Front Mol Biosci. 2025 Nov 18;12:1697083. doi: 10.3389/fmolb.2025.1697083. eCollection 2025.

ABSTRACT

BACKGROUND: EIF3M, a core subunit of eukaryotic translation initiation factor 3, plays a pivotal role in protein synthesis by regulating the assembly of the 43S initiation complex. However, its biological functions in cancer remain poorly understood. To further investigate the clinical translational value and underlying mechanisms of EIF3M in tumors, this study conducted comprehensive bioinformatic analysis of EIF3M across various tumor types.

METHODS: We utilized publicly available databases to perform a comprehensive bioinformatics analysis of EIF3M's biological roles in oncogenesis, aiming to elucidate its pan-cancer expression patterns and prognostic significance. Furthermore, we conducted an integrative multi-omics analysis incorporating methylation profiling, co-expressed gene networks, targeted miRNA interactions, and tumor immune microenvironment infiltration to decipher the complex regulatory architecture and biological pathways mediated by EIF3M across cancer types. Finally, we used HCC cell lines for in vitro functional validation, determining how EIF3M expression modulates malignant phenotypic behaviors in hepatocellular carcinoma.

RESULTS: EIF3M was overexpressed in multiple cancers and correlated with advanced tumor stage and poor survival. Its dysregulation was primarily driven by gene amplification and regulated by promoter methylation and miRNAs. EIF3M functioned as a hub in cell cycle and transcriptional networks and was linked to an immunosuppressive microenvironment. In hepatocellular carcinoma models, EIF3M modulated tumor proliferation, migration, and activated oncogenic pathways like Wnt/β-catenin.

CONCLUSION: This study reveals that EIF3M expression correlates with immune infiltration and poor prognosis in multiple cancers. In vitro experiments in hepatocellular carcinoma models demonstrated that EIF3M critically regulates malignant cell behaviors. Collectively, our findings highlight EIF3M's value as a promising pan-cancer biomarker worthy of further investigation for its utility in prognosis prediction and as an indicator of immunotherapeutic response.

PMID:41341921 | PMC:PMC12669982 | DOI:10.3389/fmolb.2025.1697083

Front Mol Biosci. 2025 Nov 18;12:1697083. doi: 10.3389/fmolb.2025.1697083. eCollection 2025.

ABSTRACT

BACKGROUND: EIF3M, a core subunit of eukaryotic translation initiation factor 3, plays a pivotal role in protein synthesis by regulating the assembly of the 43S initiation complex. However, its biological functions in cancer remain poorly understood. To further investigate the clinical translational value and underlying mechanisms of EIF3M in tumors, this study conducted comprehensive bioinformatic analysis of EIF3M across various tumor types.

METHODS: We utilized publicly available databases to perform a comprehensive bioinformatics analysis of EIF3M's biological roles in oncogenesis, aiming to elucidate its pan-cancer expression patterns and prognostic significance. Furthermore, we conducted an integrative multi-omics analysis incorporating methylation profiling, co-expressed gene networks, targeted miRNA interactions, and tumor immune microenvironment infiltration to decipher the complex regulatory architecture and biological pathways mediated by EIF3M across cancer types. Finally, we used HCC cell lines for in vitro functional validation, determining how EIF3M expression modulates malignant phenotypic behaviors in hepatocellular carcinoma.

RESULTS: EIF3M was overexpressed in multiple cancers and correlated with advanced tumor stage and poor survival. Its dysregulation was primarily driven by gene amplification and regulated by promoter methylation and miRNAs. EIF3M functioned as a hub in cell cycle and transcriptional networks and was linked to an immunosuppressive microenvironment. In hepatocellular carcinoma models, EIF3M modulated tumor proliferation, migration, and activated oncogenic pathways like Wnt/β-catenin.

CONCLUSION: This study reveals that EIF3M expression correlates with immune infiltration and poor prognosis in multiple cancers. In vitro experiments in hepatocellular carcinoma models demonstrated that EIF3M critically regulates malignant cell behaviors. Collectively, our findings highlight EIF3M's value as a promising pan-cancer biomarker worthy of further investigation for its utility in prognosis prediction and as an indicator of immunotherapeutic response.

PMID:41341921 | PMC:PMC12669982 | DOI:10.3389/fmolb.2025.1697083

BMC Bioinformatics. 2025 Dec 3;26(1):290. doi: 10.1186/s12859-025-06319-6.

ABSTRACT

Lung cancer is one of the most prevalent malignant tumors with high morbidity and mortality rates worldwide. Extensive multi-omics analyses have revealed significant intratumoral heterogeneity even within the same histopathological subtype. However, a database that systematically integrates multi-omics data for lung cancer research has long been lacking. Here, we developed LOSTdb, a molecular subtype annotation system for lung cancer that integrates multi-omics data and metadata. LOSTdb comprises 295 multi-omics datasets, including bulk RNA-seq, genomic, proteomic, methylation, and scRNA-seq data, with over 10,000 manually curated metadata entries. This resource encompasses high-quality clinical specimens, mouse models, and cell lines, totaling 34,393 samples and more than 1.2 million single cells. Each omics sample was annotated with both literature-based classical subtypes and NMF-derived meta-program (MP) subtypes. The platform supports cross-searching of omics and metadata at the gene and dataset levels, offers multiple visualization and analysis methods, and includes five tool modules, enabling functions such as integrated analysis, significance analysis between metadata as well as between genes and metadata, and target prediction for lung cancer molecular subtypes, serving as an essential tool for lung cancer precision medicine. LOSTdb is a user-friendly interactive database freely accessible at http://lostdbcancer.com:8080 .

