Comparative Metabolomics and Microbiome Analysis of Ethanol versus OMNImet/gene•GUT Fecal Stabilization.

Metabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide the stability of the fecal metabolome and microbiome at room temperature (RT). Here, we investigated the stability of various metabolites and the microbiome (16S rRNA) in feces collected in 95% ethanol (EtOH) and commercially available sample collection kits with specific preservatives OMNImet•GUT/OMNIgene•GUT. To simulate field-collection scenarios, the samples were stored at different temperatures at varying durations (24 h + 4 °C, 24 h RT, 36 h RT, 48 h RT, and 7 days RT) and compared to aliquots immediately frozen at -80 °C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar metabolites, endocannabinoids, short-chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the nonstabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet•GUT. When comparing the metabolite levels between aliquots stored at room temperature and at +4 °C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing samples at RT and stabilizing them in 95% EtOH yielded metabolomic results comparable to those from flash freezing. We also found that the overall composition of the microbiome did not vary significantly between different storage types. However, notable differences were observed in the α diversity. Altogether, the stability of the metabolome and microbiome in 95% EtOH provided results similar to those of the validated commercial collection kits OMNImet•GUT and OMNIgene•GUT, respectively.

Deep learning meets metabolomics: a methodological perspective.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

Infant gut microbiota and negative and fear reactivity.

BACKGROUND: Studies indicate that gut microbiota is related to neurodevelopmental and behavioral outcomes. Accordingly, early gut microbiota composition (GMC) has been linked to child temperament, but research is still scarce. The aim of this study was to examine how early GMC at 2.5 months is associated with child negative and fear reactivity at 8 and 12 months since they are potentially important intermediate phenotypes of later child psychiatric disorders. METHODS: Our study population was 330 infants enrolled in the longitudinal FinnBrain Birth Cohort Study. Gut microbiota composition was analyzed using stool sample 16s rRNA sequencing. Negative and fear reactivity were assessed using the Laboratory Temperament Assessment Battery (Lab-TAB) at child's age of 8 months (n =150) and the Infant Behavior Questionnaire-Revised Short Form (IBQ-R SF) at child's age of 12 months (n = 276). CONCLUSIONS: We found a positive association between alpha diversity and reported fear reactivity and differing microbial community composition based on negative reactivity for boys. Isobutyric acid correlated with observed negative reactivity, however, this association attenuated in the linear model. Several genera were associated with the selected infant temperament traits. This study adds to the growing literature on links between infant gut microbiota and temperament informing future mechanistic studies.

Classification of Common Food Lipid Sources Regarding Healthiness Using Advanced Lipidomics: A Four-Arm Crossover Study.

Prospective studies have failed to establish a causal relationship between animal fat intake and cardiovascular diseases in humans. Furthermore, the metabolic effects of different dietary sources remain unknown. In this four-arm crossover study, we investigated the impact of consuming cheese, beef, and pork meat on classic and new cardiovascular risk markers (obtained from lipidomics) in the context of a healthy diet. A total of 33 young healthy volunteers (23 women/10 men) were assigned to one out of four test diets in a Latin square design. Each test diet was consumed for 14 days, with a 2-week washout. Participants received a healthy diet plus Gouda- or Goutaler-type cheeses, pork, or beef meats. Before and after each diet, fasting blood samples were withdrawn. A reduction in total cholesterol and an increase in high density lipoprotein particle size were detected after all diets. Only the pork diet upregulated plasma unsaturated fatty acids and downregulated triglycerides species. Improvements in the lipoprotein profile and upregulation of circulating plasmalogen species were also observed after the pork diet. Our study suggests that, within the context of a healthy diet rich in micronutrients and fiber, the consumption of animal products, in particular pork meat, may not induce deleterious effects, and reducing the intake of animal products should not be regarded as a way of reducing cardiovascular risk in young individuals.

MicroRNA 452 regulates IL20RA-mediated JAK1/STAT3 pathway in inflammatory colitis and colorectal cancer.

