Harnessing single-cell and multi-omics insights: STING pathway-based predictive signature for immunotherapy response in lung adenocarcinoma DOI Creative Commons
Yang Ding, Dingli Wang,

Dali Yan

и другие.

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Апрель 16, 2025

Background Lung adenocarcinoma is the most prevalent type of small-cell carcinoma, with a poor prognosis. For advanced-stage patients, efficacy immunotherapy suboptimal. The STING signaling pathway plays pivotal role in lung adenocarcinoma; therefore, further investigation into relationship between and warranted. Methods We conducted comprehensive analysis integrating single-cell RNA sequencing (scRNA-seq) data bulk transcriptomic profiles from public databases (GEO, TCGA). pathway-related genes were identified through Genecard database. Advanced bioinformatics analyses using R packages (Seurat, CellChat) revealed heterogeneity, intercellular communication networks, immune landscape characteristics. developed signature (STINGsig) 101 machine learning frameworks. functional significance ERRFI1, key component STINGsig, was validated mouse models multicolor flow cytometry, particularly examining its enhancing antitumor immunity potential synergy α-PD1 therapy. Results Our characterized 15 distinct cell populations, including epithelial cells, macrophages, fibroblasts, T B endothelial each unique marker gene profiles. activity scoring elevated activation neutrophils, contrasting lower inflammatory macrophages. Cell-cell demonstrated enhanced interaction networks high-STING-score evident fibroblasts cells. STINGsig showed robust prognostic value microenvironment characteristics risk groups. Notably, ERRFI1 knockdown experiments confirmed significant modulating therapy response. Conclusion STING-related exhibited expression levels across high-score cells showing tumor-promoting pathways, active interactions, enrichment IFI27+ In contrast, low-score associated phenotypes reduced activity. (STINGsig), which linked to microenvironment. Through vivo experiments, we that critical within significantly enhances synergizes cancer model, underscoring therapeutic responses.

Язык: Английский

Ivonescimab in non-small cell lung cancer: harmonizing immunotherapy and anti-angiogenesis DOI
Yan Zhang, Xinyu Liu, Shengxiang Ren

и другие.

Expert Opinion on Biological Therapy, Год журнала: 2025, Номер unknown

Опубликована: Март 31, 2025

Introduction Immunotherapy combined with anti-angiogenesis has become a useful strategy in cancer treatment. Ivonescimab, the first approved bispecific antibody targeting both immune checkpoint inhibition and anti-angiogenesis, represents breakthrough over conventional dual-drug combination approach. The emerging clinical evidence demonstrates promising efficacy manageable safety profile of ivonescimab treatment non-small cell lung (NSCLC), suggesting its potential role as cornerstone next generation immunotherapy.

Язык: Английский

Процитировано

0
Unravelling Cancer Immunity: Coagulation.Sig and BIRC2 as Predictive Immunotherapeutic Architects DOI Creative Commons

Ziang Yao,

Jun Fan,

Yucheng Bai

и другие.

Journal of Cellular and Molecular Medicine, Год журнала: 2025, Номер 29(7)

Опубликована: Март 30, 2025

ABSTRACT Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals significant positive correlation between coagulation scores and immune‐related gene expression across 30 diverse types. Notably, tumours exhibiting high demonstrated enhanced infiltration of cytotoxic immune cells, including CD8 + T natural killer (NK) macrophages. Leveraging the TCGA pan‐cancer database, we developed Coagulation.Sig model, sophisticated predictive framework utilising coagulation‐related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis ten ICI‐treated cohorts, identified validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 IFNG, which form foundation our model. Functional analyses revealed that low‐risk characterised by higher cell populations, particularly superior ICI responses. These also exhibited increased mutation rates, elevated neoantigen loads, greater TCR/BCR diversity. Conversely, high‐risk displayed pronounced intratumor heterogeneity (ITH) NRF2 pathway activity, mechanisms strongly associated with evasion. Experimental validation highlighted BIRC2 as promising therapeutic target. Targeted knockdown, when combined anti‐PD‐1 significantly suppressed tumour growth, infiltration, amplified IFN‐γ TNF‐α secretion models. findings position model novel, approach personalised treatment, emerging both biomarker potential intervention point.

