scholarly journals A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Zhuoyuan Chen ◽  
Aoyu Li ◽  
Yu Xia ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Survival Prediction ◽  
Related Gene ◽  
Gene Model ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Geo Dataset ◽  
Auc Value

Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients.Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset.Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan–Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent.Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

scholarly journals A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhentao Liu ◽  
Hao Zhang ◽  
Hongkang Hu ◽  
Zheng Cai ◽  
Chengyin Lu ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Methylation Status ◽  
Survival Rates ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

Histone Deacetylase-2 Serves as an Independent Prognostic Indicator in Osteosarcoma

2020 ◽  
Author(s):  
Dawei Wang ◽  
Youchen Ye ◽  
Tingting Qu ◽  
Zhifang Zhao ◽  
Zenghui Gu ◽  
...  
Keyword(s):  
Histone Deacetylase ◽  
Expression Pattern ◽  
Prognostic Value ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Histone Deacetylase 2 ◽  
Kaplan Meier

Abstract Background Osteosarcoma is the most common primary malignant tumor of skeleton in adolescence. Histone deacetylase 2 (HDAC2), a member of class I histone deacetylase, is putatively involved in tumorigenesis of human malignancies. This study aimed to evaluate the expression pattern and prognostic value of HDAC2 in osteosarcoma.Methods Four datasets were obtained from the gene expression omnibus (GEO) database to explore the expression and prognostic value of HDAC2. Level 3 mRNA expression profiles and clinical data were obtained in The Cancer Genome Atlas (TCGA) for validation. Expression pattern of HDAC2 were illustrated in GSE16088, GSE36001 and GSE42352. The prognostic value of HDAC2 was evaluated and validated by Kaplan-Meier analyses, receiver operating characteristic (ROC), concordance index (C-index) and calibration curve in GSE21257 and TCGA. Multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were performed to assess the prognosis predictive capability. Protein-protein interaction (PPI) and gene set enrichment analysis (GSEA) were applied to further understand the molecular network and regulatory mechanisms.Results HDAC2 expression was significantly increased in osteosarcoma tissues. High HDAC2 expression was associated with tumor metastasis and chemotherapy efficacy. Kaplan-Meier analysis demonstrated that high HDAC2 predicted worse overall survival. The ROC curve showed good performance in survival prediction. Cox regression demonstrated that HDAC2 could be an independent prognostic indicator. GSEA revealed patients with high HDAC2 expression were enriched with multiple ontological signatures.Conclusions Elevated expression of HDAC2 may identify an aggressive subgroup in osteosarcoma and serve as an independent prognostic indicator in these patients.

scholarly journals An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Qian Xu ◽  
Yurong Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Staging System ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Net Benefit ◽  
Cox Regression Analysis

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

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

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.

scholarly journals Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

2021 ◽  
Vol 10 ◽  
Author(s):  
Dai Zhang ◽  
Yi Zheng ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Gene Signature ◽  
Breast Cancer Survival ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

Macrophage Polarization-Related Gene Signature for Risk Stratification and Prognosis of Survival in Gliomas 

2021 ◽  
Author(s):  
Jingwei Zhang ◽  
Shuwang Li ◽  
Fangkun Liu
Keyword(s):  
Essential Role ◽  
Cox Regression ◽  
Macrophage Polarization ◽  
Gene Signature ◽  
High Risk Group ◽  
M2 Macrophage ◽  
Related Gene ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Macrophage polarization plays an essential role in tumor immune cells infiltration and tumor growth. We selected a series of genes distinguishing between M1 and M2 macrophage and explored their prognostic value in gliomas. A total of 170 genes were included in our study. CGGA database was used as the training cohort, and the TCGA database as the validation cohort. The biological processes and functions were identified by GO and KEGG analysis. Kaplan-Meier analysis was used to compare survival differences between groups. Finally, GEPIA was applied to explore immune infiltrates in the tumor microenvironment. Importantly, we re-verified the results using our sequencing data. We build a risk score model using Cox regression analysis based on the CGGA and verified in the TCGA database and our sequencing data. Patients with gliomas in the high-risk group were associated with high grade, IDH WT status, MGMT promoter unmethylation, 1p19q non-codeletion, and prone to have a poor outcome. Moreover, these genes play an essential role in immune infiltrations in LGG and GBM microenvironments. Macrophage polarization-related gene signature can predict the malignancy and outcome of patients with gliomas and might act as a promising target for glioma immunotherapy in the future.

