Identification of a Two-Gene (PML-EPB41) Signature With Independent Prognostic Value in Osteosarcoma.
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ABSTRACT:
SUBMITTER: Liu S
PROVIDER: S-EPMC6992559 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:BackgroundOsteosarcoma is a malignant bone tumor that typically occurs in adolescents or children under 20 years of age. Developing efficient clinical prognostic markers is crucial for improving the treatment of osteosarcoma patients.MethodsThree datasets related to osteosarcoma were acquired from the Gene Expression Omnibus (GEO) database. A gene signature model was established using the Limma package in the R software, univariate and multivariate survival analyses, and least absolute shrinkage and selection operator (LASSO) algorithms. The gene signature was then verified using external datasets.ResultsFrom the GEO database, 242 differentially expressed genes were identified. A total of 590 unique genes, including 380 genes from the human protein interaction network, were found to be related to biological processes such as bone development and bone cell development. Univariate Cox survival analyses revealed 43 genes that were associated with the prognosis of osteosarcoma patients. A seven-gene signature [retinitis pigmentosa 2 (RP2), polyhydroxybutyrate (PHB), myosin VI (MYO6), mutL homolog 1 (MLH1), Casein kinase 2 beta (CSNK2B), ribosomal protein L37A (RPL37A), and CCAAT/enhancer binding protein alpha (CEBPA)] was developed using LASSO regression analysis and multivariate regression analysis. This gene signature could stratify the prognostic risk of sample cases in the training set, the test set, and the external verification set (P<0.01). The area under the receiver operating characteristic curve for the 5-year survival was higher than 0.72 in both the training and verification groups.ConclusionsIn this study, a seven-gene signature was developed that is highly efficient at predicting the prognosis of patients with osteosarcoma, and therefore, this signature may be a crucial guide in the treatment of these patients.
| S-EPMC8848427 | biostudies-literature
Project description:BackgroundCopper is an indispensably mineral element involved in various metabolic processes and functions in the active sites of many metalloproteins. Copper dysregulation is associated with cancers such as osteosarcoma (OS), the most common primary bone malignancy with invasiveness and metastasis. However, the causality between cuproptosis and OS remains elusive. We aim to identify cuproptosis-related long non-coding RNAs (lncRNAs) for osteosarcomatous prognosis, immune microenvironment response, and immunotherapy.MethodsThe Person correlation and differential expression analysis were used to identify differentially expressed cuproptosis-related lncRNAs (CRLs). The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were performed to construct the CRL signature. The Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curve, internal validation, independent prognostic analysis, and nomograph were used to evaluate the prognostic value. The functional enrichment, tumor microenvironment, immunotherapy and chemotherapy response between the two distinct groups were further explored using a series of algorithms. The expression of signature CRLs was verified by real-time quantitative polymerase chain reaction (RT-qPCR) in OS cell lines.ResultsA novel CRL signature consisting of four CRLs were successfully identified. The K-M survival analysis indicated that the OS patients in the low-risk groups had a better prognosis than that in the high-risk group. Then, the ROC curve and subgroup survival analysis confirmed the prognostic evaluation performance of the signature. Equally, the independent prognostic analysis demonstrated that the CRL signature was an independently predicted factor for OS. Friends analysis determined the hub genes that played a critical role in differentially expressed genes between two distinct risk groups. In addition, the risk score was related to immunity status, immunotherapy response, and chemotherapeutic drug sensitivity. Finally, the expression of these signature CRLs detected by RT-qPCR was consistent with the bioinformatic analysis results.ConclusionIn summary, our study confirmed that the novel CRL signature could effectively evaluate prognosis, tumor immune microenvironment, and immunotherapy response in OS. It may benefit for clinical decision-making and provide new insights for personalized therapeutics.
| S-EPMC9606239 | biostudies-literature
Project description:Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.
| S-EPMC7771722 | biostudies-literature
Project description:BackgroundAccumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC.MethodsExpression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs.ResultsTwo robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I-II) and late (III-IV) HCC detection were 0.88 and 0.93, respectively.ConclusionsThe two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.
