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Single-cell deconstruction of medulloblastoma microenvironment elucidates subtype-specific immune architectures and prognostic molecular signatures.
Medulloblastoma (MB), the most common malignant pediatric brain tumor, exhibits molecular heterogeneity classified into WNT, SHH, GP3, and GP4 subtypes, each with distinct prognoses and therapeutic vulnerabilities. Despite advances in multimodal therapies, treatment-related morbidity and incomplete understanding of tumor microenvironment (TME) heterogeneity impede precision medicine. Through integrated analysis of single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic profiles from 38 MB specimens, we systematically mapped subtype-specific TME features. WNT MB demonstrated sparse immune infiltration but enriched supportive stromal cells (pericytes, astrocytes/oligodendrocytes) and elevated angiogenesis, correlating with superior prognosis. In contrast, SHH, GP3, and GP4 subtypes exhibited immunosuppressive TMEs dominated by tumor-associated macrophages (TAMs) and T cells. Myeloid subtyping revealed 11 functionally distinct TAM clusters: WNT-enriched microglia-derived M1-polarized subsets with anti-tumor activity, SHH-specific M2-like classical TAMs promoting immune evasion, and GP4-associated ECM-remodeling TAMs driving malignancy. T cell profiling identified CD8+ exhaustion and CD4+ regulatory T cell enrichment in non-WNT subtypes, underpinning immunosuppression. Pseudotime trajectory analysis uncovered myeloid differentiation routes from proliferative TAMs to terminal immunosuppressive subsets, regulated by subtype-specific transcription factors (XBP1, STAT6). CellChat analysis highlighted MIF-CD74 and MDK-LRP1 ligand-receptor pairs as key mediators of tumor-TME crosstalk in non-WNT subtypes. Secretome-receptor clustering stratified MB into three TME-driven subtypes: SHH (matrix remodeling), GP3/GP4 (neuronal differentiation), and WNT (angiogenesis), with tumor cells as primary secretory sources. Prognostic core genes (SHH: EMILIN3, CD163; GP3/GP4: SEMA3A, TULP1) predicted survival outcomes and demonstrated diagnostic specificity. This first comprehensive TME atlas of MB subtypes elucidate mechanisms of immunosuppressive niche formation and provides actionable core targets for precision immunotherapy.
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CYB561 a potential prognostic biomarker for liver hepatocellular carcinoma.
Liver hepatocellular carcinoma (LIHC) is a malignancy characterized by a high rate of recurrence, metastasis, and poor prognosis. Cytochrome b561 (CYB561) has been previously reported to be associated with tumor progression, but it has not been revealed in LIHC. The aim of this study was to investigate the prognostic value and potential function of CYB561 in LICH. The expression level, clinical correlation, prognosis, and biological function of CYB561 in LIHC were analyzed using The Cancer Genome Atlas（TCGA), Gene Expression Omnibus (GEO), TIMER2, Kaplan-Meier Plotter, and GEPIA2 databases. The expression of CYB561 in LIHC tissue samples was analyzed by immunohistochemical staining. The effect of CYB561 on the proliferation and migration of LIHC cells was investigated by using CYB561 knockdown in vitro. GSE149614 dataset was used to analyze the expression distribution of CYB561 in LIHC on a single-cell dimension. This study showed that CYB561 mRNA and protein were highly expressed in LIHC. High expression of CYB561 suggests poor prognosis in LICH patients and is an independent risk factor for LIHC. Wound-healing experiment, transwell experiment, and clonal formation experiment confirmed that CYB561 knockdown could inhibit the proliferation and migration of LIHC cells. Functional enrichment analysis showed that CYB561 was related to biological processes such as cell adhesion and immune response. KEGG enrichment analysis showed that CYB561 interacts with tumor-related signaling pathways. Single-cell analysis showed that CYB561 was mainly expressed in hepatocytes. Cells with high CYB561 expression had a higher degree of malignancy. Our study found that abnormal expression of CYB561 in LIHC suggested poor prognosis of LIHC and was related to tumor migration and proliferation. CYB561 is a potential prognostic predictor or therapeutic biomarker.
