Ferroptosis in thyroid cancer: Potential mechanisms, effective therapeutic targets and predictive biomarker.

Thyroid cancer is a prevalent endocrine malignancy whose global incidence has risen over the past several decades. Ferroptosis, a regulated form of cell death distinguished by the excessive buildup of iron-dependent lipid peroxidates, stands out from other programmed cell death pathways in terms of morphological and molecular characteristics. Increasing evidence suggests a close association between thyroid cancer and ferroptosis, that is, inducing ferroptosis effectively suppresses the proliferation of thyroid cancer cells and impede tumor advancement. Therefore, ferroptosis represents a promising therapeutic target for the clinical management of thyroid cancer in clinical settings. Alterations in ferroptosis-related genes hold potential for prognostic prediction in thyroid cancer. This review summarizes current studies on the role of ferroptosis in thyroid cancer, elucidating its mechanisms, therapeutic targets, and predictive biomarkers. The findings underscore the significance of ferroptosis in thyroid cancer and offer valuable insights into the development of innovative treatment strategies and accurate predictors for the thyroid cancer.

CRISPR/Cas-based CAR-T cells: production and application.

Chimeric antigen receptor T cell (CAR-T) therapy has revolutionized the treatment approach for cancer, autoimmune disease, and heart disease. The integration of CAR into T cells is typically facilitated by retroviral or lentiviral vectors. However, the random insertion of CARs can lead to issues like clonal expansion, oncogenic transformation, variegated transgene expression, and transcriptional silencing. The advent of precise gene editing technology, like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), allows for controlled and precise genome modification, facilitating the translation of CAR-T research to the clinical applications. This review aims to provide a comprehensive analysis of the application of CRISPR gene editing techniques in the context of precise deletion and insertion methodologies, with a specific focus on their potential for enhancing the development and utilization of CAR-T cell therapy.

Microscopic extrathyroidal extension does not affect the prognosis of patients with papillary thyroid carcinoma: A propensity score matching analysis.

Background: Extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) can be divided into two categories based on different degrees of invasion: microscopic ETE (micro-ETE) and macroscopic ETE (macro-ETE). At present, there is a consensus that macro-ETE significantly affects PTC prognosis, while the prognostic significance of micro-ETE remains controversial. Methods: The clinicopathological and follow-up data for PTC patients who underwent surgical treatment at the Hangzhou First People's Hospital between 2015 and 2018 were retrospectively analyzed. According to the degree of ETE, patients were divided into three groups: non-ETE, micro-ETE and macro-ETE. Cox regression analysis was performed to evaluate the effect of ETE on recurrence-free survival (RFS). The propensity score matching (PSM) method was used to reduce the interference of confounding factors, and Kaplan-Meier curves were utilized to compare the RFS. Results: Both micro- and macro-ETE were associated with some aggressive tumor features, including tumor size, multifocality, and lymph node metastasis. Univariate and multivariate Cox regression analyses showed that macro-ETE was an independent risk factor for recurrence, while micro-ETE was not associated with recurrence. The K-M curves showed that RFS for micro-ETE and non-ETE were not statistically different before and after PSM, while RFS for macro-ETE was significantly shorter than that for non-ETE. Conclusion: The presence of micro-ETE in PTC did not affect prognosis of patients, suggesting that its treatment should be consistent with the treatment for intrathyroidal tumors. The surgical method and the necessity for radioiodine therapy should be carefully evaluated to reduce overtreatment.

US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?

OBJECTIVES: Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system. METHODS: The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval. RESULTS: The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively. CONCLUSIONS: The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis. CLINICAL RELEVANCE STATEMENT: Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies. KEY POINTS: • In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.

Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules.

OBJECTIVE: To investigate the application of machine learning (ML) model-based thyroid ultrasound radiomics in the evaluation of malignancy in partially cystic thyroid nodules (PCTNs). METHODS: One hundred and ninety-two patients with 197 nodules PCTNs from January 2020 to December 2020 were retrospectively analyzed. Radiomics features were extracted based on hand-crafted features from the ultrasound images, and machine learning methods were used to build a classification model by radiomics features. The least absolute shrinkage and selection operator regression was applied to select the features of nonzero coefficients from radiomics features. The prediction performance of the established model was mainly evaluated by the area under the curve (AUC) and accuracy, sensitivity, and specificity. RESULTS: Nineteen radiomics features were extracted from the original images for each nodule. Eight ML classifiers were able to differentiate malignancy in PCTNs. The AUC, accuracy, sensitivity, and specificity of k-Nearest Neighbor (KNN) model were 0.909, 82.95%, 83.33%, and 89.90%, respectively, on the test cohort. The comparative result showed statistically equivalent performance for thyroid nodule diagnosis based on image fusion and single image. In addition, the ML-Based ultrasound radiomics system showed a better AUC as compared with ACR TI-RADS model and the ultrasound features model. CONCLUSION: The novel ultrasonic-based ML model has an important clinical value for predicting malignancy in PCTNs. It can provide clinicians with a preoperative non-invasive primary screening method for PCTN diagnosis to avoid unnecessary medical investment and improve treatment outcomes.

