Comprehensive characterization of the rRNA metabolism-related genes in human cancer

Cui, Kaisa; Liu, Cheng; Xu, Li; Zhang, Qiang; Li, Youjun

doi:10.1038/s41388-019-1026-9

Cited by 45 publications

(67 citation statements)

References 52 publications

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“…It is known that ER translation and rRNA metabolic process are critical for protein synthesis, folding and trafficking, which are often regarded as conduits to human disease. 39,40 To specify pivotal regulators in the different pathology of severe and non-severe LOPE, a co-expression network analysis was performed. 18 gene modules (labelled with colours, such as MEturquoise) were generated through calculating the correlation of the HVGs screened from maternal peripheral blood transcriptomes ( Figure 4A).…”

Section: To Investigate Underlying Biological Processes Of the Diseasesmentioning

confidence: 99%

Systemic transcriptome comparison between early‐ And late‐onset pre‐eclampsia shows distinct pathology and novel biomarkers

et al. 2020

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Section: To Investigate Underlying Biological Processes Of the Diseasesmentioning

confidence: 99%

Systemic transcriptome comparison between early‐ And late‐onset pre‐eclampsia shows distinct pathology and novel biomarkers

et al. 2020

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“…We previously established a landscape of rRNA metabolismrelated genes across human cancers and explored their roles in tumorigenesis (4). Besides, another Pan-cancer analysis explored the role of ATG5 in tumor metabolism and tumor immunity (5).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of Integrin Subunit Genes in Human Cancers

Cui

Gong

et al. 2021

Front. Oncol.

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Although integrin subunit genes (ITGs) have been reported to be associated with some human cancer types, a systematic assessment of ITGs across human cancers is lacking. Hence, we performed comprehensive analyses to investigate mRNA expression, copy number variation (CNV), DNA methylation, mutation, and clinical landscapes of ITGs in more than 8000 cancer patients from The Cancer Genome Atlas (TCGA) dataset. Landscapes of ITGs were established across 20 human cancer types. We observed that ITGs are extensively dysregulated with heterogeneity in different system cancer types, part of which are driven by CNV, DNA hypomethylation or mutation. Furthermore, dysregulated prognosis-related ITGs were systematically identified in each cancer type, including ITGA11 in stomach adenocarcinoma (STAD). The models based on dysregulated ITGs with clinical relevance and TNM staging indexes are good indicators in STAD and head and neck squamous cell carcinoma. Finally, ITGA11 is overexpressed and associated with poor survival in STAD cases from the TCGA and additionally Gene Expression Omnibus cohorts. Functionally, ITGA11 knockdown inhibits malignant phenotypes in STAD cell lines AGS and MKN45, demonstrating the oncogenic role of ITGA11 in STAD. Together, this study highlights the important roles of ITGs in tumorigenesis as potential prognostic biomarkers, and provide an effective resource that identifies cancer-related genes of ITGs in human cancers.

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“…Cui et al found that the expression of EXOSC8 in colorectal cancer was higher than that in normal tissues in a public database, indicating a poor prognosis. They confirmed that the expression of EXOSC8 in colorectal cancer was higher than that in matched normal tissues in clinical samples, and verified the cancer-promoting effect of the gene in cell and animal experiments (Cui et al, 2020). As an RBP, HNRNPC was reported to be aberrantly expressed at elevated levels in a variety of tumors, besides being involved in some well-established BPs, such as RNA splicing.…”

Section: Discussionmentioning

confidence: 60%

“…The TCGA dataset was used as the training cohort, and three datasets (GSE136337, GSE24080, and GSE57317) were used for validation. Univariate Cox regression analysis and multivariate regression analysis (Cox, 1972) were chosen to screen for RBPrelated genes that were closely related to the OS of patients. In the univariate Cox regression analysis, P < 0.05 was the criterion to screen candidate genes.…”

Section: Screening For Hub Genes In the Training Datasetmentioning

confidence: 99%

Identification and Validation of a Novel RNA-Binding Protein-Related Gene-Based Prognostic Model for Multiple Myeloma

Wang

Zhu

et al. 2021

Front. Genet.

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BackgroundMultiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. RNA-binding proteins (RBPs) are involved in the development of many tumors, but their prognostic significance has not been systematically described in MM. Here, we developed a prognostic signature based on eight RBP-related genes to distinguish MM cohorts with different prognoses.MethodAfter screening the differentially expressed RBPs, univariate Cox regression was performed to evaluate the prognostic relevance of each gene using The Cancer Genome Atlas (TCGA)-Multiple Myeloma Research Foundation (MMRF) dataset. Lasso and stepwise Cox regressions were used to establish a risk prediction model through the training set, and they were validated in three Gene Expression Omnibus (GEO) datasets. We developed a signature based on eight RBP-related genes, which could classify MM patients into high- and low-score groups. The predictive ability was evaluated using bioinformatics methods. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and gene set enrichment analyses were performed to identify potentially significant biological processes (BPs) in MM.ResultThe prognostic signature performed well in the TCGA-MMRF dataset. The signature includes eight hub genes: HNRNPC, RPLP2, SNRPB, EXOSC8, RARS2, MRPS31, ZC3H6, and DROSHA. Kaplan–Meier survival curves showed that the prognosis of the risk status showed significant differences. A nomogram was constructed with age; B2M, LDH, and ALB levels; and risk status as prognostic parameters. Receiver operating characteristic (ROC) curve, C-index, calibration analysis, and decision curve analysis (DCA) showed that the risk module and nomogram performed well in 1, 3, 5, and 7-year overall survival (OS). Functional analysis suggested that the spliceosome pathway may be a major pathway by which RBPs are involved in myeloma development. Moreover, our signature can improve on the R-International Staging System (ISS)/ISS scoring system (especially for stage II), which may have guiding significance for the future.ConclusionWe constructed and verified the 8-RBP signature, which can effectively predict the prognosis of myeloma patients, and suggested that RBPs are promising biomarkers for MM.

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Comprehensive characterization of the rRNA metabolism-related genes in human cancer

Cited by 45 publications

References 52 publications

Systemic transcriptome comparison between early‐ And late‐onset pre‐eclampsia shows distinct pathology and novel biomarkers

Systemic transcriptome comparison between early‐ And late‐onset pre‐eclampsia shows distinct pathology and novel biomarkers

Comprehensive Characterization of Integrin Subunit Genes in Human Cancers

Identification and Validation of a Novel RNA-Binding Protein-Related Gene-Based Prognostic Model for Multiple Myeloma

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