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PubMed2026

RECQL correlates with immune infiltration and serves as a prognostic biomarker and therapeutic predictor in gastric cancer.

BACKGROUND: RecQ-like helicase (RECQL), a member of the RecQ-like DNA helicase family, plays a crucial role in maintaining genomic stability. However, its relevance in gastric cancer (GC) has not been fully investigated. This study aimed to explore the clinical significance, biological functions, and potential role of RECQL in the tumor immune microenvironment of GC through comprehensive bioinformatics analyses and in vitro experiments. METHODS: Weighted gene co-expression network analysis (WGCNA), differential expression analysis, and least absolute shrinkage and selection operator (LASSO) regression were performed using public datasets [The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE150290] to identify key genes associated with GC progression. Subsequently, key pathways were identified through functional enrichment analysis, while immune infiltration and spatial transcriptomic analyses were conducted to characterize RECQL expression and its association with the tumor immune microenvironment. Finally, the effects of RECQL knockdown on the biological function of GC cells were assessed through Cell Counting Kit-8 (CCK-8), colony formation, scratch, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assays. RESULTS: RECQL was significantly upregulated in GC tissues and correlated with advanced clinical stage and poor prognosis. Gene set enrichment analysis (GSEA) revealed a strong association between high RECQL expression and DNA repair pathway. Immune infiltration analysis indicated significant enrichment of M2 macrophages in the high-RECQL group, along with upregulation of immune checkpoint molecules including PDCD1, CTLA4, and CD274. Spatial transcriptomics further demonstrated co-localization of RECQL with myeloid cell-enriched regions in tumor parenchymal areas. Furthermore, in vitro experimental results indicated that RECQL was highly expressed in GC cell lines, and its knockdown effectively inhibited the viability, proliferation, and migration capabilities of HGC-27 cells, while enhancing their apoptosis. CONCLUSIONS: RECQL serves as a promising biomarker and potential therapeutic target in GC.

DNA repair↗

PubMed2026

A per- and polyfluoroalkyl substances-based gene signature links prognosis to immune landscapes in thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy with a rising global incidence and significant heterogeneity. Although per- and polyfluoroalkyl substances (PFAS) exposure is linked to thyroid dysfunction, the prognostic value of per- and polyfluoroalkyl substances-related genes (PFASRGs) and their role in the tumor immune microenvironment (TME) remain poorly understood. This study aims to systematically screen key PFASRGs and evaluate their prognostic value as biomarkers for THCA. METHODS: Utilizing The Cancer Genome Atlas (TCGA)-THCA transcriptomic data and PFASRGs, we constructed a prognostic model through differential expression analysis, univariate and multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO). The model's robustness was validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, and clinical nomograms. Furthermore, the TME, immunotherapy response, and drug sensitivities were systematically evaluated. Distinct molecular landscapes were characterized by stratifying the cohort via unsupervised consensus clustering analysis. RESULTS: The eight-gene prognostic model demonstrated robust performance, with area under the curve (AUC) values exceeding 0.85 across all validation cohorts. High-risk patients exhibited significantly shorter overall survival and an "inflamed" TME characterized by high immune scores and checkpoint expression. In contrast, the therapeutic efficacy of anti-programmed death-ligand 1 (PD-L1) agents was more pronounced in the low-risk category, as evidenced by a superior objective response. Furthermore, distinct molecular subtypes and risk-specific sensitivities to targeted agents, such as sorafenib and sunitinib, were identified, highlighting the model's clinical utility for personalized treatment. CONCLUSIONS: We established a novel THCA prognostic framework based on eight PFASRGs. This model exhibits superior performance in risk stratification, effectively distinguishing cohorts with divergent clinical trajectories, unique immune microenvironment features, and varied therapeutic responses. Our findings provide a powerful predictive tool for refining prognostic evaluation and facilitating the implementation of personalized management strategies for THCA patients.

Per- and polyfluoroalkyl substances-related genes ↗

PubMed2025

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies. OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data. METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes. RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts. CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

Humans↗

PubMed2026

Spatial biology reveals altered macrophage states in immunosuppressed non-melanoma skin cancer.

