Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples.

As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications. Our data demonstrate possible contamination and data interference caused by blank EDTA plasma tubes when employing large clinical cohorts for biomarker identification. Therefore, we propose a workflow of filtering metabolites in blank tubes prior to statistical analysis to improve the fidelity of biomarker identification.

Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes. METHODS: A total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study. Proteomics and metabolomics techniques were applied to identify potential biomarkers. An additional 29 COPD and 31 controls were enrolled for validation of the obtained proteomic signatures. Information on demographic, clinical manifestation, and blood test were collected. The ROC analyses were carried out to evaluate the diagnostic performance, and experimentally validated the final biomarkers on mild-to-moderate COPD. Next, molecular subtyping was performed using proteomics data. RESULTS: Theophylline, palmitoylethanolamide, hypoxanthine, and cadherin 5 (CDH5) could effectively diagnose advanced COPD with high accuracy (auROC = 0.98, sensitivity of 0.94, and specificity of 0.95). The performance of the diagnostic panel was superior to that of other single/combined results and blood tests. Proteome based stratification of COPD revealed three subtypes (I-III) related to different clinical outcomes and molecular feature: simplex COPD, COPD co-existing with bronchiectasis, and COPD largely co-existing with metabolic syndrome, respectively. Two discriminant models were established using the auROC of 0.96 (Principal Component Analysis, PCA) and 0.95 (the combination of RRM1 + SUPV3L1 + KRT78) in differentiating COPD and COPD with co-morbidities. Theophylline and CDH5 were exclusively elevated in advanced COPD but not in its mild form. CONCLUSIONS: This integrative multi-omics analysis provides a more comprehensive understanding of the molecular landscape of advanced COPD, which may suggest molecular targets for specialized therapy.

Proteomic Analyses Identify Differentially Expressed Proteins and Pathways Between Low-Risk and High-Risk Subtypes of Early-Stage Lung Adenocarcinoma and Their Prognostic Impacts.

The histopathological subtype of lung adenocarcinoma (LUAD) is closely associated with prognosis. Micropapillary or solid predominant LUAD tends to relapse after surgery at an early stage, whereas lepidic pattern shows a favorable outcome. However, the molecular mechanism underlying this phenomenon remains unknown. Here, we recruited 31 lepidic predominant LUADs (LR: low-risk subtype group) and 28 micropapillary or solid predominant LUADs (HR: high-risk subtype group). Tissues of these cases were obtained and label-free quantitative proteomic and bioinformatic analyses were performed. Additionally, prognostic impact of targeted proteins was validated using The Cancer Genome Atlas databases (n = 492) and tissue microarrays composed of early-stage LUADs (n = 228). A total of 192 differentially expressed proteins were identified between tumor tissues of LR and HR and three clusters were identified via hierarchical clustering excluding eight proteins. Cluster 1 (65 proteins) showed a sequential decrease in expression from normal tissues to tumor tissues of LR and then to HR and was predominantly enriched in pathways such as tyrosine metabolism and ECM-receptor interaction, and increased matched mRNA expression of 18 proteins from this cluster predicted favorable prognosis. Cluster 2 (70 proteins) demonstrated a sequential increase in expression from normal tissues to tumor tissues of LR and then to HR and was mainly enriched in pathways such as extracellular organization, DNA replication and cell cycle, and high matched mRNA expression of 25 proteins indicated poor prognosis. Cluster 3 (49 proteins) showed high expression only in LR, with high matched mRNA expression of 20 proteins in this cluster indicating favorable prognosis. Furthermore, high expression of ERO1A and FEN1 at protein level predicted poor prognosis in early-stage LUAD, supporting the mRNA results. In conclusion, we discovered key differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage LUAD. Some of these proteins could serve as potential biomarkers in prognostic evaluation.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning.

BACKGROUND: Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. METHODS: A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. RESULTS: Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism. CONCLUSIONS: We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

Comprehensive plasma metabolomic and lipidomic analyses reveal potential biomarkers for heart failure.

