Evidence map›Paper›PMID 37057114›Full record

ArticleTranslational lung cancer research2023

APOBEC mutational signature predicts prognosis and immunotherapy response in nonsmoking patients with lung adenocarcinoma.

Jianli Ma, Xudong Yang, Jing Zhang, Mara B Antonoff, Qianjiang Wu, Hongfei Ji

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Article in Translational lung cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

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5citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jianli Ma *Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China.
Xudong Yang *Institute of Cancer Prevention and Treatment, Harbin Medical University, Harbin, China.
Jing ZhangInstitute of Cancer Prevention and Treatment, Harbin Medical University, Harbin, China.
Mara B AntonoffDepartment of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Qianjiang WuInstitute of Cancer Prevention and Treatment, Harbin Medical University, Harbin, China.
Hongfei JiInstitute of Cancer Prevention and Treatment, Harbin Medical University, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer (NSCLC) with poor survival in advanced stage. Nowadays the rate of nonsmoking patients has dramatically increased and may be associated with the presence of driver mutations. Better understanding of the mutation profile data of nonsmoking LUAD patients are critical to predict survival and provide greater benefits to more patients. The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) has been shown to play an important role in molecular tumorigenesis of NSCLC. However, the clinical relevance of APOBEC in nonsmoking LUAD remains to be understood. Methods: LUAD patients with somatic mutation and RNA sequencing data obtained from The Cancer Genome Atlas (TCGA) were assessed and screened in the Gene Expression Omnibus. Transcriptome data and mutational signatures were analyzed using R package. Then, we used the least absolute shrinkage and selection operator (LASSO) regression model to construct the APOBEC3 score (APOBEC3 score) model. The prognostic value was evaluated using Kaplan-Meier analysis. Finally, the functional enrichment analysis of differential expressed genes (DEGs) and the immune-related features were also estimated using R package. Results: By analyzing the mutational profile data of NSCLC in the TCGA database, we found that different mutation patterns existed between smoking and nonsmoking patients, and the APOBEC3 family played an important role in the mutation pattern of nonsmoking patients with LUAD. We established an APOBEC3 score and found that TCW (W = A or T) mutation counts were significantly greater in the high APOBEC3 score group than in the low APOBEC3 score group. Furthermore, there were different immune feathers and prognostic values between the high and low APOBEC3 score patients, suggesting an independent prognostic factor of APOBEC3 in nonsmoking LUAD patients. Conclusions: We established a comprehensive view of APOBEC3 mutations in nonsmoking LUAD patients. Our review provides new insights into using the APOBEC3 mutation to predict prognosis and improve the immunotherapy response for future applications.

Indexed as

Apolipoprotein B mRNA editing enzyme catalytic polypeptide-like 3 (APOBEC3)immunotherapymutational signaturenonsmokingprognosis

Identifiers

PMID37057114
PMCPMC10088004

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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.