Evidence map›Paper›PMID 37711127›Full record

ArticleCurrent medicinal chemistry2024

Molecular Subtypes and Prognostic Models for Predicting Prognosis of Lung Adenocarcinoma based on MiRNA-related Genes.

Yuxi Wei, Wei Zhong, Yalan Bi, Xiaoyan Liu, Qing Zhou, Jia Liu, Mengzhao Wang, Hong Zhang, Minjiang Chen

Abstract read
PubMed Publisher
In one paragraph

Article in Current medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

9 authors.

Yuxi WeiDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Wei ZhongDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Yalan BiDepartment of Dermatology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China.
Xiaoyan LiuDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Qing ZhouDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Jia LiuDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Mengzhao WangDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Hong ZhangDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.
Minjiang ChenDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) are crucial in cancer development and progression, and therapies targeting miRNAs demonstrate great therapeutic promise.

aimWe sought to predict the prognosis and therapeutic response of lung adenocarcinoma (LUAD) by classifying molecular subtypes and constructing a prognostic model based on miRNA-related genes.

methodsThis study was based on miRNA-mRNA action pairs and ceRNA networks in the Cancer Genome Atlas (TCGA) database. Three molecular subtypes were determined based on 64 miRNA-associated target genes identified in the ceRNA network. The S3 subtype had the best prognosis, and the S2 subtype had the worst prognosis. The S2 subtype had a higher tumor mutational load (TMB) and a lower immune score. The S2 subtype was more suitable for immunotherapy and sensitive to chemotherapy. The least absolute shrinkage and selection operator (LASSO) algorithm was performed to determine eight miRNA-associated target genes for the construction of prognostic models.

resultHigh-risk patients had a poorer prognosis, lower immune score, and lower response to immunotherapy. Robustness was confirmed in the Gene-Expression Omnibus (GEO) database cohort (GSE31210, GSE50081, and GSE37745 datasets). Overall, our study deepened the understanding of the mechanism of miRNA-related target genes in LUAD and provided new ideas for classification.

conclusionSuch miRNA-associated target gene characterization could be useful for prognostic prediction and contribute to therapeutic decision-making in LUAD.

Indexed as

Adenocarcinoma of LungLung NeoplasmsMicroRNAsBiomarkers, TumorGene Expression Regulation, NeoplasticHumansPrognosisBiomarkers, TumorMicroRNAscompeting endogenous RNALUAD.Lung adenocarcinomamessenger RNAmicroRNAprognostic model

Identifiers

PMID37711127

What OpenQuestion holds

Textmetadata
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.