scholarly journals Integrated Analysis of Cell Cycle–Related and Immunity-Related Biomarker Signatures to Improve the Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Fangyu Chen ◽  
Jiahang Song ◽  
Ziqi Ye ◽  
Bing Xu ◽  
Hongyan Cheng ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Kaplan Meier ◽  
Geo Database

BackgroundLung adenocarcinoma (LUAD) is a leading malignancy and has a poor prognosis over the decades. LUAD is characterized by dysregulation of cell cycle. Immunotherapy has emerged as an ideal option for treating LUAD. Nevertheless, optimal biomarkers to predict outcomes of immunotherapy is still ill-defined and little is known about the interaction of cell cycle-related genes (CCRGs) and immunity-related genes (IRGs).MethodsWe downloaded gene expression and clinical data from TCGA and GEO database. LASSO regression and Cox regression were used to construct a differentially expressed CCRGs and IRGs signature. We used Kaplan-Meier analysis to compare survival of LUAD patients. We constructed a nomogram to predict the survival and calibration curves were used to evaluate the accuracy.ResultsA total of 61 differentially expressed CCRGs and IRGs were screened out. We constructed a new risk model based on 8 genes, including ACVR1B, BIRC5, NR2E1, INSR, TGFA, BMP7, CD28, NUDT6. Subgroup analysis revealed the risk model accurately predicted the overall survival in LUAD patients with different clinical features and was correlated with immune cells infiltration. A nomogram based on the risk model exhibited excellent performance in survival prediction of LUAD.ConclusionsThe 8 gene survival signature and nomogram in our study are effective and have potential clinical application to predict prognosis of LUAD.

scholarly journals Development and validation of a prognostic nomogram for lower-grade glioma based on an autophagy-related lncRNA signature

2022 ◽  
Author(s):  
Jiazhe Lin ◽  
Nuan Lin ◽  
Wei-jiang Zhao
Keyword(s):  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Lower Grade ◽  
Lasso Regression ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression ◽  
Kaplan Meier

IntroductionGliomas account for 75% of the primary malignant brain tumors. The prognosis and treatment planning vary in lower-grade gliomas (LGG) due to their heterogeneous clinical behaviors. The dysregulation of autophagy-related (ATG) lncRNAs plays a crucial role in LGG. We aimed to develop and validate an ATG lncRNA risk signature, and a survival nomogram with integration of novel prognostic for LGG patients.Material and methodsDifferentially expressed ATG lncRNAs were screened out based on TCGA and GTEx RNA-seq databases. ATG lncRNA prognostic signature was then established by Kaplan–Meier, univariate Cox proportional hazards regression, Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. Kaplan–Meier, univariate Cox regression and multivariate Cox proportional hazards regression were used to screen out clinical and molecular variables. A nomogram was developed and internally validated by ROC and calibration plots.ResultsAn ATG lncRNA risk signature was constructed with six differentially expressed lncRNAs (LINC00599, LINC02609, AC021739.2, AL118505.1, AL354892.2, and AL590666.2). Based on the risk signature, a nomogram was developed by addition of the significant prognostic clinical variables (age and grade) and molecular variables (IDH status and MGMT status).ConclusionsWe identified an ATG lncRNA risk signature and develop a nomogram for individualized survival prediction in LGG patients. A user-friendly free online calculator to facilitate the use of this nomogram among clinicians is also provided: https://linstu2009.shinyapps.io/LGGPRODICTORapp/?_ga=2.3154800.1506830296.1588641469-159983587.1588641469.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

scholarly journals Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolin Yu ◽  
Xiaomei Zhang ◽  
Yanxia Zhang
Keyword(s):  
Tumor Microenvironment ◽  
Confidence Interval ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Lasso Regression ◽  
The Status

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

scholarly journals Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260876
Author(s):  
Jun Yang ◽  
Jiaying Zhou ◽  
Cuili Li ◽  
Shaohua Wang
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Risk Model ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Differentially Expressed ◽  
Lasso Regression

