Evidence map›Paper›PMID 40133563›Full record

ReviewDiscover oncology2025

Exploring cell death pathways in oral cancer: mechanisms, therapeutic strategies, and future perspectives.

Chenyi Zhao

Abstract readReview
In one paragraph

Review in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. 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

1 author.

Chenyi ZhaoThe Second School of Clinical Medicine, Guangdong Medical University, Dongguan, No.1 Xincheng Blvd, Songshan Lake National High-tech Industrial Development Zone, 523808, Guangdong Province, China. chenyi_zhao@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oral squamous cell carcinoma (OSCC) represents a significant global health challenge, characterized by aggressive progression and poor therapeutic response despite advances in treatment modalities. This review provides a comprehensive analysis of diverse cell death mechanisms in OSCC, encompassing traditional pathways (apoptosis, autophagy, and necrosis), newly characterized mechanisms (ferroptosis, pyroptosis, and necroptosis), and emerging pathways (cuproptosis, anoikis, parthanatos, and entosis). By examining the molecular basis of these pathways, particularly the crucial roles of p53 signaling and miRNA regulation, we highlight how their dysregulation contributes to treatment resistance and tumor progression. The review synthesizes recent evidence demonstrating the complex interplay between these ten distinct cell death mechanisms and their impact on the tumor microenvironment and immune response. We evaluate innovative therapeutic approaches that target these pathways, including novel small molecules, combination strategies, and immunomodulatory treatments that exploit specific cell death mechanisms to enhance therapeutic efficacy. Special attention is given to emerging personalized medicine strategies that consider individual tumor characteristics and cell death pathway profiles. By integrating current challenges with future research directions, this review provides a framework for developing more effective treatments that can leverage multiple cell death pathways to overcome therapy resistance and improve outcomes for oral cancer patients.

Indexed as

Cell death pathwaysOral squamous cell carcinoma (OSCC)Personalized medicineTherapeutic resistanceTumor microenvironment

Identifiers

PMID40133563
PMCPMC11936869

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.