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Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma.
Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.
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T Cell Exhaustion-Related Gene Signatures Predict Immunotherapy and Chemotherapy Response in Kidney Renal Clear Cell Carcinoma.
Background: Understanding T cell exhaustion (TEX)-related molecular characteristics can provide novel insights into treatment response prediction. This study developed a TEX-based prognostic model to predict survival outcomes and therapy responses in kidney renal clear cell carcinoma (KIRC) patients. Methods: The authors analyzed 518 KIRC patients from The cancer genome atlas (TCGA), identifying TEX-related genes via gene set variation analysis and weighted correlation network analysis. Survival random forest and Least Absolute Shrinkage and Selection Operator-Cox analyses selected eight key genes to construct a TEX risk model. Functional analyses explored TEX-related pathways and immune infiltration. The IMvigor210 dataset assessed immunotherapy response, whereas the Genomics of Drug Sensitivity in Cancer (GDSC) database predicted chemotherapy sensitivity. Single-cell RNA sequencing and quantitative real-time polymerase chain reaction validated a key TEX gene. Results: The TEX risk model demonstrated strong prognostic performance, effectively stratifying KIRC patients into high-risk (HR) and low-risk (LR) groups with significant differences in overall survival. Gene set enrichment analysis results revealed that TEX-related pathways were enriched in tumor proliferation, migration, and immune regulation. Immune cell infiltration analysis indicated that the TEX HR group exhibited distinct immune microenvironment characteristics, including increased expression of specific immune checkpoints. The model effectively predicted clinical responses to immunotherapy, with patients in the TEX HR group showing poorer immunotherapy efficacy. In addition, drug sensitivity analysis based on the GDSC database suggested that TEX features could influence chemotherapy response, highlighting potential therapeutic vulnerabilities. Experimental validation confirmed the expression pattern of a key TEX gene in KIRC samples. Conclusion: Their TEX risk model could effectively predict patient outcomes and responses to immunotherapy and chemotherapy, supporting its potential clinical utility in personalized treatment strategies.
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G-protein Coupled Receptor 34 Promotes Gliomagenesis by Inducing Proliferation and Malignant Phenotype via TGF-Beta/Smad Signaling Pathway.
Background: G-protein coupled receptor 34 (GPR34) is involved in cell motility, differentiation, and mitosis. GPR34 was reported to be highly expressed and play an oncogenic role in several solid tumors. Here, we investigated the mechanisms underlying how GPR34 promotes glioma progression. Methods: Bioinformatic analysis was performed on RNA-seq and clinical data from the gene expression omnibus (GEO), cancer genome atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. TIMER database and single-sample GSEA (ssGAEA) method were used to investigate the association between the GPR34 expression and immune infiltration level in glioma. Cox regression analysis was employed to ascertain whether the risk signature was an independent prognostic indicator for glioma. The viability and migratory/invasive potential of glioma cells were assessed using Cell Counting Kit-8, colony formation, wound healing, and Transwell assays. Results: We found that GPR34 expression was positively correlated with immune infiltration level and that high GPR34 level may be associated with poor prognosis in glioma. We further found that GPR34 may serve as an independent prognostic marker and prediction factor for the clinicopathological features of glioma. We showed that knocking down GPR34 attenuated the viability and migratory/invasive capacity of glioma cells (U251 and LN229), while GPR34 overexpression exerted the opposite effects. Additionally, core enrichment in the GSEA analysis indicated that GPR34-mediated gliomagenesis was associated with the cell cycle arrest, epithelial-mesenchymal transition (EMT), and activation of the TGF-β/Smad pathway; furthermore, inhibiting TGF-β/Smad signaling using LY2157299, a TGF-β inhibitor, reversed the oncogenic effects and malignant phenotype associated with GPR34 overexpression. Conclusion: GPR34 enhances the malignancy and carcinogenesis of glioma by promoting an EMT-like process, G1/S phase cell cycle transition, and TGF-β/Smad signaling. Accordingly, GPR34 likely functions as an oncogene in glioma and may represent a potential therapeutic target for this cancer.
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PDE4DIP-Derived MMG8 Supports Proliferation, Migration, and Tumor Growth in Hepatocellular Carcinoma Models.
BACKGROUND: PDE4DIP encodes a scaffold protein that has been implicated in compartmentalized signaling and cytoskeletal organization, but the role of its myomegalin variant 8 (MMG8) isoform in hepatocellular carcinoma (HCC) remains unclear. To address this gap, we examined PDE4DIP expression in public HCC datasets and investigated the functional role of MMG8 in HCC models. METHODS: PDE4DIP expression was analyzed in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort and two Gene Expression Omnibus (GEO) cohorts (GSE14520, GSE36376). MMG8 function was assessed in Huh7 cells using siRNA-mediated knockdown and in Hepa1-6 cells using lentiviral Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR-associated protein 9 (CRISPR-Cas9)-mediated knockout. Cell proliferation in MMG8-KD Huh7 cells and MMG8-KO Hepa1-6 cells was assessed using Cell Counting Kit-8 (CCK-8) assays, while Huh7 cell migration was evaluated using Transwell assays. Tumor growth was assessed using a murine subcutaneous tumor model. Immunohistochemical staining for Ki67 and cleaved caspase-3 was employed to assess tumor cell proliferation and apoptosis-associated changes, respectively. Gene set enrichment analysis was performed in TCGA-LIHC tumors stratified based on PDE4DIP expression. RESULTS: PDE4DIP expression differed between tumor and non-tumor tissues across HCC cohorts, although the directionality of this difference was not uniform. MMG8 knockdown in Huh7 cells reduced proliferation and migratory activity. A single-cell-derived MMG8-KO Hepa1-6 clone exhibited reduced proliferation in vitro and formed smaller tumors in vivo, with lower Ki67 positivity but no significant difference in cleaved caspase-3 positivity between groups. In tumors from the TCGA-LIHC cohort, PDE4DIP expression was associated with distinct transcriptional programs. Specifically, PDE4DIP-high tumors presented with positive normalized enrichment score (NES) values for several metabolic pathways, whereas adhesion/extracellular matrix (ECM), cell cycle/proliferation, and translation/ribosome-related pathways exhibited negative NES values. CONCLUSIONS: These findings support a functional contribution of MMG8 to proliferative, migratory, and tumor-growth phenotypes in the tested HCC models. Bulk gene-level PDE4DIP expression in human tumors was associated with context-dependent transcriptional states and should not be interpreted as a direct surrogate for MMG8 function.
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Cell-resolved genetic risk reveals hyperandrogenism-associated immune and metabolic rewiring in the PMOS follicular microenvironment.
BACKGROUND: Polyendocrine metabolic ovarian syndrome (PMOS) is a leading cause of anovulatory infertility. Although follicular developmental arrest is a defining feature of PMOS, the cell-type-specific mechanisms linking genetic susceptibility, endocrine imbalance and follicular microenvironmental dysfunction remain incompletely understood, particularly across clinical subtypes. METHODS: We performed single-cell RNA sequencing of preovulatory follicular-fluid cells from healthy controls and women with uncomplicated PMOS, hyperandrogenic PMOS and metformin-treated insulin-resistant PMOS. We integrated these data with GWAS-derived disease relevance scores and ovarian chromatin-accessibility annotations to map genetic risk to follicular cell populations and regulatory programmes. Ligand-receptor analyses were used to infer intercellular communication networks. Transcriptomic metabolic findings were further assessed by follicular-fluid metabolic and steroid-hormone measurements, DHT-treated granulosa-cell functional assays, and reanalysis of a DHEA-induced mouse PMOS-like ovarian dataset. RESULTS: We generated a single-cell atlas of the PMOS follicular microenvironment comprising 220,983 high-quality cells. Granulosa cells showed the strongest enrichment of PMOS-related genetic risk, with regulatory-genetic support from ovarian snATAC-seq integration. Among granulosa cell states, a GC2_CYP19A1⁺ population was preferentially associated with hyperandrogenic PMOS and displayed a transcriptional programme characterized by steroidogenic remodelling coupled to reduced energy-metabolic activity. Biochemical profiling supported this endocrine-metabolic imbalance, showing increased follicular-fluid androstenedione (ASD) and estrone, reduced estradiol and E2/ASD ratio, decreased ATP, citrate and fumarate hydratase activity, and increased lactate and β-hydroxybutyrate. Prolonged DHT exposure impaired granulosa-cell mitochondrial respiration, ATP production and mitochondrial membrane potential. In parallel, PMOS subtypes showed immune remodelling and altered ligand-receptor signalling, with hyperandrogenic PMOS associated with increased inflammatory signalling and reduced homeostatic or tissue-repair pathways. Reanalysis of a DHEA-induced mouse ovarian dataset provided supportive evidence for androgen-associated steroidogenic pathway perturbation. CONCLUSIONS: This study provides a cell-resolved framework linking PMOS genetic susceptibility to endocrine-metabolic and immune remodelling in the preovulatory follicular microenvironment. The identification of a hyperandrogen-associated GC2_CYP19A1⁺ state highlights a candidate cellular programme in which steroidogenic imbalance is coupled to impaired granulosa-cell bioenergetics. These findings provide a resource for future validation and may inform microenvironment-guided stratification of PMOS and the development of strategies aimed at restoring follicular function.
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Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival.
PURPOSE: To provide a better understanding of the interplay between the immune system and brain metastases to advance therapeutic options for this life-threatening disease. EXPERIMENTAL DESIGN: Tumor-infiltrating lymphocytes (TIL) were quantified by semiautomated whole-slide analysis in brain metastases from 81 lung adenocarcinomas. Multi-color staining enabled phenotyping of TILs (CD3, CD8, and FOXP3) on a single-cell resolution. Molecular determinants of the extent of TILs in brain metastases were analyzed by transcriptomics in a subset of 63 patients. Findings in lung adenocarcinoma brain metastases were related to published multi-omic primary lung adenocarcinoma The Cancer Genome Atlas data (n = 230) and single-cell RNA-sequencing (scRNA-seq) data (n = 52,698). RESULTS: TIL numbers within tumor islands was an independent prognostic marker in patients with lung adenocarcinoma brain metastases. Comparative transcriptomics revealed that expression of three surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) was closely associated with TIL numbers. Their expression was not only prognostic in brain metastasis but also in primary lung adenocarcinoma. Correlation with scRNA-seq data revealed that brain metastases with high expression of surfactant genes might originate from tumor cells resembling alveolar type 2 cells. Methylome-based estimation of immune cell fractions in primary lung adenocarcinoma confirmed a positive association between lymphocyte infiltration and surfactant expression. Tumors with a high surfactant expression displayed a transcriptomic profile of an inflammatory microenvironment. CONCLUSIONS: The expression of surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) defines an inflamed subtype of lung adenocarcinoma brain metastases characterized by high abundance of TILs in close vicinity to tumor cells, a prolonged survival, and a tumor microenvironment which might be more accessible to immunotherapeutic approaches.
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Integrative profiling of lactylation reveals prognostic biomarkers and an immunosuppressive niche in acute myeloid leukemia.
BACKGROUND: The overall survival rate of acute myeloid leukemia (AML) remains less than 30%. Metabolic reprogramming of leukemia cells, such as the Warburg effect, enables them to adapt to the microenvironment and thereby develop. Elucidating the landscape of lactate regulation in AML helps clarify the pathogenesis from the perspective of metabolic reprogramming and identify possibilities for optimizing current treatment modalities. METHODS: RNA and single-cell sequencing data for AML were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Seurat, limma package algorithm and Weighted gene coexpression network analysis (WGCNA) were conducted to identify candidate lactylation-related genes (LRGs). Enrichment analyses and protein-to-protein interactions were used to clarify the functions. Univariate COX regression and machine learning algorithms (LASSO-logistic, SVM-RFE and Boruta) narrowed the range of LRGs.The DALEX package employed four machine learning models for validation. CIBERSORT analyzed the relationship between immune cell infiltration and key LRGs, while single-gene GSEA was utilized to evaluate the functions of LRGs. We evaluated the associations between hub LRGs and AML using a two-sample Mendelian randomization (MR) analysis. Molecular docking was used to screen for feasible drugs targeting the hub genes. Western blotting was performed to assess pan-lactylation levels in AML cell lines. qRT-PCR and immunohistochemistry were performed to detect GZMB/LSP1 expression in AML patients. RESULTS: Seven hub LRGs were identified in the AML groups: LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA and POU2F2, of which GZMB and LSP1 passed MR test. The seven hub genes were enriched in immune and inflammatory pathways. GLM ultimately emerged as the optimal model validated by GEO datasets. Compared with healthy controls, Kasumi-1 cells exhibited elevated lactylation levels, with exogenous lactate treatment further increasing lactylation levels, whereas sodium oxamate administration had the opposite effect. Exogenous lactate treatment significantly upregulated the mRNA expression of GZMB and LSP1. (-)-Gallocatechin gallate and indomethacin bound well to GZMB, while benzo(a)pyrene and benzo(e)pyrene had good binding potential with LSP1. CONCLUSIONS: We established lactylation as a critical regulator of AML, and GZMB and LSP1 were identified as lactylation-related clinical modeling indicators, which provides a foundation for choosing prognostic and therapeutic strategies for AML.
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EMP3 as a prognostic biomarker correlates with EMT in GBM.
BACKGROUND: Glioblastoma (GBM) is the most aggressive malignant central nervous system tumor with a poor prognosis.The malignant transformation of glioma cells via epithelial-mesenchymal transition (EMT) has been observed as a main obstacle for glioblastoma treatment. Epithelial membrane protein 3 (EMP3) is significantly associated with the malignancy of GBM and the prognosis of patients. Therefore, exploring the possible mechanisms by which EMP3 promotes the growth of GBM has important implications for the treatment of GBM. METHODS: We performed enrichment and correlation analysis in 5 single-cell RNA sequencing datasets. Differential expression of EMP3 in gliomas, Kaplan-Meier survival curves, diagnostic accuracy and prognostic prediction were analyzed by bioinformatics in the China Glioma Genome Atlas (CGGA) database and The Cancer Genome Atlas (TCGA) database. EMP3-silenced U87 and U251 cell lines were obtained by transient transfection with siRNA. The effect of EMP3 on glioblastoma proliferation was examined using the CCK-8 assay. Transwell migration assay and wound healing assay were used to assess the effect of EMP3 on glioblastoma migration. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were used to detect the mRNA and protein expression levels of EMT-related transcription factors and mesenchymal markers. RESULTS: EMP3 is a EMT associated gene in multiple types of malignant cancer and in high-grade glioblastoma. EMP3 is enriched in high-grade gliomas and isocitrate dehydrogenase (IDH) wild-type gliomas.EMP3 can be used as a specific biomarker for diagnosing glioma patients. It is also an independent prognostic factor for glioma patients' overall survival (OS). In addition, silencing EMP3 reduces the proliferation and migration of glioblastoma cells. Mechanistically, EMP3 enhances the malignant potential of tumor cells by promoting EMT. CONCLUSION: EMP3 promotes the proliferation and migration of GBM cells, and the mechanism may be related to EMP3 promoting the EMT process in GBM; EMP3 may be an independent prognostic factor in GBM.
