Journal of proteomics最新文献

{"title":"Exploration of potential drug targets for Glaucoma by plasma proteome screening","authors":"Zhiqi Wang ,&nbsp;Huanyu Zhou ,&nbsp;Fei Wang ,&nbsp;Haishan Huang","doi":"10.1016/j.jprot.2024.105324","DOIUrl":"10.1016/j.jprot.2024.105324","url":null,"abstract":"<div><h3>Background</h3><div>Glaucoma is the leading cause of irreversible blindness. However, the current available treatment methods are still unsatisfactory. Therefore, the exploration of new drug targets for the treatment of glaucoma is of paramount importance.</div></div><div><h3>Methods</h3><div>We conducted two-sample Mendelian randomization (MR) using plasma protein quantitative trait loci (pQTL) data from two datasets (<em>n</em> = 734, <em>n</em> = 4907) and their instrumental variables to investigate the causal relationship between plasma proteins and glaucoma. The analysis was validated by replacing the exposure and outcome cohorts. Additionally, we utilized protein-protein interaction networks to assess the associations between these potential drug targets and existing drug targets.</div></div><div><h3>Results</h3><div>Through two-sample Mendelian randomization analysis, we identified causal relationships between Glaucoma and the following proteins: AZU1, OBP2B, ENPP5, INPP5B, KREMEN1, LYPLAL1, and PTPRJ. External validation confirmed the protective effect of LYPLAL1 on Glaucoma, while ENPP5, KREMEN1, and PTPRJ increased the risk of Glaucoma. Reverse MR and Steiger filtering did not indicate any reverse causal associations of the aforementioned proteins with Glaucoma.</div></div><div><h3>Conclusion</h3><div>Our study demonstrates a causal impact of ENPP5, KREMEN1, PTPRJ, and LYPLAL1 on the risk of Glaucoma. These findings suggest that these four proteins may serve as promising drug targets for Glaucoma treatment.</div></div><div><h3>Significance</h3><div>Currently, the pharmacological treatment of glaucoma primarily focuses on lowering intraocular pressure, which has its limitations. Targeted therapy is a personalized treatment approach that aims to inhibit or block the development and progression of diseases such as cancer and inflammation by selectively acting on specific biomolecules or signaling pathways. Our research employs a two-sample Mendelian randomization (MR) method, integrating a large amount of GWAS and pQTL data to perform MR analysis. This has enabled us to explore several plasma proteins as potential drug targets for glaucoma, providing direction and a research foundation for future investigations into glaucoma drug targets.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105324"},"PeriodicalIF":2.8,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142348956","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}

{"title":"Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning","authors":"Naim Abdul-Khalek,&nbsp;Reinhard Wimmer,&nbsp;Michael Toft Overgaard,&nbsp;Simon Gregersen Echers","doi":"10.1016/j.jprot.2024.105322","DOIUrl":"10.1016/j.jprot.2024.105322","url":null,"abstract":"<div><div>Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequence and intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the prediction of the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low response. The model predicts MS1 response with a mean average percentage error of ∼11 % and a Pearson correlation coefficient of ∼0.64. While dimer representation of peptide sequences did not improve predictive capacity compared to single AA representation in earlier work, this work adds valuable insight for a better understanding of peptide response in MS analysis.</div></div><div><h3>Significance</h3><div>Mass spectrometry is not inherently quantitative, and the response of a compound relies not only on its concentration but also on the molecular composition. For mass spectrometry-based analysis of peptides, such as in bottom-up proteomics, this directly implies that the response cannot be used directly to quantify individual peptides. Moreover, the dependency of the response on the amino acid sequence of individual peptides remains poorly understood. Using a deep learning model based on a recurrent neural network with an attention mechanism, we here investigate how the presence of dimer motifs within a peptide affects the MS1 response through the analysis of intended equimolar peptide pools comprising almost 200,000 unique peptides in total. Not only do we identify certain dimer classes and specific dimers that substantially affect the MS1 response, but the model is also able to predict peptide intensity with low error rates within the independent test subset. The findings not only improve our understanding of the link between sequence and response for peptides but also highlight the potential of utilizing deep learning for developing methods allowing for absolute, label-free peptide quantification.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"309 ","pages":"Article 105322"},"PeriodicalIF":2.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142348955","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}

