Evidence map›Paper›PMID 34238310›Full record

ArticleJournal of translational medicine2021

Dissecting immune cell stat regulation network reveals biomarkers to predict ICB therapy responders in melanoma.

Jingwen Wang, Feng Li, Yanjun Xu, Xuan Zheng, Chunlong Zhang, Congxue Hu, Yingqi Xu, Wanqi Mi, Xia Li, Yunpeng Zhang

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

Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

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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

10 authors at 1 institution in 1 country.

Jingwen Wang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Feng Li *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yanjun Xu *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xuan ZhengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Chunlong ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Congxue HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yingqi XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Wanqi MiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. lixia@hrbmu.edu.cn.
Yunpeng ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. zhangyp@hrbmu.edu.cn.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmunotherapy is a revolutionary strategy in cancer therapy, but the resistance of which is one of the important challenges. Detecting the regulation of immune cells and biomarkers concerning immune checkpoint blockade (ICB) therapy is of great significance.

methodsHere, we firstly constructed regulation networks for 11 immune cell clusters by integrating biological pathway data and single cell sequencing data in metastatic melanoma with or without ICB therapy. We then dissected these regulation networks and identified differently expressed genes between responders and non-responders. Finally, we trained and validated a logistic regression model based on ligands and receptors in the regulation network to predict ICB therapy response.

resultsWe discovered the regulation of genes across eleven immune cell stats. Functional analysis indicated that these stat-specific networks consensually enriched in immune response corrected pathways and highlighted antigen processing and presentation as a core pathway in immune cell regulation. Furthermore, some famous ligands like SIRPA, ITGAM, CD247and receptors like CD14, IL2 and HLA-G were differently expressed between cells of responders and non-responders. A predictive model of gene sets containing ligands and receptors performed accuracy prediction with AUCs above 0.7 in a validation dataset suggesting that they may be server as biomarkers for predicting immunotherapy response.

conclusionsIn summary, our study presented the gene-gene regulation landscape across 11 immune cell clusters and analysis of these networks revealed several important aspects and immunotherapy response biomarkers, which may provide novel insights into immune related mechanisms and immunotherapy response prediction.

Indexed as

Biomarkers, TumorMelanomaHumansImmunotherapyBiomarkers, TumorImmune single cellsImmunotherapyRegulation networks

Identifiers

PMID34238310
PMCPMC8265039
OpenAlexW3112730926

What OpenQuestion holds

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LicenceCC BY
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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.