PROTEOMICS – Clinical Applications最新文献

Background: Triple-negative breast cancer (TNBC) has a poor prognosis, an ineffective diagnosis, and a high degree of aggressiveness. Therefore, novel therapeutic targets for TNBC urgently need to be identified.

Methods: Through a series of bioinformatics analyses, including analysis of differential gene expression, protein-protein interaction (PPI) network, univariate cox regression, immune infiltration, pathway enrichment, etc, as well as auxiliary immunohistochemistry (IHC) and protein quantitativae analysis, to explore prognostic marker for TNBC.

Results: In TNBC tissues, we found that SPDL1 (CCDC99) was considerably overexpressed at both the mRNA and protein levels compared to that in normal and non-TNBC tissues. Additionally, we found that SPDL1-high expression was strongly linked to poor prognosis in TNBC patients. Excessive SPDL1 expression was positively correlated with tumor growth and strongly linked to the cell cycle, DNA replication, and the p53 signaling pathway. In addition, CIBERSORT analysis revealed that SPDL1 can affect the tumor immune microenvironment (TME) in TNBC, encourage the development of TNBC and act as a potential prognostic biomarker for TNBC. Patients with SPDL1-high expression were more sensitive to AZD8055. Notably, we discovered that SPDL1 is highly expressed in the majority of malignancies and may have an impact on the pancancer prognosis.

Conclusions: SPDL1 can serve as a novel prognostic marker for TNBC and pancancer patients.

Purpose: Bladder cancer (BLCA) is a major cancer of the genitourinary system. Although cystoscopy is the standard protocol for diagnosing BLCA clinically, this procedure is invasive and expensive. Several urine-based markers for BLCA have been identified and investigated, but none has shown sufficient sensitivity and specificity. These observations underscore the importance of discovering novel BLCA biomarkers and developing a noninvasive method for detection of BLCA. Exploring the cancer secretome is a good starting point for the development of noninvasive biomarkers for cancer diagnosis.

Experimental design: In this study, we established a comprehensive secretome dataset of five representative BLCA cell lines, BFTC905, TSGH8301, 5637, MGH-U1, and MGH-U4, by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Expression of BLCA-specific secreted proteins at the transcription level was evaluated using the Oncomine cancer microarray database.

Results: The expressions of four candidates-COMT, EWSR1, FUSIP1, and TNPO2-were further validated in clinical human specimens. Immunohistochemical analyses confirmed that transportin-2 was highly expressed in tumor cells relative to adjacent noncancerous cells in clinical tissue specimens from BLCA patients, and was significantly elevated in BLCA urine compared with that in urine samples from aged-matched hernia patients (controls).

Conclusions: Collectively, our findings suggest TNPO2 as a potential noninvasive tumor-stage or grade discriminator for BLCA management.

Hepatocellular carcinoma (HCC) is a life-threatening disease that presents diagnostic challenges due to the absence of reliable biomarkers. Recently, plasma proteomics and glycoproteomics have emerged as powerful tools for identifying potential diagnostic biomarkers for various diseases. In this study, we conducted a comprehensive proteomic and glycoproteomic analysis of plasma samples from 11 HCC patients and 11 healthy control (HC) individuals. We identified 20 differentially expressed (DE) proteins and 32 DE intact glycosylated peptides (IGPs) that can effectively differentiate between HCC patients and HC samples. Our findings demonstrate that IGP profiles had better predictive power than protein profiles for screening HCC. Pathways associated with DE proteins and IGPs were identified. It was reported that the protein expression level of galectin 3 binding protein (LGALS3BP) and its N-linked glycosylation at the N398 and N551 sites might serve as valuable candidates for HCC diagnosis. These results highlight the importance of N-glycoproteomics in advancing our understanding of HCC and suggest possible candidates for the future diagnosis of this disease.

Background: Venous thromboembolism (VTE) remains a significant cause of morbidity and mortality worldwide. Rivaroxaban, a direct oral factor Xa inhibitor, mediates anti-inflammatory and cardiovascular-protective effects besides its well-established anticoagulant properties; yet, these remain poorly characterized. Extracellular vesicles (EVs) are considered proinflammatory messengers regulating a myriad of (patho)physiological processes and may be highly relevant to the pathophysiology of VTE. The effects of Rivaroxaban on circulating EVs in VTE patients remain unknown. We have established that differential EV biosignatures are found in patients with non-valvular atrial fibrillation anticoagulated with Rivaroxaban versus warfarin. Here, we investigated whether differential proteomic profiles of circulating EVs could also be found in patients with VTE.

Methods and results: We performed comparative label-free quantitative proteomic profiling of enriched plasma EVs from VTE patients anticoagulated with either Rivaroxaban or warfarin using a tandem mass spectrometry approach. Of the 182 quantified proteins, six were found to be either exclusive to, or enriched in, Rivaroxaban-treated patients. Intriguingly, these proteins are involved in negative feedback regulation of inflammatory and coagulation pathways, suggesting that EV proteomic signatures may reflect both Rivaroxaban's anti-coagulatory and anti-inflammatory potential.

Conclusions: These differences suggest Rivaroxaban may have pleiotropic effects, supporting the reports of its emerging anti-inflammatory and cardiovascular-protective characteristics relative to warfarin.

Purpose: This study aimed to investigate the diagnostic potential of plasma biomarkers of community-acquired pneumonia (CAP) and their severity grading.

Experimental design: Plasma proteomes from cohort I (n = 32) with CAP were analyzed by data-independent acquisition mass spectrometry (MS). MetaboAnalyst 5.0 was used to statistically evaluate significant differences in proteins from different samples, and demographic and clinical data were recorded for all enrolled patients. Cohort II (n = 80) was used to validate candidate biomarkers. Plasma protein levels were determined using quantitative enzyme-linked immunosorbent assay (ELISA). Correlations were assessed using Pearson's correlation coefficient. A receiver operating characteristic curve was used to verify the association between the variables, CAP diagnosis, and prognosis.

Results: 121 differentially expressed proteins (DEPs) were obtained between CAP and controls. These DEPs were mainly aggregated in pathways of phagosome(hsa04145) and complement and coagulation cascades (hsa04610). No significant differential proteins were detected in bacterial, viral, and mixed infection groups. The plasma levels of fetuin-A, alpha-1-antichymotrypsin (AACT), α1-acid glycoprotein (A1AG), and S100A8/S100A9 heterodimers detected by ELISA were consistent with those of MS. AACT, A1AG, S100A8/S100A9 heterodimer, and fetuin-A can potentially be used as diagnostic predictors, and fetuin-A and AACT are potential predictors of SCAP.

Conclusions and clinical relevance: Plasma protein profiling can successfully identify potential biomarkers for CAP diagnosis and disease severity assessment. These biomarkers should be further studied for their clinical application.

Dental implants have been established as successful treatment options for missing teeth with steadily increasing demands. Today, the primary areas of research in dental implantology revolve around osseointegration, soft and hard tissue grafting as well as peri-implantitis diagnostics, prevention, and treatment. This review provides a comprehensive overview of the current literature on the application of MS-based proteomics in dental implant research, highlights how explorative proteomics provided insights into the biology of peri-implant soft and hard tissues and how proteomics facilitated the stratification between healthy and diseased implants, enabling the identification of potential new diagnostic markers. Additionally, this review illuminates technical aspects, and provides recommendations for future study designs based on the current evidence.

