Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

Apolipoprotein A-IV concentrations and cancer in a large cohort of chronic kidney disease patients: results from the GCKD study

BACKGROUND

Chronic kidney disease (CKD) is highly connected to inflammation and oxidative stress. Both favour the development of cancer in CKD patients. Serum apolipoprotein A-IV (apoA-IV) concentrations are influenced by kidney function and are an early marker of kidney impairment. Besides others, it has antioxidant and anti-inflammatory properties. Proteomic studies and small case-control studies identified low apoA-IV as a biomarker for various forms of cancer; however, prospective studies are lacking. We therefore investigated whether serum apoA-IV is associated with cancer in the German Chronic Kidney Disease (GCKD) study.

METHODS

These analyses include 5039 Caucasian patients from the prospective GCKD cohort study followed for 6.5 years. Main inclusion criteria were an eGFR of 30-60 mL/min/1.73m2 or an eGFR > 60 mL/min/1.73m2 in the presence of overt proteinuria.

RESULTS

Mean apoA-IV concentrations of the entire cohort were 28.9 ± 9.8 mg/dL (median 27.6 mg/dL). 615 patients had a history of cancer before the enrolment into the study. ApoA-IV concentrations above the median were associated with a lower odds for a history of cancer (OR = 0.79, p = 0.02 when adjusted age, sex, smoking, diabetes, BMI, albuminuria, statin intake, and eGFRcreatinine). During follow-up 368 patients developed an incident cancer event and those with apoA-IV above the median had a lower risk (HR = 0.72, 95%CI 0.57-0.90, P = 0.004). Finally, 62 patients died from such an incident cancer event and each 10 mg/dL higher apoA-IV concentrations were associated with a lower risk for fatal cancer (HR = 0.62, 95%CI 0.44-0.88, P = 0.007).

CONCLUSIONS

Our data indicate an association of high apoA-IV concentrations with reduced frequencies of a history of cancer as well as incident fatal and non-fatal cancer events in a large cohort of patients with CKD.

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.

Integrated analysis of circulating and tissue proteomes reveals that fibronectin 1 is a potential biomarker in papillary thyroid cancer

Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid cancer, but 20% of cases are indeterminate (i.e., cannot be accurately diagnosed) based on preoperative cytology, which might lead to surgical removal of a normal thyroid gland. To address this concern, we performed an in-depth analysis of the serum proteomes of 26 PTC patients and 23 healthy controls using antibody microarrays and data-independent acquisition mass spectrometry (DIA-MS). We identified a total of 1091 serum proteins spanning 10-12 orders of magnitude. 166 differentially expressed proteins were identified that participate in complement activation, coagulation cascades, and platelet degranulation pathways. Furthermore, the analysis of serum proteomes before and after surgery indicated that the expression of proteins such as lactate dehydrogenase A and olfactory receptor family 52 subfamily B member 4, which participate in fibrin clot formation and extracellular matrix-receptor interaction pathways, were changed. Further analysis of the proteomes of PTC and neighboring tissues revealed integrin-mediated pathways with possible crosstalk between the tissue and circulating compartments. Among these cross-talk proteins, circulating fibronectin 1 (FN1), gelsolin (GSN) and UDP-glucose 4-epimerase (GALE) were indicated as promising biomarkers for PTC identification and validated in an independent cohort. In differentiating between patients with benign nodules or PTC, FN1 produced the best ELISA result (sensitivity = 96.89%, specificity = 91.67%). Overall, our results present proteomic landscapes of PTC before and after surgery as well as the crosstalk between tissue and the circulatory system, which is valuable to understand PTC pathology and improve PTC diagnostics in the future.

