Evidence map›Paper›PMID 34805165›Full record

ArticleFrontiers in cell and developmental biology2021

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning.

Le-Ping Liu, Lu Lu, Qiang-Qiang Zhao, Qin-Jie Kou, Zhen-Zhen Jiang, Rong Gui, Yan-Wei Luo, Qin-Yu Zhao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
4.3field-weighted citation impact, top 4% 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

37 citing papers in PubMed, 50 citations in OpenAlex.

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  14. Pyroptosis: A promising target for lung cancer therapy.Chinese medical journal pulmonary and critical care medicine · 2023
    Review
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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

8 authors at 2 institutions in 2 countries.

Le-Ping LiuDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Lu LuDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Qiang-Qiang ZhaoDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Qin-Jie KouDepartment of Laboratory Medicine, The Third Xiangya Hospital of Central South University, Changsha, China.
Zhen-Zhen JiangDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Rong GuiDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Yan-Wei LuoDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Qin-Yu ZhaoDepartment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Australian National University · AUCentral South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

Indexed as

lung adenocarcinomamachine learningprognosticpyroptosissubtype

Identifiers

PMID34805165
PMCPMC8599430
OpenAlexW3214369157

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.