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. 2022 Aug 10:12:925615.
doi: 10.3389/fonc.2022.925615. eCollection 2022.

Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis

Fu Li  1 , Jiao Cai  2   3 , Jia Liu  1 , Shi-Cang Yu  3 , Xi Zhang  1 , Yi Su  2 , Lei Gao  1
Affiliations

Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis

Fu Li et al. Front Oncol. .

Abstract

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251) data set and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to the genes in prognostic model. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.

Keywords: AML – acute myeloid leukaemia; LASSO; bioinformatic; cox regression model; prognostic model.

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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
Unsupervised clustering analysis of AML patients based on 98 survival-related immune genes. (A) All 97 TCGA-AML patients were divided into 3 clusters (green: Im1 cluster, red: Im2 cluster, blue: Im3 cluster). (B) Heatmap of 98 survival-related immune genes in different AML clusters. (C) The Kaplan-Meier survival analyses along with the Log-rank test were used to compare the overall survival (OS) of the Im1, Im2 and Im3 clusters.
Figure 2
Figure 2
Immune functional characters of 3 AML patients clusters. (A) Heatmap of the Im1, Im2 and Im3 cohorts of 97 TCGA-AML patients using ssGSEA scores from 28 immune cell types. Violin plots depict the immune score (B) and tumor purity (C) of the Im1, Im2 and Im3 cohorts (***: P<0.001).
Figure 3
Figure 3
Difference analysis of the mRNA expression dataset from TCGA-AML patients. (A) Volcano plot of differentially expressed genes between immune infiltration-lacking subtype (IL type) and immune infiltration-rich subtype (IR type). (B) Venn plot of the intersecting genes between differentially expressed genes (DEGs) and survival-related immune genes (SIGs). (C) PPI network of 42 overlap genes DEG-SIGs. (D) The bubble plot represents the GO functional enrichment analysis of 42 DEG-SIGs.
Figure 4
Figure 4
Multiomics analysis of 97 TCGA-AML patients. (A) Volcano plot of differentially expressed miRNAs between IL and IR types. (B) Correlation between mRNA expression and DNA methylation level of 6 DEG & MethylCor genes. (C) Venn plot of the intersection of DEGs, SIGs, targets of DEmiRs and MethylCor gene set.
Figure 5
Figure 5
Construction of the COX regression model. (A) LASSO coefficient profiles of 19 candidate SIGs. (B) Tuning parameter (λ) selection cross‐validation error curve. The vertical lines were drawn at the optimal values determined by the minimum criteria and the 1‐SE criteria. (C, E, G) OS in patients with high vs. low risk scores depicted by Kaplan-Meier plots in the TCGA-AML-97, TCGA-AML-151 and GSE106291 cohorts. (D, F, H) ROC curves depicting the accuracy of the Cox regression model in identifying AML subtypes with poor prognosis in the TCGA-AML-97, TCGA-AML-151 and GSE106291 cohorts.
Figure 6
Figure 6
Analysis of hub genes and mutated genes in AML. (A) Kaplan-Meier estimates of the OS according to RUNX1 mutation status. (B) Kaplan-Meier estimates of the OS according to TET2 mutation status. (wt, wild type; mut, mutation).
Figure 7
Figure 7
scRNA analysis of hub genes. (A) Clustering analysis of the UMAP plot, color coded based on cell types. (B) Overlaying gene expression on UMAP clusters to illustrate the distribution of hub genes in each cell type. (C) Violin plots of hub genes in each cell type.
Figure 8
Figure 8
Shows 2D interaction representations of the best pose of (A) CD52 with ZINC164528615 (Glecaprevir), (B) CD52 with ZINC3938684 (Toposar); (C) ICAM3 with ZINC52955754 (Ergotamine), (D) ICAM3 with ZINC1612996 (Irinotecan); (E) CLCN5 with ZINC3978005 (Dihydroergotamine), (F) CLCN5 with ZINC52955754 (Ergotamine).
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References

    1. Dohner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. New Engl J Med (2015) 373(12):1136–52. doi: 10.1056/NEJMra1406184 - DOI - PubMed
    1. Short NJ, Rytting ME, Cortes JE. Acute myeloid leukaemia. Lancet (2018) 392(10147):593–606. doi: 10.1016/S0140-6736(18)31041-9 - DOI - PMC - PubMed
    1. Döhner H, Estey EH, Amadori S, Appelbaum FR, Büchner T, Burnett AK, et al. . Diagnosis and management of acute myeloid leukemia in adults: Recommendations from an international expert panel, on behalf of the European leukemianet. Blood (2010) 115(3):453–74. doi: 10.1182/blood-2009-07-235358 - DOI - PubMed
    1. Ghobrial IM, Detappe A, Anderson KC, Steensma DP. The bone-marrow niche in mds and mgus: Implications for aml and mm. Nat Rev Clin Oncol (2018) 15(4):219–33. doi: 10.1038/nrclinonc.2017.197 - DOI - PubMed
    1. Cogle CR, Bosse RC, Brewer T, Migdady Y, Shirzad R, Kampen KR, et al. . Acute myeloid leukemia in the vascular niche. Cancer Lett (2016) 380(2):552–60. doi: 10.1016/j.canlet.2015.05.007 - DOI - PubMed

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