Literature DB >> 35557577

Weighted gene co-expression network analysis reveals prognostic and diagnostic significance of PAQR4 in patients with early and late hepatocellular carcinoma.

Tianhang Feng1, Chunyou Lai1, Deyuan Zhong1, Le Luo1, Haibo Zou1, Guan Wang1, Qinyan Yang1, Yutong Yao1, Xiaolun Huang1.   

Abstract

Background: This study aimed to reveal novel markers for prognostic and diagnostic prediction of hepatocellular carcinoma (HCC).
Methods: We applied The Cancer Genome Atlas (TCGA) data to screen differentially expressed genes (DEGs). We identified hub modules and genes using weighted gene co-expression network analysis (WGCNA). After verification with the GSE36376 dataset, hub genes were further identified. The expression of progestin and adipoQ receptor 4 (PAQR4) was confirmed in HCC by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The diagnostic and prognosis value of PAQR4 was assessed. The expression of PAQR4 was verified using the GSE76427 dataset.
Results: A total of 803 DEGs were obtained between HCC and normal tissue. Through WGCNA, 7 hub modules were screened, among which the blue module was selected to identify the hub genes associated with the HCC. After overlapping all the DEGs with 837 genes of the blue module, we obtained 466 DEGs that were defined as hub genes. Among the hub genes, 239 were related to staging. After verifying with the GSE36376 dataset, PAQR4 was identified as the real hub gene of HCC. The results of qRT-PCR revealed that PAQR4 was upregulated between HCC and normal tissue. Furthermore, PAQR4 was related to the diagnosis and prognosis of patients with HCC. Moreover, the GSE76427 verification results of PAQR4 were consistent with our integration and qRT-PCR results. Ultimately, high expression of PAQR4 was significantly related to cell cycle, DNA replication, and the p53 signaling pathway. Conclusions: The PAQR4 gene may be associated with the prognosis and diagnosis of HCC. 2022 Journal of Gastrointestinal Oncology. All rights reserved.

Entities:  

Keywords:  Gene Expression Omnibus (GEO) dataset; Hepatocellular carcinoma (HCC); PAQR4; The Cancer Genome Atlas (TCGA); weighted gene co-expression network analysis (WGCNA)

Year:  2022        PMID: 35557577      PMCID: PMC9086035          DOI: 10.21037/jgo-22-168

Source DB:  PubMed          Journal:  J Gastrointest Oncol        ISSN: 2078-6891


  33 in total

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Journal:  Carcinogenesis       Date:  2018-03-08       Impact factor: 4.944

2.  Downregulation of PRRX1 via the p53-dependent signaling pathway predicts poor prognosis in hepatocellular carcinoma.

Authors:  Mingming Fan; Jun Shen; Hu Liu; Zhijian Wen; Jue Yang; Pinghua Yang; Kai Liu; Yanxin Chang; Jicheng Duan; Kai Lu
Journal:  Oncol Rep       Date:  2017-07-03       Impact factor: 3.906

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Journal:  Oncol Rep       Date:  2017-09-20       Impact factor: 3.906

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Authors:  Frances K Turrell; Emma M Kerr; Meiling Gao; Hannah Thorpe; Gary J Doherty; Jake Cridge; David Shorthouse; Alyson Speed; Shamith Samarajiwa; Benjamin A Hall; Meryl Griffiths; Carla P Martins
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Journal:  PeerJ       Date:  2019-10-01       Impact factor: 2.984

Review 8.  Current Perspectives in Cancer Immunotherapy.

Authors:  Theodoulakis Christofi; Stavroula Baritaki; Luca Falzone; Massimo Libra; Apostolos Zaravinos
Journal:  Cancers (Basel)       Date:  2019-09-30       Impact factor: 6.639

9.  FusionCancer: a database of cancer fusion genes derived from RNA-seq data.

Authors:  Yunjin Wang; Nan Wu; Jiaqi Liu; Zhihong Wu; Dong Dong
Journal:  Diagn Pathol       Date:  2015-07-28       Impact factor: 2.644

Review 10.  Evolution of Cancer Pharmacological Treatments at the Turn of the Third Millennium.

Authors:  Luca Falzone; Salvatore Salomone; Massimo Libra
Journal:  Front Pharmacol       Date:  2018-11-13       Impact factor: 5.810

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