Evidence map›Paper›PMID 34466016›Full record

ArticleJournal of inflammation research2021

Transcriptome Analysis Reveals the Molecular Immunological Characteristics of Lesions in Patients with Halo Nevi When Compared to Stable Vitiligo, Normal Nevocytic Nevi and Cutaneous Melanoma.

Chun Pan, Jingzhe Shang, Haiqin Jiang, Ying Shi, Wenyue Zhang, Jingshu Xiong, Youming Mei, Siyu Long, Gai Ge, Zhenzhen Wang and 3 more

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 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
0.5field-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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
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  4. PD-L1Frontiers in immunology · 2025
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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

13 authors at 1 institution in 1 country.

Chun PanInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Jingzhe ShangInstitute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, People's Republic of China.
Haiqin JiangInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Ying ShiInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.ORCID 0000-0002-0301-6791
Wenyue ZhangInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Jingshu XiongInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Youming MeiInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.ORCID 0000-0003-2204-6678
Siyu LongInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Gai GeInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Zhenzhen WangInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Ziwei WuInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Hongsheng WangInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210046, People's Republic of China.
Aiping WuInstitute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, People's Republic of China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven their similar appearance and histology, halo nevi (HN) were considered as a type of vitiligo. However, whether HN have stronger immune response than stable vitiligo (VL) remains unclear. In addition, the molecular alterations in HN compared with normal nevocytic nevi (NN) and primary cutaneous melanoma (MM) must be determined. This study aimed to systematically characterize the molecular immunological features of HN.

methodsSkin samples from patients with HN, VL, NN, and MM were obtained with informed consent. Each of the four groups underwent transcriptome sequencing and data analysis were for pairwise comparison. Quantitative real-time PCR (RT-qPCR) was conducted to confirm the transcriptional expression of some differentially expressed genes (DEGs) that were closely related to immunity.

resultsA total of 441 and 1507 DEGs were found in the HN/NN and HN/MM groups, respectively. Compared with those of VL, HN lesions contained 162 up-regulated DEGs and 12 down-regulated DEGs. Bioinformatics analysis showed that the up-regulated genes in HN were substantially enriched in immune response, immune deficiency, and immune rejection; biological stimulation (virus, bacteria); and proliferation and activation of immune cells. Immune cell composition analysis also confirmed high expression levels of multiple immunocytes in HN.

conclusionThe molecular immune mechanisms of HN and VL were similar, but the immune activity of HN was stronger than that of VL. Innate and adaptive immunity were involved in the pathogenesis and progression of HN and VL.

Indexed as

differentially expressed geneshalo nevitranscriptome sequencingvitiligo

Identifiers

PMID34466016
PMCPMC8403033
OpenAlexW3193919266

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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.