The increasing availability of transcriptomic data has created new opportunities for integrating gene expression studies across biological systems and conditions. However, differences in experimental design, sequencing platforms, and sample composition introduce substantial heterogeneity, limiting direct comparability between studies. Transcriptomic meta-analysis provides a framework to address these challenges by identifying expression patterns that are reproducible across independent datasets. In this review, we outline the key methodological steps involved in transcriptomic meta-analysis, including dataset selection, preprocessing, normalization, batch-effect correction, and statistical integration. We discuss how these steps are influenced by the type of data being analyzed, from microarrays and bulk RNA sequencing to single-cell and spatial transcriptomics. Particular attention is given to the role of technical and biological heterogeneity, which must be explicitly considered to avoid misleading conclusions. Rather than treating heterogeneity solely as a source of noise, we argue that it defines the limits of reproducibility and interpretation in cross-study analyses. By focusing on consistent signals across diverse datasets, transcriptomic meta-analysis enables more robust biological inference and supports applications such as biomarker discovery and disease stratification.

The microbiota–gut–brain axis (MGBA) is a dynamic bidirectional communication network that integrates microbial, neural, endocrine, and immune signals to regulate host physiology and behavior. This review synthesizes current evidence on the molecular mechanisms by which gut microbiota modulate brain function through endocrine signaling (hypothalamic–pituitary–adrenal axis and sex hormones), neurotransmitter synthesis (serotonin, dopamine, GABA), microbial metabolites (short‐chain fatty acids, indoles, secondary bile acids), and neural pathways involving the enteric and autonomic nervous systems. It also highlights the role of cytokines and immune responses in maintaining intestinal and neurological homeostasis. Emerging findings underscore the ability of specific bacterial taxa and metabolites to influence neurodevelopment, behavior, and disease pathogenesis, including mood disorders, neurodegeneration, and autism spectrum disorders. However, the physiological relevance of many microbe‐derived signals in humans remains poorly defined. Much of the current mechanistic evidence supporting these interactions derives from animal models, underscoring the need for carefully designed human studies to establish physiological relevance and translational validity. Clarifying dose thresholds, tissue‐specific receptor responses, and bidirectional feedback loops is essential for advancing therapeutic applications. This review supports an integrative systems biology approach to understanding the MGBA, emphasizing the need for translational studies to validate preclinical findings in human physiological contexts.