Evidence map›Paper›PMID 35741714›Full record

ArticleGenes2022

Construction and Validation of a Tumor Microenvironment-Based Scoring System to Evaluate Prognosis and Response to Immune Checkpoint Inhibitor Therapy in Lung Adenocarcinoma Patients.

Pinzheng Huang, Linfeng Xu, Mingming Jin, Lixi Li, Yizhong Ke, Min Zhang, Kairui Zhang, Kongyao Lu, Gang Huang

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

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Pinzheng HuangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Linfeng XuSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Mingming JinShanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China.
Lixi LiSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yizhong KeSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Min ZhangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Kairui ZhangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Kongyao LuSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Gang HuangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
University of Shanghai for Science and Technology · CNShanghai University of Medicine and Health Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is among the most dangerous malignant tumors to human health. Lung adenocarcinoma (LUAD) accounts for about 40% of all lung cancers. Accumulating evidence suggests that the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in LUAD. However, the impact of tumor microenvironment-related signatures (TMERSs) representing the TME characteristics on the prognosis and therapeutic outcome of LUAD patients remains to be further explored. MATERIALS AND

methodsGene expression files and clinical information of 1630 LUAD samples and 275 samples with immunotherapy information from different databases such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Cancer Research Institute (CRI) iAtlas were downloaded and analyzed. Three hundred tumor microenvironment-related signatures (TMERS) based on a comprehensive collection of marker genes were quantified by single sample gene set enrichment analysis (ssGSEA), and then eight significant signatures were selected to construct the tumor microenvironment-related signature score (TMERSscore) by performing Least Absolute Shrinkage and Selection Operator (LASSO)-Cox analysis.

resultsIn this study, we constructed a TME-based prognostic stratification model for patients with LUAD and validated it in several external datasets. Furthermore, the TMERSscore was found to be positively correlated with tumor malignancy and a high TMERSscore predicted a poor prognosis. Moreover, the TMERSscore of responders treated with Immune Checkpoint Inhibitor (ICI) therapies was significantly lower than that of non-responders, and the TMERSscore was positively correlated with the tumor immune dysfunction and exclusion (TIDE) score, implying that a low TMERSscore predicts a better response to ICI treatment and may provide independent and incremental predictive value over current biomarkers.

conclusionsOverall, we constructed a TMERSscore that can be used for LUAD patient prognosis stratification as well as ICI therapeutic efficacy evaluation, supportive results from independent external validation sets showed its robustness and effectiveness.

Indexed as

Adenocarcinoma of LungLung NeoplasmsBiomarkers, TumorGene Expression Regulation, NeoplasticHumansImmune Checkpoint InhibitorsPrognosisTumor MicroenvironmentBiomarkers, TumorImmune Checkpoint Inhibitorsimmune checkpoint inhibitor therapeutic response evaluationlung adenocarcinomaprognostic stratificationTIDE scoretumor microenvironment

Identifiers

PMID35741714
PMCPMC9222903
OpenAlexW4281558504

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.