Metabolic Profiling of Formalin-Fixed Paraffin-Embedded Tissues Discriminates Normal Colon from Colorectal Cancer

Colorectal Cancer Detection via Metabolites and Machine Learning

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or  ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.

The altered metabolic pathways of diffuse large B-cell lymphoma not otherwise specified

Altered metabolic fingerprints of Diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS) may offer novel opportunities to identify new biomarkers and improve the understanding of its pathogenesis. This study aimed to investigate the modified metabolic pathways in extranodal, germinal center B-cell (GCB) and non-GCB DLBCL NOS from the head and neck. Formalin-fixed paraffin-embedded (FFPE) tissues from eleven DLBCL NOS classified according to Hans' algorithm using immunohistochemistry, and five normal lymphoid tissues (LT) were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Partial Least Squares Discriminant Analysis showed that GCB and non-GCB DLBCL NOS have a distinct metabolomics profile, being the former more similar to normal lymphoid tissues. Metabolite pathway enrichment analysis indicated the following altered pathways: arachidonic acid, tyrosine, xenobiotics, vitamin E metabolism, and vitamin A. Our findings support that GCB and non-GCB DLBCL NOS has a distinct metabolomic profile, in which GCB possibly shares more metabolic similarities with LT than non-GCB DLBCL NOS.

Regulation of Tumor Apoptosis of Poriae cutis-Derived Lanostane Triterpenes by AKT/PI3K and MAPK Signaling Pathways In Vitro

Poria cocos is traditionally used as both food and medicine. Triterpenoids in Poria cocos have a wide range of pharmacological activities, such as diuretic, sedative and tonic properties. In this study, the anti-tumor activities of poricoic acid A (PAA) and poricoic acid B (PAB), purified by high-speed counter-current chromatography, as well as their mechanisms and signaling pathways, were investigated using a HepG2 cell model. After treatment with PAA and PAB on HepG2 cells, the apoptosis was obviously increased (p < 0.05), and the cell cycle arrested in the G2/M phase. Studies showed that PAA and PAB can also inhibit the occurrence and development of tumor cells by stimulating the generation of ROS in tumor cells and inhibiting tumor migration and invasion. Combined Polymerase Chain Reaction and computer simulation of molecular docking were employed to explore the mechanism of tumor proliferation inhibition by PAA and PAB. By interfering with phosphatidylinositol-3-kinase/protein kinase B, Mitogen-activated protein kinases and p53 signaling pathways; and further affecting the expression of downstream caspases; matrix metalloproteinase family, cyclin-dependent kinase -cyclin, Intercellular adhesion molecules-1, Vascular Cell Adhesion Molecule-1 and Cyclooxygenase -2, may be responsible for their anti-tumor activity. Overall, the results suggested that PAA and PAB induced apoptosis, halted the cell cycle, and inhibited tumor migration and invasion through multi-pathway interactions, which may serve as a potential therapeutic agent against cancer.

Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline

African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC-MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.

Global Protein Profiling in Processed Immunohistochemistry Tissue Sections

Tissue sections, which are widely used in research and diagnostic laboratories and have already been examined by immunohistochemistry (IHC), may subsequently provide a resource for proteomic studies, even though only small amount of protein is available. Therefore, we established a workflow for tandem mass spectrometry-based protein profiling of IHC specimens and characterized defined brain area sections. We investigated the CA1 region of the hippocampus dissected from brain slices of adult C57BL/6J mice. The workflow contains detailed information on sample preparation from brain slices, including removal of antibodies and cover matrices, dissection of region(s) of interest, protein extraction and digestion, mass spectrometry measurement, and data analysis. The Gene Ontology (GO) knowledge base was used for further annotation. Literature searches and Gene Ontology annotation of the detected proteins verify the applicability of this method for global protein profiling using formalin-fixed and embedded material and previously used IHC slides.

Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer

BACKGROUND AND AIMS

The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes.

METHODS

A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months.

RESULTS

Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (P_FDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (P_FDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (P_FDR = 2.61 × 10^-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (P_FDR = 1.30 × 10^-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7.

CONCLUSIONS

Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.

Integrating phylogenetic and functional data in microbiome studies

MOTIVATION

Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions.

RESULTS

The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome.

