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Single-cell profiling of cross-tissue features of fetal pig macrophages and their remodeling in fetal growth restriction.
Macrophages are highly heterogeneous immune cells with context-dependent functions, yet their diversity across prenatal tissues of the whole body, sexes, and stages remains elusive. During fetal development, macrophages play essential roles in tissue formation and maintaining maternal-fetal immune balance, but their specific phenotypic and functional changes in the context of fetal growth restriction remain relatively underexplored. Herein, we constructed a cross-tissue macrophage atlas of 85 473 cells from 54 tissues of late-gestation fetal pigs. We identified 15 macrophage subtypes, among which two Hofbauer cell (HBC) subtypes were distinguished by their distinct transcriptional and functional characteristics between the amniotic and chorioallantoic membranes: HBC GPNMB+ cells upregulated glycolytic enzymes and extracellular matrix regulators such as LDHA and GPNMB, while HBC PEG10+ cells expressed immunomodulators and developmental regulators, including A2M and PEG10. Two dichotomous subtypes, LYVE1 highMHCII low and LYVE1 lowMHCII high, were delineated across 27 tissues, with a conserved transcriptional signature in pigs and humans. Transcriptional profiling of metabolic pathways revealed oxidative phosphorylation and the tricarboxylic acid cycle as core pathways across tissues, alongside tissue-specific adaptations such as enhanced glycolysis in amniotic macrophages. Cross-stage comparisons showed fetal macrophages mainly maintained developmental programs, whereas adult macrophages globally exhibited heightened immune activity. Cross-sex and cross-species analyses revealed high transcriptomic similarity in fetal macrophages. In growth-restricted fetuses, chorioallantoic HBCs showed profound remodeling, including increased inflammation, hypoxia response, and vascular dysfunction signaling, with altered intercellular communication. Collectively, this work provides a comprehensive resource for understanding fetal macrophage heterogeneity and offers insights into the potential mechanisms underlying fetal growth restriction.
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Comprehensive Investigation Illustrates the Role of M2 Macrophages and Its Related Genes in Pancreatic Cancer.
Background and Objectives: M2 macrophages play an important role in cancers. Our study aimed to illustrate the effect of M2 macrophages in pancreatic cancer (PC). Materials and Methods: The open-access data used for analysis were downloaded from The Cancer Genome Atlas Program database, as well as some online databases. R software was mainly used for data analysis based on the specific packages. Results: Here, we comprehensively investigated the role of M2 macrophages and their related genes in PC. We performed the biological enrichment of M2 macrophages in PC. Meanwhile, we identified adenosine A3 receptor (TMIGD3) as the interest gene for further analysis. The single-cell analysis showed that was mainly expressed in the Mono/Macro cells based on the data from multiple data cohorts. Biological investigation showed that TMIGD3 was primarily enriched in angiogenesis, pancreas-beta cells and TGF-beta signaling. Tumor microenvironment analysis indicated that TMIGD3 was positively correlated with monocyte_MCPCOUNTER, NK cell_MCPCOUNTER, macrophages M2_CIBERSORT, macrophage_EPIC, neutrophil_TIMER and endothelial cell_MCPCOUNTER. Interestingly, we determined that all the immune functions quantified by single sample gene set enrichment analysis algorithms were activated in the patients with high TMIGD3 expression. Conclusions: Our results provide a novel direction for the research focused on the M2 macrophages in PC. Meanwhile, TMIGD3 was identified as an M2 macrophage-related biomarker for PC.
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Upregulation of GOLPH3 mediated by Bisphenol a promotes colorectal cancer proliferation and migration: evidence based on integrated analysis.
