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LCLAT1 as a Potential Biomarker for Predicting Clinical Prognosis, Tumor Immunity, and Therapeutic Efficacy in Hepatocellular Carcinoma.
Lysocardiolipin acyltransferase 1 (LCLAT1) is a lipid acyltransferase that plays an important role in mitochondrial function maintenance and lipid metabolism. Nevertheless, there is scarce research regarding its involvement in tumors. This study sought to investigate the potential role of LCLAT1 in hepatocellular carcinoma (HCC). We obtained HCC expression profile data from The Cancer Genome Atlas and Gene Expression Omnibus databases; verified LCLAT1 expression in HCC by qRT-PCR, Western blotting, and immunohistochemical (IHC) staining; and analyzed the potential pathogenic mechanisms of LCLAT1 in HCC bioinformatically. The associations of the LCLAT1 expression level with HCC patient prognosis, immune escape, and treatment response were also explored using bioinformatics tools and IHC staining. LCLAT1 was significantly overexpressed in HCC cell lines and tissues, and its expression level was positively correlated with histologic grade, tumor status, fibrotic Ishak score, and poor patient prognosis in HCC patients. Gene set enrichment analysis suggested that patients with high LCLAT1 had significant enrichment for methylation-, immune-, and cell proliferation-related pathways. The prognosis of HCC was found to be linked to the DNA methylation status of the five CpG islands within the LCLAT1 gene. Immune infiltration and single-cell level analyses showed that LCLAT1 was closely associated with the tumor microenvironment. Moreover, high LCLAT1 expression may promote immune evasion in HCC, and the abundance of several immune cells correlated with HCC prognosis. Finally, HCC patients with low LCLAT1 responded better to treatment with transarterial chemoembolization, sorafenib, and immune checkpoint inhibitors. LCLAT1 might serve as a potential prognostic biomarker and as a multipathway target involved in the malignant progression of HCC. Importantly, LCLAT1 was related to immune infiltration and could aid in predicting treatment response in HCC.
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Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.
Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.
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Multi-omic analysis reveals the role of coagulation factor family genes and their predictive value for immune checkpoint inhibitors efficacy in gastric cancer.
Coagulation factor family genes play a pivotal role in tumor progression and immune modulation, but their relationship with tumor microenvironment and immunotherapy response in gastric cancer remains unclear. This study utilized single-cell and The Cancer Genome Atlas datasets to investigate the clinical and immune relevance of coagulation factor family genes in gastric cancer. Based on coagulation factor family genes, a risk score was constructed. The high-risk group was found to have a worse prognosis and exhibited an immunosuppressive state. Coagulation factor V (F5) emerged as a central gene, correlating negatively with immune cell recruitment and cytotoxic activity within the tumor immune cycle. In vitro knockdown experiments and in vivo 615 mouse liver metastasis models showed that F5 enhances metastasis and reduces CD8+ T cell infiltration. Immunohistochemistry (n = 64) and flow cytometry (n = 31) further confirmed F5 immunomodulatory effects. Among 37 patients receiving neoadjuvant immunotherapy, low F5 expression was associated with improved response. Finally, Gastrodin was identified via virtual screening and molecular dynamics as a potential small-molecule inhibitor of F5. These findings indicate that coagulation factor family genes, particularly F5, promote gastric cancer progression and immune evasion, and that targeting F5 may enhance immunotherapeutic outcomes.
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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.