4 Discussion
RA is an autoimmune disease that is associated with an increased risk of not only lymphoproliferative disorders, but also myeloid malignancies, increasing the risk of developing AML [
10,
11]. As these studies continue to grow and intensify, this seems to open up the possibility of exploring diagnostic biomarkers from one disease to another. Due to the chronic immune stimulation or bone marrow infiltration of RA, some risk features at the molecular level are progressively manifested before it causes AML, and these risk features are not found in normal individuals, and these molecular features underpinning the development of AML remain largely speculative. Some of the differences between RA and normal individuals at the molecular level are reflected in the immune heterogeneity of RA. Therefore, we will first investigate the immunological heterogeneity features of RA and then use this as a starting point to explore whether these features are relevant to the diagnosis of AML.
Firstly, in this study, we performed two differential expression analyses on RA and OA samples before and after calculating the immune infiltration scores of each sample, and 544 DEGs were obtained by intersecting the results of the two differential analyses. 544 DEGs were obtained through functional and pathway enrichment analyses of these DEGs, and the results showed that these DEGs were mainly enriched in the adaptive immune response, positive regulation of immune response, primary immunodeficiency, leukocyte migration, cytokine signaling in the immune system, and the expression of the immune infiltration scores of each sample response, positive regulation of immune response, primary immunodeficiency, leukocyte migration, and cytokine signaling in Immune system. These enrichment entries are from different databases but the results of functional and pathway enrichment are not very different. In addition, it has been shown that the biological processes of the immune system are crucial for the formation of a complex bone marrow micro-environment [
12,
13], so that understanding the immunological heterogeneity of RA will help to develop effective bone marrow markers for AML diagnosis.
Secondly, eight genes associated with RA immune heterogeneity were obtained by fuzzy C-mean clustering and post-tuning XGboost regression model calculations: IFNG, IL7R, CCR7, KLRB1, CXCL9, CXCL13, SELL, and PTPRC. With these eight immune heterogeneity genes of RA as a set of characterized genes to be investigated in AML, we evaluated their expression in the expression in AML. Except for IFNG, CXCL9, CXCL13, which were not statistically significant in AML above normal, the difference between AML and normal was statistically significant for the remaining five genes, and this difference was highly significant. We also validated the expression of these 5 genes using a new dataset and analyzed their diagnostic value for AML using a random ferns classifier. Typically, shed forms of SELL are often studied in conjunction with AML, and plasma from AML patients shows high levels of shed forms of selections [
14‐
16]. Little attention has been paid to the amount of SELL expressed in AML bone marrow. In this study, after analyzing a large amount of data, we found that the expression of SELL is much higher in AML bone marrow than in normal subjects, which provides a new biomarker for bone marrow diagnosis of AML. In addition, it has been found that interferon α up-regulates the expression of SELL [
17], and interferon α is often used in the treatment of RA [
18,
19], which may bring a potential risk of AML development in RA patients. In this study, KLRB1 and CCR7 were found to be barely expressed in normal human bone marrow, but highly expressed in AML bone marrow, and besides AML, they were also over-expressed in a few types of cancers, such as testicular germ cell tumors (TGCT), kidney renal clear cell carcinoma (KIRC), pancreatic cancer (PAAD), etc., and the tissues of the three cancers studied were not closely related to the bone marrow, so KLRB1 and CCR7 will be bone marrow biomarkers helpful for the diagnosis of AML. Previous studies have shown that over-expression of PTPRC can predict poor prognosis and serve as a therapeutic target for pediatric AML [
20]. In the present study PTPRC can be used as a biomarker for AML diagnosis. Earlier studies have shown that IL7R is extremely low in bone marrow [
21], which corroborates with this paper, so the presence of a significant increase in bone marrow of AML patients combined with the results of ROC analysis (AUC = 0.993) suggests that IL7R is a suitable bone marrow biomarker for AML diagnosis.