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Single-cell transcriptomics reveals the role of Macrophage-Naïve CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma.
BACKGROUND: Liver carcinoma generally presents as an immunosuppressive microenvironment that promotes tumor evasion. The intercellular crosstalk of immune cells significantly influences the construction of an immunosuppressive microenvironment. This study aimed to investigate the important interactions between immune cells and their targeting drugs in liver carcinoma, by using single-cell and bulk transcriptomic data. METHODS: Single-cell and bulk transcriptomic data were retrieved from Gene Expression Omnibus (GSE159977, GSE136103, and GSE125449) and The Cancer Genome Atlas (TGCA-LIHC), respectively. Quality control, dimension reduction, clustering, and annotation were performed according to the Scanpy workflow based on Python. Cell-cell interactions were explored using the CellPhone database and CellChat. Trajectory analysis was executed using a partition-based graph abstraction method. The transcriptomic factors (TFs) were predicted using single-cell regulatory network inference and clustering (SCENIC). The target genes from TFs were used to establish a related score based on the TCGA cohort; this score was subsequently validated by survival, gene set enrichment, and immune cell infiltration analyses. Drug prediction was performed based on the Cancer Therapeutics Response Portal and PRISM Repurposing datasets. RESULTS: Thirty-one patients at four different states, including health, hepatitis, cirrhosis, and cancer, were enrolled in this study. After dimension reduction and clustering, twenty-two clusters were identified. Cell-cell interaction analyses indicated that macrophage-naive CD4 + T cell interaction significantly affect cancerous state. In brief, macrophages interact with naive CD4 + T cells via different pathways in different states. The results of SCENIC indicated that macrophages present in cancer cells were similar to those present during cirrhosis. A macrophage-naive CD4 + T cell (MNT) score was generated by the SCENIC-derived target genes. Based on the MNT score, five relevant drugs (inhibitor of polo-like kinase 1, inhibitor of kinesin family member 11, dabrafenib, ispinesib, and epothilone-b) were predicted. CONCLUSIONS: This study reveals the crucial role of macrophage-naive CD4 + T cell interaction in the immunosuppressive microenvironment of liver carcinoma. Tumor-associated macrophages may be derived from cirrhosis and can initiate liver carcinoma. Predictive drugs that target the macrophage-naive CD4 + T cell interaction may help to improve the immunosuppressive microenvironment and prevent immune evasion. The relevant mechanisms need to be further validated in experiments and cohort studies.
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High-Resolution Spatial Transcriptomic Atlas of Mouse Soleus Muscle: Unveiling Single Cell and Subcellular Heterogeneity in Health and Denervation.
Skeletal muscle is essential for both movement and metabolic processes, characterized by a complex and ordered structure. Despite its importance, a detailed spatial map of gene expression within muscle tissue has been challenging to achieve due to the limitations of existing technologies, which struggle to provide high-resolution views. In this study, we leverage the Seq-Scope technique, an innovative method that allows for the observation of the entire transcriptome at an unprecedented submicron spatial resolution. By applying this technique to the mouse soleus muscle, we analyze and compare the gene expression profiles in both healthy conditions and following denervation, a process that mimics aspects of muscle aging. Our approach reveals detailed characteristics of muscle fibers, other cell types present within the muscle, and specific subcellular structures such as the postsynaptic nuclei at neuromuscular junctions, hybrid muscle fibers, and areas of localized expression of genes responsive to muscle injury, along with their histological context. The findings of this research significantly enhance our understanding of the diversity within the muscle cell transcriptome and its variation in response to denervation, a key factor in the decline of muscle function with age. This breakthrough in spatial transcriptomics not only deepens our knowledge of muscle biology but also sets the stage for the development of new therapeutic strategies aimed at mitigating the effects of aging on muscle health, thereby offering a more comprehensive insight into the mechanisms of muscle maintenance and degeneration in the context of aging and disease.