A Hypoxia Gene-Based Signature to Predict the Survival and Affect the Tumor Immune Microenvironment of Osteosarcoma in Children.
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ABSTRACT:
SUBMITTER: Jiang F
PROVIDER: S-EPMC8299210 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Introduction: Increasing evidences have shown that hypoxia and the immune microenvironment play vital roles in the development of osteosarcoma. However, reliable gene signatures based on the combination of hypoxia and the immune status for prognostic prediction of osteosarcoma have so far not been identified. Methods: The individual hypoxia and immune status of osteosarcoma patients were identified with transcriptomic profiles of a training cohort from the TARGET database using ssGSEA and ESTIMATE algorithms, respectively. Lasso regression and stepwise Cox regression were performed to develop a hypoxia-immune-based gene signature. An independent cohort from the GEO database was used for external validation. Finally, a nomogram was constructed based on the gene signature and clinical features to improve the risk stratification and to quantify the risk assessment for individual patients. Results: Hypoxia and the immune status were significantly associated with the prognosis of osteosarcoma patients. Seven hypoxia- and immune-related genes (BNIP3, SLC38A5, SLC5A3, CKMT2, S100A3, CXCL11 and PGM1) were identified to be involved in our prognostic signature. In the training cohort, the prognostic signature discriminated high-risk patients with osteosarcoma. The hypoxia-immune-based gene signature proved to be a stable and predictive method as determined in different datasets and subgroups of patients. Furthermore, a nomogram based on the prognostic signature was generated to optimize the risk stratification and to quantify the risk assessment. Similar results were validated in an independent GEO cohort, confirming the stability and reliability of the prognostic signature. Conclusion: The hypoxia-immune-based prognostic signature might contribute to the optimization of risk stratification for survival and personalized management of osteosarcoma patients.
| S-EPMC9791087 | biostudies-literature
Project description:BackgroundGenome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma.MethodsWe analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort.ResultsThe analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis.ConclusionsThe novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma.
| S-EPMC8138974 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.
| S-EPMC7811787 | biostudies-literature
Project description:Glioma groups, including lower-grade glioma (LGG) and glioblastoma multiforme (GBM), are the most common primary brain tumor. Malignant gliomas, especially glioblastomas, are associated with a dismal prognosis. Hypoxia is a driver of the malignant phenotype in glioma groups; it triggers a cascade of immunosuppressive processes and malignant cellular responses (tumor progression, anti-apoptosis, and resistance to chemoradiotherapy), which result in disease progression and poor prognosis. However, approaches to determine the extent of hypoxia in the tumor microenvironment are still unclear. Here, we downloaded 575 LGG patients and 354 GBM patients from Chinese Glioma Genome Atlas (GGGA), and 530 LGG patients and 167 GBM patients from The Cancer Genome Atlas (TCGA) with RNA sequence and clinicopathological data. We developed a hypoxia risk model to reflect the immune microenvironment in glioma and predict prognosis. High hypoxia risk score was associated with poor prognosis and indicated an immunosuppressive microenvironment. Hypoxia signature significantly correlated with clinical and molecular features and could serve as an independent prognostic factor for glioma patients. Moreover, Gene Set Enrichment Analysis showed that gene sets associated with the high-risk group were involved in carcinogenesis and immunosuppression signaling. In conclusion, we developed and validated a hypoxia risk model, which served as an independent prognostic indicator and reflected overall immune response intensity in the glioma microenvironment.
| S-EPMC7243125 | biostudies-literature
Project description:BackgroundThe 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC.MethodsThe gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis.Results8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK.ConclusionThe hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.
