Cell Rep Med. 2026 Oct 2:103085. doi: 10.1016/j.xcrm.2026.103085. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

PMID:42826719 | DOI:10.1016/j.xcrm.2026.103085

Cell Rep Med. 2026 Oct 2:103085. doi: 10.1016/j.xcrm.2026.103085. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

PMID:42826719 | DOI:10.1016/j.xcrm.2026.103085

Front Immunol. 2026 Mar 20;17:1787094. doi: 10.3389/fimmu.2026.1787094. eCollection 2026.

ABSTRACT

Pulmonary fibrosis (PF) significantly increases the risk of lung cancer (LC), but the mechanisms underlying this transition remain unclear. This overview positions macrophage heterogeneity as a central node within the PF-LC continuum. First, we describe important subpopulations of profibrotic and pro-tumor macrophages, including SPP1+, MERTK+, TREM2+, and MARCO+ cells, using high-resolution spatial and single-cell omics technologies. Next, we analyze the fundamental mechanisms that determine their function: the fibrotic microenvironment (e.g., extracellular matrix stiffness, hypoxia) induces profound metabolic reprogramming (e.g., Warburg effect, lipid peroxidation) and stabilizes epigenetic memory (e.g., DNA methylation, histone modifications), locking them into a pathogenic state. This reprogramming occurs through two main pathways: (1) metabolic reprogramming, characterized by aerobic glycolytic conversion and dysregulated lipid metabolism, which stimulates both pathogenic functions and suppression of T cell activity; (2) Epigenetic modifications, including stabilized alterations in DNA methylation, histone modifications, and superactivator patterns, which maintain cells in a tumor-promoting phenotype. As central nodes of communication, these macrophages interact pathologically with fibroblasts and epithelial cells through secreted factors and extracellular vesicles, forming self-reinforcing feedback loops that promote disease progression. We are studying the crucial role of new technologies, particularly multi-omic spatial models and high-precision organoids, in fostering mechanistic discoveries. These discoveries pave the way for new macrophage-focused therapeutic strategies, including the precise stratification of patients using biomarkers from liquid biopsies (such as soluble SPP1 and MARCO) and the development of targeted drug delivery systems for the selective modulation of macrophage function, thus establishing a new paradigm for therapeutic interventions in pulmonary fibrosis with concomitant lung cancer.

PMID:41939908 | PMC:PMC13046558 | DOI:10.3389/fimmu.2026.1787094

Front Immunol. 2026 Mar 20;17:1787094. doi: 10.3389/fimmu.2026.1787094. eCollection 2026.

ABSTRACT

Pulmonary fibrosis (PF) significantly increases the risk of lung cancer (LC), but the mechanisms underlying this transition remain unclear. This overview positions macrophage heterogeneity as a central node within the PF-LC continuum. First, we describe important subpopulations of profibrotic and pro-tumor macrophages, including SPP1+, MERTK+, TREM2+, and MARCO+ cells, using high-resolution spatial and single-cell omics technologies. Next, we analyze the fundamental mechanisms that determine their function: the fibrotic microenvironment (e.g., extracellular matrix stiffness, hypoxia) induces profound metabolic reprogramming (e.g., Warburg effect, lipid peroxidation) and stabilizes epigenetic memory (e.g., DNA methylation, histone modifications), locking them into a pathogenic state. This reprogramming occurs through two main pathways: (1) metabolic reprogramming, characterized by aerobic glycolytic conversion and dysregulated lipid metabolism, which stimulates both pathogenic functions and suppression of T cell activity; (2) Epigenetic modifications, including stabilized alterations in DNA methylation, histone modifications, and superactivator patterns, which maintain cells in a tumor-promoting phenotype. As central nodes of communication, these macrophages interact pathologically with fibroblasts and epithelial cells through secreted factors and extracellular vesicles, forming self-reinforcing feedback loops that promote disease progression. We are studying the crucial role of new technologies, particularly multi-omic spatial models and high-precision organoids, in fostering mechanistic discoveries. These discoveries pave the way for new macrophage-focused therapeutic strategies, including the precise stratification of patients using biomarkers from liquid biopsies (such as soluble SPP1 and MARCO) and the development of targeted drug delivery systems for the selective modulation of macrophage function, thus establishing a new paradigm for therapeutic interventions in pulmonary fibrosis with concomitant lung cancer.

