Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology. MATERIALS AND METHODS: We analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC. RESULTS: The PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence. CONCLUSION: Our deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

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

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

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Degree of Cyclooxygenase-2 Inhibition Modulates Blood Pressure Response to Celecoxib and Naproxen.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) increase the risk of adverse cardiovascular events via suppression of cyclooxygenase (COX)-2-derived prostacyclin (PGI2) formation in heart, vasculature, and kidney. The Prospective Randomized Evaluation of Celecoxib Integrated Safety versus Ibuprofen Or Naproxen (PRECISION) trial and other large clinical studies compared the cardiovascular risk of traditional NSAIDs (i.e. naproxen), which inhibit both COX isozymes, with NSAIDs selective for COX-2 (i.e. celecoxib). However, whether pharmacologically equipotent doses were used - that is, whether a similar degree of COX-2 inhibition was achieved - was not considered. We compared drug target inhibition and blood pressure response to celecoxib at the dose used by most patients in PRECISION with the lowest recommended naproxen dose for osteoarthritis, which is lower than the dose used in PRECISION. Methods: Sixteen healthy participants (19-61 years) were treated with celecoxib (100 mg every 12h), naproxen (250 mg every 12h), or placebo administered twice daily for seven days in a double-blind, crossover design randomized by order. On Day 7 when drug levels had reached steady state, the degree of COX inhibition was assessed ex vivo and in vivo. Ambulatory blood pressure was measured throughout the final 12h dosing interval. Results: Both NSAIDs inhibited COX-2 activity relative to placebo, but naproxen inhibited COX-2 activity to a greater degree (62.9±21.7%) than celecoxib (35.7±25.2%; p<0.05). Similarly, naproxen treatment inhibited PGI2 formation in vivo (48.0±24.9%) to a greater degree than celecoxib (26.7±24.6%; p<0.05). Naproxen significantly increased blood pressure compared to celecoxib (differences in least-square means of mean arterial pressure: 2.5 mm Hg (95% CI: 1.5, 3.5); systolic blood pressure: 4.0 mm Hg (95% CI: 2.9, 5.1); diastolic blood pressure: 1.8 mm Hg (95% CI: 0.8, 2.8); p<0.05 for all). The difference in systolic blood pressure relative to placebo was associated with the degree of COX-2 inhibition (p<0.05). Conclusions: Celecoxib 200 mg/day inhibited COX-2 activity to a lesser degree than naproxen 500 mg/day, resulting in a less pronounced blood pressure increase. While the PRECISION trial concluded the non-inferiority of celecoxib regarding cardiovascular risk, this is based on a comparison of doses that are not equipotent.ClinicalTrials.gov identifier: NCT02502006 (https://clinicaltrials.gov/study/NCT02502006).

HNF4A and HNF1A exhibit tissue specific target gene regulation in pancreatic beta cells and hepatocytes.

HNF4A and HNF1A encode transcription factors that are important for the development and function of the pancreas and liver. Mutations in both genes have been directly linked to Maturity Onset Diabetes of the Young (MODY) and type 2 diabetes (T2D) risk. To better define the pleiotropic gene regulatory roles of HNF4A and HNF1A, we generated a comprehensive genome-wide map of their binding targets in pancreatic and hepatic cells using ChIP-Seq. HNF4A was found to bind and regulate known (ACY3, HAAO, HNF1A, MAP3K11) and previously unidentified (ABCD3, CDKN2AIP, USH1C, VIL1) loci in a tissue-dependent manner. Functional follow-up highlighted a potential role for HAAO and USH1C as regulators of beta cell function. Unlike the loss-of-function HNF4A/MODY1 variant I271fs, the T2D-associated HNF4A variant (rs1800961) was found to activate AKAP1, GAD2 and HOPX gene expression, potentially due to changes in DNA-binding affinity. We also found HNF1A to bind to and regulate GPR39 expression in beta cells. Overall, our studies provide a rich resource for uncovering downstream molecular targets of HNF4A and HNF1A that may contribute to beta cell or hepatic cell (dys)function, and set up a framework for gene discovery and functional validation.

Disruption of the PGE2 synthesis / response pathway restrains atherogenesis in programmed cell death-1 (Pd-1) deficient hyperlipidemic mice.

Immune checkpoint inhibitors (ICIs) that target programmed cell death 1 (PD-1) have revolutionized cancer treatment by enabling the restoration of suppressed T-cell cytotoxic responses. However, resistance to single-agent ICIs limits their clinical utility. Combinatorial strategies enhance their antitumor effects, but may also enhance the risk of immune related adverse effects of ICIs. Prostaglandin (PG) E2, formed by the sequential action of the cyclooxygenase (COX) and microsomal PGE synthase (mPGES-1) enzymes, acting via its E prostanoid (EP) receptors, EPr2 and EPr4, promotes lymphocyte exhaustion, revealing an additional target for ICIs. Thus, COX inhibitors and EPr4 antagonists are currently being combined with ICIs potentially to enhance antitumor efficacy in clinical trials. However, given the cardiovascular (CV) toxicity of COX inhibitors, such combinations may increase the risk particularly of CV AEs. Here, we compared the impact of distinct approaches to disruption of the PGE2 synthesis /response pathway - global or myeloid cell specific depletion of mPges-1 or global depletion of Epr4 - on the accelerated atherogenesis in Pd-1 deficient hyperlipidemic (Ldlr-/-) mice. All strategies restrained the atherogenesis. While depletion of mPGES-1 suppresses PGE2 biosynthesis, reflected by its major urinary metabolite, PGE2 biosynthesis was increased in mice lacking EPr4, consistent with enhanced expression of aortic Cox-1 and mPges-1. Deletions of mPges-1 and Epr4 differed in their effects on immune cell populations in atherosclerotic plaques; the former reduced neutrophil infiltration, while the latter restrained macrophages and increased the infiltration of T-cells. Consistent with these findings, chemotaxis by bone-marrow derived macrophages from Epr4-/- mice was impaired. Epr4 depletion also resulted in extramedullary lymphoid hematopoiesis and inhibition of lipoprotein lipase activity (LPL) with coincident spelenomegaly, leukocytosis and dyslipidemia. Targeting either mPGES-1 or EPr4 may restrain lymphocyte exhaustion while mitigating CV irAEs consequent to PD-1 blockade.