Jul 2026· Journal of environmental biology· Vol 47, pp. 942-948· 0 citations
TL;DR
The identified genes, microRNAs, and pathways advance mechanistic understanding of disease vulnerability and may ultimately inform the development of biological markers and targeted therapeutic strategies for prostate cancer.
Abstract
Aim: This study aimed to investigate key genetic variants contributing to prostate cancer and their functional significance through integrative bioinformatic and molecular network analyses.
Methodology: GWAS data for prostate cancer were explored to identify important genetic variants and underlying biological pathways. Bioinformatic approaches were applied for enrichment analysis, protein–protein interaction network construction, and clustering to assess gene interactions. Regulatory mechanisms were examined through microRNA and transcription factor interaction analyses, with metabolomic data integrated to assess the impact of genetic variability on prostate cancer metabolism.
Results: Notable associations were identified for hsa-miR-2277-5p and hsa-miR-3944-3p, suggesting potential regulatory significance, although these did not retain significance following multiple testing correction. Reactome Pathway 2024 analysis identified Abacavir Transmembrane Transport as the most significantly enriched pathway (adjusted p = 0.00147; odds ratio = 429.33), alongside additional biologically relevant pathway associations.
Interpretation: This study highlights key genetic factors and regulatory elements potentially contributing to prostate cancer susceptibility and progression. The identified genes, microRNAs, and pathways advance mechanistic understanding of disease vulnerability and may ultimately inform the development of biological markers and targeted therapeutic strategies for prostate cancer.
Key words: Bioinformatics, GWAS, MicroRNA, Prostate cancer, Pathway enrichment
Background Prostate cancer (PCa) is a highly heterogeneous malignancy with complex genetic underpinnings. This study integrates multi-omics data to prioritize candidate susceptibility genes and evaluate their functional and clinical significance in PCa pathogenesis. Materials and methods We integrated PCa GWAS summary statistics with GTEx v8 expression quantitative trait locus reference panels to perform cross-tissue and single-tissue transcriptome-wide association studies. Candidate signals were refined using conditional analysis, MAGMA and fastBAT gene-level tests, Summary data-based Mendelian randomization, and Bayesian colocalization. Tumor-context cis-eQTL evidence from TCGA-PRAD was incorporated to prioritize regulatory signals retained in prostate cancer tissues. Prioritized candidates were further assessed using transcriptomic datasets, Human Protein Atlas immunohistochemistry, single-cell RNA-seq analysis, histological grading, preoperative PSA, and established genomic risk signatures. Gene network and pathway enrichment analyses were performed to explore potential biological context. Results The integrative genetic analyses identified 23 consensus candidate genes supported by multiple association frameworks. SMR and Bayesian colocalization further narrowed the candidate list, and tumor-context cis-eQTL analysis in TCGA-PRAD retained MLPH as the final prioritized candidate. The lead variant rs7582964 was significantly associated with MLPH expression in PRAD tumor tissues. MLPH was upregulated in PCa tissues compared with normal prostate tissues in TCGA-PRAD and showed concordant expression patterns in an independent GEO cohort and Human Protein Atlas immunohistochemistry data. Single-cell transcriptomic analysis localized MLPH expression mainly to epithelial cells, with the strongest signal observed in tumor epithelial cells. Clinically, MLPH expression was associated with histological differentiation, with reduced expression in the most poorly differentiated tumors. Lower MLPH expression also correlated with higher preoperative PSA and higher Decipher-like genomic risk scores. Functional analyses linked MLPH to vesicle-mediated transport, exocytosis, and hormone-related signaling pathways. Discussion and conclusion This integrative genomic analysis prioritizes MLPH as a candidate susceptibility gene for PCa and links its regulatory signal to tumor-context expression, protein-level evidence, cellular localization, and clinically relevant molecular features. These findings support a potential role for MLPH in PCa biology, particularly in relation to vesicle trafficking and tumor differentiation.
Runyi Wang, Jiayu Wang, Zhiyi Zhao et al.· Frontiers in Medicine· 0 citations
Aim: To identify and functionally characterise key genetic susceptibility loci for psoriasis using GWAS data integrated with pathway, network, and regulatory analyses.
Methodology: GWAS datasets were systematically analysed to identify genes and genetic variants associated with psoriasis. Functional enrichment analyses were performed to delineate key biological pathways, regulatory elements, and functional annotations. Furthermore, transcription factor and microRNA interactions with psoriasis-related genes were evaluated to provide mechanistic insights.
Results: Multiple genetic loci were identified, reaffirming the contribution of immune dysregulation and impaired skin barrier function in psoriasis. Functional enrichment highlighted significant pathways including cytokine signalling, NF-κB activation, and keratinocyte proliferation. Several transcription factors and miRNAs regulating psoriasis-associated genes were also identified, offering additional layers of gene expression control.
Interpretation: Integration of GWAS findings with pathway and regulatory network analyses enhances the understanding of psoriasis genetics. Novel susceptibility genes, transcriptional regulators, and potential biomarkers have been identified, which may serve as therapeutic targets. Future research should focus on experimental validation and clinical translation to advance precision medicine approaches in psoriasis management.
