Search Results (8)

Background/Objectives: The development of blood tests for the early detection of individual predisposition to socially significant diseases remains a pressing issue. Methods: In this pilot study, multiple reaction monitoring mass spectrometry (MRM-MS) with a BAK-270 assay was applied for protein concentrations analysis in blood plasma from 21 healthy volunteers of the European cohort. Results: The levels of 138 plasma proteins were reliably and precisely quantified in no less than 50% of samples. The quantified proteins included 66 FDA-approved markers of cardiovascular diseases (CVD), and other potential biomarkers of pathologies such as cancer, diabetes mellitus, and Alzheimer’s disease. The analysis of individual variations of the plasma proteins revealed significant differences between the male (11) and female (10) groups. In total, fifteen proteins had a significantly different concentration in plasma; this included four proteins that exhibited changes greater than ±1.5-fold, three proteins (RBP4, APCS, and TTR) with higher levels in males, and one (SHBG) elevated in females. The obtained results demonstrated considerable agreement with the data collected from 20 samples of a North American cohort, which were analyzed with the similar MRM assay. The most significant differences between the cohorts of the two continents were observed in the level of 42 plasma proteins (including 24 FDA markers), of which 17 proteins showed a ≥1.5-fold change, and included proteins increased in North Americans (APOB, CRTAC1, C1QB, C1QC, C9, CRP, HP, IGHG1, IGKV4-1, SERPING1, RBP4, and AZGP1), as well as those elevated in Europeans (APOF, CD5L, HBG2, SELPLG, and TNA). Conclusions: The results suggest a different contribution of specific (patho)physiological pathways (e.g., immune system and blood coagulation) to the development of socially significant diseases in Europeans and North Americans, and they should be taken into account when refining diagnostic panels. Full article

Preeclampsia (PE) is a complex and multifaceted obstetric syndrome characterized by several distinct molecular subtypes. It complicates up to 5% of pregnancies and significantly contributes to maternal and newborn morbidity, thereby diminishing the long-term quality of life for affected women. Due to the widespread dissatisfaction with the effectiveness of existing approaches for assessing PE risk, there is a pressing need for ongoing research to identify newer, more accurate predictors. This study aimed to investigate early changes in the maternal serum proteome and associated signaling pathways. The levels of 125 maternal serum proteins at 11–13 weeks of gestation were quantified using liquid chromatography–multiple reaction monitoring mass spectrometry (LC-MRM MS) with the BAK-125 kit. Ten serum proteins emerged as potential early markers for PE: Apolipoprotein M (APOM), Complement C1q subcomponent subunit B (C1QB), Lysozyme (LYZ), Prothrombin (F2), Albumin (ALB), Zinc-alpha-2-glycoprotein (AZGP1), Tenascin-X (TNXB), Alpha-1-antitrypsin (SERPINA1), Attractin (ATRN), and Apolipoprotein A-IV (APOA4). Notably, nine of these proteins have previously been associated with PE in prior research, underscoring the consistency and reliability of our findings. These proteins play key roles in critical molecular processes, including complement and coagulation cascades, platelet activation, and insulin-like growth factor pathways. To improve the early prediction of PE, a highly effective Support Vector Machine (SVM) model was developed, analyzing 19 maternal serum proteins from the first trimester. This model achieved an area under the curve (AUC) of 0.91, with 87% sensitivity and 95% specificity, and a hazard ratio (HR) of 13.5 (4.6–40.8) with p < 0.001. These findings demonstrate that serum protein-based SVM models possess significantly higher predictive power compared to the routine first-trimester screening test, highlighting their superior utility in the early detection and risk stratification of PE. Full article

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis. Full article

Long noncoding RNAs (lncRNAs) were recently reported to play an essential role in multiple cancer types. Herein, through next-generation sequencing, we screened metastasis-driving molecules by using tissues from early-stage gastric cancer (GC) patients with lymph node metastasis, and we identified a lncRNA LINC01094, which was associated with the metastasis of GC. According to the clinical data from the TCGA, GSE15459, and GSE62254 cohorts, the high expression of LINC01094 was associated with an unfavorable prognosis. Moreover, 106 clinical GC and paired normal samples were collected, and the qRT-PCR results showed that the high expression of LINC01094 was associated with high T and N stages and a poor prognosis. We found that LINC01094 promotes the proliferation and metastasis of GC in vitro and in vivo. AZGP1 was found as the protein-binding partner of LINC01094 by using RNA pulldown and RNA-binding protein immunoprecipitation (RIP) assays. LINC01094 antagonizes the function of AZGP1, downregulates the expression of PTEN, and further upregulates the AKT pathway. Collectively, our results suggested that LINC01094 might predict the prognosis of GC patients and become the therapy target for GC. Full article

As the currently available tests for the clinical management of prostate cancer (PCa) are still far from providing precise diagnosis and risk stratification, the identification of new molecular marker(s) remains a pertinent clinical need. Candidate PCa biomarkers from the published proteomic comparative studies of prostate tissue (2002–2020) were collected and systematically evaluated. AZGP1, MDH2, FABP5, ENO1, GSTP1, GSTM2, and EZR were chosen for further evaluation in the urine of 85 PCa patients and controls using ELISA. Statistically significant differences in protein levels between PCa and BPH showed FABP5 (p = 0.019) and ENO1 (p = 0.015). A biomarker panel based on the combination of FABP5, ENO1, and PSA provided the highest accuracy (AUC = 0.795) for PCa detection. The combination of FABP5, EZR, AZGP1, and MDH2 showed AUC = 0.889 in PCa prognosis, with 85.29% of the samples correctly classified into low and high Gleason score (GS) groups. The addition of PSA to the panel slightly increased the AUC to 0.914. AZGP1, FABP5, and EZR showed significant correlation with GS, stage, and percentage of positive biopsy cores. Although validation using larger patient cohorts will be necessary to establish the credibility of the proposed biomarker panels in a clinical context, this study opens a way for the further testing of more high-quality proteomics biomarkers, which could ultimately add value to the clinical management of PCa. Full article

