Journal of Proteome Research最新文献

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Quantitative Analysis of Nonhistone Lysine Methylation Sites and Lysine Demethylases in Breast Cancer Cell Lines. 乳腺癌细胞系非组蛋白赖氨酸甲基化位点和赖氨酸去甲基化酶的定量分析。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-08 DOI: 10.1021/acs.jproteome.4c00685
Christine A Berryhill, Taylor N Evans, Emma H Doud, Whitney R Smith-Kinnaman, Jocelyne N Hanquier, Amber L Mosley, Evan M Cornett
{"title":"Quantitative Analysis of Nonhistone Lysine Methylation Sites and Lysine Demethylases in Breast Cancer Cell Lines.","authors":"Christine A Berryhill, Taylor N Evans, Emma H Doud, Whitney R Smith-Kinnaman, Jocelyne N Hanquier, Amber L Mosley, Evan M Cornett","doi":"10.1021/acs.jproteome.4c00685","DOIUrl":"10.1021/acs.jproteome.4c00685","url":null,"abstract":"<p><p>Growing evidence shows that lysine methylation is a widespread protein post-translational modification (PTM) that regulates protein function on histone and nonhistone proteins. Numerous studies have demonstrated that the dysregulation of lysine methylation mediators contributes to cancer growth and chemotherapeutic resistance. While changes in histone methylation are well-documented with extensive analytical techniques available, there is a lack of high-throughput methods to reproducibly quantify changes in the abundances of the mediators of lysine methylation and nonhistone lysine methylation (Kme) simultaneously across multiple samples. Recent studies by our group and others have demonstrated that antibody enrichment is not required to detect lysine methylation, prompting us to investigate the use of tandem mass tag (TMT) labeling for global Kme quantification without antibody enrichment in four different breast cancer cell lines (MCF-7, MDA-MB-231, HCC1806, and MCF10A). To improve the quantification of KDMs, we incorporated a lysine demethylase (KDM) isobaric trigger channel, which enabled 96% of all KDMs to be quantified while simultaneously quantifying 326 Kme sites. Overall, 142 differentially abundant Kme sites and eight differentially abundant KDMs were identified among the four cell lines, revealing cell line-specific patterning.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PTMVision: An Interactive Visualization Webserver for Post-translational Modifications of Proteins. PTMVision:一个用于蛋白质翻译后修饰的交互式可视化web服务器。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-08 DOI: 10.1021/acs.jproteome.4c00679
Simon Hackl, Caroline Jachmann, Mathias Witte Paz, Theresa Anisja Harbig, Lennart Martens, Kay Nieselt
{"title":"PTMVision: An Interactive Visualization Webserver for Post-translational Modifications of Proteins.","authors":"Simon Hackl, Caroline Jachmann, Mathias Witte Paz, Theresa Anisja Harbig, Lennart Martens, Kay Nieselt","doi":"10.1021/acs.jproteome.4c00679","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00679","url":null,"abstract":"<p><p>Recent improvements in methods and instruments used in mass spectrometry have greatly enhanced the detection of protein post-translational modifications (PTMs). On the computational side, the adoption of open modification search strategies now allows for the identification of a wide variety of PTMs, potentially revealing hundreds to thousands of distinct modifications in biological samples. While the observable part of the proteome is continuously growing, the visualization and interpretation of this vast amount of data in a comprehensive fashion is not yet possible. There is a clear need for methods to easily investigate the PTM landscape and to thoroughly examine modifications on proteins of interest from acquired mass spectrometry data. We present PTMVision, a web server providing an intuitive and simple way to interactively explore PTMs identified in mass spectrometry-based proteomics experiments and to analyze the modification sites of proteins within relevant context. It offers a variety of tools to visualize the PTM landscape from different angles and at different levels, such as 3D structures and contact maps, UniMod classification summaries, and site specific overviews. The web server's user-friendly interface ensures accessibility across diverse scientific backgrounds. PTMVision is available at https://ptmvision-tuevis.cs.uni-tuebingen.de/.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fisetin Alleviates d-Galactose-Induced Senescence in C2C12 Myoblasts: Metabolic and Gene Regulatory Mechanisms. 非西汀缓解d-半乳糖诱导的C2C12成肌细胞衰老:代谢和基因调控机制。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-08 DOI: 10.1021/acs.jproteome.4c00939
Yue Zhang, Wenfang Wu, Caihua Huang, Donghai Lin
