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FastCCC: a permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies.
Detecting cell-cell communications (CCCs) in single-cell transcriptomics studies is fundamental for understanding the function of multicellular organisms. Here, we introduce FastCCC, a permutation-free framework that enables scalable, robust, and reference-based analysis for identifying critical CCCs and uncovering biological insights. FastCCC relies on fast Fourier transformation-based convolution to compute p-values analytically without permutations, introduces a modular algebraic operation framework to capture a broad spectrum of CCC patterns, and can leverage atlas-scale single cell references to enhance CCC analysis on user-collected datasets. To support routine reference-based CCC analysis, we constructed the first human CCC reference panel, encompassing 19 distinct tissue types, over 450 unique cell types, and approximately 16 million cells. We demonstrate the advantages of FastCCC across multiple datasets, most of which exceed the analytical capabilities of existing CCC methods. In real datasets, FastCCC reliably captures biologically meaningful CCCs, even in highly complex tissue environments, including differential interactions between endothelial and immune cells linked to COVID-19 severity, dynamic communications in thymic tissue during T-cell development, as well as distinct interactions in reference-based CCC analysis.
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Integrated Bioinformatic Analyses Reveal Thioredoxin as a Putative Marker of Cancer Stem Cells and Prognosis in Prostate Cancer.
OBJECTIVES: Prostate cancer stem cells (CSCs) play an important role in cancer cell survival, proliferation, metastasis, and recurrence; thus, removing CSCs is important for complete cancer removal. However, the mechanisms underlying CSC functions remain largely unknown, making it difficult to develop new anticancer drugs targeting CSCs. Herein, we aimed to identify novel factors that regulate stemness and predict prognosis. METHODS: We reanalyzed 2 single-cell RNA sequencing data of prostate cancer (PCa) tissues using Seurat. We used gene set enrichment analysis (GSEA) to estimate CSCs and identified common upregulated genes in CSCs between these datasets. To investigate whether its expression levels change over CSC differentiation, we performed a trajectory analysis using monocle 3. In addition, GSEA helped us understand how the identified genes regulate stemness. Finally, to assess their clinical significance, we used the Cancer Genome Atlas database to evaluate their impact on prognosis. RESULTS: The expression of thioredoxin (TXN), a redox enzyme, was approximately 1.2 times higher in prostate CSCs than in PCa cells (P < 1 × 10-10), and TXN expression decreased over CSC differentiation. In addition, GSEA suggested that intracellular signaling pathways, including MYC, may be involved in stemness regulation by TXN. Furthermore, TXN expression correlated with poor prognosis (P < .05) in PCa patients with high stemness. CONCLUSIONS: Despite the limited sample size in our study and the need for further in vitro and in vivo experiments to demonstrate whether TXN functionally regulates prostate CSCs, our findings suggest that TXN may serve as a novel therapeutic target against CSCs. Moreover, TXN expression in CSCs could be a useful marker for predicting the prognosis of PCa patients.
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Integrative analysis of polyamine metabolism-related genes in gliomas: implications for prognosis and therapy.
INTRODUCTION: Tumor transformation and progression are accompanied by multiple carcinogenic pathways that dysregulate polyamine demand and metabolism. The importance of polyamines has demonstrated that their metabolism is a potential therapeutic strategy. Yet, few prognostic models based on polyamine metabolism-related gene risk have been developed for gliomas. METHODS: The mRNA expression profiles and variations in 37 polyamine metabolism-related genes (PMRGs) were obtained from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. PMRGs-related risk model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and tested for predictive ability across two independent datasets from the Gene Expression Omnibus (GEO). The landscape of the tumor immune microenvironment and drug sensitivity were investigated systematically using multiple methods based on PMRG-related risk subtypes. Weighted gene co-expression network analysis (WGCNA) was applied to identify the key prognostic genes of the PMRGs. In addition, key genes were validated with regard to their expression and prognostic significance in human glioma tissues. To verify the cell types, single-cell RNA sequencing was performed on the cohorts available at GEO. RESULTS: Based on PMRG clusters, patients with glioma showed significant differences in PMRG expression, prognosis, and biological functions. A 11-gene risk model was constructed, and patients were categorized into high- and low-risk subtype according to the risk score. The high-risk subtype exhibited a poorer prognosis due to its immunosuppressive microenvironment. Furthermore, there were striking differences between the distinct subtypes in terms of immune cell infiltration, anticancer immunity cycle, tumor mutation burden, immune checkpoints, and response to targeted inhibitors. Spermine synthase (SMS) was identified as a key PMRG in patients with gliomas. A significant increase in SMS mRNA and protein expression was observed in tumors compared to normal controls. Single-cell sequencing analyses showed that SMS mRNA was highly expressed in all cell types, except oligodendrocytes. CONCLUSION: A PMRG-related risk model can be used as a reliable prognostic biomarker in glioma treatment. In addition, polyamine metabolism and function can be successfully targeted therapeutically.
