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Development of a Novel KCNN4-Related ceRNA Network and Prognostic Model for Renal Clear Cell Carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) accounts for more than 80% of renal cell carcinomas. Yet, it has not been fully understood about the derivation and progression of the tumor, as well as the long-term benefits from multimodality therapy. Therefore, reliable and applicable molecular markers are urgently needed for the prediction of diagnosis and prognosis of ccRCC patients. METHODS: Genetic and clinical information of 533 ccRCC patients from The Cancer Genome Atlas database was collected for comprehensive bioinformatic analyses. UALCAN was used to detect gene expression in paired tumor samples. Two data sets from Gene Expression Omnibus database were analyzed to identify differentially expressed genes (DEGs), and Gene Set Enrichment Analysis was applied for the functional enrichment of DEGs. Tumor Immune Single Cell Hub and Tumor IMmune Estimation Resource databases were separately used for analyses of single-immune cell and immune cell infiltration. Encyclopedia of RNA Interactomes database was explored to predict targeted microRNAs (miRNAs) and corresponding long non-coding RNAs (lncRNAs). Cox regression analysis was performed for the construction of risk signature and prognosis model. Finally, quantitative real-time polymerase chain reaction and western blot were conducted for KCNN4 expression detection in cell lines and clinical samples. Small interfering RNA was employed to knock down KCNN4, and corresponding functional experiments were conducted on ccRCC cells as well. RESULTS: KCNN4 showed elevated expression in tumors and prominent clinical correlation in ccRCC. In total, 41 KCNN4-related genes were enriched, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed they were intimately related to immune-related signaling pathways. Spearman's analysis revealed the significantly positive correlation of KCNN4 with immune cell infiltration. By integrating hub miRNA-let-7e-5p and four critical lncRNA, a competitive endogenous RNA network-based risk signature was constructed. The prognosis model derived from it showed considerable predictive value for survival of ccRCC patients. Finally, in vitro experiments confirmed the remarkable tumor-promoting role of KCNN4 in ccRCC cells. CONCLUSION: KCNN4 significantly affected the immune status of tumor microenvironment and immunotherapy elements, through which it promoted tumor progression in ccRCC, and it could be a potential biomarker for prognosis and immunotherapy effects of ccRCC patients.
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HAO2 protects from proximal tubular cells injured in rats with chronic kidney disease by promoting fatty acid metabolic processes.
The complex pathogenesis of kidney disease is closely related to the diversity of kidney intrinsic cells. In this study, single-cell transcriptome sequencing technology was used to sequence and analyze blood and kidney tissue cells in normal control rats and rats with chronic kidney disease (CKD), focusing on key cell populations and functional enrichment to explore the pathogenesis of CKD. Oil red O staining and enzyme-linked immunosorbent assay (ELISA) were used to detect lipid droplets and free fatty acid (FFA). Quantitative real-time polymerase chain reaction (RT-PCR), western blot (WB) were used to verify the differential gene hydroxyacid oxidase 2 (HAO2) and fatty acid metabolic process in tissue to ensure the reliability of single-cell sequencing results. We successfully established a single-cell transcriptome atlas of blood and kidney tissue in rats with CKD, which were annotated into 14 cell subsets (MPCs, PT, Tc, DCT, B-IC, A-IC, CNT, ALOH, BC, Neu, Endo, Pla, NKT, Baso) according to marker gene, and the integrated single-cell atlas of rats showed a significant increase and decrease of MPCs and PTs in the CKD group, respectively. Functional analysis found extensive enrichment of metabolic-related pathways in PT cells, includes fatty acid metabolic process, cellular amino acid metabolic process and generation of precursor metabolites and energy. Immunohistochemical experiments determined that the differential gene HAO2 was localized in the renal tubules, and its expression was significantly reduced in CKD group compared with control, and oil red O staining showed that lipid droplets increased in the CKD group, after overexpression of HAO2 the lipid droplets was inhibited. ELISA assay showed that ATP content decreased in the CKD group and FFA increased in the CKD group. Moreover, the mitochondrial membrane potential of the cells in the OE-HAO2 group was significantly increased compared with OE-NC. The acyl-CoA oxidase 1(ACOX1), peroxisome proliferator-activated receptor alpha (PPARα), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) were decreased in the CKD group, while genes and proteins were increased after overexpression of HAO2, and the AMP-activated protein kinase (AMPK) phosphorylated proteins were increased, the acetyl-CoA carboxylase (ACC) phosphorylated proteins were decreased, reversely. Therefore, HAO2 may be an important regulator of fatty acid metabolic processes in CKD, and overexpression of HAO2 can enhance fatty acid metabolism by promoting fatty acid oxidation (FAO) pathway.
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Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer.
