Evidence map›Paper›PMID 33184998›Full record

ArticleJournal of cellular and molecular medicine2020

Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

Kai Mi, Fuhui Chen, Zhipeng Qian, Jing Chen, Dongxu Lv, Chunlong Zhang, Yanjun Xu, Hongguang Wang, Yuepeng Zhang, Yanan Jiang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

14 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

11 authors at 2 institutions in 1 country.

Kai MiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Fuhui ChenDepartment of Respiratory, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Zhipeng QianCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jing ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Dongxu LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Chunlong ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID 0000-0002-1164-8182
Yanjun XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Hongguang WangSchool of Civil Engineering, Northeast Forestry University, Harbin, China.
Yuepeng ZhangDepartment of Respiratory, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Yanan JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Desi ShangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID 0000-0002-9141-393X
Harbin Medical University · CNNortheast Forestry University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non-small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 ('low infiltration' cluster), cluster 2 ('heterogeneous infiltration' cluster) and cluster 3 ('high infiltration' cluster). The immune cells were responsible for a significantly favourable prognosis for the 'high infiltration' community. Cluster 1 had the lowest cytotoxic activity, tumour-infiltrating lymphocytes and interferon-gamma (IFN-γ), as well as immune checkpoint molecules expressions. In addition, MHC-I and immune co-stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune-related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.

Indexed as

Biomarkers, TumorTumor MicroenvironmentCarcinoma, Non-Small-Cell LungComputational BiologyDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateLung NeoplasmsPhenotypePrognosisReproducibility of ResultsTranscriptomeBiomarkers, Tumornon-small cell lung cancerprognostictumour microenvironment

Identifiers

PMID33184998
PMCPMC7754023
OpenAlexW3106440520

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.