{"title":"Fisetin Alleviates d-Galactose-Induced Senescence in C2C12 Myoblasts: Metabolic and Gene Regulatory Mechanisms.","authors":"Yue Zhang, Wenfang Wu, Caihua Huang, Donghai Lin","doi":"10.1021/acs.jproteome.4c00939","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00939","url":null,"abstract":"<p><p>Skeletal muscle aging poses a major threat to the health and quality of life of elderly individuals. Fisetin, a natural polyphenolic compound, exhibits various biological activities; however, its role in preventing skeletal muscle cell aging is still unclear. This study aimed to elucidate the effects of fisetin on skeletal muscle aging using a d-galactose-induced C2C12 myoblast senescence model. Fisetin treatment effectively ameliorated d-galactose-induced aging damage and restored cellular functionality by improving cell viability, reducing the accumulation of the senescence marker enzyme SA-β-gal, and decreasing the expression of key aging marker proteins, p16 and p53. NMR-based metabolomics and RNA-seq transcriptomics analyses revealed that fisetin regulates several critical metabolic pathways, including glutathione metabolism, glycine, serine and threonine metabolism, as well as taurine and hypotaurine metabolism. This regulation led to the restoration of amino acid metabolism, stabilization of cellular energy homeostasis, and the preservation of membrane integrity. In addition, fisetin inhibited calcium signaling and JAK-STAT pathways, reduced cellular stress responses and reversed senescence-induced cell cycle arrest. Together, these findings highlight the potential of fisetin as a therapeutic agent to combat skeletal muscle aging and restore cellular homeostasis, offering a promising avenue for the development of antiaging treatments for skeletal muscle degeneration.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma. MAGPIE:一种机器学习方法来破译人类血浆中蛋白质-蛋白质相互作用。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-07 DOI: 10.1021/acs.jproteome.4c00160
Emily Hashimoto-Roth, Diane Forget, Vanessa P Gaspar, Steffany A L Bennett, Marie-Soleil Gauthier, Benoit Coulombe, Mathieu Lavallée-Adam
{"title":"MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.","authors":"Emily Hashimoto-Roth, Diane Forget, Vanessa P Gaspar, Steffany A L Bennett, Marie-Soleil Gauthier, Benoit Coulombe, Mathieu Lavallée-Adam","doi":"10.1021/acs.jproteome.4c00160","DOIUrl":"10.1021/acs.jproteome.4c00160","url":null,"abstract":"<p><p>Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Assessment of Humoral Response at SARS-CoV-2 Infection by Multipronged Functional Proteomics Approaches. 多管齐下功能蛋白质组学方法评估SARS-CoV-2感染的体液反应
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-07 DOI: 10.1021/acs.jproteome.4c00635
Pablo Juanes-Velasco, Juan Carlos Pérez-Arévalo, Carlota Arias-Hidalgo, Ana Nuño-Soriano, Alicia Landeira-Viñuela, Fernando Corrales, David Bernardo, Sara Cuesta-Sancho, Silvia Rojo-Rello, Quentin Lécrevisse, Rafael Góngora, José Manuel Sánchez-Santos, Javier De Las Rivas, Ángela-Patricia Hernández, Manuel Fuentes
{"title":"Assessment of Humoral Response at SARS-CoV-2 Infection by Multipronged Functional Proteomics Approaches.","authors":"Pablo Juanes-Velasco, Juan Carlos Pérez-Arévalo, Carlota Arias-Hidalgo, Ana Nuño-Soriano, Alicia Landeira-Viñuela, Fernando Corrales, David Bernardo, Sara Cuesta-Sancho, Silvia Rojo-Rello, Quentin Lécrevisse, Rafael Góngora, José Manuel Sánchez-Santos, Javier De Las Rivas, Ángela-Patricia Hernández, Manuel Fuentes","doi":"10.1021/acs.jproteome.4c00635","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00635","url":null,"abstract":"<p><p>In the past decade, a major goal in biomedical research has been to understand why individuals differ in disease susceptibility, disease dynamics, and progression. In many pathologies, this variability stems from evolved immune mechanisms that resist inflammatory stress from various diseases that have been encountered throughout life. These may provide advantages against other diseases, reduce comorbidities, and enhance longevity. This study evaluates prior immunity as a prognostic factor in COVID-19 patients, crucial for understanding plasmatic signaling cascades in different disease stages and their impact on disease progression. COVID-19, caused by SARS-CoV-2, primarily affects the respiratory system and presents a wide range of symptoms, posing significant challenges to medicine. This study systematically analyzed prior immunity and inflammation in two independent cohorts of infected patients. A serological profile is determined by protein microarrays, which identify IgM and IgG responses against 37 prevalent microbial pathogens and provide a comprehensive plasma analysis of 21 acute-phase proteins. Our results reveal distinct serological profiles correlating with disease severity, indicating that immune system dysregulation in COVID-19 patients is linked to existing immunity. These findings highlight the relevance of prior immunity for monitoring disease progression, particularly in infections and vaccine failure, and underscore the importance of functional proteomics in determining prognostic biomarkers.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning. 使用机器学习识别产后抑郁症风险的血浆蛋白质组学模型
