OMICS: A Journal of Integrative Biology

Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal interstitial lung disease (ILD) characterized by abnormal epithelial cell behavior and excessive extracellular matrix deposition. Despite advances in understanding its molecular pathogenesis, the lack of early diagnostic biomarkers and effective targeted therapies remains a critical barrier. Metabolomics is the comprehensive profiling of low-molecular-weight metabolites and offers an emerging lens to unpack the complex metabolic reprogramming in IPF. This expert review discusses (1) current metabolomics approaches used in IPF research and (2) the key dysregulated metabolic pathways and their potential in improving diagnosis, prognostication, and treatment response monitoring. Furthermore, the review outlines the key metabolic signatures identified in non-IPF ILDs as well and compares their roles with those observed in IPF, thereby providing a broader perspective on shared and disease-specific metabolic alterations across the ILD spectrum.

Multiple sclerosis (MS) poses a significant challenge in global health, with increasing incidence rates and profound implications that transcend the geographical boundaries. Recent literature has explored the relationship between MS and serum uric acid (SUA) levels, yielding inconclusive findings. A high SUA level is associated with several chronic disorders and has planetary health significance. Explaining person-to-person variations in SUA is therefore important. In this overarching context, despite a multitude of studies on the putative MS and SUA relationship, limitations such as small sample sizes and inconsistent outcomes persist, highlighting the current gaps in understanding this complex relationship. Here, we report a two-sample Mendelian randomization (MR) study that was conducted to estimate causal effects between MS as the exposure and SUA as the outcome. Our analysis leveraged extensive cohort datasets from publicly accessible genome-wide association studies. The inverse variance weighted method in MR indicated that the odds ratios (ORs) of SUA level per unit increase for MS were 1.649 (95% confidence interval [CI] of OR: 1.09–2.488; p = 0.017) and 23.11 (95% CI of OR: 7.04–75.84; p = 2.23 × 10⁻⁷). Leave-one-out sensitivity analyses, horizontal pleiotropy, and Cochran’s Q tests showed robustness of results. This study offers support for a causal association between MS incidence and elevated SUA levels. Pleiotropic tests and sensitivity analyses confirmed minimal horizontal pleiotropy effects and the robustness of the causal association. This MR study provides a causal effect between the incidence of MS and SUA level increase.

The classification of immune and nonimmune genes in cattle is crucial for understanding immune mechanisms and their link to disease resistance. Traditional methods rely on manual curation and conventional bioinformatics tools, which are often time-consuming and labor-intensive. We introduce ImmFinder, a multimodal fully connected neural network (FCNN) framework designed to classify immune genes by integrating genomic and transcriptomic datasets. ImmFinder achieved an accuracy of 85.67%, an F1-score of 0.85, a precision of 0.86, and a recall of 0.85, demonstrating strong predictive performance. Additionally, the area under the curve-receiver operating characteristic (AUC-ROC) curve scores of 0.9250 (test set) and 0.9264 (validation set) further validate its robustness. These findings highlight the potential of a multimodal deep learning approach for immune gene classification, advancing functional genomics in cattle. The limitations of ImmFinder include reliance on the available bovine genomic and transcriptomic datasets used for training and evaluation, which may constrain immediate generalization to other breeds or species; additional external validation and experimental follow-up will be required to confirm biological hypotheses derived from model predictions. Currently, ImmFinder demonstrates the value of multimodal data fusion for functional gene annotation and provides a scalable baseline for integrating data types, such as genomics and transcriptomics. In future work, we will expand the training cohorts, broaden the range of data modalities, and pursue experimental validation of high-confidence model predictions. ImmFinder is implemented in Python, and all datasets, training models, preprocessing, and model development scripts are available on GitHub.

Cancer is a disease with heterogenous molecular signatures that ought to be unpacked to achieve the overarching aim of precision oncology. A pan-cancer omics approach provides a systems science framework to explore shared and distinct mechanisms across cancers. We report here pan-cancer analyses of gene expression data from 17 cancers, for example, adrenocortical cancer, lung cancer, kidney cancer, and colorectal cancer, and 26 tissue types, using public datasets to construct disease-specific transcriptional networks. Using the hypergeometric test, 1005 microRNAs (miRNAs), 314 transcription factors (TFs), and 332 receptors were identified as regulatory molecules interacting with differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to explore their functional roles. Accordingly, we found miR-124-3p, miR-6799-5p, and miR-7106-5p as common miRNAs; Specificity Protein 1 (SP1), RELA Proto-Oncogene, NF-κB Subunit (RELA), and Nuclear Factor Kappa B Subunit 1 (NFKB1) as shared TFs; Cyclin-Dependent Kinase 2 (CDK2), Histone Deacetylase 1 (HDAC1), and ABL Proto-Oncogene 1, Non-Receptor Tyrosine Kinase (ABL1) as common receptors; and pathways in cancer, PI3K-Akt signaling, and p53 signaling as commonly enriched. Survival analysis in an independent dataset confirmed these findings: SP1 and NFKB1 were significant in 9 cancers, RELA in 6, whereas CDK2, HDAC1, and ABL1 were significant in 11, 10, and 10 cancers, respectively, out of the 17 cancers researched herein. In conclusion, these findings provide system-level insights on tumor heterogeneity and inform future cancer classification, for example, according to shared and distinct molecular signatures and development of therapies that might prove effective across several cancers. We underline that unpacking molecular signatures across multiple cancers also offers new prospects to move beyond the “One Drug, One Disease” paradigm of pharmaceutical innovation.