Integrated genomic analysis of the stemness index signature of mRNA expression predicts lung adenocarcinoma prognosis and immune landscape

mRNA expression-based stemness index (mRNAsi) has been used for prognostic assessment in various cancers, but its application in lung adenocarcinoma (LUAD) is limited, which is the focus of this study. Low mRNAsi in LUAD predicted a better prognosis. Eight genes (<i>GNG7, EIF5A, ANLN, FKBP4, GAPDH, GNPNAT1, E2F7, CISH</i>) associated with mRNAsi were screened to establish a risk model. The differentially expressed genes between the high and low risk groups were mainly enriched in the metabolism, cell cycle functions pathway. The low risk score group had higher immune cell scores. Patients with lower TIDE scores in the low risk group had better immunotherapy outcomes. In addition, risk score was effective in assessing drug sensitivity of LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data showed that eight genes were differentially expressed in LUAD cell lines, and knockdown of <i>EIF5A</i> reduced the invasion and migration ability of LUAD cells. This study designed a risk model based on the eight mRNAsi-related genes for predicting LUAD prognosis. The model accurately predicted the prognosis and survival of LUAD patients, facilitating the assessment of the sensitivity of patients to immunotherapy and chemotherapy.

Authors

Institutions

Publication Details

Journal
PeerJ
Published
2025-02-13
DOI
https://doi.org/10.7717/peerj.18945
Citations
1
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated genomic analysis of the stemness index signature of mRNA expression predicts lung adenocarcinoma prognosis and immune landscape

Zhimin Fu, Wei Du, Weiyang Li, Xian Huang et al.
1 citations
PeerJ
Cancer-related molecular mechanisms research
0.94
article

Integrated genomic analysis of the stemness index signature of mRNA expression predicts lung adenocarcinoma prognosis and immune landscape

Zhimin Fu, Wei Du, Weiyang Li, Xian Huang, Xiaoling Lü, Yuliang Guo, Jianping Zhou, G Chen, Zhibin Ye, Jingsheng Liao
article en
1 citations

Abstract

mRNA expression-based stemness index (mRNAsi) has been used for prognostic assessment in various cancers, but its application in lung adenocarcinoma (LUAD) is limited, which is the focus of this study. Low mRNAsi in LUAD predicted a better prognosis. Eight genes (<i>GNG7, EIF5A, ANLN, FKBP4, GAPDH, GNPNAT1, E2F7, CISH</i>) associated with mRNAsi were screened to establish a risk model. The differentially expressed genes between the high and low risk groups were mainly enriched in the metabolism, cell cycle functions pathway. The low risk score group had higher immune cell scores. Patients with lower TIDE scores in the low risk group had better immunotherapy outcomes. In addition, risk score was effective in assessing drug sensitivity of LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data showed that eight genes were differentially expressed in LUAD cell lines, and knockdown of <i>EIF5A</i> reduced the invasion and migration ability of LUAD cells. This study designed a risk model based on the eight mRNAsi-related genes for predicting LUAD prognosis. The model accurately predicted the prognosis and survival of LUAD patients, facilitating the assessment of the sensitivity of patients to immunotherapy and chemotherapy.

PeerJVol. 13
Dongguan People’s Hospital (CN), Southern Medical University (CN)
Good health and well-being
Openalex Percentile: Top 28%
Cancer-related molecular mechanisms research
0.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.