A Novel Signature Constructed by RNA-Binding Protein Coding Genes to Improve Overall Survival Prediction of Glioma Patients

RNA binding proteins (RBPs) have been reported to be involved in cancer malignancy but related functions in glioma have been less studied. Herein, we screened 14 prognostic RBP genes and constructed a risk signature to predict the prognosis of glioma patients. Univariate Cox regression was used to identify overall survival (OS)-related RBP genes. Prognostic RBP genes were screened and used to establish the RBP-signature using the least absolute shrinkage and selection operator (Lasso) method in The Cancer Genome Atlas (TCGA) cohort. The 14 RBP genes signature showed robust and stable prognostic value in the TCGA training (n = 562) cohort and in three independent validation cohorts (Chinese Glioma Genome Atlas [CGGA]seq1, CGGAseq2, and GSE16011 datasets comprising 303, 619, and 250 glioma patients, respectively). Risk scores were calculated for each patient and high-risk gliomas were defined by the median risk score in each cohort. Survival analysis in subgroups of glioma patients showed that the RBP-signature retained its prognostic value in low-grade gliomas (LGGs) and glioblastomas (GBM)s. Univariate and multivariate Cox regression analysis in each dataset and the meta cohort revealed that the RBP-signature stratification could efficiently recognize high-risk gliomas [Hazard Ratio (HR):3.662, 95% confidence interval (CI): 3.187–4.208, p < 0.001] and was an independent prognostic factor for OS (HR:1.594, 95% CI: 1.244–2.043, p < 0.001). Biological process and KEGG pathway analysis revealed the RBP gene signature was associated with immune cell activation, the p53 signaling pathway, and the PI3K-Akt signaling pathway and so on. Moreover, a nomogram model was constructed for clinical application of the RBP-signature, which showed stable predictive ability. In summary, the RBP-signature could be a robust indicator for prognostic evaluation and identifying high-risk glioma patients.

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A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

BioMed Research International ◽

10.1155/2021/8872977 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Chaocai Zhang ◽

Minjie Wang ◽

Fenghu Ji ◽

Yizhong Peng ◽

Bo Wang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Glucose Metabolism ◽

Cox Regression ◽

Gene Signature ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Survival Prediction ◽

Related Gene ◽

Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

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Construction of a Prognostic Signature in Ewing’s Sarcoma: Based on RNA-binding Genes

10.21203/rs.3.rs-482153/v1 ◽

2021 ◽

Author(s):

Bo Wu ◽

Haoqun Xie ◽

Wang Yibo ◽

Guo Zhen ◽

Hou Yuanyuan ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

High Risk ◽

Ewing’S Sarcoma ◽

Rna Binding ◽

Cox Regression ◽

Ewing's Sarcoma ◽

Risk Group ◽

Cox Regression Analysis ◽

Cox Analysis

Abstract Background: Ewing’s sarcoma is the second most prevalent primary malignant bone neoplasm. RNA-binding proteins(RBPs) play a crucial role in post-transcriptional events. In tumor cells, the alterations of post-transcription enable cells to adapt to adjacent environment rapidly. Thus, the functions of RBPs in Ewing’s sarcoma can be of high value in the prognostic[1]. The underlying mechanism between Ewing’s sarcoma and RBPs remained unclear. Methods: Based on the GEO dataset, we investigated the global protein expression profile of Ewing's sarcoma patients. Differentially expressed proteins and survival-related RNA-binding protein related genes (RRGs) were evaluated by computational difference algorithm and COX regression analysis. In addition, we also explored the mutations in these RRGs. A new prognostic indicator based on RRGs was developed and tested afterwards using multivariate COX analysis.Results: The results showed that a total of 16 RRGs which closely associated with the overall survival in Ewing’s sarcoma patients using multivariate Cox regression analysis. The prognosis-related RRGs signature established using Cox regression model consists of 8 RRGs that can divide patients into high-risk and low-risk groups. Our results suggested that overall survival rate of high-risk group patients was shorter than the patients in low-risk group. According to multivariate Cox analysis, risk score index was an independent prognosis factor for Ewing’s sarcoma. In addition, the area under the curve of the corresponding receiver operating characteristic (ROC) curve of survival is 0.947.Conclusion: The 8 RRGs marker can predict the prognosis of Ewing’s sarcoma and thus help individualized treatment of patients at different risks.

