Blood- and Urine-Based Liquid Biopsy for Early-Stage Cancer Investigation: Taken Clear Renal Cell Carcinoma as a Model.

Liquid biopsy is a noninvasive technique that can provide valuable information for disease characterization by using biofluids as a source of biomarkers. Proteins found in biofluids can offer a wealth of information for understanding pathological processes. In this study, we used early-stage clear cell renal cell carcinoma (ccRCC) as a model to explore the proteomic relationships among tissue, plasma, and urine. We analyzed samples of tumor tissue, plasma, and urine from a cohort of 27 ccRCC patients with T1-2 stage and 27 matched healthy controls, using liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis. We integrated the differential proteins found in the three types of samples to explore ccRCC-associated molecular changes. Our results showed that both plasma and urine proteomes could reflect functional changes in tumor tissue. In plasma, cytoskeletal proteins and metabolic enzymes were differentially expressed, while in urine, adhesion molecules and defense proteins showed differential levels. The differential proteins found in plasma and urine both reflect the binding and catalytic activity of tumor tissue. Additionally, proteins only changed in biofluids could reflect body immune response changes, with plasma proteins involved in actin cytoskeleton and oxidative stress, and urine proteins involved in granulocyte adhesion and leukocyte extravasation signaling. Plasma and urine proteins could effectively distinguish RCC from control, with good performances (plasma/urine: 92.6%/92.6% specificity, 96.3%/92.6% sensitivity, and an area under the curve of 0.981/0.97). In conclusion, biofluids could not only reflect functional changes in tumor tissue but also reflect changes in the body's immune response. These findings will benefit the understanding of body biomarkers in tumors and the discovery of potential disease biomarkers.

Evaluation of machine learning models on protein level inference from prioritized RNA features.

The parallel measurement of transcriptome and proteome revealed unmatched profiles. Since proteomic analysis is more expensive and challenging than transcriptomic analysis, the question of how to use messenger RNA (mRNA) expression data to predict protein level is extremely important. Here, we comprehensively evaluated 13 machine learning models on inferring protein expression levels using RNA expression profile. A total of 20 proteogenomic datasets from three mainstream proteomic platforms with >2500 samples of 13 human tissues were collected for model evaluation. Our results highlighted that the appropriate feature selection methods combined with classical machine learning models could achieve excellent predictive performance. The voting ensemble model outperformed other candidate models across datasets. Adding the mRNA proxy model to the regression model further improved the prediction performance. The dataset and gene characteristics could affect the prediction performance. Finally, we applied the model to the brain transcriptome of cerebral cortex regions to infer the protein profile for better understanding the functional characteristics of the brain regions. This benchmarking work not only provides useful hints on the inherent correlation between transcriptome and proteome, but also has practical value of the transcriptome-based prediction of protein expression levels.

Characterization of the proteome of stable and unstable carotid atherosclerotic plaques using data-independent acquisition mass spectrometry.

BACKGROUND: Currently, noninvasive imaging techniques and circulating biomarkers are still insufficient to accurately assess carotid plaque stability, and an in-depth understanding of the molecular mechanisms that contribute to plaque instability is still lacking. METHODS: We established a clinical study cohort containing 182 patients with carotid artery stenosis. After screening, 39 stable and 49 unstable plaques were included in the discovery group, and quantitative proteomics analysis based on data independent acquisition was performed for these plaque samples. Additionally, 35 plaques were included in the validation group to validate the proteomics results by immunohistochemistry analysis. RESULTS: A total of 397 differentially expressed proteins were identified in stable and unstable plaques. These proteins are primarily involved in ferroptosis and lipid metabolism-related functions and pathways. Plaque validation results showed that ferroptosis- and lipid metabolism-related proteins had different expression trends in stable plaques versus unstable fibrous cap regions and lipid core regions. Ferroptosis- and lipid metabolism-related mechanisms in plaque stability were discussed. CONCLUSIONS: Our results may provide a valuable strategy for revealing the mechanisms affecting plaque stability and will facilitate the discovery of specific biomarkers to broaden the therapeutic scope.

