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Transcriptomic and epigenetic dissection of spinal ependymoma (SP-EPN) identifies clinically relevant subtypes enriched for tumors with and without NF2 mutation.
Ependymomas encompass multiple clinically relevant tumor types based on localization and molecular profiles. Tumors of the methylation class "spinal ependymoma" (SP-EPN) represent the most common intramedullary neoplasms in children and adults. However, their developmental origin is ill-defined, molecular data are scarce, and the potential heterogeneity within SP-EPN remains unexplored. The only known recurrent genetic events in SP-EPN are loss of chromosome 22q and NF2 mutations, but neither types and frequency of these alterations nor their clinical relevance have been described in a large, epigenetically defined series. Transcriptomic (n = 72), epigenetic (n = 225), genetic (n = 134), and clinical data (n = 112) were integrated for a detailed molecular overview on SP-EPN. Additionally, we mapped SP-EPN transcriptomes to developmental atlases of the developing and adult spinal cord to uncover potential developmental origins of these tumors. The integration of transcriptomic ependymoma data with single-cell atlases of the spinal cord revealed that SP-EPN display the highest similarities to mature adult ependymal cells. Unsupervised hierarchical clustering of transcriptomic data together with integrated analysis of methylation profiles identified two molecular SP-EPN subtypes. Subtype A tumors primarily carried previously known germline or sporadic NF2 mutations together with 22q loss (bi-allelic NF2 loss), resulting in decreased NF2 expression. Furthermore, they more often presented as multilocular disease and demonstrated a significantly reduced progression-free survival as compared to SP-EP subtype B. In contrast, subtype B predominantly contained samples without NF2 mutation detected in sequencing together with 22q loss (monoallelic NF2 loss). These tumors showed regular NF2 expression but more extensive global copy number alterations. Based on integrated molecular profiling of a large multi-center cohort, we identified two distinct SP-EPN subtypes with important implications for genetic counseling, patient surveillance, and drug development priorities.
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Cisplatin resistance-related multi-omics differences and the establishment of machine learning models.
OBJECTIVES: Platinum-based chemotherapies are currently the first-line treatment of non-small cell lung cancer. This study will improve our understanding of the causes of resistance to cisplatin, especially in lung adenocarcinoma (LUAD) and provide a reference for therapeutic decisions in clinical practice. METHODS: Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA) and Zhongshan hospital affiliated to Fudan University (zs-cohort) were used to identify the multi-omics differences related to platinum chemotherapy. Cisplatin-resistant mRNA and miRNA models were constructed by Logistic regression, classification and regression tree and C4.5 decision tree classification algorithm with previous feature selection performed via least absolute shrinkage and selection operator (LASSO). qRT-PCR and western-blotting of A549 and H358 cells, as well as single-cell Seq data of tumor samples were applied to verify the tendency of certain genes. RESULTS: 661 cell lines were divided into three groups according to the IC50 value of cisplatin, and the top 1/3 (220) with a small IC50 value were defined as the sensitive group while the last 1/3 (220) were enrolled in the insensitive group. TP53 was the most common mutation in the insensitive group, in contrast to TTN in the sensitive group. 1348 mRNA, 80 miRNA, and 15 metabolites were differentially expressed between 2 groups (P < 0.05). According to the LASSO penalized logistic modeling, 6 of the 1348 mRNAs, FOXA2, BATF3, SIX1, HOXA1, ZBTB38, IRF5, were selected as the associated features with cisplatin resistance and for the contribution of predictive mRNA model (all of adjusted P-values < 0.001). Three of 6 (BATF3, IRF5, ZBTB38) genes were finally verified in cell level and patients in zs-cohort. CONCLUSIONS: Somatic mutations, mRNA expressions, miRNA expressions, metabolites and methylation were related to the resistance of cisplatin. The models we created could help in the prediction of the reaction and prognosis of patients given platinum-based chemotherapies.
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Single-cell analysis of innate spinal cord regeneration identifies intersecting modes of neuronal repair.
Adult zebrafish have an innate ability to recover from severe spinal cord injury. Here, we report a comprehensive single nuclear RNA sequencing atlas that spans 6 weeks of regeneration. We identify cooperative roles for adult neurogenesis and neuronal plasticity during spinal cord repair. Neurogenesis of glutamatergic and GABAergic neurons restores the excitatory/inhibitory balance after injury. In addition, a transient population of injury-responsive neurons (iNeurons) show elevated plasticity 1 week post-injury. We found iNeurons are injury-surviving neurons that acquire a neuroblast-like gene expression signature after injury. CRISPR/Cas9 mutagenesis showed iNeurons are required for functional recovery and employ vesicular trafficking as an essential mechanism that underlies neuronal plasticity. This study provides a comprehensive resource of the cells and mechanisms that direct spinal cord regeneration and establishes zebrafish as a model of plasticity-driven neural repair.