OBJECTIVE: MicroRNAs are a class of small, non-coding RNAs that play a key role in several biological and molecular processes, including tumorigenesis. We previously identified that MIR452 is upregulated in both colorectal cancer (CRC) and colitis. However, the functional mechanisms of MIR452 and its target genes in CRC and colitis are not well understood. So, we hypothesize that MIR452 can influence CRC and DSS-induced colitis model through the regulation of IL20RA and its downstream JAK-STATs signaling pathway. METHODS: We used a luciferase reporter assay to confirm the effect of MIR452 on IL20RA expression. The protein and mRNA expression of a target gene and its associated molecules were measured by western blot, quantitative RT-PCR, and immunohistochemistry. RESULTS: We found that the IL20RA was a direct target gene of MIR452. Overexpression of MIR452 in CRC cell lines significantly decreased IL20RA and its downstream Janus kinase 1 (JAK1), Signal transducer and activator of transcription 1 (STAT1) and STAT3. Knockdown of IL20RA in CRC cell lines by IL20RA gene silencing also decreased the expression of IL20RA, JAK1, and STAT3, but not of STAT1. CONCLUSION: Our results suggest that MIR452 regulates STAT3 through the IL20RA-mediated JAK1 pathway, but not STAT1. Overall, MIR452 acts as tumor suppressor in human CRC and in a mouse colitis model. These findings suggest that MIR452 is a promising therapeutic target in the treatment of cancer and colitis.

Cannabinoid Receptor Type 1 Regulates Drug Reward Behavior via Glutamate Decarboxylase 67 Transcription.

Interaction of cannabinoid receptor type 1 (CB1) and GABAergic neuronal activity is involved in drug abuse-related behavior. However, its role in drug-dependent Pavlovian conditioning is not well understood. In this study, we aimed to evaluate the effects of a CB1 agonist, JWH-210, on the development of conditioned place preference (CPP)-induced by methamphetamine (METH). Pretreatment with a synthetic cannabinoid, JWH-210 (CB1 agonist), increased METH-induced CPP score and METH-induced dopamine release in acute striatal slices. Interestingly, CB1 was expressed in glutamate decarboxylase 67 (GAD67) positive cells, and overexpression of CB1 increased GAD67 expression, while CB1 knockdown reduced GAD67 expression in vivo and in vitro. GAD67 is known as an enzyme involved in the synthesis of GABA. CB1 knockdown in the mice striatum increased METH-induced CPP. When GAD67 decreased in the mice striatum, mRNA level of CB1 did not change, suggesting that CB1 can regulate GAD67 expression. GAD67 knockdown in the mouse striatum augmented apomorphine (dopamine receptor D2 agonist)-induced climbing behavior and METH-induced CPP score. Moreover, in the human brain, mRNA level of GAD67 was found to be decreased in drug users. Therefore, we suggest that CB1 potentiates METH-induced CPP through inhibitory GABAergic regulation of dopaminergic neuronal activity.

Metabolic alterations in immune cells associate with progression to type 1 diabetes.

AIMS/HYPOTHESIS: Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes. METHODS: In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D, n = 34), (2) seroconverted to ≥1 islet autoantibody without progressing to type 1 diabetes (P1Ab, n = 27) or (3) remained autoantibody negative during follow-up (CTRL, n = 10). RESULTS: During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression. CONCLUSIONS/INTERPRETATION: Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes. DATA AVAILABILITY: The GEMs for PBMCs have been submitted to BioModels (www.ebi.ac.uk/biomodels/), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/), under accession number MTBLS1015.

Increased intracellular Ca2+ concentrations prevent membrane localization of PH domains through the formation of Ca2+-phosphoinositides.

