Evidence map›Paper›PMID 35860262›Full record

ArticleFrontiers in immunology2022

A Novel M6A-Related Genes Signature Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma.

Xuewen Wang, Chengfei Zhao, Dandan Huang, Zhoujie Liu, Mengmeng Liu, Fei Lin, Yingyu Lu, Jing Jia, Liqing Lin, Xinhua Lin and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. 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
0.8field-weighted citation impact, top 31% 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, 11 citations in OpenAlex.

  1. Article
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  5. Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024
    Article
  6. Review
  7. IGF2BP3 is an essential NMaterials today. Bio · 2022
    Article
  8. 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

12 authors at 3 institutions in 1 country.

Xuewen WangDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou, China.
Chengfei ZhaoDepartment of Pharmacy, School of Pharmacy and Medical Technology, Putian University, Putian, China.
Dandan HuangDepartment of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou, China.
Zhoujie LiuDepartment of Pharmacy, First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Mengmeng LiuDepartment of Pharmacy, First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Fei LinDepartment of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou, China.
Yingyu LuDepartment of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou, China.
Jing JiaDepartment of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou, China.
Liqing LinDepartment of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou, China.
Xinhua LinDepartment of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou, China.
Huangyuan LiDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou, China.
Zhiwei ChenDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou, China.
Fujian Medical University · CNFirst Affiliated Hospital of Fujian Medical University · CNPutian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) is a primary cause of cancer-related death around the world and has a poor outcome and high incidence. Treatment options are, however, restricted. One of the most critical factors in cancer and metastasis is the N6-methyladenine (m6A) alteration on RNA. This modification could alter gene expression and even function at numerous levels, such as the stability, translocation and translation of RNA splicing. This study aimed to construct an m6A-related genes signature to accurately predict the prognosis of LUAD patients. From TCGA datasets, the LUAD patient data and m6A-related genes were retrieved. LUAD patients' mutational features and differentially expressed genes (DEGs) were investigated. An univariate and LASSO model with m6A-related genes were constructed for the prediction of outcomes in LUAD. It was possible to develop a prognostic nomogram that could quantitatively predict LUAD patients' overall survival chances at 1, 3, and 5 years. Research into biological processes and cell pathways was carried out using GSEA. This study found six m6A-related DEGs in LUAD patients, and three of these DEGs(HNRNPC, IGFBP3 and IGF2BP1) were linked to the clinical outcomes of LUAD patients. We found that the overall survival rate for all LUAD patients with high-risk subgroup was considerably lower. According to ROC curves, the prognostic signature demonstrated a high degree of accuracy in predicting future outcomes. In addition, we created a novel nomogram achieved great accuracy with this one as well. The researchers also found that the novel signature might favorably modulate the immune response, and high-risk scores samples were more susceptible to numerous chemotherapeutic medicines. Overall, we developed a m6A-related gene prognostic signature that effectively predicted outcomes of LUAD patients and gave an immunological perspective for creating customized therapeutics.

Indexed as

Adenocarcinoma of LungLung NeoplasmsHumansPrognosisROC CurveSurvival Rateimmune microenvironmentlung adenocarcinomam6A related genesnomogramprognosis

Identifiers

PMID35860262
PMCPMC9289247
OpenAlexW4283816145

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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.