Search Results (78)

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. Full article

Drought significantly disrupts rice productivity under increasing climate volatility. Identifying robust molecular determinants for resilience remains a critical priority for crop improvement. Following the PRISMA guidelines, we performed a large-scale, dual-platform meta-analysis of RNA-Seq and microarray datasets to elucidate the robust transcriptomic landscape of Oryza sativa underwater deficit. Tissue-specific regulatory pathways were identified using STRING, g:Profiler, and PANTHER. Our analysis resolved distinct functional divergence, where shoots prioritize photosynthetic adjustment while roots emphasize transcriptional and chromatin reprogramming. Beyond validating core ABA signaling, we uncover a novel metabolic pivot: the activation of glyoxylate and dicarboxylate metabolism to mitigate drought-induced carbon starvation. We further identify specialized transport systems for ions and electrons across organelle membranes, alongside cellular reorganization driven by autophagy and actin-dependent cytoskeleton remodeling. These findings highlight a sophisticated network of survival strategies governing energy conservation and structural adaptation. By synthesizing heterogeneous transcriptomics, this study reveals robust pathways that are overlooked in single-platform investigations. This work provides a prioritized roadmap for utilizing functional validation and precision breeding to accelerate the development of climate-resilient rice cultivars. Full article

The purpose of this study was to compare the retinal gene expression profiles in canines with Sudden Acquired Retinal Degeneration Syndrome (SARDS) and Cancer-Associated Retinopathy (CAR) and identify shared and distinct molecular pathways. Previously published SARDS and CAR canine retinal microarray data were used for the purposes of retinal transcriptomic pathway analysis, followed by KEGG and GO pathway enrichment analysis using DAVID and MetaCore tools. Gene expression patterns were analyzed to detect the most important signaling pathways. ProteinBERT deep-learning language model, and large language models (LLM-Grok 4, ChatGPT4o) were used for analytical prediction of possible drug targets. Both diseases showed significant upregulation in T-cell co-stimulation and complement activation pathways, including CD86, DLA-79, and C5AR1. Downregulated genes were enriched in pathways associated with visual perception and cardiomyocyte signaling. CAR exhibited upregulation of tumor-related chemokine signaling (e.g., CCR5, CXCR4), while SARDS showed pronounced enrichment in vascular inflammation pathways. Analysis of drug targets identified different classes of drugs, which could be potentially utilized for SARDS and CAR treatment. SARDS and CAR share immune-related molecular signatures but potentially differ in secondary mechanisms—vascular inflammation and endothelial activation in SARDS versus paraneoplastic mimicry in CAR. These data provide potential insight into the pathogenesis of SARDS as well as CAR, and identify potential diagnostic and therapeutic targets. Full article

Background/Objective: Although most melanocytic lesions are diagnosed as benign or malignant by histopathologic evaluation, with or without the aid of immunohistochemistry, diagnosis may remain uncertain in a minority of cases. Assessment of copy number abnormalities (CNAs) may provide sufficient additional evidence to favor either a benign or malignant diagnosis in both pediatric and adult cases and in melanocytic lesions of various subtypes, including Spitzoid, mucosal, and acral. CNAs are common in melanomas, while they are rare, with few exceptions, in benign lesions. Detection of CNAs by fluorescence in situ hybridization (FISH) and chromosomal microarray (CMA) has been well established for melanocytic lesions, with advantages and disadvantages for each. The objective of this meta-analysis was to evaluate the utility of CNA testing for the diagnosis of melanoma, across subtypes, when a lesion remains ambiguous after histopathologic and immunohistochemical assessment. In addition, the utility of CNAs to determine prognosis in established diagnoses of melanoma was also evaluated. Methods: The Cancer Genomics Consortium Working Group for Melanocytic Lesions reviewed published data from January 1998 through September 2022 of CNAs in melanocytic lesions detected by either FISH or CMA and conducted a meta-analysis of the findings. Results: Specific abnormalities common in primary cutaneous melanomas of various subtypes and uveal melanomas were enumerated. Differences in CNAs found in primary versus metastatic lesions were also determined, and published evidence for prognosis was summarized. Conclusions: The working group established evidence-based recommendations for the use of CNA testing for evaluation of ambiguous melanocytic lesions. Full article

Tumor-associated macrophages (TAMs) are pivotal in the clear cell renal cell carcinoma (ccRCC) microenvironment. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), a central enzyme in one-carbon metabolism, is increasingly recognized for its oncogenic roles in both cancer cells and immune compartments. We integrated bulk and single-cell transcriptomic datasets to interrogate the expression, prognostic impact, and immunomodulatory landscape of MTHFD2 in ccRCC. Robust differential expression, meta-analysis, Cox regression, and cell type deconvolution were performed. MTHFD2 expression and its association with prognosis were validated using tissue microarrays (TMAs), multiplex IHC, and in vitro macrophage polarization assays. MTHFD2 was upregulated in ccRCC tumors and associated with poor prognosis across multiple cohorts. High MTHFD2 expression remained an independent prognostic marker after adjustment for clinical stage. Single-cell analyses identified macrophages as the principal immune subpopulation expressing MTHFD2, with MTHFD2+ macrophages displaying a transcriptional signature of immunosuppression and metabolic adaptation. In vitro, MTHFD2-induced M2 macrophage polarization was reversed by DS18561882, promoting M1 polarization. MTHFD2 is a robust biomarker for poor prognosis in ccRCC, influencing tumor–immune interactions and macrophage polarization. Targeting MTHFD2 may represent a dual-action strategy to suppress tumor growth and reprogram the tumor immune microenvironment. Full article

Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide, for which aging represents the main risk factor. As the global elderly population expands, the prevalence of Alzheimer’s disease escalates rapidly. Notably, as AD brain lesions may start 15–20 years before the appearance of the first symptoms, early diagnosis or prognosis of AD is of paramount importance for better patient treatment. Based on the absence of effective cure or early diagnosis of AD, this meta-analysis investigates the differentially expressed genes between Alzheimer’s and a healthy brain and identifies genes that can serve as risk factors for the disease or biomarkers of diagnostic, prognostic, or pharmacological value. Microarray datasets were collected from public repositories, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Quality control and data normalization were performed. Differentially expressed gene (DEG) lists were created for each study and combined through a Mosteller–Bush meta-analysis, resulting in a final list of DEGs. This list was filtered using an adjusted p-value cut-off of 0.001, and the included statistically significant DEGs were subjected to enrichment analyses. A total of eight microarray studies were identified, producing a combined list of 4218 DEGs, of which 1944 were up-regulated and enriched for immune response processes, and 2274 were down-regulated and enriched for synapse-related pathways. This meta-analysis reveals a distinct transcriptomic profile in Alzheimer’s disease characterized by the prevalence of immune response and inflammation alongside the collapse of essential synaptic and neuronal signaling. Full article

Background: Diagnostic discrimination between ischemic stroke (IS) and hemorrhagic stroke (HS) is required for successful intervention with time-critical acute treatments. The available data on blood-based RNA biomarkers and discrimination between IS and HS are limited. This systematic review aimed to examine and summarize the existing literature on potentially useful blood-based RNA biomarkers that may aid in preclinical acute diagnosis. Methods: We systematically reviewed the literature on the ability of blood-based RNA biomarkers to discriminate between IS and HS according to PRISMA guidelines. We searched PubMed, EMBASE, The Cochrane Library, and The Web of Science for eligible randomized controlled trials, observational studies, and case–control studies published in the English language without time limitation. The risk of bias was evaluated using the Newcastle–Ottawa Scale. Results: We included eight studies with a total of 728 patients (436 with IS and 292 with HS) in our review. The study quality was good in five and fair in three investigations. No meta-analysis was performed due to high heterogeneity in methods and study endpoints. Reported biomarkers include miRNA-124-3p, miRNA-16, miRNA-340-5p, lncRNA XIST (X-inactive specific transcript), PFKFB3 mRNA (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase), tRNA derivatives, tRNA fragments, extracellular miRNAs, transcriptome changes, and MCEMP1 gene expression. Assessment techniques varied widely across studies, ranging from RNA sequencing to qPCR, microarray, human transcriptome array, and ELISA. MicroRNA-124-3p, miRNA-340-5p, lncRNA XIST, PFKFB3 mRNA, and MCEMP1 gene expression differed significantly between IS and HS. In one study, principal component analysis and unsupervised learning demonstrated the utility of hierarchical clustering of differentially expressed exons to discriminate between HS and IS. Conclusions: This review demonstrates the utility of single RNA-based targets and clusters that may have diagnostic value in distinguishing IS from HS. However, the current body of evidence is limited by considerable methodological heterogeneity between studies. Registration: This systematic review was prospectively registered on PROSPERO on 21 April 2023 (CRD42023411203). Full article

