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Angiogenesis related gene signatures predict prognosis and guide therapeutic strategies in renal clear cell carcinoma.
Kidney tumors are hypervascular tumors with crucial antiangiogenic effects in tumor therapy. This study aimed to develop a predictive model for kidney renal clear cell carcinoma (KIRC) by utilizing angiogenesis-related genes to formulate targeted therapy and immunotherapy strategies. Angiogenesis-related genes were screened via the GeneCard and Molecular Signatures Database (MSigDB). The KIRC data downloaded from The Cancer Genome Atlas (TCGA) were randomly divided into an experimental cohort and a validation cohort. In the experimental cohort, a risk score prediction model was constructed through successive analyses via univariate Cox regression, LASSO regression, and multivariate Cox regression. Receiver operating characteristic (ROC) curves were employed to assess the sensitivity of the model's predictions. The model's stability and generalizability were subsequently validated in both the validation cohort and the E-MTAB-1980 cohort. Subsequently, the TCGA-KIRC dataset was stratified into two distinct groups: a localized tumor cohort and a progression/metastasis cohort, based on tumor staging criteria. The efficacy of the prognostic prediction model was evaluated within each subgroup. A nomogram model was developed in conjunction with each independent prognostic factor to accurately predict patient outcomes. Additionally, single-cell and intercellular communication analyses were conducted via KIRC single-cell data obtained from the Gene Expression Omnibus (GEO) database. The effects of immunotherapy and targeted therapy on patients were predicted via prognostic modeling. A total of 260 angiogenesis-related genes were identified through screening in the GeneCards and Molecular Signatures Database(MSigDB). We subsequently developed a risk model comprising five genes: MEOX2, PLG, PROX1, TEK, and TIMP1. Survival analysis indicated that the prognosis for high-risk patients was significantly poorer than that for low-risk patients (P < 0.001), and the model demonstrated satisfactory accuracy in predicting 1-, 3-, and 5-year survival rates. This finding was further validated in both internal and external validation cohorts. The model demonstrated applicability for prognostic predictions in both the localized tumor cohort and the progression/metastasis cohort, with proficiency in forecasting the prognosis of patients diagnosed with metastatic renal cancer. The AUC values for 1, 3, and 5 years were recorded at 0.691, 0.709, and 0.773, respectively. We successfully constructed a nomogram model to facilitate accurate prognostic predictions for patients. Analysis of single-cell data revealed that PLG was expressed predominantly in tumor cell clusters, whereas TEK was highly expressed primarily in pericytes. TIMP1 was found to be highly expressed in vascular smooth muscle cells. In contrast, MEOX2 and PROX1 were highly expressed in specific cell clusters but presented low expression levels across the overall cell population. Cell communication analysis indicated that the modeling gene TEK was involved in the angiogenic pathway, with the interaction between the ligand ANGPT2 and the receptor ITGA5-ITGB1 being particularly prominent in this study. Furthermore, the immune dysfunction and rejection scores for high-risk patients within the non-localized renal cancer cohort were markedly elevated compared to those observed in the low-risk group. In terms of targeted pharmacological intervention, individuals classified in the low-risk group exhibited a heightened sensitivity to sorafenib. The KIRC prognostic prediction model, which is based on five angiogenesis-related genes, demonstrated reliable performance, indicating that high-risk patients have a significantly poorer prognosis than low-risk patients do. The developed nomogram model effectively visualizes and accurately predicts patient prognosis. It is essential to highlight that individuals diagnosed with low-risk metastatic KIRC may experience greater advantages from the administration of immunotherapy and sorafenib.
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Multiomics analysis reveals the involvement of NET1 in tumour immune regulation and malignant progression.
Neuroepithelial cell transforming gene 1 (NET1) is a member of the Ras homologue family member A (RhoA) subfamily of guanine nucleotide exchange factors and a key protein involved in the activation of Rho guanosine triphosphatases, which act as regulators of cell proliferation, cytoskeletal organization, and cell movement and are crucial for cancer spread. Research has shown that NET1 can regulate the malignant biological functions of tumour cells, such as growth, invasion, and metastasis, and it is closely related to the progression of pancreatic cancer, gastric cancer, and liver cancer. However, the comprehensive role and mechanistic function of NET1 in other types of cancer remain largely unexplored. A deeper understanding of the role of NET1 may provide new insights into the molecular mechanisms of cancer progression and metastasis. This study aims to fill this knowledge gap and provide a more comprehensive understanding of the role of NET1 in cancer biology. The Cancer Genome Atlas and Genotype-Tissue Expression databases were utilized to analyse the differential expression of NET1 in normal and cancer tissues. The prognostic value of NET1 in cancer was evaluated through log-rank tests and Cox regression models. Further analysis was conducted to assess the relationships between NET1 expression and clinical features, as well as its diagnostic value. We investigated potential factors contributing to genetic alterations in NET1 to elucidate the role of NET1 in cancer progression. We also explored the relationships between NET1 and genes associated with epigenetic modifications, oncogenes, and tumour characteristics, such as RNA stemness scores (RNAss), DNA stemness scores (DNAss), the tumour mutation burden (TMB), and microsatellite instability (MSI). Additionally, we analysed the associations between NET1 expression and immune cell infiltration, immunoregulatory genes, and sensitivity to therapeutic drugs. We conducted gene set enrichment analysis to further investigate the signalling pathways that might be affected by changes in NET1. The prognostic value of NET1 in triple-negative breast cancer (TNBC) was further validated using real-world and Gene Expression Omnibus (GEO) data. Finally, through both in vivo and in vitro experiments, we confirmed that the overexpression of NET1 contributed to the malignant progression of TNBC cells, and we explored the potential mechanism by which NET1 regulates malignant biological behaviour through cellular experiments. Our study revealed a higher expression level of NET1 in 18 types of tumour tissues than in their corresponding normal tissues. Specifically, we observed high expression of NET1 in LIHC, LUSC, PAAD, and BRCA tumour tissues, which was associated with a poor prognosis. In terms of gene alterations, "amplification", "mutation", and "deep deletion" were identified as the main types of changes occurring in NET1. Among these, "amplification" was predominantly observed in LIHC, LUSC, PAAD, and BRCA. Furthermore, a significant positive correlation was found between copy number variations and the NET1 expression level in various tumours, including LIHC, LUSC, PAAD, and BRCA. We also discovered that NET1 expression was positively correlated with the expression of genes related to epigenetic modification in almost all types of cancer and was related to the expression levels of numerous oncogenes. In certain tumours, a significant positive correlation was noted between the expression of NET1 and TMB, MSI, DNAss, and RNAss. Intriguingly, in most tumours, NET1 expression was strongly negatively correlated with the levels of infiltrating natural killer cells and M1 macrophages. Moreover, NET1 expression was significantly positively correlated with the expression of immune genes in nearly all types of cancer. An analysis of single-cell data revealed that NET1 was expressed primarily in malignant tumour cells in most tumours, with little to no expression in immune cells. Additionally, the expression level of NET1 was associated with sensitivity to various therapeutic drugs. Data from GEO and real-world studies indicated high expression of NET1 in TNBC tissues, which was correlated with a poor prognosis. Cellular experiments indicated that NET1 could regulate the proliferation, invasion, cell cycle, and apoptosis of TNBC cells. Furthermore, NET1 may mediate the malignant proliferation of tumour cells through the AKT signalling pathway. NET1 can serve as a potential prognostic marker for LIHC, LUSC, PAAD, and BRCA tumours. Real-world data further suggest that NET1 can also serve as a prognostic indicator for TNBC. High expression of NET1 may contribute to the malignant proliferation of TNBC cells, potentially through the AKT signalling pathway. Moreover, NET1 may contribute to the formation of an immunosuppressive microenvironment that can promote tumour progression. Therefore, targeting NET1 may represents a promising approach for inhibiting tumour progression.
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A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain.
Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 ( www.isoformAtlas.com ). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.
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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.