ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Article 06 July 2023

Construction of an M1 macrophage-related lncRNA signature for predicting the tumor immune microenvironment

Cite this:
https://doi.org/10.52396/JUSTC-2022-0185
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  • Author Bio:

    Qi Wu is currently a master’s student at the School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, under the supervision of Prof. Qingsong Hu. His research mainly focuses on bioinformatics and tumor infiltrated macrophages

    Huihui Wu received her Ph.D. degree in Cell Biology from the University of Science and Technology of China. She is currently a post-doctor at the University of Science and Technology of China. Her research interests include the tumor microenvironment and its role in tumor progression

  • Corresponding author: E-mail: wuhuihui@ustc.edu.cn
  • Received Date: 30 December 2022
  • Accepted Date: 07 April 2023
  • Available Online: 06 July 2023
  • Long noncoding RNAs (lncRNAs) are considered crucial molecules associated with the tumor microenvironment (TME) and tumor immune microenvironment (TIM). Macrophages are important members of the immune system, and M1 macrophage function-associated lncRNAs still need to be further investigated. In this study, a lncRNA signature was constructed based on transcriptome differences between high and low M1 macrophage infiltration cohorts. This lncRNA signature included seven lncRNAs: LINC01494, ZDHHC20-IT1, LINC01450, LINC00871, EVX1-AS, KIF25-AS and AADACL2-AS1, and all of them were upregulated in patients lacking M1 macrophages, indicating their roles in inhibiting macrophage infiltration and polarizing to the M1 subtype, leading to an immune exclusion TME, which has been demonstrated to be closely correlated with poor prognosis. This lncRNA signature not only predicted undesirable clinical outcomes but was also associated with the immunosuppressive environment of the tumor region, which is mediated by hindering antigen presentation and processing progress. In addition, the predictive value of this lncRNA signature for immune checkpoint inhibition (ICI) therapy was also evaluated, which further enriched and strengthened the power of lncRNAs in predicting the immunotherapy response rate.
    Bioinformatic analysis screened a 7-lncRNA signature that predicted M1 macrophage infiltration and prognosis.
    Long noncoding RNAs (lncRNAs) are considered crucial molecules associated with the tumor microenvironment (TME) and tumor immune microenvironment (TIM). Macrophages are important members of the immune system, and M1 macrophage function-associated lncRNAs still need to be further investigated. In this study, a lncRNA signature was constructed based on transcriptome differences between high and low M1 macrophage infiltration cohorts. This lncRNA signature included seven lncRNAs: LINC01494, ZDHHC20-IT1, LINC01450, LINC00871, EVX1-AS, KIF25-AS and AADACL2-AS1, and all of them were upregulated in patients lacking M1 macrophages, indicating their roles in inhibiting macrophage infiltration and polarizing to the M1 subtype, leading to an immune exclusion TME, which has been demonstrated to be closely correlated with poor prognosis. This lncRNA signature not only predicted undesirable clinical outcomes but was also associated with the immunosuppressive environment of the tumor region, which is mediated by hindering antigen presentation and processing progress. In addition, the predictive value of this lncRNA signature for immune checkpoint inhibition (ICI) therapy was also evaluated, which further enriched and strengthened the power of lncRNAs in predicting the immunotherapy response rate.
    • A total of 7 prognosis-related lncRNAs were screened and constructed as a prediction signature.
    • The 7-lncRNA signature precisely predicts the tumor-associated immune microenvironment.
    • The tumor mutation landscape was depicted based on the 7-lncRNA signature.

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Catalog

    Figure  1.  M1-Mφ predicts the clinical prognosis of colon cancer patients and DElncRNA detection. (a–d) Kaplan‒Meier curve of TCGA-COAD dataset, GSE14333, integrated dataset of GSE17536 and GSE17538, GSE39582, respectively, stratified by M1 macrophage proportion. (e) Venn diagram shows DElncRNAs through intersecting DEGs (n=1634) with the lncRNA list (n=13076), 71 DElncRNAs containing 58 downregulated lncRNAs and 13 upregulated lncRNAs. (f) Volcano plot of DElncRNAs, with upregulated lncRNAs shown in red (n=13), downregulated lncRNAs shown in blue (n=58, Wilcoxon rank sum test) and nonsense lncRNAs shown in gray (n=1180, Wilcoxon rank sum test).

    Figure  2.  The constructed lncRNA signature is closely associated with clinical outcomes. (a–g) Kaplan‒Meier curve of 7 survival-related lncRNAs. (a) AADACL2-AS (log rank p=0.0037); (b) EVX1-AS (log rank p =0.0338); (c) KIF25-AS1 (log rank p =0.0013); (d) LINC00871 (log rank p =0.0013); (e) LINC01450 (log rank p =0.0117); (f) LINC01494 (log rank p =0.0011); (g) ZHDDC20-IT1 (log rank p = 0.0043). (h) Kaplan‒Meier curve of the TCGA-COAD cohort (left, log rank p=0.0007) and an independent validation dataset GSE17537 (log rank p=0.0106). (i) Box plot of the M1 macrophage infiltration proportion in the lncRNAhigh and lncRNAlow groups (p<0.0001, Mann‒Whitney test, data are represented as the mean ± SD). (j) Forest plot of the multivariate Cox regression analysis of the TCGA-COAD cohort (left) and the independent validation cohort GSE17537 (right). (k) Box plot of the lncRNA score of different COAD tumor stages (Mann‒Whitney test, compared to stage i COAD tumors, data are represented as the mean ± SD).

