RNA‐Seq analysis of differentially expressed genes relevant to mismatch repair in aging hematopoietic stem‐progenitor cells

Lian, Xiaolan; Dong, Yongpin; Zhao, Mingyi; Liang, Yajie; Jiang, Weiwei; Li, Wenfang; Zhang, Lina

doi:10.1002/jcb.28417

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…RPA3, a member of the replication protein family, plays an important role in DNA repair, recombination, replication, and cell cycle regulation. RPA3 is associated with the occurrence and poor prognosis of liver cancer, as well as the aging of hematopoietic stem cells (Xiao et al, 2018;Lian X. et al, 2019;Zhang and Yu, 2020). RRM2 has been reported to be associated with the occurrence and poor survival of prostate cancer.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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Genomic Instability in Stem Cells: The Basic Issues

González

Haider

2021

Stem Cells: From Potential to Promise

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Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis of mRNA splicing relevant proteins in aging HSPCs

Lian

Zhao

Xia³

et al. 2020

Aging Clin Exp Res

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Background: HSPC aging was closely associated with the organism aging, senile diseases and hematopoietic related diseases. Therefore, study on HSPC aging born great significance to further elucidate the mechanisms of aging and to treat hematopoietic disease resulted from HSPC aging. Little attention had been paid to mRNA splicing as a mechanism underlying HSPC senescence. Results: We used our lab's patented aging model of HSPCs in vitro to analyze mRNA splicing relevant proteins alterations with iTRAQ based proteomic analysis. We found that not only the notable mRNA splicing genes such as SR, hnRNP, WBP11, Sf3b1, Ptbp1 and U2AF1 but also the scarcely reported mRNA splicing relavant genes such as Rbmxl1, Dhx16, Pcbp2, Pabpc1 were significantly down-regulated. We further verified their genes expressions by qRT-PCR. In addition, we reported the effect of Spliceostatin A (SSA), which inhibits mRNA splicing in vivo and in vitro, on HSPC aging. Conclusions: It was concluded that mRNA splicing emerged as an important vulnerability of HSPC aging. This study improved our understanding of the role of mRNA splicing in the HSPC aging process.

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RNA‐Seq analysis of differentially expressed genes relevant to mismatch repair in aging hematopoietic stem‐progenitor cells

Cited by 5 publications

References 20 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

Genomic Instability in Stem Cells: The Basic Issues

Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis of mRNA splicing relevant proteins in aging HSPCs

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