Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma.
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ABSTRACT:
SUBMITTER: He J
PROVIDER: S-EPMC8420975 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveDespite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG.MethodsRNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER.ResultsNoted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan-Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4+ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8+T cells were significant higher in high-risk group in both TCGA and CGGA cohorts.ConclusionThe present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.
| S-EPMC7174786 | biostudies-literature
Project description:BACKGROUND:Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. METHODS:1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, P53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, PR, CD3, H3K27M, TS, and 1p/19q status. RESULTS:The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14?years old (pediatric group), 15-47?years old (youth group), 48-63?years old (middle-aged group), and???64?years old (elderly group).The proportions of glioblastoma and large tumor size (4-6?cm) increased with age (p?=?0.000, p?=?0.018, respectively). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p?=?0.000, p?=?0.017, respectively). CONCLUSIONS:Appropriate classifications of the age group for risk stratification are 0-14?years old (pediatric group), 15-47?years old (young group), 48-63?years old (middle age group) and???64?years old (elderly group). This age group classification is effective in evaluating the risk of glioblastoma in glioma patients.
| S-EPMC7439690 | biostudies-literature
Project description:BackgroundWith low response to present immunotherapy, it is imperative to identify new immune-related biomarkers for more effective immunotherapies for oral cancer.MethodsRNA profiles for 390 oral cancer patients and 32 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database and differentially expressed genes (DEGs) were analyzed. Immune genesets from ImmPort repository were overlapped with DEGs. After implementing univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, key immune-related gene pairs (IRGPs) among the overlapped DEGs for predicting the survival risk were obtained. Then, the cutoff of risk score was calculated by the receiver operating characteristic (ROC) curve to stratify oral cancer patients into high and low-risk groups. Multivariate Cox analysis was used to analyze independent prognostic indicators for oral cancer. Besides, infiltration of immune cells, functional annotation, and mutation analysis of IRGPs were conducted. Biological functions correlated with IRGPs were enriched by Gene Set Enrichment Analysis (GSEA) method.ResultsWe identified 698 differentially expressed genes (DEGs) in response to oral cancer. 17 IRGPs among the DEGs were identified and integrated into a risk score model. Patients in the high-risk group have a significantly worse prognosis than those in the low-risk group in both training (P<0.001) and test (P=0.019) cohorts. Meanwhile, the IRGP model was identified as an independent prognostic factor for oral cancer. Different infiltration patterns of immune cells were found between the high- and low-risk groups that more types of T and B cells were enriched in the low-risk group. More immune-related signaling pathways were highly enriched in the low-risk group and Tenascin C (TNC) was the most frequently mutated gene. We have developed a novel 17-IRGPs signature for risk stratification and prognostic prediction of oral cancer.ConclusionOur study provides a foundation for improved immunotherapy and prognosis and is beneficial to the individualized management of oral cancer patients.
| S-EPMC9264557 | biostudies-literature
Project description:BackgroundThe immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.MethodsDifferently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.ResultsFive DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.ConclusionsThe prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.
| S-EPMC10585734 | biostudies-literature
Project description:Cuproptosis is the most recently discovered type of regulated cell death and is mediated by copper ions. Studies show that cuproptosis plays a significant role in cancer development and progression. Lower-grade gliomas (LGGs) are slow-growing brain tumors. The majority of LGGs progress to high-grade glioma, which makes it difficult to predict the prognosis. However, the prognostic value of cuproptosis-related genes (CRGs) in LGG needs to be further explored. mRNA expression profiles and clinical data of LGG patients were collected from public sources for this study. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to build a multigene signature that could divide patients into different risk groups. The differences in clinical pathological characteristics, immune infiltration characteristics, and mutation status were evaluated in risk subgroups. In addition, drug sensitivity and immune checkpoint scores were estimated in risk subgroups to provide LGG patients with precision medication. We found that all CRGs were differentially expressed in LGG and normal tissues. Patients were divided into high- and low-risk groups based on the risk score of the CRG signature. Patients in the high-risk group had a considerably lower overall survival rate than those in the low-risk group. According to functional analysis, pathways related to the immune system were enriched, and the immune state differed across the two risk groups. Immune characteristic analysis showed that the immune cell proportion and immune scores were different in the different groups. High-risk group was characterized by low sensitivity to chemotherapy but high sensitivity to immune checkpoint inhibitors. The current study revealed that the novel CRG signature was related to the prognosis, clinicopathological features, immune characteristics, and treatment perference of LGG.
| S-EPMC9640744 | biostudies-literature
Project description:BackgroundLow-grade glioma (LGG) is a heterogeneous tumor that might develop into high-grade malignant glioma, which markedly reduces patient survival time. Endocytosis is a cellular process responsible for the internalization of cell surface proteins or external materials into the cytosol. Dysregulated endocytic pathways have been linked to all steps of oncogenesis, from initial transformation to late invasion and metastasis. However, endocytosis-related gene (ERG) signatures have not been used to study the correlations between endocytosis and prognosis in cancer. Therefore, it is essential to develop a prognostic model for LGG based on the expression profiles of ERGs.MethodsThe Cancer Genome Atlas and the Genotype-Tissue Expression database were used to identify differentially expressed ERGs in LGG patients. Gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene set enrichment analysis methodologies were adopted for functional analysis. A protein-protein interaction (PPI) network was constructed and hub genes were identified based on the Search Tool for the Retrieval of Interacting Proteins database. Univariate and multivariate Cox regression analyses were used to develop an ERG signature to predict the overall survival (OS) of LGG patients. Finally, the association between the ERG signature and gene mutation status was further analyzed.ResultsSixty-two ERGs showed distinct mRNA expression patterns between normal brain tissues and LGG tissues. Functional analysis indicated that these ERGs were strikingly enriched in endosomal trafficking pathways. The PPI network indicated that EGFR was the most central protein. We then built a 29-gene signature, dividing patients into high-risk and low-risk groups with significantly different OS times. The prognostic performance of the 29-gene signature was validated in another LGG cohort. Additionally, we found that the mutation scores calculated based on the TTN, PIK3CA, NF1, and IDH1 mutation status were significantly correlated with the endocytosis-related prognostic signature. Finally, a clinical nomogram with a concordance index of 0.881 predicted the survival probability of LGG patients by integrating clinicopathologic features and ERG signatures.ConclusionOur ERG-based prediction models could serve as an independent prognostic tool to accurately predict the outcomes of LGG.
