Skip to main content

Role of CDH23 as a prognostic biomarker and its relationship with immune infiltration in acute myeloid leukemia



Cadherin-23 (CDH23) plays an important role in intercellular adhesion and is involved in the progression of several types of cancer. However, the biological functions and effect of CDH23 expression on the prognosis of patients with acute myeloid leukemia (AML) are unexplored. Herein, we aim to characterize the role and molecular functions of CDH23 in AML.


We downloaded the transcriptomic profiles and clinical data from the Cancer Genome Atlas and Beat AML trial. The expression level of CDH23 was assessed using Gene Expression Profiling Interactive Analysis (GEPIA). Kaplan-Meier survival analysis was used to assess prognostic value of CDH23. Correlation and biological function analyses were performed using LinkedOmics and GeneMANIA. Relationship of CDH23 with immune infiltration level was determined using Tumor Immune Estimation Resource (TIMER).


We found that the CDH23 expression was aberrantly upregulated in patients with AML and could be used as an independent risk factor of overall survival using Cox multivariate analysis. Notably, we observed a negative correlation between CDH23 expression and immune cell infiltration abundance by calculating the immune and stromal scores. In addition, functional enrichment analysis established that CDH23 plays a crucial role in tumor immunity.


Our findings indicate that upregulated CDH23 expression corresponds to decreased overall survival of patients with AML. CDH23 may be involved in mediating tumor immune environment, and this highlights the potential of CDH23 as a therapeutic target in AML.

Peer Review reports


Acute myeloid leukemia (AML) is an aggressive clonal hematopoietic malignancy characterized by an accumulation of immature progenitor cells of the myeloid lineage in the bone marrow that leads to inhibition of normal hematopoietic cell proliferation [1,2,3]. Most AML patients can respond to standard chemotherapy. However, many patients who present drug resistance or have short remission durations have median overall survival (OS) of 3-7 months [4, 5]. In addition, the prognosis of older patients with AML is still dismal despite recent improvements in therapeutic and diagnostic strategies [6].

The cadherin superfamily plays important roles in a plethora of biological processes and diseases, including signal transduction, self-recognition, and tumor suppression, by regulating intercellular adhesion and cell-cell recognition in a Ca2+-dependent manner [7,8,9,10,11]. Cadherin-23 (CDH23) is defined as an atypical cadherin and dysregulation of CDH23 expression remarkably disrupts homotypic and heterotypic adhesions and increases the metastatic potential of various tumors [12,13,14,15,16,17]. Furthermore, the downregulation of CDH23 expression was significantly association with poorer outcome in patients with diffuse large B cell lymphoma [18]. However, the role of CDH23 in AML remains unknown.

In this study, we investigated the expression profile and biological functions of CDH23 in AML and further analyzed the association between CDH23 expression and the AML tumor immune microenvironment using systematic bioinformatics analysis.


Data collection and processing

Data of 145 bone marrow tissue samples from patients with AML were obtained from the Cancer Genome Atlas (TCGA) (, currently the largest cancer multi-omics information database [19]. Patients lacking survival time and/or status were excluded from the analysis. In TCGA, cytogenetic risks were categorized as poor, intermediate, and favorable risk according to National Comprehensive Cancer Network guideline.

Dato of 139 de novo AML patients were included from Beat AML trial and used as a validation cohort with complete clinical information. The aim of the Beat AML trial is to publicly provide cytogenetic and mutational data before and after treatment [20].


Gene Expression Profiling Interactive Analysis (GEPIA) ( is an analysis tool that contains RNA sequencing expression data of 9,736 tumors and 8,587 normal samples from TCGA and the GTEx projects [21]. In this study, we utilized the “Single Gene Analysis” module to perform a differential mRNA expression and prognostic analyses of CDH23 expression in AML patients and healthy donors. The P value cutoff was set at 0.05. Prognostic analysis was performed using a Kaplan-Meier curve.