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A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

10.21203/rs.3.rs-37845/v1 ◽

2020 ◽

Author(s):

Ye Liu ◽

Zhixiang Qin ◽

Hai Yang ◽

Yang Gu ◽

Kun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Data Set ◽

Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) represents one of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. This research strives to establish a prognostic model based on the RNA binding proteins (RBPs) that can predict HCC patients’ OS. Methods There was an RNA-seq data set derived from the Cancer Genome Atlas (TCGA) databank which was included in our research as well as a Microarray data set (GSE14520). The differentially expressed RBPs between HCC and normal tissues were investigated in TCGA dataset. Subsequently, the TCGA data set was randomly split into a training and a testing cohort. The prognostic model of the training cohort was developed by applying univariate Cox regression and lasso Cox regression analyses and multivariate Cox regression analysis. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted. Results A total of 133 differentially expressed RBPs were identified. Five RBPs (RPL10L, EZH2, PPARGC1A, ZNF239, IFIT1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the TCGA cohort and the GSE14520 validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these two groups was significantly different. Furthermore, the risk scores obtained by our model were highly correlated with immune cell infiltration. . Conclusions Five RBPs-related prognostic models were constructed and validated to predict OS reliably in HCC individuals. It helps to identify patients at high risk of mortality with the risk prediction score, which optimizes personalized therapeutic decision-making.

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Lymphadenectomy in Early-Stage Intermediate-/High-Risk Endometrioid Endometrial Cancer: Clinical Characteristics and Outcomes in an Australian Cohort

International Journal of Gynecological Cancer ◽

10.1097/igc.0000000000001039 ◽

2017 ◽

Vol 27 (7) ◽

pp. 1379-1386 ◽

Cited By ~ 1

Author(s):

Rhonda Farrell ◽

Suzanne C. Dixon ◽

Jonathan Carter ◽

Penny M. Webb

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

High Risk ◽

Adjuvant Treatment ◽

Cox Regression ◽

Early Stage ◽

Cox Regression Analysis ◽

Cancer Study ◽

Stage Ib ◽

Australian Cohort

ObjectiveThe role of lymphadenectomy (LND) in early-stage endometrial cancer (EC) remains controversial. Previous studies have included low-risk patients and nonendometrioid histologies for which LND may not be beneficial, whereas long-term morbidity after LND is unclear. In a large Australian cohort of women with clinical early-stage intermediate-/high-risk endometrioid EC, we analyzed the association of LND with clinicopathological characteristics, adjuvant treatment, survival, patterns of disease recurrence, and morbidity.Materials and MethodsFrom a larger prospective study (Australian National Endometrial Cancer Study), we analyzed data from 328 women with stage IA grade 3 (n = 63), stage IB grade 1 to 3 (n = 160), stage II grade 1 to 3 (n = 71), and stage IIIC1/2 grade 1 to 3 (n = 31/3) endometrioid EC. Overall survival (OS) was estimated using Kaplan-Meier methods. The association of LND with OS was assessed using Cox regression analysis adjusted for age, stage, grade, and adjuvant treatment. The association with risk of recurrent disease was analyzed using logistic regression adjusted for age, stage, and grade. Morbidity data were analyzed using χ2 tests.ResultsMedian follow-up was 45.8 months. Overall survival at 3 years was 93%. Lymphadenectomy was performed in 217 women (66%), 16% of this group having positive nodes. Median node count was 12. There were no significant differences in OS between LND and no LND groups, or by number of nodes removed. After excluding stage IB grade 1/2 tumors, there was no association between LND and OS among a “high-risk” group of 190 women with a positive node rate of 24%. However, a similar cohort (n = 71) of serous EC in the Australian National Endometrial Cancer Study had improved survival after LND. Women who underwent LND had significantly higher rates of critical events (5% vs 0%, P = 0.02) and lymphoedema (23% vs 4%, P < 0.0001).ConclusionsIn this cohort with early-stage intermediate-/high-risk endometrioid EC, LND did not improve survival but was associated with significantly increased morbidity.

