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Exploration of the underlying biological differences and targets in ovarian cancer patients with diverse immunotherapy response.
BACKGROUND: Preclinical trials of immunotherapy in ovarian cancer (OC) have shown promising results. This makes it meaningful to prospectively examine the biological mechanisms explaining the differences in response performances to immunotherapy among OC patients. METHODS: Open-accessed data was obtained from the Cancer Genome Atlas and Gene Expression Omnibus database. All the analysis was conducted using the R software. RESULTS: We firstly performed the TIDE analysis to evaluate the immunotherapy response rate of OC patients. The machine learning algorithm LASSO logistic regression and SVM-RFE were used to identify the characteristic genes. The genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were selected for molecular typing. Our result showed that the patients in Cluster1 might have a better prognosis and might be more sensitive to immunotherapy, including PD-1 and CTLA4 therapy options. Pathway enrichment analysis showed that in Cluster2, the pathway of EMT, TNFα/NF-kB signaling, IL2/STAT5 signaling, inflammatory response, KRAS signaling, apical junction, complement, interferon-gamma response and allograft rejection were significantly activated. Also, genomic instability analysis was performed to identify the underlying genomic difference between the different Cluster patients. Single-cell analysis showed that the DPT, COL6A6, LSAMP and RUNX1T1 were mainly expressed in the fibroblasts. We then quantified the CAFs infiltration in the OC samples. The result showed that patients with low CAFs infiltration might have a lower TIDE score and a higher proportion of immunotherapy responders. Also, we found all the characteristic genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were upregulated in the patients with high CAFs infiltration. Immune infiltration analysis showed that the patients in Cluster2 might have a higher infiltration of naive B cells, activated NK cells and resting Dendritic cells. CONCLUSIONS: In summary, our study provides new insights into ovarian cancer immunotherapy. Meanwhile, specific targets DPT, RUNX1T1, PTPRN, LSAMP, FDCSP, COL6A6 and CAFs were identified for OC immunotherapy.
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A novel methylation signature predicts extreme long-term survival in glioblastoma.
PURPOSE: Glioblastoma (GBM) is the most common malignant primary brain tumor with a dismal prognosis of less than 2 years under maximal therapy. Despite the poor prognosis, small fractions of GBM patients seem to have a markedly longer survival than the vast majority of patients. Recently discovered intertumoral heterogeneity is thought to be responsible for this peculiarity, although the exact underlying mechanisms remain largely unknown. Here, we investigated the epigenetic contribution to survival. METHODS: GBM treatment-naïve samples from 53 patients, consisting of 12 extremely long-term survivors (eLTS) patients and 41 median-term survivors (MTS) patients, were collected for DNA methylation analysis. 865 859 CpG sites were examined and processed for detection of differentially methylated CpG positions (DMP) and regions (DMR) between both survival groups. Gene Ontology (GO) and pathway functional annotations were used to identify associated biological processes. Verification of these findings was done using The Cancer Genome Atlas (TCGA) database. RESULTS: We identified 67 DMPs and 5 DMRs that were associated with genes and pathways - namely reduced interferon beta signaling, in MAPK signaling and in NTRK signaling - which play a role in survival in GBM. CONCLUSION: In conclusion, baseline DNA methylation differences already present in treatment-naïve GBM samples are part of genes and pathways that play a role in the survival of these tumor types and therefore may explain part of the intrinsic heterogeneity that determines prognosis in GBM patients.
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Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites.
BACKGROUND: The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand-methylated or non-methylated-and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing. RESULTS: We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a "FibroScore" correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types. CONCLUSION: This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.
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Targeting CD39 boosts PD-1 blockade antitumor therapeutic efficacy via strengthening CD8 + TILs function and recruiting B cells in cervical cancer.
Although the programmed cell death protein 1 (PD-1) blockade has been authorized for the treatment of recurrent and metastatic cervical cancer (CC) patients, a significant proportion of CC patients show low objective response rates (ORR) to immune checkpoint blockades (ICBs). Therefore, identifying novel combination treatment strategies to enhance ICBs therapeutic efficacy for CC patients is urgently needed. Here, we discovered that CD39 was highly expressed in exhausted CD8 + T cells from 10 CC patients in our center via single-cell RNA sequencing (scRNA-seq). Furthermore, we validated that CC patients with CD39 highly expressed in CD8 + T cells associated with poor prognosis and immunoevasive subtype of CC both in cohort from our center and the Cancer Genome Atlas (TCGA) database. Moreover, it was also confirmed that CD39-inhibiting not only enhanced the cytotoxicity of CD8 + tumor-infiltrating lymphocytes (TILs) but also promoted the infiltration of B cells through increasing CXCL13 secretion both in vitro experiments and subcutaneous tumor models, thereby amplifying anti-tumor immunity of PD-1 blockade. What was more, we have developed a liposome containing POM-1, which effectively enhanced the anti-tumor effect of POM-1. Our findings provide compelling evidence that targeting CD39 represents a promising "two birds with one stone" strategy for cervical cancer treatment.
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Dissecting primate early post-implantation development using long-term in vitro embryo culture.
The transition from peri-implantation to gastrulation in mammals entails the specification and organization of the lineage progenitors into a body plan. Technical and ethical challenges have limited understanding of the cellular and molecular mechanisms that underlie this transition. We established a culture system that enabled the development of cynomolgus monkey embryos in vitro for up to 20 days. Cultured embryos underwent key primate developmental stages, including lineage segregation, bilaminar disc formation, amniotic and yolk sac cavitation, and primordial germ cell-like cell (PGCLC) differentiation. Single-cell RNA-sequencing analysis revealed development trajectories of primitive endoderm, trophectoderm, epiblast lineages, and PGCLCs. Analysis of single-cell chromatin accessibility identified transcription factors specifying each cell type. Our results reveal critical developmental events and complex molecular mechanisms underlying nonhuman primate embryogenesis in the early postimplantation period, with possible relevance to human development.
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Novel pyroptosis-immune-related lncRNA signature exhibits a distinct immune cell infiltration landscape in breast cancer.
INTRODUCTION: This study investigated pyroptosis- and immunity-related long non-coding RNAs (lncRNAs) to identify promising therapeutic targets for breast cancer (BC), and constructed lncRNA signatures to determine the prognosis and immunotherapy responses of BC patients. METHODS: Pearson's correlation coefficient was used to identify pyroptosis- and immune-related differentially expressed lncRNAs (DE-pyrolncRNAs and DE-ImmlncRNAs, respectively). The Cancer Genome Atlas dataset was allocated to training and testing subsets. Prognostic lncRNA signatures were derived based on the training subset using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator methods. Stepwise Cox regression was used to refine these signatures and to select the optimal lncRNA signature. The median risk score of the training subset was applied as a threshold to divide patients into high-risk (HR) and low-risk (LR) groups. The Wilcoxon test was used to reveal differences in immune scores, cell types, functions, and checkpoint genes between these groups. Single-cell sequencing data from GSE176078 were used to validate the immune cell infiltration landscape of the identified lncRNA signatures. RESULTS: We identified a six-lncRNA pyroptosis-immune signature comprising MAPT.AS1, CTA.384, D8.34, RP11.561, I11.3, HID1.AS1, AC097713.3, and USP2.AS1. Patients in the HR group demonstrated inferior prognoses in the training, testing, and full datasets (P=3.622e-07, P=3.736e-03, and P=1.151e-08, respectively). Immune scores were significantly enhanced in the LR group, whereas tumor purity was elevated in the HR group. Fifty-eight immune scores showed significant differences between the groups (P<0.05). Immune function (APC coinhibition, CCR, and checkpoints) more significantly impaired in the HR group. Expression levels of 38 immune checkpoint genes, including KIR2DS4, KIR3DL2, CD40LG, KIR3DL1, and PDCD1, were significantly higher in the LR group. Conversely, the TDO2, PVR, and CD276 levels were elevated in the HR group. Single-cell sequencing data from GSE176078 showed sparse T cell, B cell, myeloid, and plasmablast clusters in the HR group, whereas the LR group displayed significant clustering of B cells, myeloids, and plasmablasts. CONCLUSION: The six-lncRNA pyroptosis-immune signature effectively predicted BC prognosis and highlighted distinct immune cell infiltration patterns. This holds promise for evaluating immunotherapy responses and guiding therapeutic target identification in BC.
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Tumor microenvironment characteristics of lipid metabolism reprogramming related to ferroptosis and EndMT influencing prognosis in gastric cancer.
BACKGROUND: Gastric cancer (GC) is a refractory malignant tumor with high tumor heterogeneity, a low rate of early diagnosis, and poor patient prognosis. Lipid metabolism reprogramming plays a critical role in tumorigenesis and progression, but its prognostic role and regulatory mechanism in GC are rarely studied. Thus, the identification of signatures related to lipid metabolism is necessary and may present a new avenue for improving the overall prognosis of GC. METHODS: Lipid metabolism-associated genes (LMAGs) with differential expression in tumor and tumor-adjacent tissue were acquired to identify lipid metabolism-associated subtypes. The differentially expressed genes (DEGs) between the two clusters were then utilized for prognostic analysis and signature construction. Additionally, pathway enrichment analysis and immune cell infiltration analysis were employed to identify the characteristics of the prognostic model. Further analyses were conducted at the single-cell level to better understand the model's prognostic mechanism. Finally, the prediction of immunotherapy response was used to suggest potential treatments. RESULTS: Two lipid metabolism-associated subtypes were identified and 9 prognosis-related genes from the DEGs between the two clusters were collected for the construction of the prognostic model named lipid metabolism-associated signature (LMAS). Then we found the low LMAS patients with favorable prognoses were more sensitive to ferroptosis in the Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD). Meanwhile, the tumor cells exhibiting high levels of lipid peroxidation and accumulation of reactive oxygen species (ROS) in single-cell levels were primarily enriched in the low LMAS group, which was more likely to induce ferroptosis. In addition, endothelial cells and cancer-associated fibroblasts (CAFs) facilitated tumor angiogenesis, proliferation, invasion, and metastasis through endothelial-mesenchymal transition (EndMT), affecting the prognosis of the patients with high LMAS scores. Moreover, CD1C- CD141- dendritic cells (DCs) also secreted pro-tumorigenic cytokines to regulate the function of endothelial cells and CAFs. Finally, the patients with low LMAS scores might have better efficacy in immunotherapy. CONCLUSIONS: A LMAS was constructed to guide GC prognosis and therapy. Meanwhile, a novel anti-tumor effect was found in lipid metabolism reprogramming of GC which improved patients' prognosis by regulating the sensitivity of tumor cells to ferroptosis. Moreover, EndMT may have a negative impact on GC prognosis.
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Intestinal metaplasia key molecules and UPP1 activation via Helicobacter pylori /NF-kB: drivers of malignant progression in gastric cancer.
Gastric cancer (GC) remains a significant global health challenge due to its high morbidity and mortality rates. The development of GC is a multi-hit process and the exploration of precancerous lesions is crucial. To elucidate the molecular and cellular dynamics underlying gastric carcinogenesis, we conducted an integrative single-cell RNA sequencing analysis of 26,028 high-quality cells from gastric antral mucosa biopsies across various stages, including non-atrophic gastritis, chronic atrophic gastritis, intestinal metaplasia, and early gastric cancer. By constructing a detailed single-cell atlas, we identified distinct epithelial cell subpopulations and their corresponding molecular signatures. We focused on the biological link between gastric epithelial cells and cancer cells. Notably, we observed that gland mucous cells acquired an intestinal-like stem cell phenotype during metaplasia, with MUC6, MUC2 and OLFM4 emerging as the specific markers for unique endocrine cells in early malignant lesions. Additionally, our analysis highlighted UPP1 as a key oncogene, with its expression progressively increasing from normal epithelial cells to malignant cells. UPP1 upregulation was shown to promote GC cell proliferation and migration, implicating it in the oncogenic process. Further, we explored the impact of Helicobacter pylori infection on gene expression, revealing that Helicobacter pylori infection upregulates UPP1 via the NF-κB pathway. Our cell-cell communication analysis underscored the significant role of the Macrophage migration inhibitory factor pathway in the tumor microenvironment, contributing to GC progression. Various key molecules involved in intestinal metaplasia, along with UPP1 and the Macrophage migration inhibitory factor pathway, collectively illustrate the multifaceted nature and complexity of gastric cancer evolution, highlighting the cumulative impacts that drive tumorigenesis.
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[Machine learning-based programmed cell death signature model for precise prediction of prognosis and treatment response in melanoma].
OBJECTIVES: The occurrence, metastasis, and drug resistance of melanoma pose major challenges to patient prognosis, and predictive models capable of accurately forecasting patient outcomes and guiding treatment are still lacking. This study aims to develop predictive models for melanoma prognosis and drug sensitivity based on mechanisms of programmed cell death (PCD). METHODS: Genes related to 19 PCD patterns were collected and integrated from gene set enrichment analysis (GSEA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), relevant reviews, and published studies to establish a comprehensive PCD signature gene set. Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) cohort were obtained, and 3 untreated Gene Expression Omnibus (GEO) datasets (GSE65904, GSE19234, and GSE100797) were included as external validation cohorts. In addition, immunotherapy cohorts PRJEB23709, GSE136961, and GSE215222 were collected to validate the predictive value for immunotherapy. Single-cell transcriptomic data (GSE115978 and GSE215120) were processed using Seurat for quality control, normalization, dimensionality reduction, clustering, and cell annotation; spatial transcriptomic data were obtained from 10× Genomics and combined with Cottrazm for spatial partitioning and SpaCET deconvolution. Cell-cell communication was evaluated using CellChat to assess secreted signaling, extracellular matrix (ECM)-receptor interactions, and cell contact-mediated communication patterns. Based on the TCGA training set, a machine learning strategy comprising 10 algorithms and 101 combinations was used to construct PCD-related prognostic signatures, and the optimal model was selected using the average concordance index (C-index) across multiple cohorts. Differential analysis, GSEA, CIBERSORT, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) were further applied to evaluate immune infiltration, calculate T-cell receptor (TCR) clonal diversity, cytolytic activity, and T-cell effector gene expression profiles, and to explore the association between the PCD score (PCDS) and drug sensitivity. RESULTS: The activities of the 19 PCD pathways differed significantly between normal skin and SKCM, suggesting that dysregulation is involved in melanoma progression. Mutation analysis showed that titin (TTN) and mucin 16 (MUC16) had the highest mutation frequencies. After controlling for collinearity and correlation screening, 314 candidate genes were obtained. Among the 101 model combinations, StepCox (backward)+Ridge performed best (average C-index across 4 cohorts=0.677). The PCDS constructed based on this model stably stratified prognosis: The high-PCDS group showed significantly worse survival in TCGA and 3 external validation cohorts and remained an independent prognostic indicator after adjustment for age, sex, stage, Breslow thickness, Clark level, and ulceration. Immune analyses indicated that high PCDS was associated with immunosuppression: Immune scores were reduced; CD8+ T cells, activated memory CD4+ T cells, plasma cells, and M1 macrophages were decreased, whereas M2 macrophages were increased. PCDS was significantly negatively correlated with immune checkpoint molecules, TCR Shannon index, cytolytic activity score (CYT), and T-cell effector gene expression profile (T-GEP), and was highest in the immune-desert subtype. Spatial transcriptomic and single-cell results suggested spatial and cellular heterogeneity of PCDS: PCDS was lower at boundary regions and negatively correlated with macrophage proportions; myeloid cells had the lowest overall PCDS, macrophages exhibited the strongest communication with T/natural killer (NK) cells, and after immunotherapy, macrophages were enriched with enhanced human leukocyte antigen (HLA)-A/B/C-CD8a-related interactions. In clinical applications, PCDS demonstrated greater robustness compared with 100 previously reported prognostic signatures and complemented hot-cold tumor classification by identifying high-risk patients even among "hot tumors". Validation in immunotherapy cohorts showed that PCDS was negatively associated with therapeutic benefit; high PCDS was more likely to be associated with non-response and poorer prognosis. Drug analyses suggested that PCDS was associated with sensitivity to multiple drugs and could be used for potential treatment stratification. CONCLUSIONS: PCDS is a cross-cohort robust tool for predicting melanoma prognosis and immunotherapy benefit, reflecting the degree of immunosuppression in the tumor immune microenvironment and myeloid/macrophage-related immunoregulatory features, and provides a basis for individualized risk stratification and potential drug selection. This study provides an in-depth elucidation of the regulatory mechanisms of PCD in the tumor immune microenvironment and offers an important theoretical foundation for personalized treatment decision-making in melanoma patients.
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Low PTGDS Expression Facilitates HNSCC by Suppressing Programmed Cell Death and Reducing B Cell-Mediated Immune Responses.
Head and neck squamous cell carcinoma (HNSCC) constitutes a significant global disease burden. Evasion of programmed cell death (PCD) is a hallmark of HNSCC progression. Cancer-associated fibroblasts (CAFs) are a major cellular component of the HNSCC tumor microenvironment (TME). However, the mechanism by which CAFs contribute to PCD resistance remains poorly understood. This study included 24 single-cell sequencing samples, the Cancer Genome Atlas (TCGA) data, and spatial transcriptome data from HNSCC samples. Single-cell data were clustered using the Seurat package, and pseudotemporal trajectory analysis of CAF subsets was performed with Monocle2. The random forest algorithm was used to assess the prognostic importance of candidate genes. The ssGSEA and CIBERSORT algorithms were used to assess immune cell infiltration patterns in the TCGA samples. Gain-of-function assays, Western blotting, and immunohistochemistry were conducted to validate the biological functions of key targets. Here, we identified seven CAF subsets and clarified their potential differentiation directions. Thirty-two CAF-associated PCD regulators with prognostic significance were identified, among which, prostaglandin D2 synthase (PTGDS) and squalene epoxidase (SQLE) are the most important genes. PTGDS is significantly downregulated in HNSCC and associated with favorable prognosis. PTGDS overexpression significantly inhibited clonogenicity, proliferation, and invasion while promoting apoptosis in HNSCC cells (p < 0.05). In addition, PTGDS expression is significantly enriched in B cell-related pathways. Mechanistically, PTGDS overexpression increased chemokine (C-X-C motif) ligand 13 (CXCL13) expression and enhanced immune cell infiltration. These findings identified PTGDS as a central regulator linking CAF-mediated PCD resistance and B cell immune modulation in HNSCC, suggesting its potential as both a diagnostic biomarker and therapeutic target.