Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective.

INTRODUCTION: Adaptive mutagenesis observed in colorectal cancer (CRC) cells upon exposure to EGFR inhibitors contributes to the development of resistance and recurrence. Multiple investigations have indicated a parallel between cancer cells and bacteria in terms of exhibiting adaptive mutagenesis. This phenomenon entails a transient and coordinated escalation of error-prone translesion synthesis polymerases (TLS polymerases), resulting in mutagenesis of a magnitude sufficient to drive the selection of resistant phenotypes. METHODS: In this study, we conducted a comprehensive pan-transcriptome analysis of the regulatory framework within CRC cells, with the objective of identifying potential transcriptome modules encompassing certain translesion polymerases and the associated transcription factors (TFs) that govern them. Our sampling strategy involved the collection of transcriptomic data from tumors treated with cetuximab, an EGFR inhibitor, untreated CRC tumors, and colorectal-derived cell lines, resulting in a diverse dataset. Subsequently, we identified co-regulated modules using weighted correlation network analysis with a minKMEtostay threshold set at 0.5 to minimize false-positive module identifications and mapped the modules to STRING annotations. Furthermore, we explored the putative TFs influencing these modules using KBoost, a kernel PCA regression model. RESULTS: Our analysis did not reveal a distinct transcriptional profile specific to cetuximab treatment. Moreover, we elucidated co-expression modules housing genes, for example, POLK, POLI, POLQ, REV1, POLN, and POLM. Specifically, POLK, POLI, and POLQ were assigned to the "blue" module, which also encompassed critical DNA damage response enzymes, for example. BRCA1, BRCA2, MSH6, and MSH2. To delineate the transcriptional control of this module, we investigated associated TFs, highlighting the roles of prominent cancer-associated TFs, such as CENPA, HNF1A, and E2F7. CONCLUSION: We found that translesion polymerases are co-regulated with DNA mismatch repair and cell cycle-associated factors. We did not, however, identified any networks specific to cetuximab treatment indicating that the response to EGFR inhibitors relates to a general stress response mechanism.

Spatial Heterogeneity of Immune Regulators Drives Dynamic Changes in Local Immune Responses, Affecting Disease Outcomes in Pancreatic Cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is considered a low-immunogenic (LI) tumor with a "cold" tumor microenvironment and is mostly unresponsive to immune checkpoint blockade therapies. In this study, we decipher the impact of intratumoral heterogeneity of immune determinants on antitumor responses. EXPERIMENTAL DESIGN: We performed spatial proteomic and transcriptomic analyses and multiplex immunofluorescence on multiple tumor regions, including tumor center (TC) and invasive front (IF), from 220 patients with PDAC, classified according to their transcriptomic immune signaling into high-immunogenic PDAC (HI-PDAC, n = 54) and LI PDAC (LI-PDAC, n = 166). Spatial compartments (tumor: pancytokeratin+/CD45- and leukocytes: pancytokeratin-/CD45+) were defined by fluorescence imaging. RESULTS: HI-PDAC exhibited higher densities of cytotoxic T lymphocytes with upregulation of T-cell priming-associated immune determinants, including CD40, ITGAM, glucocorticoid-induced TNF-related receptor, CXCL10, granzyme B, IFNG, and HLA-DR, which were significantly more prominent at the IF than at the TC. In contrast, LI-PDAC exhibited immune-evasive tumor microenvironments with downregulation of immune determinants and a negative gradient from TC to IF. Patients with HI-PDAC had significantly better outcomes but showed more frequently exhausted immune phenotypes. CONCLUSIONS: Our results indicate strategic differences in the regulation of immune determinants, leading to different levels of effectiveness of antitumor responses between HI and LI tumors and dynamic spatial changes, which affect the evolution of immune evasion and patient outcomes. This finding supports the coevolution of tumor and immune cells and may help define therapeutic vulnerabilities to improve antitumor immunity and harness the responsiveness to immune checkpoint inhibitors in patients with PDAC.

Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that typically manifests late patient presentation and poor outcomes. Furthermore, PDAC recurrence is a common challenge. Distinct patterns of PDAC recurrence have been associated with differential activation of immune pathway-related genes and specific inflammatory responses in their tumour microenvironment. However, the molecular associations between and within cellular components that underpin PDAC recurrence require further development, especially from a multi-omics integration perspective. In this study, we identified stable molecular associations across multiple PDAC recurrences and utilised integrative analytics to identify stable and novel associations via simultaneous feature selection. Spatial transcriptome and proteome datasets were used to perform univariate analysis, Spearman partial correlation analysis, and univariate analyses by Machine Learning methods, including regularised canonical correlation analysis and sparse partial least squares. Furthermore, networks were constructed for reported and new stable associations. Our findings revealed gene and protein associations across multiple PDAC recurrence groups, which can provide a better understanding of the multi-layer disease mechanisms that contribute to PDAC recurrence. These findings may help to provide novel association targets for clinical studies for constructing precision medicine and personalised surveillance tools for patients with PDAC recurrence.

