A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer.
Clicks: 469
ID: 88592
2020
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
72.3
/100
468 views
323 readers
Trending
AI Quality Assessment
Not analyzed
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME).There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant ( < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and < 0.001 and.The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.
| Reference Key |
li2020abiomed
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Li, Jia;Wang, Huiyu;Li, Zhaoyan;Zhang, Chenyue;Zhang, Chenxing;Li, Cheng;Yu, Haining;Wang, Haiyong; |
| Journal | BioMed research international |
| Year | 2020 |
| DOI |
10.1155/2020/2147397
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.