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Machine learning-derived natural killer cell signature predicts prognosis and therapeutic response in clear cell renal cell carcinoma.
BACKGROUND: Natural killer cells, interconnected with patient prognosis and treatment response, play a pivotal role in the tumor immune microenvironment and may serve as potential novel predictive biomarkers for renal cell carcinoma. METHODS: Clear cell renal cell carcinoma transcriptome data and the corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) database. Single-cell sequencing data were sourced from the Gene Expression Omnibus (GEO) database. A risk model was established by integrating ten different machine learning algorithms, which resulted in 101 combined models. The model with the highest average C-index was selected for further analysis, and was assessed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in immune infiltration fractions, clinicopathological features, and response to various targeted therapies and immunotherapy between high- and low-risk groups were investigated. Furthermore, qRT-PCR, IHC, colony formation test, CCK8 assay and flow cytometry were conducted to explore the expression pattern and function of ARHGAP9 in our own patient samples and renal cancer cell lines. RESULTS: Totally, 156 NK cell-related genes and 5189 prognosis-related genes were identified, and 36 genes of their intersection demonstrated prognostic value. A risk model with 18 genes was established by Coxboost plus plsRcox, which can accurately predict the prognosis of ccRCC patients. Significant correlations were determined between risk score and tumor malignancy and immune cell infiltration. Meanwhile, a combination of tumor mutation burden plus risk score could have higher accuracy of predicting clinical outcomes. Moreover, high-risk group patients were more likely to be responsive to targeted therapy but show no response to immunotherapy. CONCLUSIONS: Intricate signaling interactions between NK cells and various cellular subgroups were depicted and the developmental trajectory of NK cells was elucidated. A NK cells-related risk model was established, which can provide reliable prognostic information and identified patients with more probability of benefiting from therapy.
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Crosstalk Between Gastric Cancer and Adjacent Mucosa Reveals EDN1-EDNRA-Mediated Regulation of Cancer Stemness and Immunomodulation Networks.
Within the complex system of gastric cancer, the tumour microenvironment mediates a complex network of cellular interactions, yet its tissue-specific intracellular communication patterns have remained poorly understood. Leveraging cutting-edge single-cell RNA sequencing, we investigated two independent research studies (GSE183904 and GSE184198), creating an unprecedented map of cellular crosstalk across gastric cancer tissues, their adjacent normal tissue and gastric mucosa (GM). Our systematic analysis revealed two distinct patterns: 7557 distinct interactions from normal tissue to tumour cells, while gastric mucosa engaged in 7320 unique interactions with malignant conditions. Within this cellular network, the endothelin pathway emerged as a key regulator, specifically increased in gastric mucosa-to-tumour interaction. The Cancer Genome Atlas data demonstrated that patients harbouring elevated EDNRA expression faced significantly poorer outcomes. EDNRA, previously underexplored in this context, showed remarkable upregulation across diverse gastric cancer cell lines. Through experimental validation, we demonstrated that EDNRA overexpression, when stimulated by endothelin-1, dramatically accelerated the proliferation of human gastric epithelial GSE-1 cells. Conversely, pharmacological inhibition of EDNRA using ABT-627 suppressed both NCI-N87 and MKN-28 gastric cancer cells proliferation. Further mechanistic investigation revealed the molecular mechanism of ABT-627: simultaneously triggering both extrinsic and intrinsic apoptotic cascades. TISIDB analysis revealed significant positive correlations between EDNRA and multiple immunostimulators, suggesting the role of EDNRA in immunomodulation networks. These findings reveal a previously unidentified connection between gastric mucosa and tumour progression, positioning EDNRA not only as a molecular target, but also as a critical mediator of tissue-specific cancer communication. In conclusion, EDNRA functions as both a regulatory factor and therapeutic target, offering a promising therapeutic avenue for gastric cancer intervention.
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Lactate metabolism-related genes serve as potential biomarkers for predicting gastric cancer progression and immunotherapy.
