Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis

We constructed a prognostic-related risk prediction for patients with lung adenocarcinoma by integrating multiple omics information of lung adenocarcinoma clinical information group and genome and transcriptome. Blood samples and cancer and paracancerous lung tissue samples were collected from 480 p...

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Main Authors: Yuwang Bao, Jianxiong Luo, Tianxing Yu, Yang Liu, Xiaohua Li, Qiong Lin, Hao Wang
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Nanomaterials
Online Access:http://dx.doi.org/10.1155/2021/9997939
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spelling doaj-819e61b369674ac6ab72a0a8ae17420e2021-04-05T00:01:08ZengHindawi LimitedJournal of Nanomaterials1687-41292021-01-01202110.1155/2021/9997939Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data AnalysisYuwang Bao0Jianxiong Luo1Tianxing Yu2Yang Liu3Xiaohua Li4Qiong Lin5Hao Wang6Department of Respiratory MedicineDepartment of Respiratory MedicineDepartment of Respiratory MedicineDepartment of Respiratory MedicineDepartment of Respiratory MedicineDepartment of Respiratory MedicineTeaching Center of Experimental MedicineWe constructed a prognostic-related risk prediction for patients with lung adenocarcinoma by integrating multiple omics information of lung adenocarcinoma clinical information group and genome and transcriptome. Blood samples and cancer and paracancerous lung tissue samples were collected from 480 patients with lung adenocarcinoma. DNA and RNA sequencing was performed on DNA samples and RNA samples. The first follow-up was carried out 3 months after discharge. Clinical information of patients including age, gender, smoking history, and TNM stage was collected. The Cox proportional hazard model evaluated more than 600 potential SNPs related to the prognosis of lung adenocarcinoma. After LASSO analysis, we obtained 4 SNPs related to the prognosis of lung adenocarcinoma (including rs1059292, rs995343, rs2013335, and rs8078328). Through the Cox proportional hazard model, 260 candidate genes related to the prognosis of lung adenocarcinoma were evaluated. After subsequent analysis, 3 genes related to the prognosis of lung adenocarcinoma (LDHA, SDHC, and TYMS) were obtained. All survived patients were spilt into a high-risk group (n=170) and a low-risk group (n=170) according to 4 SNPs and 3 genes related to the prognosis of lung adenocarcinoma. The overall survival rate of patients in the high-risk group was lower than that in the low-risk group. The prognostic risk prediction index constructed by combining clinical information group and genomic and transcriptome characteristics of multiomics information can effectively distinguish the prognosis of patients with lung adenocarcinoma, which will provide effective support for the precise treatment of patients with lung adenocarcinoma.http://dx.doi.org/10.1155/2021/9997939
collection DOAJ
language English
format Article
sources DOAJ
author Yuwang Bao
Jianxiong Luo
Tianxing Yu
Yang Liu
Xiaohua Li
Qiong Lin
Hao Wang
spellingShingle Yuwang Bao
Jianxiong Luo
Tianxing Yu
Yang Liu
Xiaohua Li
Qiong Lin
Hao Wang
Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis
Journal of Nanomaterials
author_facet Yuwang Bao
Jianxiong Luo
Tianxing Yu
Yang Liu
Xiaohua Li
Qiong Lin
Hao Wang
author_sort Yuwang Bao
title Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis
title_short Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis
title_full Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis
title_fullStr Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis
title_full_unstemmed Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis
title_sort identification of gene markers for survival prediction of lung adenocarcinoma patients based on integrated multibody data analysis
publisher Hindawi Limited
series Journal of Nanomaterials
issn 1687-4129
publishDate 2021-01-01
description We constructed a prognostic-related risk prediction for patients with lung adenocarcinoma by integrating multiple omics information of lung adenocarcinoma clinical information group and genome and transcriptome. Blood samples and cancer and paracancerous lung tissue samples were collected from 480 patients with lung adenocarcinoma. DNA and RNA sequencing was performed on DNA samples and RNA samples. The first follow-up was carried out 3 months after discharge. Clinical information of patients including age, gender, smoking history, and TNM stage was collected. The Cox proportional hazard model evaluated more than 600 potential SNPs related to the prognosis of lung adenocarcinoma. After LASSO analysis, we obtained 4 SNPs related to the prognosis of lung adenocarcinoma (including rs1059292, rs995343, rs2013335, and rs8078328). Through the Cox proportional hazard model, 260 candidate genes related to the prognosis of lung adenocarcinoma were evaluated. After subsequent analysis, 3 genes related to the prognosis of lung adenocarcinoma (LDHA, SDHC, and TYMS) were obtained. All survived patients were spilt into a high-risk group (n=170) and a low-risk group (n=170) according to 4 SNPs and 3 genes related to the prognosis of lung adenocarcinoma. The overall survival rate of patients in the high-risk group was lower than that in the low-risk group. The prognostic risk prediction index constructed by combining clinical information group and genomic and transcriptome characteristics of multiomics information can effectively distinguish the prognosis of patients with lung adenocarcinoma, which will provide effective support for the precise treatment of patients with lung adenocarcinoma.
url http://dx.doi.org/10.1155/2021/9997939
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