Integrative Analysis of MicroRNA and Gene Interactions for Revealing Candidate Signatures in Prostate Cancer

Wei, Jingchao; Yin, Yinghao; Deng, Qiancheng; Zhou, Jun; Wang, Yong; Yin, Guangming; Yang, Jianfu; Tang, Yuxin

doi:10.3389/fgene.2020.00176

Cited by 43 publications

(39 citation statements)

References 64 publications

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“…RRM2 has been reported to be associated with the occurrence and poor survival of prostate cancer. The main effect mechanism is the overexpression of DNA damage repair genes, which is related to stem cell differentiation (Mazzu et al, 2019;Wei et al, 2020). These findings are consistent with our findings, demonstrating that RRM2 may control the stemness of cancer cells by repairing DNA.…”

Section: Discussionsupporting

confidence: 91%

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

Yan

et al. 2020

Front. Cell Dev. Biol.

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The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools

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Section: Discussionsupporting

confidence: 91%

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

Yan

et al. 2020

Front. Cell Dev. Biol.

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“…However, these clinical factors do not include the molecular biomarkers. With the well-application of TCGA dataset, regulatory network of miRNA, mRNA, and alternative splicing has been constructed and prognosis-associated biomarkers have also been identified for patients with PRAD [ 31 , 32 ]. Unfortunately, these prediction model do not cover the CSC-related signatures and prognostic SRGs.…”

Section: Discussionmentioning

confidence: 99%

Identification of Prognostic Biomarkers Associated with Cancer Stem Cell Features in Prostate Adenocarcinoma

Zhang

et al. 2020

Med Sci Monit

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“…We screened the DEGs between seminoma and controls in GSE8607 using the ‘limma’ R package [ 9 ]. Adjust P-value < 0.01 and |logFC| ≥2 were set as the cutoff criterion for improved accuracy and significance, as described previously [ 10 ]. Heatmap and volcano maps were drawn to present the DEGs.…”

Section: Methodsmentioning

confidence: 99%

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Chen

Pan

et al. 2020

PLoS ONE

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Testicular germ cell tumors (TGCTs) are common in young males, and seminoma accounts for a large proportion of TGCTs. However, there are limited records on the exploration of novel biomarkers for seminoma. Hence, we aimed to identify new biomarkers associated with overall survival in seminoma. mRNA-seq and clinical traits of TGCTs were downloaded from UCSC XENA and analyzed by weighted gene co-expression network analysis. After intersection with differentially expressed genes in GSE8607, common genes were subjected to protein-protein interaction (PPI) network construction and enrichment analyses. Then, the top 10 common genes were investigated by Kaplan–Meier (KM) survival analyses and univariate Cox regression analyses. Ultimately, TYROBP , CD68 , and ITGAM were considered three prognostic biomarkers in seminoma. Based on correlation analysis between these genes and immune infiltrates, we suggest that the three biomarkers influence the survival of seminoma patients, possibly through regulating the infiltration of immune cells. In conclusion, our study demonstrated that TYROBP , CD68 , and ITGAM could be regarded as prognostic biomarkers and therapeutic targets for seminoma patients.

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Integrative Analysis of MicroRNA and Gene Interactions for Revealing Candidate Signatures in Prostate Cancer

Cited by 43 publications

References 64 publications

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

Identification of Prognostic Biomarkers Associated with Cancer Stem Cell Features in Prostate Adenocarcinoma

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

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