BACKGROUND: Targeting lactate metabolism represents a promising therapeutic strategy to enhance anti-tumor immune responses. In this study, we developed a novel model based on lactate metabolism-related genes (LRGs) to predict survival, characterize the immune microenvironment, and assess immunotherapy response in gastric cancer (GC), with the potential to identify new biomarkers. METHODS: Data sets of GC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. LRGs were sourced from the MSigDB database. Five key prognostic LRGs (MMP11, MMP12, HBB, VSIG2, and SERPINE1) were identified using univariate COX regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Patients were classified into high-risk and low-risk groups based on a median risk score. We conducted prognostic analysis, gene set enrichment analysis (GSEA), immune microenvironment analysis, immunotherapy responsiveness evaluation, and drug screening in these groups. RESULTS: The high-risk group exhibited poorer prognosis compared to the low-risk group, as predicted by our nomogram for overall survival. Notably, the high-risk group, marked by higher stromal cell infiltration and RNA stemness scores (RNAss), showed increased susceptibility to immune evasion. In contrast, the low-risk group demonstrated better responses to immunotherapy and greater sensitivity to chemotherapy. Single-cell analysis revealed that SERPINE1 is predominantly positively correlated with immune checkpoint expression, while VSIG2 exhibits a negative correlation. CONCLUSIONS: We have developed and validated a novel lactate metabolism-associated model, providing new insights into the prognosis and immunotherapy of GC patients. The five identified LRGs offer potential as prognostic biomarkers and therapeutic targets in GC.
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Integrative Analysis the Role of ENG as a Metabolic and Macrophage-Related Gene in Hepatocellular Carcinoma.
High levels of M2 macrophages often correlate with poor prognosis. Endoglin (ENG) is a potential target for anti-angiogenesis therapy in various cancers, but the link between M2 macrophages and metabolism-related genes (MRGs) in hepatocellular carcinoma (HCC) is unclear. We employed cibersort analysis to identify genes associated with M2 macrophages and metabolic reprogramming in HCC, utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. TCGA data were stratified basis on ENG expression levels, and the relationships between ENG and relevant genes were assessed alongside clinical features. Furthermore, we validated ENG expression in HCC tissues and its correlation with M2 macrophages via qRT-PCR, Western blotting (WB), and immunohistochemistry (IHC). Patients with high ENG expression presented superior overall survival (OS) and longer progression-free survival (PFS). Univariate and multivariate regression analyses identified ENG as an independent prognostic predictor. Moreover, GSEA, GO, and KEGG analyses suggested a correlation between ENG-related gene expression and immunity, particularly TAMs. Additionally, ENG was found to reshape the tumor microenvironment (TME) of HCC and influence the response to immunotherapy. Single-cell analysis revealed the differential expression and distribution of ENG in the TME. In vitro experiments demonstrated lower ENG expression in HCC tissues than in paracancerous tissues, with a concomitant correlation with M2 macrophages. ENG emerges as a novel predictive marker for HCC, could reshap the TME and impacts the response to immunotherapy and provides a fresh perspective for investigating combined immunotherapy targeting MRGs in HCC.
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Prognostic value of PRAME gene promoter methylation and expression in hepatocellular carcinoma.
