Correlation analysis of bone marrow microvessel density and miRNA expression on drug resistance in patients with chronic myelogenous leukemia after tyrosine kinase inhibitor treatment

ABSTRACT

Objective:

This study analyzed the relationship between bone marrow microvessel density (MVD) and the expression of four miRNAs with chronic myelogenous leukemia (CML) resistance after tyrosine kinase inhibitor (TKI) treatment.

Methods:

234 CML patients were divided into resistance and non-resistance groups in terms of the results of the 5-year follow-up. Patients were divided into the Optimum response group and the Warning/Failure group based on TKI response. MVD was determined by immunohistochemistry, and the expression levels of four miRNAs (miR-106a, miR-155, miR-146a, and miR-340) in bone marrow biopsy specimens were examined by qPCR. We evaluated the association of MVD with four miRNAs and them predictive value for CML resistance after TKI treatment.

Results:

The MVD and the levels of miR-106a, miR-155, and miR-146a were significantly higher while the miR-340 level was lower in the resistance group than the non-resistance group. Besides, MVD had a significant correlation with the levels of miR-340 and miR-155. According to the results of survival analysis, MVD as well as miR-340 and miR-155 levels were observably correlated with 5-year survival of patients without TKI resistance. The results of the ROC curve indicated that the MVD, miR-106a, miR-340, and miR-155 had good predictive accuracy for CML resistance after TKI treatment. As for the results of multivariate analysis, disease stage, risk level (high risk), high MVD, low miR-340 expression, and high miR-155 expression were all independent risk factors for CML resistance.

Conclusion:

MVD and the expression of miR-340 and miR-155 are closely associated with CML resistance after TKI treatment.

KEYWORDS:

• Bone marrow microvessel density (MVD)
• miRNA
• tyrosine kinase inhibitor (TKI)
• chronic myelogenous leukemia (CML)
• drug resistance

Introduction

Chronic myelogenous leukemia (CML), a malignancy caused by clonal proliferation of bone marrow hematopoietic stem cells, is characterized by leukocytosis and aggregation of granulocytes and their progenitors. CML affects 1–2 in every 100,000 adults and is becoming more common over time [1]. It accounts for approximately 15% of newly diagnosed leukemia cases in adults [2]. In 2022, approximately 1022 patients will die from CML in the United States [3]. Imatinib, bosutinib, nilotinib, and dasatinib are the four tyrosine kinase inhibitors (TKIs) that are currently accepted as first-line treatments for CML both domestically and abroad [4]. If a CML patient has developed resistance to TKIs and experienced failed treatment or the patient is in an advanced state, it is challenging to treat the disease. Therefore, the search for biomarkers of prognosis in CML patients is of great importance.

Angiogenesis is associated with tumor growth, invasion, and metastasis [5]. Microvessel density (MVD) measurement is a quantitative method to assess angiogenesis [6]. CML patients exhibit a significant increase in MVD compared to those with normal bone marrow [7]. The best therapeutic response to TKI therapy in CML patients was investigated by Ćojbašić et al. [8]. In their exploration, compared with controls, CML patients had significantly higher levels of angiogenesis parameters. Furthermore, low MVD, minor axis, area, circularity, and roundness and higher aspect ratio are positive prognostic factors for achievement of complete cytogenetic response (CCgR), while only low MVD for achievement of major molecular response (MMoR). However, there has not yet been any evidence of a connection between MVD and CML resistance.