Immunosuppressed patients with non-melanoma skin cancer experience worse clinical outcomes, yet the tumor immune microenvironment associated with systemic immunosuppression remains incompletely defined. Using integrated single-cell, spatial transcriptomic, multiplex immunofluorescence, and spatial epigenomic profiling across immunocompetent and immunosuppressed tumors, we found that overall immune-cell composition was largely preserved despite differences in immune-cell distribution, spatial organization, and T cell clonality. Immunosuppressed tumors demonstrated reduced intratumoral macrophage densities, decreased T cell clonal diversity, altered antigen-presenting cell and T cell spatial interactions, and distinct fibroblast- and macrophage-associated spatial niches. Multi-cohort validation across complementary spatial and single-cell platforms identified consistent alterations in innate-adaptive immune organization in immunosuppressed tumors. Together, these findings define spatial and functional remodeling of the tumor immune microenvironment under systemic immunosuppression and provide a framework for future therapeutic investigation in high-risk patients.

Humans↗

PubMed2026

Machine learning-based integration develops a novel lysosome-related prognostic signature associated with prognosis and immune infiltration landscape in acute myeloid leukemia.

BACKGROUND: Lysosomes are essential for intracellular degradation and recycling, and changes in their function significantly contribute to tumor growth. Nonetheless, the exact role of lysosome-related genes (LRGs) in the pathogenesis of acute myeloid leukemia (AML) is still inadequately comprehended. METHODS: Differentially expressed LRGs (DE-LRGs) between AML and control groups were identified using AML-related data extracted from the Gene Expression Omnibus (GEO). The LRGs-related prognostic genes were identified and the risk model was established using univariate COX regression analysis and machine learning algorithms, based on the data obtained from The Cancer Genome Atlas (TCGA). Subsequently, we performed comprehensive analyses regarding clinical features, functional pathways, immune microenvironment, and chemotherapeutic drugs sensitivity between the high- and low-risk groups. Reverse transcription Quantitative polymerase chain reaction (RT-qPCR) and western blot were adopted to validate the expression of prognostic genes in human bone marrow-derived cell line HS-27 A and human AML cell line MOLM-13. RESULTS: Through comprehensive analysis, a risk model was developed utilizing ten LRGs (ATP6V0E2, CALCRL, TMEM165, GZMB, HCK, TCIRG1, CD1D, GPRASP1, ABCA1, and NAGA), and this model was further validated using GEO datasets. Significant differences in clinical characteristics, functional pathways, immune microenvironment characteristics, and chemotherapeutic drug sensitivity were observed between the two risk groups In vitro validation experiment illustrated that the expression trends of ATP6V0E2, TMEM165, and ABCA1 were consistent with our bioinformatics analysis. CONCLUSION: Our study demonstrates that lysosome-associated signature might forecast the prognosis of AML patients and offer guidance for subsequent immunotherapy and chemotherapy strategies.

Acute myeloid leukemia↗

PubMed2026

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗

PubMed2025

A multifaceted investigation into the impact of m6A methylation-related genes on pancreatic cancer, integrating insights from various databases and foundational experimental research.

BACKGROUND: Despite advances in surgical techniques, immunotherapy, the mortality rate associated with pancreatic cancer (PC) has been on the rise in recent years. Understanding the importance of RNA N6-methyladenosine (m6A) in PC is critical for prognosis, tumor microenvironment, and immunotherapy efficacy. The study aims to identify m6A methylation regulators that play an important role in the development and progression of PC by mining databases. The effect of insulin-like growth factor-binding protein 3 (IGFBP3) on pancreatic tumors was explored, and the related mechanisms were explored. METHODS: We analyzed the expression of m6A regulators in PC by digging deeper into the datasets of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases, and analyzed its relationship with the prognosis of patients with PC, looking for m6A methylation regulators that play an important role in the development and progression of PC. Reuse the ConsensusClusterPlus package, Cox analysis, and unsupervised clustering to delineate three distinct m6A clusters - designated as m6A cluster A, m6A cluster B, and m6A cluster C single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses evaluated the different pathway roles of these clusters in the development and progression of PC. Finally, the cell lines with IGFBP3 overexpression and knockdown were constructed by lentivirus transfection, the transfection effect was identified by WB, and the effects of IGFBP3 overexpression/knockdown on the survival and growth of PC cell lines were verified by cell cloning experiments and cell counting kit-8 experiments, and the possible related pathways were explored by KEGG. RESULTS: Most m6A regulatory factors are highly expressed in PC, and their high expression is negatively correlated with the prognosis of patients with PC. Furthermore, m6A regulatory factors may influence the occurrence and development of PC through metabolic pathways, stroma activation pathways, immune regulatory processes, and the immune microenvironment. Finally, the overexpression of IGFBP3 promoted the growth of PC cells, and vice versa. CONCLUSIONS: Most m6A regulatory factors are differentially expressed in PC and are associated with the prognosis of patients with PC, potentially influencing the occurrence and development of PC through pathways such as the immune microenvironment. The overexpression of IGFBP3 can promote the growth of PC cells and vice versa.

