scholarly journals Identification of Key Functional Gene Signatures Indicative of Dedifferentiation in Papillary Thyroid Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Weibo Xu ◽  
Cuiwei Li ◽  
Ben Ma ◽  
Zhongwu Lu ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Thyroid Cancer ◽  
Roc Curve ◽  
Differentiated Thyroid Cancer ◽  
Epigenetic Modification ◽  
Gene Signature ◽  
Papillary Thyroid ◽  
Substance Transport

Background: Differentiated thyroid cancer (DTC) is the most common type of thyroid cancer. Many of them can relapse to dedifferentiated thyroid cancer (DDTC) and exhibit different gene expression profiles. The underlying mechanism of dedifferentiation and the involved genes or pathways remained to be investigated.Methods: A discovery cohort obtained from patients who received surgical resection in the Fudan University Shanghai Cancer Center (FUSCC) and two validation cohorts derived from Gene Expression Omnibus (GEO) database were used to screen out differentially expressed genes in the dedifferentiation process. Weighted gene co-expression network analysis (WGCNA) was constructed to identify modules highly related to differentiation. Gene Set Enrichment Analysis (GSEA) was used to identify pathways related to differentiation, and all differentially expressed genes were grouped by function based on the GSEA and literature reviewing data. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to control the number of variables in each group. Next, we used logistic regression to build a gene signature in each group to indicate differentiation status, and we computed receiver operating characteristic (ROC) curve to evaluate the indicative performance of each signature.Results: A total of 307 upregulated and 313 downregulated genes in poorly differentiated thyroid cancer (PDTC) compared with papillary thyroid cancer (PTC) and normal thyroid (NT) were screened out in FUSCC cohort and validated in two GEO cohorts. WGCNA of 620 differential genes yielded the seven core genes with the highest correlation with thyroid differentiation score (TDS). Furthermore, 395 genes significantly correlated with TDS in univariate logistic regression analysis were divided into 11 groups. The areas under the ROC curve (AUCs) of the gene signature of group transcription and epigenetic modification, signal and substance transport, extracellular matrix (ECM), and metabolism in the training set [The Cancer Genome Atlas (TCGA) cohort] and validation set (combined GEO cohort) were both >0.75. The gene signature based on group transcription and epigenetic modification, cilia formation and movement, and proliferation can reflect the patient's disease recurrence state.Conclusion: The dedifferentiation of DTC is affected by a variety of mechanisms including many genes. The gene signature of group transcription and epigenetic modification, signal and substance transport, ECM, and metabolism can be used as biomarkers for DDTC.

scholarly journals A Five-Gene Prognostic Nomogram Predicting Disease-Free Survival of Differentiated Thyroid Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Pan Ruchong ◽  
Tang Haiping ◽  
Wang Xiang
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Differentiated Thyroid Cancer ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Training Dataset ◽  
Prediction Ability ◽  
Free Survival ◽  
Testing Dataset ◽  
Disease Free

Background. Differentiated thyroid cancer (DTC) is the most common type of thyroid tumor with a high recurrence rate. Here, we developed a nomogram to effectively predict postoperative disease-free survival (DFS) in DTC patients. Methods. The mRNA expressions and clinical data of DTC patients were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Seventy percent of patients were randomly selected as the training dataset, and thirty percent of patients were classified into the testing dataset. Multivariate Cox regression analysis was adopted to establish a nomogram to predict 1-year, 3-year, and 5-year DFS rate of DTC patients. Results. A five-gene signature comprised of TENM1, FN1, APOD, F12, and BTNL8 genes was established to predict the DFS rate of DTC patients. Results from the concordance index (C-index), area under curve (AUC), and calibration curve showed that both the training dataset and the testing dataset exhibited good prediction ability, and they were superior to other traditional models. The risk score and distant metastasis (M) of the five-gene signature were independent risk factors that affected DTC recurrence. A nomogram that could predict 1-year, 3-year, and 5-year DFS rate of DTC patients was established with a C-index of 0.801 (95% CI: 0.736, 0.866). Conclusion. Our study developed a prediction model based on the gene expression and clinical characteristics to predict the DFS rate of DTC patients, which may be applied to more accurately assess patient prognosis and individualized treatment.

