Evidence map›Paper›PMID 35629893›Full record

ArticleMetabolites2022

The Differential Metabolic Response of Oral Squamous Cell Carcinoma Cells and Normal Oral Epithelial Cells to Cisplatin Exposure.

Xun Chen, Sufang Kuang, Yi He, Hongyu Li, Chen Yi, Yiming Li, Chao Wang, Guanhui Chen, Shangwu Chen, Dongsheng Yu

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 34% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Biological biomarkers of oral cancer.Periodontology 2000 · 2024
    Review
  5. Review
  6. Article
  7. 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

10 authors at 1 institution in 1 country.

Xun ChenHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Sufang KuangCenter for Proteomics and Metabolomics, State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Yi HeHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Hongyu LiHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Chen YiHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Yiming LiHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Chao WangHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Guanhui ChenHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Shangwu ChenGuangdong Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory for Biocontrol, Department of Biochemistry, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.ORCID 0000-0003-0732-377X
Dongsheng YuHospital of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055, China.
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic reprogramming is one of the hallmarks of a tumor. It not only promotes the development and progression of tumor but also contributes to the resistance of tumor cells to chemotherapeutics. The difference in the metabolism between drug-resistant and sensitive tumor cells indicates that drug-resistant tumor cells have experienced metabolic adaptation. The metabolic response induced by chemotherapy is dynamic, but the early metabolic response of tumor cells to anticancer drugs and the effect of an initial response on the development of drug resistance have not been well studied. Early metabolic intervention may prevent or slow down the development of drug resistance. The differential metabolic responses of normal cells and tumor cells to drugs are unclear. The specific metabolites or metabolic pathways of tumor cells to chemotherapeutic drugs can be used as the target of metabolic intervention in tumor therapy. In this study, we used comparative metabolomics to analyze the differential metabolic responses of oral cancer cells and normal oral epithelial cells to short-term cisplatin exposure, and to identify the marker metabolites of early response in oral cancer cells. Oral cancer cells showed a dynamic metabolic response to cisplatin. Seven and five metabolites were identified as specific response markers to cisplatin exposure in oral cancer cell SCC-9 and normal oral epithelial cell HOEC, respectively. Glyoxylate and dicarboxylate metabolism and fructose, malate, serine, alanine, sorbose and glutamate were considered as specific enriched metabolic pathways and biomarkers of SCC-9 cells in response to cisplatin, respectively. The existence of differential metabolic responses lays a foundation for tumor chemotherapy combined with metabolic intervention.

Indexed as

cisplatin exposuremetabolic responsemetabolomicsnormal oral epithelial cellsoral squamous cell carcinoma cells

Identifiers

PMID35629893
PMCPMC9147301
OpenAlexW4224435916

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.