{"title":"Circulating proteomic profiles in women with morbid obesity compared to normal-weight women","authors":"Laia Bertran ,&nbsp;Elena Cristina Rusu ,&nbsp;Maria Guirro ,&nbsp;Carmen Aguilar ,&nbsp;Teresa Auguet ,&nbsp;Cristóbal Richart","doi":"10.1016/j.jprot.2024.105317","DOIUrl":"10.1016/j.jprot.2024.105317","url":null,"abstract":"<div><div>In this study, we aimed to evaluate circulating proteomic levels in women with morbid obesity (MO) compared to normal-weight (NW) women. Moreover, we have compared the proteomic profile between women with metabolically healthy (MH) MO and those with type 2 diabetes mellitus (T2DM). The study included 66 normal-weight (NW) women and 129 women with MO (54 MH and 75 with T2DM). Blood samples were processed for proteomics, involving protein extraction, quantification, digestion with peptide labelling and Nano (liquid chromatography (LC)-(Orbitrap) coupled to mass/mass spectrometry (MS/MS) analysis. Statistical analyses were performed. We identified 257 proteins. Women with MO showed significantly increased levels of 35 proteins and decreased levels of 45 proteins compared to NW women. Enrichment analysis of metabolic pathways revealed significant findings. Women with MO have an altered proteomic profile compared to normal-weight women, involving proteins significantly related to chylomicron assembly, complement cascade, clotting pathways and the insulin growth factor system. Regarding women with MO and T2DM compared to MHMO women, the proteomic profile shows alterations in mostly the same pathways associated with obesity. These findings confirmed in previous reports can help us better understand the pathophysiology of obesity and associated diseases.</div></div><div><h3>Significance</h3><div>Women with morbid obesity (MO) exhibit substantial proteomic alterations compared to normal-weight (NW) women, involving 80 proteins. These alterations are linked to significant metabolic pathways, including chylomicron assembly, complement cascade, clotting pathways and the insulin growth factor system. Additionally, women with MO and type 2 diabetes mellitus (T2DM) compared to metabolically healthy MO women share similar proteomic changes than the first comparison. These findings enhance our understanding of the pathophysiology of obesity and associated diseases, offering potential targets for therapeutic intervention.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105317"},"PeriodicalIF":2.8,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874391924002495/pdfft?md5=63f97b9b5a83a76995d04947f1193d36&pid=1-s2.0-S1874391924002495-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289774","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}

{"title":"Taxonomic identification of Morocco scorpions using MALDI-MS fingerprints of venom proteomes and computational modeling","authors":"Boujemaa Ajdi ,&nbsp;Moulay Abdelmonaim El Hidan ,&nbsp;Abdelhafed El Asbahani ,&nbsp;Michel Bocquet ,&nbsp;Mohamed Ait Hamza ,&nbsp;M'barka Elqdhy ,&nbsp;Abdessamad Elmourid ,&nbsp;Oulaid Touloun ,&nbsp;Hassan Boubaker ,&nbsp;Philippe Bulet","doi":"10.1016/j.jprot.2024.105321","DOIUrl":"10.1016/j.jprot.2024.105321","url":null,"abstract":"<div><div>The venom of scorpions has been the subject of numerous studies. However, their taxonomic identification is not a simple task, leading to misidentifications. This study aims to provide a practical approach for identifying scorpions based on the venom molecular mass fingerprint (MFP). Specimens (251) belonging to fifteen species were collected from different regions in Morocco. Their MFPs were acquired using MALDI-MS. These were used as a training dataset to generate predictive models and a library of mean spectral profiles using software programs based on machine learning. The computational model achieved an overall recognition capability of 99 % comprising 32 molecular signatures. The models and the library were tested using a new dataset for external validation and to evaluate their capability of identification. We recorded an accuracy classification with an average of 97 % and 98 % for the computational models and the library, respectively. To our knowledge, this is the first attempt to demonstrate the potential of MALDI-MS and MFPs to generate predictive models capable of discriminating scorpions from family to species levels, and to build a library of species-specific spectra. These promising results may represent a proof of concept towards developing a reliable approach for rapid molecular identification of scorpions in Morocco.</div></div><div><h3>Significance of the study</h3><div>With their clinical importance, scorpions may constitute a desirable study model for many researchers. The first step in studying scorpion is systematically identifying the species of interest. However, it can be a difficult task, especially for the non-experts. The taxonomy of scorpions is primarily based on morphometric characters. In Morocco, the high number of species and subspecies mainly endemic, and the morphological similarities between different species may result in false identifications. This was observed in many reports according to the scorpion experts. In this study, we describe a reliable practical approach for identifying scorpions based on the venom molecular mass fingerprints (MFPs). By using two software programs based on machine learning, we have demonstrated that these MFPs contains sufficient inter-specific variation to differentiate between the scorpion species mentioned in this study with a good accuracy. Using a drop of venom, this new approach could be a rapid, accurate and cost saving method for taxonomic identification of scorpions in Morocco.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105321"},"PeriodicalIF":2.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289776","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}

