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Cell type and cell signaling innovations underlying mammalian pregnancy.
UNLABELLED: How fetal and maternal cell types have co-evolved to enable mammalian placentation poses a unique evolutionary puzzle. Here, we present a multi-species atlas integrating single-cell transcriptomes from six species bracketing therian mammal diversity. We find that invasive trophoblasts share a gene-expression signature across eutherians, and evidence that endocrine decidual cells evolved stepwise from an immunomodulatory cell type retained in Tenrec with affinity to human decidua of menstruation. We recover evolutionary patterns in ligand-receptor signaling: fetal and maternal cells show a pronounced tendency towards disambiguation, but a predicted arms race dynamic between them is limited. We reconstruct cell communication networks of extinct mammalian ancestors, finding strong integration of fetal trophoblast into maternal networks. Together, our results reveal a dynamic history of cell type and signaling evolution. SYNOPSIS: The fetal-maternal interface is one of the most intense loci of cell-cell signaling in the human body. Invasion of cells from the fetal placenta into the uterus, and the corresponding transformation of maternal tissues called decidualization, first evolved in the stem lineage of eutherian mammals( 1 , 2 ). Single-cell studies of the human fetal-maternal interface have provided new insight into the cell type diversity and cell-cell interactions governing this chimeric organ( 3-5 ). However, the fetal-maternal interface is also one of the most rapidly evolving, and hence most diverse, characters among mammals( 6 ), and an evolutionary analysis is missing. Here, we present and compare single-cell data from the fetal-maternal interface of species bracketing key events in mammal phylogeny: a marsupial (opossum, Monodelphis domestica ), the afrotherian Tenrec ecaudatus, and four Euarchontoglires - guinea pig and mouse (Rodentia) together with recent macaque and human data (primates) ( 4 , 5 , 7 ). We infer cell type homologies, identify a gene-expression signature of eutherian invasive trophoblast conserved over 99 million years, and discover a predecidual cell in the tenrec which suggests stepwise evolution of the decidual stromal cell. We reconstruct ancestral cell signaling networks, revealing the integration of fetal cell types into the interface. Finally, we test two long-standing theoretical predictions, the disambiguation hypothesis( 8 ) and escalation hypothesis( 9 ), at transcriptome-wide scale, finding divergence between fetal and maternal signaling repertoires but arms race dynamics restricted to a small subset of ligand-receptor pairs. In so doing, we trace the co-evolutionary history of cell types and their signaling across mammalian viviparity.
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Machine learning-based identification of an oxidative phosphorylation signature for prognosis, immune infiltration, and drug sensitivity in ovarian cancer.
BACKGROUND: Ovarian cancer (OC) is a highly heterogeneous disease, and its metabolic characteristics also exhibit heterogeneity. However, the specific metabolic pathways that play a critical role in OC metabolism remain unclear. Additionally, the significance of genes related to the metabolic pathways in the prognosis and therapeutic outcomes has not been clearly defined. METHODS: In this study, we utilized the Cancer Genome Atlas Program (TCGA), Genotype-Tissue Expression (GTEx), and multiple Gene Expression Omnibus (GEO) datasets to perform gene set enrichment analysis (GSEA) on 84 metabolic pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Through robust rank aggregation (RRA) analysis, we identified the most significantly altered metabolic pathways. By constructing the most robust machine learning model using genes related to the most significantly altered metabolic pathways and combining it with single-cell sequencing analysis results, kinesin family member 1A (KIF1A) was selected as the gene for subsequent biological level studies. RESULTS: We identified oxidative phosphorylation (OXPHOS) as one of the core metabolic pathways in OC. The OXPHOS-related gene signature (OPRGS) was built using the random survival forest (RSF) and supervised principal components (SuperPC) methods, which emerges as a comparatively reliable risk factor for OC. Patients with high-risk scores exhibited higher ESTIMATE stromal-related scores, a significant positive correlation with tumor-associated fibroblasts, higher tumor immune dysfunction and exclusion scores, and lower programmed cell death protein-1 (PD-1) and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) immunophenoscores in the TCGA cohort, suggesting an immunosuppressive tumor microenvironment (TME) based on bioinformatic predictions. Additionally, higher OPRGS was associated with lower cancer stemness indices, resistance to paclitaxel but sensitivity to carboplatin, revealing complex biological behaviors of the tumor. Further analysis showed that high OPRGS were also correlated with high scores in cancer-related hallmark signaling pathways, such as Notch, angiogenesis, and epithelial-mesenchymal transition signaling pathways. By integrating single-cell RNA sequencing data, we identified KIF1A as a key gene for further investigation. Our findings indicated that KIF1A was upregulated in OC cell lines and might promote cell proliferation, invasion, and migration. CONCLUSION: This study constructed a new OPRGS for OC. It may serve as a potential indicator for predicting prognosis, immune infiltration, and chemotherapy drug sensitivity in OC patients.
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Integrative Bulk and Single-Cell Profiling of Premanufacture T-cell Populations Reveals Factors Mediating Long-Term Persistence of CAR T-cell Therapy.
The adoptive transfer of chimeric antigen receptor (CAR) T cells represents a breakthrough in clinical oncology, yet both between- and within-patient differences in autologously derived T cells are a major contributor to therapy failure. To interrogate the molecular determinants of clinical CAR T-cell persistence, we extensively characterized the premanufacture T cells of 71 patients with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy. We performed RNA-sequencing analysis on sorted T-cell subsets from all 71 patients, followed by paired Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on T cells from six of these patients. We found that chronic IFN signaling regulated by IRF7 was associated with poor CAR T-cell persistence across T-cell subsets, and that the TCF7 regulon not only associates with the favorable naïve T-cell state, but is maintained in effector T cells among patients with long-term CAR T-cell persistence. These findings provide key insights into the underlying molecular determinants of clinical CAR T-cell function. SIGNIFICANCE: To improve clinical outcomes for CAR T-cell therapy, there is a need to understand the molecular determinants of CAR T-cell persistence. These data represent the largest clinically annotated molecular atlas in CAR T-cell therapy to date, and significantly advance our understanding of the mechanisms underlying therapeutic efficacy.This article is highlighted in the In This Issue feature, p. 2113.
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Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment.
BACKGROUND: Immunotherapy (IO) has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with IO naïve and IO exposed primary ccRCC tumors to better understand IO resistance. Spatial molecular imaging (SMI) was obtained for tumor and adjacent stroma samples. Spatial gene set enrichment analysis (GSEA) and autocorrelation (coupling with high expression) of ligand-receptor transcript pairs were assessed. Multiplex immunofluorescence (mIF) validation was used for significant autocorrelative findings and the cancer genome atlas (TCGA) and the clinical proteomic tumor analysis consortium (CPTAC) databases were queried to assess bulk RNA expression and proteomic correlates. RESULTS: 21 patient samples underwent SMI. Viable tumors following IO harbored more stromal CD8+ T cells and neutrophils than IO naïve tumors. YES1 was significantly upregulated in IO exposed tumor cells. The epithelial-mesenchymal transition pathway was enriched on spatial GSEA and the associated transcript pair COL4A1-ITGAV had significantly higher autocorrelation in the stroma. Fibroblasts, tumor cells, and endothelium had the relative highest expression. More integrin αV+ cells were seen in IO exposed stroma on mIF validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. In CPTAC, collagen IV protein was more abundant in advanced stages of disease. CONCLUSIONS: On spatial transcriptomics, COL4A1 and ITGAV were more autocorrelated in IO-exposed stroma compared to IO-naïve tumors, with high expression amongst fibroblasts, tumor cells, and endothelium. Integrin represents a potential therapeutic target in IO treated ccRCC.
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PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer.
BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources. METHODS: Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms. RESULTS: A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery. CONCLUSIONS: As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.
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Tumor gene signatures that correlate with release of extracellular vesicles shape the immune landscape in head and neck squamous cell carcinoma.
Head and neck squamous cell carcinomas (HNSCCs) evade immune responses through multiple resistance mechanisms. Extracellular vesicles (EVs) released by the tumor and interacting with immune cells induce immune dysfunction and contribute to tumor progression. This study evaluates the clinical relevance and impact on anti-tumor immune responses of gene signatures expressed in HNSCC and associated with EV production/release. Expression levels of two recently described gene sets were determined in The Cancer Genome Atlas Head and Neck Cancer cohort (n = 522) and validated in the GSE65858 dataset (n = 250) as well as a recently published single-cell RNA sequencing dataset (n = 18). Clustering into HPV(+) and HPV(-) patients was performed in all cohorts for further analysis. Potential associations between gene expression levels, immune cell infiltration, and patient overall survival were analyzed using GEPIA2, TISIDB, TIMER, and the UCSC Xena browser. Compared to normal control tissues, vesiculation-related genes were upregulated in HNSCC cells. Elevated gene expression levels positively correlated (P < 0.01) with increased abundance of CD4(+) T cells, macrophages, neutrophils, and dendritic cells infiltrating tumor tissues but were negatively associated (P < 0.01) with the presence of B cells and CD8(+) T cells in the tumor. Expression levels of immunosuppressive factors NT5E and TGFB1 correlated with the vesiculation-related genes and might explain the alterations of the anti-tumor immune response. Enhanced expression levels of vesiculation-related genes in tumor tissues associates with the immunosuppressive tumor milieu and the reduced infiltration of B cells and CD8(+) T cells into the tumor.
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Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis.
BACKGROUND: Breast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear. METHOD: This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells. RESULTS: One hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan-Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels. CONCLUSION: ARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.
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Integrative genome-wide analysis unveils the genetic landscape of gallstone disease and highlights novel loci with therapeutic potential.
