Exploring prognostic markers for patients with acute myeloid leukemia based on cuproptosis related genes

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Abstract

Background

Acute myeloid leukemia (AML), a common form of acute leukemia, is due to tumor changes and clonal proliferation caused by genetic variants. Cuproptosis is a novel form of regulated cell death. This study aimed to explore the role of cuproptosis-related genes (CRGs) in AML.

Methods

Initially, differentially expressed genes (DEGs) between AML samples and normal samples were obtained by differential analysis, which were further intersected with the cuproptosis score-related genes (CSRGs) acquired by weighted gene co-expression network analysis (WGCNA) to obtain cuproptosis score-related differentially expressed genes (CS-DEGs). Then, a risk model was constructed by Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis. Finally, immune infiltration analysis was performed and the functions and pathways of model genes were explored by single sample gene set enrichment analysis (ssGSEA).

Results

Thirty-two CS-DEGs were obtained by overlapping 11,160 DEGs and 132 CSRGs. These 32 CS-DEGs were mainly enriched to cytoplasmic microtubule organization, RNA methylation, mTOR signaling pathway, and notch signaling pathway. Two model genes, PACS2 and NDUFV1, were finally screened for the construction of the risk model. In addition, PACS2 and NDUFV1 were significantly positively correlated with activated B cells, CD56dim natural killer (NK) cells, and negatively correlated with effector memory CD4 T cells and activated CD4 T cells. PACS2 gene was significantly enriched to inositol phosphate metabolism, histone modification, etc. NDUFV1 was mainly enriched to ncRNA metabolic process, 2-oxocarboxylic acid metabolism, and other pathways.

Conclusions

A cuproptosis-related risk model consisting of PACS2 and NDUFV1 was built, which provided a new direction for the diagnosis and treatment of AML.

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Most cited references 34

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Author and article information

Journal

Journal ID (nlm-ta): Transl Cancer Res

Journal ID (iso-abbrev): Transl Cancer Res

Journal ID (publisher-id): TCR

Title: Translational Cancer Research

Publisher: AME Publishing Company

ISSN (Print): 2218-676X

ISSN (Electronic): 2219-6803

Publication date (Electronic): 28 August 2023

Publication date (Print): 31 August 2023

Volume: 12

Issue: 8

Pages: 2008-2022

Affiliations

[1 ]deptDepartment of Biochemistry and Molecular Biology , Shanxi Medical University , Taiyuan, China;

[2 ]deptDepartment of Hematology , 2nd Hospital of Shanxi Medical University , Taiyuan, China

Author notes

Contributions: (I) Conception and design: Both authors; (II) Administrative support: None; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: X Li; (V) Data analysis and interpretation: X Li; (VI) Manuscript writing: Both authors; (VII) Final approval of manuscript: Both authors.

Correspondence to: Lianrong Xu, MD. Department of Hematology, 2nd Hospital of Shanxi Medical University, 382 Wu-Yi Rd., Taiyuan 030001, China. Email: xulrdoctor@ 123456sxmu.edu.cn .

Article

Publisher ID: tcr-12-08-2008

DOI: 10.21037/tcr-23-85

PMC ID: 10493802

PubMed ID: 37701119

SO-VID: be8b5040-d264-4dde-9698-b522d04ad8a0

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

History

Date received : 19 January 2023

Date accepted : 21 July 2023

Funding

Funded by: Social Development Project of Shanxi Province

Award ID: No. 201703D321014-3

Funded by: Research Project Supported by Shanxi Scholarship Council of China

Award ID: No. 2020-189

Funded by: Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province

Award ID: No. 20210007

Exploring prognostic markers for patients with acute myeloid leukemia based on cuproptosis related genes

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Abstract

Background

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Most cited references 34

clusterProfiler: an R package for comparing biological themes among gene clusters.

limma powers differential expression analyses for RNA-sequencing and microarray studies

WGCNA: an R package for weighted correlation network analysis

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