Evidence mapPaperPMID 40445455Full record

ReviewMolecular biology reports2025

Unraveling senescence in cancer: mechanistic complexities and therapeutic opportunities.

Prajakta Tiwari, Shreesh Kumar Shukla, Smita Rastogi Verma

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Engineering Immune Cell to Counteract Aging and Aging-Associated Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    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.

Prajakta TiwariDepartment of Biotechnology, Delhi Technological University, Delhi, 110042, India.
Shreesh Kumar ShuklaDepartment of Biotechnology, Delhi Technological University, Delhi, 110042, India.
Smita Rastogi VermaDepartment of Biotechnology, Delhi Technological University, Delhi, 110042, India. smitar@dtu.ac.in.ORCID http://orcid.org/0000-0002-3812-5314

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Senescence is a pivotal cellular process, which also plays a major role in development, immune regulation, tissue repair, and aging, triggered by stressors such as telomere shortening, oncogene activation, and DNA damage. Characterized by distinct morphological and molecular features, senescence is known to act as a tumor suppressive mechanism through irreversible cell cycle arrest. However, emerging studies reveal a paradox: prolonged senescence in cancer cells can drive tumorigenesis via the senescence-associated secretory phenotype, promoting proliferation, invasion, and metastasis. This comprehensive review elucidates the molecular intricacies of senescence to induce growth arrest, enhance immune surveillance, and favorably modulate the tumor microenvironment to inhibit cancer progression. Additionally, it examines the senescence-inducing effects of conventional therapies and explores the potential of emerging therapies, including targeted therapies and chimeric antigen receptor T cell therapy. The present review also highlights the promise of senotherapeutic strategies in selectively targeting senescent cells to improve therapeutic outcomes. It discusses the innovative integration of machine learning tools for biomarker discovery and patient stratification offering a transformative approach to improve cancer treatment efficacy.

Indexed as

Cellular SenescenceNeoplasmsAnimalsCarcinogenesisHumansSenescence-Associated Secretory PhenotypeTumor MicroenvironmentMachine learningSenescenceSenescence associated secretory phenotypeSenescence-targeted cancer therapySenocidalsSenotherapy

Identifiers

What Socratic holds

Textmetadata
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 Socratic graph.