IGFBP3↗

PubMed2026

Integrative subtyping by bile acid metabolism identifies CLCA1/UGT2A3/ZG16 as markers of immune dysfunction and poor prognosis in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the primary driver of cancer-related death and illness across the world. Despite the full-scale shift of the treatment approach for some colorectal cancer patients due to the use of immune checkpoint inhibitors (ICIs), primary resistance still poses a huge challenge to clinicians. Bile acid metabolism is involved in the pathogenesis of CRC. However, its particular function in shaping the tumor immune microenvironment (TIME) and its effect on prognosis and immune treatment response remain unclear. METHODS: Based on the transcriptome and clinical data from The Cancer Genome Atlas-Colon Adenocarcinoma (TCGA-COAD) cohort, we performed unsupervised consensus clustering and classified patients into different molecular subtypes according to bile acid metabolism. We subsequently compared overall survival (OS), immune cell infiltration levels, and differentially expressed genes among the subtypes. In addition, protein-protein interaction (PPI) network and Cox proportional hazards regression were used to identify key hub genes. Finally, the expression of these crucial hub genes was validated in the Gene Expression Omnibus (GEO) cohort and independent clinical patients. RESULTS: The bile-low group showed a significant reduction in OS time (p = 0.0049). The infiltration levels of CD8+ T cells (p < 0.05) and M1 macrophages (p < 0.01) were significantly higher in the bile-low group than in the bile-high group. We identified three key genes-CLCA1, UGT2A3, and ZG16-and found that they all were downregulated in tumor tissues across the TCGA-COAD and GEO datasets, as well as in independent clinical samples. Survival analysis showed that high CLCA1 expression was significantly associated with favorable overall survival (p < 0.001), whereas UGT2A3 (p = 0.23) and ZG16 (p = 0.17) did not reach statistical significance. The three hub genes were negatively correlated with the (TIDE) score (CLCA1: R = - 0.24, p < 0.001; UGT2A3: R = - 0.15, p = 0.0022; ZG16: R = - 0.14, p = 0.0039). CONCLUSION: Our findings suggest that bile acid metabolism could shape the TIME via key genes CLCA1, UGT2A3, and ZG16, and subsequently modify CRC prognosis and immunotherapy responses. These genes may serve as potential prognostic indicators and mechanistic mediators linking bile acid metabolism to T-cell dysfunction, offering insights for future combination strategies targeting the metabolism-barrier-immunity axis.

CLCA1↗

PubMed2026

Oral melanoma in the immunotherapy era: Immune evasion, resistance, and therapeutic opportunities.

Oral melanoma (OM) is a rare and highly aggressive mucosal malignancy associated with poor survival and limited evidence to guide immunotherapy. This narrative review synthesizes current knowledge on OM immunobiology and its therapeutic implications. OM differs from cutaneous melanoma in its origin in sun-protected sites, genomic architecture, and heterogeneous immune microenvironments, features that can contribute to attenuated responses to immune checkpoint inhibitors. Anti-PD-1-based therapy has demonstrated clinical activity in mucosal melanoma, and selected OM cases have shown meaningful responses, including in multimodal and perioperative settings. However, OM-specific prospective data remain sparse, and the available evidence is largely derived from pooled mucosal melanoma cohorts or case reports. Emerging combination strategies, such as antiangiogenic agents, radiotherapy, and perioperative immunotherapy, remain insufficiently validated. This review critically reappraises the available evidence, identifies key knowledge gaps, and outlines future directions for biomarker-driven, OM-specific translational research.

