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GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses.
BACKGROUND: GNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker. METHODS: mRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression. RESULTS: GNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects. CONCLUSION: Comprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.
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Integrated Single-Cell and Transcriptome Sequencing Analyses Develop a Ubiquitination-Associated Signature in Gastric Cancer and Identified OTULIN as a Novel Biomarker.
BACKGROUND: Gastric cancer (GC) is the most commonly diagnosed digestive system malignancy with a dismal survival outcome. The prognostic value of ubiquitination-related genes (URGs) in GC has yet to be discovered. METHODS: Two GC cohort datasets were obtained from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. Stepwise Cox analysis was employed to generate a signature. Then, we applied unsupervised clustering analysis to determine subclusters in GC based on URGs. Single-cell analysis was carried out to depict the cellular location of model genes. The CIBERSORT method was performed to estimate the immune landscape. Finally, preliminary wet lab work was utilized to disclose the potential effect of OTULIN. RESULTS: Our proposed signature was set up based on five URGs (OTULIN, UBE2C, USP1, USP2, and MAPT) which could serve as a risk classifier to categorize GC cases. In addition, it was demonstrated that the ubiquitination-associated model could depict the immune landscape and forecast immunotherapy response for GC patients. Furthermore, in vitro experiments determined the function and effect of OUTLIN in GC. We observed that the knockdown of OUTLIN could suppress cell viability and metastatic ability of GC cell lines. CONCLUSIONS: Our data lays the groundwork for a comprehensive investigation into the role of URGs in GC and determined OTULIN as a candidate GC biomarker.
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Simultaneous recognition and segmentation of cells: application in C.elegans.
MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called simultaneous recognition and segmentation (SRS) of cells, and applied it to 3D image stacks of the model organism Caenorhabditis elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a dataset of 175 C.elegans image stacks containing 14 118 manually curated BWM cells providing the 'ground-truth' for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90° rotations. With these stacks excluded from the dataset, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our web site http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d), and will be released in the V3D plugin source code repository. CONTACT: pengh@janelia.hhmi.org.
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Single-Cell Transcriptomics Identified Fibrosis-Activated Valve Interstitial Cells Involved in Functional Tricuspid Regurgitation.
BACKGROUND: The treatment of functional tricuspid regurgitation (TR) is still controversial. Characterizing the cellular composition of the tricuspid valve and identifying the molecular alterations of each cell type in valves with TR will advance our understanding of the mechanisms of TR and guide improvements in treatment. OBJECTIVES: The authors aimed to investigate the changes in cellular composition and gene expression patterns of cells in regurgitant tricuspid valves and shed light on the mechanisms of functional TR. METHODS: To improve our understanding of the pathogenesis of functional TR, we performed single-cell RNA sequencing of tricuspid valve from 10 patients, including 5 patients with moderate-to-severe functional TR and 5 nondiseased control subjects. Multiplexed fluorescence was used to detect the spatial distributions of valvular cell states and validated the cell-cell interaction. RESULTS: We assessed the transcriptional profiles of 84,102 cells and identified 6 major cell clusters, along with 25 cell subtypes, in the specimens. Valve interstitial cells (VICs) were the largest population. VICs and lymphoid cells exhibited more heterogeneity in TR patients. VICs exhibited higher transcriptional activity toward matrifibrocyte-like cells and myofibroblast-like cell differentiation, myeloid cells activated immune response, and lymphoid cells promoted fibrosis. In TR, the alternation of COMP-CD47 and FGF2-FGFR1 interaction may occur in TR specimens, which may serve as promising therapeutic targets for TR. CONCLUSIONS: Our single-cell atlas highlights the transcriptomic heterogeneity underlying the cell functions and interactions in human tricuspid valves and defines molecular and cellular perturbations in functional TR. We identified VIC clusters with fibrosis activation accumulated in TR valves.
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Ferroptosis and nutrient-metabolic pathway genes in ovarian cancer: integrated bulk and single-cell analysis of Iron-metabolic programs, immune-marker enrichment, and prognosis.
