A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients

Chen, Enguo; Wang, Pin; Lou, Hongbo; Wang, Yunshan; Yan, Hong; Bi, Lei; Liang, Liu; Li, Bin; Snijders, Antoine M.; Mao, Jian‐Hua

doi:10.18632/oncotarget.23490

Cited by 9 publications

(15 citation statements)

References 21 publications

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“…The prognostic models for LUAD has been widely studied in the context of metastasis-free, organ-specific metastasis-free, and overall survival (Chen et al, 2018; Li et al, 2017; Park et al, 2012; Shukla et al, 2017). Despite extensive researches about the combinations of gene signatures selected for prognosis prediction, the lack of robust gene signatures for LUAD overall survival prediction is still not thoroughly solved.…”

Section: Discussionmentioning

confidence: 99%

“…Although the TNM staging system had the potential to predict the prognosis, its performance was still not satisfactory (Marchevsky, 2006). Recently, many efforts were made to identify the potential molecules that are the prognostic markers of lung cancer patients (Chen et al, 2018; Li et al, 2017; Park et al, 2012; Shukla et al, 2017). With the advances in microarray and RNA sequencing technologies, gene expression signatures were widely applied to predicting the prognosis of lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

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Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

Songyang

Zhu

Liu

et al. 2019

PeerJ

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Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. High mortality in LUAD motivates us to stratify the patients into high- and low-risk groups, which is beneficial for the clinicians to design a personalized therapeutic regimen. To robustly predict the risk, we identified a set of robust prognostic gene signatures and critical pathways based on ten gene expression datasets by the meta-analysis-based Cox regression model, 25 of which were selected as predictors of multivariable Cox regression model by MMPC algorithm. Gene set enrichment analysis (GSEA) identified the Aurora-A pathway, the Aurora-B pathway, and the FOXM1 transcription factor network as prognostic pathways in LUAD. Moreover, the three prognostic pathways were also the biological processes of G2-M transition, suggesting that hyperactive G2-M transition in cell cycle was an indicator of poor prognosis in LUAD. The validation in the independent datasets suggested that overall survival differences were observed not only in all LUAD patients, but also in those with a specific TNM stage, gender, and age group. The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

Songyang

Zhu

Liu

et al. 2019

PeerJ

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“…Prognostic scores for all patients were calculated, and patients were ranked based on their scores and divided into three equal-sized cohorts. Kaplan–Meier analysis and a log-rank test were conducted to determine differences in survival, as previously described [11, 12].…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we employed a multistep bioinformatic strategy that uses omics information and clinical data to build a gene expression prognostic scoring system in HGSOC. We previously developed this approach to identify and successfully validate a 53-gene signature associated with OS of gastric cancer [11] and a 27-gene signature for lung adenocarcinoma [12]. Here, we used fifteen publicly available datasets of HGSOCs; six were used to identify an 11-gene signature associated with patient prognosis using Cox regression analysis and cross-validation.…”

Section: Introductionmentioning

confidence: 99%

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

Zhao

Yang

Jin

et al. 2019

Journal of Oncology

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The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

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“…Such classification reflects both background genetics and molecular pathogenetic features. Gene expression patterns have also recently been used as prognostic biomarkers in various types of cancer [ e.g ., [refs [10] , [11] , [12] , [13] , [14] , [15] , [16] ]]. The power of such analysis has been well demonstrated with both Oncotype DX and MammaPrint assays in predicting clinical outcome of patients with breast cancer [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

et al. 2020

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Background We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature. Methods A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts. Findings In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies. Interpretation The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen. Funding Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

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A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients

Cited by 9 publications

References 21 publications

Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

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