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Hybrid resistance in vitro. Possible role of both class I MHC and self peptides in determining the level of target cell sensitivity.

NK cells of F1 hybrids frequently exhibit an enhanced capacity to reject semisyngeneic parental bone marrow grafts, a phenomenon known as hybrid resistance. Attempts to define the mechanism whereby this occurs have been hampered by factors inherent in the use of an in vivo model, including the host immune response, microenvironment, immune cell trafficking patterns, and host irradiation. We show here that IL-2-activated NK cells (lymphokine-activated killer cells) can be used to establish an in vitro model that appears to mimic hybrid resistance. These effectors lyse parental lymphoblast targets in a pattern consistent with that observed in vivo, bear NK not T cell surface markers, and recognize target structures mapping to the MHC. By using this model, we then demonstrate that sensitivity to lysis of syngeneic F1 lymphoblasts can be augmented by exposing the targets to conditions that have been reported to permit the exchange of endogenous class I-bound self peptides for exogenous foreign sequences. These data suggest that both MHC class I molecules and the "self" peptides they bind contribute to determining the susceptibility of a target to hybrid resistance, and the data are discussed in the context of a model of NK recognition in which the sensitivity of a target to NK lysis is mediated by a complex of the class I MHC molecule and the self peptides it samples from the intracellular peptide pool.

Animals↗

NPLOC4 Constructs Tumor Immunosuppressive Microenvironment in Pan-cancer and Hepatocellular Carcinoma.

INTRODUCTION: NPLOC4 (nuclear protein localization 4 homolog) is mainly involved in DNA damage, cell cycle, and ubiquitination promotion. Nonetheless, the role of NPLOC4 in the tumor immune microenvironment (TIME) and its potential as a promising tumor therapeutic target remains unclear. METHODS: Therefore, analyses of NPLOC4 mRNA and protein expression, RNA subcellular localization, and patient prognosis associated with NPLOC4 expression were conducted across multiple tumor types. Additionally, the correlations between NPLOC4 and immune cells, non-immune cells, and immune molecules within the tumor immune microenvironment (TIME) were investigated. These analyses utilized data from various public resources, including the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), The Human Protein Atlas (HPA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), TIMER2.0, KM-Plotter, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), and Tumor Immune Single-cell Hub 2 (TISCH2). Subsequently, we utilized hepatocellular carcinoma (HCC) patients' cancer and adjacent tissues plus tumor cell lines to verify the differential RNA and protein expression of NPLOC4 via qRT-PCR and immunohistochemistry (IHC). Then, the relationship of NPLOC4 expression level with immune infiltration score, infiltration of effector immune cells, suppressive immune cells, and several vital immune checkpoints was analyzed in HCC immune microenvironment. Furthermore, the distribution of expression of NPLOC4 in various cells in the HCC microenvironment was determined through single-cell sequencing analysis. RESULTS: We discovered that NPLOC4 was up-regulated in a variety of tumors and was correlated with poor prognosis. NPLOC4 not only had the potential as a tumor prognostic marker and therapeutic target but also was strongly linked to immune cells, immune checkpoints, and immune-related molecules and pathways in HCC immune microenvironment. CONCLUSION: In summary, NPLOC4 may serve as a promising target for immunotherapy.

Humans↗

Role of the microenvironment in immune responses to transplantation.

Through the constant interplay of cellular and extracellular components, the microenvironment of tissues directs immune responses. In solid organ transplantation, one factor that significantly alters the microenvironment of tissues is reperfusion injury, which occurs to a certain extent in essentially all cadaver organs. The damage that results from reperfusion injury initiates a cascade of signals to surveillance cells such as macrophages, mast cells, and dendritic cells, augmenting both innate and allo-immune responses. Chemokines, released from surveillance cells and others, orchestrate an influx of cells into the allograft, and subsequently drive the migration of dendritic cells and lymphocytes to proper areas within lymph nodes for the efficient generation of allo-immune responses. Heparan sulfate, a component of the extracellular matrix, binds chemokines and thus regulates their localization within tissues. This association is one of a multitude of examples of the interplay between cells and their extracellular surroundings. In addition to the association with chemokines, heparan sulfate binds cytokines such as IFN-gamma and IL-2. In the spleen, heparan sulfate localizes IL-2 to the marginal zone, red pulp, and interdigitating dendritic cells of the T cell zone. Our laboratory recently determined that the contribution of heparan sulfate-bound IL-2 to immune responses is substantial, finding that bound, rather than free, IL-2 drives immune responses. This finding reiterates the critical nature of the interaction between cells and the extracellular matrix. Disruptions in these interactions may lead to dysregulation of immune responses and, in turn, pathologies such as tissue fibrosis or autoimmunity. Further studies into the exchange between cells and the extracellular matrix will likely lead to new lines of therapies aimed at correcting these abnormalities before irreversible damage occurs.