Insulin resistance, a key etiological factor in metabolic syndrome, is closely linked to ectopic lipid accumulation and increased intracellular Ca2+ concentrations in muscle and liver. However, the mechanism by which dysregulated intracellular Ca2+ homeostasis causes insulin resistance remains elusive. Here, we show that increased intracellular Ca2+ acts as a negative regulator of insulin signaling. Chronic intracellular Ca2+ overload in hepatocytes during obesity and hyperlipidemia attenuates the phosphorylation of protein kinase B (Akt) and its key downstream signaling molecules by inhibiting membrane localization of pleckstrin homology (PH) domains. Pharmacological approaches showed that elevated intracellular Ca2+ inhibits insulin-stimulated Akt phosphorylation and abrogates membrane localization of various PH domain proteins such as phospholipase Cδ and insulin receptor substrate 1, suggesting a common mechanism inhibiting the membrane targeting of PH domains. PH domain-lipid overlay assays confirmed that Ca2+ abolishes the binding of various PH domains to phosphoinositides (PIPs) with two adjacent phosphate groups, such as PI(3,4)P2, PI(4,5)P2, and PI(3,4,5)P3 Finally, thermodynamic analysis of the binding interaction showed that Ca2+-mediated inhibition of targeting PH domains to the membrane resulted from the tight binding of Ca2+ rather than PH domains to PIPs forming Ca2+-PIPs. Thus, Ca2+-PIPs prevent the recognition of PIPs by PH domains, potentially due to electrostatic repulsion between positively charged side chains in PH domains and the Ca2+-PIPs. Our findings provide a mechanistic link between intracellular Ca2+ dysregulation and Akt inactivation in insulin resistance.

Circulating metabolites in progression to islet autoimmunity and type 1 diabetes.

AIMS/HYPOTHESIS: Metabolic dysregulation may precede the onset of type 1 diabetes. However, these metabolic disturbances and their specific role in disease initiation remain poorly understood. In this study, we examined whether children who progress to type 1 diabetes have a circulatory polar metabolite profile distinct from that of children who later progress to islet autoimmunity but not type 1 diabetes and a matched control group. METHODS: We analysed polar metabolites from 415 longitudinal plasma samples in a prospective cohort of children in three study groups: those who progressed to type 1 diabetes; those who seroconverted to one islet autoantibody but not to type 1 diabetes; and an antibody-negative control group. Metabolites were measured using two-dimensional GC high-speed time of flight MS. RESULTS: In early infancy, progression to type 1 diabetes was associated with downregulated amino acids, sugar derivatives and fatty acids, including catabolites of microbial origin, compared with the control group. Methionine remained persistently upregulated in those progressing to type 1 diabetes compared with the control group and those who seroconverted to one islet autoantibody. The appearance of islet autoantibodies was associated with decreased glutamic and aspartic acids. CONCLUSIONS/INTERPRETATION: Our findings suggest that children who progress to type 1 diabetes have a unique metabolic profile, which is, however, altered with the appearance of islet autoantibodies. Our findings may assist with early prediction of the disease.

Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe.

It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted.

Metabolic Fate of 13C-Labeled Polydextrose and Impact on the Gut Microbiome: A Triple-Phase Study in a Colon Simulator.

The present study introduces a novel triple-phase (liquids, solids, and gases) approach, which employed uniformly labeled [U-13C] polydextrose (PDX) for the selective profiling of metabolites generated from dietary fiber fermentation in an in vitro colon simulator using human fecal inocula. Employing 13C NMR spectroscopy, [U-13C] PDX metabolism was observed from colonic digest samples. The major 13C-labeled metabolites generated were acetate, butyrate, propionate, and valerate. In addition to these short-chain fatty acids (SCFAs), 13C-labeled lactate, formate, succinate, and ethanol were detected in the colon simulator samples. Metabolite formation and PDX substrate degradation were examined comprehensively over time (24 and 48 h). Correlation analysis between 13C NMR spectra and gas production confirmed the anaerobic fermentation of PDX to SCFAs. In addition, 16S rRNA gene analysis showed that the level of Erysipelotrichaceae was influenced by PDX supplementation and Erysipelotrichaceae level was statistically correlated with SCFA formation. Overall, our study demonstrates a novel approach to link substrate fermentation and microbial function directly in a simulated colonic environment.