MicroRNA (miRNA) is a small non-coding RNA that plays a role in the post-transcriptional regulation of gene expression [9]. It can promote and regulate cell signaling, apoptosis, and cell development and proliferation [10]. MiRNAs have great potential as therapeutic targets and prognostic markers in different types of cancer [11]. Their roles in a variety of malignancies have also been widely documented [12–14]. These RNAs are crucial in development and regulation of T cells as well as immune reconstitution after TKI treatment [15]. Studies have shown that the development of TKIs greatly improves long-term survival in patients with CML, but amplification or mutation of the BCR-ABL1 oncogene and changes in the ATP-binding cassette (ABC) transporter can lead to drug intolerance or resistance in nearly a quarter of patients and the withdrawal of TKI therapy. Furthermore, miRNAs have also been shown to play a key role in different stages of CML [9,16]. To be more precise, miR-495-3p overexpression inhibits leukemic cell growth and TKI resistance in Imatinib-resistant T315I-mutant cells, as well as drug efflux activity through regulation of multidrug resistance 1 (MDR1) gene [17]. MiR-146a has been found to be abnormally expressed in both adults and children with acute leukemia, and circulating miR-146a level is much lower in CML patients than in healthy individuals. However, the circulating miR-146a level displays a significant rise after 14 days of imatinib treatment and a negative correlation with BCR-ABL expression level at 90 days. It is logical to speculate that circulating miR-146a has a prognostic role in the prediction of early response to imatinib treatment in CML patients [14]. According to the study of Chen et al., human umbilical cord mesenchymal stem cell (hucMSC) exosomes, via miR-146a-5p, promoted imatinib-induced apoptosis by suppressing glutaminase-1 (GLS1) ubiquitination to increase GLS protein while weakening ubiquitin-specific protease 6 (USP6) regulation of CML cells resistant to imatinib [1]. MiR-155 is highly expressed in CD34⁺ CML stem cells and promotes their proliferation, which is expected to be a therapeutic target for CML [18] Overexpression of miR-146a inhibits the NF-κB pathway, which leads to apoptosis in CML [19]. Therefore, miR-146a has been proved to be a predictor in CML [14]. MiR-106a, a target of lncRNA-BGL3, is closely related to the expression of Bcr-Abl, a regulatory gene of CML [20]. And miR-155 was found to be associated with tumor angiogenesis [21]. Collectively, the four miRNAs may possess potential clinical value as prognostic biomarkers and therapeutic targets in the field of hematologic oncology.

In this study, patients with CML who received TKI treatment were selected as the study subjects [22], and the association of CML resistance with MVD and the four miRNAs was analyzed.

Materials and methods

Study subjects and data collection

Totally 234 patients with CML who received TKI treatment in the Department of Hematology of Taihe Hospital, Hubei University of Medicine from December 2022 to January 2023 participated in this study. They were divided into resistance group (n = 59) and non-resistance group (n = 175) according to the results of the 5-year follow-up. Inclusion criteria: patients (i) diagnosed as CML; (ii) TKI treatment: Patients over 60 years of age were given imatinib (400 mg/time, orally, once a day); Patients younger than 60 years of age were given one of three drugs: Flumatinib (600 mg/time orally once a day), dasatinib (100 mg/time orally once a day), and nilotinib (300 mg/time orally twice a day). Exclusion criteria: patients (i) combined with malignancy; (ii) combined with dysfunction of important organs like heart, liver, and kidney; (iii) with incomplete clinical history and follow-up data [23]. This study was approved by the Ethics Committee of Taihe Hospital, Hubei University of Medicine (KY2308).

Basic information of patients was collected, including gender, age, white blood cells (WBC, ×10⁹/L), platelets (PLT, ×10⁹/L), histopathological type (central nervous system, lymph node, skin, etc.), (pre-TKI treatment) disease stage (chronic, accelerated, acute), (pre-TKI treatment) risk level (low, intermediate, high), EUTOS long-term survival (ELTS) score (low risk, medium risk, high risk), therapeutic drugs (imatinib, nilotinib, dasatinib), from diagnosis to TKI reception.

Microvessel count

Bone marrow biopsy specimens were formalin-fixed, decalcified with EDTA/HCL and paraffin-embedded. Then they were sectioned at a thickness of 4 μm per section for microvessel counting. Endothelial cells were labeled using polyclonal rabbit anti-human von Willebrand Factor (vWF) antibody (Beijing Zhongshan Biotechnology Co., Ltd.) [6]. The anti-rabbit IgG-HRP multimer (Beijing Zhongshan Biotechnology Co., Ltd.) was used as the secondary antibody. The obtained sections were subjected to conventional color development, hematoxylin re-staining, dehydration, transparency, and sealing.

