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Identification of tumor antigens and immune subtypes in breast cancer for mRNA vaccine development.
BACKGROUND: Poor prognosis, resistance to chemotherapy, insensitivity to radiotherapy, and a high prevalence of adverse drug reactions remain urgent issues for breast cancer (BC) patients. Increased knowledge of tumor immunobiology and vaccine development suggests the possibility of cancer vaccination. Here, we investigated potential BC-associated antigens for the development of an anti-BC mRNA vaccine and populations suitable for mRNA vaccination. METHODS: Gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). The single-cell sequencing data were obtained from the Single Cell Portal platform. cBioPortal was used to visualize and compare genetic alterations. Correlations between immune cell infiltration and antigen expression were visualized with the Tumor Immune Estimation Resource (TIMER). Immune subtypes were identified by consensus clustering and analysis of immune infiltration. Biomarkers for the assessment of mRNA vaccination suitability were investigated. RESULTS: Three tumor-associated antigens, CD74, IRF1, and PSME2, that showed overexpression, amplification, and mutation and were linked with prognosis and immune cell infiltration, were identified. Single-cell sequencing analysis showed the expression of the three tumor-associated antigens in different cells of BC. Three immune subtypes were identified among BC patients, with Cluster B patients having a tumor microenvironment conducive to immunotherapy. These subtypes also showed different expression patterns of immune checkpoints, immune cell death-promoting genes, and response to immune checkpoint inhibitor (ICI) therapy. Thus, we identified five biomarkers that could be applied for assessing vaccination suitability and predicted drugs that would be appropriate for patients unsuited for vaccination. CONCLUSIONS: Our findings suggest new directions for the development of mRNA vaccines against breast cancer.
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The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs.
The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular-resolution reference of healthy adult human tissues across multiple organ systems. Integrating spatial transcriptomics, proteomics, and histological features across over 15 million cells from more than 100 donors, SAHA maps conserved and organ-specific cellular niches in gastrointestinal and immune tissues. High-resolution profiling using CosMx SMI, 10x Xenium, RNAscope, GeoMx DSP, and single-nucleus RNA-seq reveals spatially organized cell states, rare adaptive immune populations, and tissue-specific cell-cell interactions and ligand-receptor pairs. Comparative analyses with colorectal cancer and inflammatory bowel disease demonstrate the power of SAHA to detect disease-associated spatial disruptions, including crypt dedifferentiation, perineural invasion, and therapy-resistant immune remodeling. All data are openly accessible through a FAIR-compliant interactive portal to support exploration, benchmarking, and machine learning model training. Through SAHA, we provide a foundational framework for spatial diagnostics and next-generation precision medicine grounded in a comprehensive human tissue atlas, enabling the development of context-aware models that simulate tissue behavior, decode complex pathologies, and accelerate therapeutic innovation at unprecedented scale.
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The Novel-Natural-Killer-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Hepatocellular Carcinoma.
The current understanding of the prognostic significance of natural killer (NK) cells and their tumor microenvironment (TME) in hepatocellular carcinoma (HCC) is limited. Thus, we screened for NK-cell-related genes by single-cell transcriptome data analysis and developed an NK-cell-related gene signature (NKRGS) using multi-regression analyses. Patients in the Cancer Genome Atlas cohort were stratified into high- and low-risk groups according to their median NKRGS risk scores. Overall survival between the risk groups was estimated using the Kaplan-Meier method, and a NKRGS-based nomogram was constructed. Immune infiltration profiles were compared between the risk groups. The NKRGS risk model suggests significantly worse prognoses in patients with high NKRGS risk (p < 0.05). The NKRGS-based nomogram showed good prognostic performance. The immune infiltration analysis revealed that the high-NKRGS-risk patients had significantly lower immune cell infiltration levels (p < 0.05) and were more likely to be in an immunosuppressive state. The enrichment analysis revealed that immune-related and tumor metabolism pathways highly correlated with the prognostic gene signature. In this study, a novel NKRGS was developed to stratify the prognosis of HCC patients. An immunosuppressive TME coincided with the high NKRGS risk among the HCC patients. The higher KLRB1 and DUSP10 expression levels correlated with the patients' favorable survival.
