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Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy.
INTRODUCTION: The TGF-β signaling pathway (TSP) is pivotal in tumor progression. Nonetheless, the connection between genes associated with the TSP and the clinical outcomes of breast cancer, as well as their impact on the tumor microenvironment and immunotherapeutic responses, remains elusive. METHODS: Breast cancer transcriptomic and single-cell sequencing data were obtained from the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We identified 54 genes associated with the TSP from the Molecular Signatures Database (MSigDB) and analyzed both data types to evaluate TSP activity. Using weighted gene co-expression network analysis (WGCNA), we identified modules linked to TSP activity. To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). We then examined immune activity and response to immune checkpoint inhibitors and chemotherapy in these groups. Finally, we validated ZMAT3 expression levels clinically and confirmed its relevance in breast cancer using CCK-8 and migration assays. RESULTS: At the single-cell level, TSP activity was most notable in endothelial cells, with higher activity in normal tissues compared to tumors. TSPRS was developed. This signature's accuracy was confirmed through internal and external validations. A nomogram incorporating the TSPRS was created to improve prediction accuracy. Further studies showed that breast cancer patients categorized as low-risk by the TSPRS had higher immune phenotype scores and more immune cell infiltration, leading to better prognosis and enhanced immunotherapy response. Additionally, a strong link was found between the TSPRS risk score and the effectiveness of anti-tumor agents. Silencing the ZMAT3 gene in the TSPRS significantly reduced the proliferation and invasiveness of breast cancer cells. DISCUSSION: Our study developed a TSPRS, which emerges as a potent predictive instrument for the prognosis of breast cancer, offering novel perspectives on the immunotherapeutic approach to the disease.
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Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma.
OBJECTIVE: Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. METHODS: LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. RESULTS: A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. CONCLUSION: Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.
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TFPI-high myofibroblast states and a meta-program-related five-gene prognostic signature in breast cancer.
Intratumoral heterogeneity and tumor-microenvironment interactions limit prognostic stratification in breast cancer, but the prognostic relevance and cellular context of recurrent transcriptional meta-programs remain unclear. We aimed to derive a meta-program-related prognostic signature and characterize its component transcripts at single-cell resolution. Six paired institutional tumors and adjacent non-tumor tissues served as a proof-of-concept comparison. Univariable Cox screening and least absolute shrinkage and selection operator Cox regression were used to derive a five-gene score from a prespecified meta-program-related candidate set in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) training cohort; the score was tested internally and assessed in GSE20685 using fixed coefficients and cohort-specific median cutoffs. GSE161529 single-cell transcriptomic data were used to map signature transcripts across 136,526 quality-controlled cells, while donor-aware pseudobulk analysis compared upper- and lower-quartile TFPI expression states in annotated myofibroblasts. The score comprised TCN1, FOXJ1, PIGR, SLAIN1, and TFPI and was associated with overall survival in the training, testing, and external cohorts, with concordance indices of 0.782, 0.756, and 0.721, respectively. TFPI transcripts were detected across endothelial, fibroblast, and myofibroblast compartments. TFPI-high myofibroblasts showed transcriptional enrichment of extracellular matrix and collagen fibril organization, transforming growth factor beta signaling, epithelial-mesenchymal transition, and myogenesis, together with lower oxidative phosphorylation and fatty acid metabolism programs. In bulk TCGA-BRCA tissue, TFPI expression correlated positively with stromal (r = 0.48), immune (r = 0.25), and composite microenvironment scores (r = 0.40; all p < 0.001). These findings identify a hypothesis-generating five-gene bulk-tissue prognostic signature and an expression-associated TFPI-high myofibroblast state but do not establish a discrete lineage, the cellular source of bulk TFPI, a TFPI-dependent mechanism, or clinical utility. Independent prospective cohorts, spatial and protein-level validation, and functional perturbation studies are required.
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Single-vessel transcriptome map pathological landscapes and reveal NR2F2-mediated smooth muscle cell phenotype acquisition in capillary malformations.
BACKGROUND: Capillary malformation (CM) is a congenital vascular anomaly affecting the skin, mucosa, and brain, yet the understanding of its vascular pathogenesis remains limited. METHODS: We applied spatial whole-transcriptome profiling (GeoMx) and gene set enrichment analysis within CM lesions at single vasculature level. Differentially expressed genes were validated by immunofluorescence staining. Phosphoproteomics was profiled to uncover lesion-wide phosphorylation sites on proteins. Single-cell RNA sequencing was performed on CM-derived induced pluripotent stem cells (iPSCs) to determine differentiation trajectories of lesional vascular lineages. In silico gene perturbation was used to predict candidate genes for modulating vascular pathological progression, followed by functional validation in CM iPSC-derived endothelial cells (ECs) using a Tet-on system. RESULTS: A spatial transcriptomic atlas was constructed, and pathological landscape of individual CM vasculature was delineated. CM vessels exhibited hallmarks of endothelial-to-mesenchymal transition (EndMT), including disruption of adherens junctions (AJs), vascular identity transitions, and metabolic remodeling. Phosphoproteomics confirmed that differentially phosphorylated proteins were enriched in EndMT- and AJ-related pathways. Aberrant expression of venous transcriptional factor NR2F2 was observed in lesional ECs and correlated with progressive enlargement from capillaries to larger-caliber vessels containing multiple layers of smooth muscle cells (SMCs). In CM iPSCs, differentiation course yielded reduced ECs but increased SMCs. In silico knockout simulation predicted NR2F2 as a crucial regulator of facilitating SMC phenotype in CM. Consistently, enforced NR2F2 expression during iPSC differentiation suppressed endothelial markers while inducing SMC-associated genes. CONCLUSIONS: Single CM vasculature displays pathological hallmarks characterized by EndMT and AJ disruption, leading to progressive vascular remodeling. NR2F2 functions as a central regulatory factor orchestrating the acquisition of the SMC phenotype, thereby representing a potential therapeutic target in CM.
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Altered Atlas of Exercise-Responsive MicroRNAs Revealing miR-29a-3p Attacks Armored and Cold Tumors and Boosts Anti-B7-H3 Therapy.
Increasing evidence has shown that physical exercise remarkably inhibits oncogenesis and progression of numerous cancers and exercise-responsive microRNAs (miRNAs) exert a marked role in exercise-mediated tumor suppression. In this research, expression and prognostic values of exercise-responsive miRNAs were examined in breast cancer (BRCA) and further pan-cancer types. In addition, multiple independent public and in-house cohorts, in vitro assays involving multiple, macrophages, fibroblasts, and tumor cells, and in vivo models were utilized to uncover the tumor-suppressive roles of miR-29a-3p in cancers. Here, we reported that miR-29a-3p was the exercise-responsive miRNA, which was lowly expressed in tumor tissues and associated with unfavorable prognosis in BRCA. Mechanistically, miR-29a-3p targeted macrophages, fibroblasts, and tumor cells to down-regulate B7 homolog 3 (B7-H3) expression. Single-cell RNA sequencing (scRNA-seq) and cytometry by time-of-flight (CyTOF) demonstrated that miR-29a-3p attacked the armored and cold tumors, thereby shaping an immuno-hot tumor microenvironment (TME). Translationally, liposomes were developed and loaded with miR-29a-3p (lipo@miR-29a-3p), and lipo@miR-29a-3p exhibited promising antitumor effects in a mouse model with great biocompatibility. In conclusion, we uncovered that miR-29a-3p is a critical exercise-responsive miRNA, which attacked armored and cold tumors by inhibiting B7-H3 expression. Thus, miR-29a-3p restoration could be an alternative strategy for antitumor therapy.
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Role of immune related genes in predicting prognosis and immune response in patients with hepatocellular carcinoma.
Immunotherapy has developed rapidly in recent years. This study aimed to establish a prognostic signature for immune-related genes (IRGs) and explore related potential immunotherapies. The RNA-seq transcriptome profiles and clinicopathological information of patients were obtained from The Cancer Genome Atlas. Differentially expressed IRGs in tumors and normal tissues were screened and a risk score signature was constructed to predict the prognosis in patients with hepatocellular carcinoma (HCC). Receiver operating characteristic curves, survival analyses, and correlation analyses were used to explore the clinical application of this model. We further analyzed the differences in clinical characteristics, immune infiltration, somatic mutations, and treatment sensitivity between the high- and low-risk populations characterized by the prognostic models. The immune cell infiltration score and immune-related pathway activity were calculated using the single sample gene set enrichment analysis (ssGSEA) set enrichment analysis. Gene ontology (GO), Kyoto encyclopedia of genes and genomes, and GSEA were used to explore the underlying mechanisms. We constructed a nine-IRG formula to predict the prognosis in HCC patients. The higher the risk score, the higher the malignancy of the tumor and the worse the prognosis. There were significant differences in immune related processes between the high- and low-risk groups. TP53 and CTNNB1 mutations were significantly different between different risk groups. The expression of model gene was closely related to the sensitivity of tumor cells to chemotherapeutic drugs. This risk score model, which is helpful for the individualized treatment of patients with different risk factors, could be a reliable prognostic tool for HCC patients.
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HDAC2-mediated chromatin remodeling drives hepatocellular carcinoma progression: an integrative analysis of computational pathology and multi-transcriptomics.
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer, characterized by high heterogeneity and poor prognosis. Chromatin remodeling regulates chromatin structure and function, and its dysregulation promotes tumor progression. Histone deacetylase 2 (HDAC2) is a key regulator involved in this process. This study links clinicopathological features with molecular mechanisms to elucidate the role of the chromatin-remodeling factor HDAC2 in hepatocellular carcinoma. METHOD: Based on whole-slide images (WSIs) from TCGA-LIHC, pathological features were extracted by combining ResNet-50 and CellProfiler to build a prognostic model. SHAP was subsequently applied to interpret the most influential predictive features. Through integrative multi-omics analysis, HDAC2 was identified as a core gene, and its association with malignant phenotypes was validated using the Human Protein Atlas (HPA), bulk transcriptomic data, and single-cell datasets. Malignant cells were further identified with inferCNVpy, while CellChat and Vector were used to characterize intercellular communication and developmental trajectories. Finally, spatial transcriptomics integrated with the Spotlight and MISTy algorithms revealed the spatial distribution of HDAC2 and its interactions within the tumor microenvironment. The pro-tumor capability of HDAC2 was verified by both cell-based assays and nude mouse tumorigenicity experiments. RESULTS: From WSIs, 630 features were extracted with CellProfiler and 2,048 with ResNet-50. the top nine prognostic features were used to build a Lasso + GBM model selected from 101 machine learning algorithm combinations. SHAP analysis revealed the contribution of each feature. Correlation analysis with a curated chromatin remodeling gene set identified 49 genes significantly associated with these 9 features. For HDAC2, integrative analyses across bulk, single-cell, and spatial transcriptomics revealed its biological roles and underlying mechanisms. ROC curve analysis confirmed strong predictive performance, with average AUCs of 0.84 and 0.77 in the training and validation sets, respectively. HDAC2 knockdown reduced IL-1β/IL-6/TNF-α transcripts, lowered colony formation by ~ 55%, curbed invasion/migration by ~ 30–50% (P < 0.001), and trimmed xenograft weight by ~ 35% (P < 0.01), confirming that HDAC2 drives proliferation, motility and in-vivo tumorigenicity. CONCLUSION: HDAC2 drives hepatocellular carcinoma (HCC) progression by strengthening intercellular communication and forming a spatially organized oncogenic axis. Further analyses indicate that HDAC2-high cells can be regarded as differentiation origin points of the tumor and are significantly associated with poor prognosis.
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A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma.
Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression being closely linked to metabolic reprogramming. This study integrated multiomics data (The Cancer Genome Atlas, Gene Expression Omnibus, European Nucleotide Archive) and advanced machine learning to develop prognostic and immunotherapy prediction models for melanoma, focusing on 114 metabolism-related pathways. Cox regression identified 70 genes linked to survival, with functional enrichment revealing key metabolic pathway alterations. A metabolism-related prognostic model (MRPM) was constructed using 101 combinations of machine learning algorithms, demonstrating superior predictive accuracy across 4 cohorts. High-risk patients showed worse survival and immunotherapy response in melanoma and other cancers. Tumor microenvironment analysis revealed MRPM's negative correlation with immune infiltration and positive association with tumor purity. Single-cell sequencing highlighted MRPM gene enrichment in melanocytes. Mechanistically, GYS1 (the key gene in MRPM) emerged as a pivotal prognostic gene that promotes melanoma proliferation and metastasis. Regulatory studies uncovered SP1's transcriptional control of GYS1- and PSMD14-mediated stabilization of SP1 through K48-linked ubiquitination removal. In vivo validation confirmed that PSMD14 knockdown suppressed tumor growth through SP1-GYS1 axis disruption. This work establishes MRPM as a robust predictive tool and elucidates the PSMD14-SP1-GYS1 regulatory network as a potential therapeutic target in melanoma metabolism.
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MARVELD1 as a tumor stem cell marker and therapeutic target in visceral obesity-associated ovarian cancer.
BACKGROUND: Visceral obesity-related genes (VORGs) have been implicated in cancer progression, but their biological and clinical relevance in ovarian cancer (OC) remains uncertain. METHODS: Prognostic modeling of VORGs was carried out using the The Cancer Genome Atlas (TCGA) and GSE53963 datasets. This model was further investigated in its association with tumor immunity, immune checkpoint inhibitor (ICI) response, and chemotherapy outcomes. Additionally, single-cell data were analyzed to explore gene expression within the tumor microenvironment of this model, specifically identifying high expression of the key gene MARVELD1 in tumor stem cells. The functionality of MARVELD1 was validated experimentally through Cell Counting Kit-8 (CCK-8), transwell, and colony formation assays. RESULTS: OC patients were stratified into four molecular subtypes based on overall survival (OS)-associated VORG expression profiles. Differentially expressed genes (DEGs) between subtypes with prognostic relevance were identified and subsequently analyzed using least absolute shrinkage and selection operator (LASSO) regression and stepwise Cox modeling, resulting in a robust four-gene prognostic model comprising CXCL9, IGF2, FCGBP, and MARVELD1. This model effectively classified patients into high- and low-risk groups, with the high-risk group demonstrating significantly poorer outcomes (P < 0.001). In addition, risk scores showed strong correlations with immune checkpoint gene expression and predicted chemotherapy sensitivity, underscoring their utility in forecasting therapeutic responses. Single-cell transcriptomic analysis revealed heterogeneous expression of model genes across subpopulations, with MARVELD1 prominently enriched in tumor stem cells. MARVELD1 was found to modulate early cellular processes through pathways such as reactive oxygen species. Finally, in vitro experiments confirmed that MARVELD1 knockdown significantly inhibited OC cell proliferation, invasion, and colony formation, further validating its oncogenic role. CONCLUSION: A VORG-based prognostic model was established that correlates with OC prognosis, tumor immune microenvironment, and treatment sensitivity. These findings suggest that MARVELD1 and related pathways may serve as potential targets to improve prognostic stratification and guide therapy in OC.
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Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients.
BACKGROUND: Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear. OBJECTIVE: To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets. METHODS: Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro. RESULTS: We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway. CONCLUSION: This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.