PMID:41339793 | DOI:10.1186/s12859-025-06319-6

Mol Cancer. 2025 Dec 3. doi: 10.1186/s12943-025-02521-5. Online ahead of print.

ABSTRACT

Gut microbiota-derived metabolites are emerging as systemic "remote immunoregulators" that shape tumor immunity across tissues. Integrating evidence across short-chain fatty acids, tryptophan derivatives, secondary bile acids, polyamines and other metabolites, we advance a metabolite-immune pathway-cancer framework that links receptor-mediated signaling, epigenetic remodeling and metabolic reprogramming to context-dependent, bidirectional immune effects. Importantly, in addition to the g protein-coupled receptor / aryl hydrocarbon receptor pathway, the selected microbial small molecule metabolites are the true T-cell receptor ligands of unconventional T cells, directly shaping the tissue resident immune and tumor microenvironment, supplementing the receptor signaling and epigenetic programs in our framework. We synthesize how these metabolites recalibrate the tumor immune microenvironment-modulating antigen presentation, T-cell effector fitness and exhaustion, regulatory T-cell activity, and myeloid polarization-and why the same metabolite can either potentiate immune surveillance or entrench immunosuppression depending on ligand-receptor pairing, dose and tissue niche. We compare tumor-type specific patterns (e.g., colorectal, liver, lung, breast and prostate cancers) to highlight common circuits and organ-restricted idiosyncrasies. Methodologically, we outline how single-cell and spatial multi-omics, imaging mass spectrometry and functional biosensors now enable co-registration of metabolite exposure with immune-cell states in human tumors, providing an actionable basis for biomarker discovery. Given ongoing debate about signals attributed to intratumoral microbiota in low-biomass tumor tissues, we foreground quantifiable, spatially mappable and pharmacologically tractable metabolite-receptor pathways, using microbe-associated molecular patterns / translocation as comparators to judge when chemical signals should be prioritized as intervention targets. Finally, we evaluate precision intervention avenues-including fecal microbiota transplantation, rational bacterial consortia, engineered microbes and nanoparticle-enabled metabolite delivery-and propose stratification rules that pair metabolite/receptor signatures with fit-for-purpose delivery. Together, mapping tissue-specific metabolite-immune circuits and embedding them in robust biomarker frameworks may convert microbial metabolites from correlative markers into therapeutic targets and tools, improving the efficacy and durability of cancer immunotherapy.

PMID:41339918 | DOI:10.1186/s12943-025-02521-5

BMC Bioinformatics. 2025 Dec 3;26(1):290. doi: 10.1186/s12859-025-06319-6.

ABSTRACT

Lung cancer is one of the most prevalent malignant tumors with high morbidity and mortality rates worldwide. Extensive multi-omics analyses have revealed significant intratumoral heterogeneity even within the same histopathological subtype. However, a database that systematically integrates multi-omics data for lung cancer research has long been lacking. Here, we developed LOSTdb, a molecular subtype annotation system for lung cancer that integrates multi-omics data and metadata. LOSTdb comprises 295 multi-omics datasets, including bulk RNA-seq, genomic, proteomic, methylation, and scRNA-seq data, with over 10,000 manually curated metadata entries. This resource encompasses high-quality clinical specimens, mouse models, and cell lines, totaling 34,393 samples and more than 1.2 million single cells. Each omics sample was annotated with both literature-based classical subtypes and NMF-derived meta-program (MP) subtypes. The platform supports cross-searching of omics and metadata at the gene and dataset levels, offers multiple visualization and analysis methods, and includes five tool modules, enabling functions such as integrated analysis, significance analysis between metadata as well as between genes and metadata, and target prediction for lung cancer molecular subtypes, serving as an essential tool for lung cancer precision medicine. LOSTdb is a user-friendly interactive database freely accessible at http://lostdbcancer.com:8080 .

PMID:41339793 | PMC:PMC12676782 | DOI:10.1186/s12859-025-06319-6

Mol Cancer. 2025 Dec 2. doi: 10.1186/s12943-025-02514-4. Online ahead of print.

ABSTRACT

BACKGROUND: Greater understanding of differential therapeutic sensitivity, specifically to immunotherapy, in small-cell lung cancer (SCLC) is required.

METHODS: We explored SCLC heterogeneity through integrated molecular characterization of tumor tissue samples from 159 treatment-naive patients, utilizing genetic, epigenetic, transcriptional, and proteomic profiling, immunohistochemistry staining for multiple biologically relevant markers including transcriptional subtype-defining proteins, and spatial immune profiling using multiplex immunofluorescence.

RESULTS: Multi-omics analysis confirmed high heterogeneity across/within neuroendocrine and non-neuroendocrine subtypes. Methylomics analysis identified four methylome clusters that may enhance subtype prediction, prognosis, and longitudinal monitoring of subtype evolution. Immunohistochemistry analysis showed high MHC-I expression in non-neuroendocrine subtypes, which have greatest potential benefit from adding immunotherapy to chemotherapy; high DLL3 expression associated with neuroendocrine subtypes and an immune-cold tumor microenvironment. Multiplex immunofluorescence demonstrated associations of MHC-I with spatial arrangement and phenotypic features of immune cells in the tumor microenvironment of high-MHC-I-expressing SCLC, providing mechanistic rationale for MHC-I as a potential biomarker of immunotherapy response.

CONCLUSIONS: This multimodal profiling analysis provides further insights into the biologic complexity of SCLC and highlights potential therapeutic vulnerabilities of distinct disease subtypes.

PMID:41331472 | DOI:10.1186/s12943-025-02514-4