Heart failure is a syndrome with symptoms or signs caused by cardiac dysfunction. In clinic, four stages (A, B, C, and D) were used to describe heart failure progression. This study was aimed to explore plasma metabolomic and lipidomic profiles in different HF stages to identify potential biomarkers. Metabolomics and lipidomics were performed using plasma of heart failure patients at stages A (n = 49), B (n = 61), and C+D (n = 26). Analysis of Variance (ANOVA) was used for screening dysregulated molecules. Bioinformatics was used to retrieve perturbed metabolic pathways. Univariate and multivariate receiver operating characteristic curve (ROC) analyses were used for potential biomarker screening. Stage A showed significant difference to other stages, and 142 dysregulated lipids and 134 dysregulated metabolites were found belonging to several metabolic pathways. Several marker panels were proposed for the diagnosis of heart failure stage A versus stage B-D. Several molecules, including lysophosphatidylcholine 18:2, cholesteryl ester 18:1, alanine, choline, and Fructose, were found correlated with B-type natriuretic peptide or left ventricular ejection fractions. In summary, using untargeted metabolomic and lipidomic profiling, several dysregulated small molecules were successfully identified between HF stages A and B-D. These molecules would provide valuable information for further pathological researches and biomarker development.

PTENα regulates endocytosis and modulates olfactory function.

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) α is the first identified isoform of the well-known tumor suppressor PTEN. PTENα has an evolutionarily conserved 173-aa N terminus compared with canonical PTEN. Recently, PTENα has been shown to play roles in multiple biologic processes including learning and memory, cardiac homeostasis, and antiviral immunity. Here, we report that PTENα maintains mitral cells in olfactory bulb (OB), regulates endocytosis in OB neurons, and controls olfactory behaviors in mice. We show that PTENα directly dephosphorylates the endocytic protein amphiphysin and promotes its binding to adaptor-related protein complex 2 subunit ß1 (Ap2b1). In addition, we identified mutations in the N terminus of PTENα in patients with Parkinson disease and Lewy-body dementia, which are neurodegenerative disorders with early olfactory loss. Overexpression of PTENα mutant H169N in mice OB reduces odor sensitivity. Our data demonstrate a role of PTENα in olfactory function and provide insight into the mechanism of olfactory dysfunction in neurologic disorders.-Yuan, Y., Zhao, X., Wang, P., Mei, F., Zhou, J., Jin, Y., McNutt, M. A., Yin, Y. PTENα regulates endocytosis and modulates olfactory function.

Comprehensive metabolomic and proteomic analyses reveal candidate biomarkers and related metabolic networks in atrial fibrillation.

INTRODUCTION: Atrial fibrillation (AF) is an abnormal heart rhythm characterized by an irregular beating of the atria and is associated with an increased risk of heart failure, dementia, and stroke. Currently, the perturbation of plasma content due to AF disease onset is not well known. OBJECTIVES: To investigate dysregulated molecules in blood plasma of untreated AF patients, with the goal of identifying biomarkers for disease screening and pathological studies. METHODS: LC-MS based untargeted metabolomics, lipidomics and proteomics analyses were performed to find candidate biomarkers. A targeted quantification assay and an ELISA were performed to validate the results of the omics analyses. RESULTS: We found that 24 metabolites, 16 lipids and 16 proteins were significantly dysregulated in AF patients. Pathway enrichment analysis showed that the purine metabolic pathway and fatty acid metabolism were perturbed by AF onset. FA 20:2 and FA 22:4 show great linear correlational relationship with the left atrial area and could be considered for AF disease stage monitoring or prognosis evaluation. CONCLUSION: we used a comprehensive multiple-omics strategy to systematically investigate the dysregulated molecules in the plasma of AF patients, thereby revealing potential biomarkers for diagnosis and providing information for pathological studies.

Metabolomics identifies serum and exosomes metabolite markers of pancreatic cancer.

INTRODUCTION: Pancreatic cancer (PC) is one of the most aggressive malignancies, and it's difficult to diagnosis PC at an early stage, which leads to the poor prognosis of PC. OBJECTIVES: To identifiy the possible prognosis or dignosis metabolite biomarkers in the serum exosome of PC patients. METHODS: We employed LC-DDA-MS based untargeted lipidomic analysis to search for potential candidate biomarkers in the serum exosome of PC patients. Then LC-MRM-MS based targeted lipid quantification was used to validate the trends of the candidate biomarkers in larger sample cohorts. RESULTS: About 270 lipids belonging to 20 lipid species were found significantly dysregulated between the serum exosome of PC patients and healthy controls. 61 of them were validated in larger samples size. We further analysis the correlation between these dysregulated lipids and other PC related factors, and results show that LysoPC 22:0, PC (P-14:0/22:2) and PE (16:0/18:1) are all associated with tumor stage, CA19-9, CA242 and tumor diameter. What's more, PE (16:0/18:1) is also found to be significantly correlated with the patient's overall survival. CONCLUSION: These data reveal dysregulated lipids in serum exosome of PC patients, which have potential to be biomarkers for diagnosis, or unveil pathological relationship between exosome and PC progress.

