Evidence map›Paper›PMID 38795156›Full record

ArticleArchives of dermatological research2024

Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.

Shanshan Li, Junjie Zhao, Guangyu Wang, Qingping Yao, Zhe Leng, Qinglei Liu, Jun Jiang, Wei Wang

Abstract read
PubMed Publisher
In one paragraph

Article in Archives of dermatological research, 2024. 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
–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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

8 authors.

Shanshan LiSchool of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
Junjie ZhaoSchool of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
Guangyu WangSchool of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
Qingping YaoInstitute of Mechanobiology & Medical Engineering, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Zhe LengSchool of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
Qinglei LiuSchool of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
Jun JiangDepartment of Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, 646000, China.
Wei WangSchool of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China. wangweittg@sit.edu.cn.

Funding

National Natural Science Foundation of China Nos. 82070288, 11772198the Science & Technology Department of Sichuan Province No. 2022YFS0627the young and middle-aged talent development fund of Shanghai Institute of Technology No. ZQ2021-23
6 · The paper itself

Abstract

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

Indexed as

ImmunotherapyMelanomaRNA-SeqSkin NeoplasmsTumor MicroenvironmentBiomarkers, TumorCutaneous Malignant MelanomaFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMalePrognosisSingle-Cell Gene Expression AnalysisTranscriptomeTreatment OutcomeBiomarkers, TumorBulk RNA-seqCandidate small molecule drugsImmunotherapy response predictionscRNA-seqSkin cutaneous melanomaTumor immune microenvironment

Identifiers

PMID38795156

What OpenQuestion holds

Textmetadata
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.