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Prognostic value of palmitoylation-regulated mechanisms in glioblastoma: integrated multi-omics analysis via least absolute shrinkage and selection operator (LASSO) regression and single-cell sequencing.
BACKGROUND: Accumulating evidence highlights the critical role of palmitoylation in tumorigenesis, including the regulation of oncogenic signaling pathways, metabolic reprogramming, and immune evasion mechanisms. This study was designed to systematically investigate the prognostic significance of palmitoylation-related genes in glioblastoma multiforme (GBM). METHODS: Through comparative transcriptome analysis of GBM and normal brain tissues based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), we identified 343 palmitoylation-related differentially expressed genes (DEGs). Subsequently, 80 candidate genes were extracted from the constructed protein-protein interaction (PPI) network. By applying the machine learning algorithm-least absolute shrinkage and selection operator (LASSO) regression combined with Kaplan-Meier (K-M) survival analysis, we identified core genes significantly associated with patients' overall survival (OS). Furthermore, single-cell RNA sequencing (scRNA-seq) analysis demonstrated that GABRB2, NCF2, and GRIN2A were enriched in critical oncogenic pathways, including MYC targets and KRAS signaling. Meanwhile, they exhibited a significantly heterogeneous expression pattern in GBM. RESULTS: Palmitoylation-related gene expression was higher in GBM tissues than in normal brain tissue. GABRB2 and GRIN2A inhibited the proliferation and migration of GBM cell lines. NCF2 promoted the proliferation and migration of GBM cell lines. CONCLUSIONS: This study was designed to systematically investigate the prognostic significance of palmitoylation-related genes in GBM. GABRB2, NCF2, and GRIN2A are expected to become new biomarkers for GBM.
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The emerging double-edged sword role of Sirtuins in the gastric inflammation-carcinoma sequence revealed by bulk and single-cell transcriptomes.
Histone modification and the inflammation-carcinoma sequence (ICS) have been acknowledgedly implicated in gastric carcinogenesis. However, the extremum expression of some histone modification genes (HMGs) in intestinal metaplasia (IM) rather than GC obscures the roles of HMGs in ICS. In this study, we assumed an explanation that the roles of HMGs in ICS were stage specific. Bulk RNA-seq on endoscopy biopsy samples from a total of 50 patients was accompanied by reanalysis of a set of published single-cell transcriptomes, which cross-sectionally profiled the transcriptomic features of chronic superficial gastritis (SG), atrophy gastritis (AG), IM, and early gastric cancer (GC). Differential analysis observed significantly peaked expression of SIRT6 and SIRT7 at IM. Weighted correlation network analysis on bulk transcriptome recognized significant correlations between SIRT1/6 and IM. The single-cell atlas identified one subgroup of B cells expressing high level of TFF1 (TFF1 hi naive B cell) that theoretically played important roles in defending microbial infection, while SIRT6 displayed a positive correlation with TFF1 low naive B cells. Moreover, gene set enrichment analysis at different lesions (SG-AG, AG-IM, and IM-GC) highlighted that gene sets contributing to IM, e.g., Brush Border, were largely enriched from co-expressing genes of Sirtuins (SIRTs) in AG-IM. Surveys of the genes negatively correlated with SIRT6 in public databases considered SIRT6 as tumor suppressors, which was confirmed by the cell proliferation and migration assays after transient transfection of SIRT6 overexpression vector into AGS cells. All the above observations were then confirmed by serial section-based immunohistochemistry against Ki-67, MUC2, MUC5AC, p53, and SIRT6 on the endoscopic submucosal dissection tissue. By contrast, the expression of the other HMGs varied even opposite within same family. Taken together, this study preliminarily demonstrated the two-edged sword role of SIRTs in ICS and, by extension, showed that the roles of HMGs in ICS were probably stage specific. Our study may provide new insights into and attract attention on gastric prevention and therapy targeting HMGs.
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Single-cell analysis identifies a tumor-specific T-cell metabolic signature: prognostic model and association with immunosuppressive microenvironment in ovarian cancer.
