scholarly journals Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung Adenocarcinoma

2020 ◽  
Author(s):  
Guofeng Li ◽  
Guangsuo Wang ◽  
Yanhua Guo ◽  
Shixuan Li ◽  
Youlong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Competing Risks ◽  
Risk Score ◽  
Prognostic Score ◽  
The Cancer Genome Atlas ◽  
Time Dependent ◽  
P Value ◽  
Modeling Framework ◽  
Free Survival

Abstract Background: Integrating phenotypic and genotypicinformation to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS)and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression and mutation data. Methods: We enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features, and constructed 14 different input feature sets for predictivemodel development.We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance and time-dependent AUC under competing risks scenario.We stratified patients into higher and lower-risk subgroups by the final risk score, and further investigated underlying immune phenotyping variations associated with the differential risk.Results: The model integrating all three types of data achieved the bestprediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically-definedsubgroups. The score could account for extra EFS-related variationsthat were not captured by clinicopathologic scores. Being validated forRFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p-value< 0.001). The higher-risk patients were characterized with transcriptionalaberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells. Conclusions: We developed a novel prognostic risk score with improved prediction accuracy,using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was an significant predictor of both EFS and RFS.Trail registration: This study was based on public open data from TCGA and hence the study objects were retrospectively registered.

scholarly journals Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Guofeng Li ◽  
Guangsuo Wang ◽  
Yanhua Guo ◽  
Shixuan Li ◽  
Youlong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Competing Risks ◽  
Risk Score ◽  
Prognostic Score ◽  
Open Data ◽  
The Cancer Genome Atlas ◽  
Time Dependent ◽  
Modeling Framework ◽  
Free Survival

Abstract Background Integrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data. Methods We enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features and constructed 14 different input feature sets for predictive model development. We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance, and time-dependent AUC under competing risks scenario. We stratified patients into higher- and lower-risk subgroups by the final risk score and further investigated underlying immune phenotyping variations associated with the differential risk. Results The model integrating all three types of data achieved the best prediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically defined subgroups. The score could account for extra EFS-related variations that were not captured by clinicopathologic scores. Being validated for RFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p value < 0.001). The higher-risk patients were characterized with transcriptional aberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells. Conclusions We developed a novel prognostic risk score with improved prediction accuracy, using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was a significant predictor of both EFS and RFS. Trial registration This study was based on public open data from TCGA and hence the study objects were retrospectively registered.

scholarly journals Prognostic implications of programmed death ligand 1 expression in resected lung adenocarcinoma: a systematic review and meta-analysis

2020 ◽  
Vol 58 (5) ◽  
pp. 888-898
Author(s):  
Donglai Chen ◽  
Yiming Mao ◽  
Qifeng Ding ◽  
Wei Wang ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Lung Adenocarcinoma ◽  
Messenger Rna ◽  
Disease Free Survival ◽  
Pooled Analysis ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
L1 Protein ◽  
Disease Free

Abstract OBJECTIVES Conflicting results have been reported about the prognostic value of programmed death ligand 1 (PD-L1) protein and gene expression in lung adenocarcinoma. METHODS We performed a comprehensive online search to explore the association between PD-L1 expression (protein and messenger RNA) and overall survival (OS) or disease-free survival. Outcomes also included pooled rates of high PD-L1 protein expression in different cell types, per threshold used and per antibody used. A pooled gene expression analysis was also performed on 3 transcriptomic data sets that were obtained from The Cancer Genome Atlas database and the Gene Expression Omnibus database. RESULTS A total of 6488 patients from 25 studies were included. The pooled results suggested that high PD-L1 expression was associated with shorter OS [hazard ratio (HR) 1.57; P &lt; 0.001] and disease-free survival (HR 1.341; P = 0.037) in the overall population. The overall pooled rate of high PD-L1 protein expression was 29% (95% confidence interval 23–34%) in tumour cells. In subgroup analysis, high PD-L1 protein expression in tumour cells predicted worse OS and disease-free survival. A pooled analysis of The Cancer Genome Atlas and Gene Expression Omnibus data sets revealed that higher levels of PD-L1 messenger RNA predicted poorer OS in the entire population. CONCLUSIONS This study is, to our knowledge, the largest pooled analysis on the subject to shed light on the high expression rate of PD-L1 and the prognostic value of high PD-L1 expression in resected lung adenocarcinomas. PD-L1 gene expression is a promising prognostic factor for patients with surgically resected lung adenocarcinoma. Standardization of staining should be underscored prior to routine implementation.

scholarly journals Construction of a Prognostic Model in Lung Adenocarcinoma Based on Ferroptosis-Related Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Liang ◽  
Mafeng Chen ◽  
Yinghua Zhong ◽  
Shivank Singh ◽  
Shantanu Singh
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Clinical Stage ◽  
Prognostic Significance ◽  
Low Risk ◽  
The Cancer Genome Atlas

Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma.Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observed that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score.Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes.Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways.

