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Construction of a ferroptosis-related prognostic signature and identification of SRSF9 as a key biomarker in lung adenocarcinoma.
BACKGROUND: Ferroptosis is a distinct form of regulated cell death characterized by iron-dependent lipid peroxidation, which plays crucial roles in tumor biology and therapeutic response. However, the prognostic significance of ferroptosis-related genes (FRGs) in lung adenocarcinoma (LUAD) remains incompletely understood. This study aimed to construct and validate a ferroptosis-related prognostic model for LUAD by integrating single-cell and bulk transcriptomic data. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE127465 were integrated with bulk transcriptomic data from The Cancer Genome Atlas lung adenocarcinoma cohort (TCGA-LUAD). FRGs were identified through single-cell gene set scoring with the AUCell algorithm and differential expression analysis. A least absolute shrinkage and selection operator (LASSO)-Cox regression model was constructed and externally validated using the GSE72094 and GSE68465 cohorts. Associations between the model, genomic features, and the tumor immune microenvironment were systematically analyzed. The functional role of SRSF9 was verified through in vitro experiments. RESULTS: A total of 93 candidate genes significantly associated with ferroptosis activity were identified, and an 8-gene prognostic signature (PEBP1, TXN, TIMP1, SRSF9, CFL1, OTUB1, FURIN, and PTPN6) was constructed. The signature demonstrated robust prognostic performance across validation cohorts, with time-dependent area under the curve (AUC) values ranging from 0.65 to 0.74. High-risk patients exhibited elevated tumor mutation burden (TMB) and distinct immune infiltration patterns. Consensus clustering analysis classified patients into two ferroptosis-related molecular subtypes with significantly different prognoses. Functional experiments demonstrated that SRSF9 knockdown inhibited LUAD cell proliferation, migration, and invasion. Moreover, SRSF9 knockdown downregulated glutathione peroxidase 4 (GPX4) and SLC7A11, increased lipid peroxidation and malondialdehyde (MDA) levels, and the resulting cell viability loss was rescued by ferrostatin-1, indicating that SRSF9 knockdown induces ferroptosis in LUAD cells. CONCLUSIONS: The ferroptosis-related prognostic signature established in this study may facilitate risk stratification in LUAD patients. SRSF9 may regulate ferroptosis-related pathways through modulation of GPX4 and SLC7A11, and functional experiments further confirmed that SRSF9 knockdown induces ferroptosis in LUAD cells, highlighting SRSF9 as a candidate therapeutic target that warrants further investigation.
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Single-Cell View of Tumor Microenvironment Gradients in Pleural Mesothelioma.
Immunotherapies have shown great promise in pleural mesothelioma (PM), yet most patients still do not achieve significant clinical response, highlighting the importance of improving the understanding of the tumor microenvironment (TME). Here, we utilized high-throughput, single-cell RNA sequencing (scRNA-seq) to de novo identify 54 expression programs and construct a comprehensive cellular catalog of the PM TME. We found four cancer-intrinsic programs associated with poor disease outcome and a novel fetal-like, endothelial cell population that likely responds to VEGF signaling and promotes angiogenesis. Across cellular compartments, we observe substantial difference in the TME associated with a cancer-intrinsic sarcomatoid signature, including enrichment in fetal-like endothelial cells, CXCL9+ macrophages, and cytotoxic, exhausted, and regulatory T cells, which we validated using imaging and bulk deconvolution analyses on independent cohorts. Finally, we show, both computationally and experimentally, that NKG2A:HLA-E interaction between NK and tumor cells represents an important new therapeutic axis in PM, especially for epithelioid cases. Significance: This manuscript presents the first single-cell RNA sequencing atlas of PM tumor microenvironment. Findings of translational relevance, validated experimentally and using independent bulk cohorts, include identification of gene programs predictive of survival, a fetal-like endothelial cell population, and NKG2A blockade as a promising new immunotherapeutic intervention in PM.
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Multi-omics analysis and experiments validate the tumor-suppressive role of mitochondrial lipid metabolism gene ACSM5 in lung adenocarcinoma and its impact on the immune microenvironment.
BACKGROUND: Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer, characterized by high incidence and poor prognosis. Mitochondrial lipid metabolism (MTLM) contributes to tumor proliferation and drug resistance by regulating lipid breakdown and energy supply and is strongly linked to prognosis in multiple cancers. Nevertheless, its impact on LUAD has yet to be clarified. METHODS: MTLM-associated genes were extracted from the MitoCarta 3.0 database and relevant publications. A four-gene prognostic model was formulated using Cox regression analysis, with individual gene contributions clarified through Shapley Additive Explanations (SHAP) analysis. The model was verified using The Cancer Genome Atlas and Gene Expression Omnibus datasets, and a nomogram incorporating clinical features was developed. Immunomicroenvironment characteristics and drug sensitivity were evaluated, leading to the identification of potential therapeutic agents. A competing endogenous RNAs (ceRNA) and transcription factor (TF) regulatory network was constructed for the core genes. Drug components targeting these core genes were identified through reverse pharmacology and molecular docking. Single-cell RNA sequencing (scRNA-seq) was carried out to analyze the expression of core genes across cellular subpopulations. Finally, the core gene ACSM5 was investigated for its prognostic value and immune regulatory role in LUAD, with its function further validated through clonogenic assays and Transwell experiments. RESULTS: A MTLM-related prognostic model incorporating ACSL4, ACSS3, CHPT1, and ACSM5 was established, with ACSM5 contributing most significantly to the model’s prediction. Patients in the high-risk group had notably shorter survival than those in the low-risk group, and the risk score was identified as an independent prognostic factor. The nomogram, combining clinical features and risk scores, showed an AUC value of 0.712. Notable differences were detected between distinct risk groups in terms of immune microenvironment and drug sensitivity. The ceRNA network and TF regulatory network revealed upstream and downstream targets of the core genes. Reverse pharmacological analysis identified drugs and components affecting the CYP27A1 gene. Molecular docking results showed strong binding between CHPT1 and 3-methylcholanthrene and aztreonam. ScRNA-seq revealed the expression patterns of core genes at the single-cell level. High ACSM5 expression in LUAD was positively linked to prognosis, and ACSM5 was determined to be an independent prognostic factor for LUAD. Experimental results demonstrated that ACSM5 inhibits LUAD cell proliferation, invasion, and migration. CONCLUSION: The MTLM-based four-gene prognostic model developed in this study effectively predicts LUAD patient outcomes, with ACSM5 serving as the core prognostic gene. This model provides new theoretical support for personalized treatment and precise prognosis assessment in LUAD.
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A single cell atlas of the mouse seminal vesicle.
During mammalian reproduction, sperm are delivered to the female reproductive tract bathed in a complex medium known as seminal fluid, which plays key roles in signaling to the female reproductive tract and in nourishing sperm for their onwards journey. Along with minor contributions from the prostate and the epididymis, the majority of seminal fluid is produced by a somewhat understudied organ known as the seminal vesicle. Here, we report the first single-cell RNA-seq atlas of the mouse seminal vesicle, generated using tissues obtained from 23 mice of varying ages, exposed to a range of dietary challenges. We define the transcriptome of the secretory cells in this tissue, identifying a relatively homogeneous population of the epithelial cells which are responsible for producing the majority of seminal fluid. We also define the immune cell populations - including large populations of macrophages, dendritic cells, T cells, and NKT cells - which have the potential to play roles in producing various immune mediators present in seminal plasma. Together, our data provide a resource for understanding the composition of an understudied reproductive tissue with potential implications for paternal control of offspring development and metabolism.
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Prognosis and therapeutic benefits prediction based on NK cell marker genes through single-cell RNA-seq with integrated bulk RNA-seq analysis for hepatocellular carcinoma.
Tumor-infiltrating immune cells greatly participate in regulating tumorigenesis and metastasis of hepatocellular carcinoma (HCC). Natural killer cell, as an important role of innate immunity, plays an indispensable role in antitumor immunity and regulate tumor development. In this study, we firstly identified 251 NK cell marker genes of HCC based on single-cell RNA sequencing data. Subsequently, an NK cell marker genes-related prognostic signature (NKPS) was developed in the cancer genome atlas (TCGA) cohort for risk stratification and prognosis prediction. The predictive value of the NKPS in prognosis was well validated in different clinical subgroups and three external datasets (ICGC-LIHC cohort, GSE14520 cohort and Guilin cohort). Moreover, multivariate analysis revealed the independent prognostic value of NKPS for OS in HCC. Further functional analysis indicated the NKPS was associated with basic cellular processes, that may contribute to the development and progression of HCC. Thereafter, immune characteristics as well as the therapeutic benefits in NKPS risk score-defined subgroups were analyzed. Patients with low-risk score exhibited immune-active status, manifested as higher immune scores, more infiltration of CD8+ T cells and macrophage M1, and higher T-cell receptor (TCR) richness and diversity. Remarkably, the NKPS was negatively correlated with immunotherapy response-related signatures. In addition, the low-risk group exhibited significantly improved therapeutic benefits, either from immunotherapy or traditional chemotherapy and target therapy. Overall, the NKPS showed an excellent predictive value for prognosis and therapeutic responses for HCC, which might also provide novel insights into better HCC management strategies.
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Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype.
Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with 'normal' stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.
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Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics.
Flatworms of the species Schmidtea mediterranea are immortal-adult animals contain a large pool of pluripotent stem cells that continuously differentiate into all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. By combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from transcriptional changes, and a lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterized gene expression changes during differentiation and discovered cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution.
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Prognostic significance of key immune cell functional alterations in clear cell renal cell carcinoma.
BACKGROUND: While immune cells are pivotal in clear cell renal cell carcinoma (ccRCC), functional alterations of specific subsets and their prognostic implications remain unclear. We aimed to identify key immune cells, characterize their functional states, and develop a prognostic model by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data. METHODS: scRNA-seq dataset GSE210038 was analyzed to annotate tumor-infiltrating immune cells. Key immune cells were identified from deconvolved The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) and Gene Expression Omnibus Series (GSE)105261 datasets using support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), and random forest (RF) algorithms. Immune Response Enrichment Analysis (IREA) delineated polarization states of key immune cells in response to cytokines. Cellular communication was profiled via CellChat. Prognostic genes associated with key immune cells were screened by univariate Cox and LASSO regression. A risk score (RS) model was constructed and validated in TCGA-KIRC (training, n=511) and E-MTAB-1980 (validation, n=101) cohorts. Receiver operating characteristic (ROC) curves evaluated overall survival (OS) prediction efficacy. Nomograms and drug sensitivity analyses were performed. RESULTS: Seven immune cell types infiltrated ccRCC. CD8+ T and natural killer (NK) cells were identified as the key immune cells. IREA revealed T8-c polarization in CD8+ T cells [enriched for interferon (IFN)-α1/interleukin (IL)-36α] and NK-f polarization in NK cells (enriched for IL-18/IL-2). CellChat analysis showed amplified protease activated receptors (PARs) signaling from key immune cells to cancer-associated fibroblasts, but attenuated Annexin A1 (ANXA1)-formyl peptide receptor 1 (FPR1) signaling to macrophages. Ten immune-cell-associated prognostic genes (PTTG1, CLEC2D, PLIN2, LRBA, ABCB1, MIR155HG, ADAM8, P2RY8, SORL1, CD82) were used to build the RS model. The model stratified patients into high/low-risk groups with distinct OS (log-rank P<0.001). The area under the curves (AUCs) for 1-/3-/5-year OS were 0.751/0.734/0.747 (training) and 0.742/0.740/0.778 (validation). The nomogram [incorporating RS and metastatic stage (M stage)] showed robust calibration. Drug sensitivity analysis revealed low-risk patients responded better to tyrosine kinase inhibitors, while high-risk patients were sensitive to a B-cell lymphoma-2 inhibitor. CONCLUSIONS: CD8+ T and NK cells exhibit functional polarization and altered cellular communication indicative of augmented anti-tumor immunity in ccRCC. The immune-cell-derived 10-gene signature provides a reliable prognostic tool and guides personalized therapy.
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MIF as a potential diagnostic and prognostic biomarker for triple-negative breast cancer that correlates with the polarization of M2 macrophages.
Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays a crucial role in antitumor immunity. However, the role of MIF in influencing the tumor microenvironment (TME) and prognosis of triple-negative breast cancer (TNBC) remains to be elucidated. Using R, we analyzed single-cell RNA sequencing (scRNA-seq) data of 41 567 cells from 10 TNBC tumor samples and spatial transcriptomic data from two patients. Relationships between MIF expression and immune cell infiltration, clinicopathological stage, and survival prognosis were determined using samples from The Cancer Genome Atlas (TCGA) and validated in a clinical cohort using immunohistochemistry. Analysis of scRNA-seq data revealed that MIF secreted by epithelial cells in TNBC patients could regulate the polarization of macrophages into the M2 phenotype, which plays a key role in modulating the TME. Spatial transcriptomic data also showed that epithelial cells (tumor cells) and MIF were proximally located. Analysis of TCGA samples confirmed that tumor tissues of patients with high MIF expression were enriched with M2 macrophages and showed a higher T stage. High MIF expression was significantly associated with poor patient prognosis. Immunohistochemical staining showed high MIF expression was associated with younger patients and worse clinicopathological staging. MIF secreted by epithelial cells may represent a potential biomarker for the diagnosis and prognosis of TNBC and may promote TNBC invasion by remodeling the tumor immune microenvironment.
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MIF-mediated intercellular crosstalk drives antigen-presenting-like cancer-associated fibroblast differentiation in triple-negative breast cancer via TGF-β signaling.
BACKGROUND: Triple-negative breast cancer (TNBC) is characterized by an aggressive tumor microenvironment (TME) in which cancer-associated fibroblasts (CAFs) play a pivotal role. However, the molecular mechanisms governing the transformation of quiescent fibroblasts into distinct CAF subtypes within TNBC remain poorly understood. This study aimed to elucidate the molecular mechanisms underlying fibroblast-to-CAF subtype transition in TNBC and identify key signaling pathways involved in TNBC-driven CAF activation. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA)-TNBC were analyzed using weighted gene co-expression network analysis (WGCNA). CAF-related scores and TNBC scores were computed via single-sample gene set enrichment analysis (ssGSEA). CAF infiltration was assessed using Estimating the Proportion of Immune and Cancer cells (EPIC). Survival and correlation analyses were validated in the Gene Expression Omnibus (GEO) dataset GSE58812. Single-cell RNA sequencing (scRNA-seq) data (GSE176078) were employed for cell-type annotation, pseudotime trajectory inference, and cell-cell communication analysis. Cell experiments were further performed to explore the mechanisms of fibroblast activation in TNBC. RESULTS: TNBC score was significantly associated with poor prognosis and positively correlated with CAF score/infiltration. Single-cell analysis revealed antigen-presenting CAFs (apCAFs) as the predominant CAF subtype in TNBC. Pseudotime trajectory analysis showed that 96 genes uniquely involved in quiescent-to-apCAF-like differentiation were enriched in TGF-β signaling. Cell-cell communication analysis identified macrophage migration inhibitory factor (MIF)-(CD74 + CXCR4) as significantly enhanced during TNBC-apCAF interactions but absent in TNBC-myofibroblastic CAF (myCAF) crosstalk. Four hub genes, including CNTN4, NLGN1, NLGN4X, and SEMA5A, were identified as associated with the differentiation stage: CNTN4/NLGN1 in MIF-mediated signal initiation and NLGN4X/SEMA5A in the intracellular TGF-β execution phase. Cell experiments revealed that the MIF-CD74/CXCR4 axis promotes apCAF-like differentiation via the TGF-β pathway and enhances malignant phenotypes of TNBC cells. CONCLUSIONS: A TNBC-to-apCAF signaling axis was identified wherein TNBC-secreted MIF activates CD74/CXCR4 on fibroblasts, engaging TGF-β signaling to drive apCAF-like differentiation. These findings offer potential therapeutic targets for disrupting the pro-tumorigenic microenvironment in TNBC.