A radiosensitivity gene signature in predicting glioma prognostic via EMT pathway.
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ABSTRACT:
SUBMITTER: Meng J
PROVIDER: S-EPMC4148091 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective: Glioma is the most common and fatal primary brain tumor that has a high risk of recurrence in adults. Identification of predictive biomarkers is necessary to optimize therapeutic strategies. This study investigated the predictive efficacy of a previously identified radiosensitivity signature as well as Exportin 1 (XPO1) expression levels. Methods: A total of 1,552 patients diagnosed with glioma were analyzed using the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. The radiosensitive and radioresistant groups were identified based on a radiosensitivity signature. Patients were also stratified into XPO1-high and XPO1-low groups based on XPO1 mRNA expression levels. Overall survival rates were compared across patient groups. Differential gene expression was detected and analyzed through pathway enrichment and Gene Set Enrichment Analysis (GSEA). To predict 1-, 3-, and 5-years survival rates for glioma patients, a nomogram was established combining the radiosensitivity gene signature, XPO1 status, and clinical characteristics. An artificial intelligence clustering system and a survival prediction system of glioma were developed to predict individual risk. Results: This proposed classification based on a radiosensitivity gene signature and XPO1 expression levels provides an independent prognostic factor for glioma. The RR-XPO1-high group shows a poor prognosis and may benefit most from radiotherapy-combined anti-XPO1 treatment. The nomogram based on the radiosensitivity gene signature, XPO1 expression, and clinical characteristics performs more optimally compared to the WHO classification and IDH status in predicting survival rates for glioma patients. The online clustering and prediction systems make it accessible to predict risk and optimize treatment for a special patient. The cell cycle, p53, and focal adhesion pathways are associated with more invasive glioma cases. Conclusion: Combining the radiosensitivity signature and XPO1 expression is a favorable approach to predict outcomes as well as determine optimal therapeutic strategies for glioma patients.
| S-EPMC7308508 | biostudies-literature
Project description:BackgroundNicotinamide adenine dinucleotide (NAD+) metabolism is involved in a series of cancer pathogenesis processes, and is considered a promising therapeutic target for cancer treatment. However, a comprehensive analysis of NAD+ metabolism events on immune regulation and cancer survival has not yet been conducted. Here, we constructed a prognostic NAD+ metabolism-related gene signature (NMRGS) associated with immune checkpoint inhibitor (ICI) efficacy in glioma.Methods40 NAD+ metabolism-related genes (NMRGs) were obtained from the Reactome database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Glioma cases with transcriptome data and clinical information were obtained from Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). NMRGS was constructed based on the calculated risk score using univariate analysis, Kaplan-Meier analysis, multivariate Cox regression, and nomogram. This NMRGS was verified in training (CGGA693) and validation (TCGA and CGGA325) cohorts. The immune characteristics, mutation profile, and response to ICI therapy were subsequently analyzed for different NMRGS subgroups.ResultsSix NAD+ metabolism-related genes, including CD38, nicotinamide adenine dinucleotide kinase (NADK), nicotinate phosphoribosyltransferase (NAPRT), nicotinamide/nicotinic acid mononucleotide adenylyltransferase 3 (NMNAT3), poly(ADP-Ribose) polymerase family member 6 (PARP6), and poly(ADP-Ribose) polymerase family member 9 (PARP9), were ultimately used to construct a comprehensive risk model for glioma patients. Patients in the NMRGS-high group showed a poorer survival outcome than those in the NMRGS-low group. The area under curve (AUC) indicated that NMRGS has good potential in glioma prognostic prediction. A nomogram with improved accuracy was established based on independent prognostic factors (NMRGS score, 1p19q codeletion status, and WHO grade). Furthermore, patients in the NMRGS-high group showed a more immunosuppressive microenvironment, higher tumor mutation burden (TMB), higher human leucocyte antigen (HLA) expression and a more therapeutic response to ICI therapy.ConclusionsThis study constructed a prognostic NAD+ metabolism-related signature associated with the immune landscape in glioma, which can be used for guiding individualized ICI therapy.
| S-EPMC9945104 | biostudies-literature
Project description:BackgroundGene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.MethodsRadio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The "DESeq2" R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.ResultsBased on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan-Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.ConclusionOur study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.
