor and predict recurrence of PTC after thyroidectomy. This study aimed to explore novel serum biomarkers that are relevant to the monitoring and prediction of recurrence in PTC using N-glycomics.

METHODS

A high-throughput quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to obtain serum protein N-glycomes of well-differentiated PTC, postoperative surveillance (PS), postoperative recurrence (PR), and matched healthy controls (HC) including linkage-specific sialylation information.

RESULTS

Serum N-glycan traits were found to differ among PTC, PS, PR, and HC. The differentially expressed N-glycan traits consisting of sixteen directly detected glycan traits and seven derived glycan traits indicated the response to surgical resection therapy and the potential for monitoring the PTC. Two glycan traits representing the levels of linkage-specific sialylation (H4N3F1L1 and H4N6F1E1) which were down-regulated in PS and up-regulated in PR showed high potential as biomarkers for predicting the recurrence after thyroidectomy.

CONCLUSIONS

To the best of our knowledge, this study provides comprehensive evaluations of the serum N-glycomic changes in patients with PS or PR for the first time. Several candidate serum N-glycan biomarkers including the linkage-specific sialylation have been determined, some of which have potential in the prediction of recurrence in PTC, and others of which can help to explore and monitor the response to initial surgical resection therapy. The findings enhanced the comprehension of PTC.

Safety and efficacy of ultrasound-guided thermal ablation in treating T1aN0M0 and T1bN0M0 papillary thyroid carcinoma: A meta-analysis

BACKGROUND

Papillary thyroid cancer (PTC) is the most common thyroid tumor, and early diagnosis and treatment can effectively improve prognosis. Many controversies surround the treatment method of T1N0M0 PTC. Recently, thermal ablation (TA) has shown some benefits in the treatment of PTC patients, but the safety and efficacy of its treatment remain controversial. This article performs a meta-analysis of TA in patients with T1aN0M0 and T1bN0M0 PTC.

METHODS

The PubMed, Embase, Web of Science, and Cochrane Library databases were systematically searched for retrospective or prospective studies of TA for treating patients with T1N0M0 PTC from the database establishment to May 1, 2022. Data on volume reduction rate (VRR), disease progress, and complication rate were collected. In addition, a meta-analysis was performed using the Stata 12.0 and Review Manager 5.3.

RESULTS

A total of 9 eligible studies were included. Our study demonstrated the effectiveness of VRR and disease progress. The VRR was reduced after 3 months (-75.90%; 95% CI [-118.46-33.34%]), 6 months (34.33%; 95% CI [15.01-53.65%]), 12 months (78.69%; 95% CI [71.69-85.68%]), and 24 months (89.97%; 95% CI [84.00-95.94%]). The disease progress was 1.9% (95% CI [1.1-3.0]). Safety is justified by the complication rate, which was 6.5% (95% CI [3.5-10.2]). Pain and hoarseness were the most common complications, and no life-threatening complications were reported. Egger's test demonstrated that publication bias was acceptable.

CONCLUSIONS

TA is an effective and safe method for managing T1aN0M0 and T1bN0M0 papillary thyroid nodules.

Prognostic factors in patients with persistent/recurrent differentiated thyroid carcinoma after comprehensive treatment

