Evidence map›Paper›PMID 36726781›Full record

ArticleFrontiers in pharmacology2022

A novel risk model based on anoikis: Predicting prognosis and immune infiltration in cutaneous melanoma.

Yi Zhou, Chen Wang, Yifang Chen, Wei Zhang, Zailin Fu, Jianbo Li, Jie Zheng, Minghua Xie

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Anoikis patterns via machine learning strategy and experimental verification exhibit distinct prognostic and immune landscapes in melanoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024
    Article
  6. Article
  7. 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 at 3 institutions in 1 country.

Yi ZhouDepartment of Pharmacy, First People's Hospital of Linping District, Hangzhou, Zhejiang, China.
Chen WangThe Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Yifang ChenDepartment of Pharmacy, First People's Hospital of Linping District, Hangzhou, Zhejiang, China.
Wei ZhangDepartment of Pharmacy, First People's Hospital of Linping District, Hangzhou, Zhejiang, China.
Zailin FuDepartment of Pharmacy, First People's Hospital of Linping District, Hangzhou, Zhejiang, China.
Jianbo LiDepartment of Pharmacy, First People's Hospital of Linping District, Hangzhou, Zhejiang, China.
Jie ZhengSchool of Medicine, Chongqing University, Chongqing, China.
Minghua XieDepartment of Pharmacy, First People's Hospital of Linping District, Hangzhou, Zhejiang, China.
Hangzhou First People's Hospital · CNCancer Hospital of Chinese Academy of Medical Sciences · CNChongqing University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cutaneous melanoma (CM) is a highly aggressive malignancy with a dimal prognosis and limited treatment options. Anoikis is believed to involve in the regeneration, migration, and metastasis of tumor. The exact role of anoikis-related genes (ARGs) in the development and progression of cutaneous melanoma, however, remains elusive. Four ARGs (

Indexed as

anoikis-related genescutaneous melanomaimmune microenvironmentprognosisrisk model

Identifiers

PMID36726781
PMCPMC9884695
OpenAlexW4316664915

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.