Evidence map›Paper›PMID 30039553›Full record

ReviewCancer medicine2018

Interferon gamma in cancer immunotherapy.

Ling Ni, Jian Lu

Abstract readReview
In one paragraph

Review in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 223 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
223citing 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

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

  1. Pooled it
  2. Resisting Resistance to Immune Checkpoint Therapy: A Systematic Review.International journal of molecular sciences · 2020
    Pooled it
  3. Trial
  4. Trial
  5. Trial
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Analysis of IFNγ-Induced Invasion of Ovarian Cancer Cells Using 3D Spheroids.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  15. Review
  16. Review
  17. Article
  18. Interferon-Based Therapeutics in Cancer Therapy: Past, Present, and Future.International journal of molecular sciences · 2025
    Review
  19. Review
  20. Article

163 more citing papers are in PubMed but not listed here.

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

2 authors.

Ling NiInstitute for Immunology and School of Medicine, Tsinghua University, Beijing, China.ORCID 0000-0003-1569-882X
Jian LuDepartment of Urology, Peking University Third Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune system can recognize self vs transformed self. That is why cancer immunotherapy achieves notable benefits in a wide variety of cancers. Recently, several papers reported that immune checkpoint blockade therapy led to upregulation of IFNγ and in turn clearance of tumor cells. In this review, we conducted an extensive literature search of recent 5-year studies about the roles of IFNγ signaling in both tumor immune surveillance and immune evasion. In addition to well-known functions, IFNγ signaling also induces tumor ischemia and homeostasis program, resulting in tumor clearance and tumor escape, respectively. The yin and the yang of IFNγ signaling are summarized. Thus, this review helps us to comprehensively understand the roles of IFNγ in tumor immunity, which contributes to better design and management of clinical immunotherapy approaches.

Indexed as

ImmunotherapyAnimalsBiomarkersGene Expression Regulation, NeoplasticHumansImmunologic FactorsImmunomodulationInterferon-gammaNeoplasmsSignal TransductionTumor EscapeTumor MicroenvironmentBiomarkersImmunologic FactorsInterferon-gammacancer immunotherapyIFNγ signalingimmune evasionimmune surveillancetransformed self

Identifiers

PMID30039553
PMCPMC6143921

What OpenQuestion holds

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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.