The Assessment of Ventricular Arrhythmia Indicators Based on Electrocardiography in Patients With Differentiated Thyroid Cancer Followed Up With Levothyroxine Suppression

Angiology ◽  
2021 ◽  
pp. 000331972199334
Author(s):  
Sema Hepsen ◽  
Davut Sakiz ◽  
Hilal Erken Pamukcu ◽  
Ismail Emre Arslan ◽  
Hakan Duger ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
Ventricular Arrhythmia ◽  
Differentiated Thyroid Cancer ◽  
Linear Regression Analysis ◽  
Male Gender ◽  
Qtc Interval ◽  
Inclusion Criteria ◽  
Raising Awareness ◽  
Number Of Patients

Levothyroxine suppression therapy (LST) can cause some unfavorable effects on the cardiovascular system in patients with differentiated thyroid cancer (DTC). The aim of this study was to evaluate ventricular arrhythmia predictors based on electrocardiography (ECG) in patients with DTC with LST. The ECG parameters including QT, corrected QT (QTc), Tp-e intervals, Tp-e/QT, and Tp-e/QTC ratios of 265 patients with DTC who met the inclusion criteria were compared with 100 controls. No difference was observed in the number of patients with DTC and controls with prolonged and borderline QTc interval ( P = .273). Tp-e interval, Tp-e/QT, and Tp-e/QTc ratios were significantly higher in patients ( P = .002, P = .02, P = .003; respectively). Linear regression analysis suggested that male gender was a predictor of higher Tp-e interval, Tp-e/QT, and Tp-e/QTc ratios (β = 4.322, R 2 = 0.024, P = .042; β = 0.016, R 2 = 0.048, P = .005; β = 0.015, R 2 = 0.044, P = .006, respectively). A higher serum fT4 level was found to be associated with a higher Tp-e/QT ratio (β = 0.018, R 2 = 0.089, P = .007). Ventricular arrhythmia indicators were found to be higher in patients with DTC with LST. Defining ventricular arrhythmia predictors through ECG, an easily accessible cardiac diagnostic tool, can be potentially useful in raising awareness of the possible cardiac harm of LST.

scholarly journals LDH, CRP and ALB predict nucleic acid turn negative within 14 days in symptomatic patients with COVID-19

2021 ◽  
pp. 003693302199424
Author(s):  
Gaoli Liu ◽  
Bicheng Zhang ◽  
Shaowen Zhang ◽  
Haifeng Hu ◽  
TingTing Liu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Nucleic Acid ◽  
Hospital Stay ◽  
Prognostic Marker ◽  
Roc Curve ◽  
Prognostic Value ◽  
Statistical Significance ◽  
Biochemical Indicators ◽  
The Relationship

Aims To search for biochemical indicators that can identify symptomatic patients with COVID-19 whose nucleic acid could turn negative within 14 days, and assess the prognostic value of these biochemical indicators in patients with COVID-19. Patients and methods We collected the clinical data of patients with COVID-19 admitted to our hospital, by using logistic regression analysis and AUC curves, explored the relationship between biochemical indicators and nucleic acid positive duration, the severity of COVID-19, and hospital stay respectively. Results A total of two hundred and thirty-three patients with COVID-19 were enrolled in the study. We found patients whose nucleic acid turned negative within 14 days had lower LDH, CRP and higher ALB ( P < 0.05). ROC curve results indicated that lower LDH, TP, CRP and higher ALB predicted the nucleic acid of patients turned negative within 14 days with statistical significance( P < 0.05), AST, LDH, CRP and PCT predicted the severe COVID-19 with statistical significance, and CRP predicted hospital stay >31days with statistical significance ( P < 0.05). After verification, the probability of nucleic acid turning negative within 14 days in patients with low LDH (<256 U/L), CRP (<44.5 mg/L) and high ALB (>35.8 g/L) was about 4 times higher than that in patients with high LDH, CRP and low ALB ( P < 0.05). Conclusions LDH, CRP and ALB are useful prognostic marker for predicting nucleic acid turn negative within 14 days in symptomatic patients with COVID-19.