Apolipoprotein A-IV Has Bi-Functional Actions in Alcoholic Hepatitis by Regulating Hepatocyte Injury and Immune Cell Infiltration

Alcohol abuse can lead to alcoholic hepatitis (AH), a worldwide public health issue with high morbidity and mortality. Here, we identified apolipoprotein A-IV (APOA4) as a biomarker and potential therapeutic target for AH. APOA4 expression was detected by Gene Expression Omnibus (GEO) databases, Immunohistochemistry, and qRT-PCR in AH. Bioinformatics Methods (protein-protein interaction (PPI) network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Set Enrichment Analysis (GSEA) were used to show down-stream gene and pathways of APOA4 in AH. AML-12 cells were used to evaluate the biological function of APOA4 using an ELISA kit (AST, ALT, and IL-1β) and flow cytometry (ROS activity). Both in vivo and in vitro, APOA4 expression was significantly elevated in the AH model induced by alcohol (ETOH). AML-12 cell damage was specifically repaired by APOA4 deficiency, while AST, ALT, and IL-1β activity that was increased by ETOH (200 µmol, 12 h) were suppressed. APOA4 inhibition increased intracellular ROS induced by ETOH, which was detected by flow cytometry. Functional and PPI network analyses showed Fcgamma receptor (FCGR) and platelet activation signaling were potential downstream pathways. We identified CIDEC as a downstream gene of APOA4. The CIDEC AUC values for the ROC curves were 0.861. At the same time, APOA4 silencing downregulated the expression of CIDEC, whereas the knockdown of CIDEC did not influence the expression of APOA4 in AML-12 cells. Collectively, APOA4 regulates CIDEC expression and immune cell infiltration and may hold great potential as a biomarker and therapeutic target for AH.

Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning

OBJECTIVES

To explore the molecular mechanisms underlying aggressive progression of papillary thyroid microcarcinoma and identify potential biomarkers.

METHODS

Samples were collected and sequenced using tandem mass tag-labeled liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were identified and further analyzed using Mfuzz and protein-protein interaction analysis (PPI). Parallel reaction monitoring (PRM) and immunohistochemistry (IHC) were performed to validate the DEPs.

RESULTS

Five thousand, two hundred and three DEPs were identified and quantified from the tumor/normal comparison group or the N1/N0 comparison group. Mfuzz analysis showed that clusters of DEPs were enriched according to progressive status, followed by normal tissue, tumors without lymphatic metastases, and tumors with lymphatic metastases. Analysis of PPI revealed that DEPs interacted with and were enriched in the following metabolic pathways: apoptosis, tricarboxylic acid cycle, PI3K-Akt pathway, cholesterol metabolism, pyruvate metabolism, and thyroid hormone synthesis. In addition, 18 of the 20 target proteins were successfully validated with PRM and IHC in another 20 paired validation samples. Based on machine learning, the five proteins that showed the best performance in discriminating between tumor and normal nodules were PDLIM4, ANXA1, PKM, NPC2, and LMNA. FN1 performed well in discriminating between patients with lymph node metastases (N1) and N0 with an AUC of 0.690. Finally, five validated DEPs showed a potential prognostic role after examining The Cancer Genome Atlas database: FN1, IDH2, VDAC1, FABP4, and TG. Accordingly, a nomogram was constructed whose concordance index was 0.685 (confidence interval: 0.645-0.726).

CONCLUSIONS

PDLIM4, ANXA1, PKM, NPC2, LMNA, and FN1 are potential diagnostic biomarkers. The five-protein nomogram could be a prognostic biomarker.

Vitamin D-Related Genes and Thyroid Cancer-A Systematic Review

Vitamin D, formerly known for its role in calcium-phosphorus homeostasis, was shown to exert a broad influence on immunity and on differentiation and proliferation processes in the last few years. In the field of endocrinology, there is proof of the potential role of vitamin D and vitamin D-related genes in the pathogenesis of thyroid cancer-the most prevalent endocrine malignancy. Therefore, the study aimed to systematically review the publications on the association between vitamin D-related gene variants (polymorphisms, mutations, etc.) and thyroid cancer. PubMed, EMBASE, Scopus, and Web of Science electronic databases were searched for relevant studies. A total of ten studies were found that met the inclusion criteria. Six vitamin D-related genes were analyzed (VDR-vitamin D receptor, CYP2R1-cytochrome P450 family 2 subfamily R member 1, CYP24A1-cytochrome P450 family 24 subfamily A member 1, CYP27B1-cytochrome P450 family 27 subfamily B member 1, DHCR7-7-dehydrocholesterol reductase and CUBN-cubilin). Moreover, a meta-analysis was conducted to summarize the data from the studies on VDR polymorphisms (rs2228570/FokI, rs1544410/BsmI, rs7975232/ApaI and rs731236/TaqI). Some associations between thyroid cancer risk (VDR, CYP24A1, DHCR7) or the clinical course of the disease (VDR) and vitamin D-related gene polymorphisms were described in the literature. However, these results seem inconclusive and need validation. A meta-analysis of the five studies of common VDR polymorphisms did not confirm their association with increased susceptibility to differentiated thyroid cancer. Further efforts are necessary to improve our understanding of thyroid cancer pathogenesis and implement targeted therapies for refractory cases.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Artificial intelligence defines protein-based classification of thyroid nodules

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.