High hypoxia status in pancreatic cancer is associated with multiple hallmarks of an immunosuppressive tumor microenvironment.

Introduction: Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, is a particularly lethal disease that is often diagnosed late and is refractory to most forms of treatment. Tumour hypoxia is a key hallmark of PDAC and is purported to contribute to multiple facets of disease progression such as treatment resistance, increased invasiveness, metabolic reprogramming, and immunosuppression. Methods: We used the Buffa gene signature as a hypoxia score to profile transcriptomics datasets from PDAC cases. We performed cell-type deconvolution and gene expression profiling approaches to compare the immunological phenotypes of cases with low and high hypoxia scores. We further supported our findings by qPCR analyses in PDAC cell lines cultured in hypoxic conditions. Results: First, we demonstrated that this hypoxia score is associated with increased tumour grade and reduced survival suggesting that this score is correlated to disease progression. Subsequently, we compared the immune phenotypes of cases with high versus low hypoxia score expression (HypoxiaHI vs. HypoxiaLOW) to show that high hypoxia is associated with reduced levels of T cells, NK cells and dendritic cells (DC), including the crucial cDC1 subset. Concomitantly, immune-related gene expression profiling revealed that compared to HypoxiaLOW tumours, mRNA levels for multiple immunosuppressive molecules were notably elevated in HypoxiaHI cases. Using a Random Forest machine learning approach for variable selection, we identified LGALS3 (Galectin-3) as the top gene associated with high hypoxia status and confirmed its expression in hypoxic PDAC cell lines. Discussion: In summary, we demonstrated novel associations between hypoxia and multiple immunosuppressive mediators in PDAC, highlighting avenues for improving PDAC immunotherapy by targeting these immune molecules in combination with hypoxia-targeted drugs.

Spatially restricted tumour-associated and host-associated immune drivers correlate with the recurrence sites of pancreatic cancer.

OBJECTIVE: Most patients with pancreatic ductal adenocarcinoma (PDAC) will experience recurrence after resection. Here, we investigate spatially organised immune determinants of PDAC recurrence. DESIGN: PDACs (n=284; discovery cohort) were classified according to recurrence site as liver (n=93/33%), lung (n=49/17%), local (n=31/11%), peritoneal (n=38/13%) and no-recurrence (n=73/26%). Spatial compartments were identified by fluorescent imaging as: pancytokeratin (PanCK)+CD45- (tumour cells); CD45+PanCK- (leucocytes) and PanCK-CD45- (stromal cells), followed by transcriptomic (72 genes) and proteomic analysis (51 proteins) for immune pathway targets. Results from next-generation sequencing (n=194) were integrated. Finally, 10 tumours from each group underwent immunophenotypic analysis by multiplex immunofluorescence. A validation cohort (n=109) was examined in parallel. RESULTS: No-recurrent PDACs show high immunogenicity, adaptive immune responses and are rich in pro-inflammatory chemokines, granzyme B and alpha-smooth muscle actin+ fibroblasts. PDACs with liver and/or peritoneal recurrences display low immunogenicity, stemness phenotype and innate immune responses, whereas those with peritoneal metastases are additionally rich in FAP+ fibroblasts. PDACs with local and/or lung recurrences display interferon-gamma signalling and mixed adaptive and innate immune responses, but with different leading immune cell population. Tumours with local recurrences overexpress dendritic cell markers whereas those with lung recurrences neutrophilic markers. Except the exclusive presence of RNF43 mutations in the no-recurrence group, no genetic differences were seen. The no-recurrence group exhibited the best, whereas liver and peritoneal recurrences the poorest prognosis. CONCLUSIONS: Our findings demonstrate distinct inflammatory/stromal responses in each recurrence group, which might affect dissemination patterns and patient outcomes. These findings may help to inform personalised adjuvant/neoadjuvant and surveillance strategies in PDAC, including immunotherapeutic modalities.

Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma.

BACKGROUND: Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy. METHODS: We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05). RESULTS: The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8+ cells between adjacent and tumour samples. CONCLUSION: We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8+ cells and M1 macrophages in tumour samples.

Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study.

BACKGROUND: We aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response. METHODS: Twenty-nine patients (15 with Crohn's disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4ß7 therapy. Biopsies were taken for fluorescein isothiocyanate-labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort. RESULTS: In vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn's disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7). CONCLUSIONS: Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders.

Role of microbiota and microbiota-derived short-chain fatty acids in PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC.

Integration of stool microbiota, proteome and amino acid profiles to discriminate patients with adenomas and colorectal cancer.

BACKGROUND: Screening for colorectal cancer (CRC) reduces its mortality but has limited sensitivity and specificity. Aims We aimed to explore potential biomarker panels for CRC and adenoma detection and to gain insight into the interaction between gut microbiota and human metabolism in the presence of these lesions. METHODS: This multicenter case-control cohort was performed between February 2016 and November 2019. Consecutive patients ≥18 years with a scheduled colonoscopy were asked to participate and divided into three age, gender, body-mass index and smoking status-matched subgroups: CRC (n = 12), adenomas (n = 21) and controls (n = 20). Participants collected fecal samples prior to bowel preparation on which proteome (LC-MS/MS), microbiota (16S rRNA profiling) and amino acid (HPLC) composition were assessed. Best predictive markers were combined to create diagnostic biomarker panels. Pearson correlation-based analysis on selected markers was performed to create networks of all platforms. RESULTS: Combining omics platforms provided new panels which outperformed hemoglobin in this cohort, currently used for screening (AUC 0.98, 0.95 and 0.87 for CRC vs controls, adenoma vs controls and CRC vs adenoma, respectively). Integration of data sets revealed markers associated with increased blood excretion, stress- and inflammatory responses and pointed toward downregulation of epithelial integrity. CONCLUSIONS: Integrating fecal microbiota, proteome and amino acids platforms provides for new biomarker panels that may improve noninvasive screening for adenomas and CRC, and may subsequently lead to lower incidence and mortality of colon cancer.

Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes: A cross-sectional study.

Objective: Rates of type 2 diabetes (T2D) among adolescents are on the rise. Epigenetic changes could be associated with the metabolic alterations in adolescents with T2D. Methods: We performed a cross sectional integrated analysis of DNA methylation data from peripheral blood mononuclear cells with serum metabolomic data from First Nation adolescents with T2D and controls participating in the Improving Renal Complications in Adolescents with type 2 diabetes through Research (iCARE) cohort study, to explore the molecular changes in adolescents with T2D. Results: Our analysis showed that 43 serum metabolites and 36 differentially methylated regions (DMR) were associated with T2D. Several DMRs were located near the transcriptional start site of genes with established roles in metabolic disease and associated with altered serum metabolites (e.g. glucose, leucine, and gamma-glutamylisoleucine). These included the free fatty acid receptor-1 (FFAR1), upstream transcription factor-2 (USF2), and tumor necrosis factor-related protein-9 (C1QTNF9), among others. Conclusions: We identified DMRs and metabolites that merit further investigation to determine their significance in controlling gene expression and metabolism which could define T2D risk in adolescents.

The diagnostic potential and barriers of microbiome based therapeutics.

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

Hypoxanthine is a pharmacodynamic marker of ischemic brain edema modified by glibenclamide.

Brain edema after a large stroke causes significant morbidity and mortality. Here, we seek to identify pharmacodynamic markers of edema that are modified by intravenous (i.v.) glibenclamide (glyburide; BIIB093) treatment. Using metabolomic profiling of 399 plasma samples from patients enrolled in the phase 2 Glyburide Advantage in Malignant Edema and Stroke (GAMES)-RP trial, 152 analytes are measured using liquid chromatography-tandem mass spectrometry. Associations with midline shift (MLS) and the matrix metalloproteinase-9 (MMP-9) level that are further modified by glibenclamide treatment are compared with placebo. Hypoxanthine is the only measured metabolite that associates with MLS and MMP-9. In sensitivity analyses, greater hypoxanthine levels also associate with increased net water uptake (NWU), as measured on serial head computed tomography (CT) scans. Finally, we find that treatment with i.v. glibenclamide reduces plasma hypoxanthine levels across all post-treatment time points. Hypoxanthine, which has been previously linked to inflammation, is a biomarker of brain edema and a treatment response marker of i.v. glibenclamide treatment.

Differential benefits of steroid therapies in adults following major burn injury.

BACKGROUND: Major thermal injury induces a complex pathophysiological state characterized by burn shock and hypercatabolism. Steroids are used to modulate these post-injury responses. However, the effects of steroids on acute post-burn outcomes remain unclear. METHODS: In this study of 52 thermally injured adult patients (median total burn surface area 42%, 33 males and 19 females), the effects of corticosteroid and oxandrolone on mortality, multi-organ failure (MOF), and sepsis were assessed individually. Clinical data were collected at days 1, 3, 7, and 14 post-injury. RESULTS: Twenty-two (42%) and 34 (65%) burns patients received corticosteroids and oxandrolone within the same cohort, respectively. Following separate analysis for each steroid, corticosteroid use was associated with increased odds of in-hospital mortality (OR 3.25, 95% CI: 1.32-8•00), MOF (OR 2.36, 95% CI: 1.00-1.55), and sepsis (OR 5.95, 95% CI: 2.53-14.00). Days alive (HR 0.32, 95% CI: 0.18-0.60) and sepsis-free days (HR 0.54, 95% CI: 0.37-0.80) were lower among corticosteroid-treated patients. Oxandrolone use was associated with reduced odds of 28-day mortality (OR 0.11, 95% CI: 0.04-0.30), in-hospital mortality (OR 0.19, 95% CI: 0.08-0.43), and sepsis (OR 0.24, 95% CI: 0.08-0.69). Days alive, at 28 days (HR 6.42, 95% CI: 2.77-14.9) and in-hospital (HR 3.30, 95% CI: 1.93-5.63), were higher among the oxandrolone-treated group. However, oxandrolone was associated with increased MOF odds (OR 7.90, 95% CI: 2.89-21.60) and reduced MOF-free days (HR 0.23, 95% CI: 0.11-0.50). CONCLUSION: Steroid therapies following major thermal injury may significantly affect patient prognosis. Oxandrolone was associated with better outcomes except for MOF. Adverse effects of corticosteroids and oxandrolone should be considered when managing burn patients.

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes.

BACKGROUND: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC's mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. METHODS: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. RESULTS: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. CONCLUSIONS: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

The potential of fecal microbiota and amino acids to detect and monitor patients with adenoma.

The risk of recurrent dysplastic colonic lesions is increased following polypectomy. Yield of endoscopic surveillance after adenoma removal is low, while interval colorectal cancers occur. To longitudinally assess the dynamics of fecal microbiota and amino acids in the presence of adenomatous lesions and after their endoscopic removal. In this longitudinal case-control study, patients collected fecal samples prior to bowel preparation before scheduled colonoscopy and 3 months after this intervention. Based on colonoscopy outcomes, patients with advanced adenomas and nonadvanced adenomas (0.5-1.0 cm) who underwent polypectomy during endoscopy (n = 19) were strictly matched on age, body-mass index, and smoking habits to controls without endoscopic abnormalities (n = 19). Microbial taxa were measured by 16S RNA sequencing, and amino acids (AA) were measured by high-performance liquid chromatography (HPLC). Adenoma patients were discriminated from controls based on AA and microbial composition. Levels of proline (p = .001), ornithine (p = .02) and serine (p = .02) were increased in adenoma patients compared to controls but decreased to resemble those of controls after adenoma removal. These AAs were combined as a potential adenoma-specific panel (AUC 0.79(0.64-0.94)). For bacterial taxa, differences between patients with adenomas and controls were found (Bifidobacterium spp.↓, Anaerostipes spp.↓, Butyricimonas spp.↑, Faecalitalea spp.↑ and Catenibacterium spp.↑), but no alterations in relative abundance were observed after polypectomy. Furthermore, Faecalitalea spp. and Butyricimonas spp. were significantly correlated with adenoma-specific amino acids. We selected an amino acid panel specifically increased in the presence of adenomas and a microbial signature present in adenoma patients, irrespective of polypectomy. Upon validation, these panels may improve the effectiveness of the surveillance program by detection of high-risk individuals and determination of surveillance endoscopy timing, leading to less unnecessary endoscopies and less interval cancer.

CACONET: a novel classification framework for microbial correlation networks.

MOTIVATION: Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease. RESULTS: We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility. AVAILABILITY AND IMPLEMENTATION: CACONET is available at https://github.com/yuanwxu/corr-net-classify. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Iron-mediated epigenetic activation of NRF2 targets.

The toxic effects of excess dietary iron within the colonic lumen are well documented, particularly in the context of Inflammatory Bowel Disease (IBD) and Colorectal Cancer (CRC). Proposed mechanisms that underpin iron-associated intestinal disease include: (1) the pro-inflammatory and ROS-promoting nature of iron, (2) gene-expression alterations, and (3) intestinal microbial dysbiosis. However, to date no studies have examined the effect of iron on the colonic epigenome. Here we demonstrate that chronic iron exposure of colonocytes leads to significant hypomethylation of the epigenome. Bioinformatic analysis highlights a significant epigenetic effect on NRF2 (nuclear factor erythroid 2-related factor 2) pathway targets (including NAD(P)H Quinone Dehydrogenase 1 [NQO1] and Glutathione peroxidase 2 [GPX2]); this demethylating effect was validated and subsequent gene and protein expression quantified. These epigenetic modifications were not observed upon the diminishment of cellular lipid peroxidation with endogenous glutathione and the subsequent removal of iron. Additionally, the induction of TET1 expression was found post-iron treatment, highlighting the possibility of an oxidative-stress induction of TET1 and subsequent hypomethylation of NRF2 targets. In addition, a strong time dependence on the establishment of iron-orchestrated hypomethylation was found which was concurrent with the increase in the intracellular labile iron pool (LIP) and lipid peroxidation levels. These epigenetic changes were further validated in murine intestinal mucosa in models administered a chronic iron diet, providing evidence for the likelihood of dietary-iron mediated epigenetic alterations in vivo. Furthermore, significant correlations were found between NQO1 and GPX2 demethylation and human intestinal tissue iron-status, thus suggesting that these iron-mediated epigenetic modifications are likely in iron-replete enterocytes. Together, these data describe a novel mechanism by which excess dietary iron is able to alter the intestinal phenotype, which could have implications in iron-mediated intestinal disease and the regulation of ferroptosis.

Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma.

Colon adenocarcinoma (COAD) is a common tumor of the gastrointestinal tract with a high mortality rate. Current research has identified many genes associated with immune infiltration that play a vital role in the development of COAD. In this study, we analysed the prognostic and diagnostic features of such immune-related genes in the context of colonic adenocarcinoma (COAD). We analysed 17 overlapping gene expression profiles of COAD and healthy samples obtained from TCGA-COAD and public single-cell sequencing resources, to identify potential therapeutic COAD targets. We evaluated the abundance of immune infiltration with those genes using the TIMER (Tumor Immune Estimation Resource) deconvolution method. Subsequently, we developed predictive and survival models to assess the prognostic value of these genes. The LGALS4 (Galectin-4) gene was found to be significantly (P<0.05) downregulated in COAD and bladder urothelial carcinoma (BLCA) compared to healthy samples. We identified LGALS4 as a prognostic and diagnostic marker for multiple cancer types, including COAD and BLCA. Our analysis reveals a series of novel candidate drug targets, as well as candidate molecular markers, that may explain the pathogenesis of COAD and BLCA. LGALS4 gene is associated with multiple cancer types and is a possible prognostic, as well as diagnostic, marker of COAD and BLCA.

Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India.

BACKGROUND: Non-communicable diseases (NCDs) have become a major cause of morbidity and mortality in India. Perturbation of host-microbiome interactions may be a key mechanism by which lifestyle-related risk factors such as tobacco use, alcohol consumption, and physical inactivity may influence metabolic health. There is an urgent need to identify relevant dysmetabolic traits for predicting risk of metabolic disorders, such as diabetes, among susceptible Asian Indians where NCDs are a growing epidemic. METHODS: Here, we report the first in-depth phenotypic study in which we prospectively enrolled 218 adults from urban and rural areas of Central India and used multiomic profiling to identify relationships between microbial taxa and circulating biomarkers of cardiometabolic risk. Assays included fecal microbiota analysis by 16S ribosomal RNA gene amplicon sequencing, quantification of serum short chain fatty acids by gas chromatography-mass spectrometry, and multiplex assaying of serum diabetic proteins, cytokines, chemokines, and multi-isotype antibodies. Sera was also analysed for N-glycans and immunoglobulin G Fc N-glycopeptides. RESULTS: Multiple hallmarks of dysmetabolism were identified in urbanites and young overweight adults, the majority of whom did not have a known diagnosis of diabetes. Association analyses revealed several host-microbe and metabolic associations. CONCLUSIONS: Host-microbe and metabolic interactions are differentially shaped by body weight and geographic status in Central Indians. Further exploration of these links may help create a molecular-level map for estimating risk of developing metabolic disorders and designing early interventions.