BACKGROUND: PRAME is a member of the cancer-testis antigen family and exhibits abnormally high expression in various solid tumors. Epigenetic regulation plays a critical role in the development and progression of hepatocellular carcinoma (HCC). Alterations in promoter methylation of specific genes have been widely recognized as valuable biomarkers for predicting cancer risk and assessing prognosis. OBJECTIVE: This study systematically investigated the biological function and clinical relevance of PRAME promoter methylation in HCC. METHODS: Using data from The Cancer Genome Atlas (TCGA) cohort, we identified methylation-associated prognostic genes and examined the association between PRAME promoter methylation, its mRNA expression levels, overall survival (OS), and clinicopathologic characteristics. We then validated these findings in an independent HCC cohort from Shulan (Hangzhou) Hospital using MethylTarget multiplex gene methylation detection technology. To investigate the potential role of PRAME in vascular invasion and metastasis, we conducted Gene Ontology (GO) enrichment analysis and Gene Set Enrichment Analysis (GSEA) at both histological and cellular levels. RESULTS: Analyses across three HCC cohorts consistently showed that the PRAME promoter is generally hypomethylated in tumor tissues, and this hypomethylation was significantly correlated with elevated PRAME mRNA expression. In both the TCGA and Shulan (Hangzhou) Hospital cohorts, PRAME promoter hypomethylation was associated with more advanced clinicopathologic features and shorter OS. Moreover, PRAME promoter methylation status was strongly linked to vascular invasion and distant metastasis in HCC. Further functional analyses revealed significant upregulation of Wnt signaling and cell adhesion-related pathways in tissues with high PRAME expression, a finding corroborated by single-cell RNA sequencing data. CONCLUSION: Hypomethylation of the PRAME promoter is closely associated with poor overall survival, adverse clinicopathologic features, vascular invasion, and metastasis in HCC patients. It plays a pivotal role in regulating gene expression and promoting tumor invasiveness. These findings suggest that PRAME promoter methylation may serve as a promising epigenetic biomarker for prognostic evaluation in HCC.
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UBE2J1 is identified as a novel plasma cell-related gene involved in the prognosis of high-grade serous ovarian cancer.
BACKGROUND: Immune cells within tumor tissues play important roles in remodeling the tumor microenvironment, thus affecting tumor progression and the therapeutic response. The current study was designed to identify key markers of plasma cells and explore their role in high-grade serous ovarian cancer (HGSOC). METHODS: We utilized single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify key immune cell types within HGSOC tissues and to extract related markers via the Seurat package. The effects of immune cell markers on prognosis were analyzed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and gene set variation analysis (GSVA) of bulk sequencing data from The Cancer Genome Atlas (TCGA)-HGSOC cohort. Finally, the effects of key markers on HGSOC cells were evaluated via Cell Counting Kit-8 (CCK-8), Transwell, colony formation, wound healing, immunofluorescence and in vivo tumor growth assays. RESULTS: At the single-cell level, we detected a significant increase in the proportion of plasma cells in HGSOC samples compared to that in normal ovarian samples. Within HGSOC tissues, these plasma cells were found to interact with CD8 + T cells, fibroblasts and endothelial cells. In addition, patients in the high-plasma cell-related score group had better survival rates and higher epithelial‒mesenchymal transition (EMT), apoptosis and immune scores. Moreover, univariate Cox and LASSO regression analyses revealed that ubiquitin-conjugating enzyme E2 J1 (UBE2J1) is a prognostic marker in HGSOC. Further functional studies revealed that overexpression of UBE2J1 promoted cell proliferation, invasion, migration and colony formation, whereas UBE2J1 knockdown attenuated the abovementioned cellular behaviors. Additionally, UBE2J1 overexpression promoted EMT, as evidenced by alterations in the protein expression levels of N-cadherin, snail family transcriptional repressor 2 (Slug), Twist family BHLH transcription factor 1 (Twist 1) and E-cadherin. Moreover, we found that UBE2J1 silencing was able to inhibit the tumor growth in vivo. CONCLUSIONS: Overall, this study elucidated the role of plasma cells and revealed UBE2J1 as a novel oncogene in HGSOC, uncovering new mechanisms related to HGSOC tumorigenesis and promising therapeutic targets for HGSOC patients.
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Construction and experimental validation of an acetylation-related gene signature to evaluate the recurrence and immunotherapeutic response in early-stage lung adenocarcinoma.