Key words: Genetic susceptibility, GWAS Psoriasis, Immune-related loci, Inflammatory skin disease, Psoriasis genetics
B. S. Sindhu, G.P. Chandrasekhar Naidu, Kukkapalli Prathap Kumar et al.· Journal of environmental bio...· 0 citations
Despite advances in targeted therapies, breast cancer remains one of the major challenges to global health. Although patient-derived organoids (PDOs) are physiologically relevant models, existing transcriptomic studies are limited by the poor integration of immune signals and the absence of shared biomarkers across subtypes. In this study, we hypothesised that an integrated transcriptomic and network-based analysis of PDOs could identify conserved transcriptional signatures and pathway interactions across breast cancer subtypes.
PDOs representative of multiple breast cancer subtypes were analysed and compared with non-tumour organoids. Differential gene expression analysis was performed to identify transcriptional alterations, followed by pathway enrichment analysis and protein-protein interaction network analysis to investigate functional pathways and molecular interactions. Candidate biomarkers identified through computational analyses were subsequently validated experimentally by assessing gene expression and cytokine secretion profiles in tumour PDOs.
A total of 646 differentially expressed genes were identified. Pathway enrichment analysis revealed translational machinery and ribosome biogenesis as the dominant statistically significant processes, consistent with hyperactivated protein synthesis in cancer cells. Although neurotrophin signaling pathways were not enriched at the transcriptome-wide level, network analysis identified a computationally predicted functional association between IL1RAP and NTRK3 in the STRING database. SLITRK3, IL1RAP and IRF2BP2 were consistently overexpressed in tumour PDOs and associated with activation of inflammatory pathways and increased secretion of IL-1 and IFN-γ. NTRK3 was identified as a direct network interactor of IL1RAP bridging inflammatory and neurotrophin signalling, though its transcriptional direction in BC PDOs could not be unambiguously resolved from the available data. Experimental validation confirmed the upregulation of SLITRK3, IL1RAP and IRF2BP2 together with elevated secretion of IL-1β and IFN-γ.
Our findings suggest that IL1RAP and IRF2BP2 may be important in immune-related and candidated biomarker identification in breast cancer PDOs. We integrated transcriptomic, enrichment and network-based analyses for a robust strategy for biomarker discovery. This methodologically transparent framework highlights potential targets in breast cancer. Rather than only a list of candidate genes, it provides an organoid-derived computational framework for risk stratification.
Unknown authors· Frontiers in Bioengineering...· 0 citations
Aim: This study aimed to explore the genetic basis of childhood obesity through systematic identification and examination of genes, pathways, and regulatory elements contributing to disease development using integrated bioinformatic approaches.
Methodology: Thirty genes strongly associated with childhood obesity were subjected to detailed computational analysis using DisGeNET, Gene Ontology (GO), and WikiPathways. Gene–gene interaction patterns, biological processes, molecular functions, and cellular components were examined. Regulatory layers involving transcription factor and microRNA interactions were additionally analysed to characterise genetic and post-transcriptional control mechanisms.
Results: Significantly enriched pathways included adipogenesis, orexin receptor signalling, hunger and satiety regulation, and broader metabolic control mechanisms. Key genes including LEP, IL6, POMC and ADIPOQ recurred across multiple analyses, indicating central roles within obesity-associated molecular networks. Cross-database integration revealed complex genetic interactions and molecular cross-talk influencing obesity-related phenotypes.
Interpretation: This study delineates a comprehensive genetic and molecular landscape underlying childhood obesity, reflecting its multifactorial pathogenesis. The identification of key regulatory genes, transcription factors, and microRNA interactions provides valuable insight into potential molecular targets, with implications for improving future prevention and management strategies for childhood obesity.
Key words: Bioinformatics, Childhood obesity, Gene pathways, Molecular networks, Therapeutic targets
T. Govardhan, J. Brahmaiah, P. Kumar et al.· Journal of environmental bio...· 0 citations
Ovarian cancer exhibits clinical heterogeneity; reliable prognostic stratification remains challenging despite extensive biomarker research. We performed an integrative analysis of transcriptomic and proteomic data to identify prognostically distinct molecular clusters in ovarian cancer. Using The Cancer Genome Atlas dataset, RNA sequencing and reverse–phase protein array data from 146 overlapping samples were analyzed. Genes (n = 150) and proteins (n = 67) showing nominal associations with overall survival (p < 0.05) were integrated; k–means clustering identified two patient clusters with highly significant differences in survival (p = 8.59 × 10−15). The SAR2 and ACTN4 genes and their corresponding protein expression levels showed high correlations among the significant genes and proteins (correlation coefficients > 0.80). Gene set enrichment analysis revealed that genes regulated by the PAX2 and BCL6 transcription factors were significantly enriched. The identified clusters were validated across six independent public transcriptomic datasets comprising 617 patients; consistent survival separation was observed between the patient clusters (p < 0.05). Clustering based on immunohistochemistry–derived expression profiles of nine proteins in an independent patient cohort demonstrated a significant difference in survival (p = 0.04). Integrative analysis of multi–omics data provided robust prognostic stratification in ovarian cancer while capturing the underlying molecular and biological features.
Jisup Kim, Seong Beom Cho· International Journal of Mol...· 0 citations