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A spatial transcriptomic atlas of acute neonatal lung injury across development and disease severity.
A molecular understanding of lung organogenesis requires delineation of the timing and regulation of the cellular transitions that ultimately form and support a surface capable of gas exchange. Although the advent of single-cell transcriptomics has allowed for the discovery and identification of transcriptionally distinct cell populations present during lung development, the spatiotemporal dynamics of these transcriptional shifts remain undefined. With imaging-based spatial transcriptomics, we analyzed the gene expression patterns in 17 human infant lungs at varying stages of development and injury, creating a spatial transcriptomic atlas of approximately 1.2 million cells. We applied computational clustering approaches to identify shared molecular patterns among this cohort, informing how tissue architecture and molecular spatial relationships are coordinated during development and disrupted in disease. Recognizing that all preterm birth represents an injury to the developing lung, we created a simplified classification scheme that relies upon the routinely collected objective measures of gestational age and lifespan. Within this framework, we have identified cell type patterns across gestational age and life span variables that would likely be overlooked when using the conventional "disease versus control" binary comparison. Together, these data represent an open resource for the lung research community, supporting discovery-based inquiry and identification of targetable molecular mechanisms in both normal and arrested human lung development.NEW & NOTEWORTHY Mapping the spatial and temporal transcriptional relationships during lung development is fundamental to understanding regeneration and chronic lung disease; however, the classification of samples as control or disease is especially challenging in the setting of preterm birth (itself a lung injury). Here, we report the largest neonatal lung transcriptomic atlas to date and an analysis framework based only on gestational age and lifespan, providing a new resource for hypothesis generation to the lung community.
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Single-cell multi-omics analysis of human testicular germ cell tumor reveals its molecular features and microenvironment.
Seminoma is the most common malignant solid tumor in 14 to 44 year-old men. However, its molecular features and tumor microenvironment (TME) is largely unexplored. Here, we perform a series of studies via genomics profiling (single cell multi-omics and spatial transcriptomics) and functional examination using seminoma samples and a seminoma cell line. We identify key gene expression programs share between seminoma and primordial germ cells, and further characterize the functions of TFAP2C in promoting tumor invasion and migration. We also identify 15 immune cell subtypes in TME, and find that subtypes with exhaustion features were located closer to the tumor region through combined spatial transcriptome analysis. Furthermore, we identify key pathways and genes that may facilitate seminoma disseminating beyond the seminiferous tubules. These findings advance our knowledge of seminoma tumorigenesis and produce a multi-omics atlas of in situ human seminoma microenvironment, which could help discover potential therapy targets for seminoma.
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Multi-omics nominates VDAC2 as a candidate protective locus in sepsis-associated cholesterol dysregulation.
Sepsis, a life-threatening condition, involves dysregulated cholesterol metabolism critical for immune regulation and cellular processes. This study employed multi-omics and machine learning to explore cholesterol metabolism in sepsis, aiming to identify novel therapeutic targets. Transcriptome and single-cell RNA sequencing data for sepsis were retrieved from the Gene Expression Omnibus (GEO) database. The limma package and WGCNA co-expression network were used to screen genes, hybridized with cholesterol metabolism genes (CMGs) to identify hub genes. Machine learning algorithms screened pivotal genes to construct diagnostic model, validating performance via multi-cohort Receiver Operating Characteristic (ROC) curve. Non-negative matrix factorization (NMF) based molecular typing using CMGs, and integration of 101 machine learning algorithms built prognostic models. Single-cell analysis characterized expression patterns of pivotal genes and key subsets. Causal effects and phenotypic associations of target genes were evaluated using Summary data-based Mendelian Randomization (SMR) and PheWAS. Integrated transcriptomic analysis identified three key genes (VDAC1, VDAC2, and LDLRAP1) associated with dysregulated cholesterol metabolism in sepsis. Machine learning-based diagnostic models exhibited high predictive accuracy. NMF clustering revealed two molecular subtypes, with Cluster 1 characterized by immunosuppression and metabolic reprogramming, linked to poorer prognosis. A machine learning model integrating 101 algorithms predicted 28-day mortality. The single-cell transcriptome atlas identified CD14+CD163+ monocytes as the hub cell population in the immune microenvironment of sepsis, and the active cholesterol metabolic pathway might constitute the core for regulating the immune response. Elevated VDAC2 expression was significantly correlated with reduced sepsis risk, as determined by SMR analysis. This study underscored cholesterol metabolism's critical role in sepsis pathogenesis. Multi-omics nominates VDAC2 as a candidate protective locus in sepsis-associated cholesterol dysregulation.
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ACSS2 in pan-cancer context: multi-omics insights into metabolic reprogramming and immunotherapy response.
ACSS2, a key member of the acyl-CoA synthetase short-chain family, plays a critical role in tumor metabolic reprogramming by regulating fatty acid synthesis, histone acetylation, and lactyl-CoA production. However, its immunomodulatory functions and prognostic significance across cancers remain poorly characterized. Using multiple databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and cBioPortal, we comprehensively analyzed ACSS2's pan-cancer associations with clinical survival, genomic profiles, tumor stemness, functional enrichment, single-cell heterogeneity, immune infiltration, immune checkpoints, immunotherapy responsiveness, and drug sensitivity. Our analyses revealed pronounced ACSS2 dysregulation across tumors, with elevated expression correlated with poor prognosis in multiple cancers. Genomic profiling identified copy number amplifications as the predominant ACSS2 alteration, with missense and truncating mutations constituting key functional impact variants. ACSS2 expression was negatively correlated with DNA methylation levels. Notably, ACSS2 expression showed significant associations with tumor genomic features, stemness indices, immune cell infiltration, immune regulators. These findings underscore its potential as an immune checkpoint-predictive biomarker. Single-cell resolution analysis further delineated cancer type-specific correlations between ACSS2 and immune cell spatial distribution. Crucially, ACSS2 expression demonstrated predictive value for anti-PD-1/PD-L1 therapy responsiveness, highlighting its clinical relevance in immunotherapy stratification. This study elucidates the pivotal role of ACSS2 in carcinogenesis and tumor progression through metabolism-immune interaction within the tumor microenvironment (TME). Our findings not only provide a theoretical foundation for establishing a prognostic evaluation system based on metabolic features but also highlight the potential value of ACSS2 as a pan-cancer prognostic biomarker and combination therapy target.
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Clinical and immunological implications of lymphocyte-activation gene 3 expression in metastatic colorectal cancer.
BACKGROUND: Lymphocyte-activation gene 3 (LAG3), an inhibitory immune checkpoint receptor, has emerged as a potential therapeutic target in various cancer, including colorectal cancer (CRC). However, its prognostic role and immunologic context in metastatic CRC (mCRC) remain unclear. This study investigated the prognostic impact of LAG3 expression and its biological role within the mCRC immune microenvironment. METHODS: LAG3 expression was evaluated by immunohistochemistry in primary and/or metastatic tumors from a retrospective mCRC cohort. Associations between LAG3 expression, clinicopathological features, and overall survival were analyzed and validated using data from The Cancer Genome Atlas (TCGA) COADREAD cohort. Transcriptomic data from TCGA and single-cell RNA sequencing data from 23 Korean CRC samples were used to explore pathway enrichment and cell-cell interactions. RESULTS: A total of 144 mCRC patients were included. Primary tumors showed higher LAG3 expression than metastatic tumors, with right-sided and liver metastases exhibiting the highest level. Both univariate and multivariate analyses revealed that LAG3 expression was an independent prognostic biomarker of mCRC, validated in the TCGA COADREAD cohort. Transcriptomic analysis revealed significant correlations between LAG3 expression and angiogenesis, epithelial-mesenchymal transition, and TGF-β signaling pathways, which were linked to immunosuppressive microenvironment and poor prognosis. Cell-cell communication analysis showed CD8⁺ T cells interact with various cell subpopulations, with Galectin-9-TIM-3 signaling uniquely present in LAG3⁺CD8⁺ T cells. CONCLUSIONS: LAG3 expression is an independent prognostic biomarker of mCRC. The distinct Galectin-9-TIM-3 signaling in LAG3⁺CD8⁺ T cells may contribute to limited therapeutic efficacy of LAG3 blockade, suggesting potential combinational immunotherapy strategies.
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Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model.
BACKGROUND: Apoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes. METHODS: Messenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan-Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model's predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM. RESULTS: Several apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model's score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival. CONCLUSION: Ourestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.
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Spatially distinct epithelial and mesenchymal cell subsets along progressive lineage restriction in the branching embryonic mammary gland.
How cells coordinate morphogenetic cues and fate specification during development remains a fundamental question in organogenesis. The mammary gland arises from multipotent stem cells (MaSCs), which are progressively replaced by unipotent progenitors by birth. However, the lack of specific markers for early fate specification has prevented the delineation of the features and spatial localization of MaSC-derived lineage-committed progenitors. Here, using single-cell RNA sequencing from E13.5 to birth, we produced an atlas of matched mouse mammary epithelium and mesenchyme and reconstructed the differentiation trajectories of MaSCs toward basal and luminal fate. We show that murine MaSCs exhibit lineage commitment just prior to the first sprouting events of mammary branching morphogenesis at E15.5. We identify early molecular markers for committed and multipotent MaSCs and define their spatial distribution within the developing tissue. Furthermore, we show that the mammary embryonic mesenchyme is composed of two spatially restricted cell populations, and that dermal mesenchyme-produced FGF10 is essential for embryonic mammary branching morphogenesis. Altogether, our data elucidate the spatiotemporal signals underlying lineage specification of multipotent MaSCs, and uncover the signals from mesenchymal cells that guide mammary branching morphogenesis.
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Disulfidptosis-related genes of prognostic signature and immune infiltration features in hepatocellular carcinoma supported by bulk and single-cell RNA sequencing data.
BACKGROUND: Disulfidptosis is a new type of cellular death triggered in response to disulfide stress and is strongly linked to the progression of malignancies. Hepatocellular carcinoma (HCC) is a very common malignancy. Some reports have suggested a link between disulfidptosis-related genes (DRGs) and cancer; however, further research needs to be conducted. METHODS: In this study, HCC data from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma and Gene Expression Omnibus data sets were collected and analyzed. A univariate Cox regression analysis, least absolute shrinkage and selection operator, and multivariate Cox regression analysis were conducted to identify the hub DRGs signature for prognosis. The HCC patients were allocated to high- and low-risk groups based on their disulfidptosis risk scores. The model was validated with a high degree of precision using both internal and external validation data sets. "ESTIMATE" and "CIBERSORT" packages were employed to assess the immunological landscapes and immune cell infiltration. The IMvigor210 cohort was chosen to validate the immunotherapy results. A drug sensitivity analysis was conducted to identify targeted medications. The expression of the hub DRGs in the HCC cells was confirmed using cytological techniques. RESULTS: The bioinformatic analysis revealed that 16 genes showed differential expression. A prognostic model was developed based on four genes: RPN1, SLC2A1, SLC2A4, and SLC7A11. A notable difference in prognosis was observed between the two risk groups. Based on the results of the immune microenvironment, tumor mutation burden, immunotherapy, and drug screening analyses, the DRGs signature can be employed in HCC immunotherapy decision making. Further, the expression levels of the hub DRGs were significantly upregulated in the HCC cells. CONCLUSIONS: Our four-DRGs signature could be used to predict HCC prognosis. Further, this study showed that the hub DRGs could serve as biomarkers for immunotherapy prediction and could potentially guide targeted therapies.
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LRMP Associates With Immune Infiltrates and Acts as a Prognostic Biomarker in Lung Adenocarcinoma.
Background: Lymphoid-restricted membrane protein (LRMP) is an endoplasmic reticulum-associated protein that is expressed in a developmentally regulated manner in both B and T cell lineages. However, the role of LRMP in the growth, prognosis and immune infiltration in lung adenocarcinoma (LUAD) remains unclear. Method: The expression levels of LRMP mRNA in tumor and normal tissues were analyzed using Tumor Immune Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). LRMP protein expression was examined using the Human Protein Atlas. In vitro experiments, including qRT-PCR Western blot and immunohistochemistry staining were also performed to investigate LRMP expression. GEPIA2 and Kaplan-Meier plotter databases were used to analyze the clinical prognostic significance of LRMP. To further confirm the underlying function of LRMP, the data were analyzed using gene set enrichment analysis. Moreover, we also constructed plasmids to overexpress LRMP and explored the effect of LRMP in A549 cell line. Additionally, Tumor Immune single-cell Hub was used to investigate the distribution of LRMP in the LUAD immune microenvironment; TIMER and CIBERSORT were used to investigate the relationships among LRMP, LRMP co-expressed genes, and tumor-infiltrating immune cells; Finally, the correlations between LRMP and immune checkpoints were analyzed using TIMER 2.0. Results: The expression of LRMP was significantly lower in LUAD tissues and cell lines. High LRMP expression is associated with a better prognosis in patients with LUAD. In vitro experimental studies demonstrated that overexpression of LRMP could decrease the proliferation, migration and invasion in A549 cells, and downregulated multiple oncogenic signaling pathways, including p-STAT3, p-PI3K-p-AKT, p-MEK and EMT pathways. GSEA results showed that immuno-related and cell adhesion pathways were enriched in samples with high LRMP expression. LRMP and its co-expressed genes were positively correlated with various tumor-infiltrating immune cells and their markers. Additionally, LRMP positively correlated with immune checkpoints. Conclusions: Our data suggest that LRMP may act as a tumor suppressor gene and indicates a better prognosis. Moreover, LRMP is associated with immune infiltrates which may be involved in immunotherapy response in LUAD. Further studies are needed to validate these findings.
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S100A14 as a Potential Biomarker of the Colorectal Serrated Neoplasia Pathway.
Accounting for 15-30% of colorectal cancer cases, the serrated pathway remains poorly characterized compared to the adenoma-carcinoma sequence. It involves sessile serrated lesions as precursors and is characterized by BRAF mutations (BRAFV600E), CpG island hypermethylation, and microsatellite instability (MSI). Using label-free proteomics, we compared normal tissue margins from patients with diverticular disease, sessile serrated lesions, low-grade adenomas, and high-grade adenomas. We identified S100A14 as significantly overexpressed in sessile serrated lesions compared to low-grade adenomas, high-grade adenomas, and normal tissues. This overexpression was confirmed by immunohistochemical scoring in an independent cohort. Gene expression analyses of public datasets showed higher S100A14 expression in BRAFV600E-mutated and MSI-H colorectal cancers compared to microsatellite stable BRAFwt tumors. This finding was confirmed by immunohistochemical scoring in an independent colorectal cancer cohort. Furthermore, single-cell RNA sequencing analysis from the Human Colon Cancer Atlas revealed that S100A14 expression in tumor cells positively correlated with the abundance of tumoral CD8+ cytotoxic T cells, particularly the CD8+ CXCL13+ subset, known for its association with a favorable response to immunotherapy. Collectively, our results demonstrate for the first time that S100A14 is a potential biomarker of serrated neoplasia and further suggests its potential role in predicting immunotherapy responses in colorectal cancer.