BACKGROUND: Ovarian cancer (OC) remains the most lethal gynecological malignancy, with profound tumor microenvironment (TME) heterogeneity contributing to the suboptimal clinical response to immune checkpoint inhibitors (ICIs). Therefore, delineating the metabolic heterogeneity of T cells within the TME is imperative for identifying precise prognostic biomarkers and refining individualized immunotherapeutic strategies. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data (GSE184880) with multi-omics profiles from The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) cohort. The Seurat and Harmony algorithms were employed to identify tumor-specific T-cell subpopulations (CA-T cells). Weighted gene co-expression network analysis (WGCNA) and machine learning frameworks-including the least absolute shrinkage and selection operator (LASSO) regression and Random Survival Forest-were applied to screen core prognostic genes and construct a multi-gene risk scoring model (riskScore) for predicting overall survival (OS). The model was validated through Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and independent prognostic assessment. Finally, ESTIMATE, CIBERSORT, and TIDE algorithms were applied to assess the associations between riskScore, TME immune landscapes, and ICI efficacy. RESULTS: We identified a distinct tumor-specific T-cell subset (CA-T cells) significantly enriched in malignant tissues. This subset exhibited a functional signature characterized by mitochondrial metabolic reprogramming, specifically oxidative phosphorylation and electron transport chain activity. An 8-gene riskScore model-comprising SLAMF1, CXCR4, SFT2D1, SH3KBP1, SPOCK2, CDKN1B, GNPTAB, and SNRPA1-demonstrated superior independent prognostic value, with area under the curve (AUC) values of 0.707, 0.698, and 0.724 for 1-, 3-, and 5-year survival predictions, respectively. Immunological profiling revealed that high-risk patients exhibited a prototypical 'cold' tumor phenotype, marked by significantly diminished infiltration of stromal and effector immune cells (e.g., CD8+ T cells) and a concomitant enrichment of immunosuppressive populations, including myeloid-derived suppressor cells (MDSCs) and M2-type tumor-associated macrophages (M2-TAMs). Furthermore, high-risk tumors displayed elevated immune dysfunction and exclusion scores alongside reduced IFN-γ and PD-L1 expression, suggesting limited sensitivity to ICI therapy. CONCLUSIONS: This study established a robust prognostic framework based on single-cell resolved T-cell metabolic signatures. Our findings provide a reliable independent prognostic tool for OC and elucidate the intricate interplay between metabolic reprogramming and the immunosuppressive microenvironment. These results offer critical mechanistic insights for prognostic stratification and the development of personalized combinatorial immunotherapies in OC.
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Machine learning-integrated multi-omics risk prediction for pulmonary fungal infection in COPD and lung cancer: a transcriptomic and immune profiling study.
BACKGROUND: Chronic obstructive pulmonary disease (COPD) and lung cancer are major risk factors for invasive pulmonary fungal infection (IPFI), carrying an attributable mortality of 30%-80%. Their coexistence further amplifies immunosuppression, while current diagnostic criteria remain inadequate for early risk identification. METHODS: Transcriptomic data from the GEO dataset GSE296912 (scRNA-seq; 12,078 cells from normal and COPD lung tissue) and The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) bulk RNA-seq cohort (539 tumor and 59 normal samples) underwent differential expression and cross-omics integration analysis. Five machine learning models were constructed: logistic regression, SVM, random forest, XGBoost, and LASSO. Candidate genes were validated by qRT-PCR in A549 cells and THP-1-derived macrophages stimulated with heat-inactivated Aspergillus fumigatus conidia, a protocol selected to ensure BSL-2 biosafety compliance and isolate PAMP-mediated innate immune signaling. Model performance was evaluated using 5-fold stratified cross-validation with AUC, calibration curves, and decision curve analysis. RESULTS: Single-cell transcriptomic analysis of 12,078 cells identified 14 distinct cell populations, with marked myeloid expansion and immune dysregulation in COPD lung tissue. Cross-omics integration with TCGA-LUAD data identified 1,145 shared genes (79 immune-related), converging on NF-κB, TLR4, and cytokine receptor signaling. The random forest model achieved excellent discriminative performance (5-fold CV AUC = 0.988), with Treg infiltration, TLR4, and MMP9 as the top predictors. qRT-PCR confirmed significant upregulation of all five candidate genes (DEFB4A, S100A8, IL-8, MMP9, and TLR4) in both A549 and THP-1 cells following fungal stimulation. CONCLUSION: This multi-omics machine learning model integrating scRNA-seq and TCGA transcriptomic data demonstrates excellent discriminative performance (AUC = 0.988), with mechanistic convergence of NF-κB, TLR4, and oncogenic signaling pathways identified across shared immune gene signatures. In vitro qRT-PCR validation confirms the biological relevance of five key antifungal immune genes, providing a transcriptomic foundation for future prospective IPFI risk stratification in patients with COPD and lung cancer.
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Integrated analysis of multimodal single-cell data.
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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A single-nucleus transcriptomic atlas of the adult Aedes aegypti mosquito.
The female Aedes aegypti mosquito's remarkable ability to hunt humans and transmit pathogens relies on her unique biology. Here, we present the Aedes aegypti Mosquito Cell Atlas, a comprehensive single-nucleus RNA sequencing dataset of more than 367,000 nuclei from 19 dissected tissues of adult female and male Aedes aegypti, providing cellular-level resolution of mosquito biology. We identify novel cell types and expand our understanding of sensory neuron organization of chemoreceptors across all sensory tissues. Our analysis uncovers male-specific cells and sexually dimorphic gene expression in the antenna and brain. In female mosquitoes, we find that glial cells, rather than neurons, undergo the most extensive transcriptional changes in the brain following blood feeding. Our findings provide insights into the cellular basis of mosquito behavior and sexual dimorphism. The Aedes aegypti Mosquito Cell Atlas resource enables systematic investigation of cell-type-specific expression across all mosquito tissues.
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Integration of large, complex single-cell datasets with Harmony2.
Integrating single cell RNA-seq profiles is posing new challenges as datasets are rapidly expanding, now with over 100 million cells in the public domain. We present the latest version of the Harmony integration software, which efficiently scales to >100M cells and >1K datasets without specialized hardware. Moreover, optimizations to the underlying algorithm help prevent overintegration in biologically heterogeneous datasets. Harmony2 enables efficient, accurate integration of large, complex single-cell atlases.
