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Thyroid
Small Multi-Gene DNA Panel Can Aid in Reducing the Surgical Resection Rate and Predicting the Malignancy Risk of Thyroid Nodules
Moon Young Oh, Hye-Mi Choi, Jinsun Jang, Heejun Son, Seung Shin Park, Minchul Song, Yoo Hyung Kim, Sun Wook Cho, Young Jun Chai, Woosung Chung, Young Joo Park
Endocrinol Metab. 2024;39(5):777-792.   Published online October 14, 2024
DOI: https://doi.org/10.3803/EnM.2024.2034
  • 4,294 View
  • 111 Download
  • 3 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We explored the utility of a small multi-gene DNA panel for assessing molecular profiles of thyroid nodules and influencing clinical decisions by comparing outcomes between tested and untested nodules.
Methods
Between April 2022 and May 2023, we prospectively performed fine-needle aspiration (FNA) with gene testing via DNA panel of 11 genes (BRAF, RAS [NRAS, HRAS, KRAS], EZH1, DICER1, EIF1AX, PTEN, TP53, PIK3CA, TERT promoter) in 278 consecutive nodules (panel group). Propensity score-matching (1:1) was performed with 475 nodules that consecutively underwent FNA without gene testing between January 2021 and December 2021 (control group).
Results
In the panel group, positive call rate for mutations was 41.7% (BRAF 16.2%, RAS 12.6%, others 11.5%, double mutation 1.4%) for all nodules, and 40.0% (BRAF 4.3%, RAS 19.1%, others 15.7%, double mutation 0.9%) for indeterminate nodules. Benign call rate was 69.8% for all nodules, and 75.7% for indeterminate nodules. In four nodules, additional TP53 (in addition to BRAF or EZH1) or PIK3CA (in addition to BRAF or TERT) mutations were co-detected. Sensitivity, specificity, positive predictive value, and negative predictive value were 80.0%, 53.3%, 88.1%, 38.1% for all nodules, and 78.6%, 45.5%, 64.7%, 62.5% for indeterminate nodules, respectively. Panel group exhibited lower surgical resection rates than the control group for all nodules (27.0% vs. 52.5%, P<0.001), and indeterminate nodules (23.5% vs. 68.2%, P<0.001). Malignancy risk was significantly different between the panel and control groups (81.5% vs. 63.9%, P=0.008) for all nodules.
Conclusion
Our panel aids in managing thyroid nodules by providing information on malignancy risk based on mutations, potentially reducing unnecessary surgery in benign nodules or patients with less aggressive malignancies.

Citations

Citations to this article as recorded by  
  • Integrative multi-omics refines the molecular subtypes of thyroid cancers and enhances cancer-progression prediction: a retrospective cohort study
    Yoo Hyung Kim, Joseph Wang, Jae-Kyung Won, Youngsoo Kim, Sun Wook Cho, Dohyun Han, Young Joo Park
    International Journal of Surgery.2026;[Epub]     CrossRef
  • Great Debate: Molecular Testing and Extent of Surgery in Well-Differentiated Thyroid Cancer
    Cord Sturgeon, Ashok R. Shaha, Linwah Yip
    Annals of Surgical Oncology.2025; 32(7): 4597.     CrossRef
  • Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models
    Wentian Li, Jiayu Zhu, Ying Wang, Jingxiu Li, Zhonghui Li, Cuicui Wang, Jingli Xue, Peng Zhou, Qingqing He
    Frontiers in Endocrinology.2025;[Epub]     CrossRef
  • Inside the Matrix: Integrated Cytology and Molecular Testing of Thyroid FNAC Samples Using a Commercial Synthetic 3D Scaffold
    Diana Raluca Streinu, Dana Liana Stoian, Octavian Constantin Neagoe, Mihnea Derban, Paula Diana Ciordas, Catalin Marian
    International Journal of Molecular Sciences.2025; 26(22): 11100.     CrossRef
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Miscellaneous
Differences in Type 2 Fiber Composition in the Vastus Lateralis and Gluteus Maximus of Patients with Hip Fractures
Jingwen Tian, Minchul Song, Kyu Jeong Cho, Ho Yeop Lee, Sang Hyeon Ju, Jung Ryul Lim, Ha Thi Nga, Thi Linh Nguyen, Ji Sun Moon, Hyo Ju Jang, Jung-Mo Hwang, Hyon-Seung Yi
Endocrinol Metab. 2024;39(3):521-530.   Published online June 11, 2024
DOI: https://doi.org/10.3803/EnM.2024.1935
  • 5,612 View
  • 97 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Aging leads to sarcopenia, which is characterized by reduced muscle mass and strength. Many factors, including altered muscle protein turnover, diminished neuromuscular function, hormonal changes, systemic inflammation, and the structure and composition of muscle fibers, play a crucial role in age-related muscle decline. This study explored differences in muscle fiber types contributing to overall muscle function decline in aging, focusing on individuals with hip fractures from falls.
Methods
A pilot study at Chungnam National University Hospital collected muscle biopsies from hip fracture patients aged 20 to 80 undergoing surgical treatment. Muscle biopsies from the vastus lateralis and gluteus maximus were obtained during hip arthroplasty or internal fixation. Handgrip strength, calf and thigh circumference, and bone mineral density were evaluated in individuals with hip fractures from falls. We analyzed the relationships between each clinical characteristic and muscle fiber type.
Results
In total, 26 participants (mean age 67.9 years, 69.2% male) were included in this study. The prevalence of sarcopenia was 53.8%, and that of femoral and lumbar osteoporosis was 19.2% and 11.5%, respectively. Vastus lateralis analysis revealed an age-related decrease in type IIx fibers, a higher proportion of type IIa fibers in women, and an association between handgrip strength and type IIx fibers in men. The gluteus maximus showed no significant correlations with clinical parameters.
Conclusion
This study identified complex associations between age, sex, handgrip strength, and muscle fiber composition in hip fracture patients, offering insights crucial for targeted interventions combating age-related muscle decline and improving musculoskeletal health.

