Evidence map›Paper›PMID 37160628›Full record

ArticleJournal of cancer research and clinical oncology2023

Systematic analysis of transcriptome signature for improving outcomes in lung adenocarcinoma.

Xiaoyong Ge, Hui Xu, Siyuan Weng, Yuyuan Zhang, Long Liu, Libo Wang, Zhe Xing, Yuhao Ba, Shutong Liu, Lifeng Li and 2 more

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024
    Review
  2. 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

12 authors at 5 institutions in 1 country.

Xiaoyong Ge *Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Hui Xu *Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Siyuan WengDepartment of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Yuyuan ZhangDepartment of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Long LiuDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Libo WangDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Zhe XingDepartment of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuhao BaDepartment of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Shutong LiuDepartment of Clinical Medicine, Zhengzhou University, Zhengzhou, 450052, Henan, China.
Lifeng LiMedical School, Huanghe Science and Technology University, 666 Zi Jing Shan Road, Zhengzhou, 450000, Henan, China.
Yuhui WangPrenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, No. 7, Kangfu Front Street, Erqi District, Zhengzhou, 450052, Henan, China. wangyh_1209@126.com.
Xinwei HanDepartment of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. fcchanxw@zzu.edu.cn.
First Affiliated Hospital of Zhengzhou University · CNFifth Affiliated Hospital of Zhengzhou University · CNHuanghe Science and Technology College · CNThird Affiliated Hospital of Zhengzhou University · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe updated guidelines highlight gene expression-based multigene panel as a critical tool to assess overall survival (OS) and improve treatment for lung adenocarcinoma (LUAD) patients. Nevertheless, genome-wide expression signatures are still limited in real clinical utility because of insufficient data utilization, a lack of critical validation, and inapposite machine learning algorithms.

methods2330 primary LUAD samples were enrolled from 11 independent cohorts. Seventy-six algorithm combinations based on ten machine learning algorithms were applied. A total of 108 published gene expression signatures were collected. Multiple pharmacogenomics databases and resources were utilized to identify precision therapeutic drugs.

resultsWe comprehensively developed a robust machine learning-derived genome-wide expression signature (RGS) according to stably OS-associated RNAs (OSRs). RGS was an independent risk element and remained robust and reproducible power by comparing it with general clinical parameters, molecular characteristics, and 108 published signatures. RGS-based stratification possessed different biological behaviors, molecular mechanisms, and immune microenvironment patterns. Integrating multiple databases and previous studies, we identified that alisertib was sensitive to the high-risk group, and RITA was sensitive to the low-risk group.

conclusionOur study offers an appealing platform to screen dismal prognosis LUAD patients to improve clinical outcomes by optimizing precision therapy.

Indexed as

Adenocarcinoma of LungLung NeoplasmsAlgorithmsDatabases, FactualHumansPrognosisTranscriptomeTumor MicroenvironmentGenome-wide expression signatureLung adenocarcinomaMachine learningOverall survivalPrecision therapy

Identifiers

PMID37160628
PMCPMC11798136
OpenAlexW4376121261

What Socratic 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 Socratic graph.