Humans↗

PubMed2025

m6A regulator-based molecular classification and hub genes associated with immune infiltration characteristics and clinical outcomes in diffuse gliomas.

BACKGROUND: m6A methylation modification is a new regulatory mechanism involved in tumorigenesis and tumor-immunity interaction. However, its impact on glioma immune microenvironment and clinical outcomes remains unclear. METHODS: Comprehensive expression profiles of 18 m6A regulators were used to identify molecular subtypes exhibiting distinct m6A modification patterns in 1673 glioma samples sourced from public datasets. A multi-genes signature was constructed for predicting clinical outcomes and response to immunotherapy in glioma patients. Immunohistochemistry and cellular experiments were performed for validation. RESULTS: Two m6A subtypes of gliomas were identified. The m6A-low-risk subtype was characterized by paucity of immune infiltrates; While the m6A-high-risk subtype had higher abundances of multiple immune cells including lymphocyte and macrophage as well as increased expression of PD-L1, corresponding to an immunosuppressive phenotype. The m6A-high-risk subtype had poorer survival than the m6A-low-risk subtype in both the glioblastoma and lower grade gliomas cohorts. Eight m6A-related hub genes of high prognostic significances were identified and selected for developing a scoring signature termed as m6Ascore. Elevated m6Ascore indicated worse survival for glioma patients under standard care, but showed enhanced response to immunotherapy. Moreover, we demonstrated that overexpression of FTO, a m6A demethylase, inhibited the expressions of m6A-related hub genes (PTX3, SPAG4), impaired glioma cell viability and reduced macrophage chemotaxis. CONCLUSION: This work develops an immune- and clinical-relevant m6A subtyping and a scoring model, which enhances our understanding of the role of m6A modification in regulating immune infiltration microenvironment in gliomas and helps to identify patients who are more likely to benefit from immunotherapy.

Humans↗

PubMed2025

Progressive T cell exhaustion and predominance of aging tissue associated macrophages with advancing disease stage in penile squamous cell carcinoma.

Penile squamous cell carcinoma (PSCC) is a rare malignancy with limited understanding of the tumor immune microenvironment (TIME). The interplay between PSCC and the immune system across disease progression and HPV infection status remains poorly characterized. This study aims to assess the TIME changes from localized to advanced disease and between HPV-positive versus negative tumors to identify potential immune evasion mechanisms in advanced PSCC. scRNA-seq was performed on ten PSCC tissue samples from penile, lymph node and distant metastatic sites with four matched penile and lymph node samples to understand the cellular heterogeneity within PSCC tumors. Analysis of immune cell populations and transcriptional hallmarks were performed stratified by localized (pT1-3, N0) versus advanced (N1-3, M0 or any N, M1) disease states and HPV infection status. We observed significant differences in immune cell infiltration between localized and advanced PSCC disease states and by HPV status. Advanced disease states demonstrated an exhausted immune phenotype, characterized by terminally exhausted CD8+ T cells, M2-like macrophages and hypoxic signature, while localized disease states demonstrated an active innate immune system characterized by increased DCs. HPV-negative tumors displayed low immune cell infiltration while HPV-positive tumors demonstrated an immune exhausted phenotype. These findings offer valuable insights into the evolving PSCC immune landscape, paving the way for the development of potential therapeutic approaches for advanced PSCC.

Humans↗

PubMed2025

The protective role of γδ T cells in endometrial cancer.

