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Song X, Skog S, Wei L, Qin J, Yang R, Li J, Zhou J, He E, Zhou J. Nomogram model of serum thymidine kinase 1 combined with ultrasonography for prediction of central lymph node metastasis risk in patients with papillary thyroid carcinoma pre-surgery. Front Endocrinol (Lausanne) 2024; 15:1366219. [PMID: 38887267 PMCID: PMC11180742 DOI: 10.3389/fendo.2024.1366219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 05/02/2024] [Indexed: 06/20/2024] Open
Abstract
Objective The aim of this study was to develop a nomogram, using serum thymidine kinase 1 protein (STK1p) combined with ultrasonography parameters, to early predict central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) pre-surgery. Methods Patients with PTC pre-surgery in January 2021 to February 2023 were divided into three cohorts: the observation cohort (CLNM, n = 140), the control cohort (NCLNM, n = 128), and the external verification cohort (CLNM, n = 50; NCLNM, n = 50). STK1p was detected by an enzyme immunodot-blot chemiluminescence analyzer and clinical parameters were evaluated by ultrasonography. Results A suitable risk threshold value for STK1p of 1.7 pmol/L was selected for predicting CLNM risk by receiver operating characteristic (ROC) curve analysis. Multivariate analysis identified the following six independent risk factors for CLNM: maximum tumor size >1 cm [odds ratio (OR) = 2.406, 95% confidence interval (CI) (1.279-4.526), p = 0.006]; capsule invasion [OR = 2.664, 95% CI (1.324-5.360), p = 0.006]; irregular margin [OR = 2.922; 95% CI (1.397-6.111), p = 0.004]; CLN flow signal [OR = 3.618, 95% CI (1.631-8.027), p = 0.002]; tumor-foci number ≥2 [OR = 4.064, 95% CI (2.102-7.859), p < 0.001]; and STK1p ≥1.7 pmol/L [OR = 7.514, 95% CI (3.852-14.660), p < 0.001]. The constructed nomogram showed that the area under the ROC curve for the main dataset was 0.867 and that for the validation dataset was 0.830, exhibiting effectivity, and was recalculated to a total score of approximately 383. Through monitoring the response post-surgery, all patients were assessed as tumor-free at 12 months post-surgery, which was significantly associated with a reduction in STK1p to disease-free levels. Conclusion We demonstrate for the first time that a novel nomogram including STK1p combined with ultrasonography can assist in the clinical prevention of CLNM, by facilitating timely, individualized prophylactic CLNM dissection, thereby reducing the risk of secondary surgery and the probability of recurrence.
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Affiliation(s)
- Xiaolong Song
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Sven Skog
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Long Wei
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Jinlv Qin
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Ru Yang
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Jin Li
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Ji Zhou
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Ellen He
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Jianping Zhou
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
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Wang Y, Cui Y, Zhang D, Chen C, Hou C, Cao L. Moderating Role of TSHR and PTPN22 Gene Polymorphisms in Effects of Excessive Fluoride on Thyroid: a School-Based Cross-Sectional Study. Biol Trace Elem Res 2022; 200:1104-1116. [PMID: 34050454 DOI: 10.1007/s12011-021-02753-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/05/2021] [Indexed: 01/06/2023]
Abstract
We aimed to investigate the relationship between the effects excessive of fluoride on thyroid health in children and the moderating role of thyroid stimulating hormone receptor (TSHR) or protein tyrosine phosphatase nonreceptor-22 (PTPN22) gene polymorphisms. Four hundred thirteen children (141 with dental fluorosis and 198 boys) were enrolled from both historical endemic and non-endemic areas of fluorosis in Tianjin, China. The fluoride exposure levels, thyroid health indicators, and TSHR (rs2268458) and PTPN22 (rs3765598) polymorphisms were examined. Multiple logistic models were applied to evaluate the relationship between dental fluorosis and thyroid abnormalities. Children over 9 year old with dental fluorosis have lower FT4 and TGAb levels and thyroid volume and higher TPOAb levels (all P < 0.05). In overall participants, children with dental fluorosis were more likely to have thyroid antibody single positive issues (adjusted P = 0.039) and less likely to have a goiter according to age or body surface area (age or BSA) (adjusted P = 0.003); In the TSHR (rs2268458) SNP = CC/CT or PTPN22 (rs3765598) SNP = CC subgroup, dental fluorosis may cause thyroid antibody single positive (adjusted P = 0.036; adjusted P = 0.002); in the TSHR (rs2268458) SNP = TT or PTPN22 (rs3765598) SNP = CC subgroup, dental fluorosis may protect children from goiter (age or BSA) (adjusted P = 0.018; adjusted P = 0.013). Excessive fluoride may induce thyroid antibody single positive and reduce goiter in children. Heterogeneity exists in the relationship between excessive fluoride and thyroid antibody single positive or goiter issues across children carrying different TSHR (rs2268458) or PTPN22 (rs3765598) genotypes.
