Evidence map›Paper›PMID 34321013›Full record

ArticleWorld journal of surgical oncology2021

Identification of uterine leiomyosarcoma-associated hub genes and immune cell infiltration pattern using weighted co-expression network analysis and CIBERSORT algorithm.

Xiaoqing Shen, Zhujuan Yang, Songwei Feng, Yi Li

Open access · goldAbstract read
In one paragraph

Article in World journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

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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

15 citing papers in PubMed, 20 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Xiaoqing Shen *Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Zhujuan Yang *Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Songwei FengDepartment of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Yi LiDepartment of Gynecology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University and Jiangsu Shengze Hospital, 1399 Shunxin Middle Road, Suzhou, 215228, Jiangsu Province, People's Republic of China. shengzeliyi@126.com.
Soochow University · CNJiangsu Shengze Hospital · CNSecond Affiliated Hospital of Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile large-scale genomic analyses symbolize a precious attempt to decipher the molecular foundation of uterine leiomyosarcoma (ULMS), bioinformatics results associated with the occurrence of ULMS based totally on WGCNA and CIBERSORT have not yet been reported. This study aimed to screen the hub genes and the immune cell infiltration pattern in ULMS by bioinformatics methods.

methodsFirstly, the GSE67463 dataset, including 25 ULMS tissues and 29 normal myometrium (NL) tissues, was downloaded from the public database. The differentially expressed genes (DEGs) were screened by the 'limma' package and hub modules were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, gene function annotations were performed to investigate the biological role of the genes from the intersection of two groups (hub module and DEGs). The above genes were calculated in the protein-protein interaction (PPI) network to select the hub genes further. The hub genes were validated using external data (GSE764 and GSE68295). In addition, the differential immune cell infiltration between UL and ULMS tissues was investigated using the CIBERSORT algorithm. Finally, we used western blot to preliminarily detect the hub genes in cell lines.

resultsWGCNA analysis revealed a green-yellow module possessed the highest correlation with ULMS, including 1063 genes. A total of 172 DEGs were selected by thresholds set in the 'limma' package. The above two groups of genes were intersected to obtain 72 genes for functional annotation analysis. Interestingly, it indicated that 72 genes were mainly involved in immune processes and the Neddylation pathway. We found a higher infiltration of five types of cells (memory B cells, M0-type macrophages, mast cells activated, M1-type macrophages, and T cells follicular helper) in ULMS tissues than NL tissues, while the infiltration of two types of cells (NK cells activated and mast cells resting) was lower than in NL tissues. In addition, a total of five genes (KDR, CCL21, SELP, DPT, and DCN) were identified as the hub genes. Internal and external validation demonstrated that the five genes were over-expressed in NL tissues compared with USML tissues. Finally, the correlation analysis results indicate that NK cells activated and mast cells activated positively correlated with the hub genes. However, M1-type macrophages had a negative correlation with the hub genes. Moreover, only the DCN may be associated with the Neddylation pathway.

conclusionA series of evidence confirm that the five hub genes and the infiltration of seven types of immune cells are related to USML occurrence. These hub genes may affect the occurrence of USML through immune-related and Neddylation pathways, providing molecular evidence for the treatment of USML in the future.

Indexed as

Gene Regulatory NetworksLeiomyosarcomaAlgorithmsFemaleHumansPrognosisProtein Interaction MapsCIBERSORTNeddylationUterine leiomyosarcomaWeighted co-expression network

Identifiers

PMID34321013
PMCPMC8320213
OpenAlexW3185165744

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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.