Prognosis Implication of a Novel Metabolism-Related Gene Signature in Ewing Sarcoma

Ewing sarcoma (ES) is one of the most common bone cancers in adolescents and children. Growing evidence supports the view that metabolism pathways play critical roles in numerous cancers (He et al. (2020)). However, the correlation between metabolism-associated genes (MTGs) and Ewing sarcoma has not been investigated systematically. Here, based on the univariate Cox regression analysis, we get survival genes from differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) cohort. Multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to establish the MTG signature. Comprehensive survival analyses including receiver operating characteristic (ROC) curves and Kaplan–Meier analysis were applied to estimate the independent prognostic value of the signature. The ICGC cohort served as the validation cohort. A nomogram was constructed based on the risk score of the MTG signature and other independent clinical variables. The CIBERSORT algorithm was applied to estimate immune infiltration. In addition, we explored the correlation between MTG signature and immune checkpoints. Collectively, this work presents a novel MTG signature for prognostic prediction of Ewing sarcoma. It also suggests six genes that are potential prognostic indicators and therapeutic targets for ES.

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Identification of an 8-miRNA signature as a potential prognostic biomarker for glioma

PeerJ ◽

10.7717/peerj.9943 ◽

2020 ◽

Vol 8 ◽

pp. e9943 ◽

Cited By ~ 1

Author(s):

Baowei Ji ◽

Lihua Chen ◽

Qiang Cai ◽

Qiao Guo ◽

Zhibiao Chen ◽

...

Keyword(s):

Regression Analysis ◽

Molecular Mechanisms ◽

Cox Regression ◽

Characteristic Curve ◽

Area Under The Curve ◽

Gene Expression Omnibus ◽

Related Factor ◽

Lasso Regression ◽

Mirna Signature ◽

Cox Regression Analysis

Background Glioma is the most common form of primary malignant intracranial tumor. Methods In the current study, miRNA matrix were obtained from the Chinese Glioma Genome Atlas (CGGA), and then univariate Cox regression analysis and Lasso regression analysis were utilized to select candidate miRNAs and multivariate Cox regression analysis was applied to establish a miRNA signature for predicting overall survival (OS) of glioma. The signature was assessed with the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and validated by data from Gene Expression Omnibus (GEO). Results Eight miRNAs (miR-1246, miR-148a, miR-150, miR-196a, miR-338-3p, miR-342-5p, miR-548h and miR-645) were included in the miRNA signature. The AUC of ROC analysis for 1- and 3-year OS in the CGGA dataset was 0.747 and 0.905, respectively. In the GEO dataset, The AUC for 1- and 3-year was 0.736 and 0.809, respectively. The AUC in both the CGGA and GEO datasets was similar to that based on WHO 2007 classification (0.736 and 0.799) and WHO 2016 classification (0.663 and 0.807). Additionally, Kaplan–Meier plot revealed that high-risk score patients had a poorer clinical outcome. Multivariate Cox regression analysis suggested that the miRNA signature was an independent prognosis-related factor [HR: 6.579, 95% CI [1.227−35.268], p = 0.028]. Conclusion On the whole, in the present study, based on eight miRNAs, a novel prognostic signature was developed for predicting the 1- and 3- year survival rate in glioma. The results may be conducive to predict the precise prognosis of glioma and to elucidate the underlying molecular mechanisms. However, further experimental researches of miRNAs are needed to validate the findings of this study.

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Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.676291 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zengyu Feng ◽

Hao Qian ◽

Kexian Li ◽

Jianyao Lou ◽

Yulian Wu ◽

...

Keyword(s):

Regression Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Cox Regression ◽

Predictive Accuracy ◽

Disease Free Survival ◽

Roc Curves ◽

Gene Signature ◽

Ductal Adenocarcinoma ◽

Survival Prediction ◽

Cox Regression Analysis

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported.Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature.Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways.Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

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Development and Validation of a 23-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

10.21203/rs.3.rs-272282/v1 ◽

2021 ◽

Author(s):

Jichang Liu ◽

Yadong Wang ◽

Weiqing Zhong ◽

Yong Liu ◽

Kai Wang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Protein Interactions ◽

Cox Regression ◽

External Validation ◽

Predictive Performance ◽

Gene Signature ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Prognosis Prediction

Abstract Background: Lung cancer remains the most fatal tumorous disease in the worldwide. Among that, lung adenocarcinoma (LUAD) was the most common histological type. A precise and concise prognostic model was urgently needed of LUAD. We developed a 23-gene signature for prognosis prediction based on EMT, immune and stromal datasets.Methods: Univariate Cox regression analysis was performed to select genes which were significantly associated with overall survival (OS) of the TCGA LUAD cohorts. LASSO regression and multivariate Cox regression analysis was used to build the multi-gene signature. Enrichment analyses and a protein-protein interactions (PPI) network were performed to show the interaction and functions of the signature. A nomogram was developed based on risk score and other clinical features. Predictive performance of the signature was externally validated in two independent datasets from Gene Expression Omnibus (GSE37745 and GSE13213).Results: A total of 1334 EMT, immune and stromal associated genes were obtained. After LASSO regression and multivariate Cox regression analysis, a 23-gene signature for risk stratification was built. K-M curves showed that the patients with high risk had a poorer outcome. Finally, a nomogram was built to predict prognosis. The predictive performance of the 23-gene signature was confirmed in internal and external validation.Conclusion: We developed and verified a 23-gene signature based on EMT, immune and stromal gene sets. It provided a convenient and concise tool for risk stratificationand individual medicine.

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N6-Methyladenosine-Related RNA Signature Predicting the Prognosis of Ovarian Cancer

Recent Patents on Anti-Cancer Drug Discovery ◽

10.2174/1574892816666210615164645 ◽

2021 ◽

Vol 16 ◽

Author(s):

Jiao Jiao ◽

Longyang Jiang ◽

Yang Luo

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Prognostic Indicator ◽

Gene Signature ◽

Genetic Alterations ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

Background: N6-Methyladenosine (m6A) RNA methylation is the most universal mRNA modification in eukaryotic cells. M6A mRNA modification affects almost every phases of RNA processing, including splicing, decay, export, translation and expression. Several patents have reported the application of m6A mRNA modification in cancer diagnosis and treatment. Ovarian cancer is the leading cause of death among all gynecological cancers. It is urgent to identify new biomarkers for early diagnosis and prognosis of ovarian cancer. Objective: In the current study, we aimed to evaluate the m6A RNA methylation regulators and m6A related genes and establish a new gene signature panel for prognosis of ovarian cancer. Method: We downloaded the Mutations data, FPKM data and corresponding clinical information of 373 patients with ovarian cancer (OC) from the TCGA database. We performed LASSO regression analysis and multivariate cox regression analysis to develop a risk-identifying gene signature panel. Results: A total of 317 candidate m6A RNA methylation related genes were obtained. Finally, 12 -genes (WTAP, LGR6, ZC2HC1A, SLC4A8, AP2A1, NRAS, CUX1, HDAC1, CD79A, ACE2, FLG2 and LRFN1) were selected to establish the signature panel. We analyzed the genetic alterations of the selected 12 -genes in OC using cBioPortal database. Among the 373 patients, 368 patients have mutations. The results showed that all queried genes were altered in 137 of 368 cases (37.23%). The 12-gene signature panel was confirmed as an independent prognostic indicator (P =2.29E-18, HR = 1.699, 95% CI = 1.508-1.913). Conclusion: We established an effective m6A-related gene signature panel consisted of 12 -genes, which can predict the outcome of patients with OC. The high risk score indicates unfavorable survival. Our study provided novel insights into the relationship between m6A and OC. This gene signature panel will be helpful in identifying poor prognostic patients with OC and could be a promising prognostic indicator in clinical practice.

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A Five-Gene Signature for Recurrence Prediction of Hepatocellular Carcinoma Patients

BioMed Research International ◽

10.1155/2020/4037639 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zeyu Wang ◽

Ningning Zhang ◽

Jiayu Lv ◽

Cuihua Ma ◽

Jie Gu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Early Stage ◽

Gene Signature ◽

Functional Enrichment ◽

High Recurrence Rate ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Recurrence Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim. To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods. The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results. We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion. The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

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A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

BioMed Research International ◽

10.1155/2021/6680066 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xiao Bingxiang ◽

Wu Panxing ◽

Feng Lu ◽

Yan Xiuyou ◽

Ding Chao

Keyword(s):

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Evaluation Model ◽

Gene Signature ◽

Lasso Regression ◽

Colon Tissue ◽

Cox Regression Analysis ◽

Prognostic Assessment ◽

Cox Analysis

Objective. To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model. Methods. The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model. Results. 339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage. Conclusion. The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.634116 ◽

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.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Future Oncology ◽

10.2217/fon-2020-1112 ◽

2021 ◽

Author(s):

Q Shi ◽

Z Meng ◽

XX Tian ◽

YF Wang ◽

WH Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Interaction Network ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cox Regression Analysis ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

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Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

10.21203/rs.3.rs-524410/v1 ◽

2021 ◽

Author(s):

Jixiang Cao ◽

Xi Chen ◽

Guang Lu ◽

Haowei Wang ◽

Xinyu Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Microenvironment ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Risk Scores ◽

Tumor Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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