A Prognostic Nomogram for Predicting Overall Survival in Pediatric Wilms Tumor Based on an Autophagy-related Gene Signature

2021 ◽  
Vol 24 ◽  
Author(s):  
Longkai He ◽  
Xiaotong Wang ◽  
Ya Jin ◽  
Weipeng Xu ◽  
Jun Lyu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Low Risk ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Risk Patients

Background: Wilms tumor (WT) is the most common primary renal malignancy in children. Autophagy plays dual roles in the promotion and suppression of various cancers. Objective: The goal of our study was to develop a novel autophagy-related gene (ARG) prognostic nomogram for WT. Methods: The Cancer Genome Atlas (TCGA) database was used. We screened the expression profiles of ARGs in 136 WT patients. The differentially expressed prognostic ARGs were evaluated by multivariate Cox regression analysis and survival analysis. A novel prognostic nomogram based on the ARGs and clinical characteristics was established using multivariate Cox regression analysis. Results: First, 69 differentially expressed ARGs were identified in WT patients. Then, multivariate Cox regression analysis was used to determine 4 key prognostic ARGs (CC3CL1, ERBB2, HIF-α and CXCR4) in WT. According to their ARG expression levels, the patients were clustered into high- and low-risk groups. Next, survival analysis indicated that high-risk patients had significantly poorer overall survival than low-risk patients. The results of functional enrichment analysis suggested that autophagy may play a tumor-suppressive role in the initiation of WT. Finally, a prognostic nomogram with a Harrell's concordance index (C-index) of 0.841 was used to predict the survival probability of WT patients by integrating clinical characteristics and the 4-ARG signature. The calibration curve indicated its excellent predictive performance. Conclusion: In summary, the ARG signature could be a promising biomarker for monitoring the outcomes of WT. We established a novel nomogram based on the ARG signature, which accurately predicts the overall survival of WT patients.

scholarly journals Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunlei Wu ◽  
Quanteng Hu ◽  
Dehua Ma
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

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

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.

scholarly journals Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chao Zhu ◽  
Liqun Gu ◽  
Mianfeng Yao ◽  
Jiang Li ◽  
Changyun Fang
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Oral Squamous Cell Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

scholarly journals Molecular identification of an immunity- and Ferroptosis-related gene signature in non-small cell lung Cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Taisheng Liu ◽  
Honglian Luo ◽  
Jinye Zhang ◽  
Xiaoshan Hu ◽  
Jian Zhang
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Cox Regression ◽  
Small Cell ◽  
Low Risk ◽  
Related Gene ◽  
Small Cell Lung ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the dominant causes of cancer-related deaths worldwide. Ferroptosis, an iron-dependent form of programmed cell death, plays a key role in cancer immunotherapy. However, the role of immunity- and ferroptosis-related gene signatures in non-small cell lung cancer (NSCLC) remain unclear. Methods RNA-seq data and clinical information pertaining to NSCLC were collected from The Cancer Genome Atlas dataset. Univariate and multivariate Cox regression analyses were performed to identify ferroptosis-related genes. A receiver operating characteristic (ROC) model was established for sensitivity and specificity evaluation. Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to explore the function roles of differentially expressed genes. Results A signature composed of five ferroptosis-related genes was established to stratify patients into high- and low-risk subgroups. In comparison with patients in the low-risk group, those in the high-risk one showed significantly poor overall survival in the training and validation cohorts (P < 0.05). Multivariate Cox regression analysis indicated risk score to be an independent predictor of overall survival (P < 0.01). Further, the 1-, 2-, and 3-year ROCs were 0.623 vs. 0.792 vs. 0.635, 0.644 vs. 0.792 vs. 0.634, and 0.631 vs. 0.641 vs. 0.666 in one training and two validation cohorts, respectively. Functional analysis revealed that immune-related pathways were enriched and associated with abnormal activation of immune cells. Conclusions We identified five immunity- and ferroptosis-related genes that may be involved in NSCLC progression and prognosis. Targeting ferroptosis-related genes seems to be an alternative to clinical therapy for NSCLC.

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 A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yankai Zhang ◽  
Yichao Yan ◽  
Ning Ning ◽  
Zhanlong Shen ◽  
Yingjiang Ye
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Operating Characteristic ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Aging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients. Methods The mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log‐rank test and a Cox proportional hazards regression model. Results The 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value − 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan–Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345–7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients. Conclusion Our study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.

scholarly journals Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Danfeng Li ◽  
Xiaosheng Lin ◽  
Binlie Chen ◽  
Zhiyan Ma ◽  
Yongming Zeng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Clinical Information ◽  
Low Risk ◽  
Expression Data ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC).Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC.Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB).Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

scholarly journals Identification of Prognosis-Related RBPs to Reveal The Role of RNA Binding Proteins in the Progression and Prognosis of Wilms’ Tumor

2021 ◽  
Author(s):  
Xuejiao Qi ◽  
Shuyu Wang ◽  
Yihui Dong ◽  
Xiaojie Lin ◽  
Jingqiu Chen
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Prognostic Index ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
The Relationship

