PROTEOMICS – Clinical Applications最新文献

Purpose: Breast cancer is a significant threat to women's health. Precise prognosis prediction for breast cancer can help doctors implement more rational treatment strategies. Artificial intelligence can assist doctors in decision-making and enhance prediction accuracy.

Experimental design: In this paper, a deep learning model ECMHA-PP (Energy Constrained Multi-Head Self-Attention based Prognosis Prediction) is proposed to predict the prognosis of breast cancer. ECMHA-PP utilizes patients' clinical data and extracts features through a cross-position mix and a channel mix multi-layer perceptron. Then, it incorporates an energy-constrained multi-head self-attention layer to improve feature extraction capability. The source code of ECMHA-PP has been hosted on GitHub and is available at https://github.com/xiaoliu166370/ECMHA-PP.

Results: To evaluate our proposed method, prognostic prediction experiments were performed on the METABRIC dataset, yielding outstanding results with an average accuracy of 93.0% and an average area under the curve of 0.974. To further validate the model's performance, we conducted tests on another independent dataset, BRCA, achieving an accuracy of 87.6%.

Conclusions and clinical relevance: In comparison with other widely used advanced methods, ECMHA-PP demonstrated higher comprehensive performance, making it a reliable prognostic prediction model for breast cancer. Given its robust feature extraction and prediction capabilities.

Determination of the changes in protein structure is crucial for a better understanding of their function and properties, which is highly important in identifying the causes of the disease, new drug development, and clinical applications. The Ramachandran plot, displaying the set of torsional angles, phi (Φ) and psi (Ψ), of the protein backbone, serves as a popular and convenient tool for secondary structure analysis and interpretation. However, identifying subtle changes in protein structure is often hindered in traditional Ramachandran plot, especially with the large amount of data generated by molecular dynamics (MD) simulations. In this paper, we proposed a useful and efficient tool, that is, differential Ramachandran plot (dRama), which enables to compare protein structures and extract the differences, providing a highly readable graphical representation. dRama is available at: https://github.com/MaksWolf44/dRama.

Liquid chromatography, when used in conjunction with mass spectrometry (LC/MS), is a powerful tool for conducting accurate and reproducible investigations of numerous metabolites in natural products (NPs). LC/MS has gained prominence in metabolomic research due to its high throughput, the availability of multiple ionization techniques and its ability to provide comprehensive metabolite coverage. This unique method can significantly influence various scientific domains. This review offers a comprehensive overview of the current state of LC/MS-based metabolomics in the investigation of NPs. This review provides a thorough overview of the state of the art in LC/MS-based metabolomics for the investigation of NPs. It covers the principles of LC/MS, various aspects of LC/MS-based metabolomics such as sample preparation, LC modes, method development, ionization techniques and data pre-processing. Moreover, it presents the applications of LC/MS-based metabolomics in numerous fields of NPs research such as including biomarker discovery, the agricultural research, food analysis, the study of marine NPs and microbiological research. Additionally, this review discusses the challenges and limitations of LC/MS-based metabolomics, as well as emerging trends and developments in this field.

Objective: To investigate the potential effects of products released by a resin composite on the proteome of human gingival fibroblasts.

Methods: Fifteen resin composite cylinders of a Bis-GMA-based resin composite (Tetric EvoCeram, Ivoclar) were made and placed in a culture medium for 24 h. Then, 30 mL of this medium was placed for 72 h in contact with human gingival fibroblasts and a second control group consisted of cells placed in culture medium only. Afterward, cells were collected, washed, and their proteins extracted. Three two-dimensional electrophoresis were performed per condition. Image analysis of the gels was carried out to highlight the differential protein spots. These spots were then analyzed by an ESI/qTOF mass spectrometer. Finally, specific databases provided protein identification, their interactions, and the pathways where they are implicated.

Results: Delta2D software allowed the detection of 21 spots of different proteins. The MASCOT identified 28 proteins. Five proteins from four spots were upregulated, 23 proteins from 17 spots were downregulated. The UniProt database showed that all these proteins were involved in cellular architecture, structural modifications and quality control of proteins, cellular homeostasis, and metabolic pathways. The STRING database revealed the interactions between the regulated proteins. The GO enrichment analysis showed that 19 pathways were affected.

Significance: The products released from the resin composite tested led to changes in the fibroblast proteome. Under the conditions of this study, resin composite released products can cause early adverse effects on cells, but without complete inhibition of their cellular functions.

Background: Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms.

Methods: The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations.

Results: Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms.

Conclusion: Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC.

Background: Saliva has emerged as a promising diagnostic resource due to its accessibility, noninvasiveness, and repeatability, enabling early disease detection and timely intervention. However, current studies often overlook the distinction between whole saliva (WS) and cell-free saliva (CFS). Objective This study aims to compare the proteomic profiles of WS and CFS.

Method and result: The saliva was detected with and without low-abundance protein enrichment using nanoparticles, employing DIA-MS technology. Our findings reveal a substantial enhancement in the detectability of low-abundance proteins in saliva with utilization of nanoparticles, enabling identification of 12%-15% low-abundance proteins previously undetectable in WS or CFS. In total, 3817 saliva proteins were identified, with 3413 found in WS and 2340 in CFS. More interestingly, we found that it was not the similarity of the samples that did the clustering, but rather it depended more on the different detection methods and sample types. And the predominant functions of the identified proteins in WS were related to oxidative phosphorylation and neurodegenerations, whereas those in CFS were primarily associated with nitrogen and glycosaminoglycan metabolism. And both exhibited functions in immune response and proteasome.