The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule-to-muscle gray-scale ratio.

OBJECTIVE: To investigate the efficacy of the ultrasonic nodule to muscle gray scale ratio as a predictive tool for distinguishing between benign and malignant thyroid nodules. METHODS: A retrospective study was undertaken at the First People's Hospital of Hangzhou, affiliated with the Zhejiang University School of Medicine, analyzing ultrasound and pathological data of patients with thyroid nodules between May 2020 and December 2022. The study extracted ultrasound features of nodules and employed univariate and multivariate logistic regression analyses to identify independent risk factors for malignant tumors in the nodules. Subsequently, a predictive model for distinguishing benign and malignant thyroid nodules was developed. RESULTS: A total of 466 patients were included in this retrospective study, of which 275 cases were malignant tumors. Univariate and multivariate logistic regression analyses showed that the nodular-muscle gray-scale ratio, nodule diameter, margin status, aspect ratio, and calcification were closely related to thyroid malignant tumors. The area under the curve (AUC) of training group was 0.832, with a sensitivity, specificity, and accuracy of 85.5%, 67.4%, and 76.6%, respectively. The AUC of the external validation group was 0.819, with a sensitivity, specificity, and accuracy of 76.4%, 74.5%, and 75.7%, respectively. The calibration and decision curves showed that the model had good diagnostic value. CONCLUSION: The research findings indicate that ratio is significantly associated with the malignant nature of thyroid nodules. The application of a line chart model based on these parameters exhibits a high level of predictive performance.

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

BACKGROUND: Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics. MATERIALS AND METHODS: The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system. RESULTS: The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively. CONCLUSION: This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Expression and Prognostic Value of m6A RNA Methylation-Related Genes in Thyroid Cancer.

Background: N6-methyladenosine (m6A) methylation modification is involved in tumorigenesis and progression and can affect various stages of RNA processing. We aimed to determine m6A methylation modifications on a transcriptome-wide scale in thyroid cancer. Methods: RNA samples from cancerous tissues and adjacent tissues extracted from patients with papillary thyroid carcinoma (PTC) from Hangzhou First People's Hospital, Zhejiang, China from January 2019 to January 2020 were used for m6A-sequencing. The biological function of differentially expressed genes (DEGs) was analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Correlation analysis between the results of transcriptome sequencing and m6A-sequencing was also performed. The key m6A immune-related genes were downloaded from Immport. LASSO regression was performed on the resulting genes to establish a prognostic risk model, which was verified by multivariate Cox proportional hazards regression analyses, receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis. Results: An increase in m6A content in the total RNA of PTC was observed. A total of 123 genes with significant differential expression and differential methylation sites in thyroid cancer were selected, related to protein digestion and absorption, linoleic acid metabolism, legionellosis and alpha-linolenic acid metabolism. Seven genes (GDNF, EBI3, CCL2, BMP5, TGFB2, CGB3 and RLN2) were found to be predictive of PTC. Conclusion: We analyzed the expression, enrichment pathways and functions of m6A methylation-related genes in the whole transcriptome of thyroid cancer and provided a prognostic risk model for thyroid cancer patients.

Neuroendocrine neoplasm with metastasis to the thyroid: a case report and literature review.

Thyroid cancer can be divided into two types according to its cellular origin, i.e., malignant tumors originating from thyroid cells and cancers that metastasize to the thyroid from other sites, the latter of which are, clinically rare. This article reports the diagnosis and treatment of a rectal neuroendocrine neoplasm metastasis to the thyroid. No similar cases have been reported before. This case suggests that when evaluating thyroid tumors, clinicians should not only carefully identify the clinical features of the tumor but also pay special attention to the patient's history of tumors, especially neuroendocrine neoplasms. For definite secondary thyroid malignancies, neck surgery is feasible if the thyroid is the only site of metastasis; otherwise, the subsequent diagnosis and treatment plan should be determined after a comprehensive evaluation of the primary tumor and patient's general condition.