LinkedOmics ( is a publicly accessible portal that includes multi-omics data from all 32 TCGA cancer types. The web application has three analytical modules: LinkFinder, LinkInterpreter, and LinkCompare. We used the “LinkFinder” module to investigate the transcriptional factor target enrichment of CDH23. The analysis results can be visualized using scatter, box, or Kaplan-Meier plots. To obtain biological insights from the association results, the LinkInterpreter module performs gene set enrichment analysis based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and other functional categories [22]. The enriched pathways were finally visualized using the dotplot and emapplot functions.


Tumor Immune Estimation Resource (TIMER) ( is a comprehensive resource that provides systematic analysis of immune infiltration [23]. In our study, we used the “Estimation” module to evaluate the correlation between CDH23 expression level and immune infiltration in the AML dataset from TCGA using several computational algorithms. Immune cell scores were described by the immune and stromal scores. Different immune cell types were investigated, including macrophages, neutrophils, and B, natural killer, CD8+T, CD4+T, and dendritic cells. The association of gene expression level with immune cell scores was considered significant when P<0.05.


A protein-protein interaction (PPI) network was constructed using GeneMANIA (, a useful web resource that can explore the potential functions of selected genes and construct a PPI network. Association data including protein and genetic interactions, pathways, co-expression, co-localization, and protein domain similarity can be investigated by using GeneMANIA [24].

Statistical analyses

Statistical analyses were conducted using R language (version 4.0.1). Differences between two groups were evaluated using the Mann-Whitney U-test. OS analysis was performed using the Kaplan-Meier method and compared using log-rank tests. Univariate and multivariate cox analysis was conducted using logistic model. P<0.05 indicated statistically significant differences.


Association between clinical characteristics and CDH23 expression in AML patients

Data of 145 and 139 adult patients with AML from TCGA and Beat AML, respectively, were analyzed in present study. The clinicopathologic features of AML patients between high and low CDH23 expression groups divided by the median value are shown in Table 1. We observed that there were no significant differences between two groups in gender, leukocyte count, hemoglobin value, platelet count, blast percentage, and genetic mutations (FLT3, NPM1, DNMT3A, IDH1, IDH2, RUNX1, NRAS, CEBPA, TET2, ASXL1, TP53) or fusions (Additional file 1: Fig. S1). However, the age and cytogenetic risk group were correlated with the CDH23 expression value in TCGA cohort.

Table 1 Clinicopathologic characteristics of patients in two cohorts

Aberrant expression and prognostic value of CDH23 in AML patients

As shown in Fig. 1A, we utilized the GEPIA dataset to analyze CDH23 expression level in AML patients. CDH23 mRNA expression was significantly higher in AML tissues compared to that in the corresponding healthy bone marrow samples, as seen from TCGA and GTEx data. We then investigated whether CDH23 level was predictive of survival of patients with AML. In total, 145 patients from TCGA dataset were divided into high and low CDH23 expression groups by the median mRNA level. Kaplan-Meier survival analysis demonstrated that high CDH23 mRNA levels were associated with shorter OS of patients with AML (Hazard ratio=1.9, P=0.01, Fig. 1B). A total of 139 patients from the Beat AML dataset were stratified into the high and low risk groups according to the median value of CDH23 expression. And results were consistent with those of the training set, suggesting that upregulated CDH23 expression was as a risk prognostic factor and predicted poorer outcome in the validation cohort (HR=1.9, P=0.055, Fig. 1C).

Fig. 1
figure 1

The transcription level and prognostic feature of CDH23 in AML. A The expression status of CDH23 gene in AML patients and paired normal bone marrow samples was analyzed by GEPIA. B-C Kaplan-Meier curves of CDH23 in the TCGA-LAML cohort and Beat AML cohort, respectively

Univariate and multivariate Cox analyses of the two cohorts

The univariate and multivariate Cox analyses for CDH23 expression and other prognostic risk factors were studied in the two cohorts (Table 2). Univariate analysis showed that besides high CDH23 expression level, age and cytogenetic risk group greatly correlated with the OS of patients. Furthermore, when we subsequently adjusted these clinical factors in multivariate analysis, high CDH23 expression was still identified as an independent prognostic factor for the OS of AML patients.