BACKGROUND: Acetylation is a reversible epigenetic process, playing an important role in the initiation and progression of malignant tumors. However, the prognosis value of acetylation-related genes in the early-stage lung adenocarcinoma (LUAD) remains obscure. MATERIALS AND METHODS: The acetylation-related genes were collected and clustered based on transcriptome sequencing of the patients with early-stage LUAD from the Cancer Genome Atlas. The genomic divergence analysis, protein-protein interaction network construction, Lasso regression, and univariate Cox regression were used to identify the significant biomarkers for the recurrence of the early-stage LUAD. The multivariate Cox regression was used to establish the predictive model. Gene Expression Omnibus was systemically retrieved and four independent datasets were used for external validation. 23 early-stage LUAD samples were collected from the local hospital to detect the expression difference of the genes in the model. Transfection assays were performed to verify the regulatory ability of the screened gene to the proliferation of LUAD cell lines. The single-cell RNA sequencing of the early-stage LUAD patients and two lung cancer cohorts receiving immunotherapy were utilized to explore the predictive ability of the established model to immunotherapeutic sensitivity. RESULTS: The clustering based on acetylation-related genes was significantly associated with the recurrence (P < 0.01) and immune infiltration statuses. Through a series of bioinformatical and machine learning methods, RBBP7 and YEATS2 were ultimately identified. Accordingly, a novel gene signature containing RBBP7 and YEATS2 was developed to evaluate the recurrence-free survival of early-stage LUAD, which was then validated in five independent cohorts (pooled hazard ratio = 1.88, 95% confidence interval = 1.49-2.37) and 23 local clinical samples (P < 0.01). The knock-down of YEATS2 obviously suppressed proliferation of H1975 and HCC-827 cells. Single-cell RNA sequencing analyses indicated that RBBP7 and YEATS2 were both associated with the tumor immune response, and the prognosis signature could predict the immunotherapeutic response in two cohorts receiving immunotherapy (P < 0.05; P < 0.01). CONCLUSIONS: Totally, an acetylation-related gene signature is constructed, helping to evaluate the recurrence and immunotherapeutic effectiveness of early-stage LUAD patients.
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Pan-Cancer Analysis Identifies LUC7L2 as a Prognostic Biomarker and Potential Functional Regulator of Prostate Cancer Progression.
Background/Objectives: Cancer imposes a substantial global public health burden, and its initiation and progression involve complex dysregulation of gene expression regulatory networks. Methods: This study systematically evaluated the expression characteristics and prognostic value of LUC7L2 across pan-cancer types, with a particular focus on functional validation in prostate cancer. Using transcriptomic and proteomic data from multiple databases, immunohistochemical staining from the Human Protein Atlas, and single-cell transcriptomic datasets GSE137829 and GSE176031, we analyzed the expression patterns of LUC7L2 in various cancer types. Diagnostic and prognostic potential was assessed through ROC curve analysis, Cox regression, single-cell, and spatial transcriptomics, while the functional role of LUC7L2 in prostate cancer tumorigenesis and progression was validated via in vitro assays. Results: Results showed that LUC7L2 was significantly upregulated in glioblastoma and prostate adenocarcinoma, and downregulated in lung adenocarcinoma and ovarian serous cystadenocarcinoma, with diagnostic AUC values exceeding 0.7 across multiple tumor types. In prostate cancer, high LUC7L2 expression was significantly associated with a shortened progression-free interval (multivariate HR = 3.50, 95% CI 1.95-6.29, p < 0.001), with a meta-analysis of independent cohorts showing a consistent direction of effect (pooled HR = 1.14, 95% CI 0.90-1.44). Moreover, LUC7L2 exhibited strong correlations with microsatellite instability and tumor mutational burden across multiple cancer types. Single-cell analysis revealed that its expression was markedly higher in malignant cells than in immune or stromal cells. In vitro knockdown of LUC7L2 in prostate cancer cells led to significantly suppressed proliferation, increased apoptosis, S-phase cell cycle arrest, reduced invasion and migration capacities, enhanced senescence, and aggravated DNA damage. Conclusions: This study demonstrates that LUC7L2 is specifically overexpressed in malignant prostate cancer cells and independently associated with poor prognosis. It further validates its regulatory roles in proliferation, apoptosis, migration, and DNA damage repair, providing both theoretical and experimental evidence supporting LUC7L2 as a potential biomarker for prostate cancer.