Tumor-Infiltrating Immune Cell Landscapes in the Lymph Node Metastasis of Papillary Thyroid Cancer.

Regional lymph node metastasis (LNM) increases the risk of distant metastasis in papillary thyroid cancer (PTC) patients. However, it remains unclear how tumor cells in PTC patients with LNM evade immune system surveillance and proceed to colonize distant organs. Here, we comprehensively characterize the tumor-infiltrating immune cell landscape in PTC with LNM. LNM-related genes include multiple important soluble mediators such as CXCL6, IL37, MMP10, and COL11A1, along with genes involved in areas such as extracellular matrix organization and TLR regulation by endogenous ligands. In PTC without LNM, the tumor infiltration of activated dendritic cells and M0 macrophages showed increases from normal cells, but with yet greater increases and correspondingly worse prognosis in PTC with LNM. Conversely, the tumor infiltration of activated NK cells and eosinophils was decreased in PTC without LNM, as compared to normal cells, and yet further decreased in PTC with LNM, with such decreases associated with poor prognosis. We further demonstrate that mutations of driver genes in tumor cells influence the infiltration of surrounding immune cells in the tumor microenvironment (TME). Particularly, patients carrying TG mutations tend to show increased filtration of M2 macrophages and activated NK cells in the TME, whereas patients carrying HRAS mutations tend to show reduced filtration of M0 macrophages and show enhanced filtration of activated dendritic cells in the TME. These findings increase our understanding of the mechanisms of regional lymph node metastasis in PTC and its associated tumor microenvironment, potentially facilitating the development of personalized treatment regimens to combat immunotherapy failure.

Combined fine-needle aspiration and selective intraoperative frozen section to optimize prediction of malignant thyroid nodules: A retrospective cohort study of more than 3000 patients.

Background: Preoperative fine-needle aspiration (FNA) is widely used to differentiate malignant from benign thyroid nodules, while intraoperative frozen sections (FS) are suggested as a systematic supplement for intraoperative decision-making, but limitations still remain for both procedures. Methods: Medical records of 3807 patients with thyroid nodules who underwent both pathological diagnoses (FS and FNA) at our hospital were reviewed. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FNA and FS were also evaluated. We further designed an optimal integration scheme (FNA+selective FS) to predict thyroid nodule malignancy. Finally, the efficiency of the proposed integrated diagnostic model was validated using an independent external cohort. Results: For distinguishing malignant nodules, FNA had an accuracy of 90.3%, sensitivity of 90.7%, specificity of 85.2%, PPV of 98.8% and NPV of 40.4%. In contrast, the FS represented higher discriminative power (Accuracy, 94.5%; Sensitivity, 94.1%; Specificity, 100%; PPV, 100%; and NPV, 55.6%). we proposed the selective usage of FS (removed nodules with Bethesda category VI from routine FS, ~1/3 of total). The integrated new diagnostic model of FNA plus selective FS (FNA+sFS) achieved accuracy of 96.9%, sensitivity of 97.3%, specificity of 92%, PPV of 99.4%, and NPV of 71.6% (NRI=0.135, 95% CI 0.103-0.167, P <0.001) and was successfully applied to an external cohort (N=554). Conclusion: Compared with the FNA diagnostic system, FS has an increased ability to distinguish benign and malignant thyroid nodules. The newly proposed integrated diagnostic model of FNA + selective FS can optimize the accuracy of diagnosis.

Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: construction of a dynamic predictive model.

Objective: The aim of this study was to investigate the relationships and predictive value of preoperative peripheral blood inflammatory markers as a means by which to assess risk for patients with ≤ 1 cm papillary thyroid carcinomas (PTCs). In addition, a preoperative risk stratification predictive model was constructed and validated. Methods: Clinical and pathologic data, as well as preoperative blood specimens, were collected from patients who underwent initial thyroid cancer surgery at the Hangzhou First People's Hospital, from January 2014 to January 2023. Risk assessment was performed based on postoperative pathology according to the 2015 ATA guidelines for recurrence risk stratification. Using univariate analysis and multivariate logistic regression, we identified independent risk factors associated with risk stratification. A predictive model was established and its discriminative and calibration abilities were validated. An independent validation dataset was used to verify the model, and the model was deployed as an online calculator. Results: A total of 1326 patients were included in the study, with 1047 cases (79.0%) classified as low risk and 279 cases (21.0%) classified as intermediate to high risk. The modeling group consisted of 981 cases, through univariate analysis and multivariate logistic regression analysis, preoperative blood Neutrophil/Lymphocyte Ratio (NLR), gender, tumor diameter, and multifocality were identified as independent risk factors that distinguished between low and intermediate to high risk patients with ≤ 1 cm PTCs. The clinical predictive model exhibited an AUC of 0.785, specificity of 70.6%, and sensitivity of 75.8%. For the independent validation group of 345 patients, the AUC was 0.813, specificity was 83.8%, and sensitivity was 70.4%. The calibration curve and clinical decision curve indicate that the model demonstrates excellent calibration performance. Conclusion: A dynamic clinical predictive model based on preoperative blood NLR and clinical information for patients with ≤ 1 cm PTCs was established. The model is useful for preoperative risk assessment of patients with ≤ 1 cm PTCs.