Metaproteomics Characterizes the Human Gingival Crevicular Fluid Microbiome Function in Periodontitis.

Periodontitis is the leading cause of tooth loss in adults worldwide. The human proteome and metaproteome characterization of periodontitis is not clearly understood. Gingival crevicular fluid samples were collected from eight periodontitis and eight healthy subjects. Both the human and microbial proteins were characterized by liquid chromatography coupled with high-resolution mass spectrometry. A total of 570 human proteins were found differentially expressed, which were primarily associated with inflammatory response, cell death, cellular junction, and fatty acid metabolism. For the metaproteome, 51 genera were identified, and 10 genera were found highly expressed in periodontitis, while 11 genera were downregulated. The analysis showed that microbial proteins related to butyrate metabolism were upregulated in periodontitis cases. In particular, correlation analysis showed that the expression of host proteins related to inflammatory response, cell death, cellular junction, and lipid metabolism correlates with the alteration of metaproteins, which reflect the changes of molecular function during the occurrence of periodontitis. This study showed that the gingival crevicular fluid human proteome and metaproteome could reflect the characteristics of periodontitis. This might benefit the understanding of the periodontitis mechanism.

Clinical and Proteomic-Based Molecular Characterizations of Invasive and Noninvasive Somatotroph PitNETs.

INTRODUCTION: Somatotroph pituitary neuroendocrine tumours (PitNETs) are characterized by complex and variable biological behaviours with unpredictable patterns of growth and invasiveness. The molecular mechanisms and reliable predictors of biological markers of invasiveness remain unknown. METHODS: Seventy-two acromegaly patients were consecutively enrolled. Data-independent acquisition-based proteomics and ingenuity pathway analysis were conducted between invasive and noninvasive somatotroph PitNETs. The expression of selected biomarkers was verified in PitNET tissue, and its correlation with various clinical indicators and outcomes of these tumours was assessed. The invasive phenotypes of GH3 cells were validated in vitro. RESULTS: Patients with invasive somatotroph PitNETs were significantly younger at onset and diagnosis, with significantly higher secretion and faster growth and a lower long-term biochemical response rate than patients with noninvasive somatotroph PitNETs. Proteomic data were evaluated in a consecutively collected sample of 19 (10 invasive and 9 noninvasive somatotroph PitNETs) tumours and indicated a distinct proteomic pattern. The enriched and important pathways included IL-4, PDGF, PTEN, VEGF, PI3K/AKT, FAK, and other pathways that were significantly associated with tumour proliferation, migration, and invasion. High cathepsin Z (CTSZ) expression was found in invasive somatotroph PitNETs and significantly positively correlated with parameters of tumour invasion and growth. In Ctsz-overexpressing GH3 cells, cell proliferation, invasion, and migration were consequently increased. CONCLUSION: It is more difficult for patients with invasive somatotroph PitNETs to achieve remission than those with noninvasive somatotroph PitNETs, and proteomic data analysis has revealed the high expression of CTSZ as a contributing factor to invasive transformation and poor prognosis in somatotroph PitNETs for the first time.

Proteome Characterization of Glaucoma Aqueous Humor.

Glaucoma is the leading cause of irreversible blindness worldwide. The proteome characterization of glaucoma is not clearly understood. A total of 175 subjects, including 57 primary acute angle-closure glaucoma (PAACG), 50 primary chronic angle-closure glaucoma (PCACG), 35 neovascular glaucoma (NVG), and 33 cataract patients, were enrolled and comparison proteomic analysis was provided. The samples were randomly divided into discovery group or validation group, whose aqueous humor proteome was analyzed by data-independent acquisition or by parallel reaction monitoring. The common proteome features of three types of glaucoma were immune response, lipid metabolism, and cell death. Three proteins, VTN, SERPIND1, and CD14, showed significant upregulation in glaucoma and could discriminate glaucoma from cataract. Mutual differential proteomic analysis of PAACG, PCACG, and NVG showed different proteome characterization of the three types of glaucoma. NVG was characterized with activated angiogenesis. PAACG was characterized with activation of inflammation response. SERPIND1 was discovered to play vital role in glaucoma occurrences, which is associated with eye transparency decrease and glucose metabolism. This study would provide insights in understanding proteome characterization of glaucoma and benefit the clinical application of AH proteome.