Язык: Английский

Процитировано

0
Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer DOI Creative Commons

Qinqing Wu,

Heng Cao,

J. Jin

и другие.

Frontiers in Molecular Biosciences, Год журнала: 2025, Номер 12

Опубликована: Апрель 4, 2025

Background Breast cancer (BRCA) is a significant threat to women’s health worldwide, and its progression closely associated with the tumor microenvironment gene regulation. Lactylation modification, as key epigenetic mechanism in biology, has not yet been fully elucidated context of BRCA. This study examines regulatory mechanisms lactylation-related genes (LRGs), specifically PRDX1, their prognostic significance Methods We integrated data from multiple databases, including Genome-Wide Association Study (GWAS) summary statistics, single-cell RNA sequencing, spatial transcriptomics, bulk sequencing The Cancer Genome Atlas (TCGA) Gene Expression Omnibus (GEO) databases. Using Summary-based Mendelian Randomization (SMR) analysis, we identified LRGs BRCA comprehensively analysed expression patterns cell-cell communication networks, heterogeneity. Furthermore, constructed validated model based on profile PRDX1-positive monocytes, evaluating it through Cox regression LASSO analyses. Results PRDX1 was LRG significantly risk (p_SMR = 0.0026). Single-cell analysis revealed upregulation enhanced between monocytes fibroblasts. Spatial transcriptomics uncovered heterogeneous nest regions, highlighting interaction demonstrated high accuracy predicting patient survival both training validation cohorts. High-risk patients exhibited immune-suppressive characteristics, reduced immune cell infiltration checkpoint expression. Conclusion reveals role progression, mainly regulation escape mechanisms. prediction shows robust potential, future research should focus integrating other biomarkers enhance precision personalised medicine.

Язык: Английский

Процитировано

0
Harnessing single-cell and multi-omics insights: STING pathway-based predictive signature for immunotherapy response in lung adenocarcinoma DOI Creative Commons
Yang Ding, Dingli Wang,

Dali Yan

и другие.

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Апрель 16, 2025

Background Lung adenocarcinoma is the most prevalent type of small-cell carcinoma, with a poor prognosis. For advanced-stage patients, efficacy immunotherapy suboptimal. The STING signaling pathway plays pivotal role in lung adenocarcinoma; therefore, further investigation into relationship between and warranted. Methods We conducted comprehensive analysis integrating single-cell RNA sequencing (scRNA-seq) data bulk transcriptomic profiles from public databases (GEO, TCGA). pathway-related genes were identified through Genecard database. Advanced bioinformatics analyses using R packages (Seurat, CellChat) revealed heterogeneity, intercellular communication networks, immune landscape characteristics. developed signature (STINGsig) 101 machine learning frameworks. functional significance ERRFI1, key component STINGsig, was validated mouse models multicolor flow cytometry, particularly examining its enhancing antitumor immunity potential synergy α-PD1 therapy. Results Our characterized 15 distinct cell populations, including epithelial cells, macrophages, fibroblasts, T B endothelial each unique marker gene profiles. activity scoring elevated activation neutrophils, contrasting lower inflammatory macrophages. Cell-cell demonstrated enhanced interaction networks high-STING-score evident fibroblasts cells. STINGsig showed robust prognostic value microenvironment characteristics risk groups. Notably, ERRFI1 knockdown experiments confirmed significant modulating therapy response. Conclusion STING-related exhibited expression levels across high-score cells showing tumor-promoting pathways, active interactions, enrichment IFI27+ In contrast, low-score associated phenotypes reduced activity. (STINGsig), which linked to microenvironment. Through vivo experiments, we that critical within significantly enhances synergizes cancer model, underscoring therapeutic responses.

Язык: Английский

Процитировано

0