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Liquid-liquid phase separation-related gene in gliomas: FABP5 is a potential prognostic marker.
BACKGROUND: The glioma is the most malignant human brain tumor. Early glioma detection and treatment are still difficult. New biomarkers are desperately required to aid in the evaluation of diagnosis and prognosis. METHODS: The single cell sequencing dataset scRNA-6148 for glioblastoma was obtained from the Chinese Glioma Genome Atlas database. Data were gathered for the transcriptome sequencing project. Genes involved in liquid-liquid phase separation (LLPS) were taken out of the DrLLPS database. To find the modules connected to LLPS, the weighted co-expression network was analyzed. Differential expression analysis was used to identify the differentially expressed genes (DEGs) in gliomas. Pseudo-time series analysis, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were used to investigate the role of important genes in the immunological microenvironment. We examined the function of key glioma genes using polymerase chain reaction (PCR) testing, CCK-8 assays, clone generation assays, transwell assays and wound healing assays. RESULTS: FABP5 was identified as a key gene in glioblastoma by multiomics research. Pseudo-time series analysis showed that FABP5 was highly linked with the differentiation of many different types of cells. GSEA revealed that FABP5 was strongly linked to several hallmark pathways in glioblastoma. We looked at immune cell infiltration and discovered a significant link between FABP5, macrophages and T cell follicular helpers. The PCR experiment results demonstrated that FABP5 expression was elevated in glioma samples. Cell experiments showed that FABP5 knockdown dramatically decreased the viability, proliferation, invasion and migration of the LN229 and U87 glioma cell lines. CONCLUSIONS: Our study provides a new biomarker, FABP5, for glioma diagnosis and treatment.
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Circadian-immune-related gene signature for lung squamous cell carcinoma: machine learning and multi-omics analysis.
BACKGROUND: Circadian disruption promotes tumor progression and therapy resistance, but its prognostic value and impact on the tumor immune microenvironment in lung squamous cell carcinoma (LUSC) remain unclear. This study aimed to develop a circadian-immune-related gene signature to improve LUSC risk stratification and therapy guidance. METHODS: We integrated multi-omics data from LUSC patients (n=494) across The Cancer Genome Atlas and Gene Expression Omnibus (GEO) database GSE73403 (n=69). A circadian-immune-related gene prognostic signature (CIGPS) was constructed from 1,677 circadian rhythm-related genes through weighted gene co-expression network analysis (WGCNA) and ten machine learning algorithms (StepCox + GBM optimized) and externally validated. Bioinformatics analyses assessed the tumor immune microenvironment, mutation landscape, and therapy response. Single-cell RNA sequencing data (GSE148071) explored gene expression at cellular resolution. RESULTS: A six-gene CIGPS was developed, comprising CLDN1, FGG, ALPL, TREM1, NKX2-1, and CLDN5. This signature stratified patients into high- and low-risk groups with divergent overall survival in both cohorts. Unsupervised clustering revealed C1/C2 subtypes: C1 displayed an immune-hot yet excluded phenotype with higher CD8+ T cell infiltration but elevated physical barriers; C2 exhibited an immune-cold phenotype with DNA replication and epigenetic machinery activation. Both subtypes demonstrated distinct therapeutic vulnerabilities and divergent drug sensitivity profiles. Single-cell profiling demonstrated heterogeneous spatial distribution of signature genes across distinct tumor microenvironmental compartments. CONCLUSIONS: The CIGPS serves as a potent prognostic tool that elucidates the heterogeneous tumor immune microenvironment in LUSC. It holds significant promise for guiding risk assessment and informing personalized therapeutic strategies based on distinct molecular subtypes.
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Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model.