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Five EMT-related genes signature predicts overall survival and immune environment in microsatellite instability-high gastric cancer.
BACKGROUND: Microsatellite instability-high (MSI-H) subgroup of gastric cancer (GC) is characterized by a high tumor mutational burden, increased lymphocytic infiltration, and enhanced inflammatory cytokines. GC patients with MSI-H status have a good response to immune checkpoint blockade management. However, heterogeneity within the subtype and the underlying mechanisms of shaping tumor microenvironments remain poorly understood. METHODS: RNA expression levels and clinical parameters of GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The data were analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA), univariate Cox regression, multivariate Cox regression, and Least Absolute Shrinkage Selection Operator (LASSO) regression. In addition, multiplex immunohistochemistry (mIHC) was used in our clinical cohort for the tumor microenvironment study. RESULTS: By ssGSEA and survival analysis, the EMT signaling pathway was identified as a representative pathway, which can stratify the patients with MSI-H GC with significant survival predictive power. Then, a novel representative EMT-related five-gene signature (namely CALU, PCOLCE2, PLOD2, SGCD, and THBS2) was established from EMT signaling gene set, which sensitivity and specificity were further validated in the independent GEO database (GSE62254) cohort for disease outcome prediction. Based on public single-cell data and in situ immunohistochemistry, we found that most of these five genes were abundantly expressed in cancer-associated fibroblasts. Furthermore, patients with high or low risk divided by this five-gene signature exhibited a strong correlation of the distinct patterns of tumor immune microenvironment. By mIHC staining of sections from 30 patients with MSI-H status, we showed that the patients with better prognoses had the increased infiltration of CD8+ cells in the primary tumoral tissue. CONCLUSION: Our study developed a simple five-gene signature for stratifying MSI-H GC patients with survival predictive power.
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Continuous cell-type diversification in mouse visual cortex development.
The mammalian cortex is composed of a highly diverse set of cell types and develops through a series of temporally regulated events1-3. Single-cell transcriptomics enables a systematic study of cell types across the entire timeline of cortical development. Here we present a comprehensive and high-resolution transcriptomic and epigenomic cell-type atlas of the developing mouse visual cortex. The atlas is built from a single-cell RNA sequencing dataset of 568,654 high-quality single-cell transcriptomes and a single-nucleus Multiome dataset of 200,061 high-quality nuclei, which were densely sampled across the embryonic and postnatal developmental stages (from embryonic day 11.5 to postnatal day 56). We computationally reconstructed a transcriptomic developmental trajectory map of all excitatory, inhibitory and non-neuronal cell types in the visual cortex. Branching points that mark the emergence of new cell types at specific developmental ages and molecular signatures of cellular diversification are identified. The trajectory map shows that neurogenesis, gliogenesis and early postmitotic maturation in the embryonic stage give rise to all cell classes and nearly all subclasses in a staggered parallel manner. Increasingly refined cell types emerge throughout the postnatal differentiation process, including the late emergence of many cell types during the eye-opening stage and the onset of critical period, suggesting that there is continuous cell-type diversification at different stages of cortical development. Throughout development, there are cooperative dynamic changes in gene expression and chromatin accessibility in specific cell types. We identify cell-type-specific and temporally resolved gene regulatory networks that link transcription factors and downstream target genes through accessible chromatin motifs. Collectively, our study provides a detailed dynamic molecular map directly associated with individual cell types and specific temporal events that can reveal the molecular logic underlying the complex and multifaceted cortical cell type and circuit development.