Animals↗

ZNF180 modulates tumor intrinsic immunotherapy resistance in melanoma through driving plasticity.

BACKGROUND: Based on our previous study, we have identified ZNF180, a zinc finger protein, as a pro-tumorigenic regulator in primary melanoma and a marker for poor prognosis. Herein, we report that ZNF180-regulated pathway, hence ZNF180-regulome, underlies resistance towards immune checkpoint inhibitions (ICIs). METHODS: To investigate regulatory roles of ZNF180 to confer these immune suppressive phenotypes, we performed ZNF180 knock-down in melanoma cells in vitro with different genetic backgrounds, namely A375 (BRAF-mutant) and SKMEL147 (NRAS-mutant) cells, and performed RNA- and ATAC-sequencing. We performed integrative analysis of RNA- and ATAC-sequencing data with publicly available sequencing data from ICI-treated cohorts to construct comprehensive model of ZNF180-regulome and its impacts on immune microenvironment. Further, we performed ZNF180 silencing in immune competent Yumm1.7 murine model to confirm the changes in immune microenvironments. RESULTS: ZNF180-regulome was predictive of ICI responses in independent bulk sequencing cohorts, and ZNF180+ tumors persisted after the therapy with immune-suppressive features such as MHC-I loss and CD155 expressions, the primary ligand to TIGIT inhibitory receptor. Further, ZNF180 silencing revealed its regulations on AP-1 transcription factors to drive melanoma reprogramming towards de-differentiated MITFlowAXLhigh cells, an established melanoma subtypes associated with recurrence and ICI resistance. In tandem, we observed that ZNF180+ tumor neighborhood significantly excluded with CD4 T-cells in metastatic tumor, and its silencing in immune competent murine model increased CD4 helper T-cell infiltrations with significant tumor regression in vivo. CONCLUSION: Collectively, these results indicate ZNF180 is a tumor intrinsic regulator of melanoma plasticity to drive de-differentiated phenotypes with immune-suppressive features including loss of immunogenicity, T-cell inhibitory signals through TIGIT/CD155 checkpoint and exclusion of CD4 helper T-cells. As ZNF180-regulome manifests in non-metastatic melanoma in contrast to the current focus of standard-of-care ICI on the metastatic disease, these results establish ZNF180-regulome as a biomarker and novel therapeutic avenue for early-stage, non-metastatic melanoma to intervene ICI resistance.

Immune checkpoint inhibition↗

Uncovering essential anesthetics-induced exosomal miRNAs related to hepatocellular carcinoma progression: a bioinformatic investigation.

BACKGROUND: Anesthetic drugs may alter exosomal microRNA (miRNA) contents and mediate cancer progression and tumor microenvironment remodeling. Our study aims to explore how the anesthetics (sevoflurane and propofol) impact the miRNA makeup within exosomes in hepatocellular carcinoma (HCC), alongside the interconnected signaling pathways linked to the tumor immune microenvironment. METHODS: In this prospective study, we collected plasma exosomes from two groups of HCC patients (n&#x2009;=&#x2009;5 each) treated with either propofol or sevoflurane, both before anesthesia and after hepatectomy. Exosomal miRNA profiles were assessed using next-generation sequencing (NGS). Furthermore, the expression data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) was used to pinpoint the differentially expressed exosomal miRNAs (DEmiRNAs) attributed to the influence of propofol or sevoflurane in the context of HCC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to dissect the signaling pathways and biological activities associated with the identified DEmiRNAs and their corresponding target genes. RESULTS: A total of 35 distinct DEmiRNAs were exclusively regulated by either propofol (n&#x2009;=&#x2009;9) or sevoflurane (n&#x2009;=&#x2009;26). Through TCGA-LIHC database analysis, 8 DEmiRNAs were associated with HCC. These included propofol-triggered miR-452-5p and let-7c-5p, as well as sevoflurane-induced miR-24-1-5p, miR-122-5p, miR-200a-3p, miR-4686, miR-214-3p, and miR-511-5p. Analyses revealed that among these 8 DEmiRNAs, the upregulation of miR-24-1-5p consistently demonstrated a significant association with lower histological grades (p&#x2009;<&#x2009;0.0001), early-stage tumors (p&#x2009;<&#x2009;0.05) and higher survival (p&#x2009;=&#x2009;0.029). Further analyses using GSEA and GSVA indicated that miR-24-1-5p, along with its target genes, were involved in governing the tumor immune microenvironment and potentially inhibiting tumor progression in HCC. CONCLUSIONS: This study provided bioinformatics evidence suggesting that sevoflurane-induced plasma exosomal miRNAs may have a potential impact on the immune microenvironment of HCC. These findings established a foundation for future research into mechanistic outcomes in cancer patients.

