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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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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.
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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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Multi-dimension metabolic prognostic model for gastric cancer.
BACKGROUND: Metabolic reprogramming is involved in different stages of tumorigenesis. There are six widely recognized tumor-associated metabolic pathways, including cholesterol catabolism process, fatty acid metabolism, glutamine metabolic process, glycolysis, one carbon metabolic process, and pentose phosphate process. This study aimed to classify gastric cancer patients into different metabolic bio-similar clusters. METHOD: We analyzed six tumor-associated metabolic pathways and calculated the metabolic pathway score through RNA-seq data using single sample gene set enrichment analysis. The consensus clustering analysis was performed to classify patients into different bio-similar clusters by multi-dimensional scaling. Kaplan-Meier curves were presented between different metabolic bio-similar groups for OS analysis. RESULTS: A training set of 370 patients from the Cancer Genome Atlas database with primary gastric cancer was chosen. Patients were classified into four metabolic bio-similar clusters, which were identified as metabolic non-specificity, metabolic-active, cholesterol-silence, and metabolic-silence clusters. Survival analysis showed that patients in metabolic-active cluster and metabolic-silence cluster have significantly poor prognosis than other patients (p=0.031). Patients in metabolic-active cluster and metabolic-silence cluster had significantly higher intra-tumor heterogeneity than other patients (p=0.032). Further analysis was performed in metabolic-active cluster and cholesterol-silence cluster. Three cell-cycle-related pathways, including G2M checkpoints, E2F targets, and MYC targets, were significantly upregulated in metabolic-active cluster than in cholesterol-silence cluster. A validation set of 192 gastric cancer patients from the Gene Expression Omnibus data portal verified that metabolic bio-similar cluster can predict prognosis in gastric cancer. CONCLUSION: Our study established a multi-dimension metabolic prognostic model in gastric cancer, which may be feasible for predicting clinical outcome.
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Ferroptosis-related genes identify tumor immune microenvironment characterization for the prediction of prognosis in cervical cancer.
BACKGROUND: Cervical cancer (CC) is a disease that affects female health; therefore, timely prevention and diagnosis of CC are crucial to decrease its mortality. Ferroptosis, an iron-dependent form of non-apoptotic cell death, is involved in tumor progression. However, the role of ferroptosis-related genes (FRGs) in the immune microenvironment of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) remains unclear. METHODS: The data sets of CESC patients, including RNA sequencing (RNA-seq) data and clinical information, were obtained from The Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was used to determine the stromal score, immune score, estimate score, and tumor purity in the CESC patients' data. Additionally, FRGs were identified and used to construct a signature marker for the diagnosis and prognosis of CESC. Patients were assigned to a high- or low-risk group based on their median risk score. The tumor microenvironment (TME), immune infiltration, and functional enrichment were compared between the low- and high-risk groups. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA), were conducted to explore the underlying mechanisms in the development and prognosis of CESC. RESULTS: The results showed that the estimate score was suitable for predicting the prognosis of CESC patients. Additionally, a prediction model involving four FRGs [phosphatidylethanolamine-binding protein 1 (PEBP1), dual oxidase 1 (DUOX1), iron-sulfur cluster assembly enzyme (ISCU), and cytochrome b (-245) beta subunit (CYBB)] was constructed. The performance of the prognostic model and significant clinical characteristics in predicting CESC prognosis was subsequently validated. Our results showed that the expression of CYBB affected immune cells. Gene functional enrichment analyses showed that these differentially expressed FRGs were mainly enriched in the immunity-related signaling pathways, which indicated that FRGs might affect the development and prognosis of CC by regulating the immune microenvironment. CONCLUSIONS: The expression profiles of FRGs are closely related to the TME and the prognostic survival of CESC patients. The interaction between ferroptosis and immunity in the development of CC provides new insight into the molecular mechanisms of CC.
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Association of High Tumor-Stroma Ratio with Prostate Cancer Progression: Insights from Clinical and Genomic Data.
