Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep 26:13:987398.
doi: 10.3389/fphar.2022.987398. eCollection 2022.

Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia

Yan Mao  1 , Jinwen Xu  2 , Xuejiao Xu  1 , Jiayun Qiu  1 , Zhengyun Hu  1   3 , Feng Jiang  4 , Guoping Zhou  1
Affiliations

Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia

Yan Mao et al. Front Pharmacol. .

Abstract

In malignancies, cellular senescence is critical for carcinogenesis, development, and immunological regulation. Patients with acute myeloid leukemia (AML) have not investigated a reliable cellular senescence-associated profile and its significance in outcomes and therapeutic response. Cellular senescence-related genes were acquired from the CellAge database, while AML data were obtained from the GEO and TCGA databases. The TCGA-AML group served as a training set to construct a prognostic risk score signature, while the GSE71014 set was used as a testing set to validate the accuracy of the signature. Through exploring the expression profiles of cellular senescence-related genes (SRGs) in AML patients, we used Lasso and Cox regression analysis to establish the SRG-based signature (SRGS), which was validated as an independent prognostic predictor for AML patients via clinical correlation. Survival analysis showed that AML patients in the low-risk score group had a longer survival time. Tumor immune infiltration and functional enrichment analysis demonstrated that AML patients with low-risk scores had higher immune infiltration and active immune-related pathways. Meanwhile, drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Cell line analysis in vitro further confirmed that the SRGs in the proposed signature played roles in the susceptibility to cytarabine and YM155. Our results indicated that SRGS, which regulates the immunological microenvironment, is a reliable predictor of the clinical outcome and immunotherapeutic response in AML.

Keywords: acute myeloid leukemia; cellular senescence; immunotherapy; senescence-associated secretory phenotype; tumor microenvironment; tumor mutation burden.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of DESRGs with prognostic value. (A) Volcano plot of SRGs in the TCGA dataset. (B) Eleven overlapping genes in SRGs, DESRGs, and prognostic genes. (C) Results of the univariate Cox analysis based on the identified candidate 11 SRGs. (D) Correlation network of the 11 candidate SRGs. (E) LASSO analysis on the 11 candidate SRGs. (F) Cross-validation in the LASSO regression.
FIGURE 2
FIGURE 2
Development of SRGS in TCGA-AML cohort. (A) Kaplan-Meier curve analysis of the OS in the TCGA-AML patients. (B) The risk score distribution and survival status scatter plots of TCGA-AML patients. (C) Heatmap of the eight signature genes in the risk groups. (D) Time-dependent ROC analysis of the SRGS.
FIGURE 3
FIGURE 3
Validation of the SRGS in an external GEO cohort. (A) Kaplan-Meier curve analysis of OS in AML patients from the GSE71014 dataset. (B) The risk score distribution and survival status scatter plots. (C) Heatmap of the eight genes in the proposed SRGS in two risk groups from GSE71014 dataset. (D) Time-dependent ROC analysis of the SRGS.
FIGURE 4
FIGURE 4
The independence of predictive value of the SRGS and construction of the clinical nomogram in TCGA-AML cohort. (A) Results of the univariate Cox analysis based on OS-related factors. (B) Results of the multivariate Cox analysis based on OS-related factors. (C) Nomogram constructed in conjunction with the SRGS and clinical characterization. (D) The calibration plot of the nomogram.
FIGURE 5
FIGURE 5
Analyses of SASP and immune cell infiltration levels. (A) Expression of different types of SRGS-associated secretory phenotype factors between two risk groups. (B) Correlation analysis between SRGS and immune cell infiltration abundance.
FIGURE 6
FIGURE 6
Correlation of chosen immune checkpoints and the SRGS score and their impact on clinical outcome of TCGA-AML patients. (A,D,G) Comparison of the PD-1, CTLA-4 or LAG3 expression level between different AML risk groups. (B,E,H) Correlation between SRGS score and the PD-1, CTLA-4 or LAG3 expression level. (C,F,I) Kaplan-Meier survival analyses of OS in the four groups grouped by the SRGS and the level of PD-1, CTLA-4 or LAG3.
FIGURE 7
FIGURE 7
Correlation between SRGS and TME. (A) Landscape of the immune characteristics and TME. (B) Correlations between SRGS score and TME score. (C) The relationships between SRGS scores and DNAss, RNAss, Stromal Scores, and Immune Scores. (D) Comparison of HLA gene expression levels between two risk AML groups.
FIGURE 8
FIGURE 8
Relationship of the SRGS with TMB. (A) Comparison of TMB between two risk groups. (B) Correlation between the SRG and TMB. (C) Kaplan-Meier analysis on the TMB in the TCGA-AML cohort. (D) Kaplan-Meier analysis for the groups that stratified by combining the TMB and the SRGS score. (E,F) OncoPrints constructed using the high scores (E) and the low scores (F).
FIGURE 9
FIGURE 9
Correlation between SRGS and immunotherapy. (A) Distribution of TIDE scores between two TCGA-AML risk groups. (B–E) Correlation between the SRGS score and chemotherapeutic sensitivity.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Acharya U. H., Halpern A. B., Wu Q. V., Voutsinas J. M., Walter R. B., Yun S., et al. (2018). Impact of region of diagnosis, ethnicity, age, and gender on survival in acute myeloid leukemia (AML). J. Drug Assess. 7 (1), 51–53. 10.1080/21556660.2018.1492925 - DOI - PMC - PubMed
    1. Ao R., Guan L., Wang Y., Wang J. N. (2017). Effects of PKM2 gene silencing on the proliferation and apoptosis of colorectal cancer LS-147T and SW620 cells. Cell. Physiol. biochem. 42 (5), 1769–1778. 10.1159/000479456 - DOI - PubMed
    1. Aran D., Hu Z., Butte A. J. (2017). XCell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18 (1), 220. 10.1186/s13059-017-1349-1 - DOI - PMC - PubMed
    1. Avelar R. A., Ortega J. G., Tacutu R., Tyler E. J., Bennett D., Binetti P., et al. (2020). A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 21 (1), 91. 10.1186/s13059-020-01990-9 - DOI - PMC - PubMed
    1. Basisty N., Kale A., Jeon O. H., Kuehnemann C., Payne T., Rao C., et al. (2020). A proteomic atlas of senescence-associated secretomes for aging biomarker development. PLoS Biol. 18 (1), e3000599. 10.1371/journal.pbio.3000599 - DOI - PMC - PubMed

LinkOut - more resources