Background Neuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB. Methods We obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset. Results In total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368–1.722, P = 2.69E-13). Conclusions This study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

scholarly journals A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

scholarly journals Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Guangzhao Huang ◽  
Zhi-yun Li ◽  
Yu Rao ◽  
Xiao-zhi Lv
Keyword(s):  
Survival Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

scholarly journals Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jixin Wang ◽  
Xiangjun Yin ◽  
Yin-Qiang Zhang ◽  
Xuming Ji
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Kaplan Meier

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan–Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

scholarly journals Construction and Validation of a Metabolic Risk Model Predicting Prognosis of Colon Cancer

2020 ◽  
Author(s):  
Chao Li ◽  
Tao Liu ◽  
Meng Yue ◽  
Didi Zuo ◽  
Jiantao Zhang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Metabolic Risk ◽  
Cox Regression Analysis ◽  
Metabolic Genes ◽  
Geo Database

Abstract Background Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Methods We downloaded gene expression profile from TCGA database and retrieved differentially expressed metabolic genes. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. Then we validated the risk model in TCGA and GEO datasets by Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC), risk score, univariate and multivariate cox regression analysis. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and GO (Gene Ontology) enrichment analyses were conducted to reveal the biological processes and pathways of genes by Gene Set Enrichment Analysis (GSEA). Results We extracted 753 metabolic genes and identified 139 differentially expressed metabolic genes from TCGA database. Then 15 prognostic genes were dug out and 8 genes were filtered into LASSO cox regression analysis. An eight-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by validation based on TCGA and GEO database. Finally, GSEA showed that multiplex metabolism pathways correlated with colon cancer. Conclusion We identified eight metabolic prognostic genes and developed a metabolic risk model based on TCGA and GEO database to predict overall survival rate of colon cancer.

scholarly journals The Prognostic Value of Bone Morphogenetic Proteins and Their Receptors in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wangyang Meng ◽  
Han Xiao ◽  
Rong Zhao ◽  
Dong Li ◽  
Kuo Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Bone Morphogenetic Proteins ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Significant Differential Expression ◽  
Cox Regression Analysis

BackgroundBone morphogenetic proteins (BMPs) regulate tumor progression via binding to their receptors (BMPRs). However, the expression and clinical significance of BMPs/BMPRs in lung adenocarcinoma remain unclear due to a lack of systematic studies.MethodsThis study screened differentially expressed BMPs/BMPRs (deBMPs/BMPRs) in a training dataset combining TCGA-LUAD and GTEx-LUNG and verified them in four GEO datasets. Their prognostic value was evaluated via univariate and multivariate Cox regression analyses. LASSO was performed to construct an initial risk model. Subsequently, after weighted gene co-expression network analysis (WGCNA), differential expression analysis, and univariate Cox regression analysis, hub genes co-expressed with differentially expressed BMPs/BMPRs were filtered out to improve the risk model and explore potential mechanisms. The improved risk model was re-established via LASSO combining hub genes with differentially expressed BMPs/BMPRs as the core. In the testing cohort including 93 lung adenocarcinoma patients, immunohistochemistry (IHC) was performed to verify BMP5 protein expression and its association with prognosis.ResultsBMP2, BMP5, BMP6, GDF10, and ACVRL1 were verified as downregulated in lung adenocarcinoma. Survival analysis identified BMP5 as an independent protective prognostic factor. We also found that BMP5 was significantly correlated with EGFR expression and mutations, suggesting that BMP5 may play a role in targeted therapy. The initial risk model containing only BMP5 showed a significant correlation (HR: 1.71, 95% CI: 1.28−2.28, p: 3e-04) but low prognostic accuracy (AUC of 1-year survival: 0.6, 3-year survival: 0.6, 5-year survival: 0.63). Seventy-nine hub genes co-expressed with BMP5 were identified, and their functions were enriched in cell migration and tumor metastasis. The re-established risk model showed greater prognostic correlation (HR: 2.58, 95% CI: 1.92–3.46, p: 0) and value (AUC of 1-year survival: 0.72, 3-year survival: 0.69, and 5-year survival: 0.68). IHC results revealed that BMP5 protein was also downregulated in lung adenocarcinoma and higher expression was markedly associated with better prognosis (HR: 0.44, 95% CI: 0.23–0.85, p: 0.0145).ConclusionBMP5 is a potential crucial target for lung adenocarcinoma treatment based on significant differential expression and superior prognostic value.

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

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

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

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