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Revealing the roles of TLR7, a nucleic acid sensor for COVID-19 in pan-cancer.
Recent studies suggested that cancer was a risk factor for coronavirus disease 2019 (COVID-19). Toll-like receptor 7 (TLR7), a severe acute respiratory syndrome 2 (SARS-CoV-2) virus's nucleic acid sensor, was discovered to be aberrantly expressed in many types of cancers. However, its expression pattern across cancers and association with COVID-19 (or its causing virus SARS-CoV-2) has not been systematically studied. In this study, we proposed a computational framework to comprehensively study the roles of TLR7 in COVID-19 and pan-cancers at genetic, gene expression, protein, epigenetic, and single-cell levels. We applied the computational framework in a few databases, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), lung gene expression data of mice infected with SARS-CoV-2, and the like. As a result, TLR7 expression was found to be higher in the lung of mice infected with SARS-CoV-2 than that in the control group. The analysis in the Opentargets database also confirmed the association between TLR7 and COVID-19. There are also a few exciting findings in cancers. First, the most common type of TLR7 was "Missense" at the genomic level. Second, TLR7 mRNA expression was significantly up-regulated in 6 cancer types and down-regulated in 6 cancer types compared to normal tissues, further validated in the HPA database at the protein level. The genes significantly co-expressed with TLR7 were mainly enriched in the toll-like receptor signaling pathway, endolysosome, and signaling pattern recognition receptor activity. In addition, the abnormal TLR7 expression was associated with mismatch repair (MMR), microsatellite instability (MSI), and tumor mutational burden (TMB) in various cancers. Mined by the ESTIMATE algorithm, the expression of TLR7 was also closely linked to various immune infiltration patterns in pan-cancer, and TLR7 was mainly enriched in macrophages, as revealed by single-cell RNA sequencing. Third, abnormal expression of TLR7 could predict the survival of Brain Lower Grade Glioma (LGG), Lung adenocarcinoma (LUAD), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), and Testicular Germ Cell Tumors (TGCT) patients, respectively. Finally, TLR7 expressions were very sensitive to a few targeted drugs, such as Alectinib and Imiquimod. In conclusion, TLR7 might be essential in the pathogenesis of COVID-19 and cancers.
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Identification of a seven-gene prognostic model for renal cell carcinoma associated with CD8+T lymphocyte cell.
CD8+ T lymphocytes are important elements of the tumor microenvironment, hence their involvement in the development and progression of tumors is complex. Data on the precise tumor-infiltrating lymphocytes gene signature in renal cell carcinoma (RCC) remain limited. Therefore, this study created a tumor-infiltrating lymphocytes-related predictive model for patients with RCC using data from The Cancer Genome Atlas. The most important genes associated with CD8 + T lymphocytes were identified using weighted gene co-expression network analysis. Functional categories of important genes were revealed using gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses. A CD8 + T lymphocyte-related prognostic model with 7 important genes was simultaneously created using the least absolute shrinkage and selection operator, univariate and multivariate Cox regressions, and the 7 genes were expressed particularly in CD8 + T lymphocytes according to single-cell sequencing data obtained from the Gene Expression Omnibus. This study identified a seven-gene prognostic model associated with CD8 + T lymphocytes that may significantly influence risk stratification in patients with RCC. The genes included in the model are apolipoprotein B mRNA editing catalytic polypeptide 3G, CD3 gamma, eomesodermin, protein tyrosine phosphatase, non-receptor type 7, signal regulatory protein gamma, Fas ligand, and T-cell immunoreceptor with Ig and ITIM domains.
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Prognostic Significance and Tumor Immune Microenvironment Heterogenicity of m5C RNA Methylation Regulators in Triple-Negative Breast Cancer.
PURPOSE: The m5C RNA methylation regulators are closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the gene expression, clinicopathological characteristics, and prognostic value of m5C regulators in triple-negative breast cancer (TNBC) and their correlation with the tumor immune microenvironment (TIM). METHODS: The TNBC data, Luminal BC data and HER2 positive BC data set were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and 11 m5C RNA methylation regulators were analyzed. Univariate Cox regression and the least absolute shrinkage and selection operator regression models were used to develop a prognostic risk signature. The UALCAN and cBioportal databases were used to analyze the gene characteristics and gene alteration frequency of prognosis-related m5C RNA methylation regulators. Gene set enrichment analysis was used to analyze cellular pathways enriched by prognostic factors. The Tumor Immune Single Cell Hub (TISCH) and Timer online databases were used to explore the relationship between prognosis-related genes and the TIM. RESULTS: Most of the 11 m5C RNA methylation regulators were differentially expressed in TNBC and normal samples. The prognostic risk signature showed good reliability and an independent prognostic value. Prognosis-related gene mutations were mainly amplified. Concurrently, the NOP2/Sun domain family member 2 (NSUN2) upregulation was closely related to spliceosome, RNA degradation, cell cycle signaling pathways, and RNA polymerase. Meanwhile, NSUN6 downregulation was related to extracellular matrix receptor interaction, metabolism, and cell adhesion. Analysis of the TISCH and Timer databases showed that prognosis-related genes affected the TIM, and the subtypes of immune-infiltrating cells differed between NSUN2 and NSUN6. CONCLUSION: Regulatory factors of m5C RNA methylation can predict the clinical prognostic risk of TNBC patients and affect tumor development and the TIM. Thus, they have the potential to be a novel prognostic marker of TNBC, providing clues for understanding the RNA epigenetic modification of TNBC.