Salivary Proteome, Inflammatory, and NETosis Biomarkers in Older Adult Practitioners and Nonpractitioners of Physical Exercise

Since aging has been associated with increased production of inflammatory biomarkers, the ability to monitor older adults repeatedly is highly desirable, and saliva is an interesting biofluid for the search of biomarkers, as it is easily accessible in a noninvasive manner. However, given the incipient knowledge of salivary biomarkers in aging and its relationship to physical exercise, the present study is aimed at evaluating the protein expression and the levels of inflammatory and NETosis biomarkers in the saliva of practitioners (PE) and nonpractitioners (NPE) of physical exercise older adults. Six (6) practitioner and 4 nonpractitioner older adults were enrolled in this study. Unstimulated whole saliva was collected for analysis of the proteome by label-free mass spectrometry, as well as of the inflammatory status by evaluation of C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and cytokines (TNF-α, interleukin- (IL-) 1β, and IL-8), while NETosis was assessed by myeloperoxidase (MPO) and neutrophil elastase. Regarding oral health, the decayed, missing, and filled teeth (DMF-T) index, bleeding on probing, suppuration, and probing depth measurement (mm) were evaluated. In addition, functional capacity was investigated using the General Physical Fitness Index (GPFI). In relation to the proteome analysis, 93 and 143 proteins were found exclusively in the PE and NPE groups, respectively; 224 proteins were common to both groups. Among these proteins, 10 proteins showed statistical difference ( $p<0.05$ ) between the groups: alpha-2-macroglobulin, component 3 of the complement, serotransferrin, and protein soluble in brain acid 1 were less expressed, while lactotransferrin, alpha-amylase 1, S100-A8, S100-A9, lactoperoxidase, and galectin-3 binding protein were more expressed in the PE group. No differences between groups were observed in the analysis of inflammatory and NETosis biomarkers. This study shows the potential utility of saliva for detecting protein biomarkers in a noninvasive biological sample of the elderly population.

Distinguishing Benign and Malignant Thyroid Nodules and Identifying Lymph Node Metastasis in Papillary Thyroid Cancer by Plasma N-Glycomics

BACKGROUND

Biomarkers are needed for patient stratification between benign thyroid nodules (BTN) and thyroid cancer (TC) and identifying metastasis in TC. Though plasma N-glycome profiling has shown potential in the discovery of biomarkers and can provide new insight into the mechanisms involved, little is known about it in TC and BTN. Besides, several studies have indicated associations between abnormal glycosylation and TC. Here, we aimed to explore plasma protein N-glycome of a TC cohort with regard to their applicability to serve as biomarkers.

METHODS

Plasma protein N-glycomes of TC, BTN, and matched healthy controls (HC) were obtained using a robust quantitative strategy based on MALDI-TOF MS and included linkage-specific sialylation information.

RESULTS

Plasma N-glycans were found to differ between BTN, TC, and HC in main glycosylation features, namely complexity, galactosylation, fucosylation, and sialylation. Four altered glycan traits, which were consecutively decreased in BTN and TC, and classification models based on them showed high potential as biomarkers for discrimination between BTN and TC ("moderately accurate" to "accurate"). Additionally, strong associations were found between plasma N-glycans and lymph node metastasis in TC, which added the accuracy of predicting metastasis before surgery to the existing method.

CONCLUSIONS

We comprehensively evaluated the plasma N-glycomic changes in patients with TC or BTN for the first time. We determined several N-glycan biomarkers, some of them have potential in the differential diagnosis of TC, and the others can help to stratify TC patients to low or high risk of lymph node metastasis. The findings enhanced the understanding of TC.