OMICS. 2025 Oct 16. doi: 10.1177/15578100251387518. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD) remains the most common subtype of lung cancer, characterized by high heterogeneity and poor survival outcomes. Although transcriptomic and metabolomic alterations have been individually studied, integrated multi-omics analyses are needed to uncover the convergent pathways that drive tumor progression. Differentially expressed genes (DEGs) were identified from the GSE229253 transcriptomic dataset comprising LUAD tumor and adjacent normal tissues, while significantly altered metabolites were obtained from the Lung Cancer Metabolome Database. The top 10 DEGs and metabolites were analyzed using the search tool for interacting chemicals (STITCH) to construct gene-metabolite networks, and Integrated Molecular Pathway Level Analysis (IMPaLA) was employed for integrated pathway enrichment to identify overlapping molecular processes. Transcriptomic profiling revealed 973 DEGs (410 upregulated and 563 downregulated), and metabolomic analysis identified significant alterations in metabolites linked to redox balance, amino acid derivatives, and nucleotide metabolism. Integration through STITCH generated a network of 16 nodes and 9 edges, highlighting gene-metabolite associations of probable biological relevance. Joint pathway enrichment analysis using IMPaLA consistently identified glycosylation-related pathways, particularly O-linked glycosylation of mucins, as major axes of convergence between transcriptomic and metabolomic alterations in LUAD (joint p = 0.00129-0.00434). Several genes (B3GNT6, FEZF1-AS1, and LCAL1) and metabolites (isoleucylleucine, leucylleucine, and isoleucylvaline) are probable novel candidates, warranting further investigation. These findings provide systems-level evidence that aberrant glycosylation is likely a central hallmark of LUAD, underscore the potential of glycosylation pathways as biomarkers and therapeutic targets, and demonstrate the utility of cross-omics approaches to unpack the molecular complexity of lung cancer.

PMID:41103242 | DOI:10.1177/15578100251387518

In Silico Pharmacol. 2025 Oct 17;13(3):154. doi: 10.1007/s40203-025-00440-3. eCollection 2025.

ABSTRACT

Breast, ovarian, lung, cervical, and colorectal cancers are among the most prevalent malignancies affecting women worldwide. This study aimed to elucidate the common molecular mechanisms of tumorigenesis and identify potential biomarkers using an integrative bioinformatics and network-based approach. Integrative profiling of five microarray datasets identified 66 differentially expressed genes (DEGs) that are common across five cancer types. Gene ontology and KEGG pathway analyses of common DEGs were performed using the DAVID database. The cell cycle processes were the most enriched functions, and oocyte meiosis, oocyte maturation, the p53 signaling pathway, cancer pathways, and cellular senescence were the most important pathways identified. Protein-protein interaction (PPI) networks for the DEGs were constructed using the STRING database, and the resulting networks were visualized in Cytoscape. Through PPI network analysis, ten hub genes were identified, and subsequent survival analysis confirmed that CHEK1, DLGAP5, CCNB2, and CCNA2 are significantly associated with poor patient survivability, establishing them as common biomarkers across multiple cancer types. Subsequently, ten transcription factors (TFs) and ten post-transcriptional regulators were identified through the assessment of regulatory networks involving TFs-DEGs and miRNAs-DEGs. Finally, drug-gene association analysis from the GSCA library was used to anticipate drug-like compounds using the drug repurposing approach. Overall, this comprehensive investigation holds promise for future in vitro and in vivo studies, offering a molecular foundation for the diagnosis, prognosis, and treatment of malignant cancers.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00440-3.

PMID:41113171 | PMC:PMC12534660 | DOI:10.1007/s40203-025-00440-3

Breast Cancer (Dove Med Press). 2025 Sep 25;17:851-861. doi: 10.2147/BCTT.S542704. eCollection 2025.

ABSTRACT

Despite substantial progress in the diagnosis and treatment of breast cancer, current therapeutic regimens exhibit limitations, necessitating the identification of more robust biomarkers to optimize personalized strategies. Circulating tumor DNA (ctDNA), as a non-invasive liquid biopsy modality, overcomes the inherent constraints of biopsies in capturing tumor heterogeneity. Accumulating evidence from prospective cohort studies demonstrates the clinical utility of ctDNA in risk stratification, guidance of therapeutic decision-making, recurrence surveillance and other clinical applications. Furthermore, ctDNA profiling enhances real-time pharmacodynamic monitoring and accelerates drug development by identifying molecular responders. The methodical requirements and challenges inherent in implementing liquid biopsy assessments in the clinic are examined. These encompass critical pre-analytical variables, the need for highly sensitive and specific analytical techniques, standardization of assays and bioinformatics pipelines across laboratories and the complexities of interpreting results. This review synthesizes current evidence supporting ctDNA integration into breast cancer management frameworks and systematically addresses its methodological challenges and clinical limitations.

PMID:41036092 | PMC:PMC12479222 | DOI:10.2147/BCTT.S542704

Biomedicines. 2025 Sep 11;13(9):2238. doi: 10.3390/biomedicines13092238.

ABSTRACT

Background:HTR1F (5-Hydroxytryptamine Receptor 1F) encodes a G protein-coupled receptor involved in serotonin signaling. Although dysregulated HTR1F expression has been implicated in certain malignancies, its biological functions and clinical significance across cancer types remain largely unexplored. Methods: We performed an integrative pan-cancer analysis of transcriptomic and pharmacogenomic datasets covering 34 cancer types (PAN-CAN cohort, N = 19,131; normal tissues, G = 60,499). Drug sensitivity and molecular docking analyses were conducted using the GSCALite database. The protein-protein interaction (PPI) network of HTR1F was constructed via the STRING database. Additionally, we evaluated the effects of HTR1F overexpression on proliferation and invasion in human lung squamous cell carcinoma (LUSC) cell lines NCI-H520 and NCI-H226. Results:HTR1F expression was significantly upregulated in 17 cancer types and was associated with poor prognosis, with LUSC showing an AUC of 0.912 for 1-year survival prediction. In LUSC, 695 genes were upregulated and 67 downregulated in response to HTR1F overexpression. HTR1F expression correlated with immune-related genes, immune checkpoints, tumor-infiltrating immune cells, tumor mutation burden (TMB), microsatellite instability (MSI), and drug responses. Genomic alterations, including amplification and deletion, were positively associated with HTR1F expression. Drug sensitivity analysis identified compounds such as sotrastaurin (-10.2 kcal/mol), austocystin D (-9.7 kcal/mol), and tivozanib (-9.3 kcal/mol) as potentially effective inhibitors based on predicted binding affinity. Functional enrichment analyses (GO, KEGG) and GSEA revealed that HTR1F is primarily involved in cell cycle regulation, DNA replication, cellular senescence, and immune-related pathways. Functional validation showed that HTR1F overexpression promotes proliferation of LUSC cells via the MAPK signaling pathway. Conclusions: Our integrative analysis highlights HTR1F as a potential biomarker associated with prognosis, immune modulation, and drug sensitivity across multiple cancer types. These findings provide a foundation for future experimental and clinical studies to explore HTR1F-targeted therapies.

PMID:41007799 | PMC:PMC12467612 | DOI:10.3390/biomedicines13092238

Nat Commun. 2025 Sep 25;16(1):8388. doi: 10.1038/s41467-025-63050-9.

ABSTRACT

Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration.

PMID:40998789 | PMC:PMC12462499 | DOI:10.1038/s41467-025-63050-9