scholarly journals Functions and clinical significance of KLRG1 in the development of lung adenocarcinoma and immunotherapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaodong Yang ◽  
Yuexin Zheng ◽  
Zhihai Han ◽  
Xiliang Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Killer Cell ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Lung ◽  
Using Data ◽  
Low Expression

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

scholarly journals Development and validation of a 13-m6a related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Pingfan Wu ◽  
Xiaowen Zhao ◽  
Ling Xue ◽  
Xiaojing Yang ◽  
Yuxiang Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
P53 Signaling ◽  
Selection Operator

Abstract Considerable evidence suggests that N6-methyladenosine (m6A) is involved in the regulation of long non-coding RNA (lncRNA), whichparticipates in the occurrence, development and prognosis of tumorscancerBut the relationship between m6A regulators-related lncRNA (mRlncRNA) and lung adenocarcinoma (LUAD) remains unclear. This study aims to determine a feature based on mRlncRNA for prognostic evaluation of LUAD patients. By integrating the gene expression data of LUAD and normal samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the m6A gene and mRlncRNA with imbalanced expression were screened out. Then we used the least absolute shrinkage and selection operator (LASSO) to obtain the 13-lncRNA prognostic signature in the TCGA training cohort. Patients were divided into two risk groups based on the risk score of lncRNAs characteristics, and their overall survival (OS) was significantly different. The predictive power of this signature was verified in TCGA testing cohort and entire TCGA cohort. These landmark lncRNAs were involved in several biologiocal processes and pathways related to cell cycle, DNA replication, P53 signaling pathway and mismatch repair. Besides, the high-risk group was low-response to cisplatin, while high-response to mitomycin, docetaxel and immunotherapy. In conclusion, we identified a 13-mRlncRNA model associated with prognosis and treatment sensitivity in LUAD, which may provide clues about the influence of m6A on lncRNA in LUAD and promote the further improvement of LUAD individualized treatment strategies.

scholarly journals A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5837
Author(s):  
Changwu Wu ◽  
Siming Gong ◽  
Georg Osterhoff ◽  
Nikolas Schopow
Keyword(s):  
Soft Tissue ◽  
Risk Score ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Malignant Tumours ◽  
Free Survival

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

scholarly journals Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Cheng ◽  
Kezuo Hou ◽  
Yizhe Wang ◽  
Yang Chen ◽  
Xueying Zheng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Clinical Pathology ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Therapeutic Drug ◽  
Induce Cell Cycle Arrest ◽  
Survival Difference ◽  
Incidence And Mortality

BackgroundLung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.MethodsA comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan–Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.ResultsWe identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.ConclusionThis study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

scholarly journals Increased GOLM1 Expression Independently Predicts Unfavorable Overall Survival and Recurrence-Free Survival in Lung Adenocarcinoma

2018 ◽  
Vol 25 (1) ◽  
pp. 107327481877800 ◽  
Author(s):  
Xi Liu ◽  
Lei Chen ◽  
Tao Zhang
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Survival Data ◽  
Methylation Status ◽  
Lung Squamous Cell Carcinoma ◽  
Prognostic Indicator ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Recurrence Free Survival ◽  
Free Survival

Golgi membrane protein 1 (GOLM1) is a transmembrane glycoprotein of the Golgi cisternae, which is implicated in carcinogenesis of multiple types of cancer. In this study, using data from the Gene Expression Omnibus and The Cancer Genome Atlas, we compared the expression of GOLM1 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and studied its prognostic value in terms of overall survival (OS) and recurrence-free survival (RFS) in these 2 subtypes of non-small cell lung cancer (NSCLC). Results showed that GOLM1 was significantly upregulated in both LUAD and LUSC tissues compared to the normal controls. However, GOLM1 expression was higher in LUAD tissues than in LUSC tissues. More importantly, using over 10 years’ survival data from 502 patients with LUAD and 494 patients with LUSC, we found that high GOLM1 expression was associated with unfavorable OS and RFS in patients with LUAD, but not in patients with LUSC. The following univariate and multivariate analyses confirmed that increased GOLM1 expression was an independent prognostic indicator of poor OS (hazard ratio [HR]: 1.30, 95% confidence interval [CI]: 1.11-1.54, P = .002) and RFS (HR: 1.37, 95% CI: 1.14-1.64, P = .001) in patients with LUAD. Of 511 cases with LUAD, 248 (48.5%) had heterozygous loss (−1), while 28 (5.5%) of 511 cases with LUAD had low-level copy gain (+1). In addition, we also found that the methylation status of 1 CpG site (chr9: 88,694,942-88,694,944) showed a weak negative correlation with GOLM1 expression (Pearson r = −0.25). Based on these findings, we infer that GOLM1 might serve as a valuable prognostic biomarker in LUAD, but not in LUSC. In addition, DNA copy number alterations and methylation might be 2 important mechanisms of dysregulated GOLM1 in LUAD.

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