Evidence map›Paper›PMID 33447073›Full record

ArticlePharmacogenomics and personalized medicine2021

Identification of Four Genes as Prognosis Signatures in Lung Adenocarcinoma Microenvironment.

Yan Yao, Tingting Zhang, Lingyu Qi, Ruijuan Liu, Gongxi Liu, Jie Li, Changgang Sun

Open access · goldAbstract read
In one paragraph

Article in Pharmacogenomics and personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Yan YaoClinical Medical Colleges, Weifang Medical University, Weifang, Shandong Province, People's Republic of China.
Tingting ZhangCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People's Republic of China.ORCID 0000-0001-9141-2593
Lingyu QiCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People's Republic of China.ORCID 0000-0002-7167-776X
Ruijuan LiuDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang, Shandong Province, People's Republic of China.
Gongxi LiuDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang, Shandong Province, People's Republic of China.
Jie LiCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People's Republic of China.
Changgang SunDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang, Shandong Province, People's Republic of China.ORCID 0000-0002-6648-3602
Shandong University of Traditional Chinese Medicine · CNWeifang Chinese Medicine Hospital · CNWeifang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumor microenvironment (TME) cells constitute a vital element of tumor tissues. Increasing evidence has shown that immune response in the microenvironment plays an active role in tumor invasion, metastasis, and recurrence, and is an important factor affecting tumor prognosis. Our study aimed to identify the gene signatures in lung adenocarcinoma (LUAD) microenvironment for prognosis and immunotherapy.

methodsIn this study, we evaluated, for the first time, the stromal and immune scores of 594 patients from The Cancer Genome Atlas (TCGA) database with LUAD using the ESTIMATE algorithm. Three hundred and sixty-seven dysregulated immune-related genes were identified. Then, we performed functional enrichment analysis of these genes, and found the best gene model and construct the signature through univariate, Lasso and multivariate COX regression analysis. To assess the independently prognostic ability of the signature, the Kaplan-Meier survival analysis and Cox's proportional hazards model were performed.

resultsFunctional enrichment analysis and protein-protein interaction networks showed that the immune-related genes mainly played a role in immune response, activation/proliferation of immune-related cells, and chemokine activity. A prognostic model involving 6 genes was constructed and the signature was identified as an independent prognostic factor and significantly associated with the overall survival (OS) of LUAD. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the 6 genes signature in predicting the 3-year survival rate was 0.708. Finally, four genes (FOXN4, KLHL4, FAM83F and CCR2) can be used as candidate prognostic biomarkers for LUAD.

conclusionOur findings will help evaluate the prognosis of LUAD and provide new ideas for exploring the potential relationship between TME and LUAD treatment and prognosis.

Indexed as

ESTIMATE algorithmimmune-related genelung adenocarcinomaprognosis signaturestumor microenvironment

Identifiers

PMID33447073
PMCPMC7802904
OpenAlexW3120871333

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.