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Response to Antiangiogenic Therapy Is Associated with AIMP Protein Family Expression in Glioblastoma and Lower-Grade Gliomas.
UNLABELLED: Glioblastoma (GBM) is a highly vascularized, heterogeneous tumor, yet antiangiogenic therapies have yielded limited survival benefits. The lack of validated predictive biomarkers for treatment response stratification remains a major challenge. Aminoacyl tRNA synthetase complex-interacting multicomplex proteins (AIMP) 1/2/3 have been implicated in central nervous system diseases, but their roles in gliomas remain unexplored. We investigated their association with angiogenesis and their significance as predictive biomarkers for antiangiogenic treatment response. In this multi-cohort retrospective study, we analyzed glioma samples from The Cancer Genome Atlas, Chinese Glioma Genome Atlas, REMBRANDT, Gravendeel, BELOB, and REGOMA trials, and four single-cell transcriptomic datasets. Multiomic analyses incorporated transcriptomic, epigenetic, and proteomic data. Kaplan-Meier and Cox proportional hazards models were used to assess the potential prognostic value of AIMPs in heterogeneous and homogeneous treatment groups. Using single-cell transcriptomics, we explored spatial and cell type-specific AIMP2 expression in GBM. AIMP1/2/3 expressions correlated significantly with angiogenesis across The Cancer Genome Atlas cancers. In gliomas, AIMPs were upregulated in tumor versus normal tissues, higher- versus lower-grade gliomas, and recurrent versus primary tumors (P < 0.05). Upon retrospective analysis of two clinical trials assessing different antiangiogenic drugs, we found that high-AIMP2 subgroups had improved response to therapies in GBM [REGOMA: HR, 4.75 (1.96-11.5), P < 0.001; BELOB: HR, 2.3 (1.17-4.49), P = 0.015]. AIMP2-cg04317940methylation emerged as a clinically applicable stratification marker. Single-cell analysis revealed homogeneous AIMP2 expression in tumor tissues, particularly in astrocyte-like cells, suggesting a mechanistic link to tumor angiogenesis. These findings provide novel insights into the role of AIMPs in angiogenesis, offering improved patient stratification and therapeutic outcomes in recurrent GBM. SIGNIFICANCE: This study identifies AIMP2 as a novel biomarker predictive of antiangiogenic treatment response in recurrent GBM. Through multiomic and single-cell analyses, AIMP2 is shown to be upregulated in aggressive gliomas and linked to angiogenesis. Its expression and methylation status offer a clinically applicable stratification tool, enabling more personalized therapeutic approaches and improved outcomes in patients receiving antiangiogenic therapies.
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Multi-omic analysis and validation reveal ZBP1 as a potential prognostic and immunotherapy-related biomarker in head and neck squamous cell carcinoma.
BACKGROUND: Patients with head and neck squamous cell carcinoma (HNSCC) exhibit unfavorable clinical outcomes, accompanied by high morbidity/mortality. In recent years, the management of HNSCC has encountered a significant obstacle. Z-DNA binding protein 1 (ZBP1) exerts crucial biological functions in chronic inflammatory disease and cancer. The aim of this research was to identify the possible function of ZBP1 in HNSCC. METHODS: The Cancer Genome Atlas (TCGA) database was used to collect the gene expression profile and corresponding clinical data. The gene expression, clinical prognosis, genetic alteration, immune characteristics, and subgroup analyses were performed. Meanwhile, an independent cohort (consisting of 66 tumor samples and 37 controls) was employed to validate the expression of ZBP1. RESULTS: Comparing to the normal controls, ZBP1 was upregulated in tumor samples. Low ZBP1 expression predicted undesirable clinical outcomes of HNSCC patients based on the survival analysis. Furthermore, the somatic mutations increased in low ZBP1 expression group. Immune characteristics analysis indicated a positive correlation of ZBP1 expression with the infiltration of immune cells, the expression of immunoregulatory genes and immune checkpoints. In the meantime, single-cell transcriptome analysis unveiled the expression of ZBP1 in tumor microenvironment (TME). In addition, differential gene expression analysis revealed that the expression of ZBP1 primarily contributes to the activation of T cells. Ultimately, ZBP1-associated prognostic and immune features in different subgroups of HNSCC patients were further investigated according to the subgroup analysis. CONCLUSION: Our study comprehensively disclosed that ZBP1 may have the potential to become a meaningful prognostic and immunotherapy-related biomarker for HNSCC.
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Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.
Differentially active enhancers are key drivers of cell type specific gene expression. Active enhancers are found in open chromatin, which can be mapped at genome scale across tissue and cell types. Though incompletely understood, the relationship between chromatin accessibility and enhancer activity has been exploited to identify, model, and even design functional enhancers for selected cell types, but to what extent this design strategy can generalize across human cell and tissue types remains unclear. Here, we trained deep neural networks on a large corpus of chromatin accessibility data from hundreds of human biosamples. We used these models to generate an atlas of tens of thousands of synthetic enhancers, targeting hundreds of cell lines, tissues, and differentiation states, aiming to maximize accessibility in target samples and minimize it in all off-target ones. Experimental testing of thousands of designs in a representative subset of ten human cell types and in mouse retina demonstrated their function as specific enhancers, not only in the case of one-versus-all objectives but also when targeting two or three cell types. An explainable AI analysis, enabled by our large-scale enhancer measurements, allowed us to identify similarities and differences between the sequence grammar underlying accessibility and enhancer activity. Our results show that model-guided design of enhancers can help us decipher the cis-regulatory code governing cell type specificity and generate novel tools for selective targeting of human cell states.
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Dual Roles of Signal Transducer and Activator of Transcription 1 (STAT1) in Cancer: Expression Patterns, Prognostic Significance, and Immune Associations Across Multiple Tumor Types.
BACKGROUND: Signal transducer and activator of transcription 1 (STAT1) is a pivotal transcription factor that plays a dual role in cancer biology. It behaves as a tumor suppressor and, under certain conditions, as a promoter. Few studies have analyzed STAT1 in pan-cancers and explored its role in cancer biology. This study aimed to investigate the expression of STAT1 across various cancers at different stages. Additionally, we examined the promoter methylation of STAT1 and its impact on STAT1 expression, evaluated the functional states of STAT1 in different cancer types, analyzed the correlation between STAT1 expression and survival prognosis, and studied the tumor immune infiltration. Furthermore, we analyzed STAT1 expression across diverse immune subsets in cancer. Subsequently, we conducted an analysis of STAT1-protein interactions and studied the cancer hallmarks of STAT1 and its related proteins. METHODS: Various bioinformatics tools (Gene Expression Profiling Interactive Analysis (GEPIA), Tumor Immune Estimation Resource (TIMER), University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), Kaplan-Meier Plotter, StarBase, Enrichr, and Tumor and Immune System Interaction Database (TISIDB)) and databases (Cancer Single-cell State Atlas (CancerSEA) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)) were used in this study. We comprehensively investigated STAT1 protein expression across various types of cancer and the correlation between its expression and different tumor stages, DNA methylation, survival outcomes, and immune cell infiltration. Additionally, we examined the correlation between STAT1 expression and various immune subtypes of cancer, as well as the functional states of STAT1 in different cancer types. Moreover, we investigated the STAT1 protein interactions and cancer hallmarks of STAT1 and its related proteins. RESULTS: High STAT1 expression was found in 21 cancers and was differentially expressed across different clinical tumor stages and promoter methylation levels. In addition, we found that high expression correlated with better overall survival in some tumors, whereas it correlated with worse overall survival in other tumor types. Furthermore, the expression of STAT1 was associated with the level of immune infiltration in multiple tumors and different immune subtypes. We found that STAT1 expression had dual correlations (positive and negative) with different functional states in tumors, including angiogenesis and apoptosis. STAT1 protein interaction and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that 10 STAT1-interacting proteins were significantly enriched in cancer-related pathways. CONCLUSION: Our findings suggest that STAT1 may serve as a potential diagnostic and prognostic marker for several cancer types.
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Development of a Novel KCNN4-Related ceRNA Network and Prognostic Model for Renal Clear Cell Carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) accounts for more than 80% of renal cell carcinomas. Yet, it has not been fully understood about the derivation and progression of the tumor, as well as the long-term benefits from multimodality therapy. Therefore, reliable and applicable molecular markers are urgently needed for the prediction of diagnosis and prognosis of ccRCC patients. METHODS: Genetic and clinical information of 533 ccRCC patients from The Cancer Genome Atlas database was collected for comprehensive bioinformatic analyses. UALCAN was used to detect gene expression in paired tumor samples. Two data sets from Gene Expression Omnibus database were analyzed to identify differentially expressed genes (DEGs), and Gene Set Enrichment Analysis was applied for the functional enrichment of DEGs. Tumor Immune Single Cell Hub and Tumor IMmune Estimation Resource databases were separately used for analyses of single-immune cell and immune cell infiltration. Encyclopedia of RNA Interactomes database was explored to predict targeted microRNAs (miRNAs) and corresponding long non-coding RNAs (lncRNAs). Cox regression analysis was performed for the construction of risk signature and prognosis model. Finally, quantitative real-time polymerase chain reaction and western blot were conducted for KCNN4 expression detection in cell lines and clinical samples. Small interfering RNA was employed to knock down KCNN4, and corresponding functional experiments were conducted on ccRCC cells as well. RESULTS: KCNN4 showed elevated expression in tumors and prominent clinical correlation in ccRCC. In total, 41 KCNN4-related genes were enriched, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed they were intimately related to immune-related signaling pathways. Spearman's analysis revealed the significantly positive correlation of KCNN4 with immune cell infiltration. By integrating hub miRNA-let-7e-5p and four critical lncRNA, a competitive endogenous RNA network-based risk signature was constructed. The prognosis model derived from it showed considerable predictive value for survival of ccRCC patients. Finally, in vitro experiments confirmed the remarkable tumor-promoting role of KCNN4 in ccRCC cells. CONCLUSION: KCNN4 significantly affected the immune status of tumor microenvironment and immunotherapy elements, through which it promoted tumor progression in ccRCC, and it could be a potential biomarker for prognosis and immunotherapy effects of ccRCC patients.
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HAO2 protects from proximal tubular cells injured in rats with chronic kidney disease by promoting fatty acid metabolic processes.
The complex pathogenesis of kidney disease is closely related to the diversity of kidney intrinsic cells. In this study, single-cell transcriptome sequencing technology was used to sequence and analyze blood and kidney tissue cells in normal control rats and rats with chronic kidney disease (CKD), focusing on key cell populations and functional enrichment to explore the pathogenesis of CKD. Oil red O staining and enzyme-linked immunosorbent assay (ELISA) were used to detect lipid droplets and free fatty acid (FFA). Quantitative real-time polymerase chain reaction (RT-PCR), western blot (WB) were used to verify the differential gene hydroxyacid oxidase 2 (HAO2) and fatty acid metabolic process in tissue to ensure the reliability of single-cell sequencing results. We successfully established a single-cell transcriptome atlas of blood and kidney tissue in rats with CKD, which were annotated into 14 cell subsets (MPCs, PT, Tc, DCT, B-IC, A-IC, CNT, ALOH, BC, Neu, Endo, Pla, NKT, Baso) according to marker gene, and the integrated single-cell atlas of rats showed a significant increase and decrease of MPCs and PTs in the CKD group, respectively. Functional analysis found extensive enrichment of metabolic-related pathways in PT cells, includes fatty acid metabolic process, cellular amino acid metabolic process and generation of precursor metabolites and energy. Immunohistochemical experiments determined that the differential gene HAO2 was localized in the renal tubules, and its expression was significantly reduced in CKD group compared with control, and oil red O staining showed that lipid droplets increased in the CKD group, after overexpression of HAO2 the lipid droplets was inhibited. ELISA assay showed that ATP content decreased in the CKD group and FFA increased in the CKD group. Moreover, the mitochondrial membrane potential of the cells in the OE-HAO2 group was significantly increased compared with OE-NC. The acyl-CoA oxidase 1(ACOX1), peroxisome proliferator-activated receptor alpha (PPARα), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) were decreased in the CKD group, while genes and proteins were increased after overexpression of HAO2, and the AMP-activated protein kinase (AMPK) phosphorylated proteins were increased, the acetyl-CoA carboxylase (ACC) phosphorylated proteins were decreased, reversely. Therefore, HAO2 may be an important regulator of fatty acid metabolic processes in CKD, and overexpression of HAO2 can enhance fatty acid metabolism by promoting fatty acid oxidation (FAO) pathway.