BACKGROUND: Tumor stroma ratio (TSR) is a prognostic factor in various cancers, but its role in prostate adenocarcinoma (PRAD) remains unclear. This study investigates TSR's prognostic value in PRAD using clinicopathological data, bulk/single-cell RNA sequencing to explore tumor-stroma interactions and identify therapeutic targets. METHODS: Two PRAD cohorts (The Cancer Genome Atlas cohort, TCGA; Lanzhou University Second Hospital, LUSH) were analyzed for TSR associations with clinicopathological features and biochemical recurrence (BCR). TSR was assessed via digital image analysis and expert pathologist review. Publicly available bulk/single-cell RNA sequencing data were analyzed to identify TSR-associated genes and predict drug targets, pathways, and immunotherapy responses. Quantitative real-time PCR validated mRNA expression. In vitro assays assessed cell proliferation, growth, and migration, while in vivo xenograft assays validated BGN's role in promoting tumorigenesis. RESULTS: TSR significantly correlated with clinicopathological features (age, Gleason score, stage, seminal vesicle invasion, BCR) in both TCGA (n = 453) and LUSH (n = 320) cohorts. High TSR independently predicted BCR in multivariable Cox regression. High TSR was associated with copy number variations, differentially expressed miRNAs/transcription factors, and metabolic pathways. Predicted anti-cancer drug targets, like Ki8751, showed potential benefit in high-TSR patients. High TSR may correlate with poor immunotherapy response. Notably, downregulation of BGN in cancer-associated fibroblasts (CAFs) significantly suppressed cell proliferation, migration, and invasion in vitro, and in vivo xenograft assays confirmed that BGN downregulation inhibited tumor growth. CONCLUSION: This study highlights TSR's prognostic significance in prostate cancer and its association with adverse clinical outcomes and complex tumor-stroma interactions, identifying BGN, a stromal cell-related gene, as a potential therapeutic target for CAFs. However, these findings are limited by the retrospective design, necessitating prospective validation.
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Identification and validation of a novel NK cells-related signature to predict prognosis and immune microenvironment in LUAD.
BACKGROUND: The prevalence and fatality rates of lung cancer are experiencing a rapid escalation. Natural Killer (NK) cells have been established to have a crucial role in both tumor initiation and progression. Nevertheless, uncertainties persist regarding their precise implications in the prognosis of LUAD. METHODS: The data were obtained from reputable sources, such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and our internally generated sequencing data. Utilizing the TCGA data as a background, we selected intersecting genes, validated by cluster analysis, to establish a Cox model and validated it using the GEO datasets. Furthermore, we conducted extensive analyses to investigate the significance of potential biomarkers in relation to immune cell infiltration, single-cell data, differential gene expression, and drug sensitivity. RESULTS: 67 immune-related genes associated with NK cells (NK-IRGs) were identified in the TCGA datasets, whose research potential was demonstrated by cluster analysis. A prognostic signature was identified utilizing the univariate and multivariate Cox model, resulting in the identification of five genes, which was validated using GEO datasets. Additionally, the nomogram's calibration curve demonstrated exceptional concordance between the projected and actual survival rates. Subsequent investigations uncovered that this prognostic signature demonstrated its independence as a risk factor. Notably, in the low-risk group, NK cells exhibited elevated levels of immune checkpoint molecules, indicating heightened sensitivity to immune therapy. These findings highlight the potential of utilizing this signature as a valuable tool in the selection of patients who could benefit from targeted immune interventions.
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Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis.
BACKGROUND: Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear. METHODS: ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR. RESULTS: Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers. CONCLUSIONS: Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.
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Developing a prognostic risk model based on circulating tumor cell genes to predict prognosis and provide potential therapeutic strategies in colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide. Understanding the genetic and molecular alterations in CRC can improve patient outcomes. Circulating tumor cells (CTCs) are crucial in cancer metastasis and progression. Analyzing the differentially expressed genes (DEGs) between CTCs and CRC may provide us with new therapeutic strategies. Therefore, this study aims to analyze these DEGs to construct a prognostic risk model that predicts the outcomes of CRC patients and guides clinical treatment. METHODS: We analyzed The Cancer Genome Atlas (TCGA) database to identify 1,727 DEGs between CRC and normal samples, and GSE82198 data to find 3,564 DEGs between CTCs and primary CRC samples. Using enrichment analysis, least absolute shrinkage and selection operator (LASSO) regression, and stepwise Cox regression, we derived eight model genes to construct a prognostic risk model. Various algorithms were employed in the immune microenvironment analysis. Integrating clinical factors with risk grouping, we developed a nomogram. We assessed chemotherapy sensitivity and epithelial-mesenchymal transition (EMT) scores in high-/low-risk groups and explored model gene expression at the single-cell level. RESULTS: We constructed a prognostic risk model for CRC based on eight DEGs of CTCs. The model effectively predicted treatment outcomes and correlated closely with actual prognosis. Through immune microenvironment analysis, we revealed differences in immune cell infiltration and checkpoint gene expression among different risk groups. Moreover, patients in the high-risk group showed higher sensitivity to chemotherapy drugs compared to those in the low-risk group. CONCLUSIONS: The prognosis model based on CTCs' DEGs can effectively predict patient outcomes, facilitating precision treatment for patients. This model holds significant guiding implications for immunotherapy and chemotherapy in CRC, offering potential strategies for the clinical treatment of CRC.