A qualitative and quantitative analysis of the human gingival crevicular fluid proteome and metaproteome.

Gingival crevicular fluid (GCF) is an integral part of oral fluid that plays a special role in maintaining the structure of junctional epithelium and defending against bacterial infection. In this study, we comprehensively analysed the composition of the human GCF proteome and metaproteome simultaneously to obtain multidimensional information about GCF. A total of 3680 human proteins (2540 with at least two unique peptides) were identified in the normal GCF sample, and their functions were mainly associated with immune function and inflammation. Among these proteins, 1874 proteins could be quantified by the iBAQ algorithm, and their abundances spanned a dynamic range of six orders of magnitude. For the GCF metaproteome, a total of 3082 proteins and 69 genera were found. In addition, 16 genera were not identified by GCF metagenomic analysis. Compared to the saliva metaproteome, 32 genera were found to be in common. The protein quantitative analysis showed that the abundance of GCF metaproteome contributed to approximately 4.17% of the total GCF proteome. The top three most abundant genera were Fusobacterium, Corynebacterium, and Leptotrichia. The above data will be useful for future research on GCF-related diseases.

Comprehensive Analysis of Individual Variation in the Urinary Proteome Revealed Significant Gender Differences.

Disease biomarkers are the measurable changes associated with a pathophysiological process. Without homeostatic control, urine accumulates systematic changes in the body. Thus, urine is an attractive biological material for the discovery of disease biomarkers. One of the major bottlenecks in urinary biomarker discovery is that the concentration and composition of urinary proteins are influenced by many physiological factors. To elucidate the individual variation and related factors influencing the urinary proteome, we comprehensively analyzed the urine samples from healthy adult donors (aged 20-69 years). Co-expression network analysis revealed protein clusters representing the metabolic status, gender-related differences and age-related differences in urinary proteins. In particular, we demonstrated that gender is a crucial factor contributing to individual variation. Proteins that were increased in the male urine samples include prostate-secreted proteins and TIMP1, a protein whose abundance alters under various cancers and renal diseases; however, the proteins that were increased in the female urine samples have known functions in the immune system. Nine gender-related proteins were validated on 85 independent samples by multiple reaction monitoring. Five of these proteins were further used to build a model that could accurately distinguish male and female urine samples with an area under curve value of 0.94. Based on the above results, we strongly suggest that future biomarker investigations should consider gender as a crucial factor in experimental design and data analysis. Finally, reference intervals of each urinary protein were estimated, providing a baseline for the discovery of abnormalities.

UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma.

BACKGROUND: To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC. METHODS: In total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways. RESULTS: The panel of α-CEHC, ß-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N'-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria. CONCLUSIONS: UPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC.

Differential urinary proteins to diagnose coronary heart disease based on iTRAQ quantitative proteomics.

Coronary artery disease (CAD) is a manifestation of systemic atherosclerotic disease. It is assessed by intervention or traditional scoring risk factors. Diagnosis is limited by inaccurate and invasive methods. Developing noninvasive methods to screen for the risk of CAD is a major challenge. We aimed to identify urinary proteins associated with CAD. We utilized iTRAQ labeling followed by 2D LC-MS/MS to compare the urinary proteome of CAD patients to healthy cohorts. The multiple reaction monitoring (MRM) was used to verify the differential proteins. ROC analysis based on MRM data was used to evaluate the diagnostic application. A total of 876 proteins were quantified, and 100 differential proteins were found. Functional analysis revealed that the differential proteins were mainly associated with Liver X Receptor/Retinoid X Receptor (LXR/RXR) pathway activation, atherosclerosis signaling, production of nitric oxide and reactive oxygen species, and the top upstream regulator of the differential proteins by IPA analysis indicated to the APOE. Nineteen differential proteins were verified by MRM analysis. ROC based on MRM data revealed that the combination of two proteins (APOD and TFF1) could diagnose CAD with 85% sensitivity and 99% specificity (AUC 0.95). The urinary proteome might reflect the pathophysiological changes in CAD and be used for the clinical study of CAD.