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Development of a novel copper metabolism-related risk model to predict prognosis and tumor microenvironment of patients with stomach adenocarcinoma.
Background: Stomach adenocarcinoma (STAD) is the fourth highest cause of cancer mortality worldwide. Alterations in copper metabolism are closely linked to cancer genesis and progression. We aim to identify the prognostic value of copper metabolism-related genes (CMRGs) in STAD and the characteristic of the tumor immune microenvironment (TIME) of the CMRG risk model. Methods: CMRGs were investigated in the STAD cohort from The Cancer Genome Atlas (TCGA) database. Then, the hub CMRGs were screened out with LASSO Cox regression, followed by the establishment of a risk model and validated by GSE84437 from the Expression Omnibus (GEO) database. The hub CMRGs were then utilized to create a nomogram. TMB (tumor mutation burden) and immune cell infiltration were investigated. To validate CMRGs in immunotherapy response prediction, immunophenoscore (IPS) and IMvigor210 cohort were employed. Finally, data from single-cell RNA sequencing (scRNA-seq) was utilized to depict the properties of the hub CMRGs. Results: There were 75 differentially expressed CMRGs identified, 6 of which were linked with OS. 5 hub CMRGs were selected by LASSO regression, followed by construction of the CMRG risk model. High-risk patients had a shorter life expectancy than those low-risk. The risk score independently predicted STAD survival through univariate and multivariate Cox regression analyses, with ROC calculation generating the highest results. This risk model was linked to immunocyte infiltration and showed a good prediction performance for STAD patients' survival. Furthermore, the high-risk group had lower TMB and somatic mutation counters and higher TIDE scores, but the low-risk group had greater IPS-PD-1 and IPS-CTLA4 immunotherapy prediction, indicating a higher immune checkpoint inhibitors (ICIs) response, which was corroborated by the IMvigor210 cohort. Furthermore, those with low and high risk showed differential susceptibility to anticancer drugs. Based on CMRGs, two subclusters were identified. Cluster 2 patients had superior clinical results. Finally, the copper metabolism-related TIME of STAD was concentrated in endothelium, fibroblasts, and macrophages. Conclusion: CMRG is a promising biomarker of prognosis for patients with STAD and can be used as a guide for immunotherapy.
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Single-Cell Chromatin Accessibility Analysis Reveals the Epigenetic Basis and Signature Transcription Factors for the Molecular Subtypes of Colorectal Cancers.
UNLABELLED: Colorectal cancer is a highly heterogeneous disease, with well-characterized subtypes based on genome, DNA methylome, and transcriptome signatures. To chart the epigenetic landscape of colorectal cancers, we generated a high-quality single-cell chromatin accessibility atlas of epithelial cells for 29 patients. Abnormal chromatin states acquired in adenomas were largely retained in colorectal cancers, which were tightly accompanied by opposite changes of DNA methylation. Unsupervised analysis on malignant cells revealed two epigenetic subtypes, exactly matching the iCMS classification, and key iCMS-specific transcription factors (TFs) were identified, including HNF4A and PPARA for iCMS2 tumors and FOXA3 and MAFK for iCMS3 tumors. Notably, subtype-specific TFs bind to distinct target gene sets and contribute to both interpatient similarities and diversities for both chromatin accessibilities and RNA expressions. Moreover, we identified CpG-island methylator phenotypes and pinpointed chromatin state signatures and TF regulators for the CIMP-high subtype. Our work systematically revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers. SIGNIFICANCE: Our work revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers. Moreover, interpatient minor similarities and major diversities of chromatin accessibility signatures of TF target genes can faithfully explain the corresponding interpatient minor similarities and major diversities of RNA expression signatures of colorectal cancers, respectively. This article is featured in Selected Articles from This Issue, p. 897.
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Identification of Tumor Antigens and Immune Subtypes of Esophageal Squamous Cell Carcinoma for mRNA Vaccine Development.