AVAILABILITY AND IMPLEMENTATION

These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

COL11A1 is Downregulated by miR-339-5p and Promotes Colon Carcinoma Progression

The roles of COL11A1 in cancer have been increasingly considered, but the understandings of the effects of COL11A1 on colon carcinoma progress are much limited yet. qRT-PCR and Western blot were utilized to evaluate COL11A1 expression at mRNA and protein levels, respectively, in colon carcinoma cell lines. Afterward, the tumorigenesis biological effects of COL11A1 were examined by CCK-8, colony formation, Transwell, and wound healing methods. Moreover, upstream miRNAs containing the binding sites with COL11A1 were predicted by the bioinformatics methods. The interplay between COL11A1 and miR-339-5p was identified by a dual-luciferase assay. COL11A1 expression was prominently upregulated in colon carcinoma cell lines relative to that in normal human colon mucosal epithelial cell lines, and it was related to tumor stages. The outcomes of in-vitro experiments suggested that interfering with COL11A1 remarkably repressed the malignant behaviors of SW480 and SW620 cells. MiR-339-5p was markedly lowly expressed in colon carcinoma cell lines. Furthermore, miR-339-5p directly targeted and negatively regulated COL11A1 expression. COL11A1 upregulation promoted colon carcinoma cell functions, while overexpressing miR-339-5p evidently attenuated the promotion. These results proved the modulation of the miR-339-5p/COL11A1 axis in colon carcinoma cells, and miR-339-5p repressed colon carcinoma progression via COL11A1 downregulation. These results offer new underlying targets for the accurate therapy of colon carcinoma patients.

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.

Spatially Resolved Neuropeptide Characterization from Neuropathological Formalin-Fixed, Paraffin-Embedded Tissue Sections by a Combination of Imaging MALDI FT-ICR Mass Spectrometry Histochemistry and Liquid Extraction Surface Analysis-Trapped Ion Mobility Spectrometry-Tandem Mass Spectrometry

To make the vast collections of well-documented human clinical samples archived in biobanks accessible for mass spectrometry imaging (MSI), recent developments have focused on the label-free top-down MS analysis of neuropeptides in sections of formalin-fixed, paraffin-embedded (FFPE) tissues. In analogy to immunohistochemistry (IHC), this variant of MSI has been designated MSHC (mass spectrometry histochemistry). Besides the detection and localization of neuropeptide and other biomolecular MS signals in these FFPE samples, there is great interest in their molecular identification and full characterization. We here used matrix assisted laser desorption ionization (MALDI) MSI employing ultrahigh-resolution FT-ICR MS on 2,5-dihydroxybenzoic acid (DHB) coated five-micron sections of human FFPE pituitary to demonstrate clear isotope patterns and elemental composition assignment of neuropeptides (with ∼1 ppm mass accuracy). Besides tandem MS fragmentation pattern analysis to deduce or confirm amino acid sequence information (Arg-vasopressin for the case presented here), there is a need for orthogonal primary structure characterization of the peptide-like MS signals of biomolecules desorbed directly off FFPE tissue sections. In the present work, we performed liquid extraction surface analysis (LESA) extractions on consecutive (uncoated) tissue slices. This enables the successful characterization by ion mobility MS of vasopressin present in FFPE material. Differences in sequence coverage are discussed on the basis of the mobility selected collision induced dissociation (CID), electron capture dissociation (ECD), and UV photodissociation (UVPD) MS/MS. Using Arg-vasopressin as model case (a peptide with a disulfide bridged ring structure), we illustrate the use of LESA in combination with a reduction agent for effective sequencing using mobility selected CID, ECD, and UVPD MS/MS.

Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX^® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.

Tissue fixation effects on human retinal lipid analysis by MALDI imaging and LC-MS/MS technologies

Imaging mass spectrometry (IMS) allows the location and abundance of lipids to be mapped across tissue sections of human retina. For reproducible and accurate information, sample preparation methods need to be optimized. Paraformaldehyde fixation of a delicate multilayer structure like human retina facilitates the preservation of tissue morphology by forming methylene bridge crosslinks between formaldehyde and amine/thiols in biomolecules; however, retina sections analyzed by IMS are typically fresh-frozen. To determine if clinically significant inferences could be reliably based on fixed tissue, we evaluated the effect of fixation on analyte detection, spatial localization, and introduction of artifactual signals. Hence, we assessed the molecular identity of lipids generated by matrix-assisted laser desorption ionization (MALDI-IMS) and liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) for fixed and fresh-frozen retina tissues in positive and negative ion modes. Based on MALDI-IMS analysis, more lipid signals were observed in fixed compared with fresh-frozen retina. More potassium adducts were observed in fresh-frozen tissues than fixed as the fixation process caused displacement of potassium adducts to protonated and sodiated species in ion positive ion mode. LC-MS/MS analysis revealed an overall decrease in lipid signals due to fixation that reduced glycerophospholipids and glycerolipids and conserved most sphingolipids and cholesteryl esters. The high quality and reproducible information from untargeted lipidomics analysis of fixed retina informs on all major lipid classes, similar to fresh-frozen retina, and serves as a steppingstone towards understanding of lipid alterations in retinal diseases.

Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS

Tumor cells not only show a vigorous metabolic state, but also reflect the disease progression and prognosis from their metabolites. To judge the progress and prognosis of ovarian cancer is generally based on the formation of ascites, or whether there is ascites recurrence during chemotherapy after ovarian cancer surgery. To explore the relationship between the production of ascites and ovarian cancer tissue, metabolomics was used to screen differential metabolites in this study. The significant markers leading to ascites formation and chemoresistance were screened by analyzing their correlation with the formation of ascites in ovarian cancer and the clinical indicators of patients, and then provided a theoretical basis. The results revealed that nine differential metabolites were screened out from 37 ovarian cancer tissues and their ascites, among which seven differential metabolites were screened from 22 self-paired samples. Sebacic acid and 20-COOH-leukotriene E4 were negatively correlated with the high expression of serum CA125. Carnosine was positively correlated with the high expression of serum uric acid. Hexadecanoic acid was negatively correlated with the high expression of serum γ-GGT and HBDH. 20a,22b-Dihydroxycholesterol was positively correlated with serum alkaline phosphatase and γ-GGT. In the chemotherapy-sensitive and chemotherapy-resistant ovarian cancer tissues, the differential metabolite dihydrothymine was significantly reduced in the chemotherapy-resistant group. In the ascites supernatant of the drug-resistant group, the differential metabolites, 1,25-dihydroxyvitamins D3-26, 23-lactonel and hexadecanoic acid were also significantly reduced. The results indicated that the nine differential metabolites could reflect the prognosis and the extent of liver and kidney damage in patients with ovarian cancer. Three differential metabolites with low expression in the drug-resistant group were proposed as new markers of chemotherapy efficacy in ovarian cancer patients with ascites.

The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging

Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

Lipidomic Analysis of Archival Pathology Specimens Identifies Altered Lipid Signatures in Ovarian Clear Cell Carcinoma

Cancer metabolism is associated with the enhanced lipogenesis required for rapid growth and proliferation. However, the magnitude of dysregulation of diverse lipid species still requires significant characterization, particularly in ovarian clear cell carcinoma (OCCC). Here, we have implemented a robust sample preparation workflow together with targeted LC-MS/MS to identify the lipidomic changes in formalin-fixed paraffin-embedded specimens from OCCC compared to tumor-free ovarian tissue. We quantitated 340 lipid species, representing 28 lipid classes. We observed differential regulation of diverse lipid species belonging to several glycerophospholipid classes and trihexosylceramide. A number of unsaturated lipid species were increased in OCCC, whereas saturated lipid species showed a decrease in OCCC compared to the controls. We also carried out total fatty acid analysis and observed an increase in the levels of several unsaturated fatty acids with a concomitant increase in the index of stearoyl-CoA desaturase (SCD) in OCCC. We confirmed the upregulation of SCD (the rate-limiting enzyme for the synthesis of monounsaturated fatty acids) by immunohistochemistry (IHC) assays. Hence, by carrying out a mass spectrometry analysis of archival tissue samples, we were able to provide insights into lipidomic alterations in OCCC.

A Serum Metabolomic Study Reveals Changes in Metabolites During the Treatment of Lung Cancer-Bearing Mice with Anlotinib

Background

Anlotinib is a vascular endothelial growth factor receptor tyrosine kinase inhibitor recommended for the treatment of advanced lung cancer patients after at least two previous systemic chemotherapies. Currently, many patients with lung cancer do not respond well to anlotinib treatment. Therefore, the aim of this metabolomic study was to determine the internal mechanism of anlotinib action at the molecular level and to identify the potential biomarkers and pathways associated with the therapeutic effects of anlotinib.

