FASEB J. 2025 Aug 31;39(16):e70978. doi: 10.1096/fj.202500722RR.

ABSTRACT

Ribophorin 1 (RPN1), a key component of the oligosaccharyltransferase complex, is implicated in tumor progression through glycosylation-mediated pathways, yet its pan-cancer roles remain unexplored. This study presents a comprehensive multi-omics analysis of RPN1 across 33 cancers such as sarcoma (SARC), integrating genomic, transcriptomic, and proteomic data from TCGA, GTEx, and CPTAC. RPN1 was significantly overexpressed in 14 malignancies and correlated with advanced tumor stages and poor prognosis in glioblastoma (GBM), lower-grade glioma, SARC and hepatocellular carcinoma, validated in an independent glioma cohort (n = 151). Genomically, RPN1 amplification linked to homologous recombination deficiency and elevated tumor mutational burden, suggesting a role in genomic instability. Critically, multiplex immunofluorescence demonstrates RPN1 overexpression colocalizes with CD206⁺ M2 macrophages in tumor microenvironments, while in vitro coculture experiments confirm RPN1-dependent microglial recruitment and M2 polarization. RPN1 expression negatively correlates with CD8⁺ T cell infiltration and predicts resistance to chemotherapy (GBM, ovarian cancer) and immunotherapy (GBM, esophageal carcinoma), though it associates with PD-1 inhibitor sensitivity in bladder cancer. Functional validation shows RPN1 knockdown suppresses proliferation, migration, and invasion in GBM cells. Pathway enrichment connects RPN1 to endoplasmic reticulum stress, glycosylation, DNA repair, and immune checkpoint regulation. These findings position RPN1 as a multimodal oncogenic driver promoting genomic instability, immunosuppressive microenvironment remodeling, and context-dependent therapeutic vulnerabilities across cancers.

PMID:40857034 | DOI:10.1096/fj.202500722RR

Hereditas. 2025 Jul 7;162(1):121. doi: 10.1186/s41065-025-00487-4.

ABSTRACT

Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism-related gene sets, WMRCA + outperformed widely used clustering algorithms-including iCluster, SNF, NMF, CC, and CNMF-achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA .

PMID:40624602 | PMC:PMC12235908 | DOI:10.1186/s41065-025-00487-4