scholarly journals Iodide- and Glucose-Handling Gene Expression Regulated by Sorafenib or Cabozantinib in Papillary Thyroid Cancer

2015 ◽  
Vol 100 (5) ◽  
pp. 1771-1779 ◽  
Author(s):  
Maomei Ruan ◽  
Min Liu ◽  
Qianggang Dong ◽  
Libo Chen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Signal Transduction ◽  
Cell Proliferation ◽  
Thyroid Cancer ◽  
Western Blot ◽  
Glucose Transporter ◽  
Signal Transduction Pathways ◽  
Papillary Thyroid ◽  
Cycle Arrest

Abstract Context: The aberrant silencing of iodide-handling genes accompanied by up-regulation of glucose metabolism presents a major challenge for radioiodine treatment of papillary thyroid cancer (PTC). Objective: This study aimed to evaluate the effect of tyrosine kinase inhibitors on iodide-handling and glucose-handling gene expression in BHP 2-7 cells harboring RET/PTC1 rearrangement. Main Outcome Measures: In this in vitro study, the effects of sorafenib or cabozantinib on cell growth, cycles, and apoptosis were investigated by cell proliferation assay, cell cycle analysis, and Annexin V-FITC apoptosis assay, respectively. The effect of both agents on signal transduction pathways was evaluated using the Western blot. Quantitative real-time PCR, Western blot, immunofluorescence, and radioisotope uptake assays were used to assess iodide-handling and glucose-handling gene expression. Results: Both compounds inhibited cell proliferation in a time-dependent and dose-dependent manner and caused cell cycle arrest in the G0/G1 phase. Sorafenib blocked RET, AKT, and ERK1/2 phosphorylation, whereas cabozantinib blocked RET and AKT phosphorylation. The restoration of iodide-handling gene expression and inhibition of glucose transporter 1 and 3 expression could be induced by either drug. The robust expression of sodium/iodide symporter induced by either agent was confirmed, and 125I uptake was correspondingly enhanced. 18F-fluorodeoxyglucose accumulation was significantly decreased after treatment by either sorafenib or cabozantinib. Conclusions: Sorafenib and cabozantinib had marked effects on cell proliferation, cell cycle arrest, and signal transduction pathways in PTC cells harboring RET/PTC1 rearrangement. Both agents could be potentially used to enhance the expression of iodide-handling genes and inhibit the expression of glucose transporter genes.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals Nephrectomy for Metastatic Kidney Tumor in Patients with Differentiated Thyroid Cancer: A Report of Two Cases

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Haruhiko Yamazaki ◽  
Takeshi Kishida ◽  
Go Noguchi ◽  
Hiroyuki Iwasaki ◽  
Nobuyasu Suganuma ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Differentiated Thyroid Cancer ◽  
Follicular Thyroid Carcinoma ◽  
Left Kidney ◽  
Renal Tumors ◽  
Kidney Tumor ◽  
Papillary Thyroid ◽  
Renal Metastasis ◽  
Initial Surgery ◽  
Renal Metastases

The occurrence of renal tumors originating from thyroid cancer is extremely rare with a few effective treatments for renal metastases. Here, we report the cases of two patients with differentiated thyroid cancer who underwent nephrectomy for a metastatic kidney tumor. Case 1 was a 74-year-old man who was diagnosed with right kidney tumor 10 years after initial surgery for papillary thyroid cancer (PTC). Right nephrectomy was performed, and the pathology was metastatic PTC. Case 2 was a 68-year-old woman who was diagnosed with left kidney tumor 24 years after surgery for follicular thyroid carcinoma (FTC). Left nephrectomy was performed, and the pathology was metastatic FTC. Nephrectomy for single renal metastasis could be considered a treatment option if the patients’ general condition is positive.

