Evidence map›Paper›PMID 36268280›Full record

ArticleJournal of oncology2022

Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma.

Caihou Feng, Weibi Che, Hanping Liang, Hai Zhang, Cong Lan, Bomeng Wu, Wanli Lin, Ying Chen

Abstract read
In one paragraph

Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

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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

3 citing papers in PubMed.

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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.

Caihou FengDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Weibi CheDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Hanping LiangDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Hai ZhangDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Cong LanDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Bomeng WuDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Wanli LinDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.
Ying ChenDepartment of Thoracic Surgery, Gaozhou People's Hospital, Gaozhou 525200, China.ORCID https://orcid.org/0000-0001-7266-3565

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer is emerging as one of most deadly diseases, and the mortality rate was still high with 5-year overall survival rate less than 20%. Aging is referred as protumorigenic state, and it plays a significant role in cancer development. Methods: Molecular subtype of lung cancer was identified by consensus cluster analysis. Prognostic signature was constructed using LASSO cox regression analysis. CeRNA network was constructed to explore lncRNA-miRNA-mRNA regulatory axis. Results: A total of 27 differentially expressed aging-related genes (ARGs) were obtained in LUAD. Three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy were identified. We also developed an aging-related prognostic signature that had a better performance in predicting the1-year, 3-year, and 5-year overall survival of LUAD. Further analysis suggested a significant correlation between prognostic signature gene expression and clinical stage, immune infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also identified the lncRNA UCA1/miR-143-3p/CDK1 regulatory axis in LUAD. Conclusion: Our study identified three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy. We also developed an aging-related prognostic signature that had a good performance in the prognosis of LUAD.

Identifiers

PMID36268280
PMCPMC9578794

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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.