Evidence map›Paper›PMID 35936688›Full record

ArticleFrontiers in oncology2022

Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma.

Chaojie Xu, Lishan Song, Yubin Yang, Yi Liu, Dongchen Pei, Jiabang Liu, Jianhua Guo, Nan Liu, Xiaoyong Li, Yuchen Liu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

9 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

13 authors.

Chaojie XuThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Lishan SongThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Yubin YangPeking University First Hospital, Peking University, Beijing, China.
Yi LiuThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Dongchen PeiThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Jiabang LiuShenzhen Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
Jianhua GuoThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Nan LiuThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Xiaoyong LiThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Yuchen LiuShenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Xuesong LiCollege of Pharmacy, Shantou University School of Medicine, Shantou, China.
Lin YaoCollege of Pharmacy, Shantou University School of Medicine, Shantou, China.
Zhengjun KangThe Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Numerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure. Methods: Sample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm. Results: Based on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes ( Conclusions: In conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.

Indexed as

immunotherapylung adenocarcinomaM2 macrophagesrisk scoreWGCNA

Identifiers

PMID35936688
PMCPMC9352953

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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.