scholarly journals Expression of LOX Suggests Poor Prognosis in Gastric Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Jinfeng Zhu ◽  
Chen Luo ◽  
Jiefeng Zhao ◽  
Xiaojian Zhu ◽  
Kang Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Expression Level ◽  
Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p &lt; 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

scholarly journals Association Between Triglyceride Glucose Index and Non-Small Cell Lung Cancer Risk in Chinese Population

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Yan ◽  
Yujuan Gao ◽  
Jingzhi Tong ◽  
Mi Tian ◽  
Jinghong Dai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cancer Risk ◽  
Small Cell Lung Cancer ◽  
Roc Curve ◽  
Small Cell ◽  
Small Cell Lung ◽  
Tyg Index ◽  
The Relationship

BackgroundNumerous studies showed that insulin resistance (IR) was associated with cancer risk. However, few studies investigated the relationship between IR and non-small cell lung cancer (NSCLC). The aim of this study is to explore the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, with NSCLC risk.Methods791 histologically confirmed NSCLC cases and 787 controls were enrolled in the present study. Fasting blood glucose and triglyceride were measured. The TyG index was calculated as ln [fasting triglycerides (mg/dl) ×fasting glucose (mg/dl)/2]. Logistic regression analysis was performed to estimate the relationship between NSCLC risk and the TyG index.ResultsThe TyG index was significantly higher in patients with NSCLC than that in controls (8.42 ± 0.55 vs 8.00 ± 0.45, P &lt; 0.01). Logistic regression analysis showed that the TyG index (OR = 3.651, 95%CI 2.461–5.417, P &lt; 0.001) was independently associated with NSCLC risk after adjusting for conventional risk factors. In addition, a continuous rise in the incidence of NSCLC was observed along the tertiles of the TyG index (29.4 vs 53.8 vs 67.2%, P &lt; 0.001). However, there were no differences of the TyG index in different pathological or TNM stages. In receiver operating characteristic (ROC) curve analysis, the optimal cut-off level for the TyG index to predict incident NSCLC was 8.18, and the area under the ROC curve (AUROC) was 0.713(95% CI 0.688–0.738).ConclusionsThe TyG index is significantly correlated with NSCLC risk, and it may be suitable as a predictor for NSCLC.

O-207 Which infertile patients mostly deserve to have a sperm DNA fragmentation index done? Findings from a cross-sectional study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
E Pozzi ◽  
L Boeri ◽  
L Candela ◽  
D Cignoli ◽  
G Colandrea ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Dna Fragmentation ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Sperm Concentration ◽  
Testicular Volume ◽  
Sperm Dna Fragmentation ◽  
Sperm Dna ◽  
Increased Risk