| S-EPMC10496232 | biostudies-literature
Project description:BackgroundThe correlation between epithelial-mesenchymal transition (EMT) and osteosarcoma (OS) has been widely reported. Integration of the EMT-related genes to predict the prognosis is significant for investigating the mechanism of EMT in OS. Here, we aimed to construct a prognostic EMT-related gene signature for OS.MethodsTranscriptomic and survival data of OS patients were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO). We performed univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and stepwise multivariate Cox regression analysis to construct EMT-related gene signatures. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) were applied to evaluate its predictive performance. GSVA, ssGSEA, ESTIMATE, and scRNA-seq were performed to investigate the tumor microenvironment, and the correlation between IC50 of drugs and ERG score was investigated. Furthermore, Edu and transwell experiments were conducted to assess the malignancy of OS cells.ResultsWe constructed a novel EMT-related gene signature (including CDK3, MYC, UHRF2, STC2, COL5A2, MMD, and EHMT2) for outcome prediction of OS. According to the signature, patients stratified into high- and low-ERG-score groups exhibited significantly different prognoses. ROC curves and Kaplan-Meier analysis revealed a promising performance of the signature with external validation. GSVA, ssGSEA, ESTIMATE algorithm, and scRNA-seq excavated EMT-related pathways and suggested the correlation between ERG score and immune activation. Notably, the pivotal gene CDK3 was upregulated in OS tissue and positively related to OS cell proliferation and migration.ConclusionOur EMT-related gene signature might reference OS risk stratification and guide clinical strategies as an independent prognostic factor in OS.
| S-EPMC10278480 | biostudies-literature
Project description:BackgroundGlioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.MethodsWe integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.ResultsOur study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.ConclusionOur work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.
| S-EPMC8448281 | biostudies-literature
Project description:BackgroundIt has been widely reported that epithelial-mesenchymal transition (EMT) is associated with malignant progression in gastric cancer (GC). Integration of the molecules related to EMT for predicting overall survival (OS) is meaningful for understanding the role of EMT in GC. Here, we aimed to establish an EMT-related gene signature in GC.MethodsTranscriptional profiles and clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA). We constructed EMT-related gene signature for predicting OS by using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier analysis were performed to assess its predictive value. A nomogram combining the prognostic signature with clinical characteristics for OS prediction was established. And its predictive power was estimated by concordance index (C-index), time-dependent ROC curve, calibration curve and decision curve analysis (DCA). GSE62254 dataset from Gene Expression Omnibus (GEO) was used for external validation. Quantitative real-time PCR (qRT-PCR) was used to detected the mRNA expression of the five EMT-related genes in human normal gastric mucosal and GC cell lines. To further understand the potential mechanisms of the signature, Gene Set Enrichment Analysis (GSEA), pathway enrichment analysis, predictions of transcription factors (TFs)/miRNAs were performed.ResultsA novel EMT-related gene signature (including ITGAV, DAB2, SERPINE1, MATN3, PLOD2) was constructed for OS prediction of GC. With external validation, ROC curves indicated the signature's good performance. Patients stratified into high- and low-risk groups based on the signature yielded significantly different prognosis. Univariate and multivariate Cox regression suggested that the signature was an independent prognostic variable. Nomogram for prognostication including the signature presented better predictive accuracy and clinical usefulness than the similar model without risk score to some extent with external validation. The qRT-PCR assays suggested that high expression of the five EMT-related genes could be found in human GC cell lines compared with normal gastric mucosal cell line. GSEA and pathway enrichment analysis revealed that focal adhesion and ECM-receptor interaction might be the two important pathways to the signature.ConclusionOur EMT-related gene signature may have practical application as an independent prognostic factor in GC.
| S-EPMC8264558 | biostudies-literature
Project description:BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.
| S-EPMC8365518 | biostudies-literature
Project description:BackgroundFor pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.MethodsUnivariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.ResultsLASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.ConclusionWe established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.
| S-EPMC8119901 | biostudies-literature
Project description:Gliomas are the most common and aggressive primary tumors in adults. The current approaches, such as histological classification and molecular genetics, have limitation in prediction of individual therapeutic outcomes due to heterogeneity within the tumor groups. Recent studies have proposed several gene signatures to predict glioma's prognosis. However, most of the gene expression profiling studies have been performed on relatively small number of patients and combined probes from diverse microarray chips. Here, we identified prognostic 89 common genes from diverse microarray chips. The 89-gene signature classified patients into good and bad prognostic groups which differed in the overall survival significantly, reflecting the biological characteristics and heterogeneity. The robustness and accuracy of the gene signature as an independent prognostic factor was validated in three microarray and one RNA-seq data sets independently. By incorporating into histological classification and molecular marker, the 89-gene signature could further stratify patients with 1p/19q co-deletion and IDH1 mutation. Additionally, subset analyses suggested that the 89-gene signature could predict patients who would benefit from adjuvant chemotherapy. Conclusively, we propose that the 89-gene signature would have an independent and accurate prognostic value for clinical use. This study also offers opportunities for novel targeted treatment of individual patients.
| S-EPMC5239472 | biostudies-literature
Project description:ObjectiveThis study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.MethodsBased on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.ResultsWe identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10-9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.ConclusionThe prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.
| S-EPMC6457303 | biostudies-literature