Evidence map›Paper›PMID 33511023›Full record

ArticleAnalytical cellular pathology (Amsterdam)2021

Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma.

Ai-Lan Li, Yong-Mei Zhu, Lai-Qiang Gao, Shu-Yue Wei, Ming-Tao Wang, Qiang Ma, You-You Zheng, Jian-Hua Li, Qing-Feng Wang

Open access · goldAbstract read
In one paragraph

Article in Analytical cellular pathology (Amsterdam), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 56% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

9 authors at 2 institutions in 1 country.

Ai-Lan LiDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0003-3720-1968
Yong-Mei ZhuDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0002-1599-2415
Lai-Qiang GaoDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0003-0311-9210
Shu-Yue WeiDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0002-6932-8616
Ming-Tao WangDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0002-5125-8517
Qiang MaDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0003-0168-7716
You-You ZhengDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0002-3623-1375
Jian-Hua LiDepartment of Dermatology, Dongying People's Hospital, Dongying 257091, China.ORCID https://orcid.org/0000-0002-6961-6829
Qing-Feng WangCollege of Integrated Chinese and Western Medicine, Liaoning University of traditional Chinese Medicine, Shenyang 110079, China.ORCID https://orcid.org/0000-0002-3508-0448
Wenzhou Medical University · CNLiaoning University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK-STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages (

Indexed as

AdultAgedCutaneous Malignant MelanomaFemaleGene Expression ProfilingHumansMaleMelanomaMiddle AgedSkin NeoplasmsTranscriptomeTumor Microenvironment

Identifiers

PMID33511023
PMCPMC7826228
OpenAlexW3124404948

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.