| S-EPMC8692015 | biostudies-literature
Project description:PurposeColon cancer is the main malignant tumor of the digestive tract. Hypoxia is highly related to the occurrence, progression and tumor immune microenvironment (TIME) of cancer. The aim of this study was to identify a hypoxia-associated signature with high accuracy for predicting the prognosis and TIME of colon cancer.MethodsDownload colon cancer data from the GEO and TCGA databases. A novel hypoxia risk model was identified to predict the prognosis of colon cancer patients. Subsequently, GSEA, TIME and mutation analysis were performed in the hypoxia high and low risk score groups. Finally, the signature gene ANKZF1 was selected for functional verification at the cellular level.ResultsA novel hypoxia risk model was identified. The risk score was significantly associated with poorer overall survival in colon cancer, and could be used as an independent prognostic factor for colon cancer. GSEA analysis found that the processes related to stimulate tumor proliferation and anti-apoptosis were significantly enriched in the hypoxia high risk score group. The expression of immunosuppressive cells and most immune checkpoints in the high risk score group was significantly higher than that in the low risk score group. In vitro cell experiments showed that knockdown the expression of ANKZF1 could inhibit the proliferation, migration and invasion of colon cancer cells.ConclusionHypoxia plays an important role in evaluating the TIME and predicting the prognosis of colon cancer.
| S-EPMC10281280 | biostudies-literature
Project description:BackgroundColon adenocarcinoma (COAD) is a common gastrointestinal tumor and often occurs in the left colon with a poor prognosis. The progression of COAD is closely related to the tumor microenvironment, especially the hypoxia. Currently, few studies have reported the correlation between hypoxia-related genes and the prognosis of COAD patients. Furthermore, we constructed a prognostic model using four hypoxia-related genes to predict the prognosis of COAD patients.MethodsThe mRNA expression profiles and corresponding clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The string online analysis tool was used to construct a protein-protein interaction network (PPI) of hypoxia-related genes. Kaplan-Meier curve was used to analyze the relationship of hypoxia risk score and the overall survival of COAD patients, and the receiver operating characteristic (ROC) curve was used to assess the reliability.ResultsWe screened out four hypoxia genes, including TKTL1 (transketolase like 1), SLC2A3 (solute carrier family 2 member 3), ALDOB (aldolase, fructose-bisphosphate B) and ENO3 (enolase 3), which were used to construct a hypoxia risk model to predict the overall survival of COAD patients. Besides, we also found that the hypoxia risk score was correlated with the immunosuppression of tumor microenvironment.ConclusionThe model we constructed with four survival-related hypoxia genes, including TKTL1, SLC2A3, ALDOB and ENO3, could be used to predict the overall survival of COAD patients with high stability.
| S-EPMC8687688 | 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
Project description:Background: Abnormal expression of lncRNA is closely related to the occurrence and metastasis of osteosarcoma. The tumor immune microenvironment (TIM) is considered to be an important factor affecting the prognosis and treatment of osteosarcoma. This study aims to explore the effect of immune-related lncRNAs (IRLs) on the prognosis of osteosarcoma and its relationship with the TIM. Methods: Ninety-five osteosarcoma samples from the TARGET database were included. Iterative LASSO regression and multivariate Cox regression analysis were used to screen the IRLs signature with the optimal AUC. The predict function was used to calculate the risk score and divide osteosarcoma into a high-risk group and low-risk group based on the optimal cut-off value of the risk score. The lncRNAs in IRLs signature that affect metastasis were screened for in vitro validation. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms were used to evaluate the role of TIM in the influence of IRLs on osteosarcoma prognosis. Results: Ten IRLs constituted the IRLs signature, with an AUC of 0.96. The recurrence and metastasis rates of osteosarcoma in the high-risk group were higher than those in the low-risk group. In vitro experiments showed that knockdown of lncRNA (AC006033.2) could increase the proliferation, migration, and invasion of osteosarcoma. ssGSEA and ESTIMATE results showed that the immune cell content and immune score in the low-risk group were generally higher than those in the high-risk group. In addition, the expression levels of immune escape-related genes were higher in the high-risk group. Conclusion: The IRLs signature is a reliable biomarker for the prognosis of osteosarcoma, and they alter the prognosis of osteosarcoma. In addition, IRLs signature and patient prognosis may be related to TIM in osteosarcoma. The higher the content of immune cells in the TIM of osteosarcoma, the lower the risk score of patients and the better the prognosis. The higher the expression of immune escape-related genes, the lower the risk score of patients and the better the prognosis.
| S-EPMC8529014 | biostudies-literature
Project description:Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.
| S-EPMC10518285 | biostudies-literature