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A novel adrenomedullin receptor signaling-based prognostic model predicts immunosuppressive microenvironment and informs therapeutic stratification in hepatocellular carcinoma: A retrospective bioinformatics study.
The adrenomedullin receptor signaling pathway plays a crucial role in tumor progression, yet its comprehensive implication in hepatocellular carcinoma (HCC) remains underexplored. This study aimed to develop and validate a multi-gene prognostic signature based on adrenomedullin receptor signaling-related genes (ARGs) and to elucidate its associations with clinicopathological features, immune microenvironment, drug sensitivity, and somatic mutations in HCC. Transcriptomic and clinical data of HCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Pan-cancer analysis was performed to evaluate the expression and prognostic value of ARGs. A prognostic model was constructed using 116 machine learning algorithm combinations and evaluated by C-index and receiver operating characteristic analysis. The risk score (RS) derived from the model was further correlated with clinical characteristics, immune cell infiltration, drug sensitivity, somatic mutations, and pathway activities. A nomogram was established for clinical applicability. Single-cell RNA sequencing data were analyzed to delineate the cellular heterogeneity of ARG expression within the tumor microenvironment. ADM and RAMP3 were significantly associated with HCC prognosis. The optimal model, built with Random Survival Forest, demonstrated robust predictive performance in both the Cancer Genome Atlas (HR = 9.51, P < .001) and GSE14520 (HR = 2.24, 95% CI: 1.46-3.43, P < .001) cohorts. High-risk patients exhibited advanced T stage, higher histological grade, and poorer overall survival. Computational immune profiling suggested decreased immune cell infiltration and altered immune checkpoint gene expression in the high-risk group. In silico drug sensitivity analysis predicted increased susceptibility to certain targeted agents (e.g., erlotinib) in high-risk patients, though these findings are hypothesis-generating and require experimental validation. Furthermore, RS was weakly correlated with tumor mutational burden (R = 0.16, P = .004), though TP53 mutation frequency did not differ significantly between risk groups after covariate adjustment. The nomogram integrating RS demonstrated favorable predictive accuracy for 1-, 3-, and 5-year survival. Single-cell analysis revealed predominant expression of ARGs in tumor endothelial cells. We developed and validated an ARG-based prognostic signature that effectively stratifies HCC patients into distinct risk subgroups. This model may serve as a hypothesis-generating framework for individualized prognosis prediction and for identifying potential therapeutic vulnerabilities in HCC that warrant prospective investigation.
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Identification of RING finger protein-related biomarkers in sepsis based on explainable machine learning: A comprehensive analysis.
OBJECTIVE: To systematically identify RING finger protein (RNF) biomarkers with diagnostic value in sepsis and to explore their potential as therapeutic targets. METHODS: Sepsis peripheral blood transcriptome datasets from Gene Expression Omnibus (GEO) were integrated. Sepsis-associated RNF genes were identified via differential expression analysis and weighted gene co-expression network analysis (WGCNA). Feature genes were selected using LASSO, SVM-RFE, and SHAP. Summary-data-based Mendelian randomization (SMR) assessed causality, and diagnostic performance was validated by receiver operating characteristic (ROC) curves in three independent cohorts. Single-cell RNA sequencing resolved cellular localization and perturbations. DrugReflector deep learning predicted repurposable drugs, molecular docking evaluated binding affinity, and PheWAS assessed target safety. RESULTS: A total of 13 sepsis-related RNF genes were identified, and three feature genes (ZFP36L2, RNF125, RNF175) were further selected. SMR showed that only elevated RNF175 expression significantly increased sepsis risk (OR = 1.267, 95% CI 1.002-1.603, P = 0.048). The diagnostic AUCs of RNF175 were 0.931, 0.776, and 0.835 in the training and two validation cohorts, respectively. Single-cell atlas revealed predominant RNF175 expression in T cells with significant upregulation in sepsis (P < 0.001). Deep learning predicted tadalafil as the top candidate, with a molecular docking binding energy of -9.072 kcal/mol for RNF175. PheWAS found no significant phenotype associations, indicating low off-target risk. CONCLUSIONS: RNF175 is an early sepsis biomarker with both diagnostic value and causal relevance, potentially acting through T-cell modulation. Tadalafil may be a pathway-targeted repurposable drug. This study provides new perspectives for precision medicine in sepsis.
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Self-identified African ancestry and pathomics-derived tumor-infiltrating lymphocyte scores in colon and rectal adenocarcinoma whole-slide images: an exploratory multicohort whole-slide image study.
INTRODUCTION: AI assisted computed higher tumor infiltrating lymphocyte% (TIL%) scores from hematoxylin and eosin (H&E) stained whole slide images (WSI) may serve as a useful biomarker for stratifying patients for treatment with immune checkpoint inhibitors. METHODS: To test the hypothesis that self-identified African ancestry (AA) vs. European ancestry (EA) was associated with higher TIL% scores, WSI were assembled from three diverse cohorts of formalin-fixed paraffin embedded (FFPE) colon and rectal adenocarcinoma (COAD-READ) tumor tissues: 1.) 242 AA vs. EA WSI downloaded from The Cancer Genome Atlas (TCGA)-COAD-READ database; 2.) 97 independent US self-identified AA vs. EA WSI assembled from three US medical centers; 3.) 48 (of 51) Nigerian WSI assembled from a single Nigerian medical center. There were 33 self-identified AA WSI suitable for analysis in the TCGA cohort and 49 self-identified AA WSI in the US cohort. For the TCGA cohort, 241 whole transcriptome RNA-sequence data were generated from parallel frozen tumor samples collected alongside the FFPE samples. For the US cohort, 87 RNA-seq enriched by exome capture data were generated from the same FFPE blocks as WSI. RESULTS: No difference in median TIL% scores was detected between the combined TCGA and US AA cohorts and the Nigerian cohort. Exploratory multiple regression analysis of the combined TCGA and US cohorts revealed that AA had a higher TIL% score (Estimated difference = 3.01%, 95% CI (0.54%, 5.49%), P-value = 0.0170), while controlling for MMR/MSI status and other covariates. However, this result needs to be interpreted with caution because of differences between the TCGA and US cohorts with respect to representation of self-identified AA. After adjustment for cohort in the model, the association of AA with a higher TIL% score was no longer significant (Estimated difference = 2.05%, 95% CI(-0.59%, 4.70%), P-value = 0.1277). Moderate correlations were detected in the TCGA and US cohorts analyzed separately between computed TIL% scores and CIBERSORTx estimates of lymphocyte and T-cell abundance. Moderate correlations were also detected between TIL% scores and CXCL10 and CCL5 gene expression values. CONCLUSIONS: These preliminary results underscore the need for expanding the representation of minority populations in publicly accessible datasets.
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Study on the expression level of folate receptor γ in thyroid cancer and its association with clinicopathological characteristics.
Despite generally favorable outcomes, identifying reliable biomarkers remains crucial for managing aggressive subtypes of thyroid cancer. The present study aimed to investigate the association between folate receptor γ (FOLR3) expression and the clinicopathological features of thyroid cancer, and to explore its clinical relevance using bioinformatics and experimental approaches, ultimately assessing its potential value as a diagnostic and therapeutic biomarker. Differential expression analysis of RNA-sequencing data from The Cancer Genome Atlas database was performed to identify the differentially expressed gene FOLR3, followed by standard single-cell analysis. Clinicopathological data from 293 patients with thyroid cancer were collected to investigate pathological characteristics and FOLR3 expression, which was further validated using immunohistochemistry. The functional effects of FOLR3 on the malignant behavior of thyroid cancer cells were assessed using colony formation, Cell Counting Kit-8, scratch wound healing and Transwell invasion assays. FOLR3 expression was significantly higher in normal thyroid tissue compared with cancer tissues (P<0.001). FOLR3 expression level was associated with capsular invasion, perineural invasion, striated muscle invasion and pT stage (all P<0.05). Functional experiments demonstrated that FOLR3 suppressed the proliferation, migration and invasion of TPC-1 cells (all P<0.05). In conclusion, FOLR3 expression is generally low or absent in thyroid cancer, particularly in cases demonstrating pronounced local infiltration, indicating its potential as a diagnostic biomarker for the evaluation of tumor invasiveness. Patients with high FOLR3 expression exhibit a higher survival rate, suggesting its potential prognostic value. FOLR3 inhibits the progression of thyroid cancer by inhibiting the STAT3/MAPK signaling pathways and reprogramming folate metabolism, highlighting its potential as a therapeutic target for thyroid cancer in the future.