Evidence map›Paper›PMID 38555384›Full record

ArticleScientific reports2024

Identification of molecular subtypes and a prognostic signature based on m6A/m5C/m1A-related genes in lung adenocarcinoma.

Yu Zhang, Qiuye Jia, Fangfang Li, Xuan Luo, Zhiyuan Wang, Xiaofang Wang, Yanghao Wang, Yinglin Zhang, Muye Li, Li Bian

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

9 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Identification of mDiscover oncology · 2025
    Article
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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

10 authors at 3 institutions in 1 country.

Yu Zhang *Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Qiuye Jia *Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Fangfang Li *Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Xuan LuoDepartment of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Zhiyuan WangDepartment of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Xiaofang WangDepartment of Pathology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Yanghao WangDepartment of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Yinglin ZhangWenshan People's Hospital, Yunnan, Yunnan Province, China.
Muye LiDepartment of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.
Li BianDepartment of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China. bianli@kmmu.edu.cn.
First Affiliated Hospital of Kunming Medical University · CNKunming Medical University · CNPeople’s Hospital of Wenshan Prefecture · CN

Funding

Science and Technology Innovation Team for Precision Pathological Diagnosis of Lung Malignant Tumours at Kunming Medical University CXTD202210Scientific Research Fund Project of Education Department of Yunnan Province 2024Y221the Regional Fund Project of the National Natural Science Foundation of China 82360523Xingdian Talent Plan "Famous Doctor Special Project" RLMY20220018
6 · The paper itself

Abstract

Lung cancer, specifically the histological subtype lung adenocarcinoma (LUAD), has the highest global occurrence and fatality rate. Extensive research has indicated that RNA alterations encompassing m6A, m5C, and m1A contribute actively to tumorigenesis, drug resistance, and immunotherapy responses in LUAD. Nevertheless, the absence of a dependable predictive model based on m6A/m5C/m1A-associated genes hinders accurately predicting the prognosis of patients diagnosed with LUAD. In this study, we collected patient data from The Cancer Genome Atlas (TCGA) and identified genes related to m6A/m5C/m1A modifications using the GeneCards database. The "ConsensusClusterPlus" R package was used to produce molecular subtypes by utilizing genes relevant to m6A/m5C/m1A identified through differential expression and univariate Cox analyses. An independent prognostic factor was identified by constructing a prognostic signature comprising six genes (SNHG12, PABPC1, IGF2BP1, FOXM1, CBFA2T3, and CASC8). Poor overall survival and elevated expression of human leukocyte antigens and immune checkpoints were correlated with higher risk scores. We examined the associations between the sets of genes regulated by m6A/m5C/m1A and the risk model, as well as the immune cell infiltration, using algorithms such as ESTIMATE, CIBERSORT, TIMER, ssGSEA, and exclusion (TIDE). Moreover, we compared tumor stemness indices (TSIs) by considering the molecular subtypes related to m6A/m5C/m1A and risk signatures. Analyses were performed based on the risk signature, including stratification, somatic mutation analysis, nomogram construction, chemotherapeutic response prediction, and small-molecule drug prediction. In summary, we developed a prognostic signature consisting of six genes that have the potential for prognostication in patients with LUAD and the design of personalized treatments that could provide new versions of personalized management for these patients.

Indexed as

Adenocarcinoma of LungLung NeoplasmsAdenineHumansNomogramsPrognosis6-methyladenineAdenineLung adenocarcinomam1Am5Cm6AMolecule subtypesSignature

Identifiers

PMID38555384
PMCPMC10981664
OpenAlexW4393345049

What OpenQuestion holds

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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.