And then, this study analyzed the relationship between these five genes and the AML immune microenvironment and AML therapeutic drug sensitivity, and attempted to construct a ceRNA network based on these five genes. This study found that KLRB1 can be a target for AML therapy. In a previous study on glioma, it was shown that activation of CD161 would weaken T-cell responses to tumor cells, thereby inhibiting the utility of immunotherapy. Knockdown of KLRB1, the gene encoding CD161, strongly enhanced the ability of T cells to attack glioma cells and reduced T cell exhaustion [
22]. In this study, KLRB1 showed almost negative correlation with different types of T cells and positive correlation with almost all common immune checkpoints, and its expression level was also much higher than that of normal individuals. Therefore, this study suggests that KLRB1 could be a new target for AML treatment or could be used in the development and design of novel immunotherapeutic drugs. This study also found that IL7R affects chemotherapy resistance. In fact, some studies have already confirmed that IL7R is related to chemotherapy resistance [
23‐
26], so this study found a drug that can reduce the expression of IL7R through the Drug Interaction Database: acetylcysteine, which may be helpful in reducing chemotherapy resistance. To fully understand the role of these diagnostic genes in AML, this study also constructed a ceRNA network. miRNAs and lncRNAs play key roles in gene regulation and cancer biology. miRNAs are small non-coding RNAs that are known to bind and control mRNA expression [
27‐
31]. In the ceRNA network, lncRNAs act as miRNAs to regulate gene expression and participate in cancer development. The construction of the ceRNA network will better investigate the role of diagnostic gene genes in AML and their potential mechanisms. In the results of the cell communication analysis of RA and control mice in this study, it is not difficult to see that completely different from the control group, the CD45 signaling pathway in RA is exclusively contributed by Ptprc-Cd22, which in turn is a biomarker for a variety of malignant tumors such as leukemia and non-Hodgkin’s lymphoma, which may suggest that the CD45 signaling pathway is of great importance to be investigated in the transition from RA to leukemia.
Finally, we analyzed the relationship between the expression of these five biomarkers and the different cytogenetic risks of AML patients with different CEBPA mutation status. We found that the expression of SELL was higher the worse the cytogenetic risk of AML, while the expression of PTPRC was highest in the group with Intermediate cytogenetic risk. The expression of SELL was significantly higher in patients with CEBPA mutation type as mutanted than in wild type patients. The expression of all genes except SELL was significantly higher in patients with wild type than in patients with mutated type. In addition, different expression patterns of biomarkers other than KLRBA correlated with CEBPA mutated type, although this correlation was not strong. The above results demonstrate that the expression of genes characterizing the immune heterogeneity associated with RA is associated with different cytogenetic risks and CEBPA mutation types in AML.
This study explored the relationship between genes characterizing the immune heterogeneity of RA and AML, but further research is needed for an in-depth exploration of the transition from RA to AML. Indeed, the low frequency of RA developing into AML may in some cases be associated with certain somatic mutations, such as UBA1 in Vexas syndrome. One study identified Vexas syndrome as a myeloid-driven inflammatory disorder caused by somatic mutations in the UBA1 gene, further exposing the increasingly recognized overlap between hematologic disorders and autoimmune and/or autoinflammatory manifestations [
32]. It has also been demonstrated that there is a commonality in the mechanistic pathways involved in RA, AML, and Vexas syndrome [
33], and that all involve a profound alteration of the inflammatory response and the bone marrow microenvironment. Somatic mutations in the ASXL1 gene may also be a contributing factor in the progression of RA to AML. Studies have shown that somatic mutations in the ASXL1 gene correlate with RA [
34], and ASXL1 mutations are highly specific in diagnosing AML [
35]. In addition, mutations in DNMT3A and TET2, which are closely associated with RA, may also be important in the progression of RA to AML [
36,
37].
Although this study analyzed a large amount of data using machine learning and bioinformatics methods to identify five biomarkers associated with AML, there are certain shortcomings. One is that the study may have been affected by potential confounding factors. The first is that although OA was chosen as a control in this study to reflect the immune heterogeneity of RA, after all, the mechanistic pathways of RA and OA are different, and to some extent this may interfere with the data analysis. Secondly, it is not clear whether the RA or OA samples had received treatment, as some drugs may have affected their immune function during the treatment process, which may have resulted in altered expression of certain genes. Despite some confounding, this study explored the relationship between genes that characterize immune heterogeneity in RA and AML, demonstrating that these genes are useful in distinguishing healthy cells from leukemic cells.
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