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Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer.
BACKGROUND: Antigen-presenting and processing fibroblasts (APPFs) have emerged as pivotal regulators of antitumor immunity. However, the predictive value of APPF-related genes (APPFRGs) in the prognosis and tumor immune status of gastric cancer (GC) remains largely unexplored. METHODS: Bioinformatics analysis was conducted using single-cell and bulk RNA sequencing datasets of GC retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The APPFs were identified using AUCell algorithm based on APP-associated genes obtained from the InnateDB database. CellChat algorithm was utilized to evaluate interactions between cells. The non-negative matrix factorization (NMF) clustering analysis was performed to identify APPF-related subgroups based on TCGA‑stomach adenocarcinoma cohort. LASSO and multivariate Cox regression analysis were conducted to establish the predictive model. Immunohistochemistry of GC tissue microarrays was performed to validate the model. RESULTS: Compared to non-APPFs, APPFs exhibited more interactions with myeloid cells, endothelial cells, and lymphocytes via MHC-II signaling network. The two APPF-related subgroups clustered by NMF demonstrated significant differences in prognosis and immune cell infiltration. Five APPFRGs (CPVL, ZNF331, TPP1, LGALS9, TNFAIP2) were identified to establish the predictive model and stratify GC patients based on risk score. The prognosis was significantly different between the two risk groups and was validated using GEO datasets. A nomogram that efficiently predicted the overall survival of GC patients was established by integrating the risk score with age, T stage, N stage, and M stage. Furthermore, the high-risk group exhibited reduced infiltration of activated CD4+ T cell and increased infiltration of Treg cells, higher resistance to chemotherapy and immunotherapy, and lower tumor mutation burden. Finally, the immunohistochemical results of GC tissue microarrays revealed higher expression of CPVL, ZNF331, and TPP1, and lower expression of LGALS9 and TNFAIP2 in GC compared to adjacent normal tissues. Additionally, higher risk score in GC samples was relevant with poor differentiation, positive nerve invasion, advanced T and TNM stages, and higher expression of FOXP3. CONCLUSIONS: APPFs may play an important role in the regulation of tumor immune microenvironment in GC and warrant further exploration. The predictive model based on APPFRGs effectively predicts the prognosis and tumor immune status of GC.
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Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis.
OBJECTIVE: The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD). METHODS: The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes. RESULTS: We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs. CONCLUSIONS: Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.
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Automated deconvolution of structured mixtures from heterogeneous tumor genomic data.
With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing. We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data, as well as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA and CNV data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas (TCGA). Source code is available at https://github.com/tedroman/WSCUnmix.
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Landscape of Cell States and Multicellular Ecotypes in Non-Small Cell Lung Cancer.
Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer and remains a major global health burden. Comprehensive characterization of tumor microenvironment (TME) heterogeneity is critical for understanding tumor progression and clinical outcomes in NSCLC. In this study, we identified 34 transcriptionally defined cell states and 6 multicellular ecotypes using the EcoTyper framework, thereby systematically delineating the TME landscape of NSCLC. These cell states and ecotypes were further validated across 11 independent bulk transcriptomic cohorts, one single-cell RNA sequencing cohort, and one spatial transcriptomics cohort, demonstrating strong reproducibility and robustness. Prognostic analyses revealed that several cell states and ecotypes were significantly associated with patient survival outcomes in NSCLC. Transcriptional regulatory analysis further identified SP8, MAZ, and FOS as potential regulators involved in shaping NSCLC ecotypes. Collectively, this study provides a comprehensive atlas of TME-associated cell states and ecotypes in NSCLC, offering new insights into tumor heterogeneity and potential opportunities for personalized prognostic assessment and therapeutic intervention.