The images were first analyzed at low magnification (×40) to identify the areas with the greatest number of blood vessels, the so-called ‘hot spots’. These hot spots were then observed at a high magnification (×400). The microvessels in the three hot spots were counted, and the results were averaged to express MVD. MVD is the total number of microvessels (×400) per 0.5 mm² [24].

qPCR assay

The total RNA of bone marrow biopsy specimens of patients with CML was extracted using RNA extraction kits. cDNA was synthesized from the obtained RNA with a cDNA kit and then received PCR amplification with SYBR Mix. The primer sequences are shown in Table 1, and the 2^−ΔΔCt method was used to calculate the relative expression of miR-106a, miR-340, miR-155, and miR-146a.

Table 1. qRT-PCR primer sequences.

Genes	Primer sequences
miR-106a:	F 5′AAAAGTGCTTACAGTGCAGGTAG-3′ R 5′-ACCTGCACTGCAAGCACTTTTTT-3′
miR-340:	F 5′-GCGAGCTACATTGTCTGCTGGGTT-3′ R 5′-GCGAGCTACATTGTCTGCTGGGTT-3′
miR-155:	F 5′-CTGCAAGGAT TCAGCATTA-3′ R 5′-CTGCAAGGCGTC AATATTA-3′
miR-146a:	F 5′-CGACACCGTGTCTTGTCTCG-3′ R 5′-CTCTCGGATATGTTCTACGC-3′

Statistical analysis

SPSS software was used for data analysis. The measurement data conforming to normal distribution were expressed as (mean ± standard deviation), and an independent sample t-test was used for comparison between two groups. Pearson correlation coefficient was applied to analyze the correlation between MVD and miRNA levels. The Kaplan-Meier method was employed for survival analysis. Multivariate logistic regression was used for the analysis of factors associated with CML resistance and non-resistance after TKI treatment. Time-dependent Receiver Operator Characteristic (ROC) curves were plotted to analyze the accuracy of MVD, miR-106a, miR-340, miR-155, miR-146a levels in predicting disease resistance. P < 0.05 indicates significantly different.

Results

Baseline characteristics of patients in the resistance group and the non-resistance group

The basic information of patients in the resistance group and the non-resistance group in this study is summarized in Table 2. The resistance group consisted of 16 men and 43 women, with a mean age of 57.54 ± 8.57 years. There were 10 cases in the chronic stage, 19 in the accelerated stage, and 30 in the acute stage in the resistance group. In regard to the risk level of patients in the resistance group, 9 cases were at low risk, 18 at intermediate risk, and 32 at high risk. As for the non-resistance group, there were 62 men and 113 women, with a mean age of 57.88 ± 8.57 years; 82 cases in the chronic stage, 55 cases in the accelerated stage, and 38 cases in the acute stage; 77 cases at low risk, 62 at intermediate risk, and 36 at high risk. There were no statistically significant differences in age, gender, WBC, PLT, ELTS score and therapeutic drugs between the resistance and non-resistance groups (P > 0.05). However, the differences between the two groups were statistically significant in terms of disease stage and risk level (P < 0.001).

Table 2. Baseline characteristics.

	Non-resistance group (n = 175)	Resistance group (n = 59)	χ^2/t	P
Age (years)	57.88 ± 8.57	57.54 ± 8.57	0.262	0.794
Gender			1.371	0.242
Men	62(35.4)	16(27.1)
Women	113(64.6)	43(72.9)
WBC (×10^9/L)	76.40 ± 49.53	118.30 ± 305.21	−1.05	0.298
PLT (×10^9/L)	403.90 ± 139.93	404.41 ± 158.19	−0.023	0.981
Disease stage			22.934	< 0.001
Chronic	82(46.9)	10(16.9)
accelerated	55(31.4)	19(32.2)
Acute	38(21.7)	30(50.8)
Risk level			27.442	<0.001
Low	77(44)	9(15.3)
intermediate	62(35.4)	18(30.5)
High	36(20.6)	32(54.2)
ELTS Score			1.812	0.404
Low	59(33.7)	20(33.9)
intermediate	65(37.1)	17(28.8)
High	51(29.1)	22(37.3)
Therapeutic drugs
Imatinib	72(41.1)	23(39.0)	0.085	0.770
Nilotinib	88(50.3)	32(54.2)	0.276	0.599
Dasatinib	112(64.0)	43(72.9)	1.556	0.212

Note: Values are mean ± SD or number (%). WBC, white blood cells; PLT, platelets; ELTS score, EUTOS long-term survival score.