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-07 DOI: 10.1021/acs.jproteome.4c00826
Shusheng Wang, Ru Xu, Gang Li, Songping Liu, Jie Zhu, Pengfei Gao
{"title":"A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.","authors":"Shusheng Wang, Ru Xu, Gang Li, Songping Liu, Jie Zhu, Pengfei Gao","doi":"10.1021/acs.jproteome.4c00826","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00826","url":null,"abstract":"<p><p>Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrometry, identifying 98 differentially expressed proteins (29 upregulated and 69 downregulated). Principal component analysis revealed distinct protein expression profiles between the groups. Functional enrichment and PPI analyses further explored the biological functions of these proteins. Machine learning models (XGBoost and LASSO regression) identified 17 key proteins, with the optimal logistic regression model comprising P13797 (PLS3), P56750 (CLDN17), O43173 (ST8SIA3), P01593 (IGKV1D-33), and P43243 (MATR3). The model demonstrated excellent predictive performance through ROC curves, calibration, and decision curves. These findings suggest potential biomarkers for early PPD risk assessment, paving the way for personalized prediction. However, limitations include the lack of diagnostic interviews, such as the Structured Clinical Interview for DSM-V (SCID), to confirm PPD diagnosis, a small sample size, and limited ethnic diversity, affecting generalizability. Future studies should expand sample diversity, confirm diagnoses with SCID, and validate biomarkers in larger cohorts to ensure their clinical applicability.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Novel NMR-Based Approach to Reveal the 'Metabolic Fingerprint' of Cytotoxic Gold Drugs in Cancer Cells. 基于核磁共振的新方法揭示癌细胞中细胞毒性金药物的“代谢指纹”。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-06 DOI: 10.1021/acs.jproteome.4c00904
Veronica Ghini, Ana Isabel Tristán, Giorgio Di Paco, Lara Massai, Michele Mannelli, Tania Gamberi, Ignacio Fernández, Antonio Rosato, Paola Turano, Luigi Messori
{"title":"Novel NMR-Based Approach to Reveal the 'Metabolic Fingerprint' of Cytotoxic Gold Drugs in Cancer Cells.","authors":"Veronica Ghini, Ana Isabel Tristán, Giorgio Di Paco, Lara Massai, Michele Mannelli, Tania Gamberi, Ignacio Fernández, Antonio Rosato, Paola Turano, Luigi Messori","doi":"10.1021/acs.jproteome.4c00904","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00904","url":null,"abstract":"<p><p>A combination of pathway enrichment and metabolite clustering analysis is used to interpret untargeted <sup>1</sup>H NMR metabolomics data, enabling a biochemically informative comparison of the effects induced by a panel of known cytotoxic gold(I) and gold(III) compounds in A2780 ovarian cancer cells. The identification of the most dysregulated pathways for the major classes of compounds highlights specific chemical features that lead to common biological effects. The proposed approach may have broader applicability to the screening of metal-based drug candidate libraries, which is always complicated by their multitarget nature, and support the comprehensive interpretation of their metabolic actions.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Olink Profiling of Intestinal Tissue Identifies Novel Biomarkers For Colorectal Cancer. 肠组织的链接谱识别结直肠癌的新生物标志物。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-05 DOI: 10.1021/acs.jproteome.4c00728
Chong Xiao, Hao Wu, Jing Long, Fengming You, Xueke Li
{"title":"Olink Profiling of Intestinal Tissue Identifies Novel Biomarkers For Colorectal Cancer.","authors":"Chong Xiao, Hao Wu, Jing Long, Fengming You, Xueke Li","doi":"10.1021/acs.jproteome.4c00728","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00728","url":null,"abstract":"<p><p>Comprehensive protein profiling in intestinal tissues provides detailed information about the pathogenesis of colorectal cancer (CRC). This study quantified the expression levels of 92 oncology-related proteins in tumors, paired para-carcinoma tissues, and remote normal tissues from a cohort of 52 CRC patients utilizing the Olink technology. The proteomic profile of normal tissues closely resembled that of para-carcinoma tissues while distinctly differing from that of tumors. Among the 68 differentially expressed proteins (DEPs) identified between the tumor and normal tissues, WISP-1, ESM-1, and TFPI-2 showed the most pronounced alterations and exhibited relatively strong correlations. These markers also presented the highest AUC values for distinguishing between tissue types. Bioinformatic analysis of the DEPs revealed that the plasma membrane and the PI3K-AKT signaling pathway were among the most enriched GO terms and KEGG pathways. Furthermore, although TFPI-2 is typically recognized as a tumor suppressor, both Olink and enzyme linked immunosorbent assay (ELISA) analyses have demonstrated that its expression is significantly elevated in tumors compared with paired normal tissues. To the best of our knowledge, this is the first study to profile the proteome of intestinal tissue using the Olink technology. This work offers valuable insights into potential biomarkers and therapeutic targets for CRC, complementing the Olink profiling of circulating proteins.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Proteomic Analysis of Tissue Proteins Related to Lateral Lymph Node Metastasis in Papillary Thyroid Microcarcinoma. 甲状腺乳头状微腺癌侧淋巴结转移相关组织蛋白的蛋白质组学分析
