Journal of Pathology and Translational Medicine

Original Articles

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning: Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat; J Pathol Transl Med. 2025;59(1):39-49. Published online October 24, 2024; DOI: https://doi.org/10.4132/jptm.2024.09.14

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Abstract PDF Supplementary Material: Background
Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.
Methods
We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.
Results
We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5.
Conclusions
We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

The proteomic landscape shows oncologic relevance in cystitis glandularis: Jun Yong Kim, Dohyun Han, Hyeyoon Kim, Minsun Jung, Han Suk Ryu; J Pathol Transl Med. 2023;57(1):67-74. Published online December 22, 2022; DOI: https://doi.org/10.4132/jptm.2022.10.24

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Background
The relationship between cystitis glandularis (CG) and bladder malignancy remains unclear.
Methods
We identified the oncologic significance of CG at the molecular level using liquid chromatography-tandem mass spectrometry-based proteomic analysis of 10 CG, 12 urothelial carcinoma (UC), and nine normal urothelium (NU) specimens. Differentially expressed proteins (DEPs) were identified based on an analysis of variance false discovery rate < 0.05, and their functional enrichment was analyzed using a network model, Gene Set Enrichment Analysis, and Gene Ontology annotation.
Results
We identified 9,890 proteins across all samples and 1,139 DEPs among the three entities. A substantial number of DEPs overlapped in CG/NU, distinct from UC. Interestingly, we found that a subset of DEP clusters (n = 53, 5%) was differentially expressed in NU but similarly between CG and UC. This “UC-like signature” was enriched for reactive oxygen species (ROS) and energy metabolism, growth and DNA repair, transport, motility, epithelial-mesenchymal transition, and cell survival. Using the top 10 shortlisted DEPs, including SOD2, PRKCD, CYCS, and HCLS1, we identified functional elements related to ROS metabolism, development, and transport using network analysis. The abundance of these four molecules in UC/CG than in NU was consistent with the oncologic functions in CG.
Conclusions
Using a proteomic approach, we identified a predominantly non-neoplastic landscape of CG, which was closer to NU than to UC. We also confirmed a small subset of common DEPs in UC and CG, suggesting that altered ROS metabolism might imply potential cancerous risks in CG.

Citations

Citations to this article as recorded by

Quantitative proteomics and immunohistochemistry uncover NT5DC2 as a diagnostic biomarker for papillary urothelial carcinoma
Jun Yong Kim, Jae Seok Lee, Dohyun Han, Ilias P. Nikas, Hyeyoon Kim, Minsun Jung, Han Suk Ryu
Heliyon.2024; 10(15): e35475. CrossRef
KRT18 as a Novel Biomarker of Urothelial Papilloma while Evaluating Low-Grade Papillary Urothelial Neoplasms: Bi-Center Analysis
Minsun Jung, Bohyun Kim, Jae Seok Lee, Jun Yong Kim, Dohyun Han, Kwangsoo Kim, Sunah Yang, Eun Na Kim, Hyeyooon Kim, Ilias P. Nikas, Sohyeon Yang, Kyung Chul Moon, Hyebin Lee, Han Suk Ryu
Pathobiology.2024; : 1. CrossRef

Review

The application of high-throughput proteomics in cytopathology: Ilias P. Nikas, Han Suk Ryu; J Pathol Transl Med. 2022;56(6):309-318. Published online November 9, 2022; DOI: https://doi.org/10.4132/jptm.2022.08.30

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Abstract PDF

High-throughput genomics and transcriptomics are often applied in routine pathology practice to facilitate cancer diagnosis, assess prognosis, and predict response to therapy. However, the proteins rather than nucleic acids are the functional molecules defining the cellular phenotype in health and disease, whereas genomic profiling cannot evaluate processes such as the RNA splicing or posttranslational modifications and gene expression does not necessarily correlate with protein expression. Proteomic applications have recently advanced, overcoming the issue of low depth, inconsistency, and suboptimal accuracy, also enabling the use of minimal patient-derived specimens. This review aims to present the recent evidence regarding the use of high-throughput proteomics in both exfoliative and fine-needle aspiration cytology. Most studies used mass spectrometry, as this is associated with high depth, sensitivity, and specificity, and aimed to complement the traditional cytomorphologic diagnosis, in addition to identify novel cancer biomarkers. Examples of diagnostic dilemmas subjected to proteomic analysis included the evaluation of indeterminate thyroid nodules or prediction of lymph node metastasis from thyroid cancer, also the differentiation between benign and malignant serous effusions, pancreatic cancer from autoimmune pancreatitis, non-neoplastic from malignant biliary strictures, and benign from malignant salivary gland tumors. A few cancer biomarkers—related to diverse cancers involving the breast, thyroid, bladder, lung, serous cavities, salivary glands, and bone marrow—were also discovered. Notably, residual liquid-based cytology samples were suitable for satisfactory and reproducible proteomic analysis. Proteomics could become another routine pathology platform in the near future, potentially by using validated multi-omics protocols.

Citations

Citations to this article as recorded by

Identification of NIFTP-Specific mRNA Markers for Reliable Molecular Diagnosis of Thyroid Tumors
So-Yeon Lee, Jong-Lyul Park, Kwangsoon Kim, Ja Seong Bae, Jae-Yoon Kim, Seon-Young Kim, Chan Kwon Jung
Endocrine Pathology.2023; 34(3): 311. CrossRef

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