{"title":"Ontogeny, not prey availability, underlies allopatric venom variability in insular and mainland populations of Vipera ammodytes","authors":"Margareta Lakušić ,&nbsp;Maik Damm ,&nbsp;Vukašin Bjelica ,&nbsp;Marko Anđelković ,&nbsp;Ljiljana Tomović ,&nbsp;Xavier Bonnet ,&nbsp;Dragan Arsovski ,&nbsp;Roderich D. Süssmuth ,&nbsp;Juan J. Calvete ,&nbsp;Fernando Martínez-Freiría","doi":"10.1016/j.jprot.2024.105320","DOIUrl":"10.1016/j.jprot.2024.105320","url":null,"abstract":"<div><div>Allopatric populations living under distinct ecological conditions are excellent systems to infer factors underlying intraspecific venom variation. The venom composition of two populations of <em>Vipera ammodytes</em>, insular with a diet based on ectotherms and mainland with a diet based on ectotherms and endotherms, was compared considering the sex and age of individuals. Ten toxin families, dominated by PLA₂, svMP, svSP, and DI, were identified through a bottom-up approach. The venom profiles of adult females and males were similar. Results from 58 individual SDS-PAGE profiles and venom pool analysis revealed significant differences between juveniles compared to subadults and adults. Two venom phenotypes were identified: a juvenile svMP-dominated and KUN-lacking phenotype and an adult PLA₂/svMP-balanced and KUN-containing phenotype. Despite differences in prey availability (and, therefore, diet) between populations, no significant differences in venom composition were found. As the populations are geographically isolated, the lack of venom diversification could be explained by insufficient time for natural selection and/or genetic drift to act on the venom composition of island vipers. However, substantial differences in proteomes were observed when compared to venoms from geographically distant populations inhabiting different conditions. These findings highlight the need to consider ecological and evolutionary processes when studying venom variability.</div></div><div><h3>Significance</h3><div>This study provides the first comprehensive analysis of the venom composition of two allopatric populations of <em>Vipera ammodytes</em>, living under similar abiotic (climate) but distinct biotic (prey availability) conditions. The ontogenetic changes in venom composition, coupled with the lack of differences between sex and between populations, shed light on the main determinants of venom evolution in this medically important snake. Seven new proteomes may facilitate future comparative studies of snake venom evolution. This study highlights the importance of considering ecological and evolutionary factors to understand snake venom variation.</div></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105320"},"PeriodicalIF":2.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289775","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}