Protective Effect of Octylmethoxycinnamate against UV-Induced Photoaging in Hairless Mouse via the Regulation of Matrix Metalloproteinases.

Ultraviolet (UV) irradiation damages skin and produces symptoms of photoaging, such as thickening, rough texture, wrinkles, and pigmentation. However, the cellular and molecular mechanisms underlying photoaging induced by chronic UV irradiation are not yet fully understood. Matrix metalloproteinases (MMPs) have been reported to be involved in the response to UV irradiation. In this study, we examined the effects of the sunscreen agent Octylmethoxycinnamate (OMC) on photoaging of the skin induced by chronic UV exposure in hairless albino Crl:SKH1-Hrhr (SKH-1) mice. We demonstrated that the expression of MMPs was elevated by UV irradiation, whereas the topical application of OMC inhibited the upregulation of MMPs. Furthermore, UV-induced wrinkle formation was decreased by OMC treatment. These results suggest that OMC is a potential agent for the prevention and treatment of skin photoaging.

ROS Production and ERK Activity Are Involved in the Effects of d-ß-Hydroxybutyrate and Metformin in a Glucose Deficient Condition.

Hypoglycemia, a complication of insulin or sulfonylurea therapy in diabetic patients, leads to brain damage. Furthermore, glucose replenishment following hypoglycemic coma induces neuronal cell death. In this study, we investigated the molecular mechanism underlying glucose deficiency-induced cytotoxicity and the protective effect of d-ß-hydroxybutyrate (D-BHB) using SH-SY5Y cells. The cytotoxic mechanism of metformin under glucose deficiency was also examined. Cell viability under 1 mM glucose (glucose deficiency) was significantly decreased which was accompanied by increased production of reactive oxygen species (ROS) and decreased phosphorylation of extracellular signal-regulated kinase (ERK) and glycogen synthase 3 (GSK3ß). ROS inhibitor reversed the glucose deficiency-induced cytotoxicity and restored the reduced phosphorylation of ERK and GSK3ß. While metformin did not alter cell viability in normal glucose media, it further increased cell death and ROS production under glucose deficiency. However, D-BHB reversed cytotoxicity, ROS production, and the decrease in phosphorylation of ERK and GSK3ß induced by the glucose deficiency. ERK inhibitor reversed the D-BHB-induced increase in cell viability under glucose deficiency, whereas GSK3ß inhibitor did not restore glucose deficiency-induced cytotoxicity. Finally, the protective effect of D-BHB against glucose deficiency was confirmed in primary neuronal cells. We demonstrate that glucose deficiency-induced cytotoxicity is mediated by ERK inhibition through ROS production, which is attenuated by D-BHB and intensified by metformin.

Strategy for Nuclear-Magnetic-Resonance-Based Metabolomics of Human Feces.

Metabolomic analyses of fecal material are gaining increasing attention because the gut microbial ecology and activity have an impact on the human phenotype and regulate host metabolism. Sample preparation is a crucial step, and in this study, we recommend a methodology for extraction and analysis of fresh feces by NMR-based metabolomics. The evaluation of extraction solvents showed that buffer extraction is a suitable approach to extract metabolic information in feces. Therefore, the effects of weight-to-buffer (Wf:Vb) combinations and the effect of sonication and freeze-thaw cycles on the reproducibility, chemical shift variability, and signal-to-noise ratio (SNR) of the (1)H NMR spectra were evaluated. On the basis of our results, we suggest that fresh fecal extraction with a Wf:Vb ratio of 1:2 may be the optimum choice to determine the overall metabolite composition of feces. In fact, more than 60 metabolites have been assigned in the NMR spectra obtained from the fresh fecal buffer extract, and assignments of the lipophilic signals are also presented. To our knowledge, some of the metabolites are reported here for the very first time employing (1)H NMR spectroscopy on human fecal extracts.