| S-EPMC9546683 | biostudies-literature
Project description:Background: The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. Latestresearch hasdisplayed that tumor immune cell infiltration (ICI) is associated with the clinical outcome of patients with osteosarcoma (OS). This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes. Methods: The TARGET-OS dataset was used for model training, while the GSE21257 dataset was taken forvalidation. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan-Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO regression analysis and produced a gene signature, which was next assessed with the Kaplan-Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. GSEAswere deployed to further study the functional mechanism of the signature. We conducted an analysis of 22 TICsfor identifying the role of TICs in the gene signature's prognosis ability. Results: Data from the training cohort were used to generate a nine-gene signature. The Kaplan-Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS validated the signature's capacity and independence in predicting the outcomes of OS patients in the validation cohort. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis revealed the mechanism related to the gene signature specifically. The immune-infiltration analysis uncoveredkey roles for activated mast cells in the prognosis of OS. Conclusion: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can predict OS outcome precisely and is strongly linked to activated mast cells.
| S-EPMC8450587 | biostudies-literature
Project description:BackgroundBreast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.MethodsGene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.ResultsWe constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).ConclusionOur study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.
| S-EPMC8165796 | biostudies-literature
Project description:Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P-value <0.01 in the training set. Next, using robust likelihood-based survival modeling, we developed a six-gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six-gene signature. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer overall survival (OS) outcomes than those in the low-risk group (log-rank test P-value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six-gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.
| S-EPMC6247034 | biostudies-literature
Project description:BackgroundOsteosarcoma is the most prevalent bone cancer that affects young adults and adolescents. It is the most frequent malignancy of the bone. In spite of the fact that complete surgical resection and chemotherapy have increased the overall survival of osteosarcoma patients considerably, the prognosis remains dismal in patients with recurring and/or metastasized osteosarcoma. Thus, finding predictive biomarkers representing osteosarcoma's biological variability may result in more effective treatment for osteosarcoma patients.MethodsIn this research, RNA data and clinical information were obtained from TARGET database. The risk score was calculated using a technique that incorporated both univariate and multivariate Cox regression. A variety of statistical methods were employed to assess the risk score's accuracy. These included ROC curves, nomograms, and Kaplan-Meier curves. Following that, bioinformatics studies were carried out in order to investigate the possible biological processes that influence the prognosis of osteosarcoma patients. GSEA was used to investigate the variations in pathway enrichment among the different groups of genes. To examine the disparities in the immune microenvironment, the analytical methods CIBERSORT and ssGSEA were employed.ResultsWe discovered three differentially expressed lncRNAs (RPARP-AS1, AC009159.3, and AC124312.3) that are linked to osteosarcoma prognosis. Kaplan-Meier analysis showed the presence of a signature of high-risk lncRNAs linked with a poor prognosis for osteosarcoma. Furthermore, the AUC of the lncRNAs signature was 0.773, indicating that they are useful in predicting osteosarcoma prognosis in certain cases. In predicting osteosarcoma prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA showed the presence of tumor-related pathways as well as immune-related pathways. Furthermore, TARGET revealed that immune-related functions such as checkpoint, T-cell coinhibition, and costimulation were significantly different between the high-risk and low-risk groups. LAIR1, LAG3, CD44, and CD22, as well as other immune checkpoints, were shown to be expressed differentially across the two risk groups.ConclusionThis study established that pyroptosis-derived lncRNAs had a significant predictive value for osteosarcoma patients' survival, indicating that they may be a viable target for future therapy.
| S-EPMC8601829 | biostudies-literature
Project description:AbstractBladder cancer (BC) is one of the most common malignancies worldwide. Several biomarkers related to the prognosis of patients with BC have previously been identified. However, these prognostic models use only one gene and are thus not reliable or accurate enough. The purpose of our study was to develop an innovative gene signature that has greater prognostic value in BC. So, in this study, we performed mRNA expression profiling of glycolysis-related genes in BC (n = 407) cohorts by mining data from The Cancer Genome Atlas (TCGA) database. The glycolysis-related gene sets were confirmed using the Gene Set Enrichment Analysis (GSEA). Using Cox regression analysis, a risk score staging model was built based on the genes that were determined to be significantly associated with BC outcome. Eventually, the system of risk score was structured to predict a patient's survival, and we identified four genes (CHPF, AK3, GALK1, and NUP188) that were associated with the outcomes of BC patients. According to the above-mentioned gene signature, patients were divided into two risk subgroups. The analysis showed that our constructed risk model was independent of clinical features and that the risk score was a highly powerful tool for predicting the overall survival (OS) of BC patients. Taking together, we identified a gene signature associated with glycolysis that could effectively predict the prognosis of BC patients. Our findings offer a new perspective for the clinical research and treatment of BC.
| S-EPMC7837905 | biostudies-literature