BACKGROUND: Antigen-presenting and processing fibroblasts (APPFs) have emerged as pivotal regulators of antitumor immunity. However, the predictive value of APPF-related genes (APPFRGs) in the prognosis and tumor immune status of gastric cancer (GC) remains largely unexplored. METHODS: Bioinformatics analysis was conducted using single-cell and bulk RNA sequencing datasets of GC retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The APPFs were identified using AUCell algorithm based on APP-associated genes obtained from the InnateDB database. CellChat algorithm was utilized to evaluate interactions between cells. The non-negative matrix factorization (NMF) clustering analysis was performed to identify APPF-related subgroups based on TCGA‑stomach adenocarcinoma cohort. LASSO and multivariate Cox regression analysis were conducted to establish the predictive model. Immunohistochemistry of GC tissue microarrays was performed to validate the model. RESULTS: Compared to non-APPFs, APPFs exhibited more interactions with myeloid cells, endothelial cells, and lymphocytes via MHC-II signaling network. The two APPF-related subgroups clustered by NMF demonstrated significant differences in prognosis and immune cell infiltration. Five APPFRGs (CPVL, ZNF331, TPP1, LGALS9, TNFAIP2) were identified to establish the predictive model and stratify GC patients based on risk score. The prognosis was significantly different between the two risk groups and was validated using GEO datasets. A nomogram that efficiently predicted the overall survival of GC patients was established by integrating the risk score with age, T stage, N stage, and M stage. Furthermore, the high-risk group exhibited reduced infiltration of activated CD4+ T cell and increased infiltration of Treg cells, higher resistance to chemotherapy and immunotherapy, and lower tumor mutation burden. Finally, the immunohistochemical results of GC tissue microarrays revealed higher expression of CPVL, ZNF331, and TPP1, and lower expression of LGALS9 and TNFAIP2 in GC compared to adjacent normal tissues. Additionally, higher risk score in GC samples was relevant with poor differentiation, positive nerve invasion, advanced T and TNM stages, and higher expression of FOXP3. CONCLUSIONS: APPFs may play an important role in the regulation of tumor immune microenvironment in GC and warrant further exploration. The predictive model based on APPFRGs effectively predicts the prognosis and tumor immune status of GC.
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Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis.
OBJECTIVE: The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD). METHODS: The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes. RESULTS: We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs. CONCLUSIONS: Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.
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Automated deconvolution of structured mixtures from heterogeneous tumor genomic data.
With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing. We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data, as well as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA and CNV data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas (TCGA). Source code is available at https://github.com/tedroman/WSCUnmix.
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Landscape of Cell States and Multicellular Ecotypes in Non-Small Cell Lung Cancer.
Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer and remains a major global health burden. Comprehensive characterization of tumor microenvironment (TME) heterogeneity is critical for understanding tumor progression and clinical outcomes in NSCLC. In this study, we identified 34 transcriptionally defined cell states and 6 multicellular ecotypes using the EcoTyper framework, thereby systematically delineating the TME landscape of NSCLC. These cell states and ecotypes were further validated across 11 independent bulk transcriptomic cohorts, one single-cell RNA sequencing cohort, and one spatial transcriptomics cohort, demonstrating strong reproducibility and robustness. Prognostic analyses revealed that several cell states and ecotypes were significantly associated with patient survival outcomes in NSCLC. Transcriptional regulatory analysis further identified SP8, MAZ, and FOS as potential regulators involved in shaping NSCLC ecotypes. Collectively, this study provides a comprehensive atlas of TME-associated cell states and ecotypes in NSCLC, offering new insights into tumor heterogeneity and potential opportunities for personalized prognostic assessment and therapeutic intervention.
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DOO: integrated multi-omics resources for deep ocean organisms.
The deep ocean is one of Earth's most vast and least explored frontiers, characterized by extreme conditions such as high pressure, limited light, and nutrient scarcity. These environments pose unparalleled challenges to life, making them invaluable for studying genetic and molecular adaptations to extreme conditions. Emerging omics resources have recently provided significant insights into the advanced understanding of deep ocean ecosystems and evolution. However, a centralized resource for deep ocean multi-omics data remains lacking. To bridge this gap, the Deep Ocean Omics (DOO, https://DeepOceanOmics.org) database, a multi-omics atlas for deep ocean organisms, is presented. DOO integrates diverse omics resources from 68 species across seven phyla and 16 classes, encompassing 72 genomes, 950 bulk transcriptomes, 15 single-cell transcriptomes, and 1112 metagenomes, alongside functional support toolkits for functional and comparative analysis. DOO provides a systematic view of genomic information, including genome assembly, phylogeny, gene annotation, BUSCO genes, transcription factors/ubiquitin family, gene clusters, symbiont and mitochondrial genomes, and fossil records. Moreover, DOO offers co-expression networks with expression views across different tissues, and developmental stages and micro- and macrosynteny analyses to elucidate the pan-evolutionary features of genome structure. As the first comprehensive multi-omics resource dedicated to deep ocean organisms, DOO serves as a pivotal platform for uncovering multi-omics underpinnings of deep ocean organisms and offering insights into the understanding of deep ocean biodiversity, evolution, and genetic adaptation under extreme conditions.