BACKGROUND: Intra-tumor heterogeneity (ITH) encompasses cellular differences in tumors and is related to clinical outcomes such as drug resistance. However, little is known about the dynamics of ITH, owing to the lack of time-series analysis at the single-cell level. Mouse models that recapitulate cancer development are useful for controlled serial time sampling. RESULTS: We performed single-cell exome and transcriptome sequencing of 200 cells to investigate how ITH is generated in a mouse colorectal cancer model. In the model, a single normal intestinal cell is grown into organoids that mimic the intestinal crypt structure. Upon RNAi-mediated downregulation of a tumor suppressor gene APC, the transduced organoids were serially transplanted into mice to allow exposure to in vivo microenvironments, which play relevant roles in cancer development. The ITH of the transcriptome increased after the transplantation, while that of the exome decreased. Mutations generated during organoid culture did not greatly change at the bulk-cell level upon the transplantation. The RNA ITH increase was due to the emergence of new transcriptional subpopulations. In contrast to the initial cells expressing mesenchymal-marker genes, new subpopulations repressed these genes after the transplantation. Analyses of colorectal cancer data from The Cancer Genome Atlas revealed a high proportion of metastatic cases in human subjects with expression patterns similar to the new cell subpopulations in mouse. These results suggest that the birth of transcriptional subpopulations may be a key for adaptation to drastic micro-environmental changes when cancer cells have sufficient genetic alterations at later tumor stages. CONCLUSIONS: This study revealed an evolutionary dynamics of single-cell RNA and DNA heterogeneity in tumor progression, giving insights into the mesenchymal-epithelial transformation of tumor cells at metastasis in colorectal cancer.
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Leveraging diverse cell-death patterns to predict the prognosis, immunotherapy and drug sensitivity of clear cell renal cell carcinoma.
Clear cell renal cell carcinoma (ccRCC) poses clinical challenges due to its varied prognosis, tumor microenvironment attributes, and responses to immunotherapy. We established a novel Programmed Cell Death-related Signature (PRS) for ccRCC assessment, derived through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. We validated PRS using the E-MTAB-1980 dataset and created PCD-related clusters via non-negative matrix factorization (NMF). Our investigation included an in-depth analysis of immune infiltration scores using various algorithms. Additionally, we integrated data from the Cancer Immunome Atlas (TCIA) for ccRCC immunotherapy insights and leveraged the Genomics of Drug Sensitivity in Cancer (GDSC) database to assess drug sensitivity models. We complemented our findings with single-cell sequencing data and employed the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and qRT-PCR to compare gene expression profiles between cancerous and paracancerous tissues. PRS serves as a valuable tool for prognostication, immune characterization, tumor mutation burden estimation, immunotherapy response prediction, and drug sensitivity assessment in ccRCC. We identify five genes with significant roles in cancer promotion and three genes with cancer-suppressive properties, further validated by qRT-PCR and CPTAC analyses, showcasing gene expression differences in ccRCC tissues. Our study introduces an innovative PCD model that amalgamates diverse cell death patterns to provide accurate predictions for clinical outcomes, mutational profiles, and immune characteristics in ccRCC. Our findings hold promise for advancing personalized treatment strategies in ccRCC patients.
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ECT2+ cell group acts as cancer stem cell in malignant pleomorphic adenoma.
Salivary carcinoma ex pleomorphic adenoma (CXPA) is a carcinoma that develops from benign pleomorphic adenoma, and it is characterized by aggressiveness and poor prognosis. Recent advancements in cancer stem cell (CSC) research have identified CSCs as critical contributors to tumor carcinogenesis due to their unlimited self-renewal ability. Single-cell RNA sequencing has enabled a comprehensive analysis of CSC heterogeneity. However, CSCs in CXPA remain largely unexplored. In this study, we constructed a comprehensive atlas of CXPA at single-cell resolution and demonstrated that CXPA exhibited intratumoral heterogeneity. We identified four tumor epithelial cell subgroups characterized by epithelial development, cell cycle, extracellular matrix remodeling, and salivary secretion signatures. ECT2+ tumor cells in the cell cycle cluster were identified as CSCs in CXPA. Analysis of tumor tissues from patients with CXPA revealed that ECT2 expression was strongly associated with invasiveness and prognosis. Targeting the ECT2 gene significantly inhibited tumor sphere formation and aggressiveness in CXPA cell lines. Our findings provide new insights into CXPA diversity and origin, with substantial clinical implications for targeting the ECT2 gene in CXPA treatment.
