Non-coding RNAs in drug and radiation resistance of bone and soft tissue sarcoma: a systematic review

  1. Huan-Huan Chen
  2. Tie-Ning Zhang
  3. Fang-Yuan Zhang  Is a corresponding author
  4. Tao Zhang  Is a corresponding author
  1. China Medical University, China

Abstract

Background: Sarcomas comprise approximately 1% of all human malignancies; treatment resistance is one of the major reasons for the poor prognosis of sarcomas. Accumulating evidence suggests that non-coding RNAs, including microRNAs, long non-coding RNAs, and circular RNAs, are important molecules involved in the crosstalk between resistance to chemotherapy, targeted therapy, and radiotherapy via various pathways.

Methods: We searched the PubMed (MEDLINE) database for articles regarding sarcoma-associated non-coding RNAs from inception to August17, 2022. Studies investigating the roles of host-derived microRNAs, long non-coding RNAs, and circular RNAs in sarcoma were included. Data regarding the roles of ncRNAs in therapeutic regulation and their applicability as biomarkers for predicting therapeutic response of sarcomas were extracted. Two independent researchers assessed the quality of the studies using Würzburg Methodological Quality Score(W-MeQS).

Results: Observational studies revealed ectopic expression of non-coding RNAs in sarcoma patients with different responses to antitumor treatments. Experimental studies have confirmed crosstalk between cellular pathways pertinent to chemotherapy, targeted therapy, and radiotherapy resistance. Of the included studies, W-MeQS scores ranged from 3 to 10 (average score = 5.42). Of the twelve articles that investigated non-coding RNAs as biomarkers, none included a validation cohort. Selective reporting of the sensitivity, specificity, and receiver operating curves was common.

Conclusion: Although non-coding RNAs appear to be good candidates as biomarkers for predicting treatment response and therapeutics for sarcoma, their differential expression across tissues complicates their application. Further research regarding their potential for inhibiting or activating these regulatory molecules to reverse treatment resistance may be useful.

Funding: This study's literature retrieval cost was supported by the 345 Talent Project of Shengjing Hospital of China Medical University(M0949 to Tao Zhang).

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file. The data has also been deposited to Dryad

The following data sets were generated
    1. Zhang T
    2. Chen H
    3. Zhang T
    4. Zhang F
    (2022) 212 orginal articles
    Dryad Digital Repository, doi:10.5061/dryad.kd51c5b8t.

Article and author information

Author details

  1. Huan-Huan Chen

    Department of Oncology, China Medical University, Shenyang, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Tie-Ning Zhang

    Department of Pediatrics, China Medical University, Shenyang, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Fang-Yuan Zhang

    Department of General Surgery, China Medical University, Shenyang, China
    For correspondence
    fyzhang@cmu.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
  4. Tao Zhang

    Department of Pediatrics, China Medical University, Shenyang, China
    For correspondence
    zhangtaocmu7@126.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5341-8249

Funding

345 Talent of Shengjing Hospital of China Medical University

  • Tao Zhang

The funder supported the data collection for the study.

Reviewing Editor

  1. Renata Pasqualini, Rutgers University, United States

Version history

  1. Preprint posted: April 7, 2022 (view preprint)
  2. Received: April 21, 2022
  3. Accepted: November 2, 2022
  4. Accepted Manuscript published: November 3, 2022 (version 1)
  5. Version of Record published: November 24, 2022 (version 2)

Copyright

© 2022, Chen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 400
    Page views
  • 57
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Huan-Huan Chen
  2. Tie-Ning Zhang
  3. Fang-Yuan Zhang
  4. Tao Zhang
(2022)
Non-coding RNAs in drug and radiation resistance of bone and soft tissue sarcoma: a systematic review
eLife 11:e79655.
https://doi.org/10.7554/eLife.79655

Share this article

https://doi.org/10.7554/eLife.79655

Further reading

    1. Cancer Biology
    Wanyoung Lim, Inwoo Hwang ... Sungsu Park
    Research Article

    Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium. In vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high NUPR1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.

    1. Cancer Biology
    2. Computational and Systems Biology
    Bingrui Li, Fernanda G Kugeratski, Raghu Kalluri
    Research Article

    Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.