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Affiliation(s)
- Yang Wang
- Tianjin Centers for Disease Control and Prevention, 6 Huayue Road, Hedong District, Tianjin, 300011, People's Republic of China
| | - Yushan Cui
- Tianjin Centers for Disease Control and Prevention, 6 Huayue Road, Hedong District, Tianjin, 300011, People's Republic of China
| | - Dandan Zhang
- Tianjin Centers for Disease Control and Prevention, 6 Huayue Road, Hedong District, Tianjin, 300011, People's Republic of China
| | - Chen Chen
- Tianjin Health Promotion Center, 76 Hualong Road, Hedong District, Tianjin, 300011, People's Republic of China
| | - Changchun Hou
- Tianjin Centers for Disease Control and Prevention, 6 Huayue Road, Hedong District, Tianjin, 300011, People's Republic of China.
| | - Lichun Cao
- Dazhangzhuang Community Health Service Center, 31 Yongkang Road, Beichen District, Tianjin, 300400, People's Republic of China.
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He CY, Chen LZ, Wang ZX, Sun LP, Peng JJ, Wu MQ, Wang TM, Li YQ, Yang XH, Zhou DL, Ye ZL, Ma JJ, Li XZ, Zhang PF, Ju HQ, Mo HY, Zhang ZC, Zeng ZL, Shao JY, Jia WH, Cai SJ, Yuan Y, Xu RH. Performance of common genetic variants in risk prediction for colorectal cancer in Chinese: A two-stage and multicenter study. Genomics 2021; 113:867-873. [PMID: 33545268 DOI: 10.1016/j.ygeno.2021.01.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 11/25/2022]
Abstract
The efficacy of susceptible variants derived from genome-wide association studies (GWAs) optimizing discriminatory accuracy of colorectal cancer (CRC) in Chinese remains unclear. In the present validation study, we assessed 75 recently identified variants from GWAs. A risk predictive model combining 19 variants using the least absolute shrinkage and selection operator (LASSO) statistics offered certain clinical advantages. This model demonstrated an area under the receiver operating characteristic (AUC) of 0.61 during training analysis and yielded robust AUCs from 0.59 to 0.61 during validation analysis in three independent centers. The individuals carrying the highest quartile of risk score revealed over 2-fold risks of CRC (ranging from 2.12 to 2.90) compared with those who presented the lowest quartile of risk score. This genetic model offered the possibility of partitioning risk within the average risk population, which might serve as a first step toward developing individualized CRC prevention strategies in China.
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Affiliation(s)
- Cai-Yun He
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Le-Zong Chen
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zi-Xian Wang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Li-Ping Sun
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China
| | - Jun-Jie Peng
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Min-Qing Wu
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Tong-Min Wang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ya-Qi Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xin-Hua Yang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Da-Lei Zhou
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zu-Lu Ye
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Jiang-Jun Ma
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xi-Zhao Li
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Pei-Fen Zhang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Huai-Qiang Ju
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Hai-Yu Mo
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Zi-Chen Zhang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zhao-Lei Zeng
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Jian-Yong Shao
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Wei-Hua Jia
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
| | - San-Jun Cai
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yuan Yuan
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China.
| | - Rui-Hua Xu
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
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Li Y, He HC, Zhou DL, Liu Q, Zhang X, Yang XH, Ye ZL, Peng JL, Tang T, Su X, He CY. Associations between lncRNA-related polymorphisms and hepatocellular carcinoma risk: A two-stage case-control study. J Gastroenterol Hepatol 2021; 36:233-239. [PMID: 32453900 DOI: 10.1111/jgh.15118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/12/2020] [Accepted: 05/19/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND AIM Single-nucleotide polymorphisms (SNPs) in long non-coding RNAs (lncRNAs) are potential biomarkers for cancer risk, but their association with hepatocellular carcinoma (HCC) is unclear. We examined the association of lncRNA-related SNPs with HCC susceptibility and explored the optimal genetic models for SNPs. METHODS Five candidate SNPs linked with digestive tumors were first genotyped in a screening population of 700 HCC and 2800 control cases. The association between each SNP and HCC risk was estimated by multivariate logistic regression adjusted by sex and age and recorded as odds ratio (OR) with 95% confidence interval. Significant associations were further tested in a validation population with 1140 HCC and 5115 control cases. Finally, the most appropriate genetic models for HCC-associated SNPs were identified using pairwise allele differences; the overall gene effects of each SNP were further evaluated based on optimal genetic models. RESULTS Three candidate SNPs, rs7315438, rs6983267, and rs10795668, showed statistical connections with HCC risk in the discovery stage. Among these, rs7315438 remained steadily significant in the validation stage; rs7315438 and rs10795668 both reached statistical threshold in the combined analysis of both stages. SNP rs7315438 (TC vs TT/CC, OR = 1.410, P < 0.001) was associated with increased risk of HCC in a complete overdominant model, whereas rs10795668 (AG vs AA/GG, OR = 0.892, P = 0.035) exerted a protective effect on HCC risk in a complete overdominant model. CONCLUSIONS Long non-coding RNA-related SNPs rs7315438 and rs10795668 are potential biomarkers for HCC susceptibility, especially when evaluated based on their optimal genetic models.