Objective: To investigate the prognostic factors of patients with persistent/recurrent differentiated thyroid carcinoma (DTC) especially with external invasive persistent recurrent DTC after comprehensive treatment. Methods: The clinical data of 525 patients with persistent/recurrent DTC who underwent surgical treatment from August 2011 to June 2021 in the Department of Head and Neck Surgery of Jiangsu Cancer Hospital were retrospectively analyzed. The prognostic factors affecting overall survival (OS) and relapse-free survival (RFS) of persistent/recurrent DTC, especially external invasive persistent/recurrent DTC were analyzed. Results: Among 525 patients, 318 patients underwent thyroidectomy, 359 patients underwent central lymph node dissection, and 409 patients underwent lateral cervical lymph node dissection. Among 493 followed-up patients, 5-year OS and RFS were 95.10% and 89.60%, 8-year OS and RFS were 91.80% and 81.30%. Cox regression analysis showed that in patients with persistent/recurrent DTC after comprehensive treatment, age ≥55 years at reoperation after recurrence, male gender and distant metastasis were independent risk factors of OS (all P<0.05); while the simultaneous invasion of thyroid and lymph nodes, multiple organ invasion and the number of previous operations ≥2 were independent risk factors of RFS (all P<0.05). In patients with external invasive persistent/recurrent DTC after comprehensive treatment, age ≥55 years at reoperation after recurrence and male gender were independent risk factors of OS (both P<0.05); while multiple organ invasion and the number of previous operations ≥2 were independent risk factors of RFS (both P<0.05). Conclusions: Male patients aged 55 years old and above, with distant metastasis have a higher risk of poorer prognosis in persistent/recurrent DTC; while patients with simultaneous external invasion of thyroid and lymph nodes, multiple organ invasion and the number of previous operations ≥2 are more likely to relapse. For external invasive persistent/recurrent DTC, male patients aged 55 years old and above have a higher risk of poorer prognosis; while patients with multiple organ invasion and the number of previous operations ≥2 are more likely to have recurrence.

Development and validation of a novel 14-gene signature for predicting lymph node metastasis in papillary thyroid carcinoma

Background

There is still no reasonably accurate method of preoperatively predicting central lymph node metastasis (LNM), and it is essential to develop an effective evaluation model for predicting LNM in papillary thyroid carcinoma (PTC) patients.

Methods

PTC samples were collected from The Cancer Genome Atlas database. Candidate genes were identified as continuously upregulated or downregulated genes in the process of N0 to N1a and N1a to N1b. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct the predictive model for LNM. Multivariate logistic regression analysis was performed to screen the potential factors related to LNM, and a nomogram was established. The risk score of the gene signature model for predicting disease-free survival (DFS) was evaluated by Kaplan-Meier analysis.

Results

A 14-gene signature was developed by LASSO regression for predicting LNM based on 69 differential expression genes (DEGs) that were continuously upregulated or downregulated in the progress of PTC. The receiver operating characteristic (ROC) curves of the 14-gene signature predicting LNM, central LNM and lateral LNM were generated. The area under the ROC (AUC) values were 0.806 [95% confidence interval (CI): 0.7608-0.8815], 0.755 (95% CI: 0.6839-0.8263) and 0.821 (95% CI: 0.7608-0.8815). The nomogram's C-index value, including the 14-gene signature and other potential risk factors, was 0.786 (95% CI: 0.7296-0.8425), and the calibration exhibited fairly good consistency with the perfect prediction. Based on the 14-gene risk score, high-risk PTC patients had a worse DFS.

Conclusions

A novel 14-gene signature was developed for predicting LNM in PTC patients. The risk score also correlated with DFS in PTC patients.

MicroRNA-Based Risk Score for Predicting Tumor Progression Following Radioactive Iodine Ablation in Well-Differentiated Thyroid Cancer Patients: A Propensity-Score Matched Analysis

Simple Summary

The three-tiered American Thyroid Association (ATA) risk stratification helps clinicians tailor decisions regarding follow-up modalities and the need for postoperative radioactive iodine (RAI) ablation and radiotherapy. However, a significant number of well-differentiated thyroid cancers (DTC) progress after treatment. Current follow-up modalities have also been proposed to detect disease relapse and recurrence but have failed to be sufficiently sensitive or specific to detect, monitor, or determine progression. Therefore, we assessed the predictive accuracy of the microRNA-based risk score in DTC with and without postoperative RAI. We confirm the prognostic role of triad biomarkers (miR-2f04, miR-221, and miR-222) with higher sensitivity and specificity for predicting disease progression than the ATA risk score. Compared to indolent tumors, a higher risk score was found in progressive samples and was associated with shorter survival. Consequently, our prognostic microRNA signature and nomogram provide a clinically practical and reliable ancillary measure to determine the prognosis of DTC patients.