{"title":"Drug response-based precision therapeutic selection for tamoxifen-resistant triple-positive breast cancer","authors":"Vinod S. Bisht ,&nbsp;Deepak Kumar ,&nbsp;Mohd Altaf Najar ,&nbsp;Kuldeep Giri ,&nbsp;Jaismeen Kaur ,&nbsp;Thottethodi Subrahmanya Keshava Prasad ,&nbsp;Kiran Ambatipudi","doi":"10.1016/j.jprot.2024.105319","DOIUrl":"10.1016/j.jprot.2024.105319","url":null,"abstract":"<div><p>Breast cancer adaptability to the drug environment reduces the chemotherapeutic response and facilitates acquired drug resistance. Cancer-specific therapeutics can be more effective against advanced-stage cancer than standard chemotherapeutics. To extend the paradigm of cancer-specific therapeutics, clinically relevant acquired tamoxifen-resistant MCF-7 proteome was deconstructed to identify possible druggable targets (<em>N</em> = 150). Twenty-eight drug inhibitors were used against identified druggable targets to suppress non-resistant (NC) and resistant cells (RC). First, selected drugs were screened using growth-inhibitory response against NC and RC. Seven drugs were shortlisted for their time-dependent (10–12 days) cytotoxic effect and further narrowed to three effective drugs (e.g., cisplatin, doxorubicin, and hydroxychloroquine). The growth-suppressive effectiveness of selected drugs was validated in the complex spheroid model (progressive and regressive). In the progressive model, doxorubicin (RC: 83.64 %, NC: 54.81 %), followed by cisplatin (RC: 76.66 %, NC: 68.94 %) and hydroxychloroquine (RC: 68.70 %, NC: 61.78 %) showed a significant growth-suppressive effect. However, in fully grown regressive spheroid, after 4th drug treatment, cisplatin significantly suppressed RC (84.79 %) and NC (40.21 %), while doxorubicin and hydroxychloroquine significantly suppressed only RC (76.09 and 76.34 %). Our in-depth investigation effectively integrated the expression data with the cancer-specific therapeutic investigation. Furthermore, our three-step sequential drug-screening approach unbiasedly identified cisplatin, doxorubicin, and hydroxychloroquine as an efficacious drug to target heterogeneous cancer cell populations.</p></div><div><h3>Significance statement</h3><p>Hormonal-positive BC grows slowly, and hormonal-inhibitors effectively suppress the oncogenesis. However, development of drug-resistance not only reduces the drug-response but also increases the chance of BC aggressiveness. Further, alternative chemotherapeutics are widely used to control advanced-stage BC. In contrast, we hypothesized that, compared to standard chemotherapeutics, cancer-specific drugs can be more effective against resistant-cancer. Although cancer-specific treatment identification is an uphill battle, our work shows proteome data can be used for drug selection. We identified multiple druggable targets and, using ex-vivo methods narrowed multiple drugs to disease-condition-specific therapeutics. We consider that our investigation successfully interconnected the expression data with the functional disease-specific therapeutic investigation and selected drugs can be used for effective resistant treatment with higher therapeutic response.</p></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105319"},"PeriodicalIF":2.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874391924002513/pdfft?md5=b8fd894e3cc744d22eedc45bed0e8399&pid=1-s2.0-S1874391924002513-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274222","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}

{"title":"Tandem mass tag-based proteomic analysis of granulosa and theca interna cells of the porcine ovarian follicle following in vitro treatment with vitamin D3 and insulin alone or in combination","authors":"Kinga Kamińska ,&nbsp;Bianka Świderska ,&nbsp;Agata Malinowska ,&nbsp;Małgorzata Grzesiak","doi":"10.1016/j.jprot.2024.105318","DOIUrl":"10.1016/j.jprot.2024.105318","url":null,"abstract":"<div><p>This study was performed to investigate the proteomic basis underlying the interaction between vitamin D₃ (VD) and insulin (I) within ovarian follicle using the pig as a model. Porcine antral follicles were incubated <em>in vitro</em> for 12 h with VD alone and I alone or in combination (VD + I) or with no treatment as the control (C). In total, 7690 and 7467 proteins were identified in the granulosa and theca interna compartments, respectively. Comparative proteomic analysis revealed 97 differentially abundant proteins (DAPs) within the granulosa layer and 11 DAPs within the theca interna layer. In the granulosa compartment, VD affected proteome leading to the promotion of cell proliferation, whereas I influenced mainly proteins related to cellular adhesion. The VD + I treatment induced granulosa cell proliferation probably <em>via</em> the DAPs involved in DNA synthesis and the cell cycle regulation. In the theca interna layer, VD alone or in co-treatment with I affected DAPs associated with cholesterol transport and lipid and steroid metabolic processes that was further confirmed by diminished lipid droplet accumulation.</p></div><div><h3>Significance</h3><p>The application of quantitative proteomics demonstrated for the first time the complexity of VD and I interactions in porcine ovarian follicle, providing a framework for understanding the molecular mechanisms underlying their cross-talk. Although identified DAPs were related to crucial ovarian processes, including the granulosa cell proliferation and cholesterol transport in the theca interna layer, novel molecular pathways underlying these processes have been proposed. The identified unique proteins may serve as indicators of VD and I interactions in both follicle layers, and could be useful biomarkers of ovarian pathologies characterized by impaired VD and I levels, such as polycystic ovary syndrome.</p></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105318"},"PeriodicalIF":2.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874391924002501/pdfft?md5=01ed789897643acd081eef443def7947&pid=1-s2.0-S1874391924002501-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241651","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}