Analysis of the differential urinary protein profile in IgA nephropathy patients of Uygur ethnicity.

BACKGROUND: IgA nephropathy (IgAN) is one of the most common forms of idiopathic glomerular diseases and might lead to end-stage kidney disease. Accurate and non-invasive biomarkers for early diagnosis are required for early intervention and consequent therapy for IgAN patients. Because variance in the disease incidence and predisposing genes of IgAN has been detected among different ethnicities, the ethnicity factor should be considered in IgAN biomarker discovery. The differences in the protein profiles and pathological mechanisms of IgAN in patients of Uygur ethnicity need to be clearly illustrated. METHODS: In this study, we used urinary proteomics to discover candidate biomarkers of IgAN in patients of Uygur ethnicity. The urinary proteins from Uygur normal control and Uygur IgAN patients were extracted and analyzed using 2D-LC-MS/MS and isobaric tags for relative and absolute quantitation (iTRAQ) analysis. RESULTS: A total of 277 proteins were found to be differentially represented in Uygur IgAN compared with the respective normal controls. The bioinformatics analysis revealed that the immune response, cell survival, and complement system were activated in Uygur IgAN. Many differentially expressed proteins were found to be related to nephropathy and kidney injuries. Four candidate biomarkers were validated by Western blot, and these results were consistent with the iTRAQ results. ICAM1, TIMP1, SERPINC1 and ADIPOQ were upregulated in Uygur IgAN. Bioinformatic analysis revealed that the increase of ICAM1 and TIMP1 might be caused by IgAN, but the increase of SERPINC1 and ADIPOQ might be caused by proteinuria. SERPINC1 and ICAM1 were identified as the candidate biomarkers with excellent area-under-the-curve (AUC) values (0.84) for distinguishing Uygur IgAN from normal controls. CONCLUSIONS: Using urinary proteomic analysis, we identified several candidate biomarkers for IgAN in patients of Uygur ethnicity. These results will prove helpful for exploring the pathological mechanism of IgAN in patients of Uygur ethnicity and for developing better treatments for these patients.

Comprehensive Map and Functional Annotation of Human Pituitary and Thyroid Proteome.

Knowledge about human tissue proteome will provide insights into health organ physiology. To construct a comprehensive data set of human pituitary and thyroid proteins, post-mortem pituitaries and thyroids from 10 normal individuals were used. The pooled samples were prepared using two methods. One part of the sample was processed using 14 high-abundance proteins immunoaffinity column. The other part was directly subjected to digestion. Finally, a total of 7596 proteins in pituitary and 5602 proteins in thyroid with high confidence were identified, with 6623 and 4368 quantified, respectively. A total of 5781 of pituitary and 3178 of thyroid proteins have not been previously reported in the normal pituitary and thyroid proteome. Comparison of pituitary and thyroid proteome indicated that thyroid prefers to be involved in nerve system regeneration and metabolic regulation, while pituitary mainly performs functions of signal transduction and cancer modulation. Our results, for the first time, comprehensively profiled and functionally annotated the largest high-confidence data set of proteome of two important endocrine glands, pituitary and thyroid, which is important for further studies on biomarker identification and molecular mechanisms of pituitary and thyroid disorders. The mapping results can be freely downloaded at http://www.urimarker.com/pituitary/ and http://www.urimarker.com/thyroid/ . The raw data are available via ProteomeXchange with identifier PXD006471.

A qualitative and quantitative evaluation of the peptide characteristics of microwave- and ultrasound-assisted digestion in discovery and targeted proteomic analyses.

RATIONALE: Fast digestion methods can dramatically accelerate enzyme digestion and increase the throughput of proteomic analysis. However, the peptide characteristics of fast digestion methods and their performance in discovery and targeted proteomic analysis must be systematically evaluated. METHODS: Three digestion methods, including overnight digestion, microwave-assisted protein enzymatic digestion (MAPED), and high-intensity focused ultrasonic-assisted enzymatic digestion (HIFUSAED), in trypsin or in trypsin/Lys-C were comprehensively compared in both discovery and targeted proteomics analysis using the HeLa cell proteome. In discovery proteomic analysis, the highest numbers of peptides and proteins were identified when the sample was digested via the MAPED method with trypsin/Lys-C. RESULTS: The fast digestion methods showed a higher mis-cleavage rate and a lower semi-tryptic rate than the overnight digestion method. In both label-free quantitative analysis and targeted proteomic analysis, both fully cleaved peptides (FCPs) and mis-cleaved peptides (MCPs) from the fast digestion methods and the overnight digestion method showed good reproducibility if they showed good abundance. CONCLUSIONS: When both the FCPs and MCPs were included in the analysis, the MAPED with trypsin/Lys-C method showed the best results for both discovery proteomic analysis and relative quantitative targeted proteomic analysis. These results will be beneficial for the application of fast digestion methods to proteomics.

Differential urinary glycoproteome analysis of type 2 diabetic nephropathy using 2D-LC-MS/MS and iTRAQ quantification.

BACKGROUND: Diabetic nephropathy (DN) is the leading cause of chronic kidney failure and end-stage kidney disease. More accurate and non-invasive test for the diagnosis and monitoring the progression of DN is urgently needed for the better care of such patients. METHODS: In this study we utilized urinary glycoproteome to discover the differential proteins during the course of type 2 DN. The urinary glycoproteins from normal controls, normalbuminuira, microalbuminura, and macroalbuminuria patients were enriched by concanavalin A (ConA) and analyzed by 2DLC/MS/MS and isobaric tags for relative and absolute quantitation quantification. RESULTS: A total of 478 proteins were identified and 408 were annotated as N-linked glycoproteins. A total of 72, 107 and 123 differential proteins were identified in normalbuminuria, microalbuminuria and macroalbuminuria, respectively. By bioinformatics analysis, in normalbuminruia state, cell proliferation and cell movement were activated, which might reflect the compensatory phase during the disease development. In micro- and macro-albuminuria, cell death and apoptosis was activated, which might reflect the de-compensatory phase. Pathway analysis showed acute phase proteins, the member of high density lipoprotein and low density lipoprotein proteins were changed, indicating the role of the inflammatory response and lipid metabolism abnormality in the pathogenesis of DN. Six selected differential proteins were validated by Western Blot. Alpha-1-antitrypsin (SERPINA1) and Ceruloplasmin are the two markers with excellent area under curve values (0.929 and 1.000 respectively) to distinguish the microalbuminuria and normalbuminuria. For the first time, we found pro-epidermal growth factor and prolactin-inducible protein were decreased in macroalbuminuria stage, which might reflect the inhibition of cell viability and the activation of cell death in kidney. CONCLUSIONS: Above data indicated that urinary glycoproteome could be useful to distinguish the differences in protein profiles in different stages in DN, which will help better individualized care of patients in DN.

A preliminary quantitative proteomic analysis of glioblastoma pseudoprogression.

BACKGROUNDS: Pseudoprogression disease (PsPD) is commonly observed during glioblastoma (GBM) follow-up after adjuvant therapy. Because it is difficult to differentiate PsPD from true early progression of GBM, we have used a quantitative proteomics strategy to identify molecular signatures and develop predictive markers of PsPD. RESULTS: An initial screening of three PsPD and three GBM patients was performed, and from which 530 proteins with significant fold changes were identified. By conducting biological functional analysis of these proteins, we found evidence that the protein synthesis network and the cellular growth and proliferation network were most significantly affected. Moreover, six of the proteins (HNRNPK, ELAVL1, CDH2, FBLN1, CALU and FGB) involved in the two networks were validated (n = 18) in the same six samples and in twelve additional samples using immunohistochemistry methods and the western blot analysis. The receiver operating characteristic (ROC) curve analysis in distinguishing PsPD patients from GBM patients yielded an area under curve (AUC) value of 0.90 (95% confidence interval (CI), 0.662-0.9880) for CDH2 and.0.92 (95% CI, 0.696-0.995) for CDH2 combined with ELAVL1. CONCLUSIONS: The results of the present study both revealed the biological signatures of PsPD from a proteomics perspective and indicated that CDH2 alone or combined with ELAVL1 could be potential biomarkers with high accuracy in the diagnosis of PsPD.

LC-MS-based urine metabolomics analysis of chronic subdural hematoma for biomarker discovery.

BACKGROUND: Chronic subdural hematoma (CSDH) is one of the most common neurosurgical diseases with atypical manifestations. The aim of this study was to utilize urine metabolomics to explore potential biomarkers for the diagnosis and prognosis of CSDH. METHODS: Seventy-seven healthy controls and ninety-two patients with CSDH were enrolled in our study. In total, 261 urine samples divided into the discovery group and validation group were analyzed by LC-MS. The statistical analysis and functional annotation were applied to discover potential biomarker panels and altered metabolic pathways. RESULTS: A total of 53 differential metabolites were identified in this study. And the urinary metabolic profiles showed apparent separation between patients and controls. Further functional annotation showed that the differential metabolites were associated with lipid metabolism, fatty acid metabolism, amino acid metabolism, biotin metabolism, steroid hormone biosynthesis, and pentose and glucuronate interconversions. Moreover, one panel of Capryloylglycine, cis-5-Octenoic acid, Ethisterone, and 5,6-DiHETE showed good predictive performance in the diagnosis of CSDH, with an AUC of 0.89 in discovery group and an AUC of 0.822 in validation group. Another five metabolites (Trilobinol, 3'-Hydroxyropivacaine, Ethisterone, Arginyl-Proline, 5-alpha-Dihydrotestosterone glucuronide) showed the levels of them returned to a healthy state after surgery, showing good possibility to monitor the recovery of CSDH patients. CONCLUSION AND CLINICAL RELEVANCE: The findings of the study revealed urine metabolomic differences between CSDH and controls. The potentially diagnostic and prognostic biomarker panels of urine metabolites were established, and functional analysis demonstrated deeper metabolic disorders of CSDH, which might conduce to improve early diagnose of CSDH clinically.

Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness.

BACKGROUND: Patients with disorders of consciousness (DoC) exhibit varied revival outcomes based on different etiologies and diagnoses, the mechanisms of which remain largely unknown. The fluctuating clinical presentations in DoC pose challenges in accurately assessing consciousness levels and prognoses, often leading to misdiagnoses. There is an urgent need for a deeper understanding of the physiological changes in DoC and the development of objective diagnostic and prognostic biomarkers to improve treatment guidance. METHODS: To explore biomarkers and understand the biological processes, we conducted a comprehensive untargeted metabolomic analysis on serum samples from 48 patients with DoC. Patients were categorized based on etiology (TBI vs. non-TBI), CRS-R scores, and prognosis. Advanced analytical techniques, including PCA and OPLS-DA models, were employed to identify differential metabolites. RESULTS: Our analysis revealed a distinct separation in metabolomic profiles among the different groups. The primary differential metabolites distinguishing patients with varying etiologies were predominantly phospholipids, with a notable decrease in glycerophospholipids observed in the TBI group. Patients with higher CRS-R scores exhibited a pattern of impaired carbohydrate metabolism coupled with enhanced lipid metabolism. Notably, serum concentrations of both LysoPE and PE were reduced in patients with improved outcomes, suggesting their potential as prognostic biomarkers. CONCLUSIONS: Our study underscores the critical role of phospholipid metabolism in the brain's metabolic alterations in patients with DoC. It identifies key biomarkers for diagnosis and prognosis, offering insights that could lead to novel therapeutic targets. These findings highlight the value of metabolomic profiling in understanding and potentially treating DoC.

A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database.

Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.

Susceptibility to preoperative seizures in glioma patients with elevated homocysteine levels.

OBJECTIVE: Seizures are a common clinical presentation in patients with glioma and substantially impact patients' quality of life. Hyperhomocysteinemia is defined as abnormally high serum levels of homocysteine (Hcy) and is reportedly linked to susceptibility to various nervous system diseases. However, it remains unclear whether and how hyperhomocysteinemia and its associated genetic polymorphisms promote seizures in glioma patients. METHODS: We retrospectively reviewed all medical data from 127 patients with malignant gliomas, who underwent initial tumor resection by our team between July 2019 and June 2021 and had preoperative measurements of serum Hcy levels. According to whether they had at least one seizure before surgery, they were divided into the seizure and nonseizure groups. We also detected polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene and measured intratumoral Hcy levels in these patients. RESULTS: Hyperhomocysteinemia was a susceptibility factor for preoperative seizures in glioma patients according to both univariate analyses (P < 0.001) and multivariate logistic regression analyses (OR 1.239, 95% CI 1.062-1.445, P = 0.007). Patients with the MTHFR C677T variant exhibited elevated serum Hcy levels (P = 0.027) and an increased prevalence of preoperative seizures (P = 0.019). Intratumoral Hcy levels were positively correlated with serum Hcy levels (R = 0.231, P = 0.046) and were elevated in patients with hyperhomocysteinemia (P = 0.031), the MTHFR C677T variant (P = 0.002) and preoperative seizures (P = 0.003). High intratumoral Hcy levels, rather than hyperhomocysteinemia or the MTHFR C677T variant, emerged as an independent risk factor for preoperative seizures (OR 1.303, 95% CI 1.015-1.673, P = 0.038). Furthermore, the effects of hyperhomocysteinemia on epileptic susceptibility were reduced to nonsignificance when intratumoral Hcy was controlled to the same level between groups. SIGNIFICANCE: Glioma patients with hyperhomocysteinemia and the MTHFR C677T variant were susceptible to preoperative seizures, suggesting their potential as biomarkers for the management of seizures in glioma patients. The elevation of intratumoral Hcy is a possible mechanism underlying this susceptibility.

Urinary complement proteins in IgA nephropathy progression from a relative quantitative proteomic analysis.

Aim: IgA nephropathy (IgAN) is one of the leading causes of end-stage renal disease (ESRD). Urine testing is a non-invasive way to track the biomarkers used for measuring renal injury. This study aimed to analyse urinary complement proteins during IgAN progression using quantitative proteomics. Methods: In the discovery phase, we analysed 22 IgAN patients who were divided into three groups (IgAN 1-3) according to their estimated glomerular filtration rate (eGFR). Eight patients with primary membranous nephropathy (pMN) were used as controls. Isobaric tags for relative and absolute quantitation (iTRAQ) labelling, coupled with liquid chromatography-tandem mass spectrometry, was used to analyse global urinary protein expression. In the validation phase, western blotting and parallel reaction monitoring (PRM) were used to verify the iTRAQ results in an independent cohort (N = 64). Results: In the discovery phase, 747 proteins were identified in the urine of IgAN and pMN patients. There were different urine protein profiles in IgAN and pMN patients, and the bioinformatics analysis revealed that the complement and coagulation pathways were most activated. We identified a total of 27 urinary complement proteins related to IgAN. The relative abundance of C3, the membrane attack complex (MAC), the complement regulatory proteins of the alternative pathway (AP), and MBL (mannose-binding lectin) and MASP1 (MBL associated serine protease 2) in the lectin pathway (LP) increased during IgAN progression. This was especially true for MAC, which was found to be involved prominently in disease progression. Alpha-N-acetylglucosaminidase (NAGLU) and α-galactosidase A (GLA) were validated by western blot and the results were consistent with the iTRAQ results. Ten proteins were validated in a PRM analysis, and these results were also consistent with the iTRAQ results. Complement factor B (CFB) and complement component C8 alpha chain (C8A) both increased with the progression of IgAN. The combination of CFB and mucosal addressin cell adhesion molecule-1 (MAdCAM-1) also showed potential as a urinary biomarker for monitoring IgAN development. Conclusion: There were abundant complement components in the urine of IgAN patients, indicating that the activation of AP and LP is involved in IgAN progression. Urinary complement proteins may be used as biomarkers for evaluating IgAN progression in the future.