Stratification of follicular thyroid tumours using data‐independent acquisition proteomics and a comprehensive thyroid tissue spectral library

Sun, Yaoting; Lü, Li; Zhou, Yan; Ge, Weigang; Wang, He; Wu, Runxin; Liu, Wei; Chen, Hao; Xiao, Qin; Cai, Xue; Dong, Zhen; Zhang, Fangfei; Xiao, Jian‐Kang; Wang, Guangzhi; He, Yi; Gao, Jinlong; Kon, Oi Lian; Iyer, N. Gopalakrishna; Guan, Haixia; Teng, Xiaodong; Zhu, Yi; Zhao, Yongfu; Guo, Tiannan

doi:10.1002/1878-0261.13198

Cited by 21 publications

(17 citation statements)

References 55 publications

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“…To analyze the DIA data, we built a thyroid-specific spectral library from FFPE tissues as we described previously 19 . The library contained 925,330 transition groups, 157,548 peptide precursors, 121,960 peptides, 9941 protein groups, and 9826 proteins from proteotypic peptides.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence defines protein-based classification of thyroid nodules

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence defines protein-based classification of thyroid nodules

et al. 2022

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“…Therefore, identification of specific blood biomarkers remains an urgent need for increasing the diagnostic accuracy. In general, the prevailing diagnostic model after DIA is the verification of parallel reaction monitoring (PRM) [ 35 , 36 ]. However, PRM is expensive and it is not suitable for clinical testing.…”

Section: Discussionmentioning

confidence: 99%

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion

Han

Zhang

Sun

et al. 2022

JCM

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Objective: The process of normal cervix changing into high grade squamous intraepithelial lesion (HSIL) and invasive cervical cancer is long and the mechanisms are still not completely clear. This study aimed to reveal the protein profiles related to HSIL and cervical cancer and find the diagnostic and prognostic molecular changes. Methods: Data-independent acquisition (DIA) analysis was performed to identify 20 healthy female volunteers, 20 HSIL and 20 cervical patients in a cohort to screen differentially expressed proteins (DEPs) for the HSIL and cervical cancer. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used for functional annotation of DEPs; the protein–protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) were performed for detection of key molecular modules and hub proteins. They were validated using the Enzyme-Linked Immunosorbent Assay (ELISA). Results: A total of 243 DEPs were identified in the study groups. GO and KEGG analysis showed that DEPs were mainly enriched in the complement and coagulation pathway, cholesterol metabolism pathway, the IL-17 signaling pathway as well as the viral protein interaction with cytokine and cytokine receptor pathway. Subsequently, the WGCNA analysis showed that the green module was highly correlated with the cervical cancer stage. Additionally, six interesting core DEPs were verified by ELISA, APOF and ORM1, showing nearly the same expression pattern with DIA. The area under the curve (AUC) of 0.978 was obtained by using ORM1 combined with APOF to predict CK and HSIL+CC, and in the diagnosis of HSIL and CC, the AUC can reach to 0.982. The high expression of ORM1 is related to lymph node metastasis and the clinical stage of cervical cancer patients as well as the poor prognosis. Conclusion: DIA-ELSIA combined analysis screened and validated two previously unexplored but potentially useful biomarkers for early diagnosis of HSIL and cervical cancer, as well as possible new pathogenic pathways and therapeutic targets.

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“…To perform diaPASEF acquisition, we defined two 15 Th isolation windows: from m/z 384 to 1008 and from m/z 475 to 1099. Spectronaut™ (version 14.6) was used to compare all PulseDIA data against a renal-specific spectral library ( 20 ) including 539,631 peptide precursors, 448,338 peptides, 13,624 protein groups, and 9205 proteins with a false discovery rate of 0.01. The other parameters were set to the default values.…”

Section: Methodsmentioning

confidence: 99%

Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens

Huang

Fei²,

Zhong³

et al. 2022

Front. Endocrinol.

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AimThe aims of this study were to analyze the proteomic differences in renal tissues from patients with diabetes mellitus (DM) and diabetic kidney disease (DKD) and to select sensitive biomarkers for early identification of DKD progression.MethodsPressure cycling technology–pulse data-independent acquisition mass spectrometry was employed to investigate protein alterations in 36 formalin-fixed paraffin-embedded specimens. Then, bioinformatics analysis was performed to identify important signaling pathways and key molecules. Finally, the target proteins were validated in 60 blood and 30 urine samples.ResultsA total of 52 up- and 311 down-regulated differential proteins were identified as differing among the advanced DKD samples, early DKD samples, and DM controls (adjusted p<0.05). These differentially expressed proteins were mainly involved in ion transport, apoptosis regulation, and the inflammatory response. UniProt database analysis showed that these proteins were mostly enriched in signaling pathways related to metabolism, apoptosis, and inflammation. NBR1 was significantly up-regulated in both early and advanced DKD, with fold changes (FCs) of 175 and 184, respectively (both p<0.01). In addition, VPS37A and ATG4B were significantly down-regulated with DKD progression, with FCs of 0.140 and 0.088, respectively, in advanced DKD and 0.533 and 0.192, respectively, in early DKD compared with the DM control group (both p<0.01). Bioinformatics analysis showed that NBR1, VPS37A, and ATG4B are closely related to autophagy. We also found that serum levels of the three proteins and urine levels of NBR1 decreased with disease progression. Moreover, there was a significant difference in serum VPS37A and ATG4B levels between patients with early and advanced DKD (both p<0.05). The immunohistochemistry assaay exhibited that the three proteins were expressed in renal tubular cells, and NBR1 was also expressed in the cystic wall of renal glomeruli.ConclusionThe increase in NBR1 expression and the decrease in ATG4B and VPS37 expression in renal tissue are closely related to inhibition of the autophagy pathway, which may contribute to DKD development or progression. These three proteins may serve as sensitive serum biomarkers for early identification of DKD progression.

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Stratification of follicular thyroid tumours using data‐independent acquisition proteomics and a comprehensive thyroid tissue spectral library

Cited by 21 publications

References 55 publications

Artificial intelligence defines protein-based classification of thyroid nodules

Artificial intelligence defines protein-based classification of thyroid nodules

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion

Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens

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