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Multi-omics characterization of a GPRC5A+ epithelial subpopulation associated with malignant features in colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) exhibits marked cellular heterogeneity, and the cellular context of malignancy-associated epithelial programs remains incompletely defined. METHODS: We integrated 2,993 CRC samples spanning bulk RNA-seq (n = 2,568; two OS/RFS cohorts), scRNA-seq (281,961 cells/152 specimens), spatial transcriptomics (n = 6), and proteomics (n = 267). Analyses included single-cell integration/annotation, GSVA/HALLMARK, interactome, pseudotime, and ligand-receptor mapping; functional CRISPR assays, EMT immunoblotting, and xenografts; TF profiling (SCENIC/JASPAR/ChIP-qPCR); and exploratory drug-response prediction (OncoPredict), cell-sensitivity assays, and docking/MD modeling. RESULTS: We constructed a stage-stratified single-cell atlas and resolved eleven malignant epithelial subsets, characterizing Epi_4 as late-stage-enriched with EMT, hypoxia, and inflammatory programs and adverse OS/RFS. GPRC5A marked this subset, which we define as GPRC5A+Epi; its expression rose from stage I→IV and was associated with poor outcomes across cohorts, with concordant spatial/proteomic observations. GPRC5A perturbation affected CRC proliferation, migration/invasion, EMT, and xenograft tumorigenicity, supporting a functionally important role in the tested models. SCENIC and ChIP-qPCR supported FOSL1 as an upstream regulator that occupies the GPRC5A promoter. Spatial and ligand-receptor analyses predicted close association and potentially reciprocal signaling between GPRC5A+Epi and POSTN+fibroblasts (COL1A1-SDC4, COL1A1/1A2-ITGA2/ITGB1, PPIA-BSG); concurrent high GPRC5A+Epi/POSTN+Fib signatures were associated with inferior OS/RFS. Drug-response analyses identified an association between GPRC5A status and trametinib sensitivity. Docking/MD produced a computational model of a possible trametinib-GPRC5A interaction, which remains experimentally unvalidated. CONCLUSIONS: GPRC5A⁺Epi is a malignancy-associated epithelial state in CRC, and GPRC5A is functionally important for malignant phenotypes in the tested models. Its inferred relationships with POSTN⁺ fibroblasts and the trametinib findings should be regarded as hypothesis-generating pending functional crosstalk, direct-binding, and therapeutic validation.
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Single-cell multiregion dissection of Alzheimer's disease.
Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology.
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Single-nucleus transcriptomic landscape of primate hippocampal aging.
The hippocampus plays a crucial role in learning and memory, and its progressive deterioration with age is functionally linked to a variety of human neurodegenerative diseases. Yet a systematic profiling of the aging effects on various hippocampal cell types in primates is still missing. Here, we reported a variety of new aging-associated phenotypic changes of the primate hippocampus. These include, in particular, increased DNA damage and heterochromatin erosion with time, alongside loss of proteostasis and elevated inflammation. To understand their cellular and molecular causes, we established the first single-nucleus transcriptomic atlas of primate hippocampal aging. Among the 12 identified cell types, neural transiently amplifying progenitor cell (TAPC) and microglia were most affected by aging. In-depth dissection of gene-expression dynamics revealed impaired TAPC division and compromised neuronal function along the neurogenesis trajectory; additionally elevated pro-inflammatory responses in the aged microglia and oligodendrocyte, as well as dysregulated coagulation pathways in the aged endothelial cells may contribute to a hostile microenvironment for neurogenesis. This rich resource for understanding primate hippocampal aging may provide potential diagnostic biomarkers and therapeutic interventions against age-related neurodegenerative diseases.