Nomogram model based on preoperative clinical characteristics of unilateral papillary thyroid carcinoma to predict contralateral medium-volume central lymph node metastasis.

Objectives: To explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN). Methods: Clinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People's Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results: Univariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value. Conclusion: Gender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.

Artificial intelligence defines protein-based classification of thyroid nodules.

Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

Blood immune indexes can predict lateral lymph node metastasis of thyroid papillary carcinoma.

Objective: To explore the clinical significance of blood immune indexes in predicting lateral lymph node metastasis (LLNM) of thyroid papillary carcinoma (PTC). Methods: The pathological data and preoperative blood samples of 713 patients that underwent thyroid surgery at affiliated Hangzhou First People's Hospital Zhejiang University School of Medicine from January 2013 to June 2021 were collected as the model group. The pathological data and preoperative blood samples of 177 patients that underwent thyroid surgery in the same hospital from July 2021 to October 2021 were collected as the external validation group. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors of LLNM in PTC patients. A predictive model for assessing LLNM in PTC patients was established and externally validated using the external data. Results: According to univariate and multivariate logistic regression analyses, tumor diameter (P < 0.001, odds ratios (OR): 1.205, 95% confidence interval (CI): 1.162-1.249) and the preoperative systemic immune-inflammation index (SII) (P = 0.032, OR: 1.001, 95% CI: 1.000-1.002) were independent risk factors for distinguishing LLNM in PTC patients. When the Youden index was the highest, the area under the curve (AUC) was 0.860 (P < 0.001, 95% CI: 0.821-0.898). The externally validated AUC was 0.827 (P < 0.001, 95% CI: 0.724-0.929), the specificity was 86.4%, and the sensitivity was 69.6%. The calibration curve and the decision curve indicated that the model had good diagnostic value. Conclusion: Blood immune indexes can reflect the occurrence of LLNM and the biological behavior of PTC. The predictive model established in combination with SII and tumor diameter can effectively predict the occurrence of LLNM in PTC patients.

Total tumor diameter is a better indicator of multifocal papillary thyroid microcarcinoma: A propensity score matching analysis.

Background: Accurate evaluation of the risk of papillary thyroid microcarcinoma (PTMC) is the key to treatment. However, the maximum diameter (MD), which is currently used in various staging systems, may not truly reflect the aggressiveness of multifocal tumors. Methods: Clinical and pathological data for 1001 patients with papillary thyroid carcinoma who underwent surgery at the Hangzhou First People's Hospital were retrospectively analyzed. First, the relationship between total tumor diameter (TTD) and clinicopathological features in multifocal PTMC was explored. Then, patients were divided into subgroups according to the TTD. The baseline was consistent after using the propensity score matching method, and the differences between groups were compared. In addition, the effectiveness of TTD and MD in evaluating central lymph node metastasis (CLNM) was analyzed and compared. Results: TTD is associated with a range of clinicopathological features, including lymph node metastasis, extrathyroidal extension, and risk stratification. Assuming the same MD and number of foci, the invasiveness of multifocal PTMC with TTD >1 cm was significantly higher than that with TTD <1 cm, and even higher than unifocal non-PTMC. Moreover, the efficiency of TTD in predicting CLNM was also significantly higher than that of MD. Conclusion: For multifocal PTMC, TTD is a more realistic indicator of tumor biological characteristics than MD. The aggressiveness of PTMC with TTD >1 cm was significantly enhanced, and surgical treatment should be actively sought in such cases.

Occurrences and Functions of Ly6Chi and Ly6Clo Macrophages in Health and Disease.