Table 2 Univariable and multivariable Cox regression analysis of OS in two cohorts

Correlation gene expression analysis in AML patients

To illustrate the potential mechanisms and functions of CDH23 in AML, we used the LinkedOmics database for correlation analysis between CDH23 and various genes. The results in Fig. 2A-C are presented as heat and volcano maps of the top 50 genes that are either positively or negatively correlated with CDH23 expression. CDH23 was positively correlated with PPM1M, C10orf105, TFEB, FGD2, and IGF2R. By contrast, CDH23 demonstrated a significant negative correlation with CASP6, KDM5B, KCNQ5, and SCCPDH. We chose the most frequently altered neighboring genes, including ITGAM (Spearman’s correlation: 0.6822, P=1.765e-24), TFEB (Spearman’s correlation: 0.7165, P=0), PPM1M (Spearman’s correlation: 0.7524, P=4.274e-32), and SLC8A1 (Spearman’s correlation: 0.6635, P=0), to conduct a correlation analysis using LinkedOmics (Fig. 2D-G). As shown in Fig. 3, we further analyzed the prognostic value of these four genes in AML. The results demonstrated that overexpression of ITGAM was highly associated with poor prognosis. Similarly, high TFEB, PPM1M, and SLC8A1 mRNA expression corresponded to poor OS.

Fig. 2
figure 2

Correlated genes analysis of CDH23 in TCGA-LAML cohort (LinkedOmics). A The volcano plots show the correlated genes of CDH23. B-C The heatmaps demonstrate positively and negatively differential expression genes, respectively. D-G The scatter plots show Spearman-correlation of CDH23 expression with ITGAM, TFEB, PPM1M, SLC8A1

Fig. 3
figure 3

The prognostic value of genes significantly associated with CDH23 in TCGA-LAML (GEPIA). A The OS curves of high and low ITGAM expression in AML patients. B The OS curves of high and low TFEB expression in AML patients. C The OS curves of high and low PPM1M expression in AML patients. D The OS curves of high and low SLC8A1 expression in AML patients

Effect of CDH23 expression on immune cell infiltration and tumor microenvironment (TME) in AML patients

Tumor-infiltrating immune cells (TIICs) are a major component of the TME and are involved in the occurrence, progression, and metastasis of cancer. Tumor-infiltrating lymphocyte grade is a powerful independent predictor of sentinel lymph node status and clinical survival in some cancer types [25, 26]. To explore the relationship between CDH23 expression and immune cell infiltration, we embarked on a comprehensive analysis using the TIMER database with the sequencing data of AML patients from TCGA. The online database stratified patients with AML into high and low CDH23 expression groups by the median value. Subsequently, we used several computational algorithms to estimate the abundance of various TIICs (B cells, CD4+T cells, CD8+T cells, macrophages, monocytes, etc.) among the high and low CDH23 expression groups. As shown in Fig. 4A, compared to that in the patients with low CDH23 expression, the patients with high CDH23 expression had a higher proportion of monocytes, memory activated CD4+T cells, and regulatory T cells, whereas the percentage of naïve B cells, eosinophils, resting mast cells, plasma cells, memory resting CD4+T cells, and CD8+T cells was lower in patients with low CDH23 expression levels. Other immune cells were not significantly different between the two groups. This result indicated that high and low CDH23 expression patients had differences in immune cell infiltration in the TME, which has potential roles in tumor occurrence, progression, prognosis, and tumor sensitivity to immunotherapy of patients with AML. Additionally, we used the TIMER database to further investigate the role of CDH23 in the TME. Recently, the ESTIMATE algorithm has been widely utilized to assess immune and stromal scores, which provided in-depth insights into the TME of AML. The analysis revealed that average ESTIMATE, immune, and stromal scores (P<0.001) of the high CDH23 expression group were higher than those of the low CDH23 expression group, and there was a statistically significant positive correlation between CDH23 expression levels and the immune and stromal scores (Fig. 4B-D, P<0.001). Collectively, these results suggested that the expression level of CDH23 was highly correlated with tumor immune infiltration and the TME.