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Comparative analysis of single-cell transcriptome reveals heterogeneity in the tumor microenvironment of lung adenocarcinoma and brain metastases.
PURPOSE: Solid tumors such as lung adenocarcinoma include not only the tumor cells but also the microenvironment in which the tumor cells continuously interact with each other. An in-depth understanding of the oncological features and tumor microenvironment (TME) of lung adenocarcinoma and brain metastases at the single-cell level could provide new therapeutic strategies for brain metastases from lung adenocarcinoma. METHODS: To solve this problem, we performed single-cell RNA sequencing (scRNA-seq) analysis on 15 lung adenocarcinoma samples and 10 brain metastasis samples. RESULTS: A total of 86,282 single cells were obtained and divided into 8 cell types, including epithelial cells, endothelial cells, fibroblasts, oligodendrocytes, T/NK cells, B cells, mast cells, and macrophages. In brain metastases, we found a significantly lower proportion of T/NK cells and mast cells, and more severe immune dysregulation. In addition, we found a subpopulation of macrophages with high expression of metastasis-promoting-related genes enriched in brain metastatic tissues. Moreover, in brain metastases, we found a significantly increased proportion of myofibroblastic cancer-associated fibroblasts (myCAFs) and a higher angiogenic capacity of endothelial cells. Epithelial cells in brain metastases were more malignant and underwent genomic reprogramming. Next, we found that DNA damage-inducible transcript 4 (DDIT4) expression was upregulated in epithelial cells in brain metastases and was associated with poor prognosis. Finally, we experimentally validated that the downregulation of DDIT4 inhibited the proliferation, migration, and invasion of lung cancer cells. CONCLUSIONS: This study depicts a single-cell atlas of lung adenocarcinoma and brain metastases by scRNA-seq and paves the way for the development of future therapeutic targets for brain metastases from lung cancer.
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A Tertiary Lymphoid Structure-Related Gene Signature Predicts Prognosis and Treatment Response in Hepatocellular Carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) carries a poor prognosis with limited treatment options. Tertiary lymphoid structures (TLS) impact tumor immunity, but their role in HCC requires clarification. This study aimed to develop and validate a TLS-related gene signature for predicting survival and therapeutic response in HCC, and to explore its mechanisms. METHODS: We analyzed transcriptomic data from public databases (The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)) using LASSO-Cox regression to identify a six-gene TLS signature (CCL20, CD200, PLAC8, DNASE1L3, C7, and SKAP1). We validated this TLS score across multiple independent HCC cohorts, including patients receiving transarterial chemoembolization (TACE), programmed cell death protein-1 (PD-1)/ligand 1 (PD-L1) inhibitors, or lenvatinib. Through single-cell RNA sequencing (scRNA-seq), we characterized immune microenvironment differences between score groups. RESULTS: The TLS score effectively stratified patients' survival outcomes across all validation cohorts. Low TLS scores significantly correlated with improved overall survival, enhanced therapeutic response (especially to immune checkpoint inhibitors (ICIs)), and lower immune evasion potential. Mechanistically, scRNA-seq revealed distinct immune microenvironments: low-score tumors were enriched in cytotoxic and exhausted CD8+ T cells (Tex), favorable for immunotherapy, showing beneficial immune remodeling post-treatment (decreased Tex, increased effector memory T cells). High-score tumors featured dense regulatory T-cell (Treg) infiltration, contributing to immunosuppression. CONCLUSION: Our findings suggest a potential TLS-based biomarker for HCC prognosis and therapeutic response. This work offers preliminary insights into tumor immune microenvironment (TIME) heterogeneity, which may be modulated by the Treg/Tex balance, and proposes a possible tool for improving patient stratification.
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Resolving floral development dynamics using genome and single-cell temporal transcriptome of Dendrobium devonianum.
Dendrobium devonianum, a species of the Orchidaceae family, is notable for its unique floral characteristics, which include two yellow spots and purple tips on its labellum, as well as fringed edges. However, the molecular mechanisms underlying flower pattern formation in D. devonianum remain poorly understood, hindering advancements in its breeding process. Here, a chromosome-scale genome of D. devonianum was presented for the first time, revealing two significant polyploidization events. Additionally, a high-resolution single-cell transcriptomic atlas was constructed, capturing 11 distinct cell clusters. Expression patterns of MADS-box genes were identified through temporal and spatial bulk RNA-Seq, revealing alignment with the ABCDE model of flower formation. Meanwhile, mass spectrometry imaging and scRNA analyses showed that the yellow spots were primarily associated with carotenoid biosynthesis gene expression, while the purple colour is predominantly linked to anthocyanin biosynthesis gene expression. These genes were mainly expressed in the epidermis and vascular cells. Developmental trajectory analyses of epidermal cells further uncovered a gene regulatory network and several transcription factors likely responsible for fringes formation along the labellum margin. This study provides valuable insights into the molecular mechanisms driving floral colour differentiation and structural traits in D. devonianum, contributing to a deeper understanding of orchid evolution, diversification and breeding.