Γδ T cells are non-conventional T cells that are not MHC restricted and have T cell receptors (TCRs) that are stimulated by phosphoantigens, stress-induced proteins, lipids, and other antigens. These cells are prognostic across cancer types in The Cancer Genome Atlas (TCGA) but have not been well studied in endometrial cancer, which has a rising incidence and mortality rate. Endometrial cancer patients have variable responses to checkpoint inhibitors which are related to the molecular subtype of their cancer. As such, there is a pressing need to understand the immune microenvironment in endometrial cancer. This study addresses this gap in knowledge by investigating γδ T cell repertoires and transcriptomes in this disease site. γδ T cell repertoires were obtained for 543 endometrial cancer patients within the TCGA and from 5 endometrial cancer patients in the single cell dataset SRP349751 using TRUST4. GLIPH2 was used to identify TCRs predicted to bind the same antigen. Transcriptomes were investigated in the single cell dataset. DNA Polymerase Epsilon Exonuclease (POLE) and Microsatellite Instability High (MSI-H) endometrial cancer subtypes had the most γδ T cell infiltration. Vδ1 and Vδ3 γδ T cell infiltration was prognostic independent of stage and molecular subtype. GLIPH2 analysis revealed TCRδ motifs for TDK, YTD, and GEL were public across all four molecular subtypes and were present in the single cell data set. Vδ1 γδ T cell transcriptomes were associated with cytotoxicity and recent TCR stimulation. These data support further investigation of immunotherapies targeting γδ T cells in endometrial cancer.

Humans↗

PubMed2026

Myeloid-Mediated Immunoregulation and Resistance to Immune Checkpoint Inhibitor Therapy Across Squamous Cell Carcinomas: Mechanisms and Reprogramming Strategies.

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 have improved outcomes across squamous cell carcinomas (SCCs) of the head and neck, lung, esophagus, and skin, yet durable responses remain confined to a subset of patients in every subtype. Objective response rates vary substantially across SCCs despite overlapping genomic alterations, comparable tumor mutational burden, and high PD-L1 expression, indicating that tumor-intrinsic biomarkers alone do not explain this variability. Growing evidence points to the tumor immune microenvironment, and in particular the myeloid compartment, as a critical determinant of immunotherapy responsiveness. In this review, we synthesize current evidence on myeloid-mediated immune regulation across SCC subtypes, focusing on tumor-associated macrophages, myeloid-derived suppressor cells/tumor-associated neutrophils, and dendritic cells, and the mechanisms by which these populations impair antigen presentation, restrict T cell infiltration, and sustain immunologically "cold" tumor states. We further examine therapeutic strategies aimed at reprogramming rather than simply depleting suppressive myeloid populations, including radiation therapy, STING agonism, and myeloid-targeted agents (CSF1R, PI3Kγ, and CXCR2 inhibition), each of which has shown encouraging preclinical and early clinical activity in combination with ICI. Collectively, this evidence supports a model in which the myeloid compartment functions as an actionable, convergent determinant of ICI resistance across SCC subtypes, rather than merely a passive biomarker. We propose that through the integration of spatial and single-cell profiling of myeloid states with clinical history it will be possible to predict response to immune checkpoint therapy and personalize myeloid-directed combination strategies, though the specific biomarkers needed to match individual patients to a given myeloid-targeted approach remain to be defined. We further discuss the toxicity considerations associated with both immune checkpoint blockade and radiation-based combination approaches, the early-phase status of most myeloid-targeted agents currently in clinical development, and the extent to which mechanistic insight, derived predominantly from HNSCC, generalizes to squamous cell carcinomas arising at other anatomic sites.

dendritic cells↗

PubMed2026

MYBL2 promotes malignant phenotypes and M2-like macrophage polarization through CCL2 in non-small cell lung cancer.

Hub genes associated with non-small cell lung cancer (NSCLC) were identified through bioinformatics screening. In vitro experiments analyzed the potential mechanisms by which these genes regulate tumor malignant phenotypes and macrophage polarization. Differentially expressed genes were identified from The Cancer Genome Atlas (TCGA)-NSCLC and GSE32175 datasets, followed by protein-protein interaction (PPI) network analysis to screen hub genes. The effects of MYB Proto-Oncogene Like 2 (MYBL2) on NSCLC progression and macrophage polarization were evaluated using in vitro models. The regulatory relationship between MYBL2 and C-C motif chemokine ligand 2 (CCL2) was investigated by Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays, and rescue experiments were performed to validate the role of the MYBL2-CCL2 axis. Bioinformatics screening identified BUB1B, CDCA2 and MYBL2 as key hub genes with high expression in NSCLC, among which MYBL2 was significantly upregulated in NSCLC cells. Functional experiments confirmed that MYBL2 silencing markedly inhibited the malignant proliferation, migration and invasion of NSCLC cells. Tumor cell MYBL2 knockdown effectively reversed M2-like polarization and promoted M1-like polarization in the co-culture system. Mechanistically, MYBL2 directly bound to the CCL2 promoter region to enhance CCL2 transcriptional activity and upregulate CCL2 expression in NSCLC cells. Exogenous CCL2 supplementation significantly rescued the inhibitory effect of MYBL2 knockdown on macrophage M2-like polarization, verifying the mediating role of CCL2 in this regulatory axis. MYBL2 is strongly expressed in NSCLC cells and is associated with enhanced malignant phenotypes. It may affect macrophage M2-like polarization by upregulating CCL2, thus participating in NSCLC immune microenvironment remodeling.

CCL2↗

PubMed2024

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy.

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

Humans↗

PubMed2026

Intratumoral PD-1+LAG-3+CD8+ T cells are associated with improved prognosis in gastric cancer.

PURPOSE: PD-1 and LAG-3 are frequently used as markers of T cell exhaustion, yet the prognostic relevance and phenotypic characteristics of PD-1+LAG-3+CD8+ T cells in gastric cancer (GC) remain poorly defined. This study aimed to investigate their association with clinical outcomes and characterize their immune characteristics across independent GC cohorts. METHODS: Four independent GC cohorts were analyzed: the Zhongshan Hospital cohort (ZSGC, n = 298), The Cancer Genome Atlas cohort (TCGA, n = 371), an Immune Checkpoint Blockade cohort (ICB, n = 45), and the Yonsei cohort (n = 433). Intratumoral PD-1+LAG-3+CD8+ T cell infiltration was quantified by immunofluorescence staining and transcriptomic gene signature scoring. Survival analysis was performed using Kaplan-Meier estimation and multivariate Cox regression. Functional characterization was performed by flow cytometry on resected GC tissue. The immune microenvironment composition was evaluated using computational analyses. RESULTS: PD-1+LAG-3+CD8+ T cells were enriched within tumors compared to adjacent normal mucosa, and their infiltration correlated with advanced tumor stage, poor differentiation, microsatellite instability, and Epstein-Barr virus (EBV)-positive molecular subtypes. High intratumoral infiltration was significantly associated with improved overall survival in both the ZSGC and TCGA cohorts, whereas single-positive PD-1+CD8+ or LAG-3+CD8+ T cells showed no such association. In the ICB cohort, higher infiltration was associated with a higher response rate to pembrolizumab. Intratumoral PD-1+LAG-3+CD8+ T cells exhibit an activated phenotype characterized by increased expression of CD137, IFN-γ, perforin, and CXCL13, along with elevated TCF7 and lower PD-1 levels, suggesting a tumor-reactive, pre-exhausted state. High infiltration was further associated with an immune-active tumor microenvironment. CONCLUSIONS: High intratumoral infiltration of PD-1+LAG-3+CD8+ T cells is associated with favorable prognosis and an immune-active microenvironment in GC. These cells display phenotypic features consistent with a pre-exhausted state and may serve as independent prognostic biomarkers and candidate predictive biomarkers for immunotherapy stratification.

Humans↗

PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗

PubMed2026

Splenic extramedullary hematopoiesis in myelofibrosis is shaped by transcriptomic and epigenetic dysregulation.

Myelofibrosis (MF) is a chronic, progressive myeloproliferative neoplasm characterized by bone marrow fibrosis, ineffective blood cell production, and neoplastic extramedullary hematopoiesis (EMH) occurring primarily within the spleen. To explore the molecular mechanisms underlying splenic EMH, we performed single-cell transcriptional and chromatin profiling of cells from MF spleens that had been surgically removed. We demonstrate significant expansion of hematopoietic stem and progenitor cells, coupled with aberrant differentiation toward the erythroid and megakaryocytic lineages, associated with a significant enrichment of inflammatory pathways with enhanced NF-κB signaling and IFN responses, as well as dysregulation of the inferred function of differentiation-defining transcription factors. Finally, we report a significant remodeling of the immune microenvironment in MF spleens, characterized by emergence of dysfunctional T cell subsets and inflammatory memory B cells, suggesting the concomitant establishment of a pro-inflammatory and immune-tolerant tumor microenvironment within the spleen that influences hematopoietic cell differentiation and impairs tumor immune surveillance.

Primary Myelofibrosis↗