Fig. 4
figure 4

Correlation analysis between CDH23 expression in AML and tumor immune infiltration and tumor microenvironment (TIMER). A Different algorithms were conducted to investigate the potential correlation between the expression level of CDH23 and the various immune cell infiltration level in AML. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. B-D Correlation analysis between CDH23 expression in AML and immune score and stromal score

Functional enrichment analysis of CDH23 and related genes in AML patients

To further study the molecular functions and biological processes associated with CDH23, we employed the LinkFinder module of the LinkedOmics database to analyze the mRNA sequencing data of patients with AML in TCGA. The GO enrichment analysis data indicated that the expression of CDH23 and most of the related genes were correlated to pathways or biological processes of immune response and cytokine production such as neutrophil activation involved in immune response, neutrophil degranulation, positive regulation of cytokine production, phagocytosis, cellular response to biotic stimulus, cellular response to molecules of bacterial origin, cellular response to lipopolysaccharide, and so on (Fig. 5A and B). Moreover, the significant transcription factor targets of CDH23 included PEA3, ELF1, IRF, PU.1, and so on (Fig. 5C). Furthermore, the main pathways related to the functions of CDH23 expression level in AML were illustrated through KEGG analysis and included osteoclast differentiation, phagosome, lysosome, chemokine signaling pathway, endocytosis, NOD-like receptor signaling pathway, regulation of actin cytoskeleton and others (Fig. 5D). In conclusion, CDH23 had an extensive influence on the regulation of several pathways and processes involved in tumor immunity.

Fig. 5
figure 5

Gene set enrichment analysis and transcription factor target of CDH23. A-B Based on the LinkedOmics dataset, we supplied the dotplot and emapplot to perform the GO pathway analysis. C-D We also analyzed the transcription factor target and KEGG pathways of CDH23 in AML

PPI network of CDH23 constructed using GeneMANIA

To further elucidate the role of CDH23 in tumorigenesis, we constructed an integrated PPI network using GeneMANIA to identify CDH23-binding proteins. As described in Fig. 6, the interaction network showed that CDH23 is highly linked with usher syndrome 1C , abl-interactor 1, NCK-associated protein 1 , cytoplasmic FMR1 interacting protein 1, protocadherin-related 15, and other vital proteins. The biological functions of these proteins were related to hearing loss, regulation of actin cytoskeleton, cell adhesion, cancer progression, and so on. Among the interacting proteins, we observed enrichment of biological processes associated with actin-based cell projection, extrinsic components of the plasma membrane, clusters of actin-based cell projections, and extrinsic components of membranes. These results strongly support the hypothesis that CDH23 is involved in the tumorigenesis and pathogenesis of AML.

Fig. 6
figure 6

PPI network analysis of CDH23. We used the GeneMANIA database to perform protein-protein interaction of CDH23. The colors of the line illustrate different type of their relationships. The distinct colors inside the gene dots indicate the biological function which these genes involved in, including actin-based cell projection, extrinsic component of plasma membrane, neuromuscular process, cluster of actin-based cell projections, retina homeostasis, stereocilium bundle and extrinsic component of membrane


Although the role of CDH23 in cancer progression and prognosis has been illustrated earlier [18, 27], systematic and comprehensive analysis of its role in AML using bioinformatics tools has not yet been conducted. We first explored the association between CDH23 mRNA expression level and prognosis of AML patients.

Here, we revealed that CDH23 expression was higher in AML than in normal counterpart samples. The results showed that higher mRNA expression of CDH23 corresponded to shorter OS and was determined as an independent predictor of AML patient OS. These findings raise the possibility that CDH23 may serve as a potential prognostic marker for AML.