Methods

A total of 20 male nude mice were randomly divided into 2 groups and treated with anlotinib or physiological saline. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was performed to analyze the serum samples and determine the differential metabolites and pathways between anlotinib and control groups.

Results

We observed significant differences between the anlotinib and control groups, and 13 endogenous differential metabolites and 5 potential metabolic pathways were identified. Glyoxylate and dicarboxylate metabolism, tryptophan metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism and valine, leucine and isoleucine biosynthesis were the most important pathways regulated by anlotinib in vivo. Notably, these 5 differential pathways were highly associated with the TCA cycle, which is important in the proliferation and apoptosis of cancer cells.

Conclusion

This serum metabolomic study revealed distinct metabolic profiles in lung cancer-bearing mice treated with anlotinib and identified differential metabolites and pathways between the anlotinib and control groups, which may provide new ideas for the clinical application of anlotinib.

Integrative analysis reveals the prognostic value and functions of splicing factors implicated in hepatocellular carcinoma

Splicing factors (SFs) play critical roles in the pathogenesis of various cancers through regulating tumor-associated alternative splicing (AS) events. However, the clinical value and biological functions of SFs in hepatocellular carcinoma (HCC) remain obscure. In this study, we identified 40 dysregulated SFs in HCC and established a prognostic model composed of four SFs (DNAJC6, ZC3H13, IGF2BP3, DDX19B). The predictive efficiency and independence of the prognostic model were confirmed to be satisfactory. Gene Set Enrichment Analysis (GSEA) illustrated the risk score calculated by our prognostic model was significantly associated with multiple cancer-related pathways and metabolic processes. Furthermore, we constructed the SFs-AS events regulatory network and extracted 108 protein-coding genes from the network for following functional explorations. Protein–protein interaction (PPI) network delineated the potential interactions among these 108 protein-coding genes. GO and KEGG pathway analyses investigated ontology gene sets and canonical pathways enriched by these 108 protein-coding genes. Overlapping the results of GSEA and KEGG, seven pathways were identified to be potential pathways regulated by our prognostic model through triggering aberrant AS events in HCC. In conclusion, the present study established an effective prognostic model based on SFs for HCC patients. Functional explorations of SFs and SFs-associated AS events provided directions to explore biological functions and mechanisms of SFs in HCC tumorigenesis.

Sequential Orbitrap Secondary Ion Mass Spectrometry and Liquid Extraction Surface Analysis-Tandem Mass Spectrometry-Based Metabolomics for Prediction of Brain Tumor Relapse from Sample-Limited Primary Tissue Archives

We present here a novel surface mass spectrometry strategy to perform untargeted metabolite profiling of formalin-fixed paraffin-embedded pediatric ependymoma archives. Sequential Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) and liquid extraction surface analysis-tandem mass spectrometry (LESA-MS/MS) permitted the detection of 887 metabolites (163 chemical classes) from pediatric ependymoma tumor tissue microarrays (diameter: <1 mm; thickness: 4 μm). From these 163 classes, 60 classes were detected with both techniques, whilst LESA-MS/MS and 3D OrbiSIMS individually allowed the detection of another 83 and 20 unique metabolite classes, respectively. Through data fusion and multivariate analysis, we were able to identify key metabolites and corresponding pathways predictive of tumor relapse, which were retrospectively confirmed by gene expression analysis with publicly available data. Altogether, this sequential mass spectrometry strategy has shown to be a versatile tool to perform high-throughput metabolite profiling on sample-limited tissue archives.

Indication of high lipid content in epithelial-mesenchymal transitions of breast tissues

The epithelial-mesenchymal transition (EMT) is a crucial process in cancer progression and metastasis. Study of metabolic changes during the EMT process is important in seeking to understand the biochemical changes associated with cancer progression, not least in scoping for therapeutic strategies aimed at targeting EMT. Due to the potential for high sensitivity and specificity, Raman spectroscopy was used here to study the metabolic changes associated with EMT in human breast cancer tissue. For Raman spectroscopy measurements, tissue from 23 patients were collected, comprising non-lesional, EMT and non-EMT formalin-fixed and paraffin embedded breast cancer samples. Analysis was made in the fingerprint Raman spectra region (600-1800 cm-1) best associated with cancer progression biochemical changes in lipid, protein and nucleic acids. The ANOVA test followed by the Tukey's multiple comparisons test were conducted to see if there existed differences between non-lesional, EMT and non-EMT breast tissue for Raman spectroscopy measurements. Results revealed that significant differences were evident in terms of intensity between the non-lesional and EMT samples, as well as the EMT and non-EMT samples. Multivariate analysis involving independent component analysis, Principal component analysis and non-negative least square were used to analyse the Raman spectra data. The results show significant differences between EMT and non-EMT cancers in lipid, protein, and nucleic acids. This study demonstrated the capability of Raman spectroscopy supported by multivariate analysis in analysing metabolic changes in EMT breast cancer tissue.

