Evidence map›Paper›PMID 37710052›Full record

ArticleJournal of cancer research and clinical oncology2023

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma.

Ming Yi, Jiaying Shi, Xiaolan Tan, Xinyue Zhang, Dachang Tao, Yuan Yang, Yunqiang Liu

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Ming Yi *Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Jiaying Shi *Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Xiaolan TanDepartment of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Xinyue ZhangDepartment of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Dachang TaoDepartment of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Yuan YangDepartment of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
Yunqiang LiuDepartment of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China. yq_liu@scu.edu.cn.
State Key Laboratory of BiotherapySichuan University · CN

Funding

the National Natural Science Foundation of China 81773159the National Natural Science Foundation of China 81871203
6 · The paper itself

Abstract

purposeThis study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD).

methodsRisk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD.

resultsA risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients.

conclusionsThe constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.

Indexed as

Adenocarcinoma of LungLung NeoplasmsAlgorithmsEpithelial CellsHumansPrognosisDeconvolutionLung adenocarcinomaPrognosis-related signaturesSingle cell transcriptome

Identifiers

PMID37710052
PMCPMC11798305
OpenAlexW4386739343

What OpenQuestion holds

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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.