Furthermore, using gene set enrichment analysis, we identified the most correlated genes, including ITGAM, TFEB, PPM1M, and SLC8A1. Notably, ITGAM, a CD11 antigen-like family member B (CD11b), encodes the integrin α M chain and plays key roles in regulating various immune cell adhesion and ingestion of coated particles. It also involved in activation, chemotaxis, and cytotoxicity of leukemia cells in the TME, and a number of studies have reported that the prognosis of AML patients with upregulated ITGAM expression was poor [28]. The transcription factor EB (TFEB) belongs to a member of the basic helix-loop-helix leucine zipper family of transcription activators and acts as the master modulator of lysosomal biogenesis [29]. TFEB was associated with tumorigenesis and its elevated expression could promote autophagy and lysosomal biogenesis activating related signaling pathways to control cell proliferation and tumor survival and progression [30]. Taken together, these results highlight the potential ability of these correlated genes to be used as novel prognostic biomarkers and therapeutic targets in the future.

We also investigated the potential correlation between CDH23 expression and tumor immune infiltration in AML. Previous studies have explored the correlations between immune infiltration with prognostic outcome in AML. The proportion of macrophages was reported to be related to the survival and chemotherapy resistance of patients with AML [31]. Regulatory T cells were also revealed to play an important role in occurrence, progression, and immune response of AML [32]. In the present study, we first demonstrated a significant correlation between CDH23 expression and the infiltration level of various immune cells in AML. Notably, high CDH23 expression corresponded with a significantly higher infiltration of monocytes. Accordingly, it has been reported that a higher frequency of monocytes in peripheral blood mononuclear cells is observed in immunotherapy responders compared to non-responders and allows for the prediction of responsiveness prior to the initiation of immunotherapy [33, 34]. These findings indicated that the differences of immune cell infiltration existed between the high and low CDH23 expression patients in the TME, which might provide a guidance for disease outcomes and tumor sensitivity to immunotherapy.

We further explored the functions of CDH23, and the results showed that CDH23 might regulate the pathways associated with osteoclast differentiation, endocytosis, clusters of actin-based cell projections, and so on. Previously, it has been revealed that osteoclasts and osteoblasts are involved in the formation of hematopoietic stem and progenitor cell microenvironment, which provides a support for the subtypes of cells that directly play roles in hematopoiesis and habitat for leukemic blasts [35]. In brief, these findings suggest that CDH23 may be involved in tumor processes including tumor invasion, phagocytosis, granulocyte activation, and neutrophil-mediated immunity.

Collectively, our bioinformatics analysis systematically disclosed statistical correlations of CDH23 expression with the prognosis and extent of immune cell infiltration of AML. We also comprehensively analyzed the functional pathways of CDH23, which provided bioinformatics and computational biology-based insights for further understanding of the role played by CDH23 in tumor processes.


In this study, we revealed that CDH23 was overexpressed in patients with AML. In addition, higher mRNA expression of CDH23 was found to be related to a reduced OS of AML patients. Additionally, the expression of CDH23 may mediate immune infiltration of tumor. In summary, we have determined that CDH23 might be a potential prognostic biomarker and a promising therapeutic target for AML.

Availability of data and materials

The datasets supporting the conclusions of this article are available in The Cancer Genome Atlas ( and the Beat AML trial ( NCT03013998).



Acute myeloid leukemia


Overall survival


Cadherin-related 23


Hazard ratio


Tumor microenvironment


The Cancer Genome Atlas


Gene Expression Profiling Interactive Analysis


Tumor Immune Estimation Resource


Gene Ontology


Kyoto Encyclopedia of Genes and Genomes


Protein-protein interaction


  1. van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S, et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176(6):1265-1281.e1224.

    Article  Google Scholar 

  2. Witkowski MT, Lasry A, Carroll WL, Aifantis I. Immune-based therapies in acute leukemia. Trends Cancer. 2019;5(10):604–18.

    Article  CAS  Google Scholar 

  3. Estey EH. Acute myeloid leukemia: 2019 update on risk-stratification and management. Am J Hematol. 2018;93(10):1267–91.