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Integration of Machine Learning, Bioinformatics, and Experimental Validation to Identify Novel Diagnostic and Prognostic Biomarkers Associated With Succinylation in Lung Adenocarcinoma.
Lung adenocarcinoma (LUAD) is the most prevalent and lethal subtype of non-small cell lung cancer (NSCLC), with its progression closely associated with aberrant succinylation modifications. This study aimed to systematically identify succinylation-related genes in LUAD and evaluate their diagnostic and prognostic significance. By integrating four Gene Expression Omnibus (GEO) datasets, 45 differentially expressed succinylation-related candidate genes were identified. Feature selection using three machine learning methods-Lasso regression, support vector machine recursive feature elimination (SVM-RFE), and Random Forest-yielded seven core genes: TIMP1, SLC2A1, JUP, F12, B3GALNT1, DSP, and MMP1. ROC analysis showed that all core genes achieved AUC values greater than 0.7, indicating strong diagnostic potential. A diagnostic model constructed from these seven genes achieved an AUC of 0.912 in the training cohort, significantly outperforming single-gene models, and was validated in The Cancer Genome Atlas (TCGA) cohort (AUC = 0.893). Prognostic analysis revealed that Kaplan-Meier curves for all seven core genes demonstrated p < 0.05 and HR > 1, indicating that high expression was associated with poor outcomes. A risk prediction nomogram was also developed based on these genes. SHAP analysis clarified each gene's contribution to the model, while drug enrichment and transcriptional regulatory network analyses provided further insights into potential therapeutic targets. Notably, JUP exhibited the highest diagnostic efficacy (AUC = 0.921) and was significantly correlated with immune cell infiltration and tumor microenvironment regulation. Molecular docking suggested stable binding between JUP and potential therapeutic compounds, single-cell analysis confirmed its marked overexpression in tumor and epithelial cells, and experimental validation further established its oncogenic role. In conclusion, this study systematically defines the diagnostic and prognostic value of seven succinylation-related core genes in LUAD, with JUP playing a particularly pivotal role. These findings provide robust evidence supporting its potential as a novel biomarker and therapeutic target.
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A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia.
BACKGROUND: Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML). METHODS: RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated. RESULTS: Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents. CONCLUSION: A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.
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RAB37 Exerts a Context-Dependent Role in Cutaneous Melanoma Progression by Promoting Tumor Cell Proliferation and Being Associated with Favorable Immune Characteristics.
BACKGROUND: Rab proteins and small GTPase-mediated vesicle trafficking pathways play important roles in tumor progression and the tumor microenvironment. However, the biological function and clinical significance of RAB37 in cutaneous melanoma (CM) remain unclear. This study investigated the expression pattern, prognostic value, biological function, and immune-related characteristics of RAB37 in CM. METHODS: RAB37 expression and prognostic significance were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Kaplan-Meier and Cox regression analyses were performed to evaluate prognostic value. Spearman correlation analysis assessed associations between RAB37 expression and immune cell infiltration, immune checkpoint blockade (ICB)-related signatures, and inflammatory cytokines. Functional roles of RAB37 were validated using loss- and gain-of-function experiments in A375 and WM-115 melanoma cells. CCK-8, EdU, and Transwell assays evaluated cell proliferation and migration. Single-cell functional analysis was performed using CancerSEA. RESULTS: RAB37 expression was significantly higher in metastatic than in primary CM tissues. Elevated RAB37 expression and hypermethylation of the cg26263675 CpG site were associated with favorable survival outcomes. A prognostic nomogram integrating clinicopathological characteristics and RAB37 methylation was established. Functional experiments demonstrated that RAB37 knockdown significantly inhibited, whereas RAB37 overexpression promoted, melanoma cell proliferation and migration in both A375 and WM-115 cells. RAB37 expression was significantly associated with immune cell infiltration, ICB-related signatures, inflammatory cytokines, and immune-related pathways, including T-cell receptor signaling, PD-1 checkpoint signaling, IL-2/STAT5 signaling, and inflammatory responses. Single-cell analysis further revealed a positive association between RAB37 expression and inflammation-related functional states. CONCLUSION: RAB37 exhibits a context-dependent role in CM by promoting malignant cellular behaviors while being associated with immune-related characteristics and favorable clinical outcomes. These findings suggest that RAB37 may serve as a potential prognostic biomarker and provide new insights into the interplay between CM progression and the tumor immune microenvironment.
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Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer.
Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients. Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC). Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-β and other pathway signals were correlated with progression. Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration.
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Melanoma cell-derived LAG-3 enhances CXCL1/8-driven MDSCs recruitment and immune escape via TRIM28-mediated IκBα degradation.
INTRODUCTION: Lymphocyte activation gene 3 (LAG-3) is recognized as an important inhibitory receptor on T cells, and LAG-3-targeted therapy has been approved for advanced melanoma treatment. Recently, LAG-3 has been reported to be expressed on tumor cells. However, the potential effects and underlying mechanisms of tumor cell-derived LAG-3 in mediating tumor growth and immunosuppression remain unclear. OBJECTIVES: This study aimed to evaluate the role of tumor cell-derived LAG-3 within the tumor microenvironment (TME) and the efficacy of anti-LAG-3 combination therapy against melanoma. METHODS: The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, single-nuclear RNA sequencing database and tissue microarray analyses were conducted to detect the LAG-3 gene and protein expressions. The biological functions and underlying mechanism of tumor cell-derived LAG-3 in tumor growth and immunosuppression were investigated using phospho-array, RNA-seq, co-immunoprecipitation, flow cytometry, co-culture systems, and various tumor-bearing mouse models. RESULTS: Our findings revealed that LAG-3 was present in a subpopulation of cancer cells and clinical melanoma specimens associated with poorer prognosis. High LAG-3 expression on tumor cells not only contributed to the proliferation and metastasis of melanoma cells, but also promoted the recruitment of myeloid-derived suppressor cells (MDSCs) and dysfunctional CD8+ T cells into the TME. Notably, depletion of MDSCs counteracted the pro-tumor activity of tumor cell-derived LAG-3. Mechanistically, LAG-3 overexpression triggered CXCL1 and CXCL8 production of melanoma cells by enhancing TRIM28-mediated IκBα ubiquitination and degradation, which in turn activated the NF-κB pathway and fostered tumor growth and an immunosuppressive microenvironment. Intriguingly, mice bearing LAG-3-overexpressing melanoma cells showed reduced survival but responded to anti-LAG-3 antibody therapy. Furthermore, inhibiting the CXCL1/8-CXCR2 axis sensitized LAG-3-overexpressing melanoma to anti-LAG-3 antibody. CONCLUSION: This study firstly identified LAG-3 as a pro-tumor factor and a predictor for anti-LAG-3 immunotherapy efficacy. Combined CXCL1/8-CXCR2 inhibitor and anti-LAG-3 treatment may represent a promising therapeutic strategy for LAG-3-driven tumor.
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Loss of OBSCN expression promotes bladder cancer progression but enhances the efficacy of PD-L1 inhibitors.
BACKGROUND: As the objective overall response rate to immune checkpoint inhibitors (ICIs) is less than 30% in late stage or metastatic bladder cancer (BLCA), elucidating the intrinsic mechanisms of immune evasion is of great importance for the discovery of predictive and prognostic biomarkers and the exploration of novel targets for intervention. Recent studies have shown that OBSCN and the cytoskeletal protein it encodes, obscurin, play an important role in tumour progression. However, no studies have reported the role of OBSCN in BLCA. METHODS: RNA sequencing and clinical data were downloaded from multiple public databases including The Cancer Genome Atlas and the Gene Expression Omnibus. Immunohistochemistry (IHC) was performed on tissue microarrays including 80 BLCA patients from Shuguang Hospital. Kaplan-Meier curves with log-rank test, univariate and multivariate COX regression were performed to evaluate the prognostic efficacy of OBSCN expression. In vitro experiments were conducted to determine the role of OBSCN deficiency in promoting BLCA progression. Pan-cancer tumour immune microenvironment (TIME) analysis was performed to explore the potential correlation between OBSCN deficiency and immune evasion. RESULTS: Pan-cancers and single-cell sequencing analysis revealed that the expression level and proportion of OBSCN was significantly decreased in BLCA cells compared to normal urothelium. Survival curves showed that BLCA patients with low OBSCN expression had a worse prognosis, yet a better clinical response to PD-L1 ICIs. Gene set variation analysis and Gene set enrichment analysis revealed that epithelial-mesenchymal transition (EMT) and immune-related processes were significantly enriched in BLCA samples with low OBSCN expression. In vitro experiments identified that OBSCN-deficient BLCA cells enhanced invasion, migration and EMT. Pan-cancer analysis of TIME revealed that neoantigen, tumor mutation burden, CD8+T cells and immune checkpoints were significantly negatively associated with OBSCN expression. IHC and Western blot assay identified that BLCA samples with low OBSCN expression had more CD8+ T-cell infiltration and higher PD-L1 expression. CONCLUSIONS: This study confirmed that BLCA patients with low OBSCN expression had a worse prognosis but a superior response to ICIs, providing a reference for individualised treatment of BLCA patients.
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Construction of a human epidermal growth factor receptor 2-related gene risk model for predicting breast cancer prognosis.
The present study aimed to construct a human epidermal growth factor receptor 2 (HER2)-related gene risk model to predict breast cancer prognosis. Gene expression and clinical follow-up data were extracted from The Cancer Genome Atlas database, while the GSE7390 dataset was obtained from the Gene Expression Omnibus database. Prognostic and clinical feature analyses were performed. In addition, differentially expressed genes (DEGs) between HER2-negative and -positive groups were screened, followed by enrichment analysis. Subsequently, a prognostic model was established, and prognosis was predicted using a nomogram. In addition, the association of risk score with immunity was analyzed, and single-cell analysis was performed. Next, key genes were identified by reverse transcription-quantitive PCR (RT-qPCR) analysis. The results revealed that HER2 was significantly associated with estrogen receptor status, progesterone receptor status, N stage, American Joint Committee on Cancer stage, mutation count and tumor mutation burden of breast cancer. AS601245, AP.24534 and roscovitine were the top three chemotherapeutic agents showing the highest sensitivity differences between the risk groups. A total of 251 DEGs between HER2-negative and -positive groups were screened, which were found to be significantly involved in the Kyoto Encyclopedia of Genes and Genomes pathway of estrogen signaling, PI3K-AKT signaling pathway and chemical carcinogenesis-receptor activation. Eight prognostic gene models were constructed, and it was found that patients in the high-risk group had significantly shorter survival times than those in the low-risk group. A nomogram, incorporating risk groups and clinicopathological features, demonstrated strong predictive ability and high accuracy. The RT-qPCR results indicated that the expression of electron transfer flavoprotein subunit α, rap guanine nucleotide exchange factor-like 1, keratin 7, cluster of differentiation 24, proline rich 15-like, arachidonate 15-lipoxygenase type B, ELOVL fatty acid elongase 2 and C-X-C motif chemokine ligand 9 was consistent with the results of bioinformatic analysis. In conclusion, the HER2-related risk model and nomogram developed in the present study demonstrated high accuracy in predicting patient survival.