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-03 Epub Date: 2024-11-27 DOI: 10.1021/acs.jproteome.4c00737
Qiyao Zhang, Zhen Cao, Yuanyang Wang, Hao Wu, Zejian Zhang, Ziwen Liu
{"title":"Proteomic Analysis of Tissue Proteins Related to Lateral Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.","authors":"Qiyao Zhang, Zhen Cao, Yuanyang Wang, Hao Wu, Zejian Zhang, Ziwen Liu","doi":"10.1021/acs.jproteome.4c00737","DOIUrl":"10.1021/acs.jproteome.4c00737","url":null,"abstract":"<p><p>Patients with lateral lymph node metastasis (LLNM) may experience higher locoregional recurrence rates and poorer prognoses compared to those without LLNM, highlighting the need for effective preoperative stratification to reliably assess risk LLNM. In this study, we collected PTMC samples from Peking Union Medical College Hospital and employed data-independent acquisition mass spectrometry proteomics technique to identify protein profiles in PTMC tissues with and without LLNM. Pseudo temporal analysis and single sample gene set enrichment analysis were conducted in combination with The Cancer Genome Atlas Thyroid Carcinoma for functional coordination analysis and the construction of a prediction model based on random forest. Non-negative matrix factorization (NMF) clustering was utilized to classify molecular subtypes of PTMC. Our findings revealed that the differential activation of pathways such as MAPK and PI3K was critical in enhancing the lateral lymph node metastatic potential of PTMC. We successfully screened biomarkers via machine learning and public databases, creating an effective prediction model for metastasis. Additionally, we explored the mechanism of metastasis-associated PTMC subtypes via NMF clustering. These insights into LLNM mechanisms in PTMC may contribute to future biomarker screening and the identification of therapeutic targets.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"256-267"},"PeriodicalIF":3.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142724273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multiomics Approach Identifies Key Proteins and Regulatory Pathways in Colorectal Cancer. 多组学方法鉴定结直肠癌的关键蛋白和调控途径。
IF 3.8 2区 生物学
Journal of Proteome Research Pub Date : 2025-01-03 Epub Date: 2024-12-19 DOI: 10.1021/acs.jproteome.4c00902
Jun Rao, Xing Wang, Xianghui Wan, Chao Chen, Xiaopeng Xiong, Aihua Xiong, Zhiqing Yang, Lanyu Chen, Ting Wang, Lihua Mao, Chunling Jiang, Jiquan Zeng, Zhi Zheng
{"title":"Multiomics Approach Identifies Key Proteins and Regulatory Pathways in Colorectal Cancer.","authors":"Jun Rao, Xing Wang, Xianghui Wan, Chao Chen, Xiaopeng Xiong, Aihua Xiong, Zhiqing Yang, Lanyu Chen, Ting Wang, Lihua Mao, Chunling Jiang, Jiquan Zeng, Zhi Zheng","doi":"10.1021/acs.jproteome.4c00902","DOIUrl":"10.1021/acs.jproteome.4c00902","url":null,"abstract":"<p><p>The prevalence rate of colorectal cancer (CRC) has dramatically increased in recent decades. However, robust CRC biomarkers with therapeutic value for early diagnosis are still lacking. To comprehensively reveal the molecular characteristics of CRC development, we employed a multiomics strategy to investigate eight different types of CRC samples. Proteomic analysis revealed 2022 and 599 differentially expressed tissue proteins between CRC and control groups in CRC patients and CRC mice, respectively. In patients with colorectal precancerous lesions, 25 and 34 significantly changed proteins were found between patients and healthy controls in plasma and white blood cells, respectively. Notably, vesicle-associated membrane protein-associated protein A (VAPA) was found to be consistently and significantly decreased in most types of CRC samples, and its level was also significantly correlated with increased overall survival of CRC patients. Furthermore, 37 significantly enriched pathways in CRC were further validated via metabolomics analysis. Ten VAPA-related pathways were found to be significantly enriched in CRC samples, among which PI3K-Akt signaling, central carbon metabolism in cancer, cholesterol metabolism, and ABC transporter pathways were also enriched in the premalignant stage. Our study identified VAPA and its associated pathways as key regulators, suggesting their potential applications in the early diagnosis and prognosis of CRC.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"356-367"},"PeriodicalIF":3.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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