Citations

Citations to this article as recorded by  
  • Tourniquet Use During Anterior Cruciate Ligament Reconstruction Is Associated With Postoperative Quadriceps Atrophy and Pain but No Negative Effects in the Long Term: A Systematic Review
    Caleb V. Hayes, Saad M. Ibrahim, Anna E. Crawford, James R. Jones, Mathew D. Hargreaves, Clay A. Rahaman, Eugene W. Brabston, Thomas B. Evely, Aaron J. Casp, Kevin E. Wilk, Amit M. Momaya
    Arthroscopy, Sports Medicine, and Rehabilitation.2025; 7(2): 101040.     CrossRef
  • Volume and quality of the gluteal muscles are associated with early physical function after total hip arthroplasty
    Makoto Iwasa, Keisuke Uemura, Mazen Soufi, Yoshito Otake, Tomofumi Kinoshita, Tatsuhiko Kutsuna, Kazuma Takashima, Hidetoshi Hamada, Yoshinobu Sato, Nobuhiko Sugano, Seiji Okada, Masaki Takao
    International Journal of Computer Assisted Radiology and Surgery.2025; 20(4): 703.     CrossRef
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Review Article
Thyroid
Metabolic Reprogramming in Thyroid Cancer
Sang-Hyeon Ju, Minchul Song, Joung Youl Lim, Yea Eun Kang, Hyon-Seung Yi, Minho Shong
Endocrinol Metab. 2024;39(3):425-444.   Published online June 10, 2024
DOI: https://doi.org/10.3803/EnM.2023.1802
  • 12,580 View
  • 254 Download
  • 15 Web of Science
  • 17 Crossref
AbstractAbstract PDFPubReader   ePub   
Thyroid cancer is a common endocrine malignancy with increasing incidence globally. Although most cases can be treated effectively, some cases are more aggressive and have a higher risk of mortality. Inhibiting RET and BRAF kinases has emerged as a potential therapeutic strategy for the treatment of thyroid cancer, particularly in cases of advanced or aggressive disease. However, the development of resistance mechanisms may limit the efficacy of these kinase inhibitors. Therefore, developing precise strategies to target thyroid cancer cell metabolism and overcome resistance is a critical area of research for advancing thyroid cancer treatment. In the field of cancer therapeutics, researchers have explored combinatorial strategies involving dual metabolic inhibition and metabolic inhibitors in combination with targeted therapy, chemotherapy, and immunotherapy to overcome the challenge of metabolic plasticity. This review highlights the need for new therapeutic approaches for thyroid cancer and discusses promising metabolic inhibitors targeting thyroid cancer. It also discusses the challenges posed by metabolic plasticity in the development of effective strategies for targeting cancer cell metabolism and explores the potential advantages of combined metabolic targeting.