Deuterium Depletion Inhibits Cell Proliferation, RNA and Nuclear Membrane Turnover to Enhance Survival in Pancreatic Cancer

The effects of deuterium-depleted water (DDW) containing deuterium (D) at a concentration of 25 parts per million (ppm), 50 ppm, 105 ppm and the control at 150 ppm were monitored in MIA-PaCa-2 pancreatic cancer cells by the real-time cell impedance detection xCELLigence method. The data revealed that lower deuterium concentrations corresponded to lower MiA PaCa-2 growth rate. Nuclear membrane turnover and nucleic acid synthesis rate at different D-concentrations were determined by targeted [1,2-13C2]-D-glucose fate associations. The data showed severely decreased oxidative pentose cycling, RNA ribose 13C labeling from [1,2-13C2]-D-glucose and nuclear membrane lignoceric (C24:0) acid turnover. Here, we treated advanced pancreatic cancer patients with DDW as an extra-mitochondrial deuterium-depleting strategy and evaluated overall patient survival. Eighty-six (36 male and 50 female) pancreatic adenocarcinoma patients were treated with conventional chemotherapy and natural water (control, 30 patients) or 85 ppm DDW (56 patients), which was gradually decreased to preparations with 65 ppm and 45 ppm deuterium content for each 1 to 3 months treatment period. Patient survival curves were calculated by the Kaplan-Meier method and Pearson correlation was taken between medial survival time (MST) and DDW treatment in pancreatic cancer patients. The MST for patients consuming DDW treatment (n = 56) was 19.6 months in comparison with the 6.36 months' MST achieved with chemotherapy alone (n = 30). There was a strong, statistically significant Pearson correlation (r = 0.504, p < 0.001) between survival time and length and frequency of DDW treatment.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach

BACKGROUND

To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis.

RESULTS

Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity.

CONCLUSION

The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

miR-1184 regulates the proliferation and apoptosis of colon cancer cells via targeting CSNK2A1

MicroRNA (miRNA) exerts an important part in colon cancer cell proliferation and apoptosis. Meanwhile, the dysregulation of some miRNAs is detected in colon cancer cells. However, it remains unclear about the underlying mechanism of their effects on tumor pathogenesis. The current work aimed to examine the miR-1184 effect on colon cancer cells. The differentially expressed miRNAs (DEMs), including miR-9-3p, miR-1184, miR-492, miR-92a-1-5p and miR-20a-3p, were obtained from the GSE115108 and GSE132619 data sets using the 'GEO2R' online tool. Based on the findings, miR-1184 was significantly down-regulated within colon cancer cells and tissues. Moreover, the experimental results of CCK8, flow cytometry, colony formation and Western blotting assays showed that, miR-1184 over-expression suppressed colon cancer cell proliferation through inhibiting Ki67 expression and promoted their apoptosis through up-regulating cleaved caspase-3 and down-regulating Bcl-2 expression. By contrast, miR-1184 inhibition exerted the opposite effects. A total of 110 target genes of miR-1184 were predicted using the TargetScan and miRTarBase databases, which were then used to construct the protein-protein interaction (PPI) network based on the DAVID and STRING websites and to perform GO and KEGG pathway enrichment analyses. The MCODE plug-in of cytoscape was utilized to verify that CSNK2A1 was the target gene and key gene in significant modules. MiR-1184 directly targets CSNK2A1 via using RNA immunoprecipitation assay and luciferase reporter gene assay. According to the results, CSNK2A1 over-expression reversed the functions of miR-1184 over-expression in suppressing colon cancer cell proliferation and enhancing their apoptosis. In conclusion, over-expression of miR-1184 inhibits colon cancer cell proliferation but promotes their apoptosis through down-regulating CSNK2A1 expression.

Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (¹H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.