Evidence map›Paper›PMID 35950215›Full record

ArticleMolecular therapy. Nucleic acids2022

Xingxing Jian, Linfeng Xu, Jingjing Zhao, Yanhui Wang, Wen Zhou, Lu Xie

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular therapy. Nucleic acids, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
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

6 authors at 3 institutions in 1 country.

Xingxing JianBioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Linfeng XuShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China.
Jingjing ZhaoShanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China.
Yanhui WangCollege of Food Science and Technology, Shanghai Ocean University, Shanghai, China.
Wen ZhouKey Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Sciences, Central South University, Changsha, Hunan, China.
Lu XieBioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Central South University · CNShanghai Ocean University · CNUniversity of Shanghai for Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neoantigen provides a promising breakthrough in tumor immunotherapy, although only a subset of patients responds well due to the quality of their immune response. However, few biomarkers have been reported to measure the quality of immune response to neoantigens and to predict prognosis of patients with multiple myeloma (MM). Here, we first developed a neoantigen-prediction pipeline starting from outcomes of somatic mutations and gene-expression profiles. Given the expression of some specific marker genes, the human leukocyte antigen (HLA)-I score and the cytolytic score were evaluated respectively to reflect HLA-I molecular expression and CD8+ T/natural killer (NK) cell abundance. According to the process of the immune response to neoantigens, we comprehensively took neoantigen load, cytolytic score, and HLA-I score to construct a neoantigen immune response score (

Indexed as

multiple myelomaNAIRscoreneoantigenneoantigen immune response scoreneoantigen prediction pipelineoverall-survival predictionquality of immune response to neoantigens

Identifiers

PMID35950215
PMCPMC9352810
OpenAlexW4285020118

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.