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A Novel Ferroptosis-Related Gene Model for Overall Survival Predictions of Bladder Urothelial Carcinoma Patients.
INTRODUCTION: Bladder cancer is the most common urinary tract malignancy, and 90% of bladder tumors are urothelial cell carcinomas. Ferroptosis is a new form of cell death discovered in recent years, which is an iron-dependent form of cell death characterized by the lethal intracellular accumulation of lipid-based reactive oxygen species. Ferroptosis is considered to be a double-edged sword for cancer and cancer therapy. MATERIALS AND METHODS: In the current study, expression profiles of bladder cancer (BLCA) specimens were obtained from The Cancer Genome Atlas (TCGA) RNA-Seq database. Ferroptosis-related genes were downloaded from the FerrDb website. The ferroptosis-related differentially expressed genes (DEGs) which were related to overall survival (OS) were first identified. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression methods were utilized to develop a ferroptosis-related prognostic model (FRPM). In addition, a nomogram model based on FRPM and clinicopathological features was successfully constructed and validated. In addition, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) methods were utilized in this study in order to compare the DEGs between the high-risk and low-risk groups. This study also adopted RT-qPCR, CCK-8 assay, and scratch assay methods to perform experimental verification processes. RESULTS AND DISCUSSION: A 7-gene FRPM was constructed in this research investigation in order to stratify the patients into two groups according to their risk scores. The results of this study's survival analysis and time-dependent receiver operating characteristic (ROC) analysis demonstrated that the model had achieved a stable performance level. This multivariate Cox regression results revealed that the FRPM was an independent prognostic predictor for the OS of BLCA patients and the results were displayed using a nomogram. In addition, the ROC analysis, concordance index (C-index), calibration plots, and decision curve analysis (DCA) curves further indicated that this study's nomogram method enabled valuable prediction results. The functional enrichment analysis results suggested that the DEGs between the high- and low-risk groups played vital roles in the progression of the ferroptosis. Also, the ssGSEA indicated that the immune status was different between the two groups. This study found that the RT-qPCR results had confirmed the differential expressions of DEGs in the tissue samples, and the CCK-8 assay and scratch assay results confirmed the promoting effects of SCD on the proliferation and migration of tumor cells. CONCLUSIONS: This study defined a novel prognostic model of seven ferroptosis-related genes, which proved to be independently associated with the OS of BLCA. A nomogram method was developed for the purpose of providing further insight into the accurate predictions of BLCA prognoses.
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Prognostic value and immunotherapy analysis of immune cell-related genes in laryngeal cancer.
BACKGROUND: Laryngeal cancer (LC) is a prevalent head and neck carcinoma. Extensive research has established a link between immune cells in the tumor microenvironment (TME) and cancer progression, as well as responses to immunotherapy. This study aims to develop a prognostic model based on immune cell-related genes and examine the TME in LC. METHODS: RNA-seq data for LC were sourced from The Cancer Genome Atlas (TCGA), and GSE27020 and GSE51985 datasets were retrieved from the Gene Expression Omnibus (GEO) database. Key genes were identified through the intersection of differentially expressed genes (DEGs) between normal and LC samples and module genes derived from weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. The prognostic risk model was constructed using univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Gene Set Variation Analysis (GSVA) was subsequently performed for hallmark and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses in high- and low-risk groups. Immune infiltration analysis between risk groups was conducted via Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and single sample gene set enrichment analysis (ssGSEA). Finally, the relationship between the risk model and immunotherapy response was explored. RESULTS: A total of 124 key genes were identified through the overlap analysis, predominantly enriched in GO terms such as defense response to viruses and regulation of response to biotic stimuli, as well as KEGG pathways related to phagosome and Epstein-Barr virus infection. Machine learning indicated that the optimal prognostic model was constructed from two biomarkers, RENBP and OLR1. GSVA revealed that in the high-risk group, epithelial-mesenchymal transition and ECM-receptor interaction were the most significantly enriched pathways, while autoimmune thyroid disease, ribosome, and oxidative phosphorylation predominated in the low-risk group. Additionally, the stromal score was significantly higher in the high-risk group, while CD8+ T cells, cytolytic activity, inflammation promotion, and T cell co-stimulation were elevated in the low-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) analysis showed higher TIDE and exclusion scores in the high-risk group, whereas the CD8 score was higher in the low-risk group. Finally, CD274 (PD-L1) expression was significantly elevated in the low-risk group. CONCLUSIONS: This study identified two key prognostic biomarkers, RENBP and OLR1, and characterized TME differences across risk groups, offering novel insights into the diagnosis and treatment of LC.
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Induction of the p21/CDK6 pathway and alteration of the immune microenvironment by the stem cell marker CBX3 in melanoma.
BACKGROUND: As one of the stem cell markers, chromobox protein homolog 3 (CBX3) participates in multiple signaling pathways that affect the progression of various tumors. However, the role of CBX3 in melanoma remains unclear, and the mechanisms by which CBX3 may regulate immunotherapy outcome remain largely unknown. METHODS: We used the Cancer Genome Atlas, Genotype-Tissue Expression portal, and Gene Expression Omnibus database to estimate CBX3 expression and its prognostic effect in melanoma. The role of CBX3 in proliferation and migration of melanoma cells were examined using the CCK8, cloning, wound healing, and transwell assays. The effect of CBX3 on melanoma tumorigenesis was assessed using an in vivo animal model. The role of CBX3 in cell cycle was examined using flow cytometry, and expression levels of cell cycle-related genes and proteins in cells with altered CBX3 levels were analyzed using qPCR and western blotting. The function of CBX3 in the immune microenvironment of melanoma was studied using single-cell RNA sequencing and public databases. RESULTS: We found that CBX3 was highly expressed in melanoma with poor prognosis. CBX3 promoted the proliferation and migration of melanoma cells in vivo and in vitro. Functional analysis revealed that CBX3 regulates cell cycle, as it accelerated the G1 to S transition, decreased p21 expression, and increased CDK6 expression. Finally, single-cell sequencing and immune-related assays showed that CBX3 is immunogenic and can change the immune microenvironment of melanoma. CONCLUSIONS: We conclude that the stem cell marker, CBX3 activates the p21/CDK6 pathway and alters the immune microenvironment in melanoma.
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Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer.
Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40-80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer. Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression. Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues. Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.
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THAP3 recruits SMYD3 to OXPHOS genes and epigenetically promotes mitochondrial respiration in hepatocellular carcinoma.
Mitochondria harbor the oxidative phosphorylation (OXPHOS) system to sustain cellular respiration. However, the transcriptional regulation of OXPHOS remains largely unexplored. Through the cancer genome atlas (TCGA) transcriptome analysis, transcription factor THAP domain-containing 3 (THAP3) was found to be strongly associated with OXPHOS gene expression. Mechanistically, THAP3 recruited the histone methyltransferase SET and MYND domain-containing protein 3 (SMYD3) to upregulate H3K4me3 and promote OXPHOS gene expression. The levels of THAP3 and SMYD3 were altered by metabolic cues. They collaboratively supported liver cancer cell proliferation and colony formation. In clinical human liver cancer, both of them were overexpressed. THAP3 positively correlated with OXPHOS gene expression. Together, THAP3 cooperates with SMYD3 to epigenetically upregulate cellular respiration and liver cancer cell proliferation.
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Pan-cancer profiling of FZD2 as a prognostic biomarker: integrative multi-omics analysis with experimental validation and functional characterization in gastric cancer.
BACKGROUND: Frizzled class receptor 2 (FZD2), is a critical protein in the Wnt signaling pathway, which plays significant roles in various cancers. However, its role in cancer progression, prognosis, and diagnosis remains largely unexplored. This study investigates the correlation between FZD2 expression and clinical outcomes, as well as its underlying molecular mechanisms in pan-cancer. METHODS: A comprehensive bioinformatic analysis was performed using pan-cancer data from The Cancer Genome Atlas (TCGA), which included 33 cancer types. Gene set enrichment analysis (GSEA) was conducted to explore functional pathways, while a protein-protein interaction (PPI) network was constructed to further elucidate the role of FZD2 in tumor biology. The relationship between FZD2 expression and immune cell infiltration across 22 categories was assessed using CIBERSORT. Additionally, single-cell analysis was employed to examine FZD2 expression levels across different cell types. To investigate the functional impact of FZD2, loss-of-function experiments were carried out in gastric cancer cell lines using siRNA-mediated knockdown. Subsequent assays, including Polymerase Chain Reaction (PCR), Western blotting (WB), Cell Counting Kit-8 (CCK8), Flow Cytometry, wound healing, and transwell migration and invasion assays, were performed to assess cellular responses. A subcutaneous gastric cancer xenograft model was established in nude mice to investigate the effect of FZD2 knockdown on tumor growth in vivo. RESULTS: Our analysis revealed significant upregulation of FZD2 in multiple malignancies, including stomach adenocarcinoma (STAD), bladder cancer (BLCA), and cholangiocarcinoma (CHOL). FZD2 expression was correlated with various cancer characteristics, including stemness score, matrix score, immune score, tumor mutational burden (TMB), microsatellite instability (MSI), RNA modification genes, and drug sensitivity. Notably, FZD2 was associated with altered sensitivity to several anticancer agents, suggesting its role in modulating treatment responses. FZD2 knockdown was demonstrated by both in vitro and in vivo experiments to suppress tumor cell proliferation, migration, and invasion in gastric cancer cell lines, indicating its critical role in tumor progression. Furthermore, FZD2 exhibited significant correlations with other Wnt pathway genes (e.g., Wnt2, Wnt4, Wnt5B), indicating a complex interaction network contributing to tumorigenesis. CONCLUSION: FZD2 is widely upregulated in various tumor types, with its expression closely associated with key clinical outcomes, including overall survival, disease-specific survival, disease-free interval, as well as tumor mutations, drug sensitivity, immune cell infiltration, and immunotherapy-related biomarkers such as TMB and MSI. These findings highlight the pivotal role of FZD2 in cancer prognosis and treatment, offering potential for novel therapeutic approaches and the development of personalized medicine strategies in oncology.
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Identification and Characterization of Key Differentially Expressed Genes Associated With Metronomic Dosing of Topotecan in Human Prostate Cancer.
Repetitive, low-dose (metronomic; METRO) drug administration of some anticancer agents can overcome drug resistance and increase drug efficacy in many cancers, but the mechanisms are not understood fully. Previously, we showed that METRO dosing of topotecan (TOPO) is more effective than conventional (CONV) dosing in aggressive human prostate cancer (PCa) cell lines and in mouse tumor xenograft models. To gain mechanistic insights into METRO-TOPO activity, in this study we determined the effect of METRO- and CONV-TOPO treatment in a panel of human PCa cell lines representing castration-sensitive/resistant, androgen receptor (+/-), and those of different ethnicity on cell growth and gene expression. Differentially expressed genes (DEGs) were identified for METRO-TOPO therapy and compared to a PCa patient cohort and The Cancer Genome Atlas (TCGA) database. The top five DEGs were SERPINB5, CDKN1A, TNF, FOS, and ANGPT1. Ingenuity Pathway Analysis predicted several upstream regulators and identified top molecular networks associated with METRO dosing, including tumor suppression, anti-proliferation, angiogenesis, invasion, metastasis, and inflammation. Further, the top DEGs were associated with increase survival of PCa patients (TCGA database), as well as ethnic differences in gene expression patterns in patients and cell lines representing African Americans (AA) and European Americans (EA). Thus, we have identified candidate pharmacogenomic biomarkers and novel pathways associated with METRO-TOPO therapy that will serve as a foundation for further investigation and validation of METRO-TOPO as a novel treatment option for prostate cancers.
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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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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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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.