Low levels of serum serotonin and amino acids identified in migraine patients.

Migraine is a highly disabling primary headache associated with a high socioeconomic burden and a generally high prevalence. The clinical management of migraine remains a challenge. This study was undertaken to identify potential serum biomarkers of migraine. Using Liquid Chromatography coupled to Mass Spectrometry (LC-MS), the metabolomic profile of migraine was compared with healthy individuals. Principal component analysis (PCA) and Orthogonal partial least squares-discriminant analysis (orthoPLS-DA) showed the metabolomic profile of migraine is distinguishable from controls. Volcano plot analysis identified 10 serum metabolites significantly decreased during migraine. One of these was serotonin, and the other 9 were amino acids. Pathway analysis and enrichment analysis showed tryptophan metabolism (serotonin metabolism), arginine and proline metabolism, and aminoacyl-tRNA biosynthesis are the three most prominently altered pathways in migraine. ROC curve analysis indicated Glycyl-l-proline, N-Methyl-dl-Alanine and l-Methionine are potential sensitive and specific biomarkers for migraine. Our results show Glycyl-l-proline, N-Methyl-dl-Alanine and l-Methionine may be as specific or more specific for migraine than serotonin which is the traditional biomarker of migraine. We propose that therapeutic manipulation of these metabolites or metabolic pathways may be helpful in the prevention and treatment of migraine.

Lipidomic profiling of plasma samples from patients with mitochondrial disease.

Mitochondrial disease (MD) is a rare mitochondrial respiratory chain disorder with a high mortality and extremely challenging to treat. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of MD, the role of metabolomics in MD, particularly of lipidomics remains unclear. This study was undertaken to identify potential lipid biomarkers of MD. An untargeted lipidomic approach was used to compare the plasma lipid metabolites in 20 MD patients and 20 controls through Liquid Chromatography coupled to Mass Spectrometry. Volcano plot analysis was performed to identify the different metabolites. Receiver operating characteristic (ROC) curves were constructed and the area under the ROC curves (AUC) was calculated to determine the potentially sensitive and specific biomarkers. A total of 41 lipids were significantly different in MD patients and controls. ROC curve analysis showed the top 5 AUC values of lipids (phosphatidylinositols 38:6, lysoPC 20:0, 19:0, 18:0, 17:0) are more than 0.99. Multivariate ROC curve based exploratory analysis showed the AUC of combination of top 5 lipids is 1, indicating they may be potentially sensitive and specific biomarkers for MD. We propose combination of these lipid species may be more valuable in predicting the development and progression of MD, and this will have important implications for the diagnosis and treatment of MD.

Unbiased lipidomic profiling reveals metabolomic changes during the onset and antipsychotics treatment of schizophrenia disease.

INTRODUCTION: Schizophrenia (SCH) is one of the most common psychiatric disorders, which involves impairments in motivation and cognition. The pathological mechanisms underlying SCH are still unknown, and no effective therapies can prevent or treat perfectly the cognitive impairments and deficit symptoms caused by SCH. OBJECTIVES: We aimed to find the lipid expression change in plasma that underlie SCH onset and antipsychotics treatment. METHODS: We performed a data independent acquisition-based untargeted lipidomic approach on a quadrupole-time of flight liquid chromatography coupled to mass spectrometry platform. The plasma lipidomic profiles of SCH patients (n = 20) pre- and post-antipsychotics treatment were acquired as well as healthy controls (n = 29). Grouped or paired t-test were used to analyze the data. RESULTS: Over 1000 features were detected by our lipidomic analysis, of which 445 lipids belonging to 17 lipid species were reliably identified by tandem mass spectrometry. After statistical analysis, 47 lipids belonging to 9 lipid species were found to be dysregulated between naive SCH patients and healthy controls, and 50 lipids belonging to 9 lipid species were found to be dysregulated after antipsychotics treatment. These findings include several new SCH-relevant lipid species such as sphingomyelin, acylcarnitine and ceramide. Four types of lipid expression regulative patterns can be concluded from the above mentioned findings, revealing information about mechanism, side-effect and potential target of antipsychotics. CONCLUSIONS: The work presented here have revealed several new lipid species which are significantly dysregulated in SCH disease development or antipsychotics treatment. These lipids provide new evidence for the pathological studies of SCH and new antipsychotics development, or can be considered as potentially candidate biomarkers for further validation.

