Evidence map›Paper›PMID 35387121›Full record

ArticleFrontiers in oncology2022

Development and Validation of a Novel Survival Model for Cutaneous Melanoma Based on Necroptosis-Related Genes.

Zehao Niu, Xin Wang, Yujian Xu, Yan Li, Xiaojing Gong, Quan Zeng, Biao Zhang, Jiafei Xi, Xuetao Pei, Wen Yue and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.9field-weighted citation impact, top 8% 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

10 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

11 authors at 3 institutions in 1 country.

Zehao NiuMedical School of Chinese PLA, Beijing, China.
Xin WangDepartment of Ophthalmology, PLA Strategic Support Force Characteristic Medical Center, Beijing, China.
Yujian XuDepartment of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Yan LiMedical School of Chinese PLA, Beijing, China.
Xiaojing GongMedical School of Chinese PLA, Beijing, China.
Quan ZengStem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.
Biao ZhangStem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.
Jiafei XiStem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.
Xuetao PeiStem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.
Wen YueStem Cell and Regenerative Medicine Lab, Institute of Health Service and Transfusion Medicine, AMMS, Beijing, China.
Yan HanDepartment of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Chinese PLA General Hospital · CNCenter for High Pressure Science & Technology Advanced Research · CNAcademy of Military Medical Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Necroptosis is crucial for organismal development and pathogenesis. To date, the role of necroptosis in skin cutaneous melanoma (SKCM) is yet unveiled. In addition, the part of melanin pigmentation was largely neglected in the bioinformatic analysis. In this study, we aimed to construct a novel prognostic model based on necroptosis-related genes and analysis the pigmentation phenotype of patients to provide clinically actionable information for SKCM patients. Methods: We downloaded the SKCM data from the TCGA and GEO databases in this study and identified the differently expressed and prognostic necroptosis-related genes. Patients' pigmentation phenotype was evaluated by the GSVA method. Then, using Lasso and Cox regression analysis, a novel prognostic model was constructed based on the intersected genes. The risk score was calculated and the patients were divided into two groups. The survival differences between the two groups were compared using Kaplan-Meier analysis. The ROC analysis was performed and the area under curves was calculated to evaluate the prediction performances of the model. Then, the GO, KEGG and GSEA analyses were performed to elucidate the underlying mechanisms. Differences in the tumor microenvironment, patients' response to immune checkpoint inhibitors (ICIs) and pigmentation phenotype were analyzed. In order to validate the mRNA expression levels of the selected genes, quantitative real-time PCR (qRT-PCR) was performed. Results: Altogether, a novel prognostic model based on four genes (BOK, CD14, CYLD and FASLG) was constructed, and patients were classified into high and low-risk groups based on the median risk score. Low-risk group patients showed better survival status. The model showed high accuracy in the training and the validation cohort. Pathway and functional enrichment analysis indicated that immune-related pathways were differently activated in the two groups. In addition, immune cells infiltration patterns and sensitivity of ICIs showed a significant difference between patients from two risk groups. The pigmentation score was positively related to the risk score in pigmentation phenotype analysis. Conclusion: In conclusion, this study established a novel prognostic model based on necroptosis-related genes and revealed the possible connections between necroptosis and melanin pigmentation. It is expected to provide a reference for clinical treatment.

Indexed as

biomarkersmelanomanecroptosisprognostic predictionTCGAtreatment

Identifiers

PMID35387121
PMCPMC8979066
OpenAlexW4220667437

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.