Abstract Background: Despite the various key functions of RBPs in posttranscriptional events, the mechanism of their influence on Wilms’ tumor has not been well elucidated. Therefore, we constructed the research to identify several RBPs related to Wilmes’ tumor progression and prognosis, for the better understanding of RBPs’ role in the occurrence and development of Wilmes’ tumor, and to provide effective reference targets for new drug development.Methods: A total of 127 samples of different clinical characteristics including gender, race and stage were selected from TARGET to carry out our study. After the gene functional enrichment pathways, univariate Cox regression analysis and lasso regression analysis were performed to test the prognostic effect of the differentially-expressed genes and establish the prognostic index . Further Cox regression analyses were utilized to identify the independence of our model and to analyze the relationship between our model and clinical parameters. What’s more, gene set enrichment analysis (GSEA) was also performed to elucidate the biological characteristics of genes involved in Wilms’ tumor. P< 0.05 was considered to be statistically significant.Results: 20 RBPs were statistically correlated with Wilms’ tumor. After the construction of a prognostic index , patients were divided into high-and low-risk scores group. Kaplan-Meier (K-M) analyses showed that patients with high risk scores possessed poorer survival probability than patients with low risk scores in both training group and test group. Furthermore, multivariate Cox regression analysis explored the relationship between our prognostic model and clinical parameters and confirmed that our model was an independent predicted factor for Wilms’ tumor. Conclusion: Our study clarifies the application of RBPs in the prognosis of Wilms’ tumor. We are confident that our risk scoring model can provide ideas for the development of new targets for broad-spectrum anticancer drugs and has great potential in clinical practice.

scholarly journals The Identification and Validation of a Robust Immune-Associated Gene Signature in Cutaneous Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Le-Bin Song ◽  
Jiao-Chen Luan ◽  
Qi-Jie Zhang ◽  
Lin Chen ◽  
Hao-Yang Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
Cox Model ◽  
Critical Role ◽  
Gene Signature ◽  
Low Risk ◽  
Cox Regression Analysis

Background. Cutaneous melanoma is defined as one of the most aggressive skin tumors in the world. An increasing body of evidence suggested an indispensable association between immune-associated gene (IAG) signature and melanoma. This article is aimed at formulating an IAG signature to estimate prognosis of melanoma. Methods. 434 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database, and 1811 IAGs were downloaded from the ImmPort database in our retrospective study. The Cox regression analysis and LASSO regression analysis were utilized to establish a prognostic IAG signature. The Kaplan-Meier (KM) survival analysis was performed, and the time-dependent receiver operating characteristic curve (ROC) analysis was further applied to assess the predictive value. Besides, the propensity score algorithm was utilized to balance the confounding clinical factors between the high- and low-risk groups. Results. A total of six prognostic IAGs comprising of INHA, NDRG1, IFITM1, LHB, GBP2, and CCL8 were eventually filtered out. According to the KM survival analysis, the results displayed a shorter overall survival (OS) in the high-risk group compared to the low-risk group. In the multivariate Cox model, the gene signature was testified as a remarkable prognostic factor ( HR = 45.423 , P < 0.001 ). Additionally, the ROC curve analyses were performed which demonstrated our IAG signature was superior to four known biomarkers mentioned in the study. Moreover, the IAG signature was significantly related to immunotherapy-related biomarkers. Conclusion. Our study demonstrated that the six IAG signature played a critical role in the prognosis and immunotherapy of melanoma, which might help clinicians predict patients’ survival and provide individualized treatment.

scholarly journals Identification of Prognosis-related RBPs to Reveal the Role of RNA Binding Proteins in the Progression and Prognosis of Wilms’ Tumor

2020 ◽  
Author(s):  
Xuejiao Qi ◽  
Yihui Dong ◽  
Xiaojie Lin ◽  
Jingqiu Chen
Keyword(s):  
Regression Analysis ◽  
Binding Proteins ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Prognostic Index ◽  
Low Risk ◽  
Risk Scores ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
The Relationship

Abstract BackgroundRNA-binding proteins (RBPs), the ubiquitous regulators that can bind to RNA, mediate the function of RNA in the process of its maturation, translation, transport and localization [1, 2]. Despite the various key functions of RBPs in posttranscriptional events, the mechanism of their influence on Wilms’ tumor has not been well elucidated. So we construct the research to identify several RBPs related to Wilmes’ tumor progression and prognosis, for the better understanding of RBPs’ role in the occurrence and development of Wilmes’ tumor, and to provide effective reference targets for new drug development.MethodsA total of 127 samples of different clinical characteristics including gender, race and stage were selected from TCGA to carry out our study. After the gene functional enrichment pathways, univariate Cox regression analysis and lasso regression analysis were performed to test the prognostic effect of the differentially-expressed genes and establish the prognostic index . Further Cox regression analyses were utilized to identify the independence of our model and to analyze the relationship between our model and clinical parameters. What’s more, gene set enrichment analysis (GSEA) was also performed to elucidate the biological characteristics of genes involved in Wilms’ tumor. P< 0.05 was considered to be statistically significant.Results26 RBPs were statistically correlated with Wilms’ tumor. After the construction of a prognostic index , patients were divided into high-and low-risk scores group. Kaplan-Meier (K-M) analyses showed that patients with high risk scores possessed poorer survival probability than patients with low risk scores in both training group and test group. Furthermore, multivariate Cox regression analysis explored the relationship between our prognostic model and clinical parameters and confirmed that our model was an independent predicted factor for Wilms’ tumor. ConclusionOur study clarifies the application of RBPs in the prognosis of Wilms’ tumor. We are confident that our risk scoring model can provide ideas for the development of new targets for broad-spectrum anticancer drugs and has great potential in clinical practice.Trial registrationretrospectively registered

Sign in / Sign up

Export Citation Format

Share Document