Lipidomic analysis of serum samples from migraine patients.

BACKGROUND: Migraine is a prevalent, disabling type of primary headache disorder associated with a high socioeconomic burden. The clinical management of migraine is challenging. This study was to identify potential serum lipidomic biomarkers of migraine. METHODS: The serum lipidomic profile of migraine sufferers was compared with healthy individuals using Liquid Chromatography coupled to Mass Spectrometry (LC-MS). Volcano plot analysis by Student's t-test was performed to identify the differential metabolites. Receiver operating characteristic (ROC) curves were constructed and the area under ROC curves (AUC) was calculated to evaluate whether the metabolites could be efficiently exploited for constructing a sensitive biomarker of migraine. RESULTS: A total of 29 serum metabolites from 4 classes of lipids including acylcarnitines, non-alpha-hydroxy-sphingosine ceramides (Cer_NSs), lysophosphatidylcholines (lysoPCs) and lysophosphatidylethanolamines (lysoPEs) were significantly different in migraine patients and controls. Of note, Cer_NSs were significantly elevated and lysoPEs were significantly decreased in migraine patients. LysoPE 18:1, lysoPE 18:2 and lysoPE 22:5 were found to be decreased in both positive and negative ion mode. Moreover, except for lysoPC 20:0, other lysoPCs were decreased in migraine patients. ROC curve analysis indicated that lysoPC 16:0 and lysoPC 20:0 are potential sensitive and specific biomarkers for migraine. CONCLUSION: LysoPC 16:0 and lysoPC 20:0 may be potential biomarkers for migraine. We suggest therapeutic management of these metabolites may be helpful in the prevention and treatment of migraine.

Quantitative proteomics reveals EVA1A-related proteins involved in neuronal differentiation.

EVA1A is an autophagy-related protein, which plays an important role in embryonic neurogenesis. In this study, we found that loss of EVA1A could decrease neural differentiation in the brain of adult Eva1a-/- mice. To determine the mechanism underlying this phenotype, we performed label-free quantitative proteomics and bioinformatics analysis using the brains of Eva1a-/- and wild-type mice. We identified 11 proteins that were up-regulated and 17 that were down-regulated in the brains of the knockout mice compared to the wild-type counterparts. Bioinformatics analysis indicated that biological processes, including ATP synthesis, oxidative phosphorylation, and the TCA cycle, are involved in the EVA1A regulatory network. In addition, gene set enrichment analysis showed that neurodegenerative diseases, such as Alzheimer's disease and Huntington's disease, were strongly associated with Eva1a knockout. Western blot experiments showed changes in the expression of nicotinamide nucleotide transhydrogenase, an important mitochondrial enzyme involved in the TCA cycle, in the brains of Eva1a knockout mice. Our study provides valuable information on the molecular functions and regulatory network of the Eva1a gene, as well as new perspectives on the relationship between autography-related proteins and neural differentiation.

Development and Evaluation of a Parallel Reaction Monitoring Strategy for Large-Scale Targeted Metabolomics Quantification.

Recent advances in mass spectrometers which have yielded higher resolution and faster scanning speeds have expanded their application in metabolomics of diverse diseases. Using a quadrupole-Orbitrap LC-MS system, we developed an efficient large-scale quantitative method targeting 237 metabolites involved in various metabolic pathways using scheduled, parallel reaction monitoring (PRM). We assessed the dynamic range, linearity, reproducibility, and system suitability of the PRM assay by measuring concentration curves, biological samples, and clinical serum samples. The quantification performances of PRM and MS1-based assays in Q-Exactive were compared, and the MRM assay in QTRAP 6500 was also compared. The PRM assay monitoring 237 polar metabolites showed greater reproducibility and quantitative accuracy than MS1-based quantification and also showed greater flexibility in postacquisition assay refinement than the MRM assay in QTRAP 6500. We present a workflow for convenient PRM data processing using Skyline software which is free of charge. In this study we have established a reliable PRM methodology on a quadrupole-Orbitrap platform for evaluation of large-scale targeted metabolomics, which provides a new choice for basic and clinical metabolomics study.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry.