A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Bladder cancer

2021 ◽  
Author(s):  
Liyuan Wu ◽  
Feiya Yang ◽  
Nianzeng Xing
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

Abstract Background Bladder cancer (BC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis is related to a variety of biological pathways, including those involved in the metabolism of amino acids, lipids, and iron. However, the prognostic value of ferroptosis-related genes in BC remains to be further elucidated. Methods In this study, the mRNA expression profiles and corresponding clinical data of BC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature and validated it. Results Our results showed 12 differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 9-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups, especially in humoral immune response process. Conclusion In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in BC. Targeting ferroptosis may be a therapeutic alternative for BC.

scholarly journals Construction of a Pseudogene‐associated ceRNA Network and Identification of Prognostic Pseudogene Biomarkers for Colorectal Cancer

2021 ◽  
Author(s):  
Zhuoqi Li ◽  
Jing Zhou ◽  
Liankun Gu ◽  
Baozhen Zhang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.

scholarly journals Network analysis of miRNA targeting m6A-related genes in patients with esophageal cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11893
Author(s):  
Lili Li ◽  
Rongrong Xie ◽  
Qichun Wei
Keyword(s):  
Esophageal Cancer ◽  
Gene Network ◽  
Cox Regression ◽  
Genomic Analysis ◽  
Roc Curves ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Background We investigated the miRNA-m6A related gene network and identified a miRNA-based prognostic signature in patients with esophageal cancer using integrated genomic analysis. Methods We obtained expression data for m6A-related genes and miRNAs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Survival analysis was conducted to identify potential prognostic biomarkers. LASSO Cox regression was performed to construct the overall survival (OS) associated prediction signature. We used the Kaplan–Meier (K–M) curve and receiver operating characteristic (ROC) curves to explore the signature’s efficiency and accuracy. Interactions between the m6A-related genes and miRNAs were identified in starBase3.0 and used to construct the miRNA-m6A related gene network. Results We found that HNRNPC, YTHDF, ZC3H13, YTHDC2, and METTL14 were dysregulated in esophageal cancer tissues. Multivariate Cox regression analysis revealed that HNRNPC may be an independent risk factor for OS. Five hundred twenty-two potential upstream miRNAs were obtained from starBase3.0. Four miRNAs (miR-186, miR-320c, miR-320d, and miR-320b) were used to construct a prognostic signature, which could serve as a prognostic predictor independent from routine clinicopathological features. Finally, we constructed a key miRNA-m6A related gene network and used one m6A-related gene and four miRNAs associated with the prognosis. The results of our bioinformatics analysis were successfully validated in the human esophageal carcinoma cell lines KYSE30 and TE-1. Conclusion Our study identified a 4-miRNA prognostic signature and established a key miRNA-m6A related gene network. These tools may reliably assist with esophageal cancer patient prognosis.

Study of the Relationship of Product Infused with Survival in Patients with Hodgkin’s Disease and No Hodgkin’s Lymphoma Undergoing Autologus Stem Cell Transplantation

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4443-4443
Author(s):  
Eduardo Salido ◽  
Maria Juliana Majado ◽  
Alfredo Minguela ◽  
Jose Maria Moraleda ◽  
Consuelo Gonzalez ◽  
...  
Keyword(s):  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Cell Transplantation ◽  
Hodgkin’S Lymphoma ◽  
Cox Regression ◽  
Statistical Significance ◽  
Hodgkin's Lymphoma ◽  
Rank Test ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Absolute lymphocyte count (ALC) at diagnosis (ALC-D), as well as ALC recovery, have been described as prognostic factors in overall survival (OS) and progression free survival (PFS) in patients with non-Hodgkin’s and Hodgkin’s lymphoma(NHL and HL) who underwent autologous stem cell transplantation (ASCT). The aim of this study is to verify those findings, as well as the influence of infused lymphocyte, MNC, CD19 and CD3 in OS and PFS in our patients, in order to give some support to the importance of the immune system for disease control. A total of 55 patients were reviewed, 35 males and 20 females, 44 had NHL and 12 HL. Variables analyzed with regard to PFS and OS were: ALC-D, ALC at day 15 post-transplant (ALC15), infused lymphocyte ×106/Kg, MNC X108 / Kg, CD19 X106/ Kg and CD3×106/ Kg. Variables were dichotomized at the median, and PFS and OS estimates were calculated with the Kaplan-Meier method and compared using log-rank test; regression Cox test was used for multivariate study. Results: The median follow-up was 71 months (14–180). Relapse or progression occurred in 20 patients (36%) between 1 and 148 months (median 32). Eighteen patients (33%) died at a median of 28 months (14–110). Median ALC-D was 1.5×109/L (0.2–5.2 ×109/L). ALC15 count was 0.6 ×109/L (0.1–1.9). Infused MNC were 5.0×108/Kg (0.04– 22.2). Infused lymphocytes were 241.6 ×106/Kg (0.1–1222). Median CD19 cells infused were 1.12 ×106/Kg (0.0–79.7). Infused CD3 cells were 106.8 ×106/Kg (0.02–678.2). No difference was found in OS or PFS in patients in groups above and below median in ALC-D, ALC15 counts, neither in MNC, lymphocytes, CD19 nor CD3 cells infused in Kaplan-Meier study. MNC infused have statistical significance in OS (p=0.042) and PFS (p=0.023) in Cox regression analysis. In our series we do not find a clear predictor in relation to the immune reconstitution in NHL and HL patients receiving an ASCT; althoug it is possible that among MNC should be immune progenitors able to offer a better outcome to these patients, that could be studied in future.

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