Evidence map›Paper›PMID 38178073›Full record

ArticleRespiratory research2024

A gene-based score for the risk stratification of stage IA lung adenocarcinoma.

Yanlu Xiong, Yongfu Ma, Kun Liu, Jie Lei, Jinbo Zhao, Jianfei Zhu, Wenchen Wang, Miaomiao Wen, Xuejiao Wang, Ying Sun and 4 more

Open access · goldAbstract read
In one paragraph

Article in Respiratory research, 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
0.4field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  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

14 authors at 3 institutions in 1 country.

Yanlu Xiong *Department of Thoracic Surgery, First Medical Center, Chinese PLA General Hospital and PLA Medical School, Beijing, China.
Yongfu Ma *Department of Thoracic Surgery, First Medical Center, Chinese PLA General Hospital and PLA Medical School, Beijing, China.
Kun Liu *Department of Epidemiology, Ministry of Education Key Laboratory of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China.
Jie LeiDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Jinbo ZhaoDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Jianfei ZhuDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Wenchen WangDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Miaomiao WenDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Xuejiao WangDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Ying SunDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Yabo ZhaoDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Yong HanDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China. hanyong_td@163.com.
Tao JiangDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China. jiangtaochest@163.com.
Yang LiuDepartment of Thoracic Surgery, First Medical Center, Chinese PLA General Hospital and PLA Medical School, Beijing, China. sunny301x@sina.com.
Air Force Medical University · CNChinese PLA General Hospital · CNShaanxi Provincial People's Hospital · CN

Funding

National Natural Science Foundation of China 82002421
6 · The paper itself

Abstract

objectiveWe aim to molecularly stratify stage IA lung adenocarcinoma (LUAD) for precision medicine.

methodsTwelve multi-institution datasets (837 cases of IA) were used to classify the high- and low-risk types (based on survival status within 5 years), and the biological differences were compared. Then, a gene-based classifying score (IA score) was trained, tested and validated by several machine learning methods. Furthermore, we estimated the significance of the IA score in the prognostic assessment, chemotherapy prediction and risk stratification of stage IA LUAD. We also developed an R package for the clinical application. The SEER database (15708 IA samples) and TCGA Pan-Cancer (1881 stage I samples) database were used to verify clinical significance.

resultsCompared with the low-risk group, the high-risk group of stage IA LUAD has obvious enrichment of the malignant pathway and more driver mutations and copy number variations. The effect of the IA score on the classification of high- and low-risk stage IA LUAD was much better than that of classical clinicopathological factors (training set: AUC = 0.9, validation set: AUC = 0.7). The IA score can significantly predict the prognosis of stage IA LUAD and has a prognostic effect for stage I pancancer. The IA score can effectively predict chemotherapy sensitivity and occult metastasis or invasion in stage IA LUAD. The R package IAExpSuv has a good risk probability prediction effect for both groups and single stages of IA LUAD.

conclusionsThe IA score can effectively stratify the risk of stage IA LUAD, offering good assistance in precision medicine.

Indexed as

Adenocarcinoma of LungLung NeoplasmsDatabases, FactualDNA Copy Number VariationsHumansPrognosisRisk AssessmentIAExpSuvIA scoreLUADPrognosisRisk stratificationStage IA

Identifiers

PMID38178073
PMCPMC10765678
OpenAlexW4390583050

What OpenQuestion holds

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LicenceCC BY
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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.