Chronic kidney disease (CKD) is characterized by a long-term loss of kidney function and, in most cases, by progressive fibrosis. Zinc-alpha2-glycoprotein (AZGP1) is a secreted protein, which is expressed in many different tissues and has been associated with a variety of functions. In a previous study, we have shown in cell culture and in AZGP1 deficient mice that AZGP1 has protective anti-fibrotic effects. In the present study, we tested the therapeutic potential of an experimental increase in AZGP1 using two different strategies. (1) C57Bl/6J mice were treated systemically with recombinant AZGP1, and (2) a transgenic mouse strain was generated to overexpress AZGP1 conditionally in proximal tubular cells. Mice underwent unilateral uretic obstruction as a pro-fibrotic kidney stress model, and kidneys were examined after 14 days. Recombinant AZGP1 treatment was accompanied by better preservation of tubular integrity, reduced collagen deposition, and lower expression of injury and fibrosis markers. Weaker but similar tendencies were observed in transgenic AZGP1 overexpressing mice. Higher AZGP1 levels led to a significant reduction in stress-induced accumulation of tubular lipid droplets, which was paralleled by improved expression of key players in lipid metabolism and fatty acid oxidation. Together these data show beneficial effects of elevated AZGP1 levels in fibrotic kidney disease and highlight a novel link to tubular cell lipid metabolism, which might open up new opportunities for CKD treatment. Full article

Biomarkers for predicting the risk of castration-resistant prostate cancer (CRPC) in men treated with primary androgen deprivation therapy (ADT) are lacking. We investigated whether Zinc-alpha 2 glycoprotein (AZGP1) expression in the diagnostic biopsies of men with hormone-naïve prostate cancer (PCa) undergoing primary ADT was predictive of the development of CRPC and PCa-specific mortality. The study included 191 patients who commenced ADT from 2000 to 2011. The AZGP1 expression was evaluated using immunohistochemistry and scored as high or low expression. The risks of CRPC and PCa-specific mortality were analyzed using stratified cumulative incidences and a cause-specific COX regression analysis for competing risk assessment. The median follow-up time was 9.8 (IQR: 6.1–12.7) years. In total, 94 and 97 patients presented with low and high AZGP1 expression, respectively. A low AZGP1 expression was found to be associated with a shorter time to CRPC when compared to patients with a high AZGP1 expression (HR: 1.5; 95% CI: 1.0–2.1; p = 0.03). However, the multivariable analysis demonstrated no added benefit by adding the AZGP1 expression to prediction models for CRPC. No differences for PCa-specific mortality between the AZGP1 groups were observed. In conclusion, a low AZGP1 expression was associated with a shorter time to CRPC for PCa patients treated with first-line ADT but did not add any predictive information besides well-established clinicopathological variables. Full article

Zinc-α-2-glycoprotein (AZGP1) is a 41-kDa secreted glycoprotein, which has been detected in several malignancies. The diagnostic value of AZGP1 in serum of prostate and breast cancer patients has been reported. Analyzing “The Cancer Genome Atlas” data, we found that in colon cancer AZGP1 gene expression was upregulated at transcriptional level. We hypothesized that AZGP1 could be used as a diagnostic marker of colon cancer. First, we confirmed AZGP1 expression was higher in a set of 28 tumor tissues than in normal colonic mucosa tissues by real-time quantitative PCR and western blot in a Chinese population. We verified that serum concentration of AZGP1 was higher in 120 colon cancer patients compared with 40 healthy controls by ELISA (p < 0.001). Then receiver-operating characteristic (ROC) curve analysis was used to evaluate the predictive diagnostic value of AZGP1 in serum. The area under the curve (AUC) of AZGP1 was 0.742 (p < 0.001, 95% confidence interval (CI) = 0.656–0.827) in between the AUC of carcinoembryonic antigen (CEA) and the AUC of CA19-9, suggesting that predictive diagnostic value of AZGP1 is between CEA and Carbohydrate 19-9 (CA19-9). The combination of AZGP1 with traditional serum biomarkers, CEA and CA19-9, could result in better diagnostic results. To further validate the diagnostic value of AZGP1, a tissue microarray containing 190 samples of primary colon cancer tissue paired with normal colonic tissue was analysed and the result showed that AZGP1 was significantly upregulated in 68.4% (130 of 190) of the primary cancer lesions. In contrast, there was a weakly positive staining in 29.5% (56 of 190) of the normal colonic tissue samples (p < 0.001). Leave-one-out cross-validation was performed on the serum data, and showed that the diagnostic value of AZGP1 had 63.3% sensitivity and 65.0% specificity. Combination of AZGP1, CEA and CA19-9 had improved diagnosis value accuracy with 74.2% sensitivity and 72.5% specificity. These results suggest that AZGP1 is a useful diagnostic biomarker in tissues and serum from a Chinese population. Full article