ArticleTranslational cancer research2020
Effects of autophagy-associated genes on the prognosis for lung adenocarcinoma.
Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of DAPK2 as a key regulatory element in various human cancers: a systematic review.Molecular biology reports · 2024Pooled it
- Machine learning-driven development of a novel unfolded protein response-related gene signature for predicting lung adenocarcinoma patient prognosis.BMC cancer · 2026Article
- The oncogenic miR-429 promotes triple-negative breast cancer progression by degrading DLC1.Aging · 2023Article
- A novel defined cuproptosis-related gene signature for predicting the prognosis of lung adenocarcinoma.Frontiers in genetics · 2022Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral studies show that autophagy plays an important part in the biological processes of lung adenocarcinoma. Therefore, this work aimed to establish one scoring system on the basis of the expression profiles of differentially expressed autophagy-related genes (DEARGs) in patients with lung adenocarcinoma.
methodsThe Cancer Genome Atlas (TCGA) was applied to retrieve lung adenocarcinoma data. The overall survival (OS)-associated DEARGs were selected for the DEARG scoring scale. Moreover, the online database Kaplan-Meier Plotter (www.Kmplot.com) was employed to verify the accuracy of our results.
resultsThe expression patterns of DEARG were detected in lung adenocarcinoma as well as normal lung tissues. A gene set related to autophagy was identified, along with 9 genes that showed marked significance in predicting the lung adenocarcinoma prognosis. According to the cox regression results, DEARGs (including ITGB4, BIRC5, ERO1A, and NLRC4) were applied to calculate the DEARGs risk score. Patients with lower DEARGs risk scores were associated with better OS. Moreover, based on analysis with the receiver operating characteristic (ROC) curve, DEARGs accurately distinguished the healthy tissues from lung adenocarcinoma tissues [area under the curve (AUC) value of >0.6].
conclusionsA scoring system is constructed based on the primary DEARGs, which accurately predicts the outcomes of lung adenocarcinoma.
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