Heme Oxygenase-1 Predicts Risk Stratification and Immunotherapy Efficacy in Lower Grade Gliomas

Background: Gliomas are the most common tumors in human brains with unpleasing outcomes. Heme oxygenase-1 (HMOX1, HO-1) was a potential target for human cancers. However, their relationship remains incompletely discussed.Methods: We employed a total of 952 lower grade glioma (LGG) patients from TCGA and CGGA databases, and 29 samples in our hospital for subsequent analyses. Expression, mutational, survival, and immune profiles of HMOX1 were comprehensively evaluated. We constructed a risk signature using the LASSO Cox regression model, and further generated a nomogram model to predict survival of LGG patients. Single-cell transcriptomic sequencing data were also employed to investigated the role of HMOX1 in cancer cells.Results: We found that HMOX1 was overexpressed and was related to poorer survival in gliomas. HMOX1-related genes (HRGs) were involved in immune-related pathways. Patients in the high-risk group exhibited significantly poorer overall survival. The risk score was positively correlated with the abundance of resting memory CD4+ T cells, M1, M2 macrophages, and activated dendritic cells. Additionally, immunotherapy showed potent efficacy in low-risk group. And patients with lower HMOX1 expression were predicted to have better response to immunotherapies, suggesting that immunotherapies combined with HMOX1 inhibition may execute good responses. Moreover, significant correlations were found between HMOX1 expression and single-cell functional states including angiogenesis, hypoxia, and metastasis. Finally, we constructed a nomogram which could predict 1-, 3-, and 5-year survival in LGG patients.Conclusion:HMOX1 is involved in immune infiltration and predicts poor survival in patients with lower grade glioma. Importantly, HMOX1 were related to oncological functional states including angiogenesis, hypoxia, and metastasis. A nomogram integrated with the risk signature was obtained to robustly predict glioma patient outcomes, with the potential to guide clinical decision-making.

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Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

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The Role of Surgery in IDH-Wild-Type Lower-Grade Gliomas: Threshold at a High Extent of Resection Should be Pursued

Neurosurgery ◽

10.1093/neuros/nyab052 ◽

2021 ◽

Author(s):

Peng Wang ◽

Chen Luo ◽

Peng-jie Hong ◽

Wen-ting Rui ◽

Shuai Wu

Keyword(s):

Cox Regression ◽

Progression Free Survival ◽

Extent Of Resection ◽

Lower Grade ◽

Wild Type ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Lower Grade Glioma ◽

Clinical Benefits

Abstract BACKGROUND While maximizing extent of resection (EOR) is associated with longer survival in lower-grade glioma (LGG) patients, the number of cases remains insufficient in determining a EOR threshold to elucidate the clinical benefits, especially in IDH-wild-type LGG patients. OBJECTIVE To identify the effects of EOR on the survival outcomes of IDH-wild-type LGG patients. METHODS IDH-wild-type LGG patients were retrospectively reviewed. The effect of EOR and other predictor variables on overall survival (OS) and progression-free survival (PFS) was analyzed using Cox regression models and the Kaplan-Meier method. RESULTS A total of 94 patients (median OS: 48.9 mo; median follow-up: 30.6 mo) were included in this study. In the multivariable Cox regression analysis, postoperative residual volume was associated with prolonged OS (HR = 2.238; 95% confidence interval [CI], 1.130-4.435; P = .021) and PFS (HR = 2.075; 95% CI, 1.113-3.869; P = .022). Thresholds at a minimum EOR of 97.0% or a maximum residue of 3.0 cm3 were necessary to impact OS positively. For the telomerase reverse transcriptase (TERT)p-wild-type group, such an association was absent. Significant differences in survival existed between the TERTp-wild-type and mutant patients who underwent relatively incomplete resections (residual ≥2.0 cm3 + TERTp wild type: median OS of 62.6 mo [95% CI: 39.7-85.5 mo]; residual ≥2.0 cm3 + TERTp mutant: median OS of 20.0 mo [95% CI:14.6-25.4 mo]). CONCLUSION Our results support the core role of maximal safe resection in the treatment of IDH-wild-type LGGs, especially for IDH-wild-type + TERTp-mutant LGGs. Importantly, the survival benefits of surgery could only be elucidated at a high EOR cut-off point.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

