Survival prediction for patients with lung adenocarcinoma: A prognostic risk model based on gene mutations

2020 ◽  
Vol 27 (4) ◽  
pp. 525-532
Author(s):  
Hua Geng ◽  
Shixiong Li ◽  
Yixian Guo ◽  
Fang Yan ◽  
Yuebin Han ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Model ◽  
Gene Mutations ◽  
Survival Prediction ◽  
Model Based

scholarly journals A Novel Risk Model Based on Autophagy Pathway Related Genes for Survival Prediction in Lung Adenocarcinoma

2020 ◽  
Vol 26 ◽  
Author(s):  
Fan Zhang ◽  
Suzhen Xie ◽  
Zhenyu Zhang ◽  
Huanhuan Zhao ◽  
Zijun Zhao ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Model ◽  
Survival Prediction ◽  
Model Based ◽  
Autophagy Pathway

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 The roles of risk model based on the 3-XRCC genes in lung adenocarcinoma progression

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Qun-Xian Zhang ◽  
Ye Yang ◽  
Heng Yang ◽  
Qiang Guo ◽  
Jia-Long Guo ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Model ◽  
Model Based

scholarly journals Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

scholarly journals Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma

2020 ◽  
Vol 21 (22) ◽  
pp. 8479
Author(s):  
Zhaodong Li ◽  
Fangyuan Qi ◽  
Fan Li
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Proportional Hazards ◽  
Risk Model ◽  
Expression Profiles ◽  
Clinical Stage ◽  
Gene Signature ◽  
Risk Scores ◽  
Survival Prediction ◽  
Signature Genes

Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (TTK, HMMR, ASPM, CDCA8, KIF2C, CCNA2, CCNB2, and MKI67) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

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