Evidence map›Paper›PMID 41409301›Full record

ArticleFrontiers in immunology2025

Identification and validation of prognostic genes and prognostic models associated with cutaneous melanoma and integrative stress response.

Zhaoqi Zhang, Ying Wang, Wei Yin, Qingyang Lei, Yuqiao Fu, Yingzi Liang, Ruilei Li, Ke Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Targeting the BMX in cancer: molecular mechanisms and emerging therapeutic strategies.Journal of enzyme inhibition and medicinal chemistry · 2026
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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

8 authors.

Zhaoqi Zhang *Key Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Ying Wang *Key Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Wei Yin *Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qingyang LeiKey Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Yuqiao FuKey Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Yingzi LiangKey Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Ruilei LiKey Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.
Ke LiKey Laboratory of Melanoma Research, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Skin cutaneous melanoma (SKCM) is a highly invasive cancer with dismal prognosis. Integrated stress response (ISR) is associated with tumorigenesis and progression, but its relationship with SKCM prognosis is unclear. This research aimed to identify relevant prognostic genes for SKCM prognosis and treatment insights. Methods: Data were obtained from public databases. Differential expression and regression analyses were used to identify prognostic genes. Based on these genes and independent prognostic factors, risk models and nomograms were constructed to assess their clinical application potential in SKCM. Then, immune microenvironment changes in SKCM were explored according to risk-group grouping, providing a basis for stratified treatment decisions for SKCM patients. Finally, RT-qPCR was used to validate the results. Results: Conclusion: The identification of ISR-featured prognostic genes and risk score stratification provide new insights into targeting SKCM and enhancing the efficacy of immunotherapy.

Indexed as

Biomarkers, TumorMelanomaSkin NeoplasmsCutaneous Malignant MelanomaFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedNomogramsPrognosisTumor MicroenvironmentBiomarkers, Tumorimmune micro-environmentimmunohistochemistryintegrated stress responseprognostic modeskin cutaneous melanoma

Identifiers

PMID41409301
PMCPMC12705592

What Socratic 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 Socratic graph.