Abstract

To identify molecular markers that can accurately predict aggressive tumor behavior at the time of surgery, a propensity-matching score analysis of archived specimens yielded two similar datasets of DTC patients (with and without RAI). Bioinformatically selected microRNAs were quantified by qRT-PCR. The risk score was generated using Cox regression and assessed using ROC, C-statistic, and Brier-score. A predictive Bayesian nomogram was established. External validation was performed, and causal network analysis was generated. Within the eight-year follow-up period, progression was reported in 51.5% of cases; of these, 48.6% had the T1a/b stage. Analysis showed upregulation of miR-221-3p and miR-222-3p and downregulation of miR-204-5p in 68 paired cancer tissues (p < 0.001). These three miRNAs were not differentially expressed in RAI and non-RAI groups. The ATA risk score showed poor discriminative ability (AUC = 0.518, p = 0.80). In contrast, the microRNA-based risk score showed high accuracy in predicting tumor progression in the whole cohorts (median = 1.87 vs. 0.39, AUC = 0.944) and RAI group (2.23 vs. 0.37, AUC = 0.979) at the cutoff >0.86 (92.6% accuracy, 88.6% sensitivity, 97% specificity) in the whole cohorts (C-statistics = 0.943/Brier = 0.083) and RAI subgroup (C-statistic = 0.978/Brier = 0.049). The high-score group had a three-fold increased progression risk (hazard ratio = 2.71, 95%CI = 1.86–3.96, p < 0.001) and shorter survival times (17.3 vs. 70.79 months, p < 0.001). Our prognostic microRNA signature and nomogram showed excellent predictive accuracy for progression-free survival in DTC.

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors

Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients' response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.

Identification of a Signature Comprising 5 Soluble Carrier Family Genes to Predict the Recurrence of Papillary Thyroid Carcinoma

RNA-sequencing data and relevant clinical data in The Cancer Genome Atlas for 502 samples of papillary thyroid cancer (PTC) were analyzed to determine the prognostic value of soluble carrier family genes in PTC. We analyzed soluble carrier family gene expression and function in the samples. Clustering identified 2 clusters in the data. Risk characteristics were identified using LASSO and Univariate Cox regression analysis, which divided the patients into low and high-risk groups. The expression levels of 88 soluble carrier genes were significantly different between tumors and normal tissue. The 2 PTC clusters had different clinical outcomes and distributions of gene expression. The expression levels of SFXN1, SLC12A4, SLC35A1, SLC35E1, and SLCO1C1 were markedly different between the 2 groups. The high risk and low risk groups had significant different prognoses (P < 0.05). Significant differences were identified for disease free survival (DFS), sex and T stage between the 2 subgroups. The risk score was identified as an independent prognostic variable (P < 0.05) and as a predictor of clinicopathological variables. In patients with PTC, solute carrier gene expression showed differential associations with clinicopathological variables. The 5 genes could be used as prognostic factors for PTC, particularly to predict PTC recurrence.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Differences in Gene Expression Profile of Primary Tumors in Metastatic and Non-Metastatic Papillary Thyroid Carcinoma-Do They Exist?

Molecular mechanisms of distant metastases (M1) in papillary thyroid cancer (PTC) are poorly understood. We attempted to analyze the gene expression profile in PTC primary tumors to seek the genes associated with M1 status and characterize their molecular function. One hundred and twenty-three patients, including 36 M1 cases, were subjected to transcriptome oligonucleotide microarray analyses: (set A-U133, set B-HG 1.0 ST) at transcript and gene group level (limma, gene set enrichment analysis (GSEA)). An additional independent set of 63 PTCs, including 9 M1 cases, was used to validate results by qPCR. The analysis on dataset A detected eleven transcripts showing significant differences in expression between metastatic and non-metastatic PTC. These genes were validated on microarray dataset B. The differential expression was positively confirmed for only two genes: IGFBP3, (most significant) and ECM1. However, when analyzed on an independent dataset by qPCR, the IGFBP3 gene showed no differences in expression. Gene group analysis showed differences mainly among immune-related transcripts, indicating the potential influence of tumor immune infiltration or signal within the primary tumor. The differences in gene expression profile between metastatic and non-metastatic PTC, if they exist, are subtle and potentially detectable only in large datasets.