Proline-rich acidic protein 1 upregulates mitotic arrest deficient 1 to promote cisplatin-resistance of colorectal carcinoma by restraining mitotic checkpoint complex assembly

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Abstract

Background: The mechanism underlying cisplatin resistance in colorectal carcinoma (CRC) has not yet been elucidated. This study is aimed to illustrate the indispensable role of proline-rich acidic protein 1 (PRAP1) in cisplatin-resistant CRC.

Methods: Cell viability and apoptosis were monitored using cell counting kit-8 and flow cytometry. Immunofluorescence and morphological analysis were used to determine mitotic arrest in cells. In vivo drug resistance was evaluated using a tumor xenograft assay.

Results: PRAP1 was highly expressed in cisplatin-resistant CRC. PRAP1-upregulation in HCT-116 cells increased chemoresistance to cisplatin, whereas RNAi-mediated knockdown of PRAP1 sensitized cisplatin-resistant HCT-116 cells (HCT-116/DDP) to cisplatin. PRAP1-upregulation in HCT-116 cells hindered mitotic arrest and the formation of mitotic checkpoint complexes (MCC), followed by an increase in multidrug-resistant proteins such as p-glycoprotein 1 and multidrug resistance-associated protein 1, while PRAP1-knockdown in HCT-116/DDP cells partly restored colcemid-induced mitotic arrest and MCC assembly, resulting in decreased multidrug-resistant protein levels. PRAP1 downregulation-mediated sensitization to cisplatin in HCT-116/DDP cells was abolished by the inhibition of mitotic kinase activity by limiting MCC assembly. Additionally, PRAP1-upregulation increased cisplatin-resistance in CRC in vivo. Mechanistically, PRAP1 increased the expression of mitotic arrest deficient 1 (MAD1), that competitively binds to mitotic arrest deficient 2 (MAD2) in cisplatin-resistant CRC cells, leading to failed assembly of MCC and subsequent chemotherapy resistance.

Conclusion: PRAP1-overexpression caused cisplatin resistance in CRC. Possibly, PRAP1 induced an increase in MAD1, which competitively interacted with MAD2 and subsequently restrained the formation of MCC, resulting in CRC cells escape from the supervision of MCC and chemotherapy resistance.

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The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

Paul Kleihues, Otmar D. Wiestler, Guido Reifenberger … (2016)

The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.

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starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

Jun-Hao Li, Shun Liu, Hui Zhou … (2013)

Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA–RNA and protein–RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285 000 protein–RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10 000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

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GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

Zefang Tang, Boxi Kang, Chenwei Li … (2019)

Abstract Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.

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Author and article information

Journal

Journal ID (nlm-ta): J Cancer

Journal ID (iso-abbrev): J Cancer

Journal ID (publisher-id): jca

Title: Journal of Cancer

Publisher: Ivyspring International Publisher (Sydney )

ISSN (Electronic): 1837-9664

Publication date Collection: 2023

Publication date (Electronic): 21 May 2023

Volume: 14

Issue: 9

Pages: 1515-1530

Affiliations

[1 ]Department of Gastroenterology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China.

[2 ]Department of Abdominal Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian Province, China.

[3 ]Fujian Medical University, FuzhouCity 350000, Fujian Province, Fujian Province, China.

[4 ]Department of pharmacy, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Rd., Jin'an District, Fuzhou City 350014, China.

[5 ]Fujian Clinical Research Center for Digestive System Tumors and Upper Gastrointestinal Diseases, Fujian Medical University, Fuzhou 350001, Fujian Province, China.

Author notes

✉ Corresponding author: Dan Li, Department of Gastroenterology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China. Email: songjintian@ 123456fjmu.edu.cn .

Competing Interests: The authors have declared that no competing interest exists.

Article

Publisher ID: jcav14p1515

DOI: 10.7150/jca.84048

PMC ID: 10266255

PubMed ID: 37325046

SO-VID: 400479fd-0c59-4f2f-ba04-94027b9b45cc

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

Proline-rich acidic protein 1 upregulates mitotic arrest deficient 1 to promote cisplatin-resistance of colorectal carcinoma by restraining mitotic checkpoint complex assembly

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Karger: Oncology

Most cited references 58

The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

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