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CXCL13 as a Prognostic Biomarker and Immune Microenvironment-Associated Gene in Endometrial Carcinoma: A Multi-Omics Investigation.
Immune remodeling within the tumor microenvironment (TME) influences the progression and clinical outcome of uterine corpus endometrial carcinoma (UCEC), but the contribution of chemokine-related regulatory genes remains incompletely characterized. This study aimed to evaluate the prognostic relevance of CXCL13 and its association with immune microenvironmental features in UCEC using publicly available transcriptomic and single-cell datasets. RNA-sequencing profiles and clinical annotations from 589 UCEC cases in The Cancer Genome Atlas (TCGA) were analyzed to assess TME composition using ESTIMATE (Estimation of Stromal and Immune cells in MAlignant Tumours using Expression data) and CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), followed by survival analysis, differential gene expression analysis, protein-protein interaction network construction, Cox regression, and gene set enrichment analysis. A public single-cell RNA-sequencing dataset from the Gene Expression Omnibus (GEO; GSE173682) was further used to infer the cellular sources of CXCL13. Elevated CXCL13 expression was associated with favorable overall survival and enrichment of immune-activation pathways. CIBERSORT-based analysis indicated that high CXCL13 expression correlated with increased estimated fractions of CD8+ T cells and plasma cells, together with transcriptional features related to tertiary lymphoid structure-associated immune activation, whereas several immunosuppressive cell populations showed lower estimated abundance. Single-cell analysis suggested that CXCL13 was mainly expressed by follicular helper T cells and exhausted CD8+ T cells. These findings indicate that CXCL13 may serve as a prognostic biomarker associated with an immune-active TME in UCEC. Further histological, spatial, and functional validation is warranted to confirm its mechanistic role and translational potential.
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Case Report: Transformation From Cold to Hot Tumor in a Case of NSCLC Neoadjuvant Immunochemotherapy Pseudoprogression.
A 56-year-old male was diagnosed with right lung upper lobe squamous cancer with right hilar and mediastinum lymph node metastasis. After four cycles of neoadjuvant immunochemotherapy, reexamination by computed tomography showed progressive disease of the primary lesion. Then, the patient underwent a right lung upper lobectomy, and hilar and mediastinum lymph node dissection. Surgical pathology showed a partial response to immunochemotherapy. Single-cell RNA sequencing was used to characterize the infiltrating immune cell atlas after neoadjuvant immunochemotherapy; the most common infiltrating immune cell types were cytotoxic CD8+ T cells, monocyte-derived dendritic cells, and macrophages. Imaging mass cytometry revealed a transformation from cold to hot tumor after neoadjuvant immunochemotherapy. In this case study, we are the first to report a case of neoadjuvant immunochemotherapy pseudoprogression, proved by surgical pathology, single-cell RNA sequencing, and imaging mass cytometry. Both single-cell RNA sequencing and imaging mass cytometry revealed an activated immune microenvironment after neoadjuvant immunochemotherapy.
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Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.
BACKGROUND: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. METHODS: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. RESULTS: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. CONCLUSION: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.
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Prognostic value and immune infiltration of novel markers TNRC6C/AMPD1 in pancreatic cancer microenvironment.