Macrophages originating from the yolk sac or bone marrow play essential roles in tissue homeostasis and disease. Bone marrow-derived monocytes differentiate into Ly6Chi and Ly6Clo macrophages according to the differential expression of the surface marker protein Ly6C. Ly6Chi and Ly6Clo cells possess diverse functions and transcriptional profiles and can accelerate the disease process or support tissue repair and reconstruction. In this review, we discuss the basic biology of Ly6Chi and Ly6Clo macrophages, including their origin, differentiation, and phenotypic switching, and the diverse functions of Ly6Chi and Ly6Clo macrophages in homeostasis and disease, including in injury, chronic inflammation, wound repair, autoimmune disease, and cancer. Furthermore, we clarify the differences between Ly6Chi and Ly6Clo macrophages and their connections with traditional M1 and M2 macrophages. We also summarize the limitations and perspectives for Ly6Chi and Ly6Clo macrophages. Overall, continued efforts to understand these cells may provide therapeutic approaches for disease treatment.

Identification of Long Noncoding RNAs Associated With the Clinicopathological Features of Papillary Thyroid Carcinoma Complicated With Hashimoto's Thyroiditis.

Long noncoding RNAs (lncRNAs) play a significant role in cancer biology. This study aimed to determine the roles of lncRNAs in establishing the differences in clinical features between patients with papillary thyroid carcinoma (PTC) without Hashimoto's thyroiditis (HT) and patients with PTC and HT. In the present study, we detected the differentially expressed lncRNAs between tumor tissues of patients with PTC with or without HT through lncRNA microarrays. The data were verified and analyzed through qRT-PCR, cell viability, cell cycle and bioinformatics analyses. We found that 1031 lncRNAs and 1338 mRNAs were abnormally expressed in 5 tissue samples of PTC complicated with HT [PTC/HT (+)] compared with 5 samples of PTC without HT [PTC/HT (-)]. Gene Ontology and pathway analyses of the mRNAs suggested that several biological processes and pathways, particularly immune system processes, were induced in the PTC/HT (+) tissues. Twenty lncRNAs were verified in 31 PTC/HT (+) and 64 PTC/HT (-) specimens by qRT-PCR, and the results were consistent with the microarray data. Specifically, ENST00000452578, a downregulated lncRNA in PTC/HT(+), was negatively correlated with the tumor size. Cell viability assays revealed that ENST00000452578 could inhibit cell proliferation. Our results indicate that lncRNAs and mRNAs play an important role in establishing the different clinical characteristics between patients with PTC/HT(+) and patients with PTC/HT(-), and might provide new insights from the perspective of RNA for obtaining a further understanding of the clinical features related to PTC with HT.

Risk factors of skip lateral cervical lymph node metastasis in papillary thyroid carcinoma: a systematic review and meta-analysis.

OBJECTIVE: To analyze and explore the risk factors of skip lateral cervical lymph node metastasis (SLLNM) in papillary thyroid carcinoma (PTC). METHODS: PubMed, Web of Science, Embase, Cochrane, Wanfang, China National Knowledge Infrastructure, and China Science and Technology Journal databases, updated to April 4, 2021, were systematically searched for literature on the risk factors of SLLNM in PTC. The meta-analysis was completed using Stata 15.0 software after quality evaluation. The odds ratio (OR) and 95% confidence interval (CI) of each variable were calculated using fixed or random-effects models, and the publication bias was evaluated by the Egger's test. RESULTS: A total of 28 studies with 10,682 cases were included in our meta-analysis; 1592 (14.90%) cases were positive for SLLNM. The meta-analysis showed that female sex (OR = 1.16, 95% CI = 1.02-1.31, P = 0.021), age ≥45 (OR = 1.60, 95% CI = 1.19-2.15, P = 0.002), tumor diameter ≤10 mm (OR = 2.23, 95% CI = 1.62-3.06, P < 0.001), and upper location of tumor (OR = 3.60, 95% CI = 2.65-4.89, P < 0.001) were risk factors for SLLNM in PTC patients. Hashimoto's thyroiditis (OR = 1.02, 95% CI = 0.88-1.19, P = 0.777), multifocality (OR = 0.98, 95% CI = 0.75-1.28, P = 0.873), bilateral tumors (OR = 0.92, 95% CI = 0.70-1.19, P = 0.515), extrathyroidal extensions (OR = 1.07, 95% CI = 0.83-1.39, P = 0.598), and capsular invasion (OR = 0.93, 95% CI = 0.65-1.31, P = 0.660) were not closely related to SLLNM risk. CONCLUSION: This study confirmed significant associations between SLLNM and female sex, age ≥45, tumor diameter ≤10 mm, and upper location of the tumor.