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Multi-omics decipher the immune microenvironment and unveil therapeutic strategies for postoperative ovarian cancer patients.
BACKGROUND: Ovarian cancer (OC) is a highly aggressive and often fatal disease that frequently goes undetected until it has already metastasized. The classic treatment for OC involves surgery followed by chemotherapy. However, despite the effectiveness of surgery, relapse is still a common occurrence. Unfortunately, there is currently no ideal predictive model for the progression and drug sensitivity of postoperative OC patients. Cell death patterns play an important role in tumor progression. So we aimed to investigate their potential to be used as indicators of postoperative OC prognosis and drug sensitivity. METHODS: A total of 12 programmed cell death (PCD) patterns were employed to construct novel classification and prognosis model. Bulk transcriptome, genomics, and clinical information were collected from The Cancer Genome Atlas (TCGA) Program-OV, GSE9891, GSE26712, GSE49997 and GSE63885. In addition, single-cell transcriptome data GSE210347 were procured from the Gene Expression Omnibus (GEO) database for subsequent analysis. RESULTS: In this study, a novel PCD classification has been employed to phenotype postoperative OC patients, revealing that patients in cluster 1 exhibited heightened sensitivity to immune-based therapies combined with high expression of chemokines, interleukins, interferons, and checkpoints. Meanwhile, a programmed cell death index (PCDI) was established using an 8-gene signature with the help of a machine learning algorithm. The patients with high-PCDI had a worse prognosis after surgery in OC. In addition, we also found that patients with low PCDI patients may exhibit sensitivity to immunotherapy, while those with high PCDI patients may display increased responsiveness to tyrosine kinase inhibitors. CONCLUSIONS: This study provides a novel PCD model and nomogram that can effectively predict the clinical prognosis and drug sensitivity of OC patients post-surgery.
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Human brain vascular multi-omics elucidates disease-risk associations.
Cerebrovascular dysfunction underlies many neurological disorders, yet how genetic variants in brain vascular cells drive disease risk remains unknown. We developed MultiVINE-seq to simultaneously profile RNA and chromatin accessibility in vascular, perivascular, and immune cells from 30 human brains. Mapping genome-wide association study (GWAS) data to our multi-omic atlas linked thousands of GWAS disease-risk variants to target cell types and genes, including 2,605 previously unmapped. We found cerebrovascular and neurodegenerative disease variants have distinct mechanisms: cerebrovascular disease variants disrupt extracellular matrix genes in endothelial, mural, and fibroblast cells important for vessel structural integrity, while Alzheimer's disease (AD) variants dysregulate inflammatory adaptor proteins in endothelial and immune cells. Notably, a lead AD variant enhances PTK2B expression in brain CD8 T cells, providing genetic evidence for adaptive immunity in AD pathogenesis. This work provides a key resource for interpreting genetic risk and reveals how variants in vascular cells drive divergent pathogenic mechanisms across neurological diseases.
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A Single-cell Atlas of Juvenile Nasopharyngeal Angiofibroma Reveals VEGF-Driven Angiogenic Remodeling as a Therapeutic Vulnerability.
BACKGROUND: Juvenile nasopharyngeal angiofibroma (JNA) is a rare locally aggressive vascular sinonasal tumor that primarily affects adolescent males. Despite advances in endoscopic surgery and preoperative embolization, JNA can be associated with major operative bleeding risk and clinically meaningful recurrence, while non-surgical treatment options remain limited. METHODS: To define the cellular programs underlying JNA vascularity, we performed single-cell RNA sequencing of JNA tumors (n=2), tumor-adjacent mucosa, and control sinonasal tissue. We analyzed cell composition, differential gene expression, pathway enrichment, and cell-cell communication, followed by Drug2cell-based mapping of transcriptional states to candidate therapeutic targets. RESULTS: JNA contained an expanded fibrovascular compartment composed of endothelial cells, fibroblasts, pericytes, vascular smooth muscle cells, and neural crest-like cells. Neural crest- like cells were enriched in JNA but showed relatively limited transcriptional differences from tumor-adjacent tissue. By contrast, endothelial cells demonstrated the strongest disease-associated remodeling, with enrichment of angiogenesis, extracellular matrix organization, hypoxia response, and cell migration pathways. Endothelial cells also showed downregulation of adaptive immune signaling pathways, suggesting reduced immune engagement within the tumor microenvironment. Intercellular communication analyses revealed dense endothelial-stromal signaling across the JNA fibrovascular network. Drug2cell analysis nominated VEGF/VEGFR signaling as a candidate therapeutic vulnerability, with VEGFR-targeting agents predicted to act primarily on vascular and lymphatic endothelial populations. CONCLUSIONS: JNA is organized around an angiogenesis-dominant fibrovascular program driven by endothelial-centered signaling. These data support further investigation of VEGF/VEGFR-directed therapy as a potential adjunctive strategy for patients with recurrent, unresectable, or surgically high-risk JNA.