Evidence map›Paper›PMID 31718573›Full record

ArticleBMC medical genetics2019

Association of PD-1 polymorphisms with the risk and prognosis of lung adenocarcinoma in the northeastern Chinese Han population.

Kun Huang, Erqiang Hu, Wan Li, Junjie Lv, Yuehan He, Gui Deng, Jinling Xiao, Chengcheng Yang, Xinyu Zhao, Lina Chen and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 24% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Genomic insights on lung cancer risk: identifying new variants in Amazonian indigenous populations.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. ACancers · 2021
    Article
  8. Article
  9. Article
  10. 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 1 institution in 1 country.

Kun HuangDepartment of Respiratory, the Second Affiliated Hospital, Harbin Medical University, Harbin, 150086, People's Republic of China.
Erqiang HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Junjie LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Yuehan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Gui DengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Jinling XiaoDepartment of Respiratory, the Second Affiliated Hospital, Harbin Medical University, Harbin, 150086, People's Republic of China.
Chengcheng YangDepartment of Respiratory, the Second Affiliated Hospital, Harbin Medical University, Harbin, 150086, People's Republic of China.
Xinyu ZhaoDepartment of Respiratory, the Second Affiliated Hospital, Harbin Medical University, Harbin, 150086, People's Republic of China.
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China. chenlina@ems.hrbmu.edu.cn.ORCID 0000-0002-4576-6814
Xinyan WangDepartment of Respiratory, the Second Affiliated Hospital, Harbin Medical University, Harbin, 150086, People's Republic of China. wxylyfll@163.com.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is a leading cause of death from cancer worldwide, especially non-small cell lung cancer (NSCLC). The marker of progression in lung adenocarcinoma, the main type of NSCLC, has been rarely studied. Programmed death 1 (PD-1) is an effective drug target for the treatment of NSCLC. Our study aimed to examine the PD-1 role in the disease process. The study of the effect of polymorphisms on the progression of lung adenocarcinoma in the Han population of Northeast China may provide a valuable reference for the research and application of these drugs.

methodsChi-square test, Wilcoxon rank sum test, and classification efficiency assessment were used to test SNPs of PD-1 in 287 patients and combined with clinical information.

resultsWe successfully identified biomarkers (rs2227981, rs2227982, and rs3608432) that could distinguish between lung adenocarcinoma patients of early stages and late stages. Multiple clinical indicators showed significant differences among different SNPs and cancer stages. Furthermore, this gene was confirmed to effectively distinguish the stages of lung adenocarcinoma with RNA-seq data in TCGA.

conclusionsOut study indicated that the PD-1 gene and the SNPs on it could be used as markers for distinguishing lung adenocarcinoma staging in the Northeast Han population. Our investigation into the link between PD-1 polymorphisms and lung adenocarcinoma would help to provide guidance for the treatment and prognosis of lung adenocarcinoma.

Indexed as

Genetic Predisposition to DiseasePolymorphism, Single NucleotideAdenocarcinomaAdultAgedAged, 80 and overCase-Control StudiesChinaCross-Sectional StudiesDisease ProgressionEthnicityFemaleHumansLung NeoplasmsMaleMiddle AgedProgrammed Cell Death 1 ReceptorLung adenocarcinoma stagingPD-1PolymorphismSNP

Identifiers

PMID31718573
PMCPMC6849296
OpenAlexW2985661060

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.