M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

Zhe Liu; Fang Liu; Olutomilayo Olayemi Petinrin; Muhammad Toseef; Nanjun Chen; Zhongxu Zhu; Ka-Chun Wong

doi:10.37349/emed.2024.00254

M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

Zhe Liu (Co-first Author), Fang Liu (Co-first Author), Olutomilayo Olayemi Petinrin, Muhammad Toseef, Nanjun Chen^*, Zhongxu Zhu^*, Ka-Chun Wong^*

^*Corresponding author for this work

Department of Computer Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Abstract

Aim: Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most common subtype. Despite recent advancements in diagnostics and therapies, only a small percentage of patients benefit from immunotherapies. This underscores the urgent need to identify prognostic biomarkers for accurately assessing outcomes and providing treatment recommendations for NSCLC patients. Single-cell RNA sequencing (scRNA-seq) has revealed the heterogeneity of tumor-associated macrophages. Macrophages consist of M0, M1, and M2 subsets. M1 macrophages are often associated with improved clinical outcomes in various malignancies. However, there are no systematic studies on risk biomarkers for prognosticating NSCLC. Methods: CIBERSORT was used to calculate the macrophage subset infiltration percentage in bulk RNA-seq data from TCGA and GEO. The M1-related module was identified using the WGCNA algorithm. Potential M1 macrophage prognosis-associated genes were defined as the overlapping genes between marker genes in the M1 subpopulation from scRNA-seq data and prognosis-associated genes in M1 infiltrating cells. Results: Four risk genes (ADAM19, ICAM3, WIPF1, and LAP3) were identified through LASSO and multivariate Cox regression analysis. Forest plots demonstrated that the scoring model was an independent risk factor. A nomogram was provided to predict the prognosis of NSCLC patients. Finally, we validated the four risk genes at the protein expression levels and for copy number variations. Conclusions: In summary, our studies identified four risk genes related to M1 macrophages and presented a risk-scoring system to predict the outcomes of patients with NSCLC by integrating bulk and single-cell data. © The Author(s) 2024.

Original language	English
Pages (from-to)	750-773
Journal	Exploration of Medicine
Volume	5
Issue number	6
Online published	6 Nov 2024
DOIs	https://doi.org/10.37349/emed.2024.00254
Publication status	Published - 2024

Funding

This research was substantially sponsored by the research projects [No. 32170654; No. 32000464] supported by the National Natural Science Foundation of China and was substantially supported by the Shenzhen Research Institute, City University of Hong Kong. This project is substantially funded by the Strategic Interdisciplinary Research Grant of City University of Hong Kong [No. 2021SIRG036]. The work described in this paper was substantially supported by the grant from the Health and Medical Research Fund, the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region [07181426]. The work described in this paper was partially supported by the grants from City University of Hong Kong [CityU 11203520, CityU 11203221]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Research Keywords

M1 macrophage
NSCLC
prognosis
scRNA-seq
TCGA
WGCNA

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC",

abstract = "Aim: Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most common subtype. Despite recent advancements in diagnostics and therapies, only a small percentage of patients benefit from immunotherapies. This underscores the urgent need to identify prognostic biomarkers for accurately assessing outcomes and providing treatment recommendations for NSCLC patients. Single-cell RNA sequencing (scRNA-seq) has revealed the heterogeneity of tumor-associated macrophages. Macrophages consist of M0, M1, and M2 subsets. M1 macrophages are often associated with improved clinical outcomes in various malignancies. However, there are no systematic studies on risk biomarkers for prognosticating NSCLC. Methods: CIBERSORT was used to calculate the macrophage subset infiltration percentage in bulk RNA-seq data from TCGA and GEO. The M1-related module was identified using the WGCNA algorithm. Potential M1 macrophage prognosis-associated genes were defined as the overlapping genes between marker genes in the M1 subpopulation from scRNA-seq data and prognosis-associated genes in M1 infiltrating cells. Results: Four risk genes (ADAM19, ICAM3, WIPF1, and LAP3) were identified through LASSO and multivariate Cox regression analysis. Forest plots demonstrated that the scoring model was an independent risk factor. A nomogram was provided to predict the prognosis of NSCLC patients. Finally, we validated the four risk genes at the protein expression levels and for copy number variations. Conclusions: In summary, our studies identified four risk genes related to M1 macrophages and presented a risk-scoring system to predict the outcomes of patients with NSCLC by integrating bulk and single-cell data. {\textcopyright} The Author(s) 2024.",

keywords = "M1 macrophage, NSCLC, prognosis, scRNA-seq, TCGA, WGCNA",

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year = "2024",

doi = "10.37349/emed.2024.00254",

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M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC. / Liu, Zhe (Co-first Author); Liu, Fang (Co-first Author); Petinrin, Olutomilayo Olayemi et al.
In: Exploration of Medicine, Vol. 5, No. 6, 2024, p. 750-773.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