Purpose: The applicability of mRNA vaccines against esophageal squamous cell carcinoma (ESCC) remains unclear. Here, we identified potential antigens for developing mRNA vaccines against ESCC and characterized immune subtypes to select appropriate patients for vaccination. Methods: RNA-seq, genetic alteration data, and corresponding clinical information of ESCC patients were obtained from the Cancer Genome Atlas (TCGA) database. The RNA-seq data of normal esophageal tissue were obtained from the Genotype-Tissue Expression (GTEx) database. Potential tumor antigens were screened by analyzing differentially expressed and mutated genes and potential antigens with significant differences in prognosis were screened using the Kaplan-Meier method. The proportion of immune cell infiltration in the tumor microenvironment was estimated using CIBERSORT and MCPcounter, and the correlation of potential antigens with antigen-presenting cells and major histocompatibility complex class II was analyzed. Subsequently, immune subtypes were constructed using consensus clustering analysis and characterized by single-sample gene set enrichment analysis and weighted gene co-expression network analysis (WGCNA). The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to analyze the drug sensitivity of different immune subtypes. Results: Four overexpressed and mutated tumor antigens associated with antigen presentation and poor prognosis were identified in ESCC, including NLRC5, FCRL4, TMEM229B, and LCP2. By consensus clustering, we identified two immune-associated ESCC subtypes, immune subtype 1 (IS1) and immune subtype 2 (IS2); the prognosis of the two subtypes was statistically different. In addition, the two immune subtypes had distinctly different cellular, molecular, and clinical characteristics. IS1 patients have a distinct immune "hot" phenotype with strong immune tolerance, whereas patients with IS2 have an immune "cold" phenotype. Differential expression of immune checkpoints and immunogenic cell death modulators was observed between the different immune subtypes. Finally, we found that IS1 and IS2 patients showed different drug sensitivities to common anti-tumor drugs, possibly facilitating the development of individualized treatment regimens for patients. Conclusion: NLRC5, LCP2, TMEM229B, and FCRL4 are potential antigens for ESCC mRNA vaccines, and such vaccines may be more suitable for IS2 patients. This study provides a theoretical basis for mRNA vaccines against ESCC, by identifying the critical characteristics to predict ESCC prognosis and select suitable patients for vaccination.
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A prognostic gene signature derived from aging-related genes predicts survival, immune landscape and therapy response in glioma.
Gliomas, the most common primary brain tumors, show diverse prognostic outcomes. Differences in gene expression between low-grade gliomas and glioblastoma and the role of aging-related genes highlight the need for robust prognostic models. The present study identified differentially expressed genes (DEGs) and developed a predictive risk model. Using The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, 29 overlapping aging-related DEGs were identified (|LogFC|>1, adjusted P<0.05). Cox and LASSO regression analyses selected 8 genes for a risk scoring model, validated across datasets and subgroups. Functional and single-cell analyses explored immune microenvironments and drug sensitivities. Additionally, reverse transcription-quantitative PCR (RT-qPCR) was performed to validate the differential expression of these genes in normal astrocytes (HA) and glioblastoma (GBM) cell lines (U251 and U87). The 8-gene model (Netrin-4, retinol-binding protein 1, Twist Family BHLH Transcription Factor 1, growth arrest and DNA damage inducible gamma (GADD45G), NUAK2, glutamate ionotropic receptor kainate type subunit 2, WEE1 and ribonucleotide reductase regulatory subunit) stratified patients into high- and low-risk groups, with high-risk patients showing significantly poorer survival (TCGA, HR=6.84; CGGA, HR=3.72; P<0.001). High-risk tumors were enriched in cell cycle and senescence pathways and exhibited elevated immune checkpoint expression and reduced chemotherapeutic sensitivity. Single-cell analysis revealed differential GADD45G expression in M1 and M2 macrophages, suggesting a role in immune evasion. RT-qPCR results further confirmed differential expression patterns of the 8 genes between normal and GBM cells, supporting their involvement in GBM pathogenesis. This 8-gene risk model effectively predicts glioma prognosis and supports personalized treatment strategies by highlighting immune microenvironment differences and drug sensitivities between risk groups.
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BMPER induces the adipogenic differentiation of fibro/adipogenic progenitors and promotes intramuscular fat deposition in chickens.
BACKGROUND: Fibro-adipogenic progenitors (FAPs) serve as the developmental origin of intramuscular adipocytes in skeletal muscle. Enhancing their adipogenic transition could increase intramuscular fat (IMF) deposition, thereby improving meat flavor in chickens. However, critical aspects of FAPs including their cellular composition, dynamic changes during skeletal muscle growth, and associated regulatory mechanisms remain poorly understood. Therefore, we comprehensively characterized FAPs in Langshan chickens from d 1 to d 98 using both in vivo and in vitro approaches combined with single-nucleus RNA sequencing (snRNA-seq) analysis. RESULTS: Our analysis of IMF contents and adipogenesis-related gene expression in the pectoralis major muscle of Langshan chickens revealed that the adipogenic properties of FAPs peaked at d 1, reached its lowest point at d 14, and subsequently increased until d 98. The snRNA-seq analysis successfully identified the population of FAPs along with their 5 subtypes including the pre-adipogenic, adipogenic, and fibrotic FAPs. The ratio of the pre-adipogenic subtype decreased from d 14 to d 98, which was reversely correlated with the changes of the adipogenic subtype, suggesting a differentiating process. Furthermore, RNA velocity and pseudo-trajectory analysis revealed that the initial FAPs had superior fibrotic capacity but decreased over time which contrasted with their enhanced adipogenic capacity with development. Notably, BMPER was identified as an important regulator for the adipogenic differentiation of FAPs, which was also confirmed by in vitro over-expression studies. In addition, the expression of BMPER in the adipogenic portion of FAPs was found to be highly conserved across human and mouse skeletal muscles. CONCLUSIONS: Our study provides the first comprehensive atlas of FAPs in the skeletal muscle of chickens and identifies BMPER as a key regulator for the adipogenic differentiation of FAPs. The findings will not only provide novel targets for breeding chickens with high IMF content but also offer significant insights into understanding the cell fate decision of FAPs under both physiological and pathological conditions across species.
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Divide-and-conquer analysis reveals hidden immune cell influencers across the placenta in preeclampsia.
BACKGROUND: Placenta is a focal point of cellular interactions of maternal and fetal immune systems, fostering immune tolerance essential for a successful pregnancy. However, the mechanisms underlying immune dysregulation in disorders such as preeclampsia remain poorly understood. METHODS: We constructed a multilayered atlas of the human placenta by performing single-cell RNA sequencing across distinct placental layers from both normal and severe preeclamptic pregnancies. Rather than focusing on specific regions of placenta, such as the villi and decidua, we explore placental architecture by collecting pair-matched tissues from individual pregnancies to suggest appropriate placental-wide immune atlas. To interpret intercellular communication in those unexplored placental layers, we designed two conceptual models: one based on immune interaction frequency (IIF) and another on immune tolerance influencers (IT). We applied machine learning classifiers to identify gene signatures associated with preeclampsia. RESULTS: We observed extensive admixture of semi-allogeneic fetal and maternal cells across all placental layers, regardless of disease status. This contradicts the IIF-based model, which is premised on that such intermixing frequency is a pathologic feature specific to preeclampsia. Instead, analysis under the IT framework revealed key molecular determinants of preeclampsia. Notably, classifier-prioritized genes associated with preeclampsia were enriched for ligands and receptors supporting a role for intercellular immune interactions. Among them, the ligand-receptor pair SPP1-CD44 between fetus and maternal immune cells emerged as peculiarly associated influencers of preeclampsia. Spatial image analysis confirmed co-localization of SPP1-CD44 expression within immune cell populations in preeclamptic placental tissue. Our study provides a comprehensive map of the human placenta and identifies disease-specific immune signaling pathways in preeclampsia using the divide and rule approach. The findings highlight SPP1-CD44 as a putative target of immune dysregulation, offering new insight into the cellular basis of maternal-fetal tolerance and its breakdown in pregnancy-related disorders. CLINICAL TRIAL NUMBER: Not applicable.
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FAT10 is a Prognostic Biomarker and Correlated With Immune Infiltrates in Skin Cutaneous Melanoma.
Background: Skin Cutaneous Melanoma (SKCM) is the deadliest cutaneous neoplasm. Previous studies have proposed ubiquitin-like protein FAT10 plays key roles in the initiation and progression of several types of human cancer, but little is known about the interrelation between FAT10 gene expression, tumor immunity, and prognosis of patients with SKCM. Methods: Here, we first performed pan-cancer analysis for FAT10's expression and prognosis using the Cancer Genome Atlas and the Genotype-Tissue Expression data. Subsequently, we investigated the mRNA expression level, prognostic value, and gene-gene interaction network of FAT10 in SKCM using the Oncomine databases, GEPIA, TIMER, UALCAN, and starBase. The relationship between FAT10 expression and tumor immune invasion was studied by using the TIMER database. Additionally, the expression and functional status of FAT10 in SKCM were evaluated by the single-cell RNA sequencing and CancerSEA databases. Results: In this study, we found that FAT10 expression was increased in SKCM and was correlated with a better survival rate in patients with SKCM. Moreover, we identified FAT10 level was significantly positively associated with immune infiltrates, biomarkers of immune cells, and immune checkpoint expression, and negatively correlated with tumor cell invasion and DNA damage, indicating that increased FAT10 expression in SKCM was a favorable response to immune checkpoint inhibitors. Conclusion: Our findings suggest that upregulation of FAT10 correlated with better prognosis and tumor immune infiltration in SKCM.