Serum lipidomic profiling for liver cancer screening using surface-assisted laser desorption ionization MS and machine learning

Transformative potentials, challenges and innovative solutions of lipidomics in multiple clinical applications

Lipidomics, a rapidly evolving field within metabolomics, provides comprehensive insights into lipid profiles and their roles in health and disease. Advances in lipidomics have enabled the discovery of novel biomarkers with significant clinical applications, revolutionizing the diagnosis, prognosis, and therapeutic monitoring of various diseases. Emerging methodologies, including high-resolution mass spectrometry (HRMS), Ion mobility spectrometry (IMS), and Supercritical Fluid Chromatography (SFC) have enhanced lipid identification and quantification with remarkable analytical whip hands. These advancements are complemented by innovative sample preparation techniques ensuring the recovery of diverse lipid species with minimal degradation. Biomarker discovery with lipidomics has illuminated critical pathways in numerous diseases, including cardiovascular disorders, neurodegenerative conditions, metabolic syndromes, and cancers. Specific lipid classes, such as sphingolipids (SLs) and phospholipids (PLs) have been linked to Alzheimer's disease and diabetes, respectively, while oxylipins and eicosanoids are emerging as inflammatory biomarkers. Furthermore, lipidomic profiles have shown promise in personalized medicine, enabling the stratification of patient sub-populations and tailoring treatment strategies. This review emphasizes the latest innovative developments in analytical technologies, advanced sample preparation techniques and challenges for lipidomics research including bioinformatic tools on multiple clinical conditions. By exploring these cutting-edge developments, this review highlights the transformative potential of lipidomics in biomarker discovery across diverse clinical applications.

Improving detection sensitivity of SALDI-MS by constructing patterned composite hierarchical structures

Surface-Assisted Laser Desorption/Ionization Mass Spectrometry (SALDI-MS) is a prominent tool for detecting small molecules; however, enhancing its detection sensitivity remains a significant challenge. Two strategies are commonly employed to enhance detection sensitivity: enriching analytes through substrate modification and improving the laser desorption/ionization efficiency of the substrate. In this study, we developed a patterned composite hierarchical structure as a SALDI-MS substrate to improve detection sensitivity. The substrate consists of Si nanopillars and Au nanoparticles, and is patterned with hydrophilic spots and hydrophobic surrounding area. The signal intensity of tetrabutylammonium iodide and sulfacetamide is enhanced by 100-fold and 60-fold, respectively, compared to testing on the Si nanopillars array. The sensitivity enhancement on this substrate is primarily attributed to two factors: first, the analytes are enriched on the hydrophilic spots; and second, the laser desorption/ionization efficiency is improved by the introduction of Schottky barriers through the deposition of Au nanoparticles on Si nanopillars, which extends the lifetime of electron-holes. This design offers high sensitivity, with the lowest detection concentrations for dyes, amino acids and sulfonamides reaching the attomole level. Patterned modifications overcome common issues with super-hydrophobic substrate, such as difficult analyte addition and droplet slippage. The pattern also ensures excellent detection reproducibility, with relative standard deviations (RSD) of 2.4 % across different areas of the same substrate and 6.36 % across different substrates. The substrate is suitable for detecting trace levels of dyes and sulfonamides in river water and seafood extract, demonstrating its potential for analyzing real samples.

Establishment of MS LOC platform and its pilot application in clinical lipidomics

Lipidomics has demonstrated significant potential for disease diagnosis and prediction. The development and optimization of a robust mass spectrometry (MS) platform for lipidome analysis is critically important, as it can facilitate biomarker discovery, cohort testing, and performance evaluation in clinical lipidomics studies. In this work, we developed a high-throughput and reliable platform, termed MS Lab on a Chip (MS LOC), which integrates the MetArray chip, an automated lipidomics pretreatment protocol, and the reflectron matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) instrument. The MetArray chip, produced through a mass production process, exhibited exceptional stability as an MS substrate. The integration of automated lipid pretreatment and MS detection processes ensures high throughput, stability and efficiency during sample preparation. The analysis of various lipid standards and different types of biological samples enabled comprehensive investigation of lipid features and annotation using the MS LOC. Furthermore, a small cohort study, consisting of hepatocellular carcinoma (HCC) and non-HCC groups, was conducted on this platform, providing preliminary validation of its performance and suggesting that this platform offers a comprehensive protocol for clinical lipidomics testing.

Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence

Background and Objectives: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identification. This study aims to employ explainable artificial intelligence (XAI) to identify lipidomic biomarkers for liver cancer and to develop a robust predictive model for early diagnosis. Materials and Methods: This study included 219 patients diagnosed with liver cancer and 219 healthy controls. Serum samples underwent untargeted lipidomic analysis with LC-QTOF-MS. Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), t-tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. Machine learning models (AdaBoost, Random Forest, Gradient Boosting) were developed and evaluated utilizing these biomarkers to differentiate liver cancer. The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model's predictive decisions. Results: Notable alterations in lipid profiles were observed: decreased sphingomyelins (SM d39:2, SM d41:2) and increased fatty acids (FA 14:1, FA 22:2) and phosphatidylcholines (PC 34:1, PC 32:1). AdaBoost exhibited a superior classification performance, achieving an AUC of 0.875. SHAP identified PC 40:4 as the most efficacious lipid for model predictions. The SM d41:2 and SM d36:3 lipids were specifically associated with an increased risk of low-onset cancer and elevated levels of the PC 40:4 lipid. Conclusions: This study demonstrates that untargeted lipidomics, in conjunction with explainable artificial intelligence (XAI) and machine learning, may effectively identify biomarkers for the early detection of liver cancer. The results suggest that alterations in lipid metabolism are crucial to the progression of liver cancer and provide valuable insights for incorporating lipidomics into precision oncology.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development

BACKGROUND

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. This systematic review, conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current state of the art in using machine learning for the detection and classification of antimicrobial resistance from MALDI-TOF mass spectrometry data.

METHODS

A comprehensive review of the literature on machine learning applications for antimicrobial resistance detection was performed using databases such as Web Of Science, Scopus, ScienceDirect, IEEE Xplore, and PubMed. Only original articles in English were included. Studies applying machine learning without using MALDI-TOF mass spectra were excluded.

RESULTS

Forty studies met the inclusion criteria. Staphylococcus aureus, Klebsiella pneumoniae and Escherichia coli were the most frequently cited bacteria. The antibiotics resistance most studied corresponds to methicillin for S. aureus, cephalosporins for K. pneumoniae, and aminoglycosides for E. coli. Random forest, support vector machine and logistic regression were the most employed algorithms to predict antimicrobial resistance. Additionally, seven studies reported using artificial neural networks. Most studies reported metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (AUROC) above 0.80.

CONCLUSIONS

Our study indicates that random forest, support vector machine, and logistic regression are effective for predicting antimicrobial resistance using MALDI-TOF MS data. Recent studies also highlight the potential of deep learning techniques in this area. We recommend further exploration of deep learning and multi-label supervised learning for comprehensive antibiotic resistance prediction in clinical practice.

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine

The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.