Metabolic characterization of triple negative breast cancer.

BACKGROUND: The aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ER(pos)/PgR(pos)/HER-2(pos) status which for the sake of simplicity is called triple positive breast cancer (TPBC). METHODS: The study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data. RESULTS: Choline levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or "Glutamine addiction" has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2(pos) tumors, which support Glycine as potential marker for tumor aggressiveness. CONCLUSIONS: Metabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes.

Tetrandrine for Targeting Therapy Resistance in Cancer.

During the last five decades, there has been tremendous development in our understanding of cancer biology and the development of new and novel therapeutics to target cancer. However, despite these advances, cancer remains the second leading cause of death across the globe. Most cancer deaths are attributed to the development of resistance to current therapies. There is an urgent and unmet need to address cancer therapy resistance. Tetrandrine, a bis-benzyl iso-quinoline, has shown a promising role as an anti-cancer agent. Recent work from our laboratory and others suggests that tetrandrine and its derivatives could be an excellent adjuvant to the current arsenal of anti-cancer drugs. Herein, we provide an overview of resistance mechanisms to current therapeutics and review the existing literature on the anti-cancer effects of tetrandrine and its potential use for overcoming therapy resistance in cancer.

BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.

BACKGROUND: Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. RESULTS: We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. CONCLUSIONS: BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

MicroRNA 452 regulates SHC1 expression in human colorectal cancer and colitis.

BACKGROUND: Human microRNA 452 (MIR452) has been linked to both colorectal cancer (CRC) tissues and dextran sulfate sodium (DSS)-induced colitis. OBJECTIVE: We analyzed the correlation between MIR452 and its putative target gene in human CRC cells and in mouse colitis tissues. METHODS: Luciferase reporter assay confirmed that Src homologous and collagen adaptor protein 1 (SHC1) is a direct target of MIR452. Furthermore, the expression of proteins or mRNA was assessed by immunohistochemical analysis, Western blot, or quantitative RT-PCR (qRT-PCR). RESULTS: We found that MIR452 has a potential binding site at 3'-UTR of SHC1. Likewise, MIR452 or siSHC1 transfection dramatically reduced the level of cellular SHC1 in CRC cells. The expression of SHC1 was frequently downregulated in both human CRC tissues and mouse colitis tissues. In CRC cells, we demonstrated that MIR452 regulated the expression of genes involved in the SHC1-mediated KRAS-MAPK signal transduction pathways. CONCLUSION: These findings suggest a potential defense mechanism in which MIR452 regulation of the adaptor protein SHC1 maintains cellular homeostasis during carcinogenesis or chronic inflammation. Therefore, MIR452 may have therapeutic value for human early-stage CRC and colitis.

MicroRNA 133A Regulates Cell Proliferation, Cell Migration, and Apoptosis in Colorectal Cancer by Suppressing CDH3 Expression.

MicroRNAs are endogenous, non-coding RNA that play an essential role in colorectal carcinoma (CRC) pathogenesis by targeting specific genes. This research aimed to determine and validate the target genes of the MIR133A associated with CRC. We verified that cadherin 3 (CDH3) is the direct target gene of MIR133A using a luciferase reporter assay, quantitative RT-PCR, and western blot analyses. CDH3 mRNA and protein expression were reduced significantly in CRC cells after transfection with MIR133A or siCDH3. We also verified that MIR133A regulated CDH3-mediated catenin, matrix metalloproteinase, apoptosis, and the epithelial-mesenchymal transition (EMT) pathway. Knockdown of CDH3 in CRC cell lines by siCDH3 produced similar results. Compared with adjacent non-tumor tissues, CDH3 protein expression was upregulated in CRC tissues, which is further confirmed by immunohistochemistry. Additionally, molecular and functional studies revealed that cell viability, migration, and colony formation were significantly reduced, and apoptosis was increased in CRC cell lines transfected with MIR133A or siCDH3. Our results suggest that MIR133A regulates CDH3 expression in human CRC.

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.

Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine.