Comprehensive Analysis of the Tumor Microenvironment and Ferroptosis-Related Genes Predict Prognosis with Ovarian Cancer.
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ABSTRACT:
SUBMITTER: Li XX
PROVIDER: S-EPMC8634641 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThere is a significant role for peroxisome proliferator-activated receptors (PPARs) in the development of cancer. Nevertheless, the role of PPARs-related genes in ovarian cancer (OC) remains unclear.MethodsThe open-accessed data used for analysis were downloaded from The Cancer Genome Atlas database, which was analyzed using the R software.ResultsIn our study, we comprehensively investigated the PPAR target genes in OC, including their biological role. Meanwhile, a prognosis signature consisting of eight PPAR target genes was established, including apolipoprotein A-V, UDP glucuronosyltransferase 2 family, polypeptide B4, TSC22 domain family, member 1, growth hormone inducible transmembrane protein, renin, dedicator of cytokinesis 4, enoyl CoA hydratase 1, peroxisomal (ECH1), and angiopoietin-like 4, which showed a good prediction efficiency. A nomogram was constructed by combining the clinical feature and risk score. Immune infiltration and biological enrichment analysis were applied to investigate the difference between high- and low-risk patients. Immunotherapy analysis indicated that low-risk patients might respond better to immunotherapy. Drug sensitivity analysis indicated that high-risk patients might respond better to bleomycin, nilotinib, pazopanib, pyrimethamine, and vinorelbine, yet worse to cisplatin and gefitinib. Furthermore, the gene ECH1 was selected for further analysis.ConclusionsOur study identified a prognosis signature that could effectively indicates patients survival. Meanwhile, our study can provide the direction for future studies focused on the PPARs in OC.
| S-EPMC10195182 | biostudies-literature
Project description:BackgroundCervical cancer (CC) is a disease that affects female health; therefore, timely prevention and diagnosis of CC are crucial to decrease its mortality. Ferroptosis, an iron-dependent form of non-apoptotic cell death, is involved in tumor progression. However, the role of ferroptosis-related genes (FRGs) in the immune microenvironment of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) remains unclear.MethodsThe data sets of CESC patients, including RNA sequencing (RNA-seq) data and clinical information, were obtained from The Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was used to determine the stromal score, immune score, estimate score, and tumor purity in the CESC patients' data. Additionally, FRGs were identified and used to construct a signature marker for the diagnosis and prognosis of CESC. Patients were assigned to a high- or low-risk group based on their median risk score. The tumor microenvironment (TME), immune infiltration, and functional enrichment were compared between the low- and high-risk groups. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA), were conducted to explore the underlying mechanisms in the development and prognosis of CESC.ResultsThe results showed that the estimate score was suitable for predicting the prognosis of CESC patients. Additionally, a prediction model involving four FRGs [phosphatidylethanolamine-binding protein 1 (PEBP1), dual oxidase 1 (DUOX1), iron-sulfur cluster assembly enzyme (ISCU), and cytochrome b (-245) beta subunit (CYBB)] was constructed. The performance of the prognostic model and significant clinical characteristics in predicting CESC prognosis was subsequently validated. Our results showed that the expression of CYBB affected immune cells. Gene functional enrichment analyses showed that these differentially expressed FRGs were mainly enriched in the immunity-related signaling pathways, which indicated that FRGs might affect the development and prognosis of CC by regulating the immune microenvironment.ConclusionsThe expression profiles of FRGs are closely related to the TME and the prognostic survival of CESC patients. The interaction between ferroptosis and immunity in the development of CC provides new insight into the molecular mechanisms of CC.
| S-EPMC8848400 | biostudies-literature
Project description:BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and resultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.
| S-EPMC8710702 | biostudies-literature
Project description:Cuproptosis, a newly discovered form of programmed cell death, plays a vital role in the occurrence and development of tumors. However, the role of cuproptosis in the bladder cancer tumor microenvironment remains unclear. In this study, we developed a method for predicting the prognostic outcomes and guiding the treatment selection for patients with bladder cancer. We obtained 1001 samples and survival data points from The Cancer Genome Atlas database and Gene Expression Omnibus database. Using cuproptosis-related genes (CRGs) identified in previous studies, we analyzed CRG transcriptional changes and identified two molecular subtypes, namely high- and low-risk patients. The prognostic features of eight genes (PDGFRB, COMP, GREM1, FRRS1, SDHD, RARRES2, CRTAC1, and HMGCS2) were determined. The CRG molecular typing and risk scores were correlated with clinicopathological features, prognosis, tumor microenvironment cell infiltration characteristics, immune checkpoint activation, mutation burden, and chemotherapy drug sensitivity. Additionally, we constructed an accurate nomogram to improve the clinical applicability of the CRG_score. qRT-PCR was used to detect the expression levels of eight genes in bladder cancer tissues, and the results were consistent with the predicted results. These findings may help us to understand the role of cuproptosis in cancer and provide new directions for the design of personalized treatment and prediction of survival outcomes in patients with bladder cancer.
| S-EPMC9989218 | biostudies-literature
Project description:BackgroundOvarian cancer (OC) is a highly malignant disease with a poor prognosis and high recurrence rate. At present, there is no accurate strategy to predict the prognosis and recurrence of OC. The aim of this study was to identify gene-based signatures to predict OC prognosis and recurrence.MethodsmRNA expression profiles and corresponding clinical information regarding OC were collected from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and LASSO analysis were performed, and Kaplan-Meier curves, time-dependent ROC curves, and nomograms were constructed using R software and GraphPad Prism7.ResultsWe first identified several key signalling pathways that affected ovarian tumorigenesis by GSEA. We then established a nine-gene-based signature for overall survival (OS) and a five-gene-based-signature for relapse-free survival (RFS) using LASSO Cox regression analysis of the TCGA dataset and validated the prognostic value of these signatures in independent GEO datasets. We also confirmed that these signatures were independent risk factors for OS and RFS by multivariate Cox analysis. Time-dependent ROC analysis showed that the AUC values for OS and RFS were 0.640, 0.663, 0.758, and 0.891, and 0.638, 0.722, 0.813, and 0.972 at 1, 3, 5, and 10 years, respectively. The results of the nomogram analysis demonstrated that combining two signatures with the TNM staging system and tumour status yielded better predictive ability.ConclusionIn conclusion, the two-gene-based signatures established in this study may serve as novel and independent prognostic indicators for OS and RFS.
| S-EPMC7718801 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer.MethodsThe Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted.ResultsBased on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor.ConclusionsIn this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.
| S-EPMC11313097 | biostudies-literature
Project description:BackgroundCutaneous Melanoma (CM) is a malignant disease with increasing incidence and high mortality. Ferroptosis is a new kind of cell death and related to tumor blood and lymphatic metastasis. This study aims at using bioinformatics technology to construct a prognostic signature and identify ferroptosis-related biomarkers to improve the prognosis and treatment of cutaneous melanoma.MethodsWe used bioinformatics tools to analyze RNA sequencing expression data with clinical information from multiple databases, utilized varieties of statistical methods to construct a ferroptosis-related prognostic signature of cutaneous melanoma and screened out specific genes with independent prognostic ability.ResultsWe obtained 22 ferroptosis-related (P < 0.05) prognostic DEGs in the uniCox regression analysis, among which 10 high-expressed genes (ATG5, CHAC1, FANCD2, FBXL5, HMOX2, HSPB1, NQO1, PEBP1, PRNP, SLC3A2) were screened out by LASSO regression analysis to establish a predictive model. Meanwhile, the ferroptosis-related signature and the nomogram we drew performed an excellent performance based on Kaplan-Meier (K-M), Receiver operating characteristic (ROC) and calibration curves. Univariate and multivariable cox analyses displayed that our model was greater than other prognostic features. GO and KEGG analyses revealed that 10-biomarker signature was mainly related to epidermis differentiation and immunity. ssGSEA analysis indicated that the immune status between the two risk groups was highly different. Besides, we found that two genes (CP, ZEB1) had independent prognostic ability and can be applied for drug research. Both genes were highly related to immunity. GSEA illustrated that ZEB1 may be involved in cellular functions such as proliferation, apoptosis, and migration, while CP was closely connected to immune cell related functions.ConclusionThe present study suggested a 10-biomarker signature can be clinically used to predict the prognosis of cutaneous melanoma, which was better than conventional factors. CP and ZEB1 were independent prognostic genes and can be applied to guide treatment. In addition, ZEB1 mutation was highly related to overall survival in cutaneous melanoma, while CP may be associated with tumor progression. Our study comprehensively analyzed the relationship between iron metabolism, ferroptosis-related genes, and the prognosis of cutaneous melanoma, provided new insight for molecular mechanisms and treatment of ferroptosis and cutaneous melanoma.
