AUTHOR=Hua Jingsheng , Ding Tianling , Shao Yanping 

TITLE=A transient receptor potential channel-related model based on machine learning for evaluating tumor microenvironment and immunotherapeutic strategies in acute myeloid leukemia

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.1040661

DOI=10.3389/fimmu.2022.1040661

ISSN=1664-3224

ABSTRACT=Background
Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy. TRP channels in AML still needs to be further explored. A TRP channel-related model based on machine learning was established in this study.
Methods
The data was downloaded from TCGA-LAML and GTEx. We extracted TRP-related genes (TRGs) from previous literature. Using ssGSEA, TRP-enrichment scores (TESs) were calculated. The limma was used to identify differentially expressed genes (DEGs) and Univariate Cox regression analysis was performed to identify prognostic DEGs. The above prognostic DEGs were analyzed by Random Survival Forest and Lasso analysis to creating the TRP-signature. The Kaplan–Meier and ROC curves were plotted to investigate the efficiency and accuracy of prognostic prediction. Besides, genomic mutation analysis was based on GISTIC analysis. Based on ESTIMATE, TIMER, MCPcounter, and ssGSEA, tumor microenvironment and immunological characteristics was expressly evaluated to explore immunotherapeutic strategies. Enrichment analysis for TRP-signature was based on KEGG, GO, ORA and GSEA. The GDSC and pRRophetic were used to carry out drug sensitivity analysis. Conclusively, we randomly selected SCHIP1 to perform in vitro cyto-functional experiments.
Results
The worse clinical outcomes of patients with higher TESs were observed. There were 107 differentially expressed TRGs identified. Our data revealed 57 prognostic TRGs. We obtained eight TRGs to establish the prognostic TRP-signature, and we found the worse clinical outcomes of patients with higher TRP-scores. The efficiency and accuracy of TRP-signature in predicting prognosis was confirmed by ROC curves and five external validation datasets. Our data revealed the mutation rates of DNMT3A, IDH2, MUC16, and TTN were relatively high. The level of infiltrating immune cell populations, stromal, immune, ESTIMATE scores increased as the TRP-scores increased. Nevertheless, AML patients with lower TRP-scores were observed more tumor purity. The TRP-scores were found to be correlated with immunomodulators and immune checkpoints, thus revealing immune characteristics and immunotherapeutic strategies. The IC50 values of six chemotherapeutics were lower in HTS-group. Finally, we found that SCHIP1 may be the oncogenic gene.
Conclusion
The results of this study will understand the role of TRP and SCHIP1 in the prognosis and development of AML.