Evidence map›Paper›PMID 34741022›Full record

ArticleCell death & disease2021

Dihydroartemisinin is potential therapeutics for treating late-stage CRC by targeting the elevated c-Myc level.

Xianjing Hu, Sarwat Fatima, Minting Chen, Tao Huang, Yuen Wa Chen, Ruihong Gong, Hoi Leong Xavier Wong, Rongmin Yu, Liyan Song, Hiu Yee Kwan and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cell death & disease, 2021. 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
0.4field-weighted citation impact, top 40% 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, 13 citations in OpenAlex.

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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

11 authors at 2 institutions in 2 countries.

Xianjing Hu *Centre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.ORCID 0000-0001-8541-8542
Sarwat Fatima *Centre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Minting ChenCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Tao HuangCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Yuen Wa ChenCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Ruihong GongCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Hoi Leong Xavier WongCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Rongmin YuBiotechnological Institute of Chinese Materia Medical, Jinan University, Guangzhou, China.
Liyan SongDepartment of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China.
Hiu Yee KwanCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China. hykwan@hkbu.edu.hk.ORCID 0000-0002-6088-7323
Zhaoxiang BianCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China. bzxiang@hkbu.edu.hk.ORCID 0000-0001-6206-1958
Hong Kong Baptist University · HKJinan University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) #81503303Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation) 2018A0303130122Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation) 2021A1515010655
6 · The paper itself

Abstract

Currently, no frontline treatment is effective for the late-stage colorectal cancer (CRC). Understanding the molecular differences in different stages of CRC can help us to identify the critical therapeutic targets for designing therapeutic strategy. Our data show that c-Myc protein is highly expressed in late-stage CRC when compared with early-stage CRC in both clinical samples and in cell lines representing different cancer stages. Given that c-Myc is a well-known oncogenic driver in CRC, its high expression in the late-stage CRC may represent a critical therapeutic target for treating the cancer. Dihydroartemisinin treatment significantly increases c-Myc protein degradation and hence reduces its expression in CRC. The treatment also reduces CRC cell viability. Interestingly, dihydroartemisinin exhibits a more potent growth-inhibitory effect in late-stage CRC than the early-stage CRC. The treatment also possesses potent growth-inhibitory effects in mouse models bearing c-Myc-overexpressed CRC. The reduced c-Myc level and its reduced transcriptional activity reduce the expressions of acetyl-CoA carboxylase, fatty acid synthase, carnitine-palmitoyltransferase-1, and medium-chain acyl-CoA dehydrogenase in the cancer cells. Lipidomics study also shows that dihydroartemisinin treatment changes the metabolic phenotypes in CRC, reduces oxygen consumption, respiration, and ATP production, hence reduces the cell proliferation and induces apoptosis. Our study provides strong pharmacological evidence to support the translation of dihydroartemisinin for the treatment of late-stage CRC by targeting c-Myc.

Indexed as

AnimalsApoptosisArtemisininsCell Line, TumorCell ProliferationColorectal NeoplasmsDisease Models, AnimalEnergy MetabolismFatty AcidsGene Expression Regulation, NeoplasticHumansInhibitory Concentration 50Ki-67 AntigenLipid MetabolismLuminescent MeasurementsMiceArtemisininsartenimolFatty AcidsKi-67 AntigenMYC protein, humanProto-Oncogene Proteins c-myc

Identifiers

PMID34741022
PMCPMC8571272
OpenAlexW3211103017

What OpenQuestion holds

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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.