Carcinoma, Hepatocellular↗

Prognostic and immunological implications of sialylation-associated gene signatures in hepatocellular carcinoma.

OBJECTIVE: The absence of effective biomarkers continues to limit early diagnostic accuracy and prognostic evaluation in patients with hepatocellular carcinoma (HCC). Aberrant sialylation (SI) has been demonstrated to contribute to therapeutic resistance and tumor progression. The aim of this investigation was to identify a sialylation-related gene (SRG) signature, evaluate its prognostic significance, and investigate associated immunological characteristics in HCC. METHODS: Transcriptomic profiles and corresponding clinical data for patients with HCC were obtained from UCSC Xena, the International Cancer Genome Consortium (ICGC), and the Molecular Signatures Database (MsigDB). Differential expression analysis, Cox regression analysis modeling, and least absolute shrinkage and selection operator (LASSO) regression analysis were applied to identify independent prognostic markers and develop predictive models. The tumor immune microenvironment and its relationship with the identified SRGs were assessed by evaluating immune infiltration patterns. A gene co-expression network for the prognostic SRGs was constructed using GeneMANIA to identify potentially targetable signaling pathways. RESULTS: Four SRGs (ST6GALNAC4, B4GALT5, B4GALNT1, and NEU1) were significantly associated with the prognosis of patients with HCC. Prognostic models constructed using these genes demonstrated strong predictive performance. Notable differences were observed in immune cell populations and immune checkpoint expression between the high-risk and low-risk groups. Additionally, the half-maximal inhibitory concentration values for 101 therapeutic compounds varied between these groups. Lipopolysaccharide and sphingolipid metabolism were identified as key biological processes linked to tumor progression and modulation of the immune microenvironment. CONCLUSION: The four identified SRGs were significantly associated with clinical outcomes and immunological features in HCC. These findings provide a foundation for advancing early diagnostic strategies, refining prognostic assessments, and guiding personalized therapeutic approaches for patients with HCC.

Humans↗

Identification of JAML as an Immune-Associated Prognostic Marker in Non-Small Cell Lung Cancer.

INTRODUCTION: Non-small cell lung cancer (NSCLC) remains a major cause of cancer-related mortality worldwide, and the identification of novel prognostic biomarkers associated with tumor immunity is urgently needed. Junctional adhesion molecule-like (JAML), a member of the junctional adhesion molecule family, participates in leukocyte adhesion, migration, and T-cell activation. Although JAML has been implicated in immune regulation and tumor progression in other cancers, its expression pattern, prognostic significance, and association with the immune microenvironment in NSCLC remain unclear. This study aimed to investigate the clinical and immunological significance of JAML in NSCLC. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to evaluate JAML expression patterns in NSCLC subtypes. The prognostic value of JAML was assessed using Kaplan-Meier survival analysis and Cox regression models. The association between JAML expression and immune cell infiltration was investigated using TIMER2.0, CIBERSORT, and TISIDB analyses. Functional enrichment analyses were performed to explore potential biological pathways associated with JAML expression. In addition, JAML expression was validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in paired NSCLC and adjacent normal tissues. RESULTS: JAML expression was significantly decreased in NSCLC tissues compared with normal tissues (P < 0.005), with the lowest expression observed in lung squamous cell carcinoma (LUSC) and reduced expression in lung adenocarcinoma (LUAD). Survival analysis demonstrated that patients with high JAML expression had significantly improved overall survival compared with those with low expression (univariate HR = 0.68, 95% CI: 0.54-0.86, P = 0.001; multivariate HR = 0.76, 95% CI: 0.57-1.00, P = 0.049). Immune infiltration analysis revealed that JAML expression was significantly associated with multiple immune cell populations, including CD8+ T cells (r = 0.42, P < 0.001), suggesting a close relationship between JAML expression and the tumor immune microenvironment. qRT-PCR validation confirmed that JAML expression was approximately 2.3-fold higher in adjacent normal tissues than in NSCLC tissues (P < 0.05). CONCLUSION: JAML is downregulated in NSCLC and its high expression is associated with favorable overall survival and distinct immune infiltration patterns. These findings indicate that JAML may serve as a potential prognostic biomarker and provide insights into the relationship between JAML expression and the tumor immune microenvironment in NSCLC.

JAML protein↗

Tumor microenvironments, the immune system and cancer survival.

The study of cancer immunology has recently been reinvigorated by the application of new research tools and technologies, as well as by refined bioinformatics methods for interpretation of complex datasets. Recent microarray analyses of lymphomas suggest that the prognosis of cancer patients is related to an interplay between cancer cells and their microenvironment, including the immune response.

Cell Transformation, Neoplastic↗

Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. METHODS: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. RESULTS: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: PARP1, MBD4, TELO2, NSMCE3, SMUG1, and BABAM1. A nomogram incorporating risk score (RS) and clinical variables achieved strong predictive accuracy for 1- to 3-year survival [area under the curve (AUC) >0.7]. High-HRD tumors exhibited distinct mutational patterns (TP53 and TTN) and enriched glutathione metabolism and cytochrome P450 pathways. Immune infiltration analysis revealed significant differences in plasma cell and neutrophil infiltration between risk groups (P<0.05), suggesting HRD-associated immune microenvironment remodeling. CONCLUSIONS: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗

Identification of a novel human gut microbes and microbial metabolites related genes signature for prognostic implication in head and neck squamous carcinomas.

BACKGROUND: The gut microbiota acts as a critical driver influencing the pathogenesis, therapeutic response, and clinical outcomes across various cancer types. This study aimed to investigate the prognostic value of human gut microbes and microbial metabolites related genes (HGMMMRGs) in head and neck squamous cell carcinoma (HNSCC). METHODS: We constructed a prognostic risk model comprising 19 core HGMMMRGs using LASSO penalized regression and a multivariate Cox proportional hazards model. The predictive performance of the model was evaluated through Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, nomograms, and concordance index. In addition, functional enrichment analysis was performed on the differentially expressed risk genes. Furthermore, the relationship between the immune microenvironment of HNSCC and the risk diagnostic model was examined. Western blot analysis was used to assess the expression levels of IL10 in both HNSCC tissues and adjacent normal tissues. Finally, the correlation between IL10 and the gut microbiota was explored. RESULTS: This study developed a risk score model integrating 19 HGMMMRG genes, which can serve as a tool to guide prognosis and immune microenvironment assessment in HNSCC patients. Survival analysis showed that patients in the high-risk group had significantly worse outcomes (P&#x2009;<&#x2009;0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant enrichment of differentially expressed genes (DRLs) and immune-related pathways. Western blot analysis further confirmed that IL10 was highly expressed in HNSCC, and the abundance of Faecalibacterium prausnitzii and Enterococcus durans colonies was correlated with IL10 expression. CONCLUSION: We developed a prognostic model for HGMMMRGs that can be effectively used to predict OS in patients with HNSCC. Second, Faecalibacterium prausnitzii and Enterococcus durans can influence the prognosis of patients with HNSCC by mediating the expression IL10 and thereby affecting the prognosis of HNSCC patients. Thus, human gut microbes and microbial metabolite-related genes may be another promising strategy for the treatment of patients with HNSCC.

HNSCC↗

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↗

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 ↗

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↗

Number of CD4+ cells and location of forkhead box protein P3-positive cells in diagnostic follicular lymphoma tissue microarrays correlates with outcome.

PURPOSE: To examine the immune microenvironment in diagnostic follicular lymphoma (FL) biopsies and evaluate its prognostic significance. PATIENTS AND METHODS: Immunohistochemistry was used to study numbers and location of cells staining positive for immune cell markers CD4, CD7, CD8, CD25, CD68, forkhead box protein P3 (FOXP3), T-cell intracellular antigen-1, and Granzyme B in tissue microarrays of paraffin-embedded, diagnostic lymph node biopsies taken from 59 FL patients who lived less than 5 years (short-survival group; n = 34) and more than 15 years (long-survival group; n = 25). RESULTS: CD4 and FOXP3 expression were significantly different between the two groups. Samples from the long-survival group were more likely than those from the short-survival group to have CD4+ staining cells and to have FOXP3-positive cells in a perifollicular location. CONCLUSION: This study has identified differences in immune cell composition of the diagnostic FL lymph node immune microenvironment and these have the potential for use as prognostic biomarkers in a routine histopathology setting.

CD4-Positive T-Lymphocytes↗

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↗

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&#xa0;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↗

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)↗

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↗