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Affiliation(s)
- Yue Li
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hui-Chan He
- Department of Blood Transfusion, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Da-Lei Zhou
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Qing Liu
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiao Zhang
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xin-Hua Yang
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zu-Lu Ye
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jun-Ling Peng
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Tao Tang
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xuan Su
- Department of Head and Neck, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Cai-Yun He
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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5
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Kollati Y, Akella RRD, Naushad SM, Borkar D, Thalla M, Nagalingam S, Lingappa L, Patel RK, Reddy GB, Dirisala VR. Newborn screening and single nucleotide variation profiling of TSHR, TPO, TG and DUOX2 candidate genes for congenital hypothyroidism. Mol Biol Rep 2020; 47:7467-7475. [PMID: 32930933 DOI: 10.1007/s11033-020-05803-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 09/03/2020] [Indexed: 12/28/2022]
Abstract
High prevalence of congenital hypothyroidism (CH) among Indian newborns prompted us to establish population-specific reference ranges of TSH and to explore the contribution of the common genetic variants in TSHR, TPO, TG and DUOX2 genes towards CH. A total of 1144 newborns (593 males and 551 females) were screened for CH. SNV profiling (n = 22) spanning three candidate genes, i.e. TSHR, TPO and TG was carried out in confirmed CH cases (n = 45). In screen negative cases (n = 700), ten TSHR variants were explored to establish association with CH. No mutation found in DUOX2. The 2.5th to 97.5th percentiles of TSH in these newborns were 0.5 to 12.2 mU/L. In newborns with optimal birth weight, the cut-off was 10 mU/L. Lower or higher birth weight resulted in slightly higher TSH. Two TSHR variants, i.e. rs7144481 and rs17630128 were associated with agenesis, hypoplasia and goiter. The rs2268477 was associated with agenesis and hypoplasia. The rs1991517, rs2075176 and rs2241119 were associated with agenesis only. The rs7144481, rs17630128, rs1991517 and rs2268477 were associated with 2.17, 4.62, 2.91 and 2.29-fold increased risk for CH, respectively. Among the TPO variants, rs867983 and rs2175977 were associated with agenesis and goiter, respectively. Among the TG variants, rs2076740 showed association with agenesis and goiter. Two rare variants i.e. TPO g.IVS14-19 G>C and TG c.1262 C>T were observed in CH cases. No genetic variant identified in the two exons of DUOX2. To conclude, the current study established Indian population-specific normative values for TSH and demonstrates specific genotype-phenotype correlations among three candidate genes.
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Affiliation(s)
- Yedukondalu Kollati
- Department of Biotechnology, Vignan's Foundation for Science, Technology & Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, 522213, India
| | - Radha Rama Devi Akella
- Department of Genetics, Rainbow Children's Hospital, Banjara Hills, Hyderabad, Telangana, 500009, India
- Department of Biochemical Genetics and Pharmacogenomics, Sandor Speciality Diagnostics Pvt. Ltd, Banjara Hills, Hyderabad, Telangana, 500034, India
| | - Shaik Mohammad Naushad
- Department of Biochemical Genetics and Pharmacogenomics, Sandor Speciality Diagnostics Pvt. Ltd, Banjara Hills, Hyderabad, Telangana, 500034, India
| | - Divya Borkar
- Department of Biochemical Genetics and Pharmacogenomics, Sandor Speciality Diagnostics Pvt. Ltd, Banjara Hills, Hyderabad, Telangana, 500034, India
| | - Maunika Thalla
- Department of Biochemical Genetics and Pharmacogenomics, Sandor Speciality Diagnostics Pvt. Ltd, Banjara Hills, Hyderabad, Telangana, 500034, India
| | - Swapna Nagalingam
- Biochemistry Division, National Institute of Nutrition, Hyderabad, Telangana, 500007, India
| | - Lokesh Lingappa
- Department of Pediatric Neurology, Rainbow Children's Hospital, Banjara Hills, Hyderabad, Telangana, 500009, India
| | - Rajesh K Patel
- Department of Genetics, Genetic Group of Gujarat Diagnostic Centre, Mehsana, Gujarat, 384002, India
| | - G Bhanuprakash Reddy
- Biochemistry Division, National Institute of Nutrition, Hyderabad, Telangana, 500007, India.
| | - Vijaya R Dirisala
- Department of Biotechnology, Vignan's Foundation for Science, Technology & Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, 522213, India.
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