    Article  Google Scholar 

  4. DeAngelo DJ, Jonas BA, Liesveld JL, Bixby DL, Advani AS, Marlton P, et al. Phase 1/2 study of uproleselan added to chemotherapy in patients with relapsed or refractory acute myeloid leukemia. Blood. 2021;139(8):1135–46.

    Article  CAS  Google Scholar 

  5. Vadakekolathu J, Minden MD, Hood T, Church SE, Reeder S, Altmann H, et al. Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia. Sci Transl Med. 2020;12(546):eaaz0463.

    Article  CAS  Google Scholar 

  6. DiNardo CD, Jonas BA, Pullarkat V, Thirman MJ, Garcia JS, Wei AH, et al. Azacitidine and Venetoclax in previously untreated acute myeloid leukemia. N Engl J Med. 2020;383(7):617–29.

    Article  CAS  Google Scholar 

  7. van Roy F. Beyond E-cadherin: roles of other cadherin superfamily members in cancer. Nat Rev Cancer. 2014;14(2):121–34.

    Article  Google Scholar 

  8. Biswas KH. Molecular mobility-mediated regulation of E-cadherin adhesion. Trends Biochem Sci. 2020;45(2):163–73.

    Article  CAS  Google Scholar 

  9. Leckband D, Sivasankar S. Cadherin recognition and adhesion. Curr Opin Cell Biol. 2012;24(5):620–7.

    Article  CAS  Google Scholar 

  10. Cao ZQ, Wang Z, Leng P. Aberrant N-cadherin expression in cancer. Biomed Pharmacother. 2019;118:109320.

    Article  CAS  Google Scholar 

  11. Gul IS, Hulpiau P, Saeys Y, van Roy F. Evolution and diversity of cadherins and catenins. Exp Cell Res. 2017;358(1):3–9.

    Article  CAS  Google Scholar 

  12. Onder TT, Gupta PB, Mani SA, Yang J, Lander ES, Weinberg RA. Loss of E-cadherin promotes metastasis via multiple downstream transcriptional pathways. Cancer Res. 2008;68(10):3645–54.

    Article  CAS  Google Scholar 

  13. Vanniya SP, Srisailapathy CRS, KunkaMohanram R. The tip link protein Cadherin-23: from hearing loss to cancer. Pharmacol Res. 2018;130:25–35.

    Article  Google Scholar 

  14. Sannigrahi MK, Srinivas CS, Deokate N, Rakshit S. The strong propensity of Cadherin-23 for aggregation inhibits cell migration. Mol Oncol. 2019;13(5):1092–109.

    Article  CAS  Google Scholar 

  15. Gorski M, Tin A, Garnaas M, McMahon GM, Chu AY, Tayo BO, et al. Genome-wide association study of kidney function decline in individuals of European descent. Kidney Int. 2015;87(5):1017–29.

    Article  CAS  Google Scholar 

  16. Singaraju GS, Sagar A, Kumar A, Samuel JS, Hazra JP, Sannigrahi MK, et al. Structural basis of the strong cell-cell junction formed by cadherin-23. FEBS J. 2019;287(11):2328–47.

    Article  Google Scholar 

  17. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–8.

    Article  Google Scholar 

  18. Cao B, Guo X, Huang L, Wang B, Wang W, Han D, et al. Methylation silencing CDH23 is a poor prognostic marker in diffuse large B-cell lymphoma. Aging. 2021;13(13):17768–88.

    Article  CAS  Google Scholar 

  19. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1a):A68-77.

    Google Scholar 

  20. Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562(7728):526–31.

    Article  CAS  Google Scholar 

  21. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98-w102.

    Article  CAS  Google Scholar 

  22. Vasaikar SV, Straub P, Wang J, Zhang B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018;46(D1):D956-d963.

    Article  CAS  Google Scholar 

  23. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509-w514.

    Article  CAS  Google Scholar 

  24. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38(Web Server issue):W214-220.

