Clinical proteomics最新文献

{"title":"Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers.","authors":"Deborah Wenk, Charlotte Zuo, Thomas Kislinger, Lusia Sepiashvili","doi":"10.1186/s12014-024-09452-1","DOIUrl":"10.1186/s12014-024-09452-1","url":null,"abstract":"<p><p>Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC-MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10826105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139575428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frozen tissue coring and layered histological analysis improves cell type-specific proteogenomic characterization of pancreatic adenocarcinoma","authors":"Sara R. Savage, Yuefan Wang, Lijun Chen, Scott Jewell, Chelsea Newton, Yongchao Dou, Qing Kay Li, Oliver F. Bathe, Ana I. Robles, Gilbert S. Omenn, Mathangi Thiagarajan, Hui Zhang, Galen Hostetter, Bing Zhang","doi":"10.1186/s12014-024-09450-3","DOIUrl":"https://doi.org/10.1186/s12014-024-09450-3","url":null,"abstract":"Omics characterization of pancreatic adenocarcinoma tissue is complicated by the highly heterogeneous and mixed populations of cells. We evaluate the feasibility and potential benefit of using a coring method to enrich specific regions from bulk tissue and then perform proteogenomic analyses. We used the Biopsy Trifecta Extraction (BioTExt) technique to isolate cores of epithelial-enriched and stroma-enriched tissue from pancreatic tumor and adjacent tissue blocks. Histology was assessed at multiple depths throughout each core. DNA sequencing, RNA sequencing, and proteomics were performed on the cored and bulk tissue samples. Supervised and unsupervised analyses were performed based on integrated molecular and histology data. Tissue cores had mixed cell composition at varying depths throughout. Average cell type percentages assessed by histology throughout the core were better associated with KRAS variant allele frequencies than standard histology assessment of the cut surface. Clustering based on serial histology data separated the cores into three groups with enrichment of neoplastic epithelium, stroma, and acinar cells, respectively. Using this classification, tumor overexpressed proteins identified in bulk tissue analysis were assigned into epithelial- or stroma-specific categories, which revealed novel epithelial-specific tumor overexpressed proteins. Our study demonstrates the feasibility of multi-omics data generation from tissue cores, the necessity of interval H&E stains in serial histology sections, and the utility of coring to improve analysis over bulk tissue data.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139584257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An LC-MS-based designated comparison method with similar performance to the Lp(a) reference measurement procedure to guide molar Lp(a) standardization.","authors":"Nina M Diederiks, L Renee Ruhaak, Fred P H T M Romijn, Mervin M Pieterse, Nico P M Smit, Christa M Cobbaert","doi":"10.1186/s12014-023-09446-5","DOIUrl":"10.1186/s12014-023-09446-5","url":null,"abstract":"<p><strong>Background: </strong>The 2022 consensus statement of the European Atherosclerosis Society (EAS) on lipoprotein(a) (Lp(a)) recognizes the role of Lp(a) as a relevant genetically determined risk factor and recommends its measurement at least once in an individual's lifetime. It also strongly urges that Lp(a) test results are expressed as apolipoprotein (a) (apo(a)) amount of substance in molar units and no longer in confounded Lp(a) mass units (mg/dL or mg/L). Therefore, IVD manufacturers should transition to molar units. A prerequisite for this transition is the availability of an Lp(a) Reference Measurement Procedure (RMP) that allows unequivocal molecular detection and quantification of apo(a) in Lp(a). To that end an ISO 17511:2020 compliant LC-MS based and IFCC-endorsed RMP has been established that targets proteotypic peptides of apolipoprotein(a) (apo(a)) in Lp(a). The RMP is laborious and requires highly skilled operators. To guide IVD-manufacturers of immunoassay-based Lp(a) test kits in the transition from mass to molar units, a Designated Comparison Method (DCM) has been developed and evaluated.</p><p><strong>Methods: </strong>To assess whether the DCM provides equivalent results compared to the RMP, the procedural designs were compared and the analytical performance of DCM and RMP were first evaluated in a head-to-head comparison. Subsequently, apo(a) was quantified in 153 human clinical serum samples. Both DCM and RMP were calibrated using external native calibrators that produce results traceable to SRM2B. Measurement uncertainty (MU) was checked against predefined allowable MU.</p><p><strong>Results: </strong>The major difference in the design of the DCM for apo(a) is the use of only one enzymatic digestion step. The analytical performance of the DCM and RMP for apo(a) is highly similar. In a direct method comparison, equivalent results were obtained with a median regression slope 0.997 of and a median bias of - 0.2 nmol/L (- 0.2%); the intermediate imprecision of the test results was within total allowable error (TEa) (CVa of 10.2% at 90 nmol/L).</p><p><strong>Conclusions: </strong>The semi-automated, higher throughput, LC-MS-based method for Lp(a) meets the predefined analytical performance specifications and allowable MU and is hence applicable as a higher order Designated Comparison Method, which is ideally suited to guide IVD manufacturers in the transition from Lp(a) mass to molar units.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10809433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139544871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling.","authors":"Allison L Hunt, Nicholas W Bateman, Waleed Barakat, Sasha C Makohon-Moore, Tamara Abulez, Jordan A Driscoll, Joshua P Schaaf, Brian L Hood, Kelly A Conrads, Ming Zhou, Valerie Calvert, Mariaelena Pierobon, Jeremy Loffredo, Katlin N Wilson, Tracy J Litzi, Pang-Ning Teng, Julie Oliver, Dave Mitchell, Glenn Gist, Christine Rojas, Brian Blanton, Kathleen M Darcy, Uma N M Rao, Emanuel F Petricoin, Neil T Phippen, G Larry Maxwell, Thomas P Conrads","doi":"10.1186/s12014-024-09451-2","DOIUrl":"10.1186/s12014-024-09451-2","url":null,"abstract":"<p><strong>Background: </strong>Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics.</p><p><strong>Methods: </strong>Tumor epithelium, tumor-involved stroma, and whole \"bulk\" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor.</p><p><strong>Results: </strong>LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ).</p><p><strong>Conclusions: </strong>Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10804562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139520144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinase inhibitor pulldown assay (KiP) for clinical proteomics.","authors":"Alexander B Saltzman, Doug W Chan, Matthew V Holt, Junkai Wang, Eric J Jaehnig, Meenakshi Anurag, Purba Singh, Anna Malovannaya, Beom-Jun Kim, Matthew J Ellis","doi":"10.1186/s12014-023-09448-3","DOIUrl":"10.1186/s12014-023-09448-3","url":null,"abstract":"<p><p>Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential. For breast cancer treatment, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method. Data are available via ProteomeXchange with identifiers PXD044655 and PXD046169.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10790396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139472174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma","authors":"Yuqi Huang, Sheng Ma, Jun-Yu Xu, Kun Qian, Yaru Wang, Yi Zhang, Minjia Tan, Ting Xiao","doi":"10.1186/s12014-023-09449-2","DOIUrl":"https://doi.org/10.1186/s12014-023-09449-2","url":null,"abstract":"Despite recent innovations in imaging and genomic screening promotes advance in diagnosis and treatment of lung adenocarcinoma (LUAD), there remains high mortality of LUAD and insufficient understanding of LUAD biology. Our previous study performed an integrative multi-omic analysis of LUAD, filling the gap between genomic alterations and their biological proteome effects. However, more detailed molecular characterization and biomarker resources at proteome level still need to be uncovered. In this study, a quantitative proteomic experiment of patient-derived benign lung disease samples was carried out. After that, we integrated the proteomic data with previous dataset of 103 paired LUAD samples. We depicted the proteomic differences between non-cancerous and tumor samples and among diverse pathological subtypes. We also found that up-regulated mitophagy was a significant characteristic of early-stage LUAD. Additionally, our integrative analysis filtered out 75 potential prognostic biomarkers and validated two of them in an independent LUAD serum cohort. This study provided insights for improved understanding proteome abnormalities of LUAD and the novel prognostic biomarker discovery offered an opportunity for LUAD precise management.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139102530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay","authors":"Joohyun Ryu, Kristin L. M. Boylan, Carly A. I. Twigg, Richard Evans, Amy P. N. Skubitz, Stefani N. Thomas","doi":"10.1186/s12014-023-09447-4","DOIUrl":"https://doi.org/10.1186/s12014-023-09447-4","url":null,"abstract":"Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera. To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates. We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions. A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sBioSITe enables sensitive identification of the cell surface proteome through direct enrichment of biotinylated peptides.","authors":"Kishore Garapati, Husheng Ding, M Cristine Charlesworth, Yohan Kim, Roman Zenka, Mayank Saraswat, Dong-Gi Mun, Sandip Chavan, Ashish Shingade, Fabrice Lucien, Jun Zhong, Richard K Kandasamy, Akhilesh Pandey","doi":"10.1186/s12014-023-09445-6","DOIUrl":"10.1186/s12014-023-09445-6","url":null,"abstract":"<p><strong>Background: </strong>Cell surface proteins perform critical functions related to immune response, signal transduction, cell-cell interactions, and cell migration. Expression of specific cell surface proteins can determine cell-type identity, and can be altered in diseases including infections, cancer and genetic disorders. Identification of the cell surface proteome remains a challenge despite several enrichment methods exploiting their biochemical and biophysical properties.</p><p><strong>Methods: </strong>Here, we report a novel method for enrichment of proteins localized to cell surface. We developed this new approach designated surface Biotinylation Site Identification Technology (sBioSITe) by adapting our previously published method for direct identification of biotinylated peptides. In this strategy, the primary amine groups of lysines on proteins on the surface of live cells are first labeled with biotin, and subsequently, biotinylated peptides are enriched by anti-biotin antibodies and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS).</p><p><strong>Results: </strong>By direct detection of biotinylated lysines from PC-3, a prostate cancer cell line, using sBioSITe, we identified 5851 peptides biotinylated on the cell surface that were derived from 1409 proteins. Of these proteins, 533 were previously shown or predicted to be localized to the cell surface or secreted extracellularly. Several of the identified cell surface markers have known associations with prostate cancer and metastasis including CD59, 4F2 cell-surface antigen heavy chain (SLC3A2) and adhesion G protein-coupled receptor E5 (CD97). Importantly, we identified several biotinylated peptides derived from plectin and nucleolin, both of which are not annotated in surface proteome databases but have been shown to have aberrant surface localization in certain cancers highlighting the utility of this method.</p><p><strong>Conclusions: </strong>Detection of biotinylation sites on cell surface proteins using sBioSITe provides a reliable method for identifying cell surface proteins. This strategy complements existing methods for detection of cell surface expressed proteins especially in discovery-based proteomics approaches.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138486897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative proteomics analysis based on data-independent acquisition reveals the effect of Shenling Baizhu powder (SLP) on protein expression in MAFLD rat liver tissue.","authors":"Sufei Song, Jixian Zheng, Dongmei Zhao, Anni Zheng, Ye Zhu, Qiuling Xu, Tao Liu","doi":"10.1186/s12014-023-09442-9","DOIUrl":"10.1186/s12014-023-09442-9","url":null,"abstract":"<p><strong>Background: </strong>Metabolic associated fatty liver disease (MAFLD) has become the most common chronic liver disease worldwide, and it is also a high-risk factor for the development of other metabolic diseases. Shenling Baizhu powder (SLP) is a traditional Chinese herbal formula with good clinical efficacy against MAFLD. However, its molecular mechanism for the treatment of MAFLD is still not fully understood. This study used quantitative proteomics analysis to reveal the SLP action mechanism in the treatment of MAFLD by discovering the effect of SLP on protein expression in the liver tissue of MAFLD rats.</p><p><strong>Materials and methods: </strong>Q-Orbitrap LC-MS/MS was used to identify the incoming blood compounds of SLP. The 18 SD male rats were randomly divided into 3 groups (n = 6): control group, HFD group and SLP group. The HFD group and SLP group were established as MAFLD rat models by feeding them a high-fat diet for 4 weeks. Afterwards, the SLP group was treated with SLP (10.89 g/kg/d) for 3 weeks. Biochemical parameters and liver pathological status were measured. Rat liver tissue was analyzed using DIA-based quantitative proteomics and the DEPs were validated by western blotting analysis.</p><p><strong>Results: </strong>A total of 18 active compounds of SLP were identified and isolated to enter the bloodstream. Comparison of DEPs between control group vs. HFD group and HFD group vs. SLP group revealed that SLP restored the expression of 113 DEPs. SLP catalyzes oxidoreductase activity and binding activity on mitochondria and endoplasmic reticulum to promote lipid oxidative catabolism, maintain oxoacid metabolic homeostasis in vivo and mitigate oxidative stress-induced hepatocyte injury. And 52 signaling pathways including PPAR signaling, arachidonic acid metabolism and glycine, serine and threonine metabolism were enriched by KEGG. PPI topology analysis showed that Cyp4a2, Agxt2, Fabp1, Pck1, Acsm3, Aldh1a1, Got1 and Hmgcs2 were the core DEPs. The western blotting analysis verified that SLP was able to reverse the increase in Fabp1 and Hmgcs2 and the decrease in Pck1 induced by HFD, and the results were consistent proteomic data.</p><p><strong>Conclusion: </strong>SLP ameliorates hepatic steatosis to exert therapeutic effects on MAFLD by inhibiting the expression of lipid synthesis genes and inhibiting lipid peroxidation in mitochondria. This study provides a new idea and basis for the study of SLP in the treatment of MAFLD and provides an experimental basis for the clinical application of SLP.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138458353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.","authors":"Philip M Hemken, Xuzhen Qin, Lori J Sokoll, Laurel Jackson, Fan Feng, Peng Li, Susan H Gawel, Bailin Tu, Zhihong Lin, James Hartnett, David Hawksworth, Bryan C Tieman, Toru Yoshimura, Hideki Kinukawa, Shaohua Ning, Enfu Liu, Fanju Meng, Fei Chen, Juru Miao, Xuan Mi, Xin Tong, Daniel W Chan, Gerard J Davis","doi":"10.1186/s12014-023-09444-7","DOIUrl":"10.1186/s12014-023-09444-7","url":null,"abstract":"<p><strong>Background: </strong>Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts.</p><p><strong>Methods: </strong>To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model.</p><p><strong>Results: </strong>GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls.</p><p><strong>Conclusions: </strong>GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138451078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}