BACKGROUND: Ovarian cancer (OC), particularly high-grade serous ovarian carcinoma (HGSOC), remains a lethal gynecological malignancy characterized by frequent recurrence and treatment resistance. Ferroptosis is governed by nutrient-related metabolic processes involving iron handling, cystine-glutathione antioxidant defense, lipid peroxidation, amino-acid metabolism, and cellular energy metabolism. However, the organization of these programs across bulk tumors and individual tumor-microenvironment compartments remains incompletely understood. METHODS: Bulk RNA-sequencing data from The Cancer Genome Atlas ovarian cancer cohort (TCGA-OV) were analyzed in 421 primary tumors, with 419 patients evaluable for overall survival, using a curated 40-gene ferroptosis-nutrient-metabolic panel. Gene correlations, module scores, survival associations, a 10-fold cross-validated LASSO-Cox ferroptosis risk score (FRS), immune-marker enrichment, and immune-checkpoint correlations were evaluated. Single-cell RNA-sequencing data from GSE146026 were analyzed in a 10x cohort of 9,609 cells from 6 patients, with a Smart-seq2 cohort of 1,297 cells used for consistency analyses. Selected genes were further validated by RT-qPCR in hTERT FNE1, NIH:OVCAR-3, and Caov-3 cells. RESULTS: FTL, RPL8, FTH1, LDHA, and GPX4 showed the highest mean expression in TCGA-OV. Lower SLC7A11 expression was associated with shorter survival (HR = 0.83 per SD, 95% CI 0.73-0.94, p = 0.004), whereas higher LPCAT3 expression was associated with shorter survival (HR = 1.19, p = 0.010), although neither survived false-discovery-rate correction. The two-gene FRS separated survival groups (log-rank p = 0.019) but showed modest discrimination (C-index = 0.56; held-out concordance = 0.55). The ferroptosis-nutrition score correlated most strongly with macrophage marker enrichment (rho = 0.46). At single-cell resolution, the overall score was highest in fibroblasts, epithelial tumor cells, and macrophages, whereas the iron-storage module was highest in macrophages. Cross-platform concordance was observed between 10x and Smart-seq2 (rho = 0.63) and between epithelial pseudobulk and TCGA-OV bulk expression (rho = 0.65). RT-qPCR confirmed higher expression of FTL, FTH1, GPX4, SLC7A11, and LPCAT3 in both ovarian-cancer cell lines than in hTERT FNE1. CONCLUSION: Ferroptosis-related nutrient-metabolic genes display coordinated but cell-type-specific organization in ovarian cancer and are linked to immune-marker enrichment. RT-qPCR provides orthogonal expression-level validation of selected candidates. The FRS remains exploratory, and the transcriptomic scores should not be interpreted as direct measures of ferroptotic activity or clinical nutritional status.
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An early invasive signature from the adenocarcinoma in situ-to-invasive adenocarcinoma single-cell trajectory stratifies stage I lung adenocarcinoma overall survival.
