Evidence map›Paper›PMID 36157481›Full record

ReviewGenes & diseases2022

Role of Receptor Interacting Protein (RIP) kinases in cancer.

Kaylee Ermine, Jian Yu, Lin Zhang

Open access · diamondAbstract readReview
In one paragraph

Review in Genes & diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 15% 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. TNF-alpha: Roles in pathogenesis and therapeutics in cancer.The Journal of biological chemistry · 2026
    Review
  5. Review
  6. Article
  7. Review
  8. Targeting RIPK1 to modulate cell death and tumour microenvironment in cancer therapy.Journal of enzyme inhibition and medicinal chemistry · 2025
    Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Leucine-rich repeat kinase 2 at a glance.Journal of cell science · 2023
    Article
  15. Article
  16. Article
  17. 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

3 authors at 1 institution in 1 country.

Kaylee ErmineDepartment of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
Jian YuUPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA.
Lin ZhangDepartment of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
University of Pittsburgh · US

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CHRISTOPHER J. BAKKENIST · 1988 to 2026
$158.0M
PREDOCTORAL TRAINING IN PHARMACOLOGICAL SCIENCEST32GM008424 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI PAGANO, PATRICK J · 1994 to 2019
$3.4M
Novel Mcl-1 inhibitors for overcoming therapeutic resistance in colorectal cancerR01CA217141 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NIKOLOVSKA-COLESKA, ZANETA, ZHANG, LIN · 2017 to 2021
$2.6M
Translation addiction and targeting in colon cancerR01CA215481 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI YU, JIAN · 2018 to 2022
$2.3M
Targeting defective necroptosis in colorectal cancerR01CA247231 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ZHANG, LIN · 2020 to 2024
$2.3M
Role of necroptosis in colorectal cancer therapyR01CA236271 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ZHANG, LIN · 2019 to 2023
$2.2M
BET degraders for improving colorectal cancer therapyR01CA248112 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ZHANG, LIN · 2021 to 2025
$2.1M
Predoctoral Training in Pharmacological Sciences (Resubmission)T32GM133332 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Tija C. Jacob, Francisco Jose Schopfer · 2020 to 2026
$1.8M
Role of Mcl-1 degradation in colorecal cancer therapyR01CA203028 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ZHANG, LIN · 2016 to 2020
$1.7M
NCI NIH HHS R01 CA203028NCI NIH HHS R01 CA215481NCI NIH HHS R01 CA217141NCI NIH HHS R01 CA236271NCI NIH HHS R01 CA247231NCI NIH HHS R01 CA248112NIGMS NIH HHS T32 GM008424NIGMS NIH HHS T32 GM133332
6 · The paper itself

Abstract

The Receptor Interacting Protein (RIP) kinase family consists of seven Serine/Threonine kinases, which plays a key signaling role in cell survival and cell death. Each RIP family member contains a conserved kinase domain and other domains that determine the specific kinase function through protein-protein interactions. RIP1 and RIP3 are best known for their critical roles in necroptosis, programmed necrosis and a non-apoptotic inflammatory cell death process. Dysregulation of RIP kinases contributes to a variety of pathogenic conditions such as inflammatory diseases, neurological diseases, and cancer. In cancer cells, alterations of RIP kinases at genetic, epigenetic and expression levels are frequently found, and suggested to promote tumor progression and metastasis, escape of antitumor immune response, and therapeutic resistance. However, RIP kinases can be either pro-tumor or anti-tumor depending on specific tumor types and cellular contexts. Therapeutic agents for targeting RIP kinases have been tested in clinical trials mainly for inflammatory diseases. Deregulated expression of these kinases in different types of cancer suggests that they represent attractive therapeutic targets. The focus of this review is to outline the role of RIP kinases in cancer, highlighting potential opportunities to manipulate these proteins in cancer treatment.

Indexed as

CancerCell deathImmune responseNecroptosisRIP kinases

Identifiers

PMID36157481
PMCPMC9485196
OpenAlexW3215509516

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.