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A novel association of pyroptosis-related gene signature with the prognosis of hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the global leading lethal tumors. Pyroptosis has recently been defined as an inflammatory programmed cell death, which is closely linked to cancer progression. However, the significance of pyroptosis-related genes (PRGs) in the prognosis of HCC remains elusive. METHODS: RNA sequencing (RNA-seq) data of HCC cases and their corresponding clinical information were collected from the Cancer Genome Atlas (TCGA) database, and differential PRGs were explored. The prognostic PRGs were analyzed with univariate COX regression and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to build a prognostic model in the TCGA training cohort. The predictive model was further validated in the TCGA test cohort and ICGC validation cohort. Differential gene function and associated pathway analysis were performed by Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG). Single-sample gene set enrichment analysis (ssGSEA) was used to identify distinct immune cell infiltration. The mRNA and protein expression of prognostic PRGs was examined by quantitative RT-qPCR and immunohistochemistry. RESULTS: We identified 46 PRGs that were differentially expressed between normal and HCC tissues in a TCGA cohort, and HCC patients could be well categorized into two clusters associated with distinct survival rates based on expression levels of the PRGs. A three-PRG prognostic model comprising CHMP4A, HMGB1 and PLK1 was constructed in the training cohort, and HCC patients could be classified into the high- and low-risk subgroups based on the median risk score. High-risk patients exhibited shorter overall survival (OS) than low-risk ones, which was validated in the test cohort and ICGC validation cohort. The risk score of this model was confirmed as an independent prognostic factor to predict OS of HCC patients. GO, KEGG and ssGSEA demonstrated the differential immune cell infiltrations were associated with the risk scores. The higher expression of CHMP4A, HMGB1 and PLK1 were validated in HCC compared to normal in vivo and in vitro. CONCLUSION: The three-PRG signature (CHMP4A, HMGB1, and PLK1) could act as an independent factor to predict the prognosis of HCC patients, which would shed light upon a potent therapeutic strategy for HCC treatment.
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[A risk prediction model for prognosis and immunotherapy response in prostate cancer patients based on immunosuppressive neutrophil Neu_2 subsets].
OBJECTIVES: To identify immunosuppressive neutrophil subsets in patients with prostate cancer (PCa) and construct a risk prediction model for prognosis and immunotherapy response of the patients based on these neutrophil subsets. METHODS: Single-cell and transcriptome data from PCa patients were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Neutrophil subsets in PCa were identified through unsupervised clustering, and their biological functions and effects on immune regulation were analyzed by functional enrichment, cell interaction, and pseudo-time series analyses. Lasso-Cox regression was utilized to construct a prognostic risk model based on the immunosuppressive neutrophil subsets, and survival analysis and ROC curve analysis were used to compare the prognosis of PCa patients with high and low risks stratified using this model. The relationship of the prognostic risk model with PCa immune infiltration and immune response was evaluated using CIBERSORT and TIDE scores. RESULTS: PCa tissues showed a significantly greater proportion of infiltrating neutrophils than the adjacent normal tissues (P<0.05). PCa-associated neutrophils could be clustered into two independent cell subsets: Neu_1 and Neu_2. Neu_2 cells exhibited highly enriched immunoregulatory functions and were highly differentiated and mature, with upregulated immunosuppressive cytokines such as TGFB1, ITGB2, and LGALS3. Based on the genetic characteristics of Neu_2 cell subsets, the prognostic risk model was constructed. The patients in the high-risk group identified by the model had a shorter biochemical recurrence time (P<0.05) and a higher proportion of Tregs and M2-TAMs cell infiltration (P<0.05) with a higher risk of immune rejection and poorer immune response scores. CONCLUSIONS: PCa-associated neutrophils are highly heterogeneous. The prognostic risk model constructed based on the immunosuppressive neutrophil Neu_2 subset can effectively predict both the survival outcomes and immune response of PCa patients.