BACKGROUND: Stage I lung adenocarcinoma (LUAD) shows significant variability in prognosis that the traditional tumor-node-metastasis (TNM) system fails to fully detect. Most current prognostic signatures are based on bulk-level statistical analyses and do not have a clear biological basis. This research focused on identifying an early-invasive gene set (EIGS) through single-cell pseudotime trajectory analysis and creating a simple risk score for better prognostic classification in stage I LUAD. METHODS: Single-cell RNA sequencing data from adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) were analyzed to reconstruct malignant epithelial pseudotime trajectories using Monocle 2. A 200-gene EIGS was derived from the invasion-associated branch and condensed into a multi-gene EIGS Score via stepwise Cox forward selection with Ridge penalization in The Cancer Genome Atlas (TCGA) stage I training cohort (n=269). The model was externally validated in three independent cohorts (GSE37745, n=70; GSE50081, n=92; GSE72094, n=254) and assessed by pooled analysis. Immune microenvironment characterization, drug-sensitivity profiling, and virtual gene-knockout analysis were conducted to assess the biological and therapeutic relevance of the EIGS Score. RESULTS: The EIGS Score stratified overall survival (OS) in the training cohort [hazard ratio (HR) = 4.53, P<0.001; concordance index (C-index) =0.698] and was confirmed in pooled external validation across three cohorts [pooled OS: HR =2.50, P<0.001, C-index =0.640; pooled disease-free survival (DFS): HR =3.26, P=0.001, C-index =0.653]. Multivariable Cox regression confirmed the EIGS Score as an independent prognostic factor after adjusting for age, sex, and substage (HR =1.93; P<0.001). High-EIGS tumors exhibited lower immune infiltration scores, enrichment of the high-plasticity cell state, and a selective drug-sensitivity profile. The virtual knockout of ARL4C disrupted immune-related transcriptional programs, supporting a regulatory role for ARL4C in connecting the EIGS network to immune microenvironment remodeling. CONCLUSIONS: The EIGS Score, derived from a single-cell invasive trajectory, consistently demonstrates prognostic value in stage I LUAD and may complement existing risk-stratification tools, though further prospective validation is needed.
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The Immune Cell Atlas of "Longevity Molecular Tag": Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms.
Immunosenescence represents a critical aspect of the aging process. Centenarians, serving as a nature model of "healthy aging," demonstrate a distinctive immune "compensatory adaptation" mechanism that contributes to the maintenance of immune homeostasis. However, the specific immune cell subsets involved and the molecular mechanisms underlying these phenotypic traits remain incompletely understood. In this study, we integrated single-cell RNA sequencing data spanning the entire lifespan of East Asian populations with bulk transcriptomic data from a centenarian cohort in Guangxi. Utilizing the Scissor algorithm, we identified immune cell subpopulations positively (Scissor+) and negatively (Scissor-) associated with longevity phenotypes, thereby constructing an immune cell atlas of "Longevity Molecular Tag." Our findings indicate that Scissor+ cells predominantly comprise natural killer (NK) cells, CD8+ T cells, and γδ T cells, characterized by enhanced cytotoxic and immunomodulatory functions. Conversely, Scissor- cells mainly include CD4+ T cells, B cells, and dendritic cells (DCs), which are linked to inflammatory signaling pathways and Th17/Th1 differentiation. Trajectory analysis elucidated the differentiation pathways of NK, CD8+ T cells, CD4+ T cells, and B cells. Differentially expressed genes were enriched in pathways such as NF-κB signaling, T cell receptor signaling, and NK cell cytotoxicity. Furthermore, co-localization analysis revealed five eQTL-colocalized events (rs3793537-GLIPR2/CD72/TLN1 and rs8019902-TRDV2/TRDC) associated with longevity. Collectively, these results suggest that centenarians achieve immune equilibrium by remodeling cytotoxic immune lineages and finely tuning inflammatory responses, thereby promoting health span and longevity. This study offers novel insights into potential strategies for modulating immunosenescence.
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Comprehensive analysis of pain genes in prognosis of kidney renal clear cell carcinoma and tumor immunotherapy: A comprehensive bioinformatic study.
BACKGROUND: The effect of pain genes (NAV1, EHMT2, SP1, SLC6A4, COMT, OPRM1, OPRD1, CYP2D6, and CYP3A4) have not been reported previously in kidney renal clear cell carcinoma (KIRC) patients and thus we made a comprehensive analysis of pain genes in the prognosis of KIRC and tumor immunotherapy. METHODS: In this study, TCGA, Kaplan-Meier plotter, Metascape, STRING, Human Protein Atlas, Single Cell Expression Atlas database, LinkedOmics, cBioPortal, MethSurv, CancerSEA, COSMIC database and R package (ggplot2, version 3.3.3) were used for comprehensive analysis of pain genes in KIRC. Pearson and Spearman correlation coefficients were for co-expression analysis. Immunotherapy and TISIDB database were used for tumor Immunotherapy. RESULTS: Representative pain genes (SP1, SLC6A4, COMT, OPRD1, CYP2D6, and CYP3A4) were statistically significant (p < 0.0001) in the prognosis of KIRC. Immunotherapy (anti-PD-1 therapy, anti-PD-L1 therapy, and anti-CTLA4 therapy) and immunomodulator (immunoinhibitor, immunostimulator, and MHC molecule) in KIRC were significant associated with pain genes (SP1, SLC6A4, COMT, OPRD1, CYP2D6, and CYP3A4), which were the important addition to clinical decision making for patients. CONCLUSIONS: Our study uncovered a mechanism for the effect of pain genes on KIRC outcome via the modulation of associated co-expression gene networks, gene variation, and tumor Immunotherapy.