    Article  CAS  Google Scholar 

  25. Fridman WH, Galon J, Dieu-Nosjean MC, Cremer I, Fisson S, Damotte D, et al. Immune infiltration in human cancer: prognostic significance and disease control. Curr Top Microbiol Immunol. 2011;344:1–24.

    CAS  PubMed  Google Scholar 

  26. Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, et al. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012;30(21):2678–83.

    Article  Google Scholar 

  27. Zhang Q, Peng C, Song J, Zhang Y, Chen J, Song Z, et al. Germline mutations in CDH23, encoding cadherin-related 23, are associated with both familial and sporadic pituitary adenomas. Am J Hum Genet. 2017;100(5):817–23.

    Article  CAS  Google Scholar 

  28. Wang J, Hao JP, Uddin MN, Wu Y, Chen R, Li DF, et al. Identification and validation of inferior prognostic genes associated with immune signatures and chemotherapy outcome in acute myeloid leukemia. Aging. 2021;13(12):16445–70.

    Article  CAS  Google Scholar 

  29. Napolitano G, Esposito A, Choi H, Matarese M, Benedetti V, Di Malta C, et al. mTOR-dependent phosphorylation controls TFEB nuclear export. Nat Commun. 2018;9(1):3312.

    Article  Google Scholar 

  30. Paquette M, El-Houjeiri L, L CZ, Puustinen P, Blanchette P, Jeong H, et al. AMPK-dependent phosphorylation is required for transcriptional activation of TFEB and TFE3. Autophagy. 2021,

  31. Guo R, Lü M, Cao F, Wu G, Gao F, Pang H, et al. Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment. Biomark Res. 2021;9(1):15.

    Article  Google Scholar 

  32. Dong Q, Li G, Fozza C, Wang S, Yang S, Sang Y, et al. Levels and clinical significance of regulatory B cells and T cells in acute myeloid leukemia. Biomed Res Int. 2020;2020:7023168.

    PubMed  PubMed Central  Google Scholar 

  33. Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM, et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med. 2018;24(2):144–53.

    Article  CAS  Google Scholar 

  34. Park JA, Wang L, Cheung NV. Modulating tumor infiltrating myeloid cells to enhance bispecific antibody-driven T cell infiltration and anti-tumor response. J Hematol Oncol. 2021;14(1):142.

    Article  CAS  Google Scholar 

  35. Shafat MS, Gnaneswaran B, Bowles KM, Rushworth SA. The bone marrow microenvironment - home of the leukemic blasts. Blood Rev. 2017;31(5):277–86.

    Article  Google Scholar 

Download references


Not applicable.


This work was supported by grants from the Distinguished Taishan Scholars in Climbing Plan (tspd20210321), the National Natural Science Foundation of China (81770159, 82070160, 82170182), the Major Research plan of the National Natural Science Foundation of China (91942306), the key Program of Natural Science Foundation of Shandong Province (ZR2020KH016), the Clinical Practical new Technology and Development Fund of Qilu Hospital, Shandong University (2019-5), the horizontal subject of Shandong University (6010120072), and Shandong Provincial Natural Science Foundation (ZR2021MH302).

Author information

Authors and Affiliations



FL and JY designed the experiments. GXM and JY extracted the data and performed the statistical analysis. YHP, YNZ, and JJY contributed to the study design. JY wrote the manuscript. TS, DXM, and CYJ revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chunyan Ji.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of the Qilu Hospital of Shandong University. Authors confirm that the experiments involving human data were conducted in accordance with the Declaration of Helsinki. All datasets were retrieved from the online databases, and it was confirmed that informed written consent had already been obtained.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Fig. S1. The correlation of CDH23 and blasts percentage. A The different proportion of blast cells between high and low CDH23 mRNA level in TCGA cohort (P=0.291). B The spearman correlation analysis between the CDH23 expression level and blast percentage in TCGA cohort (Spearman’s correlation: -0.078, P=0.35).

Rights and permissions

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, J., Lu, F., Ma, G. et al. Role of CDH23 as a prognostic biomarker and its relationship with immune infiltration in acute myeloid leukemia. BMC Cancer 22, 568 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: