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NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing.
Further research is needed to investigate the association between netosis and clear cell renal cell carcinoma (ccRCC). We developed a prognostic framework for netosis using univariate, Lasso, and multivariate Cox regression analyses. The CIBERSORT algorithm was employed to compute immune infiltration metrics for The Cancer Genome Atlas (TCGA) dataset. These scores, combined with Cox regression analysis and patient survival data, contribute to the establishment of a prognostic model for the tumor microenvironment (TME). A combined prognostic model incorporating netosis and TME was then developed, stratifying patients based on median results. Further evaluation of the variations in the pathways within the model was conducted using Fast Gene Set Enrichment Analysis (FGSEA) and Weighted Correlation Network Analysis (WGCNA). Additionally, single-cell data integration allowed us to examine netosis-related genes in the context of cell communication and tumor development using the CellChat and Monocle packages. Netosis and TME scores exhibited a high degree of predictive power for patient survival, as illustrated by Kaplan-Meier (KM) curves. Gene set enrichment analysis (GSEA) revealed significant disparities in pathways associated with tumor occurrence between netosis and TME scores. A combined prognostic model incorporating both netosis and TME scores showed excellent performance in the validation set and TCGA data. FGSEA and WGCNA revealed significant differences in pathways associated with traditional tumor development and occurrence within distinct groups of the combined model. Furthermore, single-cell data analysis revealed substantial variations in intercellular communication levels among groups of netosis model genes with high and low expression. Pseudotime analysis highlighted increased expression of EREG, LYZ, S100A8, and S100A9. The combined netosis and TME prognostic model demonstrated high accuracy and efficacy, underscoring its potential value in guiding the treatment and prognosis of future ccRCC patients.
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Pan-cancer analysis of acetylcholinesterase (AChE) expression by a data-driven approach: future directions of AChE in cancer neuroscience.
There is evidence that the expression of acetylcholinesterase (AChE) is associated with the development of certain cancers. However, there are still insufficient studies to reveal the correlation between AChE expression and cancer development and prognosis from the perspective of pan-cancer. In this study, a comprehensive bioinformatics analysis of the pan-cancer expression signature of AChE was conducted through a data-driven approach. Pan-cancer datasets were obtained through online databases, including the Cancer Genome Atlas and Genotypic Tissue Expression databases. The correlation between ACHE expression and cancer prognosis was predicted by survival analysis. The results showed differences in the expression levels of the ACHE gene in cancer and paired normal tissues. However, the alterations in cancer tissue ACHE expression were different depending on the cancer type, e.g., higher ACHE expression was found in pancreatic adenocarcinoma, and lower ACHE expression was found in glioblastoma multiforme (GBM). In addition, ACHE expression was found to have dual prognostic significance, e.g., cutaneous melanoma with higher levels of ACHE expression had better overall survival, while the opposite outcome was found in uveal melanoma. The function of ACHE-expressing cells in primary and metastatic tumors was predicted using single-cell RNA sequencing datasets from patients with GBM. Differences in cell type preferences for ACHE expression were found in primary and metastatic GBM, and different functional enrichment features were identified, suggesting a potential role of ACHE expression in GBM progression. Overall, our study highlights the potential of AChE as a target for cancer research.
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Single-cell transcriptome unveils unique transcriptomic signatures of human organ-specific endothelial cells.
The heterogeneity of endothelial cells (ECs) across human tissues remains incompletely inventoried. We constructed an atlas of > 210,000 ECs derived from 38 regions across 24 human tissues. Our analysis reveals significant differences in transcriptome, phenotype, metabolism and transcriptional regulation among ECs from various tissues. Notably, arterial, venous, and lymphatic ECs shared more common markers in multiple tissues than capillary ECs, which exhibited higher heterogeneity. This diversity in capillary ECs suggests their greater potential as targets for drug development. ECs from different tissues and vascular beds were found to be associated with specific diseases. Importantly, tissue specificity of EC senescence is more determined by somatic site than by tissue type (e.g. subcutaneus adipose tissue and visceral adipose tissue). Additionally, sex-specific differences in brain EC senescence were observed. Our EC atlas offers valuble resoursce for identifying EC subclusters in single-cell datasets from body tissues or organoids, facilitating the screen of tissue-specific targeted therapies, and serving as a powerful tool for future discoveries.
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Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks.
OBJECTIVE: Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. METHODS: The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. RESULTS: Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. CONCLUSION: This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.
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Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome.
Understanding the dynamics of eukaryotic transcriptome is essential for studying the complexity of transcriptional regulation and its impact on phenotype. However, comprehensive studies of transcriptomes at single base resolution are rare, even for modern organisms, and lacking for rice. Here, we present the first transcriptome atlas for eight organs of cultivated rice. Using high-throughput paired-end RNA-seq, we unambiguously detected transcripts expressing at an extremely low level, as well as a substantial number of novel transcripts, exons, and untranslated regions. An analysis of alternative splicing in the rice transcriptome revealed that alternative cis-splicing occurred in approximately 33% of all rice genes. This is far more than previously reported. In addition, we also identified 234 putative chimeric transcripts that seem to be produced by trans-splicing, indicating that transcript fusion events are more common than expected. In-depth analysis revealed a multitude of fusion transcripts that might be by-products of alternative splicing. Validation and chimeric transcript structural analysis provided evidence that some of these transcripts are likely to be functional in the cell. Taken together, our data provide extensive evidence that transcriptional regulation in rice is vastly more complex than previously believed.
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Single-nucleus transcriptomics and chromatin accessibility analysis of musk gland development in Chinese forest musk deer (Moschus berezovskii).
Musk secreted by male forest musk deer (Moschus berezovskii) musk glands is an invaluable component of medicine and perfume. Musk secretion depends on musk gland maturation; however, the mechanism of its development remains elusive. Herein, using single cell multiome ATAC + gene expression coupled with several bioinformatic analyses, a dynamic transcriptional cell atlas of musk gland development was revealed, and key genes and transcription factors affecting its development were determined. Twelve cell types, including two different types of acinar cells (Clusters 0 and 10) were identified. Single-nucleus RNA and single-nucleus ATAC sequencing analyses revealed that seven core target genes associated with musk secretion (Hsd17b2, Acacb, Lss, Vapa, Aldh16a1, Aldh7a1, and Sqle) were regulated by 12 core transcription factors (FOXO1, CUX2, RORA, RUNX1, KLF6, MGA, NFIC, FOXO3, ETV5, NR3C1, HSF4, and MITF) during the development of Cluster 0 acinar cells. Kyoto Encyclopedia of Genes and Genomes enrichment showed significant changes in the pathways associated with musk secretion during acinar cell development. Gene set variation analysis also revealed that certain pathways associated with musk secretion were enriched in 6-year-old acinar cells. A gene co-expression network was constructed during acinar cell development to provide a precise understanding of the connections between transcription factors, genes, and pathways. Finally, intercellular communication analysis showed that intercellular communication is involved in musk gland development. This study provides crucial insights into the changes and key factors underlying musk gland development, which serve as valuable resources for studying musk secretion mechanisms and promoting the protection of this endangered species.
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TMPO-AS1-hsa-let-7b-5p-EZH2-RNA network predicts poor survival in basal-like breast cancer patients.
BACKGROUND: The study explores the role of non-coding ribonucleic acids (ncRNAs) and higher enhancer of zeste homolog 2 (EZH2) gene in breast cancer progression, examining how microRNA (miRNA) and long noncoding RNA (lncRNA) control EZH2, potentially influencing oncogene growth and treatment failure. MATERIALS AND METHODS: The databases used in the study included Cyclebase 3.0 and CellTracer to determine EZH2's role in cell cycle, Oncomine, OncoMX and The University of Alabama At Birmingham Cancer Data Analysis Portal (UALCAN) for Pan-cancer analysis, The Cancer Genomic Atlas Portal (TCGA Portal), Gene Expression Profiling Interactive Analysis (GEPIA2), OncoDB, CR2Cancer, Encyclopaedia of RNA Interactomes (ENCORI), and The Cancer Genome Altas Analyzer (TCGAnalyzeR v1.0) for differential expression analysis, CR2Cancer, OncoDB, MethMarkerDB, and Wanderer databases for epigenetic alteration analysis, Kaplan-Meier Plotter for survival analysis, Breast Cancer Gene Expression Miner (bc-GenExMiner v5.0) for hormone receptor analysis, Tumor-Immune System Interaction Database (TISIDB), Cancer Single Cell State Atlas (CancerSEA), TNMplot, DriverDBv4, and ENCORI for biological processes, cell cycle checkpoints and metastasis analysis, Enrichr, Tumor Immune Estimation Resource (TIMER 2.0), Gepia2 for transcription factor analysis, miRNet, Transcriptome Alterations in Cancer Omnibus (TACCO), and CancerMIRome for miRNA analysis, Enrichr, UALCAN, and ENCORI for lncRNA analysis. RESULTS: The EZH2 gene is overexpressed in breast cancer (BRCA) tumors, metastatic tissues, and circulating tumor cells, potentially leading to cancer progression. Patients with high EZH2 expression have shorter overall survival (OS), distant metastasis-free survival (DMFS), and relapse-free survival (RFS) compared to those with low expression. Estrogen receptot (ER)-negative BRCA tumors and PR-negative tumors have high EZH2 gene and eukaryotic transcription factor (E2F2) levels. The EZH2/E2F2 axis may assist ER/PR-negative BRCA by sponging homosapiens microRNA family (hsa-let-7b-5p) through lncRNA-thymopoietin antisense transcript 1 (TMPO-AS1). Overexpression of the EZH2 protein is associated with BRCA metastasis. CONCLUSION: EZH2 overexpression in basal-like BRCA is mediated by a competing endogenous RNA (ceRNA) network and regulating their expression levels may facilitate better survival outcomes.
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Single cell RNA sequencing to identify an Immunogenic cell death related prognostic signature in intrahepatic cholangiocarcinoma.
Immunogenic cell death (ICD) is crucial for cancer development and enhances the effectiveness of immunotherapy by triggering an adaptive immune response. However, the impact of ICD-related genes (ICDRGs) on iCCA progression and patient prognosis remains unclear. iCCA samples from The Cancer Genome Atlas (TCGA) were categorized into two ICD-associated subtypes by consensus clustering, and patient prognosis in these subgroups was assessed. Single-cell RNA sequencing was conducted on viable cells from 8 iCCA patients to understand their transcriptomic profiles, heterogeneity, and immune microenvironment. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analysis were applied to identified 13 cell subpopulations. The gene set enrichment analysis (GSEA) was used to investigate pathway heterogeneity among these subpopulations. CellChat was employed for intercellular communication analysis. Pseudotime trajectory analyses with CytoTRACE and Monocle2 were used to explore differentiation trajectories and functional differences across various states. 30,485 single cells from human iCCA and normal tissues were sequenced, revealing two ICD-associated subtypes. The ICD-low subtype correlates with better clinical outcomes, whereas the ICD-high subtype is linked to increased immune cell infiltration and immune response signaling activity. Distinct biological functions and metabolic characteristics were observed in iCCA between high- and low-ICD groups. High infiltration of ICD-low subtype in iCCA is associated with poor prognosis, potentially through the ANXA-FPR pathway affecting macrophage proliferation and differentiation. Our study constructed an immunogenic cell death-related signature that can promise prognosis prediction and personalized medicine in iCCA. This finding might help guide clinicians for iCCA patients.
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Prognostic Signature of NETs-Related Genes in Hepatocellular Carcinoma Based on Bulk and Single-Cell Transcriptomics.
BACKGROUND: Inflammation drives tumor development, with neutrophil extracellular traps (NETs) promoting progression through metastasis, immune suppression, and microenvironment modulation. However, the role of NETs-related genes in hepatocellular carcinoma (HCC) immunity response is still unclear. METHODS: We integrated single-cell RNA sequencing (GSE202642, seven tumor samples and four normal liver samples) and The Cancer Genome Atlas (TCGA, n=312) transcriptomic data to identify NETs-related gene signatures. Weighted gene co-expression network analysis (WGCNA) identified NETs-correlated gene modules, and LASSO-COX regression selected prognostic genes for risk stratification. A nomogram was developed to predict survival, while functional, mutation, immune, and drug sensitivity analyses highlighted intergroup differences. EdU and CCK-8 cell proliferation assays confirmed the role of NETs-related genes in HCC cell proliferation. RESULTS: The analysis revealed significant differences in survival time between high- and low-NETs groups. GAS2L3 and RTN3 were identified and validated as independent prognostic factors. ROC and decision curve analysis (DCA) demonstrated that the nomogram model combining NETs risk scores with clinical parameters exhibited robust prognostic performance. The high-risk subgroup was enriched in glycosphingolipid biosynthesis pathways and showed higher mutati843on rates, especially in TP53, CTNNB1, and MUC16, along with overexpression of immunosuppressive genes (VTCN1, LAIR1). In vitro experiments confirmed that GAS2L3 knockdown inhibited HepG2 and Huh7 cell proliferation. CONCLUSION: Integrated multi-omics analysis revealed NETs-associated prognostic signatures in HCC, with GAS2L3 identified as a key gene linking NETs to tumor progression and therapeutic potential.
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Comprehensive analysis of mitochondrial solute carrier family 25 (SLC25) identifies member 19 (SLC25A19) as a regulatory factor in hepatocellular carcinoma.
BACKGROUND: The mitochondrial solute carrier family 25 (SLC25) is known to play a pivotal role in oncogenesis, yet its specific involvement in hepatocellular carcinoma (HCC) remains poorly elucidated. METHODS: In this study, we performed a clustering analysis of HCC patients in the Cancer Genome Atlas database based on the expression levels of SLC25 members, and conducted clinical feature analysis for each patient within the clusters. Subsequently, we developed a prognostic model using a Lasso regression approach with SLC25A19, SLC25A49, and SLC25A51 as features, and generated a risk score for each HCC patient. We then identified SLC25A19 as a potential prognostic marker for HCC through single-cell analysis, and validated this finding using in vitro and in vivo experiments. RESULTS: Our results revealed significant differences in the expression of most SLC25 family members in HCC patients, enabling the stratification of patients into three clusters, with those in cluster 1 exhibiting the most favorable prognosis and showing a correlation with enhanced immune infiltration. The risk scores derived from the features SLC25A19, SLC25A49, and SLC25A51 effectively predicted the prognosis of HCC patients, with area under the curve (AUC) values exceeding 0.7 in the test group. Single-cell analysis further demonstrated h eightened expression of SLC25A19 in the immune microenvironment of HCC, and in vitro experiments indicated that SLC25A19 may regulate the proliferation, migration, invasion, cycle, and apoptosis of liver cancer cells through the Wnt pathway. In the HepG2 animal model, overexpression of SLC25A19 significantly promotes tumor growth, while knockdown inhibits tumor growth. Analysis of patient tumor tissues shows that SLC25A19 is highly expressed in liver cancer tissues and is associated with CD8+ T cell infiltration. CONCLUSIONS: In conclusion, our comprehensive analysis of the role of SLC25 in HCC unveiled SLC25A19 as a potential regulatory factor in HCC.