BACKGROUND: The interaction between environmental endocrine-disrupting chemicals, such as Bisphenol A (BPA), and their influence on cancer progression, particularly regarding the GOLPH3 gene in colorectal cancer, remains unclear. METHODS: We performed an integrated analysis of transcriptional profiling, clinical data, and bioinformatics analyses utilizing data from the Comparative Toxicogenomics Database and The Cancer Genome Atlas. The study employed ClueGO, Gene Set Enrichment Analysis, and Gene Set Variation Analysis for functional enrichment analysis, alongside experimental assays to examine the effects of BPA exposure on colorectal cancer cell lines, focusing on GOLPH3 expression and its implications for cancer progression. RESULTS: Our findings demonstrated that BPA exposure significantly promoted the progression of colorectal cancer by upregulating GOLPH3, which in turn enhanced the malignant phenotype of colorectal cancer cells. Comparative analysis revealed elevated GOLPH3 protein levels in cancerous tissues versus normal tissues, with single-cell analysis indicating widespread GOLPH3 presence across various cell types in the cancer microenvironment. GOLPH3 was also associated with multiple carcinogenic pathways, including the G2M checkpoint. Furthermore, our investigation into the colorectal cancer microenvironment and genomic mutation signature underscored the oncogenic potential of GOLPH3, exacerbated by BPA exposure. CONCLUSION: This study provides novel insights into the complex interactions between BPA exposure and GOLPH3 in the context of colorectal cancer, emphasizing the need for heightened awareness and measures to mitigate BPA exposure risks. Our findings advocate for further research to validate these observations in clinical and epidemiological settings and explore potential therapeutic targets within these pathways.
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Cancer Stemness Associated With Prognosis and the Efficacy of Immunotherapy in Adrenocortical Carcinoma.
BACKGROUND: Cancer stem cells (CSCs) have been proven to influence drug resistance, recurrence, and metastasis in tumors. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in adrenocortical carcinoma. METHODS: RNA-seq data and clinical characteristics were downloaded from The Cancer Genome Atlas (TCGA). The stemness indexes, mDNAsi and mRNAsi, were calculated to classify all samples into low-score and high-score groups. Two algorithms, based on the R language, ESTIMATE and single-sample Gene Set Enrichment Analysis (ssGSEA) were used to assess the immune cell infiltration states of adrenocortical carcinoma patients. Weighted Gene Co-expression Network Analysis (WGCNA) was used to find genes that were related to the stemness of cancer. By bioinformatics methods, the correlations between biomarkers capable of predicting immune checkpoint inhibitors (ICIs) responses and stemness of cancer were explored. RESULTS: High-mRNAsi predicted shorter overall survival (OS) and a higher metastatic trend in adrenocortical carcinoma (ACC) patients. Compared with the low-mRNAsi group, the high-mRNAsi group had a lower ImmuneScore and StromalScroe. Twenty-two stemness-related prognostic genes were obtained by WGCNA, which focused on the function of the cell cycle and cell mitosis. Immune cell infiltration, especially CD8+T cell, increased in the low-mRNAsi group compared with the high-mRNAsi group. Lower expression of PD-L1, CTLA-4, and TIGHT was evaluated in the high-mRNAsi group. CONCLUSIONS: ACC patients with high-mRNAsi have poor prognosis and less immune cell infiltration. Combined with the finding of lower expression of CTLA-4, TIGHT, and PD-L1 in the high-mRNAsi group, we came to the conclusion that stemness index is a potential biomarker to predict the effectiveness of ICIs.
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Analysis of cancer-associated fibroblasts related genes identifies COL11A1 associated with lung adenocarcinoma prognosis.
BACKGROUND: The treatment of lung adenocarcinoma is difficult due to the limited therapeutic options. Cancer-associated fibroblasts play an important role in the development of cancers. This study aimed to identify a promising molecular target associated with cancer-associated fibroblasts for the treatment of lung adenocarcinoma. METHODS: The Cancer Genome Atlas lung adenocarcinoma dataset was used to screen hub genes associated with cancer-associated fibroblasts via the EPIC algorithm and Weighted Gene Co-expression Network Analysis. Multiple databases were used together with our data to verify the differential expression and survival of COL11A1. Functional enrichment analysis and the single-cell TISCH database were used to elucidate the mechanisms underlying COL11A1 expression. The correlation between COL11A1 and immune checkpoint genes in human cancers was also evaluated. RESULTS: Using the EPIC algorithm and Weighted Gene Co-expression Network Analysis, 13 hub genes associated with cancer-associated fibroblasts in lung adenocarcinoma were screened. Using the GEPIA database, Kaplan-Meier Plotter database, GSE72094, GSE75037, GSE32863, and our immunohistochemistry experiment data, we confirmed that COL11A1 overexpresses in lung adenocarcinoma and that high expression of COL11A1 is associated with a poor prognosis. COL11A1 has a genetic alteration frequency of 22% in patients with lung adenocarcinoma. COL11A1 is involved in the extracellular matrix activities of lung adenocarcinoma. Using the TISCH database, we found that COL11A1 is mainly expressed by cancer-associated fibroblasts in the tumor microenvironment rather than by lung adenocarcinoma cells. Finally, we found that COL11A1 is positively correlated with HAVCR2(TIM3), CD274 (PD-L1), CTLA4, and LAG3 in lung adenocarcinoma. CONCLUSION: COL11A1 may be expressed and secreted by cancer-associated fibroblasts, and a high expression of COL11A1 may result in T cell exhaustion in the tumor microenvironment of lung adenocarcinoma. COL11A1 may serve as an attractive biomarker to provide new insights into cancer therapeutics.
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EDomics: a comprehensive and comparative multi-omics database for animal evo-devo.
Evolutionary developmental biology (evo-devo) has been among the most fascinating interdisciplinary fields for decades, which aims to elucidate the origin and evolution of diverse developmental processes. The rapid accumulation of omics data provides unprecedented opportunities to answer many interesting but unresolved evo-devo questions. However, the access and utilization of these resources are hindered by challenges particularly in non-model animals. Here, we establish a comparative multi-omics database for animal evo-devo (EDomics, http://edomics.qnlm.ac) containing comprehensive genomes, bulk transcriptomes, and single-cell data across 40 representative species, many of which are generally used as model organisms for animal evo-devo study. EDomics provides a systematic view of genomic/transcriptomic information from various aspects, including genome assembly statistics, gene features and families, transcription factors, transposable elements, and gene expressional profiles/networks. It also exhibits spatiotemporal gene expression profiles at a single-cell level, such as cell atlas, cell markers, and spatial-map information. Moreover, EDomics provides highly valuable, customized datasets/resources for evo-devo research, including gene family expansion/contraction, inferred core gene repertoires, macrosynteny analysis for karyotype evolution, and cell type evolution analysis. EDomics presents a comprehensive and comparative multi-omics platform for animal evo-devo community to decipher the whole history of developmental evolution across the tree of life.
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TMEM171 is a potential biomarker associated with lung injury and lung adenocarcinoma.
BACKGROUND: Lung cancer therapy frequently induces lung injury, leading to treatment interruption and poor prognosis; however, specific biomarkers linking cancer therapy and therapy-related lung injury remain poorly defined. This study aimed to identify a biomarker relevant to both lung cancer progression and lung injury, with emphasis on prognostic significance and immune infiltration. METHODS: RNA-sequencing data from Gene Expression Omnibus (GEO) (GSE166340, GSE27128) were used to identify lung injury-related genes (LIRGs), and The Cancer Genome Atlas (TCGA) data were analyzed to evaluate their expression in lung adenocarcinoma (LUAD). Single-cell RNA-sequencing data (PRJNA846488) and in vivo lung injury models were used to validate TMEM171 expression. Prognostic value was assessed using Kaplan-Meier analysis, univariate and multivariate Cox regression, and a least absolute shrinkage and selection operator (LASSO) prognostic model. Immune infiltration, immune checkpoint correlations, and pathway enrichment were analyzed using single-sample gene set enrichment analysis (ssGSEA). Functional effects of TMEM171 were examined by in vitro knockdown experiments. RESULTS: A total of 10 LIRGs were identified. TMEM171 was significantly upregulated in LUAD tissues and immunotherapy-induced lung injury models. High TMEM171 expression was an independent predictor of poor prognosis and was associated with TP53 mutations, apoptosis, and inflammatory response pathways. TMEM171 expression positively correlated with neutrophil infiltration and immune checkpoint expression. TMEM171 knockdown suppressed malignant behavior of LUAD cells and induced G1/S cell-cycle arrest in vitro. CONCLUSIONS: TMEM171 represents a potential biomarker linking LUAD and therapy-related lung injury and may serve as a candidate for future mechanistic investigation.