BACKGROUND: Pancreatic cancer (PC) is a highly lethal malignancy with limited treatment options. Identifying novel prognostic biomarkers and therapeutic targets is crucial for improving patient outcomes. METHODS: A comprehensive bioinformatics analysis was conducted on the Gene Expression Omnibus (GEO, GSE79668, GSE183795) and The Cancer Genome Atlas- Pancreatic Adenocarcinoma (TCGA-PAAD) datasets to identify prognostic biomarkers. The prognostic value of these biomarkers was validated through survival analysis and a Cox proportional hazards model (Cox model). A clinical phenotypic prediction model was constructed using AMPD1 and TNRC6C expression levels, with logistic regression models being built for their combination. The nomogram was constructed to visually represent the model's predictive power. Additionally, immune infiltration and single-cell analyses were performed to explore the underlying mechanisms. Functional experiments were conducted to validate the effects of these biomarkers on PC cell behavior. RESULTS: Adenosine Monophosphate Deaminase 1 (AMPD1) and Trinucleotide Repeat Containing Adaptor 6C (TNRC6C) were identified as key prognostic biomarkers for PC. High expression of these genes was associated with improved patient survival. Furthermore, AMPD1 and TNRC6C were found to be positively correlated with various immune cells, suggesting their potential role in modulating the tumor immune microenvironment. Functional experiments confirmed that these genes inhibited cancer cell proliferation, migration, invasion, and promoted apoptosis. The prognostic model based on AMPD1 and TNRC6C expression showed significant predictive accuracy, suggesting its potential clinical utility. CONCLUSION: This study highlights the prognostic significance of AMPD1 and TNRC6C in PC. These findings provide potential new therapeutic targets for PC and warrant further investigation. The developed clinical prediction model further supports their potential utility as biomarkers for patient stratification and prognosis.
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The role of MRO as an M2 macrophage-associated gene in non-small cell lung cancer: insights into immune infiltration, prognostic significance, and therapeutic implications.
BACKGROUND: Non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), is influenced by tumor-immune interactions. M2 macrophages play a significant role in the tumor microenvironment. This study explores the role of MRO, an M2 macrophage-associated gene, in NSCLC, focusing on immune infiltration, prognostic significance, and therapeutic potential. METHODS: NSCLC samples from The Cancer Genome Atlas (TCGA) were analyzed using the CIBERSORT algorithm to quantify immune cell compositions. Differential gene expression and correlation studies examined MRO's association with M2 macrophages. Univariate Cox regression and Kaplan-Meier analyses assessed prognostic significance. Single-cell RNA sequencing data from TISCH2 evaluated MRO expression in different cell types. The ESTIMATE algorithm analyzed correlations between MRO expression and immune scores, while TIDE and Submap analyses predicted immunotherapy responses. RESULTS: MRO was highly expressed in NSCLC, particularly in LUAD and LUSC, and associated with M2 macrophages. MRO correlated with key immune pathways, including TNFα signaling via NFκB, inflammatory response, and IL6 JAK STAT3 signaling. High MRO expression correlated with poorer overall survival (OS) and disease-specific survival (DSS). Single-cell analysis confirmed MRO expression in macrophages. The ESTIMATE algorithm showed positive correlations between MRO expression and immune scores. TIDE and Submap analyses suggested low MRO expression in LUSC patients might predict better immunotherapy responses. CONCLUSIONS: MRO is a critical M2 macrophage-associated gene in NSCLC, influencing immune infiltration and prognosis. It may serve as a biomarker for prognostication and a target for therapeutic intervention in NSCLC.
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Integrating innovative multiomics and machine learning strategies for prognostic biomarker discovery in hepatocellular carcinoma: guiding personalized treatment strategies with single-cell analysis.
BACKGROUND: Precise treatments for hepatocellular carcinoma (HCC), a highly invasive and heterogeneous cancer, are lacking. We aimed to analyze multiomics data with machine learning and single-cell analysis to develop biomarkers for predicting HCC prognosis and guiding clinical practice. METHODS: Obtaining data from The Cancer Genome Atlas dataset, we analyzed messenger RNA, long noncoding RNA, and microRNA expression profiles, genomic mutations, and DNA methylation data to construct cancer subtypes (CSs) and identify differentially expressed genes (DEGs). Using AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA), we identified cancer subtype1-related genes (CS1RGs) at the single-cell and bulk transcriptome levels. To construct a consensus CS1-related signature (CS1RS), we developed a novel machine learning framework with 13 combinations and evaluated it in the external set. Multiomics analysis provided comprehensive insight into the prognostic signature. We then created a CS1RS-integrated nomogram for prognostic prediction, and we assessed immunotherapy responses and identified drugs for targeted personalized medicine. RESULTS: We identified two CSs of HCC through multiomics consensus clustering. CS1 showed unfavorable survival outcomes. Comprehensive analyses revealed distinct molecular features that differentiated these subtypes, including gene expression patterns, chromatin remodeling, and immune infiltration. Single-cell RNA sequencing identified 2,028 DEGs between the high- and low-CS1 groups. We constructed a CS1RS using 13 machine learning approaches that demonstrated robust predictive accuracy across the training and validation sets. When we combined the CS1RS with clinical characteristics, we observed that they together offered a reliable tool for personalized prognosis prediction in HCC. Additionally, we observed significant correlations between the CS1RS and tumor microenvironment, immune infiltration, and drug sensitivity, providing insights into HCC progression and potential therapeutic targets. CONCLUSIONS: Developing a new risk stratification and prediction model (CS1RS) can help in the prognosis assessment, early risk warning of HCC patients, and can also be used to formulate individualized treatment plans. Our innovation is poised to significantly impact clinical intelligence and management approaches.
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[Omics and cell controlling technology for drug discovery].
Knowledge Palette, Inc. is a start-up company that aims to overcome incurable diseases by applying the world's most accurate single-cell level and bulk level transcriptome technology to obtain large-scale data on the state of cells treated with various types of drugs and media, and using this information to highly control cells for improving human health. We are working on new phenotypic drug discovery and higher quality cells for regenerative medicine using big data. As one of its core technologies, the company is utilizing a single-cell-level whole gene expression analysis technology, Quartz-Seq2, which was originally developed in RIKEN. This technology received first place in accuracy of genes detection as well as marker identification, and was ranked No. 1 in overall score in the benchmarking in the international Human Cell Atlas project. By applying this technology to the bulk level analysis of ultra-multiple samples, it has enabled drug screening, analysis of human clinical specimens, and evaluation of numerous culture environments in a high-throughput way. This paper presents an omics-driven drug discovery and cell regulation approach that is combined with large-scale data and artificial intelligence technology.
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Prognostic Potential of Secreted Modular Calcium-Binding Protein 1 in Low-Grade Glioma.
Background: Secreted modular calcium-binding protein 1 (SMOC1) belongs to a family of matricellular proteins; it was involved in embryo development, endothelial cell proliferation, angiogenesis, integrin-matrix interactions, cell adhesion, and regulation of glucose metabolism. Previous studies showed that the expression of SMOC1 was increased in some tumors. However, the prognostic value and the biological function of SMOC1 in tumor remain unclear. Methods: In this study, we explored the expression profile and prognostic value of SMOC1 in pan-cancers, especially glioma, via multiple databases, including Oncomine, Gene Expression Profiling Interactive 2, PrognoScan, Kaplan-Meier plotter, and the Chinese Glioma Genome Atlas database. Furthermore, LinkedOmics was used to identify the genes coexpressed with SMOC1 and to perform Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology analysis in low-grade glioma (LGG). Also, the Cancer Single-Cell State Atlas database was used to evaluate the correlation between SMOC1 expression and functional state activities in glioma cells. In addition, the Tumor Immune Estimation Resource and TISIDB databases were used to evaluate the correlations between SMOC1 expression and tumor-infiltrating immune cells in the tumor microenvironment. Results: Compared with normal brain tissues, the expression of SMOC1 was increased in LGG tissues. The higher expression of SMOC1 was significantly correlated with better survival of LGG patients. Additionally, functional analyses showed that the SMOC1 coexpressed genes were inhibited in processes such as response to type I interferon and interferon-gamma, lymphocyte-mediated immunity, leukocyte migration, adaptive immune response, neutrophil-mediated immunity, T cell activation, and pathways including EMC-receptor interaction, Th17 cell differentiation, and leukocyte trans-endothelial migration in LGG. Moreover, the expression of SMOC1 was correlated with stemness, hypoxia, EMT, and metastasis of glioma cells. Additionally, the expression of SMOC1 expression was negatively correlated with levels of infiltrating B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells, and gene markers of most immune cells in LGG. Conclusion: Our results suggest that SMOC1 could be a potential biomarker to determine prognosis and might play a specific role in the tumor microenvironment of glioma, thereby influencing the development and progression of glioma. These findings provide some new insights for further investigation.