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Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

10.21203/rs.2.22601/v1 ◽

2020 ◽

Author(s):

Peng Wang ◽

Kai Huang ◽

Miaojing Wu ◽

Qing Hu ◽

Chuming Tao ◽

...

Keyword(s):

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Roc Curves ◽

Clinicopathological Features ◽

Gene Set Enrichment Analysis ◽

Consensus Clustering ◽

Score Model ◽

Genome Atlas

Abstract Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

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miR-181 Expression is Associated With The Prognosis in Lung Cancer.

10.21203/rs.3.rs-44942/v1 ◽

2020 ◽

Author(s):

Yue Zhao ◽

Xiangjun Kong ◽

Hongbing Wang

Keyword(s):

Lung Cancer ◽

Overall Survival ◽

Prognostic Value ◽

Cox Regression ◽

Suppressor Gene ◽

Rank Test ◽

High Morbidity ◽

Cox Regression Analysis ◽

Log Rank Test ◽

Cancer Tissues

Abstract Background: Lung cancer is one of the most common cancers, with high morbidity and mortality. MiRNAs are proved to play important roles in various human cancers. In our study, we aimed to explore the prognostic value of miR-181 in lung cancerMethods: Quantitative real-time polymerase chain reaction (QRT-PCR) was used to detect the expression level of miR-181 in lung cancer tissues and the paired non-cancerous tissues. The relationship between miR-181 expression and clinicopathologic parameters were analyzed by chi-square test. Kaplan-Meier method with log rank test was applied for overall survival analysis. Furthermore, the Cox regression analyses were performed to evaluate the prognostic value of miR-181 in lung cancer.Results: Down-regulated miR-181 expression was observed in lung cancer tissues (P<0.001), moreover, its expression was significantly correlated with TNM stage (P=0.015) and metastasis (P=0.000). In addition, lung cancer patients with lower miR-181 expression level had poorer overall survival than those with higher expression (log rank test, P=0.011). Cox regression analysis suggested that miR-181 was an independent prognostic factor for lung cancer (HR=1.961, 95%CI=1.135-3.388, P=0.016).Conclusion: MiR-181 may be a tumor suppressor gene in lung cancer, which can predict outcomes for the patients.

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Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

Disease Markers ◽

10.1155/2021/8886897 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Shuyan Zhang ◽

Shanshan Li ◽

Jian-Lin Guo ◽

Ningyi Li ◽

Cai-Ning Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Proportional Hazards ◽

Integrated Analysis ◽

Survival Prediction ◽

Survival Prognosis ◽

Cox Regression Analysis ◽

Cerna Network

Background. Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction. Methods. The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs. Results. Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells. Conclusions. The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

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Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/1586312 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jie Zhao ◽

Rixiang Zhao ◽

Xiaocen Wei ◽

Xiaojing Jiang ◽

Fan Su

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Ovarian Cancer Risk ◽

Cox Regression Analysis ◽

The Impact

Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

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Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.726745 ◽

2021 ◽

Vol 11 ◽

Author(s):

Kebing Huang ◽

Xiaoyu Yue ◽

Yinfei Zheng ◽

Zhengwei Zhang ◽

Meng Cheng ◽

...

Keyword(s):

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

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Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2191709 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Jimin He ◽

Chun Zeng ◽

Yong Long

Keyword(s):

Risk Stratification ◽

Prognostic Model ◽

Cox Regression ◽

Prognostic Indicator ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Immune Related Genes ◽

Genome Atlas

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

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