Abstract Study question Current scientific guidelines do not clearly suggest which patients would benefit the most from a sperm DNA fragmentation (SDF) test. Summary answer We aimed to investigate potential predictive factors for altered SDF in a homogenous cohort of white-European men presenting for primary couple’s infertility. What is known already High SDF has been associated with reduced fertilization rates, reduced chances of natural conception and an increased risk of early pregnancy loss. Study design, size, duration Data from 478 consecutive men with normal or altered SDF were analysed. Infertility was defined according to the WHO criteria. Semen analysis, SDF (according to SCSA) and serum hormones were measured in every patient. Health significant comorbidities were scored with the Charlson Comorbidity Index (CCI). Altered SDF was considered with a threshold of &gt; 30%. Participants/materials, setting, methods Descriptive statistics compared the overall characteristics of patients with normal SDF and altered SDF. Logistic regression analysis tested potential predictors of altered SDF. ROC curve was used to test the accuracy of the model in predicting SDF alteration Main results and the role of chance Of 478 patients, 253 (57.7%) had altered SDF. Median (IQR) age and BMI of the whole cohort were 38 (35-42) years and 25.1 (23.3-27.1) kg/m2 respectively. Patients with altered SDF were older (median (IQR) age: 39 (36-43) vs. 37 (34-38) years, p &lt; 0.0001), had lower sperm concentration (5 (1.1–18) vs. 17 x106/mL (6–38.8), p &lt; 0.0001), testicular volume (15.1 (12 –20) vs. 16.8 (12 – 25) Prader, p = 0.0005), and total motile sperm count (TMSC) (1.8 (0.21–10.71) vs. 11.8x106 (2–37.26), p &lt; 0.0001). Conversely, men with altered SDF had higher FSH (6.1 (3.85–9.7) vs. 4.8 (3.85 – 7.9) mIU/mL, p &lt; 0.0001) and prolactin levels (9.8 (7.43–14.04) vs. 8.3 (6.6–11.3) pg/mL, p = 0.0004) than those with normal SDF. At multivariable logistic regression analysis, patients’ age &gt;35 years (OR: 2.45, p = 0.0009), FSH &gt; 8.0 mIU/mL (OR: 2.23, p &lt; 0.0001) and lower TMSC (OR: 2.04, p = 0.002) were identified as indipendent predictors of altered SDF, after adjusting for testicular volume and CCI≥1. ROC curve (Figure 1) revealed that the model has a good predictive ability to identify patients with SDF alteration (AUC: 0.72, 95%CI: 0.67 - 0.77). Limitations, reasons for caution It is a retrospective analysis at a single, tertiary-referral academic centre, thus raising the possibility of selection biases. In spite of this, all patients have been consistently analysed over time with a rigorous follow-up, thus limiting potential heterogeneity in terms of data reporting Wider implications of the findings Primary infertile men older than 35 years, with high serum FSH and low TMSC at baseline are the ones who mostly deserve a SDF test over their diagnostic work-up and that would potentially benefit the most of certain treatments to improve SDF value, thus increasing chances of conceiving. Trial registration number Not applicable

scholarly journals BCL2 and hsa-miR-181a-5p are potential biomarkers associated with papillary thyroid cancer based on bioinformatics analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Cong Zhang ◽  
Chunrui Bo ◽  
Lunhua Guo ◽  
Pingyang Yu ◽  
Susheng Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Gene Regulatory Network ◽  
Papillary Thyroid Cancer ◽  
Regulatory Network ◽  
Mirna Gene ◽  
Cancer Type ◽  
Papillary Thyroid ◽  
Ppi Network ◽  
Gene Regulatory

Abstract Background The morbidity of thyroid carcinoma has been rising worldwide and increasing faster than any other cancer type. The most common subtype with the best prognosis is papillary thyroid cancer (PTC); however, the exact molecular pathogenesis of PTC is still not completely understood. Methods In the current study, 3 gene expression datasets (GSE3678, GSE3467, and GSE33630) and 2 miRNA expression datasets (GSE113629 and GSE73182) of PTC were selected from the Gene Expression Omnibus (GEO) database and were further used to identify differentially expressed genes (DEGs) and deregulated miRNAs between normal thyroid tissue samples and PTC samples. Then, Gene Ontology (GO) and pathway enrichment analyses were conducted, and a protein-protein interaction (PPI) network was constructed to explore the potential mechanism of PTC carcinogenesis. The hub gene detection was performed using the CentiScaPe v2.0 plugin, and significant modules were discovered using the MCODE plugin for Cytoscape. In addition, a miRNA-gene regulatory network in PTC was constructed using common deregulated miRNAs and DEGs. Results A total of 263 common DEGs and 12 common deregulated miRNAs were identified. Then, 6 significant KEGG pathways (P < 0.05) and 82 significant GO terms were found to be enriched, indicating that PTC was closely related to amino acid metabolism, development, immune system, and endocrine system. In addition, by constructing a PPI network and miRNA-gene regulatory network, we found that hsa-miR-181a-5p regulated the most DEGs, while BCL2 was targeted by the most miRNAs. Conclusions The results of this study suggested that hsa-miR-181a-5p and BCL2 and their regulatory networks may play important roles in the pathogenesis of PTC.

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