{"title":"Corrigendum to “Unbiased differential proteomic profiling between cancer-associated fibroblasts and cancer cell lines” [Journal of Proteomics (2023) Volume 288, Article number 104973]","authors":"Rachel Lau ,&nbsp;Lu Yu ,&nbsp;Theodoros I. Roumeliotis ,&nbsp;Adam Stewart ,&nbsp;Lisa Pickard ,&nbsp;Ruth Riisanes ,&nbsp;Bora Gurel ,&nbsp;Johann S. de Bono ,&nbsp;Jyoti S. Choudhary ,&nbsp;Udai Banerji","doi":"10.1016/j.jprot.2024.105306","DOIUrl":"10.1016/j.jprot.2024.105306","url":null,"abstract":"","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"309 ","pages":"Article 105306"},"PeriodicalIF":2.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874391924002380/pdfft?md5=7087b31f0774e725f8996eb7a2530f81&pid=1-s2.0-S1874391924002380-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145889","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}

{"title":"Unravelling the role of NDUFAF4 in Colon Cancer: Insights from multi-omics analysis","authors":"Taimoor Riaz,&nbsp;Muhammad Zubair","doi":"10.1016/j.jprot.2024.105309","DOIUrl":"10.1016/j.jprot.2024.105309","url":null,"abstract":"<div><p>Colon cancer is a significant public health issue, and a deeper understanding of the molecular fundamentals [<span><span>16</span></span>] ehind is required to improve sensitivity and curability. This research explored the gene NDUFAF4 as a target of concern due to its link to a mitochondrial function and protein “Relatively of liver tumorigenesis”, which remains unclear is attributable to its inclusion into the complex I (CI) pathway. The gene ontology analysis, in turn, showed that NDUFAF4 is a key player in several critical biological phases linked to mitochondrial function and energy metabolism. Furthermore, survival analysis displayed that there was a strong correlation between NDUFAF4 expression and the patients' longevity suggesting that this factor may be important in colon cancer prognosis as well. The TCGA data proved that NDUFAF4 is elevated in colon cancer making the results of the analysis reported credible. All of the above justified the understanding of the role and importance of NDUFAF4 in treating each colon cancer patient as a molecular target. The findings help in understanding the colon cancer pathogenesis and suggest ways for developing more efficient diagnosis and treatment of the disease.</p></div><div><h3>Significance</h3><p>This research explored the gene NDUFAF4 as a target of concern due to its link to a mitochondrial function and protein “Relatively of liver tumorigenesis”, which remains unclear is attributable to its inclusion into the complex I (CI) pathway. Using a comprehensive approach to Gene Ontology analysis, Protein-Protein Interaction network modelling, survival analysis, KEGG pathway analysis, and validation using TCGA data, we identified the activities of NDUFAF4 in colon cancer. The Gene Ontology analysis, in turn, showed that NDUFAF4 is a key player in several critical biological phases linked to mitochondrial function and energy metabolism. The construction of the PPI network illustrates the interactors of NDUFAF4, the functional association protein within the cellular regulatory networks. In addition, survival analysis indicated that there was a considerable relationship between the expression of NDUFAF4 and patient survival, indicating its potential role as a prognostic factor in colon cancer. KEGG pathway analysis suggested that NDUFAF4 plays a role in thermogenesis and mitochondrial biogenesis, biological processes that should be targeted due to their implication in cellular metabolism and cancer onset. The use of TCGA information confirmed the upregulation of NDUFAF4 in colon cancer, thus making the findings of the analysis reported dependable. Overall, our study provided necessary information on the role and significance of NDUFAF4, a potential molecular target in colon cancer cases. These present findings enhance our knowledge of the pathogenesis of colon cancer and open new opportunities for designing novel diagnostic and therapeutic approaches to improve patient outcomes.</p></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"310 ","pages":"Article 105309"},"PeriodicalIF":2.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874391924002410/pdfft?md5=f0ea68db32bb49d0dd7f86a1e1d0a44a&pid=1-s2.0-S1874391924002410-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145888","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}

{"title":"Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data","authors":"Dahrii Paul,&nbsp;Vigneshwar Suriya Prakash Sinnarasan,&nbsp;Rajesh Das,&nbsp;Md Mujibur Rahman Sheikh,&nbsp;Amouda Venkatesan","doi":"10.1016/j.jprot.2024.105298","DOIUrl":"10.1016/j.jprot.2024.105298","url":null,"abstract":"<div><p>Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, <em>ESM1</em>, <em>FCN2</em>, <em>MDK</em>, <em>GPC3</em>, <em>CTHRC1</em> and <em>COL6A6</em>, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation.</p></div><div><h3>Significance</h3><p>Liver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.</p></div>","PeriodicalId":16891,"journal":{"name":"Journal of proteomics","volume":"309 ","pages":"Article 105298"},"PeriodicalIF":2.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142108597","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}