Evidence map›Paper›PMID 36151761›Full record

ArticleCancer medicine2023

Identification of a pyroptosis-based model for predicting clinical outcomes from immunotherapy in patients with metastatic melanoma.

Guanghao Wu, Biying Chen, Junjie Jiang, Yiran Chen, Yanyan Chen, Haiyong Wang

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2023. 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
1.2field-weighted citation impact, top 22% 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, 12 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. The Therapeutic Potential of Pyroptosis in Melanoma.International journal of molecular sciences · 2023
    Review
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

6 authors at 3 institutions in 1 country.

Guanghao WuSchool of Clinical Medicine, Hangzhou Normal University Medical College, Hangzhou, China.
Biying ChenDepartment of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Junjie JiangDepartment of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yiran ChenDepartment of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yanyan ChenDepartment of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Haiyong WangDepartment of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID 0000-0003-0124-5301
First Affiliated Hospital Zhejiang University · CNHangzhou First People's Hospital · CNHangzhou Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy has greatly improved outcomes for patients with advanced melanoma, but good predictive biomarkers remain lacking in clinical practice. Although increasing evidence has revealed a vital role of pyroptosis in the tumor microenvironment (TME), it remains unclear for pyroptosis as a predictive biomarker for immunotherapy in melanoma. RNA sequencing data and annotated clinical information of melanoma patients were obtained from four published immunotherapy datasets. LASSO regression analysis was conducted to develop a pyroptosis-based model for quantifying a pyroptosis score in each tumor. Based on four clinical cohorts, we evaluated the predictive capability of the model using multiple immunotherapeutic outcomes, including clinical benefits, overall survival (OS), and progression-free survival (PFS). Furthermore, we depicted the distinctive TME features associated with pyroptosis. Compared with the group with low pyroptosis scores, the group with high pyroptosis scores consistently achieved better durable clinical benefits in four independent cohorts and the meta-cohort. ROC analysis validated that the pyroptosis-based model was a reliable biomarker for predicting clinical benefits from immunotherapy in melanoma. Survival analyses showed that the group with high pyroptosis scores harbored more favorable OS and PFS than those with low pyroptosis scores. Molecular analysis revealed that tumors with high pyroptosis scores displayed a typical immune-inflamed phenotype in TME, including enrichment of immunostimulatory pathways, increased level of tumor-infiltrating lymphocytes, upregulation of immune effectors, and activation of the antitumor immune response. Our findings suggested that the pyroptosis-related model associated with multiple immune-inflamed characteristics might be a reliable tool for predicting clinical benefit and survival outcomes from immunotherapy in melanoma.

Indexed as

MelanomaNeoplasms, Second PrimaryHumansImmunizationImmunotherapyPrognosisPyroptosisTumor Microenvironmentdurable clinical benefitimmunotherapymelanomapredictive modelpyroptosis

Identifiers

PMID36151761
PMCPMC9972144
OpenAlexW4297174606

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.