Evidence map›Paper›PMID 41436784›Full record

ArticleScientific reports2025

Comprehensive analysis of malignant subtypes of lung adenocarcinoma based on multi-omics landscape and functional validation of prognostic biomarker BAIAP2L2.

Bowei Jiang, Yuan Gan, Wanshuo Wei, Meichun Yang, Jiahui Long, Xiujuan He, Hanbo Yu, Jianjun Wen, Zhongheng Wei, Qijun Long

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

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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

10 authors.

Bowei Jiang *School of Clinical Medicine, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Yuan Gan *School of Clinical Medicine, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Wanshuo Wei *School of Clinical Medicine, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Meichun Yang *Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Jiahui LongMedical College, Xinjiang University of Science & Technology, Korla, 841009, Xinjiang, China.
Xiujuan HeCollege of Humanities and Management, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Hanbo YuSchool of Basic Medicine, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Jianjun WenCenter of International Cooperation and Exchange, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China. 13877699698@163.com.
Zhongheng WeiAffiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China. Weizhongh1968@126.com.
Qijun LongCollege of Humanities and Management, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China. longqijun248@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma, the predominant pathogenic type of lung cancer, exhibits intricate biological behaviors and molecular pathways, which have consistently been a focal point and challenge in tumor research. The integration of multi-omics technologies and diverse analytical methodologies facilitates an in-depth study of lung adenocarcinoma's characteristics at cellular, molecular, and clinical dimensions, providing a theoretical foundation and prospective targets for precise diagnosis and effective treatment. scRNA-seq and RNA-seq data were obtained from public databases. The epithelial cells of lung adenocarcinoma were analyzed by methods such as CNV level assessment, cell trajectory analysis, malignant cell identification, and cell communication analysis, and the marker genes of malignant cells were extracted. In the RNA-seq data, CoxBoost was used for prognostic modeling to mine the risk gene BAIAP2L2 for further analysis. The effect of BAIAP2L2 on lung adenocarcinoma was verified by cell experiments. There is obvious heterogeneity among the epithelial cells of lung adenocarcinoma, and significant differences exist between different cell subgroups. The malignant differentiation of lung adenocarcinoma cells has a clear trajectory, and epithelial cell subgroups may influence the differentiation direction through cell communication. The CoxBoost model established based on the marker genes of malignant cells has a relatively good predictive effect, and BAIAP2L2 can serve as a prognostic factor for lung adenocarcinoma. In vitro cellular experiments demonstrated that interfering with BAIAP2L2 can impede the proliferation, migration, and invasion of lung adenocarcinoma cells, along with the epithelial-mesenchymal transition of these cells. BAIAP2L2 is a prognostic factor for lung adenocarcinoma, and interfering with BAIAP2L2 can inhibit the growth, metastasis, and epithelial-mesenchymal transition of lung adenocarcinoma.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsCell Line, TumorCell ProliferationEpithelial-Mesenchymal TransitionGene Expression Regulation, NeoplasticHumansMultiomicsPrognosisBiomarkers, TumorEMTLung adenocarcinomaMulti-omicsPrognostic factor

Identifiers

PMID41436784
PMCPMC12830839

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