BioMed Research International ◽

10.1155/2020/3708231 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Jingwei Zhao ◽

Le Wang ◽

Bo Wei

Keyword(s):

Energy Metabolism ◽

Risk Score ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Coexpression Network ◽

Normal Brain ◽

Lower Grade ◽

Prognostic Signature ◽

Lower Grade Glioma ◽

Genome Atlas

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

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Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

10.21203/rs.3.rs-212352/v1 ◽

2021 ◽

Author(s):

Cheng Lijing ◽

Yuan Meiling ◽

Li Shu ◽

Chen Junjing ◽

Zhong Shupeng ◽

...

Keyword(s):

Overall Survival ◽

Clinical Decision Making ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Individual Treatment ◽

Normal Brain ◽

Data Set ◽

Cox Regression Analysis ◽

Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

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A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.763636 ◽

2022 ◽

Vol 12 ◽

Author(s):

Xitao Wang ◽

Xiaolin Dou ◽

Xinxin Ren ◽

Zhuoxian Rong ◽

Lunquan Sun ◽

...

Keyword(s):

High Risk ◽

Single Cell ◽

Risk Model ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Assessment Model ◽

Sequencing Data ◽

Ductal Cell ◽

Significant Difference

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p < 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p < 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p < 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p < 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.

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Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma

Bioscience Reports ◽

10.1042/bsr20201012 ◽

2020 ◽

Vol 40 (10) ◽

Author(s):

Ming Wu ◽

Yu Xia ◽

Yadong Wang ◽

Fei Fan ◽

Xian Li ◽

...

Keyword(s):

Prognostic Model ◽

Clinical Decision Making ◽

Cox Regression ◽

Risk Group ◽

Prognostic Models ◽

Related Gene ◽

Cox Regression Analysis ◽

Model Based ◽

Immune Related Gene ◽

Stomach Adenocarcinoma

Abstract Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis. Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested. Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group. Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

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MOMC-3. Hypermethylation and overexpression of HOX genes are poor prognosticators in Lower-Grade Glioma

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab070.013 ◽

2021 ◽

Vol 3 (Supplement_2) ◽

pp. ii4-ii4

Author(s):

Yasin Mamatjan ◽

Mathew Voisin ◽

Farshad Nassiri ◽

Fabio Moraes ◽

Severa Bunda ◽

...

Keyword(s):

Hox Genes ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

P Value ◽

Low Grade ◽

Lower Grade ◽

Cox Regression Analysis ◽

Hox Gene ◽

Lower Grade Glioma ◽

Diffuse Gliomas

Abstract Diffuse gliomas represent over 80% of malignant brain tumors ranging from low-grade to aggressive high-grade lesions. Molecular characterization of these tumors led to the development of new classification system comprising specific glioma subtypes. While this provides novel molecular insight into gliomas it does not fully explain the variability in patient outcome. To identify and characterize a predictive signature of outcome in diffuse gliomas, we utilized an integrative molecular analysis (methylation, mRNA, copy number variation (CNV) and mutation data) using multiple molecular platforms, including a total of 310 IDH mutant glioma samples from University Health Network (UHN) and German Cancer Research Center (DKFZ) together with 419 samples from The Cancer Genome Atlas (TCGA). Cox regression analysis of methylation data from the UHN cohort identified CpG-based signatures that split the glioma cohort into two prognostic groups strongly predicting survival (p-value < 0.0001). The CpG-based signatures were reliably validated using two independent datasets from TCGA and DKFZ cohorts (both p-values < 0.0001). The results show that the methylation signatures that predict poor outcome also correlated with G-CIMP low status, elevated CNV instability and hypermethylation of a set of HOX gene probes. Further study in diffuse lower-grade glioma (LGG) using integrated mRNA and methylation (iRM) analyses showed that parallel HOX gene overexpression and hypermethylation in the same direction were significantly associated with increased mutational load, high aneuploidy and worse survival (p-value < 0.0001). Furthermore, this iRM high group was characterized by a 7-HOX gene signature showed significant survival differences not only in IDH mutant LGG but also in IDH wildtype LGG. These results demonstrate the importance of HOX genes in predicting the outcome of diffuse gliomas to identify relevant molecular subtyping independent of histology.