PMID:41939908 | PMC:PMC13046558 | DOI:10.3389/fimmu.2026.1787094

Cancers (Basel). 2026 Feb 25;18(5):735. doi: 10.3390/cancers18050735.

ABSTRACT

BACKGROUND: Lactate accumulation is increasingly recognized as a feature of tumor metabolic reprogramming that can coincide with immune dysregulation and aggressive phenotypes. The prognostic and immunologic relevance of lactate-associated heterogeneity in lung cancer remains to be clarified.

METHODS: We curated lactate-related genes and identified prognostic candidates in lung cancer cohorts. Consensus clustering was applied to define lactate-associated molecular subtypes, followed by characterization of survival and tumor microenvironment features. A LASSO-based gene signature was developed to generate an individual-level risk score and an integrated nomogram. Multi-omics analyses were used to evaluate concordance between transcriptomic and proteomic alterations. Single-cell transcriptomic data were analyzed to explore cellular heterogeneity in lactate-related programs. In vitro assays evaluated the response of candidate genes to lactate exposure and assessed cell migration and invasion under proliferation-inhibited conditions after genetic perturbation.

RESULTS: Two lactate-associated molecular subtypes were identified with distinct overall survival and divergent immune microenvironment features. Subtype 1 was associated with better outcomes and a more immune-inflamed profile, whereas Subtype 2 was associated with poorer outcomes and a myeloid-enriched, immunosuppressive contexture. Pathway analyses indicated subtype-associated differences in extracellular matrix-related processes and apoptosis-associated signaling. We developed an 11-gene prognostic signature and nomogram that stratified patients by risk across TCGA and GEO cohorts. Multi-omics integration highlighted ANLN, FGA, and DKK1 as consistently dysregulated at both transcript and protein levels. Among these candidates, DKK1 showed lactate-responsive induction in vitro. DKK1 perturbation altered lactate-enhanced migratory and invasive phenotypes and was accompanied by changes in intracellular lactate levels and global protein lactylation, supporting a potential feedforward relationship between lactate exposure, DKK1 expression, and lactylation.

CONCLUSIONS: This study characterizes lactate-associated molecular heterogeneity in lung cancer and provides a lactate-related subtype framework and prognostic risk model for patient stratification. The findings nominate DKK1 as a lactate-responsive candidate linked to migration/invasion phenotypes and lactate/lactylation changes in vitro.

PMID:41827671 | PMC:PMC12985219 | DOI:10.3390/cancers18050735

Cancers (Basel). 2026 Feb 25;18(5):735. doi: 10.3390/cancers18050735.

ABSTRACT

BACKGROUND: Lactate accumulation is increasingly recognized as a feature of tumor metabolic reprogramming that can coincide with immune dysregulation and aggressive phenotypes. The prognostic and immunologic relevance of lactate-associated heterogeneity in lung cancer remains to be clarified.

METHODS: We curated lactate-related genes and identified prognostic candidates in lung cancer cohorts. Consensus clustering was applied to define lactate-associated molecular subtypes, followed by characterization of survival and tumor microenvironment features. A LASSO-based gene signature was developed to generate an individual-level risk score and an integrated nomogram. Multi-omics analyses were used to evaluate concordance between transcriptomic and proteomic alterations. Single-cell transcriptomic data were analyzed to explore cellular heterogeneity in lactate-related programs. In vitro assays evaluated the response of candidate genes to lactate exposure and assessed cell migration and invasion under proliferation-inhibited conditions after genetic perturbation.

RESULTS: Two lactate-associated molecular subtypes were identified with distinct overall survival and divergent immune microenvironment features. Subtype 1 was associated with better outcomes and a more immune-inflamed profile, whereas Subtype 2 was associated with poorer outcomes and a myeloid-enriched, immunosuppressive contexture. Pathway analyses indicated subtype-associated differences in extracellular matrix-related processes and apoptosis-associated signaling. We developed an 11-gene prognostic signature and nomogram that stratified patients by risk across TCGA and GEO cohorts. Multi-omics integration highlighted ANLN, FGA, and DKK1 as consistently dysregulated at both transcript and protein levels. Among these candidates, DKK1 showed lactate-responsive induction in vitro. DKK1 perturbation altered lactate-enhanced migratory and invasive phenotypes and was accompanied by changes in intracellular lactate levels and global protein lactylation, supporting a potential feedforward relationship between lactate exposure, DKK1 expression, and lactylation.

CONCLUSIONS: This study characterizes lactate-associated molecular heterogeneity in lung cancer and provides a lactate-related subtype framework and prognostic risk model for patient stratification. The findings nominate DKK1 as a lactate-responsive candidate linked to migration/invasion phenotypes and lactate/lactylation changes in vitro.

PMID:41827671 | PMC:PMC12985219 | DOI:10.3390/cancers18050735

Mol Divers. 2026 Mar 12. doi: 10.1007/s11030-026-11508-3. Online ahead of print.

ABSTRACT

Gastric cancer (GC) risk is shaped by environmental exposures such as benzo[a]pyrene (BaP). Here, we systematically identified BaP-toxicological targets and dissected their contribution to GC development. BaP-related targets were independently predicted with stringent filters from ChEMBL, Similarity Ensemble Approach (SEA) and PharmMapper databases, while GC-related targets were mined from the Comparative Toxicogenomics Database (CTD), GeneCards and OMIM databases. Overlapping targets were subjected to protein-protein interaction (PPI) network construction, functional enrichment analysis and molecular docking. We then integrated multi-omics data using ten clustering algorithms to identify the consensus GC subtypes, which were subsequently employed 101 machine learning combinations to develop a consensus benzo[a]pyrene-related signature (CBRS) for GC patients. As a result, we identified seven hub toxicological targets: ALB, HSP90AA1, ESR1, INS, TP53, TNF, and EGFR, underscoring their potential central roles in BaP-driven GC pathogenesis. These targets are enriched in the MAPK, Lipid and atherosclerosis, and PI3K-Akt signaling pathway. The BaP-toxicological classifiers and the CBRS prognostic model could provide useful support for risk stratification and inform personalized therapeutic strategies for GC patients. Molecular docking results suggest that BaP exhibits relatively strong binding affinity with these key toxicological targets, potentially implicating their involvement in BaP-induced gastric cancer toxicity. Therefore, this study integrates multi-dimensional omics data with advanced machine learning algorithms to establish a comprehensive analytical framework for the toxicological effects of between BaP and GC, which transcends the limitations of traditional analyses and offers unprecedented insights and evidence chains for elucidating the pathogenesis of GC.

PMID:41817952 | DOI:10.1007/s11030-026-11508-3

Genes Immun. 2026 Mar 10. doi: 10.1038/s41435-026-00382-6. Online ahead of print.

ABSTRACT

The prognosis for hepatocellular carcinoma remains grim. Combining radiotherapy with immune checkpoint blockade (ICB) has shown potential to enhance therapeutic outcomes, yet there is a pressing need for further advancements. Our previous research demonstrated that this combined approach suppresses ALKBH5 gene expression and increases m6A modification levels in hepatocellular carcinoma tissues. High-throughput sequencing and detailed molecular analysis revealed that inhibiting ALKBH5 amplifies CIITA m6A modifications post-therapy. This modulation triggers MHC II molecule expression in tumors, facilitating the presentation of tumor-associated antigens to CD4 + T lymphocytes and the recruitment of CD8 + T cells for an anti-tumor immune response. Building on these findings, we engineered a CIITA vector with a specific site mutation to confirm that the regulation of CIITA by the combined radiotherapy and immunotherapy is mediated through m6A methylation. Consequently, we established a comprehensive network involving ALKBH5, CIITA, MHC II, and CD4+ and CD8 + T cells. To elucidate the role and underlying molecular mechanisms of this combined therapy in reshaping the tumor immune microenvironment for hepatocellular carcinoma, we employed multi-omics approaches across in vitro, animal model, and clinical multi-dimensional studies, offering novel insights for enhancing treatment efficacy.

PMID:41807814 | DOI:10.1038/s41435-026-00382-6