Background: The signal transducer and activator of transcription 5 (STAT5) proteins, STAT5A and STAT5B, are highly homologous transcription factors with distinct roles in cancer biology. While STAT5A has been characterized as a context-dependent modulator of tumor progression, the prognostic significance of STAT5B remains less clear. Here, we conducted a systematic meta-analysis of STAT5B to evaluate its association with overall survival across cancers and to compare its prognostic role with that of STAT5A, as reported previously. Methods: Microarray datasets from the Prognoscan database were analyzed for STAT5B expression and overall survival. Hazard ratios (HRs) were estimated using Cox proportional hazards models, and results from 42 datasets were synthesized by meta-analysis. Subgroup analyses were performed by cancer type, and heterogeneity was assessed using Cochran’s Q Test and I² statistics. Results: Pooled analysis showed that high STAT5B expression was significantly associated with favorable overall survival (lnHR = −0.4009; 95% CI: −0.6007 to −0.2011; p < 0.0001), albeit with notable heterogeneity (I² = 64%). Subgroup analyses indicated that STAT5B was particularly protective in lung cancers (lnHR = −0.5170; p = 0.0042) and hematologic malignancies (lnHR = −0.6988; p < 0.0001). In contrast, STAT5A demonstrated divergent effects, conferring favorable survival in breast cancer but poorer outcomes in hematologic cancers. Conclusions: Elevated STAT5B expression is associated with improved survival in multiple cancers, supporting a potential tumor-suppressive role distinct from STAT5A. These findings underscore the importance of isoform-specific STAT5 evaluation in cancer prognosis and suggest that STAT5B may serve as a potential biomarker and therapeutic target. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Huntington’s disease (HD) is a severe and progressive neurodegenerative disease for which therapeutic options have so far been confined to symptomatic treatment. Currently, the diagnosis relies on the signs and symptoms shown by patients; however, by that stage, the psychomotor issues have progressed to a point where reversal of the condition is unattainable. Although numerous clinical trials have been actively investigating therapeutic agents aimed at preventing the onset of disease or slowing down the disease progression, there has been a constant need for reliable biomarkers to assess neurodegeneration, monitor disease progression, and assess the efficacy of treatments accurately. Therefore, to discover the key biomarkers associated with the progression of HD, we employed bioinformatics and machine learning (ML) to create a robust pipeline that integrated differentially expressed gene (DEG) analysis with ML to select potential biomarkers. We performed a meta-analysis to identify DEGs using three Gene Expression Omnibus (GEO) microarray datasets from different platforms related to HD-affected brain tissue, applying both relaxed and strict criteria to identify differentially expressed genes. Subsequently, focusing only on genes identified through the inclusive threshold, we employed 19 diverse ML techniques to explore the common genes that contributed to the top three selected ML algorithms and the shared genes that had an impact on the ML algorithms and were observed in the meta-analysis using the stringent condition were selected. Additionally, a receiver operating characteristic (ROC) analysis was conducted on external datasets to validate the discriminatory power of the identified genes. Based on the results of an inverse variance weighted meta-analysis of the AUCs across both human and mouse cohorts, GABRD and PHACTR1 were identified as the most robust candidates and were selected as key biomarkers for HD. Our comprehensive methodology, which integrates DEG meta-analysis with ML techniques, enabled a systematic prioritization of these biomarkers, providing valuable insights into their biological significance and potential for further validation in clinical research. Full article

Rice blast caused by Magnaporthe oryzae (MOR) reigns as the top-most devastating disease affecting global rice production. Pattern-triggered immunity (PTI) is crucial for mitigating plant responses to pathogens. However, the transcriptional dynamics of PTI-related genes in rice response to MOR infection remain largely unexplored. In this study, we performed a meta-analysis of 201 RNA sequencing and 217 microarray datasets to investigate the transcriptional dynamics of rice under MOR infection at various infection stages. The transcriptional dynamics of extracellular/cytoplasmic receptor kinase genes (RLKs, RLCKs, WAKs) and downstream signaling intermediates, including mitogen-activated protein kinases (MAPKs) and Ca²⁺-related signaling genes, were identified as immunity hubs for PTI. Extracellular/cytoplasmic receptors were predominantly induced, in contrast to a marked decrease in the repression of these genes. Notably, a maximum of 141 and 154 receptor-based genes were frequently induced from the microarray and RNA-seq datasets, respectively. Moreover, 31 genes were consistently induced across all the transcriptomic profiles, highlighting their pivotal role in PTI-activating immunity regulation in rice under MOR stress. Furthermore, protein–protein interaction (PPI) analysis revealed that cytoplasmic receptor-based genes (RLCKs) and MAPK(K)s were highly interconnected. Among them, four core MAPKK genes, including SMG1, MKK1, MKK6, and MPKK10.2, were identified as the most frequently interconnected with receptor-based genes or other MAPKs under MOR infection, suggesting their critical role as intermediates during downstream signaling networks in response to MOR infection. Together, our comprehensive analysis provides insights into the transcriptional dynamics of receptor-based genes and downstream signaling intermediates as core PTI-related genes that can play crucial roles in modulating rice immune responses to MOR infection. Full article

Diabetic Nephropathy (DN) stands as a primary cause of end-stage renal disease and its etiology remains unclear. Thus, this study aims to construct a genetic panel with potential biomarkers linked to the inflammatory pathway of DN associated with the pathology’s susceptibility. Through a systematic review and meta-analysis, we selected observational studies in English, Portuguese, and Spanish, selected from the PubMed, SCOPUS, Virtual Health Library, Web of Science, and EMBASE databases. Additionally, a protein–protein interaction network was constructed to list hub genes, with differential expression analysis by microarray of kidneys with DN from the GSE30529 database to further refine results. Seventy-two articles were included, and 54 polymorphisms in 37 genes were associated with the inflammatory pathway of DN. Meta-analysis indicated a higher risk of complication associated with SNPs 59029 G/A, −511 C/T, VNTR 86 bp, −308 G/A, and −1031 T/C. Bioinformatics analyses identified differentially expressed hub genes, underscoring the scarcity of studies on CCL2 and VEGF-A genes in relation to DN. This study highlighted the intrinsic relationship between inflammatory activity in the etiology and progression of DN, enabling the effective application of precision medicine in diabetic patients for potential prognosis of the complications and contributing to cost reduction in the public health system. Full article

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon’s rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers. Full article

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson’s Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Models—GMMs, Expectation Maximization—EM, Logistic Regression—LoR, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data. Full article