| S-EPMC8450508 | biostudies-literature
Project description:A substantial proportion of prostatic adenocarcinoma (PRAD) patients experience biochemical failure (BCF) after radical prostatectomy (RP). The immune microenvironment plays a vital role in carcinogenesis and the development of PRAD. This study aimed to identify a novel immune-related gene (IRG)-based signature for risk stratification and prognosis of BCF in PRAD. Weighted gene coexpression network analysis was carried out to identify a BCF-related module in a discovery cohort of patients who underwent RP at the Massachusetts General Hospital. The median follow-up time was 70.32 months. Random forest and multivariate stepwise Cox regression analyses were used to identify an IRG-based signature from the specific module. Risk plot analyses, Kaplan-Meier curves, receiver operating characteristic curves, univariate and multivariate Cox regression analyses, stratified analysis, and Harrell's concordance index were used to assess the prognostic value and predictive accuracy of the IRG-based signature in the internal discovery cohort; The Cancer Genome Atlas database was used as a validation cohort. Tumor immune estimation resource database analysis and CIBERSORT algorithm were used to assess the immunophenotype of PRAD. A novel IRG-based signature was identified from the specific module. Five IRGs (BUB1B, NDN, NID1, COL4A6, and FLRT2) were verified as components of the risk signature. The IRG-based signature showed good prognostic value and predictive accuracy in both the discovery and validation cohorts. Infiltrations of various immune cells were significantly different between low-risk and high-risk groups in PRAD. We identified a novel IRG-based signature that could function as an index for assessing tumor immune status and risk stratification in PRAD.
| S-EPMC8486177 | biostudies-literature
Project description:Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.
| S-EPMC7873765 | biostudies-literature
Project description:As the malignancy with the highest global incidence, breast cancer represents a significant threat to women's health. Recent advances have shed light on the importance of mitochondrial function in cancer, particularly in metabolic reprogramming within tumors. Recognizing this, we developed a novel risk signature based on mitochondrial-related genes to improve prognosis prediction and risk stratification in breast cancer patients. In this study, transcriptome data and clinical features of breast cancer samples were extracted from two sources: the TCGA, serving as the training set, and the METABRIC, used as the independent validation set. We developed the signature using LASSO-Cox regression and assessed its prognostic efficacy via ROC curves. Furthermore, the signature was integrated with clinical features to create a Nomogram model, whose accuracy was validated through clinical calibration curves and decision curve analysis. To further elucidate prognostic variations between high and low-risk groups, we conducted functional enrichment and immune infiltration analyses. Additionally, the study encompassed a comparison of mutation landscapes and drug sensitivity, providing a comprehensive understanding of the differing characteristics in these groups. Conclusively, we established a risk signature comprising 8 mitochondrial-related genes-ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3, and BCL2A1. This signature was identified as an independent risk predictor for breast cancer patient survival, exhibiting a significant high hazard ratio (HR = 3.028, 95%CI 2.038-4.499, P < 0.001). Patients in the low-risk group showed a more favorable prognosis, with enhanced immune infiltration, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs. In contrast, the high-risk group exhibited an adverse trend in these aspects. This risk signature represents a novel and effective prognostic indicator, suggesting valuable insights for patient stratification in breast cancer.
| S-EPMC10838276 | biostudies-literature
Project description:Immune-related gene pairs (IRGPs) have been associated with prognosis in various cancer types, but few studies have examined their prognostic capabilities in glioma patients. Here, we gathered the gene expression and clinical profile data of primary lower-grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA, containing CGGAseq1 and CGGAseq2), the Gene Expression Omnibus (GEO: GSE16011), and Rembrandt datasets. In the TCGA dataset, univariate Cox regression was performed to detect overall survival (OS)-related IRGs, Lasso regression, and multivariate Cox regression were used to screen robust prognosis-related IRGs, and 19 IRGs were selected for the construction of an IRGP prognostic signature. All patients were allotted to high- and low-risk subgroups based on the TCGA dataset median value risk score. Validation analysis indicated that the IRGP signature returned a stable prognostic value among all datasets. Univariate and multivariate Cox regression analyses indicated that the IRG -signature could efficiently predict the prognosis of primary LGG patients. The IRGP-signature-based nomogram model was built, revealing the reliable ability of the IRGP signature to predict clinical prognosis. The single-sample gene set enrichment analysis (ssGSEA) suggested that high-risk samples contained higher numbers of immune cells but featured lower tumor purity than low-risk samples. Finally, we verified the prognostic ability of the IRGP signature using experiments performed in LGG cells. These results indicated that the IRGP signature could be regarded as a stable prognostic assessment predictor for identifying high-risk primary LGG patients.
| S-EPMC8190397 | biostudies-literature