Identification of a four-gene panel predicting overall survival for lung adenocarcinoma

Li, Chunyu; Qizhong, Long; Danni, Zhang; Li, Jun; Zhang, Xianming

doi:10.1186/s12885-020-07657-9

Cited by 26 publications

(16 citation statements)

References 44 publications

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“…Therefore, with the development of bioinformatics, an increasing number of novel biomarkers have been constructed to get involved in the diagnosis and prediction of cancer [ 28 , 29 ]. These signatures are associated with metabolism [ 30 ], immune [ 31 ], and DNA methylation [ 32 ]. The standards to judge the model are convenient, credible, and precise.…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

Deng

et al. 2021

World J Surg Onc

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Background Breast cancer (BC) has a high incidence and mortality rate in females. Its conventional clinical characteristics are far from accurate for the prediction of individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods We analyzed the data of a training cohort from the Cancer Genome Atlas (TCGA) database and a validation cohort from the Gene Expression Omnibus (GEO) database. After the applications of Gene Set Enrichment Analysis (GSEA) and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed; the signature contained AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Furthermore, on the basis of expression levels of the six-gene signature, we constructed a risk score formula to classify the patients into high- and low-risk groups. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Later, a nomogram was developed to predict the outcomes of patients with risk score and clinical features over a period of 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues, which were taken as control. Results We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in the high-risk group showed poor prognosis than those in the low-risk group. The area under the curve (AUC) values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients with BC without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues; however, AK3 was an exception. Conclusion We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate prediction and might be useful in expanding the hypothesis in clinical research.

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Section: Discussionmentioning

confidence: 99%

Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

Deng

et al. 2021

World J Surg Onc

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“…Several studies have reported the predictive value of gene expression for the prognosis of patients suffering from cancer, including breast, colorectal, lung, gastric, and ovarian cancer (Li et al, 2020a;Pei et al, 2020;Xie et al, 2020;Zhang et al, 2020b). Most of these studies involved the selection of differentially expressed genes followed by the screening of prognosis-related genes.…”

Section: Discussionmentioning

confidence: 99%

“…Gene sequencing has developed rapidly over the last few years and has allowed us to identify an increasing number of molecular prognostic factors for several cancers, including colon cancer, lung cancer, and breast cancer (Pei et al, 2020;Yang et al, 2020;Zhang et al, 2020a). Indeed, recent studies have demonstrated that genomic data are superior to the traditional staging system for estimating the risk of a worse prognosis and predicting the benefit of adjuvant chemotherapy (He & Zuo, 2019).…”

Section: Introductionmentioning

confidence: 99%

A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

Liu

Peng

et al. 2021

PeerJ

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Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

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“…Therefore, an increasing number of novel biomarkers have been constructed to involve in the diagnosis and prediction on cancer with the development of bioinformatics. These signatures are associated with metabolism [24], immune [25], DNA methylation [26]. The standards to judge the model are whether it is convenient, credible, and precise.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

Deng

et al. 2021

Preprint

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Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

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Identification of a four-gene panel predicting overall survival for lung adenocarcinoma

Cited by 26 publications

References 44 publications

Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

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