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IRF1 expression might be a biomarker of CD8+ T cell infiltration in cutaneous melanoma.
OBJECTIVE: This study aimed to explore the expression profile of interferon regulatory factor (IRF) genes in skin cutaneous melanoma (SKCM), their association with CD8 + T cell infiltration, and the potential regulatory network in melanoma and non-melanoma cells. METHODS: Bioinformatic analysis was conducted using the SKCM subset of The Cancer Genome Atlas (TCGA) Pan-Cancer, Genotype-Tissue Expression Project (GTEx), and single-cell RNA-seq data from the Human Protein Atlas and Jerby-Arnon et al. 2018's dataset. RESULTS: IRF1 expression is robustly associated with moderate to strong CD8 + T cell infiltration in the tumor microenvironment. It is ubiquitously expressed in tumor and non-tumor cells in melanoma. Melanoma tumor cells and macrophages had 16/36 and 9/27 cell-specific IRF1-correlated genes, respectively. The methylation of four CpG sites (cg00255919, cg21138405, cg15375424, and cg27587780) within the IRF1 gene locus showed moderate to strong negative correlations with IRF1 expression. CONCLUSION: IRF1 expression might serve as a biomarker indicating CD8 + T cell infiltration in skin melanoma. It might exert different regulatory effects in melanoma and non-melanoma cells in the tumor microenvironment. Cg00255919, cg21138405, cg15375424, and cg27587780 are four critical CpG sites that might modulate the transcription of IRF1.
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Cell Atlas technologies and insights into tissue architecture.
Since Robert Hooke first described the existence of 'cells' in 1665, scientists have sought to identify and further characterise these fundamental units of life. While our understanding of cell location, morphology and function has expanded greatly; our understanding of cell types and states at the molecular level, and how these function within tissue architecture, is still limited. A greater understanding of our cells could revolutionise basic biology and medicine. Atlasing initiatives like the Human Cell Atlas aim to identify all cell types at the molecular level, including their physical locations, and to make this reference data openly available to the scientific community. This is made possible by a recent technology revolution: both in single-cell molecular profiling, particularly single-cell RNA sequencing, and in spatially resolved methods for assessing gene and protein expression. Here, we review available and upcoming atlasing technologies, the biological insights gained to date and the promise of this field for the future.
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Single-Cell RNA-Seq of Pituitary and Ovary Identifies Regulators of Reproduction in Yellow Catfish (Pelteobagrus fulvidraco).
The pituitary and gonads serve as central regulatory hubs and functional organs for gametogenesis and maturation. In this study, we performed single-cell RNA sequencing (scRNA-seq) of the pituitary and ovary in pre-spawning Pelteobagrus fulvidraco to elucidate the cellular landscape and regulatory pathways governing gonadal development and oocyte maturation. Pituitaries from four female fish (weight: 43.8 ± 3.2 g; length: 14.1 ± 0.7 cm) and four male fish (weight: 78.2 ± 11.2 g; length: 18.9 ± 1.1 cm) were subjected to single-cell transcriptomic analysis. A total of 17 distinct cell types were identified in the female pituitary, whereas 15 cell types were detected in the male pituitary. Both male and female pituitaries comprised multiple hormone-secreting endocrine populations, indicating a largely conserved cellular composition. However, sex-specific differences were observed in thyrotrope subtypes, suggesting potential sexual dimorphism in pituitary endocrine regulation. Examination of receptor gene expression revealed cell-type-specific regulatory capacities, highlighting gonadotropin, steroid, and neuropeptide responsiveness across pituitary populations. In the ovary, 10 cell types were identified, with granulosa cells (~22.9%) and theca cells (~8.6%) showing distinct transcriptional profiles. Follicle-stimulating hormone receptor (fshr) was highly expressed in granulosa cells, whereas luteinizing hormone receptor (lhcgr) and steroidogenic genes (hsd3b1, pgr) were predominantly localized in theca cells, indicating functional compartmentalization of gonadotropin and steroid signaling. Prostaglandin (PG) and melatonin (MT) pathways were implicated in paracrine regulation: the prostaglandin synthase ptgs2a was expressed in theca, germ, and immune cells, while ptger2a was expressed in granulosa cells; melatonin synthesis genes (aanat1, aanat2, asmtl) were confined to granulosa cells, with receptors (mtnr1ab) in germ cells. These findings suggest that ovarian paracrine signaling complements systemic endocrine control to modulate oocyte maturation and ovulation. This single-cell atlas provides a high-resolution framework of reproductive cell types and signaling networks in P. fulvidraco, offering insights for improving artificial breeding and reproductive management in aquaculture.
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Integrated Transcriptomic Analysis Leveraging Single-Cell and Bulk RNA Sequencing Data to Uncover Pyroptosis-Related Prognostic Signatures in HCC.
BACKGROUND: The influence of pyroptosis on tumors is complex and diverse. However, its specific impact on hepatocellular carcinoma (HCC) is still not well understood. Therefore, the objective of this study was to develop a prognostic signature for HCC based on pyroptosis-related genes. METHODS: The single-cell RNA sequencing (scRNA-seq) data, mRNA expression files and corresponding clinical information of HCC were obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. Python was used to process scRNA-seq data and calculated the enrichment score of pyroptosis-related genes (PRGs). Weight Co-Expression Network Analysis was used to identify pyroptosis-related hub genes. By overlapping the PRGs from scRNA-seq analysis and bulk RNA-seq analysis, respectively. Then, Univariate cox and LASSO regression were used to construct the pyroptosis prognostic model. Multivariate cox was used to identify independent factors for HCC and then developed a nomogram. The biological functions, survival analysis, immune characteristics, therapy response, and m6A modification status were analyzed. RESULTS: Based on the scRNA-seq analysis and bulk RNA-seq analysis, hub PRGs were identified in HCC. Of those genes, five PRGs (ADGRE2, FCER1G, SLC9A9, CYBB, SLAMF6) were selected as a prognostic signature. The risk score established from the prognostic signature was an independent prognostic factor for HCC. The high-risk score group is associated with a poor prognosis, characterized by immunosuppressive features. CONCLUSION: This study uniquely integrates single-cell and bulk transcriptomic data to systematically identify pyroptosis-related prognostic biomarkers, pinpointing their cellular origin within the tumor microenvironment.
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Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis.
Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: "Anti-apoptosis", "Immune cell interactions", "Melanogenesis", "Ribosomal biogenesis", "Extracellular structure organization", and "Epithelial-Mesenchymal Transition (EMT)"). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs ("Immune cell interactions", "Melanogenesis", "Ribosomal biogenesis", and "Extracellular structure organization") were significantly correlated with prognosis (p = 1.08 × 10-5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10-6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.
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[Identification of SULF1 as a Shared Gene in Idiopathic Pulmonary Fibrosis and Lung Adenocarcinoma].
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an idiopathic chronic, progressive interstitial lung disease with a diagnosed median survival of 3-5 years. IPF is associated with an increased risk of lung cancer. Therefore, exploring the shared pathogenic genes and molecular pathways between IPF and lung adenocarcinoma (LUAD) holds significant importance for the development of novel therapeutic approaches and personalized precision treatment strategies for IPF combined with lung cancer. METHODS: Bioinformatics analysis was conducted using publicly available gene expression datasets of IPF and LUAD from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis was employed to identify common genes involved in the progression of both diseases, followed by functional enrichment analysis. Subsequently, additional datasets were used to pinpoint the core shared genes between the two diseases. The relationship between core shared genes and prognosis, as well as their expression patterns, clinical relevance, genetic characteristics, and immune-related functions in LUAD, were analyzed using The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing datasets. Finally, potential therapeutic drugs related to the identified genes were screened through drug databases. RESULTS: A total of 529 shared genes between IPF and LUAD were identified. Among them, SULF1 emerged as a core shared gene associated with poor prognosis. It exhibited significantly elevated expression levels in LUAD tissues, concomitant with high mutation rates, genomic heterogeneity, and an immunosuppressive microenvironment. Subsequent single-cell RNA-seq analysis revealed that the high expression of SULF1 primarily originated from tumor-associated fibroblasts. This study further demonstrated an association between SULF1 expression and tumor drug sensitivity, and it identified potential small-molecule drugs targeting SULF1 highly expressed fibroblasts. CONCLUSIONS: This study identified a set of shared molecular pathways and core genes between IPF and LUAD. Notably, SULF1 may serve as a potential immune-related biomarker and therapeutic target for both diseases.
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A cuproptosis-related signature predicts prognosis and indicates cross-talk with immunocyte in ovarian cancer.
PURPOSE: Cuproptosis, programmed cell death by intracellular copper-mediated lipoylated protein aggregation, is involved in various tumorigenesis and drug resistance abilities by mediating the tumor microenvironment. Previous studies have demonstrated that serum copper levels are higher in OC patients than in normal subjects. However, the exact relationship between cuproptosis and ovarian cancer progression remains to be further elucidated. METHODS: The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) datasets were utilized to establish a cuproptosis-related prognostic signature in ovarian cancer. Subsequently, the bulk RNA-seq analysis and single-cell RNA-seq analysis were used to identify the relationship between signature with immune cell infiltration, chemotherapy, and cuproptosis-related scoring (CuRS) system. Finally, the potential biological functional roles of target genes in cuproptosis were validated in vitro. RESULTS: By using LASSO-Cox regression analysis to establish the cuproptosis-related prognostic model, our works demonstrated the accuracy and efficiency of our model in the TCGA (583 OC patients) and GEO (260 OC patients) OC cohorts, and the high-scoring groups showed worse survival outcomes. Notably, there were substantial differences between the high and low-risk groups in extensive respects, such as the activating transcription factors, cell pseudotime features, cell intercommunication patterns, immunocytes infiltration, chemotherapy response, and potential drug resistance. KIF26B was selected to construct a prognostic model from the identified 33 prognosis-related genes, and high expression of KIF26B predicted poorer prognosis in ovarian cancer. Ultimately, further in vitro experiments demonstrated that KIF26B participated in the proliferation and cisplatin resistance of OC cells. Knockdown of KIF26B increased the sensitivity of OC cells to elesclomol, a cuproptosis agonists. CONCLUSION: This study constructed a new cuproptosis-related gene signature that has a good prognostic capacity in assessing the outcome of OC patients. This study enhances our understanding of cuproptosis associated with ovarian cancer aggressiveness, cross-talk with immunocytes, and serves as a novel chemotherapy strategy.
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Uncovering somatic genetic drivers in prostate cancer through comprehensive genome-wide analysis.
Given that hereditary prostate cancer (PCa) accounts for only a small fraction of PCa phenotypes, there is still a substantial journey ahead in exploring the somatic genetic drivers contributing to sporadic PCa. The expression quantitative trait loci (eQTLs) data were sourced from the GTEx dataset for prostate-specific genes, and the summary statistic information was collected for 5854 genes. Genetic associations with PCa were extracted from three well-established consortiums: the UK Biobank (9131 cases and 173,493 controls), the PRACTICAL study (79,148 cases and 61,106 controls), and the FinnGen cohort (13,216 cases and 119,948 controls). To prioritize potential causal targets, additional analysis, including the protein-protein interaction (PPI), The Cancer Genome Atlas (TCGA) dataset, and the single-cell-type expression analysis, was performed. Generally, a total of 150 common significant genes with the same causal association with PCa were identified. Out of the 150 genes examined, 67.33% (101/150) were found to have protein-coding functions, while only 30.67% (46/150) of these genes had prior mentions in the scientific literature. Notably, the analysis of the TCGA dataset showed that only 44.67% (67/150) of the genes produced consistent results with the Mendelian randomization (MR) analysis. Furthermore, the evaluation of single-cell RNA-seq data and colocalization analysis singled out MSMB as a critical gene associated with the occurrence of PCa. We pinpointed a range of prostate-specific genes that display causal associations with the onset of PCa. Among these, the MSMB gene emerged as a pivotal factor linked to PCa, demonstrating robust consistency across all four assessments, including the MR, TCGA dataset, single-cell RNA-seq data, and colocalization analysis. These findings provided fresh perspectives on the pathogenesis of PCa and presented potential targets for drug development.
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Single-Cell RNA-seq Reveals a Subpopulation of Prostate Cancer Cells with Enhanced Cell-Cycle-Related Transcription and Attenuated Androgen Response.
Increasing evidence suggests the presence of minor cell subpopulations in prostate cancer that are androgen independent and poised for selection as dominant clones after androgen deprivation therapy. In this study, we investigated this phenomenon by stratifying cell subpopulations based on transcriptome profiling of 144 single LNCaP prostate cancer cells treated or untreated with androgen after cell-cycle synchronization. Model-based clustering of 397 differentially expressed genes identified eight potential subpopulations of LNCaP cells, revealing a previously unappreciable level of cellular heterogeneity to androgen stimulation. One subpopulation displayed stem-like features with a slower cell doubling rate, increased sphere formation capability, and resistance to G2-M arrest induced by a mitosis inhibitor. Advanced growth of this subpopulation was associated with enhanced expression of 10 cell-cycle-related genes (CCNB2, DLGAP5, CENPF, CENPE, MKI67, PTTG1, CDC20, PLK1, HMMR, and CCNB1) and decreased dependence upon androgen receptor signaling. In silico analysis of RNA-seq data from The Cancer Genome Atlas further demonstrated that concordant upregulation of these genes was linked to recurrent prostate cancers. Analysis of receiver operating characteristic curves implicates aberrant expression of these genes and could be useful for early identification of tumors that subsequently develop biochemical recurrence. Moreover, this single-cell approach provides a better understanding of how prostate cancer cells respond heterogeneously to androgen deprivation therapies and reveals characteristics of subpopulations resistant to this treatment.Significance: Illustrating the challenge in treating cancers with targeted drugs, which by selecting for drug resistance can drive metastatic progression, this study characterized the plasticity and heterogeneity of prostate cancer cells with regard to androgen dependence, defining the character or minor subpopulations of androgen-independent cells that are poised for clonal selection after androgen-deprivation therapy. Cancer Res; 78(4); 853-64. ©2017 AACR.
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Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF.
MOTIVATION: The rapid proliferation of single-cell RNA-sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface. RESULTS: We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse non-negative matrix factorization, cluster 'fitness', support vector machine) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from multiple cell atlases, we show that the PageRank algorithm effectively downsamples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar yet distinct cell types and while recovering novel transcriptionally distinct cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets. AVAILABILITY AND IMPLEMENTATION: ICGS2 is implemented in Python. The source code and documentation are available at http://altanalyze.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.