Evidence map›Paper›PMID 39093712›Full record

ArticleSkin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)2024

Establishment of a CD8+ T cells-related prognostic risk model for acral melanoma based on single-cell and bulk RNA sequencing.

Wenwen Wang, Pu Liu, Jie Ma, Jun Li, Ling Leng

Abstract read
In one paragraph

Article in Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI), 2024. 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
–field-weighted citation impact
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.

  1. Establishment of a CD8+ T cells-related prognostic risk model for acral melanoma based on single-cell and bulk RNA sequencing.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024
    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

5 authors.

Wenwen WangDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Pu LiuChongqing Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
Jie MaState Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
Jun LiDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ling LengStem Cell and Regenerative Medicine Lab, Department of Medical Science Research Center, State Key Laboratory for Complex, Severe, and Rare Diseases, Center for Translational Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCD8+ T cells have been recognized as crucial factors in the prognosis of melanoma. However, there is currently a lack of gene markers that accurately describe their characteristics and functions in acral melanoma (AM), which hinders the development of personalized medicine.

methodsFirstly, we explored the composition differences of immune cells in AM using single-cell RNA sequencing (scRNA-seq) data and comprehensively characterized the immune microenvironment of AM in terms of composition, developmental differentiation, function, and cell communication. Subsequently, we constructed and validated a prognostic risk scoring model based on differentially expressed genes (DEGs) of CD8+ T cells using the TCGA-SKCM cohort through Lasso-Cox method. Lastly, immunofluorescence staining was performed to validate the expression of four genes (ISG20, CCL4, LPAR6, DDIT3) in AM and healthy skin tissues as included in the prognostic model.

resultsThe scRNA-seq data revealed that memory CD8+ T cells accounted for the highest proportion in the immune microenvironment of AM, reaching 70.5%. Cell-cell communication analysis showed extensive communication relationships among effector CD8+ T cells. Subsequently, we constructed a prognostic scoring model based on DEGs derived from CD8+ T cell sources. Four CD8+ T cell-related genes were included in the construction and validation of the prognostic model. Additionally, immunofluorescence results demonstrated that ISG20 and CCL4 were downregulated, while LPAR6 and DDIT3 were upregulated in AM tissues compared to normal skin tissues.

conclusionIdentifying biomarkers based on the expression levels of CD8+ T cell-related genes may be an effective approach for establishing prognostic models in AM patients. The independently prognostic risk evaluation model we constructed provides new insights and theoretical support for immunotherapy in AM.

Indexed as

CD8-Positive T-LymphocytesMelanomaSingle-Cell AnalysisSkin NeoplasmsTumor MicroenvironmentBiomarkers, TumorFemaleHumansMaleMiddle AgedPrognosisRisk AssessmentSequence Analysis, RNABiomarkers, Tumoracral melanomaCD8+ T cellsprognostic modelsingle‐cell RNA sequencing

Identifiers

PMID39093712
PMCPMC11296306

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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.