Evidence map›Paper›PMID 38627760›Full record

ArticleCancer cell international2024

Comprehensive analysis of m6A modification in immune infiltration, metabolism and drug resistance in hepatocellular carcinoma.

Yunxing Shi, Kai Li, Yichuan Yuan, Chenwei Wang, Zhiwen Yang, Dinglan Zuo, Yi Niu, Jiliang Qiu, Binkui Li, Yunfei Yuan and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancer cell international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. TargetingJournal of Zhejiang University. Science. B · 2026
    Article
  2. Article
  3. Article
  4. 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

11 authors at 4 institutions in 1 country.

Yunxing Shi *Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat- sen University, Guangzhou, China.
Kai Li *Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Yichuan YuanState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Chenwei WangState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Zhiwen YangState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Dinglan ZuoState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Yi NiuState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Jiliang QiuState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Binkui LiState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Yunfei YuanState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Wei HeState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China. hewei@sysucc.org.cn.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CNFirst Affiliated Hospital of Guangzhou Medical University · CNSixth Affiliated Hospital of Sun Yat-sen University · CN

Funding

GuangDong Basic and Applied Basic Research Foundation No. 2022A1515110486Science and Technology Projects in Guangzhou No. SL2022A04J01959
6 · The paper itself

Abstract

N6-methyladenosine (m6A) is important in regulating mRNA stability, splicing, and translation, and it also contributes to tumor development. However, there is still limited understanding of the comprehensive effects of m6A modification patterns on the tumor immune microenvironment, metabolism, and drug resistance in hepatocellular carcinoma (HCC). In this study, we utilized unsupervised clustering based on the expression of 23 m6A regulators to identify m6A clusters. We identified differential m6A modification patterns and characterized m6A-gene-cluster A, which exhibited poorer survival rates, a higher abundance of Treg cells, and increased expression of TGFβ in the tumor microenvironment (TME). Additionally, m6A-gene-cluster A demonstrated higher levels of glycolysis activity, cholesterol metabolism, and fatty acid biosynthesis. We also found that the m6A score was associated with prognosis and drug resistance. Patients with a low m6A score experienced worse prognoses, which were linked to an abundance of Treg cells, upregulation of TGFβ, and increased metabolic activity. HCC patients with a higher m6A score showed improved prognosis following sorafenib treatment and immunotherapy. In conclusion, we reveals the association between m6A modification patterns and the tumor immune microenvironment, metabolism, and drug resistance in HCC. Furthermore, the m6A score holds potential as a predictive factor for the efficacy of targeted therapy and immunotherapy in HCC.

Indexed as

Drug resistanceImmunotherapyM6A modificationMetabolismTargeted-therapyTumor immune microenvironment

Identifiers

PMID38627760
PMCPMC11022358
OpenAlexW4394843880

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.