| S-EPMC8886785 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common subtype of lung cancer which typically exhibits a diverse progression trajectory. Our study sought to explore the cell differentiation trajectory of LUAD and its clinical relevance.MethodsUtilizing a single-cell RNA-sequencing dataset (GSE117570), we identified LUAD cells of distinct differential status along with differentiation-related genes (DRGs). DRGs were applied to the analysis of bulk-tissue RNA-sequencing dataset (GSE72094) to classify tumors into different subtypes, whose clinical relevance was further analyzed. DRGs were also applied to gene co-expression network analysis (WGCNA) using another bulk-tissue RNA-sequencing dataset (TCGA-LUAD). Genes from modules that demonstrated a significant correlation with clinical traits and were differentially expressed between normal tissue and tumors were identified. Among these, genes with significant prognostic relevance were used for the development of a prognostic nomogram, which was tested on TCGA-LUAD dataset and validated in GSE72094. Finally, CCK-8, EdU, cell apoptosis, cell colony formation, and Transwell assays were used to verify the functions of the identified genes.ResultsFour clusters of cells with distinct differentiation status were characterized, whose DRGs were predominantly correlated with pathways of immune regulation. Based on DRGs, tumors could be clustered into four subtypes associated with distinct immune microenvironment and clinical outcomes. DRGs were categorized into four modules. A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes. Finally, we verified that knockdown of WFDC2 inhibited proliferation, migration, and invasion but promoted the apoptosis of A549 cells in vitro.ConclusionThe cellular composition and cellular differentiation status of tumor mass can predict the clinical outcomes of LUAD patients. It also plays an important role in shaping the tumor immune microenvironment.
| S-EPMC9339705 | biostudies-literature
Project description:Background: Ferroptosis is a form of regulated cell death that occurs as a consequence of lethal lipid peroxidation. A wealth of studies has demonstrated that ferroptosis profoundly modulated numerous biological behaviors of tumor. However, its natural functions in gastric cancer (GC) remain to be explored. Methods: Firstly, a total of over 1,000 GC patients from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database were included in our study. Secondly, 32 ferroptosis-related genes were extracted from the ferrDb website. Then, unsupervised clustering was performed to classify patients into three distinct ferroptosis-related clusters. Subsequently, we systematically and comprehensively explored the biological characteristics of each cluster. Finally, we constructed a scoring system, named ferroptosis score, to quantify each cluster and also investigated the predictive therapeutic value of the ferroptosis score for chemotherapy and immunotherapy. Results: Based on the expressions of 32 ferroptosis-related genes, three distinct ferroptosis-related subtypes with various biological characteristics were determined. Integrated analysis showed that cluster 1 is a microsatellite instability (MSI)-like subtype, cluster 2 is an epithelial-mesenchymal transition (EMT)-like subtype, while cluster 3 tends to be a metabolic-like subtype. Prognostic analysis revealed that patients in cluster 2 had a worse overall survival and relapse-free survival. The distribution of the ferroptosis score was significantly different in clusters and gene clusters. The ferroptosis score could predict the biological characteristics of each cluster, the stromal activity, and progression of tumor. The low ferroptosis score group was characterized by the activation of antigen processing and presentation, DNA damage repair pathways, and metabolic pathways, while the high ferroptosis score group was characterized by stromal activation. In response to anticancer drugs, the ferroptosis score was highly negatively associated with drugs targeting MAPK signaling and PI3K/mTOR signaling, while it was positively correlated with drugs targeting the cell cycle, mitosis, and metabolism. Finally, we also proved that the ferroptosis score could serve as a reliable biomarker to predict response to immunotherapy. Conclusion: This work revealed that tumor cells and their surrounding microenvironment could be shaped by varying the activation degrees of ferroptosis. Establishing ferroptosis-related subtypes would guide in predicting the biological features of individual tumors and selecting appropriate treatment protocols for patients.
| S-EPMC8517126 | biostudies-literature
Project description:BackgroundHypoxia will trigger a series of immunosuppressive process in tumor microenvironment, leading to the progression in gastric cancer (GC). This research aims to establish a prognostic model made up of hypoxia-risk-related genes in GC.MethodsHypoxic genes were outlined via the protein-protein interaction network. And a prognostic model was developed using univariate cox analysis and lasso regression from data in TCGA. Two independent queues of GEO were used for validation.ResultsWe set up a hypoxic model presented as an independent prognostic factor for GC. And a nomogram combined this model with clinical features can predict OS with great performance. Furthermore, DNA methylation, IHC and cell line analyses validated the expression of hypoxic genes in GC.ConclusionsIn summary, we proposed and verified a hypoxia-risk-related model, which could reflect the immune microenvironment and predict prognosis in GC.
| S-EPMC9755770 | biostudies-literature