    Figure  3.  The tumor immune microenvironment is negatively correlated with the lncRNA signature. (a) GSEA plots of differentially enriched cancer hallmark pathways between the lncRNA scorehigh and lncRNA scorelow cohorts. (b) Spearman’s correlation between lncRNA score and ESTIMATE immune score (Rs=−0.302, p=3.935×10−10). (c) Reactome enrichment plot of mRNAs that negatively correlated with the lncRNA set (n=23, Fisher’s exact test). (d) GO:BP enrichment plot of mRNAs that negatively correlated with the lncRNA signature. The top 20 enriched biological processes are displayed (Fisher’s exact test).

    Figure  4.  Antigen-presenting processes were downregulated in the lncRNA scorehigh group. (a) Spearman correlation between lncRNA score and antigen presenting and processing score (Rs=−0.307, p=2.054×10−10). (b) Box plot of differences in chemotaxis score between the high lncRNA group and the low lncRNA group (**: p<0.05, ***:p<0.0001, Mann‒Whitney test, data are represented as the mean ± SD). (c) Spearman correlation of lncRNA score and genes that positively regulate antigen processing and presentation. (d) Spearman correlation of lncRNA score and genes that positively regulate chemotaxis.

    Figure  5.  LncRNA signature predicts ICI therapy outcomes. (a) Swarm diagram shows the differences in PD1 (left) and PD-L1 (right) between the lncRNA scorehigh and lncRNA scorelow groups (data are represented as the mean ± SD, Mann‒Whitney test). (b) Predictive value of PD1 blocker efficiency between the lncRNA set scorehigh and scorelow groups (p=0.015, Student’s t test) (c) Kaplan‒Meier curve of four patient groups stratified by lncRNA score and PD1 expression (left) and lncRNA score and PD-L1 expression (right). (d) Stacked histograms of MSS, MSI-L and MSI-H patient numbers in the lncRNA scorehigh and lncRNA scorelow groups (p<0.05, chi-square test).

    Figure  6.  Relationship between tumor mutation burden and lncRNA signature. (a) Swarm diagram shows the differences in TMB between the lncRNA scorehigh and lncRNA scorelow groups (data are represented as the mean ± SD, Mann‒Whitney test). (b) Waterfall plot shows the top 30 mutated genes in the lncRNA scorehigh (upper panel) and lncRNA scorelow groups (lower panel). Genes in black suggest shared mutated genes across the lncRNA scorehigh and lncRNA scorelow groups. Genes in red were highly mutated in the lncRNA scorehigh group. Genes in cyan were highly mutated in the lncRNA scorelow group. (c) Comparing MSI-related gene mutations between the lncRNA scorehigh and lncRNA scorelow groups.

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    [3]
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    [4]
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    [5]
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    [6]
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    [7]
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    [8]
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    [9]
    Bian J, Dannappel M, Wan C, et al. Transcriptional regulation of Wnt/β-catenin pathway in colorectal cancer. Cells, 2020, 9 (9): 2125. doi: 10.3390/cells9092125
    [10]
    Yang L, Lin C, Jin C, et al. lncRNA-dependent mechanisms of androgen-receptor-regulated gene activation programs. Nature, 2013, 500 (7464): 598–602. doi: 10.1038/nature12451
    [11]
    Li G, Kryczek I, Nam J, et al. LIMIT is an immunogenic lncRNA in cancer immunity and immunotherapy. Nat. Cell Biol., 2021, 23 (5): 526–537. doi: 10.1038/s41556-021-00672-3
    [12]
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    [13]
    Jiang W X, Pan S Y, Chen X, et al. The role of lncRNAs and circRNAs in the PD-1/PD-L1 pathway in cancer immunotherapy. Mol. Cancer, 2021, 20 (1): 116. doi: 10.1186/s12943-021-01406-7
    [14]
    Xu H, Jiang Y, Xu X, et al. Inducible degradation of lncRNA Sros1 promotes IFN-γ-mediated activation of innate immune responses by stabilizing Stat1 mRNA. Nat. Immunol., 2019, 20 (12): 1621–1630. doi: 10.1038/s41590-019-0542-7
    [15]
    Mills C D, Lenz L L, Harris R A. A breakthrough: Macrophage-directed cancer immunotherapy. Cancer Res., 2016, 76 (3): 513–516. doi: 10.1158/0008-5472.CAN-15-1737
    [16]
    Choo Y W, Kang M, Kim H Y, et al. M1 macrophage-derived nanovesicles potentiate the anticancer efficacy of immune checkpoint inhibitors. ACS Nano, 2018, 12 (9): 8977–8993. doi: 10.1021/acsnano.8b02446
    [17]
    Li M, Sun X, Zhao J, et al. CCL5 deficiency promotes liver repair by improving inflammation resolution and liver regeneration through M2 macrophage polarization. Cell. Mol. Immunol., 2020, 17 (7): 753–764. doi: 10.1038/s41423-019-0279-0
    [18]
    Vogel D Y S, Glim J E, Stavenuiter A W D, et al. Human macrophage polarization in vitro: Maturation and activation methods compared. Immunobiology, 2014, 219 (9): 695–703. doi: 10.1016/j.imbio.2014.05.002
    [19]
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