Surface plasmon resonance aptasensor for soluble ICAM-1 protein in blood samples

Dursun, Ali Doğan; Doğan, Soner; Kavruk, Murat; Taşbaşi, Behiye Büşra; Sudağıdan, Mert; Yilmaz, M. Deniz; Yılmaz, Bayram; Özalp, Veli Cengiz; Tuna, Bilge Güvenç

doi:10.1039/d1an02332b

Cited by 3 publications

(3 citation statements)

References 28 publications

(30 reference statements)

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“…These ndings align with prior research indicating the involvement of C-FOS, the gene product of FOS, in atherosclerosis development. Moreover, alterations in C-FOS expression levels have been linked to lipid accumulation, potentially impacting thrombosis [23] .ICAM1, a cell adhesion molecule, serves as an early indicator of atherosclerosis and is considered a pivotal gene for venous thrombosis treatment [24] .HSP90AA1, acting as a regulatory gene, may play a crucial role in COVID-19associated thrombosis [25] .CASP3, a marker of cardiac metabolism risk factors, exhibits associations with coronary artery ion scores, potentially offering predictive evidence [26] .TThese ndings provide evidence supporting the potential utilization of herbal medicine in recurrent venous embolism treatment through gene modulation.Further investigation of the raw data involved operations like clustering and the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, along with machine learning techniques. Ultimately, we identi ed two pivotal genes, PPP2R1A and RPL9.The PPP2R1A gene encodes Protein Phosphatase 2A (PP2A), a crucial protein phosphatase with vital roles in cellular signaling and regulation [27] .RPL9 (Ribosomal Protein L9) encodes a ribosomal protein essential for ribosome assembly, a critical machinery in cellular protein synthesis [28] .Speci cally, Gene Set Enrichment Analysis (GSEA) of the PPP2R1A gene indicated its positive regulation of processes such as protein-DNA complex disassembly, actin lament bundling, actin binding, and tissue H3-methyltransferase activity.…”

Section: Discussionmentioning

confidence: 99%

Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA Networks

Li,

Zhong,

et al. 2024

Preprint

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BACKGROUND: Venous thrombosis presents a significant global health challenge, characterized by its high incidence and limited therapeutic effectiveness. Our objective is to leverage the pharmacological insights offered by traditional Chinese medicine networks to identify potential therapeutic targets for venous thrombosis treatment and elucidate their underlying mechanisms of action. METHODS: We initiated our study by isolating the active ingredients and targets of Chinese medicine compounds from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Concurrently, we acquired venous thromboembolism (VTE) patient data from the Gene Expression Omnibus (GEO) dataset. Utilizing Cytoscape, we constructed a network connecting TCM ingredients, disease targets, and related interactions. Molecular subtypes were determined through target information clustering and typing using the "ConsensusClusterPlus" software package. Key genes were subsequently identified through a combination of weighted gene co-expression network analysis (WGCNA) and machine learning techniques. Functional enrichment analysis was performed on these key genes. Subsequently, we investigated immune cell and immune function differences related to the identified key genes. Lastly, we constructed the competing endogenous RNA (ceRNA) network associated with PPP2R1A. RESULTS: Our initial analysis identified several key genes, including FOS, ICAM1, CASP3, and HSP90AA1. Subsequent functional and downstream analyses revealed that two central hub genes, PPP2R1A and RPL9, may represent novel targets for VTE therapy. Notably, these hub genes are not only associated with thrombospondin and platelet function regulation but also play a role in modulating T cell expression in immunoassays. In conclusion, our findings suggest that ten lncRNAs may compete with two miRNAs for binding, thereby regulating PPP2R1A target genes, with potential implications for improving VTE treatment efficacy. CONCLUSION: Our current findings offer a novel direction and serve as a theoretical foundation for identifying therapeutic targets in the treatment of VTE. Furthermore, these findings hold the potential to facilitate the translation of our research into clinical applications in the future.

