Evidence map›Paper›PMID 40502851›Full record

ArticleMedicine international

Effects of sertraline and sorafenib on HepG2 cells with a possible link to autophagy.

Gamze Demirel, Nil Pinarer, Mert Emre Ergin, Zeynep Saraçoğlu, Ceren Bingöl, Zeynep Güneş Özünal, Yaprak Dönmez Çakil

Abstract read
In one paragraph

Article in Medicine international. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Gamze DemirelCancer and Stem Cell Center, Maltepe University, 34858 Istanbul, Türkiye.
Nil PinarerFaculty of Medicine, Maltepe University, 34858 Istanbul, Türkiye.
Mert Emre ErginFaculty of Medicine, Maltepe University, 34858 Istanbul, Türkiye.
Zeynep SaraçoğluFaculty of Medicine, Maltepe University, 34858 Istanbul, Türkiye.
Ceren BingölFaculty of Medicine, Maltepe University, 34858 Istanbul, Türkiye.
Zeynep Güneş ÖzünalDepartment of Medical Pharmacology, Faculty of Medicine, Atlas University, 34403 Istanbul, Türkiye.
Yaprak Dönmez ÇakilDepartment of Medical Biology and Genetics, Faculty of Medicine, Maltepe University, 34858 Istanbul, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cancer is one of the leading causes of cancer-related mortality worldwide. The current range of treatment options for patients with advanced-stage disease, including the first approved systemic therapy, sorafenib, has been demonstrated to have limited efficacy. A significant number of patients develop resistance to sorafenib treatment or discontinue its use due to adverse effects. Depression is a common complication of cancer, and the antidepressant, sertraline, has recently garnered considerable attention due to its anticancer activity. Accumulating evidence suggests that autophagy may represent a highly promising target for cancer therapy. Previously, the authors demonstrated that the sorafenib and sertraline combination exerted a synergistic anticancer effect on HepG2 cells. The present study examined the intracellular localization and mRNA expression levels of key autophagy markers, including Beclin-1, p62 and LC3, as well as the formation of acidic vesicular organelles using acridine orange staining, to further elucidate the link between autophagy and combined treatment of HepG2 cancer cells with sorafenib and sertraline. Drug treatment did not result in significant alterations in the expression levels of the

Indexed as

autophagyliver cancermicroscopesertralinesorafenib

Identifiers

PMID40502851
PMCPMC12152409

What OpenQuestion holds

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Registered trials

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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.