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Identification of a chemoresistance-related prognostic gene signature by comprehensive analysis and experimental validation in pancreatic cancer.
BACKGROUND: Chemoresistance is a major hurdle to improving the prognosis of pancreatic cancer (PC). This study aimed to identify key genes regulating chemoresistance and develop a chemoresistance-related gene signature for prognosis prediction. METHODS: A total of 30 PC cell lines were subtyped according to gemcitabine sensitivity data from the Cancer Therapeutics Response Portal (CTRP v2). Differentially expressed genes (DEGs) between gemcitabine-resistant and gemcitabine-sensitive cells were subsequently identified. These upregulated DEGs associated with prognostic values were incorporated to build a LASSO Cox risk model for The Cancer Genome Atlas (TCGA) cohort. Four datasets (GSE28735, GSE62452, GSE85916, and GSE102238) from the Gene Expression Omnibus (GEO) were used as an external validation cohort. Then, a nomogram was developed based on independent prognostic factors. The responses to multiple anti-PC chemotherapeutics were estimated by the "oncoPredict" method. Tumor mutation burden (TMB) was calculated using the "TCGAbiolinks" package. Analysis of the tumor microenvironment (TME) was performed using the "IOBR" package, while the TIDE and "easier" algorithms were employed to estimate immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducted to validate the expression and functions of ALDH3B1 and NCEH1. RESULTS: A five-gene signature and a predictive nomogram were developed from six prognostic DEGs, including EGFR, MSLN, ERAP2, ALDH3B1, and NCEH1. Bulk and single-cell RNA sequencing analyses indicated that all five genes were highly expressed in tumor samples. This gene signature was not only an independent prognostic factor but also a biomarker forecasting chemoresistance, TMB, and immune cells. In vitro experiments suggested that ALDH3B1 and NCEH1 were involved in PC progression and gemcitabine chemoresistance. CONCLUSION: This chemoresistance-related gene signature links prognosis with chemoresistance, TMB, and immune features. ALDH3B1 and NCEH1 are two promising targets for treating PC.
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Tissue and cellular spatiotemporal dynamics in colon aging.
Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.
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Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry.
Single-nucleus analysis allows robust cell-type classification and helps to establish relationships between chromatin accessibility and cell-type-specific gene expression. Here, using samples from 92 women of several genetic ancestries, we developed a comprehensive chromatin accessibility and gene expression atlas of the breast tissue. Integrated analysis revealed ten distinct cell types, including three major epithelial subtypes (luminal hormone sensing, luminal adaptive secretory precursor (LASP) and basal-myoepithelial), two endothelial and adipocyte subtypes, fibroblasts, T cells, and macrophages. In addition to the known cell identity genes FOXA1 (luminal hormone sensing), EHF and ELF5 (LASP), TP63 and KRT14 (basal-myoepithelial), epithelial subtypes displayed several uncharacterized markers and inferred gene regulatory networks. By integrating breast epithelial cell gene expression signatures with spatial transcriptomics, we identified gene expression and signaling differences between lobular and ductal epithelial cells and age-associated changes in signaling networks. LASP cells and fibroblasts showed genetic ancestry-dependent variability. An estrogen receptor-positive subpopulation of LASP cells with alveolar progenitor cell state was enriched in women of Indigenous American ancestry. Fibroblasts from breast tissues of women of African and European ancestry clustered differently, with accompanying gene expression differences. Collectively, these data provide a vital resource for further exploring genetic ancestry-dependent variability in healthy breast biology.