Conclusion: This study represents the first comparison of WS and CFS, providing valuable experimental evidence for guiding the selection of research subjects in future saliva omics studies.

Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD.

Purpose: The recurrence of esophageal squamous cell carcinoma (ESCC) in radiation therapy treatment presents a complex challenge due to its resistance to radiation. However, the mechanism underlying the development of radioresistance in ESCC remains unclear. In this study, we aim to uncover the mechanisms underlying radioresistance in ESCC cells and identify potential targets for radiosensitization.

Methods: We established two radio-resistant cell lines, TE-1R and KYSE-150R, from the parental ESCC cell lines TE-1 and KYSE-150 through fractionated irradiation. A TMT-based quantitative proteomic profiling approach was applied to identify changes in protein expression patterns. Cell Counting Kit-8, colony formation, γH2AX foci immunofluorescence and comet assays were utilized to validate our findings. The downstream effectors of the DNA repair pathway were confirmed using an HR/NHEJ reporter assay and Western blot analysis. Furthermore, we evaluated the expression of potential targets in ESCC tissues through immunohistochemistry combined with mass spectrometry.

Results: Over 2,000 proteins were quantitatively identified in the ESCC cell lysates. A comparison with radio-sensitive cells revealed 61 up-regulated and 14 down-regulated proteins in the radio-resistant cells. Additionally, radiation treatment induced 24 up-regulated and 12 down-regulated proteins in the radio-sensitive ESCC cells. Among the differentially expressed proteins, S100 calcium binding protein A6 (S100A6), glutamine gamma-glutamyltransferase 2 (TGM2), glycogen phosphorylase, brain form (PYGB), and Thymosin Beta 10 (TMSB10) were selected for further validation studies as they were found to be over-expressed in the accumulated radio-resistant ESCC cells and radio-resistant cells. Importantly, high S100A6 expression showed a positive correlation with cancer recurrence in ESCC patients. Our results suggest that several key proteins, including S100A6, TGM2, and PYGB, play a role in the development of radioresistance in ESCC.

Conclusions: Our results revealed that several proteins including Protein S100-A6 (S100A6), Protein-glutamine gamma-glutamyltransferase 2 (TGM2), Glycogen phosphorylase, brain form (PYGB) were involved in radio-resistance development. These proteins could potentially serve as biomarkers for ESCC radio-resistance and as therapeutic targets to treat radio-resistant ESCC cells.

Objective: To investigate the potential effects of BvfA in reproductive system damage caused by Brucella.

Methods: Brucella intracellular multiplication ability was determined by a gentamicin protection assay; the LDH method was used to determine the lethal effect of Brucella on TM4 cells. Afterward, Label-free proteomics and LC-MS/MS metabolomics assays were combined to reveal differential abundant proteins and metabolites of TM4 cells infected with bvfA-deletion strains and parental strains. Finally, PRM mass spectrometry and western blot analysis were carried out to confirm differential expression of proteins.

Results: This report demonstrated that bvfA-deletion strains failed to invade TM4 cells and reconstitution of invasion when a strain with gene bvfA was reintroduced to the deletion strain in 3 h. The bvfA-deletion exhibited weakened intracellular multiplication compared with parental strains in TM4 cells in 12 h; however, the death rate of TM4 cells infected with bvfA-deletion strains was higher than that of TM4 cells infected with parental strains. Combined proteomics and metabolomics analyses revealed that the differential abundant proteins and metabolites in TM4 cells infected with bvfA-deletion and parental strains mainly involved the mineral absorption-related pathway, NADH:ubiquinone oxidoreductase subunit-related mitochondrial respiratory signaling pathway, and sphingolipid signaling pathway of TM4 cells. These three signaling pathways were involved in expression changes of TRPM6/7, STEAP1, Gnaq, Trp53, Pbk, Tns2, Akt2, and the NADH:ubiquinone oxidoreductase subunit, as well as content changes of l-Valine, l-Isoleucine, l-Methionine, PC, PE DG, and SM metabolites.

Significance: These results indicated that BvfA of Brucella abortus S19 affected the above proteins and metabolites in TM4 cells.

Aims: The pathophysiological of diabetic distal symmetric polyneuropathy (DSPN) remains to be elucidated and there are no diagnostic or prognostic biomarkers for the condition. In this explorative proteomic study, metabolic proteome profiling of serum in patients with/without DSPN was analyzed. We aimed to discover proteins with different abundance ranges through proximity extension assay (PEA) technology.

Methods: Temperature quantitative sensory testing (QST) and electromyography (EMG) were used to access the small- and large-fiber function of all participants, respectively. The metabolic proteome profile of serum was analyzed using PEA technology (Olink Target 96 METABOLISM panel).

Results: We evaluated serum from patients without DSPN (n = 27), with small-fiber neuropathy (SFN, n = 25) and with mixed small- and large-fiber neuropathy (MSLFN, n = 24). Fifteen proteins, which were especially related to immune response, insulin resistance, and lipid metabolism, were significantly different between patients without DSPN and with MSLFN. Besides, seven proteins, especially related to extracellular structure organization, were significantly different between serum from patients with SFN and with MSLFN. What's more, serum from patients without DSPN showed that three proteins, related to immune response, altered significantly compared to serum from patients with SFN.

Conclusions: This was the first study that characterized the metabolic proteomic profile of serum in DSPN patients by analyzing a panel of 92 metabolic proteins using PEA technology.