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Identification and Verification on Prognostic Index of Lower-Grade Glioma Immune-Related LncRNAs

Frontiers in Oncology ◽

10.3389/fonc.2020.578809 ◽

2020 ◽

Vol 10 ◽

Author(s):

Jing Wen ◽

Youjun Wang ◽

Lili Luo ◽

Lu Peng ◽

Caixia Chen ◽

...

Keyword(s):

Risk Score ◽

Immune Cells ◽

Risk Group ◽

Prognostic Index ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Validation Data ◽

Lower Grade Glioma ◽

Immune Group

Previous studies have shown that the prognosis of patients with lower-grade glioma (LGG) is closely related to the infiltration of immune cells and the expression of long non-coding RNAs (lncRNAs). In this paper, we applied single-sample gene set enrichment analysis (ssGSEA) algorithm to evaluate the expression level of immune genes from tumor tissues in The Cancer Genome Atlas (TCGA) database, and divided patients into the high immune group and the low immune group, which were separately analyzed for differential expression. Venn analysis was taken to select 36 immune-related lncRNAs. To construct a prognostic model of LGG based on immune-related lncRNAs, we divided patients into a training set and a verification set at a ratio of 2:1. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to select 11 immune-related lncRNAs associated with the prognosis of LGG, and based on these selected lncRNAs, the risk scoring model was constructed. Through Kaplan-Meier analysis, the overall survival (OS) of patients in the high-risk group was significantly lower than that of the low-risk group. Then, established a nomogram including age, gender, neoplasm histologic grade, and risk score. Meanwhile, the predictive performance of the model was evaluated by calculating the C-index, drawing the calibration chart, the clinical decision curve as well as the Receiver Operating Characteristic (ROC) curve. Similar results were obtained by utilizing the validation data to verify the above consequences. Based on the TIMER database, the correlation analysis showed that the 11 immune-related lncRNAs risk score of LGG were in connection with the infiltration of the subtypes of immune cells. Subsequently, we performed enrichment analysis, whose results showed that these immune-related lncRNAs played important roles in the progress of LGG. In conclusion, these 11 immune-related lncRNAs have the potential to predict the prognosis of patients with LGG, which may play a key role in the development of LGG.

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Analyses of metastasis-associated genes in IDH wild-type glioma

BMC Cancer ◽

10.1186/s12885-020-07628-0 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Xiaozhi Li ◽

Yutong Meng

Keyword(s):

Single Cell ◽

Signaling Pathway ◽

Prognostic Model ◽

Cox Regression ◽

Metastatic Tumor ◽

Cellular Protein ◽

Signal Pathways ◽

Clustering Methods ◽

Sequencing Data ◽

Single Cell Sequencing

Abstract Background Glioma is the most common malignant tumor of the brain. The existence of metastatic tumor cells is an important cause of recurrence even after radical glioma resection. Methods Single-cell sequencing data and high-throughput data were downloaded from GEO database and TCGA/CGGA database. By means of PCA and tSNE clustering methods, metastasis-associated genes in glioma were identified. GSEA explored possible biological functions that these metastasis-associated genes may participate in. Univariate and multivariate Cox regression were used to construct a prognostic model. Results Glioma metastatic cells and metastasis-associated genes were identified. The prognostic model based on metastasis-associated genes had good sensitivity and specificity for the prognosis of glioma. These genes may be involved in signal pathways such as cellular protein catabolic process, p53 signaling pathway, transcriptional misregulation in cancer and JAK-STAT signaling pathway. Conclusion This study explored glioma metastasis-associated genes through single-cell sequencing data mining, and aimed to identify prognostic metastasis-associated signatures for glioma and may provide potential targets for further cancer research.

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