High-throughput technologies now make it possible to simultaneously generate genomic, transcriptomic, epigenomic, proteomic, and metabolomic data. Spatial and single-cell platforms, in turn, provide cellular and spatial information in greater detail. Relying on a single omics method captures only a fraction of this complexity and often overlooks interactions across different layers. Despite these advances, clinical applications remain limited. This review examines integration strategies, along with their applications in various diseases, and the machine learning methods used to implement them. Rather than focusing solely on predictive performance, the aim is to support biological reasoning by formulating testable hypotheses, drawing causal inferences, and gaining insights that can guide clinical decisions. A reanalysis of a publicly available chronic lymphocytic leukemia dataset revealed that one data type was missing in approximately one-third of patients. When early and intermediate integration were combined with simple zero imputation, agreement dropped to chance and near-chance levels. In contrast, late integration, which does not require data imputation, was largely unaffected by this issue. This demonstrates that findings regarding integration strategies cannot be considered in isolation from how missing data are handled. The review also covers approaches that directly address missing data. Major barriers to clinical application include confounding batch effects, unreliable feature attribution when features are correlated, and deficiencies in clinical quality assurance. This review proposes four criteria to help identify models that not only ensure statistical validity but also genuinely support biological reasoning: mechanistic specificity, causal grounding, failure transparency, and decision relevance.
The present study investigated the transcriptional differences between black skinned Kadaknath and white skinned Broiler chickens using RNA sequencing analysis of skin tissue. A total of 339 high confidence, differentially expressed genes with a log₂ fold change ≥ 4 were identified, of which 294 were upregulated in Kadaknath while 45 were upregulated in Broiler skin samples. Comparative gene expression analysis revealed strong activation of melanogenesis related pathways in black skin, indicating enhanced pigment synthesis and melanocyte activity. Pathway enrichment also underscored the role of biological processes related to melanin biosynthesis, ion homeostasis and mitochondrial regulation in Kadaknath. In contrast, Broiler skin showed enrichment of pathways associated with developmental regulation and tissue patterning, suggesting a greater role of transcriptional programs involved in skin development and structural organization. Gene co-expression network analysis also revealed distinct regulatory architectures between the two groups. Key genes identified in black skin were associated with structural and cytoskeletal functions, while developmental transcription factors and regulatory genes were enriched in white skin. These findings suggest that transcriptomic differences between the two chicken types are regulated by pigment synthesis pathways and developmental regulatory networks in in black and white skin, respectively. This study provides valuable insights into the transcriptional profiles associated with diverse skin types in chicken.
Biliary atresia (BA) is a devastating neonatal cholestatic disease characterized by progressive fibro-obliteration of the extrahepatic bile ducts. Although adipokine signaling is implicated in liver pathology, its key mediators in BA remain unclear. We analyzed BA transcriptomic data (GSE46960) to identify differentially expressed genes (DEGs) and derived an adipokine-associated gene set (AdipoBA-GeneSet). An ensemble of 101 machine learning algorithms was employed to prioritize key genes. The diagnostic value, associated biological pathways, and immune infiltration patterns of key genes were investigated. The role of Leucine-Rich Alpha-2-Glycoprotein 1 (LRG1) was validated in a biliatresone-induced BA mouse model by upregulating its expression. We identified 522 DEGs in BA livers, refined to a 30-gene AdipoBA-GeneSet. Machine learning prioritized seven key genes from this adipokine-related gene set (LEPR, SPP1, LRG1, IL32, FBN1, IGF1, and SAA1). These genes showed excellent performance in distinguishing BA patients from normal controls and were linked to extracellular matrix organization and immune pathways. Immune infiltration analysis revealed that key gene expression correlated with specific immune cell subsets. Notably, the adipokine LRG1 was downregulated in BA, and its overexpression alleviated the disease phenotype, as evidenced by improved body weight, decreased bile duct wall-to-lumen ratio, attenuated hepatic fibrosis, and ameliorated hepatic steatosis. This study identifies a novel adipokine-related gene signature in BA and highlights LRG1 as a key protector. LRG1 overexpression mitigates cholestatic injury and fibrotic progression in vivo, positioning it as a promising therapeutic target.
Jasmonic acid (JA) is a central precursor in jasmonate metabolism, and its conjugate jasmonoyl-isoleucine (JA-Ile) serves as the major bioactive jasmonate that regulates plant growth, development, and responses to biotic and abiotic stresses. While extensive research has focused on JA-Ile-mediated signal transduction, particularly the COI1-JAZ-MYC module, less attention has been paid to the regulatory and broader systemic roles of genes involved in jasmonate biosynthesis and catabolism. This review systematically summarizes the multifaceted roles of key genes in the jasmonate biosynthesis and degradation pathway, such as LOXs, AOSs, AOCs, OPRs, ACXs, MFPs, KATs, JAR1, CYP94s and JOXs/JAOs, in plant growth and development, as well as in responses to biotic and abiotic stresses. We highlight that current evidence suggests that jasmonate biosynthesis and degradation are regulated through a complex network involving multiple gene families operating across different subcellular compartments. These genes are also reported to be associated with interactions between jasmonate metabolism and other signaling pathways, including light and hormones such as auxin, cytokinin, and ethylene. Finally, we identify current research gaps and propose future directions, including tissue-specific mechanisms, multi-signal integration, and applications in non-model plants.
Anoikis resistance is a hallmark of malignant tumor cells, particularly those with high metastatic potential, and it has recently been closely linked to tumor immune evasion. Accumulating evidence links anoikis resistance to the immunosuppressive tumor microenvironment in which an anoikis-related index derived from a prognostic anoikis-related gene (ARG) signature stratifies patients into distinct immune landscapes and immune phenotypes. This review systematically summarizes the multiple roles of genes comprising this prognostic ARG signature in tumor immune regulation, including their association with immune cell recruitment and function, their utility as predictive biomarkers for prognosis and immunotherapy response, and therapeutic strategies targeting anoikis-related pathways to overcome resistance and reverse immune suppression. Moreover, the associations between the ARG signature characteristics, immunophenotype, and treatment response are not entirely consistent, varying not only across cancer types but also within the same cancer type when different gene signatures are used. Notably, while numerous transcriptomic studies have correlated ARG signatures with immune features across cancers, functional validation remains limited. Although the tumor cell-autonomous functions of some ARG signatures have been supported experimentally, systematic evidence directly demonstrating a causal relationship between specific ARG signatures and immune cell function remains scarce. By highlighting current progress and mechanistic gaps, this review aims to provide a theoretical foundation and potential targets for developing next-generation combination immunotherapies. Clinical trial number: Not applicable.