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Section: Discussionmentioning

confidence: 99%

Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA Networks

Li,

Zhong,

et al. 2024

Preprint

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“…28 It can be used for kinetic analysis of biological processes and biomarker identification like exosomal proteins from nonsmall cell lung cancer (NSCLC), 29,30 due to its high sensitivity (limit of detection: 0.2-100 ng/ml). 31,32 However, limited throughput and high cost limit wider applications of SPR in the field of IVD. 33 Recently, optical spectroscopy and mass spectrometry are more and more widely applied in the field of IVD, such as surface-enhanced Raman spectroscopy (SERS) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS).…”

Section: Introductionmentioning

confidence: 99%

“…SPR is a label‐free biosensing analysis technique that can obtain specific signals of interactions between biomolecules through refractive index changes 28 . It can be used for kinetic analysis of biological processes and biomarker identification like exosomal proteins from nonsmall cell lung cancer (NSCLC), 29,30 due to its high sensitivity (limit of detection: 0.2–100 ng/ml) 31,32 . However, limited throughput and high cost limit wider applications of SPR in the field of IVD 33 .…”

Section: Introductionmentioning

confidence: 99%

Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis

Chen

Shu

Zhao

et al. 2022

VIEW

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In vitro diagnosis (IVD) is one vital component of medical tests that detects biological samples of tissues or bio‐fluids. Recently, mass spectrometry and spectroscopy have been increasingly employed in the field of IVD, due to their high accuracy, facile sample preparation, and rapid detection. Notably, the large datasets generated by these two technology methods provide a wealth of information but subsequently involve complex and time‐consuming processing works. Machine learning (ML), an important branch of artificial intelligence (AI), has emerged as a promising solution for the decoding of big data. ML imitates the human brain to process data, significantly improving accuracy and efficiency compared with traditional processing methods. In this review, we first introduce the commonly used ML algorithms and advanced mass spectrometry and spectroscopy techniques in the field of IVD, respectively. The ML algorithms are summarized as four aspects according to different learning tasks. Then, the combinations of ML with mass spectrometry, spectroscopy, and multi‐modal analysis for IVD are presented, and the roles of ML in these combinations are elucidated by some representative examples. This review aims to provide a systematic and comprehensive summary of the literature on ML‐assisted mass spectrometry or spectroscopy. We believe that it will facilitate researchers to select suitable ML algorithms for supplementing existing detection techniques or to develop the potential of coupling more detection techniques with ML, thus promoting the development of mass spectrometry and spectroscopy in IVD.

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Aptamers Targeting Membrane Proteins for Sensor and Diagnostic Applications

et al. 2023

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Many biological processes (physiological or pathological) are relevant to membrane proteins (MPs), which account for almost 30% of the total of human proteins. As such, MPs can serve as predictive molecular biomarkers for disease diagnosis and prognosis. Indeed, cell surface MPs are an important class of attractive targets of the currently prescribed therapeutic drugs and diagnostic molecules used in disease detection. The oligonucleotides known as aptamers can be selected against a particular target with high affinity and selectivity by iterative rounds of in vitro library evolution, known as Systematic Evolution of Ligands by EXponential Enrichment (SELEX). As an alternative to antibodies, aptamers offer unique features like thermal stability, low-cost, reuse, ease of chemical modification, and compatibility with various detection techniques. Particularly, immobilized-aptamer sensing platforms have been under investigation for diagnostics and have demonstrated significant value compared to other analytical techniques. These “aptasensors” can be classified into several types based on their working principle, which are commonly electrochemical, optical, or mass-sensitive. In this review, we review the studies on aptamer-based MP-sensing technologies for diagnostic applications and have included new methodological variations undertaken in recent years.

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Surface plasmon resonance aptasensor for soluble ICAM-1 protein in blood samples

Abstract: Intercellular Adhesion Molecule 1 (ICAM-1) is considered to be a cancer biomarker in the assessment of metastatic potential of patients and an early indicator for atherosclerosis. A labelless biosensor based...

Cited by 3 publications

References 28 publications

Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA Networks

Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA Networks

Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis

Aptamers Targeting Membrane Proteins for Sensor and Diagnostic Applications

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