Complement C4-A and Plasminogen as Potential Biomarkers for Prediction of Papillary Thyroid Carcinoma

Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks

Although papillary thyroid cancer (PTC) has a good prognosis, its recurrence rate is high and remains a core concern in the clinic. Molecular factors contributing to different recurrence risks (RRs) remain poorly defined. Here, we perform an integrative proteogenomic and metabolomic characterization of 102 Chinese PTC patients with different RRs. Genomic profiling reveals that mutations in MUC16 and TERT promoter as well as multiple gene fusions like NCOA4-RET are enriched by the high RR. Integrative multi-omics analyses further describe the multi-dimensional characteristics of PTC, especially in metabolism pathways, and delineate dominated molecular patterns of different RRs. Moreover, the PTC patients are clustered into four subtypes (CS1: low RR and BRAF-like; CS2: high RR and metabolism type, worst prognosis; CS3: high RR and immune type, better prognosis; CS4: high RR and BRAF-like) based on the omics data. Notably, the subtypes display significant differences considering BRAF and TERT promoter mutations, metabolism and immune pathway profiles, epithelial cell compositions, and various clinical factors (especially RRs and prognosis) as well as druggable targets. This study can provide insights into the complex molecular characteristics of PTC recurrences and help promote early diagnosis and precision treatment of recurrent PTC.

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review)

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Prognostic-Related Biomarkers in Pancreatic Ductal Adenocarcinoma Correlating with Immune Infiltrates Based on Proteomics

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) accounts for 85% of pancreatic carcinoma cases. Patients with PDAC have a poor prognosis. The lack of reliable prognostic biomarkers makes treatment challenging for patients with PDAC. Using a bioinformatics database, we sought to identify prognostic biomarkers for PDAC. MATERIAL AND METHODS Using proteomic analysis of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database, we were able to identify core differential proteins between early and advanced pancreatic ductal adenocarcinoma tissue, and then we used survival analysis, Cox regression analysis, and area under the ROC curves to screen for more significant differential proteins. Additionally, the Kaplan-Meier plotter database was utilized to determine the relationship between prognosis and immune infiltration in PDAC. RESULTS We identified 378 differential proteins in early (n=78) and advanced stages (n=47) of PDAC (P<0.05). PLG, COPS5, FYN, ITGB3, IRF3, and SPTA1 served as independent prognostic factors of patients with PDAC. Patients with higher COPS5 expression had shorter overall survival (OS) and recurrence-free survival, and those with higher PLG, ITGB3, and SPTA1, and lower FYN and IRF3 expression had shorter OS. More importantly, COPS5, IRF3 were negatively associated with macrophages and NK cells, but PLG, FYN, ITGB3, and SPTA1 were positively related to the expression of CD8+ T cells and B cells. COPS5 affected the prognosis of PDAC patients by acting on B cells, CD8+ T cells, macrophages, and NK cells immune infiltration, while PLG, FYN, ITGB3, IRF3, and SPTA1 affected PDAC patient prognosis through some immune cells. CONCLUSIONS PLG, COPS5, FYN, IRF3, ITGB3 and SPTA1 could be potential immunotherapeutic targets and valuable prognostic biomarkers of PDAC.

CircRTN1 stimulates HMGB1 to regulate the malignant progression of papillary thyroid cancer by sponging miR-101-3p

BACKGROUND

The important role played by circular RNA (circRNA) in promoting the progression of papillary thyroid cancer (PTC) is attracting ever more attention among medical researchers. However, what the precise contribution is of circRTN1 in PTC progression remains unclear. The study was designed to analyze the role and mechanism of circRTN1 in regulating PTC progression.

METHODS

Human PTC cell lines (TPC-1 and IHH-4) and human thyroid normal cells (Nthy-ori 3-1) were used for in vitro assays. mRNA or protein expression of circRTN1, miR-101-3p, and high mobility group box 1 (HMGB1) were detected by quantitative real-time polymerase chain reaction or western blot. Cell proliferation was investigated by cell counting kit-8 assay, cell colony formation assay, and 5-ethynyl-2'-deoxyuridine assay. Wound-healing assay and transwell invasion assay were conducted to evaluate cell migration and invasion. Dual-luciferase reporter assay and RNA immunoprecipitation assay were applied to verify the target relations between circRTN1, miR-101-3p, and HMGB1. A xenograft tumor model was established to demonstrate the effect of circRTN1 on tumor formation in vivo. An immunohistochemistry assay was used to detect protein expression of HMGB1, ki-67, E-cadherin, and vimentin.

RESULTS

In comparison with healthy thyroid tissues and cells, PTC tissues and cells displayed high circRTN1 RNA expression and high HMGB1 mRNA and protein expression but low miR-101-3p expression. Silencing of circRTN1 suppressed PTC cell proliferation, migration, and invasion in vitro. MiR-101-3p was a target of circRTN1, and the knockdown of miR-101-3p relieved circRTN1 absence-mediated suppressive effects on PTC cell malignancy. HMGB1 was identified as a target gene of miR-101-3p, and overexpressed HMGB1 almost reverted the inhibitory impacts induced by miR-101-3p mimic in PTC cells. Moreover, circRTN1 silencing hampered tumor formation in vivo.

CONCLUSION

CircRTN1 depletion impeded PTC cell malignancy via the miR-101-3p/HMGB1 pathway, which provided a possible circRNA-targeted therapeutic strategy for PTC.

Quantitative proteomics reveal three potential biomarkers for risk assessment of acute myocardial infarction

Acute myocardial infarction (AMI) is the one of the main cause of death worldwide. Exosomes carry important information about intercellular communication and could be diagnostic marker for many diseases. Here, we aimed to find potential key proteins for the early diagnosis of AMI. A label free proteomics strategy was used to identify the differentially expressed proteins (DEPs) of AMI patients’ plasma exosome. By bioinformatics analysis and enzyme-linked immunosorbent assay to validate the candidate proteins. Compared to healthy control plasma exosome, we totally identified 72 differentially expressed proteins (DEPs) in AMI patients. Also, we found that complement and coagulation cascades was activated by KEGG analysis and GSEA. PLG, C8B and F2 were selected as candidate molecules for further study, and then validated another 40 plasma samples using enzyme-linked immunosorbent assay. Finally, we found that the expression levels of these three proteins (PLG, C8B and F2) were significantly higher than those of healthy controls (P < 0.05). ROC analysis revealed that PLG, C8B and F2 had potential value for AMI early diagnosis. In conclusion, our study identified three potential biomarkers for AMI diagnosis. But there remains a need to further study the mechanism of the biomarkers.