Alzheimer's disease (AD), one of the most common dementias, is a neurodegenerative disease characterized by cognitive impairment and decreased judgment function. The expected number of AD patient is increasing in the context of the world's advancing medical care and increasing human life expectancy. Since current molecular mechanism studies on AD pathogenesis are incomplete, there is no specific and effective therapeutic agent. Mass spectrometry (MS)-based unbiased proteomics studies provide an effective and comprehensive approach. Many advances have been made in the study of the mechanism, diagnostic markers, and drug targets of AD using proteomics. This paper focus on subcellular level studies, reviews studies using proteomics to study AD-associated mitochondrial dysfunction, synaptic, and myelin damage, the protein composition of amyloid plaques (APs) and neurofibrillary tangles (NFTs), changes in tissue extracellular vehicles (EVs) and exosome proteome, and the protein changes in ribosomes and lysosomes. The methods of sample separation and preparation and proteomic analysis as well as the main findings of these studies are involved. The results of these proteomics studies provide insights into the pathogenesis of AD and provide theoretical resource and direction for future research in AD, helping to identify new biomarkers and drugs targets for AD.

Purpose: This study was aimed to analyze serum amino acid metabolite profiles in frailty patients, gain a better understanding of the metabolic mechanisms in frailty, and assess the diagnostic value of metabolomics-based biomarkers of frailty.

Experimental design: This study utilized the ultra-performance liquid chromatography tandem mass spectrometry to examine amino acids associated with frailty. Additionally, we employed multivariate statistical methods, metabolomic data analysis, receiver operating characteristic (ROC) curve analysis, and pathway enrichment analysis.

Results: Among the assayed amino acid metabolites, we identified biomarkers for frailty. ROC curve analysis for frailty diagnosis based on the modified Fried's frailty index showed that the areas under ROC curve of tryptophan, phenylalanine, aspartic acid, and combination were 0.775, 0.679, 0.667, and 0.807, respectively. ROC curve analysis for frailty diagnosis based on Frail Scale showed that the areas under ROC curve of cystine, phenylalanine, and combination of amino acids (cystine, L-Glutamine, citrulline, tyrosine, kynurenine, phenylalanine, glutamin acid) were 0.834, 0.708, and 0.854 respectively.

Conclusion and clinical relevance: In this study, we explored the serum amino acid metabolite profiles in frailty patients. These present metabolic analyses may provide valuable information on the potential biomarkers and the possible pathogenic mechanisms of frailty.

Clinical significance: Frailty is a clinical syndrome, as a consequence it is challenging to identify at early course of the disease, even based on the existing frailty scales. Early diagnosis and appropriate patient management are the key to improve the survival and limit disabilities in frailty patients. Proven by the extensive laboratory and clinical studies on frailty, comprehensive analysis of metabolic levels in frail patients, identification of biomarkers and study of pathogenic pathways of metabolites contribute to the prediction and early diagnosis of frailty. In this study, we explored the serum amino acid metabolite profiles in frailty patients. These present metabolic analyses may provide valuable information on the potential biomarkers and the possible pathogenic mechanisms of frailty.

Purpose: Glioblastoma (GBM) is the most common and aggressive primary brain tumor characterized by poor prognosis and high recurrence. The underlying molecular mechanism that drives tumor progression and recurrence is unclear. This study is intended to look for molecular and biological changes that play a key role in GBM recurrence.

Experimental design: An integrative transcriptomic and proteomic analysis was performed on three primary GBM and three recurrent GBM tissues. Omics analyses were conducted using label-free quantitative proteomics and whole transcriptome sequencing.

Results: A significant difference was found between primary GBM and recurrent GBM at the transcriptional level. Similar to other omics studies of cancer, a weak overlap was observed between transcriptome and proteome, and Procollagen C-Endopeptidase Enhancer 2 (PCOLCE2) was observed to be upregulated at mRNA and protein levels. Analysis of public cancer database revealed that high expression of PCOLCE2 is associated with poor prognosis of patients with GBM and that it may be a potential prognostic indicator. Functional and environmental enrichment analyses revealed significantly altered signaling pathways related to energy metabolism, including mitochondrial ATP synthesis-coupled electron transport and oxidative phosphorylation.

Conclusions and clinical relevance: This study provides new insights into the recurrence process of GBM through combined transcriptomic and proteomic analyses, complementing the existing GBM transcriptomic and proteomic data and suggesting that integrated multi-omics analyses may reveal new disease features of GBM.

Background: Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection.

Methods: The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA).

Results: A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis.

Conclusions: Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.