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine.

Metabolomics is a fast-developing technique used in biomedical researches focusing on pathological mechanism illustration or novel biomarker development for diseases. The ability of simultaneously quantifying thousands of metabolites in samples makes metabolomics a promising technique in predictive or personalized medicine-oriented researches and applications. Liquid chromatography-mass spectrometry is the most widely employed analytical strategy for metabolomics. In this current mini-review, we provide a brief update on the recent developments and novel applications of LC-MS based metabolomics in the predictive and personalized medicine sector, such as early diagnosis, molecular phenotyping or prognostic evaluation. COVID-19 related metabolomic studies are also summarized. We also discuss the prospects of metabolomics in precision medicine-oriented researches, as well as critical issues that need to be addressed when employing metabolomic strategy in clinical applications.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.

BACKGROUND: Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. METHODS: Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. FINDINGS: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. INTERPRETATION: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis.

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.

Integrated proteomics and phosphoproteomics reveal perturbed regulative pathways in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis largely owing to its inefficient diagnosis, rapid progress, and tenacious drug resistance. Here, we aimed to analyze the expressive patterns of proteins and phosphorylation in PDAC tissue samples and compare them to normal pancreatic tissue to investigate the underlying mechanisms and to reveal potential protein targets for diagnosis and drug development. Liquid chromatography coupled to mass spectrometry (LC-MS) based proteomics and phosphoproteomics analyses were performed using 20 pairs of patient-derived PDAC tissue and normal pancreatic tissue samples. Protein identification and quantification were conducted using MaxQuant software. Bioinformatics analysis was used to retrieve PDAC-relevant pathways and gene ontology (GO) terms. 4985 proteins and 3643 phosphoproteins were identified with high confidence; of these, 322 proteins and 235 phosphoproteins were dysregulated in PDAC. Several pathways, including several extracellular matrix-related pathways, were found to be strongly associated with PDAC. Further, the expression levels of filamin A (FLNA), integrin alpha-V (ITGAV), thymidine phosphorylase (TYMP), medium-chain specific acyl-CoA dehydrogenase, mitochondrial (ACADM), short-chain specific acyl-CoA dehydrogenase, mitochondrial (ACADS), and acetyl-CoA acetyltransferase, mitochondrial (ACAT1) were examined through western blot and immunohistochemistry analysis, and the results confirmed the validity of the proteomics analysis results. These findings provide comprehensive insight into the dysregulated regulative networks in PDAC tissue samples at the protein and phosphorylation levels, and they provide clues for further pathological studies and drug-target development.

Proteomic analysis reveals distinctive protein expression patterns of thrombus in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a type of malignant tumor of the urinary system. The renal vein or vena cava thrombus can be found in a subset of ccRCC patients in whom it leads to worse prognosis. However, the protein expression profile and molecular features of ccRCC thrombus remain largely unclear. Here, a comparative proteomic analysis was performed using the 2D-LC-MS strategy for the thrombus-tumor-normal tissue triples of 15 ccRCC patients. Statistical analysis, GO enrichment analysis, protein-protein interaction network construction, and mRNA-based survival analysis were used to interpret the proteomic data. Three dysregulated proteins, GGT5 (gamma-glutamyl transferase 5), KRT7 (keratin 7) and CFHR1 (complement factor H related 1), were analyzed using western blot (WB) and immunohistochemistry (IHC) to validate the reliability of the proteomic analysis. The result of this analysis revealed 251 dysregulated proteins, which could be divided into 11 clusters depending on the changing trends, among the thrombus, tumor, and normal tissues. Several pathways and regulation networks were found to be associated with the thrombus, and some dysregulated proteins showed potential values for prognosis prediction. WB and IHC results were in accordance with the proteomic results, further validating the reliability of this study. In conclusion, our findings provide an overview of the thrombus at the molecular level as well as valuable information for further pathological studies or research on biomarkers and therapeutic targets.