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Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibin subunit βb as a novel therapeutic target.
BACKGROUND: Colon adenocarcinoma (COAD) is a malignancy with a high mortality rate and complex biological characteristics and heterogeneity, which poses challenges for clinical treatment. Anoikis is a type of programmed cell death that occurs when cells lose their attachment to the extracellular matrix (ECM), and it plays a crucial role in tumor metastasis. However, the specific biological link between anoikis and COAD, as well as its mechanisms in tumor progression, remains unclear, making it a potential new direction for therapeutic strategy research. METHODS: We employed transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to pinpoint differentially expressed anoikis-related genes (ARGs) in COAD. Using Cox proportional hazards models and Lasso regression analysis, we developed a prognostic signature derived from these ARGs. We also investigated the roles and interactions of these genes in the tumor microenvironment by analyzing single-cell RNA sequencing data. Additionally, we employed molecular docking techniques to evaluate the potential of inhibin subunit beta B (INHBB) as therapeutic targets and to assess the binding affinity of candidate drugs. Finally, we used gene knockout techniques to silence the key gene INHBB and explored its biological functions in vitro. RESULTS: In our study, by analyzing the expression differences of ARGs, we successfully classified patients with COAD. Kaplan-Meier survival analysis demonstrated that patients with elevated risk scores experienced poorer prognosis, a finding that was confirmed in both the training and validation cohorts. Additionally, immune infiltration analysis revealed a notable increase in immune cell presence within the tumor microenvironment of high-risk patients. Molecular docking identified potential drug candidates with high binding affinity to INHBB, including risperidone. Furthermore, in vitro experiments with INHBB showed that downregulation of its expression in COAD cell lines significantly reduced cellular viability and migration capacity. CONCLUSION: In summary, our research, based on the expression characteristics of ARGs, provides new insights into the precise classification, prognosis assessment, and identification of potential therapeutic targets in COAD. It also validates the key role of INHBB in the progression of COAD, establishing the foundation for future personalized treatment strategies.
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Integrating Single-Cell and Spatial Transcriptomics Reveals Heterogeneity of Early Pig Skin Development and a Subpopulation with Hair Placode Formation.
The dermis and epidermis, crucial structural layers of the skin, encompass appendages, hair follicles (HFs), and intricate cellular heterogeneity. However, an integrated spatiotemporal transcriptomic atlas of embryonic skin has not yet been described and would be invaluable for studying skin-related diseases in humans. Here, single-cell and spatial transcriptomic analyses are performed on skin samples of normal and hairless fetal pigs across four developmental periods. The cross-species comparison of skin cells illustrated that the pig epidermis is more representative of the human epidermis than mice epidermis. Moreover, Phenome-wide association study analysis revealed that the conserved genes between pigs and humans are strongly associated with human skin-related diseases. In the epidermis, two lineage differentiation trajectories describe hair follicle (HF) morphogenesis and epidermal development. By comparing normal and hairless fetal pigs, it is found that the hair placode (Pc), the most characteristic initial structure in HFs, arises from progenitor-like OGN+/UCHL1+ cells. These progenitors appear earlier in development than the previously described early Pc cells and exhibit abnormal proliferation and migration during differentiation in hairless pigs. The study provides a valuable resource for in-depth insights into HF development, which may serve as a key reference atlas for studying human skin disease etiology using porcine models.