Citations

Citations to this article as recorded by  
  • Proteomics combined with ultrasound radiomics predicts central lymph node metastasis in papillary thyroid carcinoma
    Ya Sun, Jian Jiang, Pengfei Zhang, Zhiyong Lv, Jiajun Wang, Nan Zhou, Lei Liang
    Current Radiopharmaceuticals.2026; 19(2): 100016.     CrossRef
  • The molecular mechanisms and potential therapeutic implications of the crosstalk between DNA methylation and metabolic reprogramming in thyroid cancer
    Tianying Zhang, Hengtong Han, Yating Zhang, Tingting Zhang, Libin Ma, Ze Yang, Yong-xun Zhao
    Cell Death Discovery.2026;[Epub]     CrossRef
  • The critical role of ferroptosis in thyroid cancer development and potential therapeutic implications
    Yinghao Li, Tao Qian, Zhongyu Han, Chuchu Wang, Meiqi Zhang, Chi Huang, Qingqing Gu, Shuangyan Zhang, Yumeng Lin, Jianhua Wang, Shouqiang Chen
    Frontiers in Oncology.2026;[Epub]     CrossRef
  • Acetate Metabolism in Thyroid Cancer Progression
    Enke Baldini, Silvia Cardarelli, Eleonora Lori, Poupak Fallahi, Camilla Virili, Marco Centanni, Vito D’Andrea, Alessandro Antonelli, Salvatore Sorrenti, Salvatore Ulisse
    International Journal of Molecular Sciences.2026; 27(4): 2013.     CrossRef
  • Bilateral chylothorax following total thyroidectomy with neck lymph node dissection for thyroid cancer: a case report and literature review
    Yunsheng Wang, Xudong Liu, Xingyue Wang, Youxin Tian, Qinjiang Liu, Jun Wang, Jincai Xue
    Frontiers in Oncology.2025;[Epub]     CrossRef
  • The emerging landscape of small nucleolar RNA host gene 10 in cancer mechanistic insights and clinical relevance
    Jingyu Zhu, Zihao Jian, Fangteng Liu, Lulu Le
    Cellular Signalling.2025; 127: 111590.     CrossRef
  • The Role of WDR77 in Cancer: More than a PRMT5 Interactor
    Isaac Silverman, Aaron Shaykevich, Radhashree Maitra
    Molecular Cancer Research.2025; 23(4): 269.     CrossRef
  • Systems biology approach delineates critical pathways associated with papillary thyroid cancer: a multi-omics data analysis
    Febby Payva, Santhy K. S., Remya James, Amrisa Pavithra E, Venketesh Sivaramakrishnan
    Thyroid Research.2025;[Epub]     CrossRef
  • Identification of oxidative stress-related subgroups and signature genes for the prediction of prognosis and immune microenvironment in thyroid cancer
    Zhenwei Qiu, Jing Li, Mei Tian
    Molecular Genetics and Genomics.2025;[Epub]     CrossRef
  • The current state and influencing factors of negative social expectations among thyroid cancer patients: a single-center cross-sectional analysis
    Zhitong Wang, Cui Chen, Jing Lu, Zhenfan Liu
    Frontiers in Psychology.2025;[Epub]     CrossRef
  • Turning tumor microenvironmental foes to friends: A new opportunity for thyroid cancer therapy and redifferentiation
    Liya Zhu, Xiuli Jing, Byeong-Cheol Ahn
    Oral Oncology.2025; 168: 107513.     CrossRef
  • Targeting the ATG12–ATG3 protein-protein interaction: From structural insights to therapeutic opportunities in autophagy modulation
    Emadeldin M. Kamel, Sally Mostafa Khadrawy, Ahmed A. Allam, Noha A. Ahmed, Faris F. Aba Alkhayl, Al Mokhtar Lamsabhi
    Pathology - Research and Practice.2025; 273: 156156.     CrossRef
  • Association between metabolic dysfunction-associated steatotic liver disease and the risk of thyroid cancer: a nationwide cohort study
    Jeongmin Lee, Jeongeun Kwak, Min-Hee Kim, Seung-Hwan Lee, Jae-Hyoung Cho, Dong-Jun Lim, Jung Min Lee, Sang-Ah Chang, Hun-Sung Kim
    The Korean Journal of Internal Medicine.2025; 40(5): 813.     CrossRef
  • Thyroid cancer: From molecular insights to therapy (Review)
    Zhuozheng Li, Nuofan Wang, Xiao Li, Yongfang Xie, Zemin Dou, Hongbing Xin, Yuzhuo Lin, Yan Si, Tingting Feng, Guohui Wang
    Oncology Letters.2025; 30(5): 1.     CrossRef
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    Zhongqiang Li, Dongxia Lv, Lingjuan Niu, Yacun Wan, Junli Li
    Pathology - Research and Practice.2025; 275: 156231.     CrossRef
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    Shouhua Li, Hengtong Han, Kaili Yang, Xiaoxiao Li, Libin Ma, Ze Yang, Yong-xun Zhao
    Molecular Cancer.2025;[Epub]     CrossRef
  • Oncogenic mutation-driven metabolism-immunity regulatory axis: Potential prospects for thyroid cancer precision therapy
    Tingting Zhang, Hengtong Han, Tianying Zhang, Yating Zhang, Libin Ma, Ze Yang, Yong-xun Zhao
    Biochimica et Biophysica Acta (BBA) - Reviews on Cancer.2025; 1880(6): 189459.     CrossRef
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