OBJECTIVE: Gallstone disease (GD) is a common gastrointestinal disorder with a significant genetic component. Despite known risk factors, the genetic basis of GD remains incompletely understood. We aimed to identify novel genetic loci associated with GD, explore their clinical implications and investigate their therapeutic potential. METHODS: We conducted a genome-wide association study from the UK Biobank followed by a meta-analysis, integrating summary statistics from the FinnGen R11, with further replication from Biobank Japan. Using systematic bioinformatic approaches, we performed gene prioritisation, colocalisation analysis, transcriptome-wide association study, Mendelian randomisations, cross-trait genetic correlations, phenome-wide association study, clinical investigations and gene-environment interactions by leveraging data from the FinnGen, Genotype-Tissue Expression project and Liver Cell Atlas single-cell transcriptomics data set. RESULTS: Our study highlighted novel susceptibility loci near candidate genes (ie, UGT1A4, FADS1/3) associated with GD, expanding the known genetic landscape. Functional annotation and colocalisation analysis implicated that the independent variants are involved in various hepatocyte functions, including bile secretion, cellular glucuronidation and cholesterol gallstone pathway. Mendelian randomisation established causal relationships between the level of unsaturated fatty acids and GD risk. We also demonstrated the implications of indirect bilirubin level in GD risk stratification and the protective effect of oily fish intake in genetically susceptible individuals. CONCLUSIONS: This study provides new insights into the genetic basis of GD and highlights the role of hepatocytes in GD pathogenesis. These findings have implications for the personalised prevention strategies and new therapeutic interventions in individuals predisposed to GD.
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Pan-Cancer Analysis of a Depression-Related Gene Signature Identifies an Immunosuppressive Tumor Microenvironment and Links to Immunotherapy Response in Breast Cancer.
BACKGROUND: Depression has been increasingly associated with cancer progression and patient outcomes, yet its molecular and immunological correlates within tumors remain incompletely understood. This study aimed to investigate depression-associated gene signature across cancers and evaluate their relationship with tumor biology, immune microenvironment, and clinical outcomes, with a focus on breast cancer. METHODS: A depression-related gene signature comprising 382 genes was obtained from the Molecular Signatures Database and used to compute a DScore via single-sample gene set enrichment analysis (ssGSEA) across over 10,000 tumor samples from 33 cancer types in The Cancer Genome Atlas (TCGA). Tumor microenvironment features were assessed using ESTIMATE and CIBERSORT, and cell-type-specific patterns were evaluated using single-cell RNA sequencing. CACNA1H was validated using qPCR and IHC in breast cancer samples. RESULTS: DScore exhibited tissue-specific patterns across cancers and was elevated in several tumor types, including gastrointestinal, brain, renal, and endocrine malignancies. Higher DScore was associated with older age, advanced stage, and poorer survival across multiple malignancies, including breast, ovarian, uterine, and lung cancers, whereas inverse associations with survival were observed in selected brain, kidney, and endocrine cancers. Higher DScore was further associated with genomic instability, an immunosuppressive tumor microenvironment (TME), and poorer survival across multiple cancers, particularly in breast and ovarian cancers. In breast cancer, this phenotype was characterized by monocytic-lineage enrichment, increased M2 macrophages, and reduced CD8⁺ T-cell infiltration. Consistent with these findings, higher DScore was associated with reduced benefit from immune checkpoint therapy in breast and bladder cancers, with non-responders exhibiting higher DScore predominantly enriched in monocytic-lineage cells. CACNA1H was identified as a depression-related gene correlated with poor prognosis in breast cancer and was validated in independent cohorts. CONCLUSION: Depression-associated gene expression patterns are linked to tumor biology and immune features across cancers, with particularly strong relevance in breast cancer. These findings highlight the need for further research into how psychological, molecular, and immune factors interact in cancer progression and treatment response.
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UPR-CEBPB-MIF Signaling Links to Macrophage Polarization and an Immunosuppressive Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits marked metabolic reprogramming, and the unfolded protein response (UPR) plays a crucial role in stress adaptation and immune regulation; however, its underlying mechanisms remain unclear. METHODS: Multi-omics data from The Cancer Genome Atlas (TCGA) program, Gene Expression Omnibus (GEO), and ArrayExpress were integrated, and UPR key genes were identified using an autoencoder combined with XGBoost. ConsensusClusterPlus and Weighted Gene Co-expression Network Analysis (WGCNA) were applied to determine the intersection genes of UPR-WGCNA-macrophage (UWMG). A prognostic model was then constructed using least absolute shrinkage and selection operator-Cox regression and validated across multiple cohorts. Single-cell transcriptome analysis was used to map UPR activity, trace myeloid differentiation paths, and examine CellChat-based communication patterns, aiming to clarify how UPR signaling connects to immune polarization. RESULTS: In the metabolic model, the UPR pathway showed the strongest contribution, and its activity was mainly observed in macrophages. From 17 UWMGs, a five-gene prognostic model (CEBPB, PLAUR, ARHGAP24, SNHG8, and ZBTB16) was developed and performed consistently across multiple independent cohorts. The high-risk group exhibited concurrent features of immune activation and suppression. Communication analysis revealed that tumor cells with high CEBPB expression exhibited enhanced MIF-CXCR4 signaling and were associated with macrophage M2-like polarization and an immunosuppressive microenvironment. CONCLUSIONS: The UPR-CEBPB-MIF signaling axis is associated with macrophage polarization and an immunosuppressive immune microenvironment in ccRCC. The proposed multi-omics model demonstrates stable prognostic value and provides potential targets for optimizing immunotherapy.