Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis

Yang, Jun; Li, Cuili; Zhou, Jianyi; Liu, Xiaoquan; Wang, Shaohua

doi:10.3389/fgene.2019.01408

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Hub gene analysis showed that although all hub genes were significant with OS in the green and red modules, they were not the most significant genes or one with the least HR related to the OS, except for PYCR1. The effect of hub genes in survival was investigated in many studies and hub genes were introduced as important prognostic markers 14,41 . It is important to note that we should look at relapse as a consequence of complex mechanisms, and nodes, hub genes, are not the best options for predicting them.…”

Section: Discussionmentioning

confidence: 99%

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Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Darzi

Gorgin

Majidzadeh‐A

et al. 2021

Sci Rep

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The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR = 0.6, 95% CI = 0.4–0.89, P = 0.0125), (HR = 0.65, 95% CI = 0.44–0.98, P = 0.04) and poor (HR = 1.55, 95% CI = 1.06–2.27, P = 0.025) prognosis to the overall survival (OS) (time = 3740 days). The first one was significant in multivariate HR estimates (HR = 0.4, 95% CI = 0.28–0.69, P = 0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell–cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

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Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, no research has focused on non-uterine leiomyosarcoma (NULMS) based on gene interaction networks in recent years. However, a study was published that investigated all types of LMSs together 14 .…”

mentioning

confidence: 99%

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Darzi

Gorgin

Majidzadeh‐A

et al. 2021

Sci Rep

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“…Weighted gene co-expression network analysis (WGCNA) uses systems biology to identify modules of highly related genes and associate these modules with clinical traits. Therefore, WGCNA is widely used to identify and screen for biomarkers ( 9 ), and has been successfully used to discover therapeutic targets for a variety of cancer types, including, but not limited to, laryngeal cancer ( 10 ), leiomyosarcoma ( 11 ) and advanced gastric cancer ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Weighted gene co‑expression network analysis of the association between upregulated AMD1, EN1 and VGLL1 and the progression and poor prognosis of breast cancer

Yang

Luo

et al. 2021

Exp Ther Med

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Breast cancer is the most prevalent malignancy among females, but the molecular mechanisms involved in its pathogenesis and progression have remained to be fully elucidated. The aim of the present study was to identify novel potential therapeutic targets for breast cancer. The dataset GSE76275 was downloaded from the Gene Expression Omnibus database and weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes. Furthermore, the dataset GSE25055, containing gene expression data and clinical information, was downloaded to validate the expression and survival association of these hub genes. In addition, the datasets GSE25065 and GSE42568 were used to validate the association between hub gene expression levels and clinical features. Immunohistochemistry (IHC), reverse transcription-quantitative PCR, as well as proliferation, migration, invasion and apoptosis assays, were used to verify gene expression and function. A total of 4,052 genes were selected for WGCNA and 18 modules were established; the red module was identified as the key module, as it had a strong positive correlation with the tumor grade. Survival analyses of hub genes [S-adenosylmethionine decarboxylase proenzyme (AMD1), homeobox protein engrailed-1 (EN1) and vestigial-like protein (VGLL1)] indicated that higher levels of gene expression were associated with poor prognosis of patients with breast cancer. This association was based on survival analysis of GSE25055 using the Kaplan-Meier plotter tool. Expression validation revealed that the upregulation of hub genes was associated with advanced tumor grade and malignant molecular subtype (basal-like). IHC results from the Human Protein Atlas also demonstrated that protein expression levels of the hub genes were higher in tumor tissues compared with those in adjacent normal tissues. Furthermore, the expression levels of AMD1, EN1 and VGLL1 were strongly correlated with each other. These results demonstrated that AMD1 is highly expressed in breast cancer tissues and cells and AMD1 knockdown decreased the proliferation and metastatic potential, while increasing apoptosis of breast cancer cells. These results suggested that AMD1, EN1 and VGLL1 are likely to contribute to breast cancer progression and unfavorable prognosis.

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Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer

Zhao¹,

Gu²,

Bao³

et al. 2020

Briefings in Bioinformatics

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An accurate prognosis assessment for cancer patients could aid in guiding clinical decision-making. Reliance on traditional clinical features alone in a complex clinical environment is challenging and unsatisfactory in the era of precision medicine; thus, reliable prognostic biomarkers are urgently required to improve a patient staging system. In this study, we proposed a patient-level computational framework from mechanistic and translational perspectives to establish a personalized prognostic signature (named PLPPS) in high-grade serous ovarian carcinoma (HGSOC). The PLPPS composed of 68 immune genes achieved accurate prognostic risk stratification for 1190 patients in the meta-training cohort and was rigorously validated in multiple cross-platform independent cohorts comprising 792 HGSOC patients. Furthermore, the PLPPS was shown to be the better prognostic factor compared with clinical parameters in the univariate analysis and retained a significant independent association with prognosis after adjusting for clinical parameters in the multivariate analysis. In benchmark comparisons, the performance of PLPPS (hazard ratio (HR), 1.371; concordance index (C-index), 0.604 and area under the curve (AUC), 0.637) is comparable to or better than other published gene signatures (HR, 0.972 to 1.340; C-index, 0.495 to 0.592 and AUC, 0.48–0.624). With further validation in prospective clinical trials, we hope that the PLPPS might become a promising genomic tool to guide personalized management and decision-making of HGSOC in clinical practice.

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Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis

Cited by 4 publications

References 41 publications

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Weighted gene co‑expression network analysis of the association between upregulated AMD1, EN1 and VGLL1 and the progression and poor prognosis of breast cancer

Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer

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