AU - Liu, Zhe

AU - Liu, Fang

AU - Petinrin, Olutomilayo Olayemi

AU - Toseef, Muhammad

AU - Chen, Nanjun

AU - Zhu, Zhongxu

AU - Wong, Ka-Chun

PY - 2024

Y1 - 2024

N2 - Aim: Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most common subtype. Despite recent advancements in diagnostics and therapies, only a small percentage of patients benefit from immunotherapies. This underscores the urgent need to identify prognostic biomarkers for accurately assessing outcomes and providing treatment recommendations for NSCLC patients. Single-cell RNA sequencing (scRNA-seq) has revealed the heterogeneity of tumor-associated macrophages. Macrophages consist of M0, M1, and M2 subsets. M1 macrophages are often associated with improved clinical outcomes in various malignancies. However, there are no systematic studies on risk biomarkers for prognosticating NSCLC. Methods: CIBERSORT was used to calculate the macrophage subset infiltration percentage in bulk RNA-seq data from TCGA and GEO. The M1-related module was identified using the WGCNA algorithm. Potential M1 macrophage prognosis-associated genes were defined as the overlapping genes between marker genes in the M1 subpopulation from scRNA-seq data and prognosis-associated genes in M1 infiltrating cells. Results: Four risk genes (ADAM19, ICAM3, WIPF1, and LAP3) were identified through LASSO and multivariate Cox regression analysis. Forest plots demonstrated that the scoring model was an independent risk factor. A nomogram was provided to predict the prognosis of NSCLC patients. Finally, we validated the four risk genes at the protein expression levels and for copy number variations. Conclusions: In summary, our studies identified four risk genes related to M1 macrophages and presented a risk-scoring system to predict the outcomes of patients with NSCLC by integrating bulk and single-cell data. © The Author(s) 2024.

AB - Aim: Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most common subtype. Despite recent advancements in diagnostics and therapies, only a small percentage of patients benefit from immunotherapies. This underscores the urgent need to identify prognostic biomarkers for accurately assessing outcomes and providing treatment recommendations for NSCLC patients. Single-cell RNA sequencing (scRNA-seq) has revealed the heterogeneity of tumor-associated macrophages. Macrophages consist of M0, M1, and M2 subsets. M1 macrophages are often associated with improved clinical outcomes in various malignancies. However, there are no systematic studies on risk biomarkers for prognosticating NSCLC. Methods: CIBERSORT was used to calculate the macrophage subset infiltration percentage in bulk RNA-seq data from TCGA and GEO. The M1-related module was identified using the WGCNA algorithm. Potential M1 macrophage prognosis-associated genes were defined as the overlapping genes between marker genes in the M1 subpopulation from scRNA-seq data and prognosis-associated genes in M1 infiltrating cells. Results: Four risk genes (ADAM19, ICAM3, WIPF1, and LAP3) were identified through LASSO and multivariate Cox regression analysis. Forest plots demonstrated that the scoring model was an independent risk factor. A nomogram was provided to predict the prognosis of NSCLC patients. Finally, we validated the four risk genes at the protein expression levels and for copy number variations. Conclusions: In summary, our studies identified four risk genes related to M1 macrophages and presented a risk-scoring system to predict the outcomes of patients with NSCLC by integrating bulk and single-cell data. © The Author(s) 2024.

KW - M1 macrophage

KW - NSCLC

KW - prognosis

KW - scRNA-seq

KW - TCGA

KW - WGCNA

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U2 - 10.37349/emed.2024.00254

DO - 10.37349/emed.2024.00254

M3 - RGC 21 - Publication in refereed journal

SN - 2692-3106

VL - 5

SP - 750

EP - 773

JO - Exploration of Medicine

JF - Exploration of Medicine

IS - 6

ER -

M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

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HMRF: Development of Big Data Tools for High-Throughput Sequencing Data with Applications to Colorectal Cancer Genomes

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M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

UN SDGs

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HMRF: Development of Big Data Tools for High-Throughput Sequencing Data with Applications to Colorectal Cancer Genomes

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