Artificial Neural Network Analysis-Based Immune-Related Signatures of Primary Non-Response to Infliximab in Patients With Ulcerative Colitis

Multi-Omics Biomarkers for Predicting Efficacy of Biologic and Small-Molecule Therapies in Adults With Inflammatory Bowel Disease: A Systematic Review

The heterogeneity and suboptimal efficacy of biological treatments and small molecule drugs necessitate their precise selection based on biomarkers that predict therapeutic responses in inflammatory bowel disease. Recent studies have identified numerous novel biomarkers predictive of responses to biologics and small molecule modulators, utilizing a variety of omics approaches in inflammatory bowel disease. In this review, we systematically examine baseline omics biomarkers that predict responses to biological therapies and small molecule drugs, drawing on literature from PubMed. Our analysis spans multiple omics disciplines, including genomics, transcriptomics (both bulk RNA and single-cell RNA sequencing), proteomics, microbiomics, and metabolomics, with particular emphasis on the impact of models integrating multiple omics datasets. Additionally, to further the field of precision medicine, we evaluated specific biomarkers that may exhibit distinct effects on responses to multiple therapeutic interventions.

Artificial Intelligence in Inflammatory Bowel Disease

Artificial intelligence (AI) is being increasingly studied and implemented in gastroenterology. In inflammatory bowel disease (IBD), numerous AI models are being developed to assist with IBD diagnosis, standardization of endoscopic and radiologic disease activity, and predicting outcomes. Further prospective, multicenter studies representing diverse populations and novel applications are needed prior to routine implementation in clinical practice and expected improved outcomes for clinicians and patients.

Genetic Polymorphisms (ApaI, FokI, BsmI, and TaqI) of the Vitamin D Receptor (VDR) Influence the Natural History and Phenotype of Crohn's Disease

Vitamin D receptor (VDR) single-nucleotide polymorphisms (SNPs) modulate vitamin D/VDR signaling, a key pathway in inflammatory bowel disease (IBD) pathogenesis. This study investigates how ApaI, BsmI, TaqI, and FokI SNPs affect IBD phenotype and progression. A total of 76 Crohn's disease (CD) and 68 ulcerative colitis (UC) patients were genotyped. On initial bivariate analysis, the AA genotype of ApaI was accompanied by higher rates of penetrating (B3) CD (36.7% vs. 8.7%; p = 0.012). The FokI SNP was associated with disease location, with the ff genotype predisposing to CD and affecting the upper GI (36.4% vs. 7.7%; p = 0.044) or the colon (90.9% vs. 50.8%; p = 0.038). Moreover, patients harboring the ApaI A allele (AA/Aa) experienced higher rates of steroid-refractory or steroid-dependent CD. In multivariate analyses, the aa genotype showed a protective effect against hospitalization (aOR = 0.17; p = 0.013) in CD, whereas the TT genotype emerged as an independent risk factor (aOR = 4.79; p = 0.044). Moreover, the aa genotype was independently associated with a decreased risk of IBD-related surgery (aOR = 0.055; p = 0.014). VDR SNPs, particularly ApaI, influence disease phenotype, progression, and treatment response in CD. The aa genotype of ApaI appears to confer protection against adverse disease outcomes.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.

Artificial intelligence and machine learning technologies in ulcerative colitis

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis

BACKGROUND AND OBJECTIVES

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition.

METHOD

A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English.

RESULTS

Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis.

CONCLUSIONS

When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Role of Nox4 in Mitigating Inflammation and Fibrosis in Dextran Sulfate Sodium-Induced Colitis

BACKGROUND & AIMS

Fibrosis development in ulcerative colitis is associated directly with the severity of mucosal inflammation, which increases the risk of colorectal cancer. The transforming growth factor-β (TGF-β) signaling pathway is an important source of tissue fibrogenesis, which is stimulated directly by reactive oxygen species produced from nicotinamide adenine dinucleotide phosphate oxidases (NOX). Among members of the NOX family, NOX4 expression is up-regulated in patients with fibrostenotic Crohn's disease (CD) and in dextran sulfate sodium (DSS)-induced murine colitis. The aim of this study was to determine whether NOX4 plays a role in fibrogenesis during inflammation in the colon using a mouse model.

METHODS

Acute and recovery models of colonic inflammation were performed by DSS administration to newly generated Nox4-/- mice. Pathologic analysis of colon tissues was performed, including detection of immune cells, proliferation, and fibrotic and inflammatory markers. RNA sequencing was performed to detect differentially expressed genes between Nox4-/- and wild-type mice in both the untreated and DSS-treated conditions, followed by functional enrichment analysis to explore the molecular mechanisms contributing to pathologic differences during DSS-induced colitis and after recovery.

RESULTS

Nox4-/- mice showed increased endogenous TGF-β signaling in the colon, increased reactive oxygen species levels, intensive inflammation, and an increased fibrotic region after DSS treatment compared with wild-type mice. Bulk RNA sequencing confirmed involvement of canonical TGF-β signaling in fibrogenesis of the DSS-induced colitis model. Up-regulation of TGF-β signaling affects collagen activation and T-cell lineage commitment, increasing the susceptibility for inflammation.

CONCLUSIONS

Nox4 protects against injury and plays a crucial role in fibrogenesis in DSS-induced colitis through canonical TGF-β signaling regulation, highlighting a new treatment target.

Mise au point de l’utilisation de l’intelligence artificielle dans la prise en charge des maladies inflammatoires chroniques de l’intestin

Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.