Evidence map›Paper›PMID 36059641›Full record

ArticleFrontiers in oncology2022

Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma.

Binyu Song, Hao Chi, Gaoge Peng, Yajuan Song, Zhiwei Cui, Yuhan Zhu, Guo Chen, Junzheng Wu, Wei Liu, Chen Dong and 4 more

Abstract 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 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Article
  2. Mutation-informed gene pairs to predict melanoma metastasis.Cell communication and signaling : CCS · 2026
    Article
  3. Article
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  7. Article
  8. The Dynamic Landscape of the Coagulome of Metastatic Malignant Melanoma.International journal of molecular sciences · 2025
    Article
  9. Review
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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

14 authors.

Binyu SongDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Hao ChiClinical Medical College, Southwest Medical University, Luzhou, China.
Gaoge PengClinical Medical College, Southwest Medical University, Luzhou, China.
Yajuan SongDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Zhiwei CuiDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Yuhan ZhuDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Guo ChenDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Junzheng WuDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Wei LiuDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Chen DongDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Yuanyong WangDepartment of Thoracic Surgery, Tangdu Hospital of Air Force Military Medical University, Xi'an, China.
Ke XuClinical Medical College, Southwest Medical University, Luzhou, China.
Zhou YuDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Baoqiang SongDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Backgroud: Skin cutaneous melanoma (SKCM) is an extremely metastatic form of skin cancer. However, there are few valuable molecular biomarkers, and accurate diagnosis is still a challenge. Hypercoagulable state encourages the infiltration and development of tumor cells and is significantly associated with poor prognosis in cancer patients. However, the use of a coagulation-related gene (CRG) signature for prognosis in SKCM, on the other hand, has yet to be determined. Method: We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves. Moreover, the Gene Expression Omnibus (GEO), GSE54467 was used as an independent validation. The correlation between risk score and clinicopathological characteristics, tumor microenvironment (TME), and immunotherapy was further analyzed. Results: To develop a prognostic model, seven CRGs in SKCM patients related to overall survival (OS) were selected: ANG, C1QA, CFB, DUSP6, KLKB1, MMP7, and RABIF. According to the Kaplan-Meier survival analysis, an increased OS was observed in the low-risk group than in the high-risk group (P<0.05). Immunotherapy was much more beneficial in the low-risk group, as per immune infiltration, functional enrichment, and immunotherapy analysis. Conclusions: The prognosis of SKCM patients may now be predicted with the use of a CRG prognostic model, thus guiding the development of treatment plans for SKCM patients and promoting OS rates.

Indexed as

coagulationprognostic signatureSKCMTCGATME

Identifiers

PMID36059641
PMCPMC9434152

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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.