The prognosis biomarkers based on m6A-related lncRNAs for myeloid leukemia patients

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Abstract

Background

Chronic myeloid leukemia (CML) and acute myeloid leukemia (AML) are two common malignant disorders in leukemia. Although potent drugs are emerging, CML and AML may still relapse after the drug treatment is stopped. N6-methyladenosine (m6A) and lncRNAs play certain roles in the occurrence and development of tumors, but m6A-modified LncRNAs in ML remain to be further investigated.

Methods

In this study, we extracted and analyzed the TCGA gene expression profile of 151 ML patients and the clinical data. On this basis, we then evaluated the immune infiltration capacity of ML and LASSO-penalized Cox analysis was applied to construct the prognostic model based on m6A related lncRNAs to verify the prognostic risk in clinical features of ML. Quantitative reverse transcription PCR was used to detect the expression level of LncRNA in in ML cell lines K562, MOLM13 and acute monocytic leukemia cell line THP-1.

Results

We found 70 m6A-related lncRNAs that were related to prognosis, and speculated that the content of stromal cells and immune cells would correlate with the survival of patients with ML. Next, Prognostic risk model of m6A-related lncRNAs was validated to have excellent consistency in clinical features of ML. Finally, we verified the expression levels of CRNDE, CHROMR and NARF-IT1 in ML cell lines K562, MOLM13 and acute monocytic leukemia cell line THP-1, which were significant.

Conclusions

The research provides clues for the prognosis prediction of ML patients by using the m6A-related lncRNAs model we have created, and clarifies the accuracy and authenticity of it.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02428-3.

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Most cited references 30

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ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

Matthew D. Wilkerson, D. Hayes (2010)

Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

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The prognostic landscape of genes and infiltrating immune cells across human cancers.

Andrew Gentles, Aaron Newman, Chih Long Liu … (2015)

Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

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Long non-coding RNAs: insights into functions.

Tim Mercer, Marcel E Dinger, John S. Mattick (2009)

In mammals and other eukaryotes most of the genome is transcribed in a developmentally regulated manner to produce large numbers of long non-coding RNAs (ncRNAs). Here we review the rapidly advancing field of long ncRNAs, describing their conservation, their organization in the genome and their roles in gene regulation. We also consider the medical implications, and the emerging recognition that any transcript, regardless of coding potential, can have an intrinsic function as an RNA.

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Author and article information

Contributors

Zhao-Wei Teng: tengzhaowei2003@163.com

Jun Zhang:

ORCID: http://orcid.org/0000-0002-7690-4132

Zhangjun2021123@163.com

Journal

Journal ID (nlm-ta): Cancer Cell Int

Journal ID (iso-abbrev): Cancer Cell Int

Title: Cancer Cell International

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-2867

Publication date (Electronic): 7 January 2022

Publication date PMC-release: 7 January 2022

Publication date Collection: 2022

Volume: 22

Electronic Location Identifier: 10

Affiliations

[1 ]GRID grid.460068.c, ISNI 0000 0004 1757 9645, Department of Oncology, , The Third People’s Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, ; 82 Qinglong Road, Chengdu, 610031 Sichuan China

[2 ]GRID grid.285847.4, ISNI 0000 0000 9588 0960, Kunming Medical University, ; Kunming, 650000 Yunnan China

[3 ]GRID grid.414918.1, Yunnan Key Laboratory of Digital Orthopedics, Department of Orthopedic, , The First People’s Hospital of Yunnan Province, ; Kunming, 650000 Yunnan China

[4 ]GRID grid.459918.8, The Sixth Affiliated Hospital of Kunming Medical University, The People’s Hospital of Yuxi City, ; Yunnan, 653100 Yuxi China

[5 ]GRID grid.440773.3, ISNI 0000 0000 9342 2456, Center for Life Sciences, School of Life Sciences, , Yunnan University, ; Kunming, 650000 China

Author information

Jun Zhang http://orcid.org/0000-0002-7690-4132

Article

Publisher ID: 2428

DOI: 10.1186/s12935-021-02428-3

PMC ID: 8739709

PubMed ID: 34996458

SO-VID: 886a7f0f-2b95-4dd6-b466-ff66b476f467

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 24 November 2021

Date accepted : 23 December 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 31960136

Award ID: 81760136

Award Recipient : Yun Zhu Zhao-Wei Teng

Funded by: Joint special fund of Applied Fundamental Research of Kunming Medical University granted by Science and Technology Office of Yunnan

Award ID: 2018FE001(-174)

Award ID: 2018FE001(-175)

Award Recipient : Yun Zhu Zhao-Wei Teng

Funded by: Yunnan Province Young and Middle-aged Academic and Technical Leaders Reserve Talent Project

Award ID: 202005AC160124

Award Recipient : Zhao-Wei Teng

Custom metadata

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: m6a,m6a-related lncrnas,crnde,chromr,narf-it1,myeloid leukemia,prognosis

Data availability:

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: m6a, m6a-related lncrnas, crnde, chromr, narf-it1, myeloid leukemia, prognosis

The prognosis biomarkers based on m6A-related lncRNAs for myeloid leukemia patients

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Abstract

Background

Methods

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Conclusions

Supplementary Information

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Karger: Oncology

Most cited references 30

ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

The prognostic landscape of genes and infiltrating immune cells across human cancers.

Long non-coding RNAs: insights into functions.

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