Patients with CML respond to TKI drug therapy

Subsequently, the therapeutic response to TKI was evaluated. There were 172 cases in the Optimum response and 62 cases in the Warning/Failure group. And the results were shown in Table 3. There were no significant differences in age, gender, PLT (×10⁹/L), risk level (%), ELTS score and therapeutic drugs between CML patients in the Optimum response group and the Warning/Failure group (P > 0.05). There were significant differences in WBC (×10⁹/L) and disease stage between the two groups (P < 0.05).

Table 3. Comparison of clinicopathologic features between groups.

	Optimum response group (n = 172)	Warning/Failure group (n = 62)	χ^2/t	P
Age (years)	58.27 ± 8.25	56.48 ± 9.30	1.411	0.16
Gender			0.702	0.402
Men	60 (34.9)	18 (29.0)
Women	112 (65.1)	44 (71.0)
WBC (×10^9/L)	72.86 ± 34.94	126.09 ± 302.16	−2.277	0.024
PLT (×10^9/L)	405.76 ± 140.07	399.23 ± 156.89	0.305	0.761
Disease stage			19.208	<0.001
Chronic	78 (45.3)	14 (22.6)
accelerated	57 (33.1)	17 (27.4)
Acute	37 (21.5)	31 (50)
Risk level			1.623	0.444
Low	77 (44.8)	22 (35.5)
intermediate	58 (33.7)	24 (38.7)
High	37 (21.5)	16 (25.8)
ELTS Score (%)
Low	57 (33.1)	22 (35.5)
intermediate	66 (38.4)	16 (25.8)
High	49 (28.5)	24 (38.7)
Therapeutic drugs (%)
Imatinib	69 (40.1)	26 (41.9)	0.063	0.803
Nilotinib	85 (49.4)	35 (56.5)	0.902	0.342
Dasatinib	110 (64)	45 (72.6)	1.517	0.218

Values are mean ± SD or number (%). WBC, white blood cells; PLT, platelets; ELTS score, EUTOS long-term survival score.

Differences in microvessel density and levels of 4 miRNAs between the resistance group and non-resistance group

As shown in Figure 1A–E, MVD, miR-106a, miR-155 and miR-146a levels were significantly higher in the resistance group than those in the non-resistance group, while miR-340 level was much lower. And the difference was statistically significant (P < 0.05). According to Figure 2A–D, the MVD in bone marrow biopsy specimens were correlated with miR-155 (Figure 2B) and miR-340 (Figure 2D) levels (P < 0.05), but not correlated with miR-106a (Figure 2A) and miR-146a (Figure 2C) levels (P > 0.05). In Figure 3 and Table 4, the results of ROC curve analysis showed that MVD, miR-155 and miR-340 levels had good accuracy in predicting CML resistance after allo-HSCT, with optimal cut-off values of 32.5, 1.252 and 0.823, respectively. The area under the curve (AUC) of miR-106a and miR-146a were 0.594 and 0.574, suggesting that they were not associated with prognosis of CML resistance.

Figure 1. Differences in MVD and miRNA levels between the resistance group and the non-resistance group. The differences in MVD (A), miR-106a level (B), miR-155 level (C), miR-146a level (D), miR-340 level (E) between the resistance group and the non-resistance group. *P < 0.05; **P < 0.01.

Figure 2. Correlation analysis of MVD with the levels of the 4 miRNAs. A: Correlation analysis of MVD with miR-106a. B: Correlation analysis of MVD with miR-155. C: Correlation analysis of MVD with miR-146a. D: Correlation analysis of MVD with miR-340.

Figure 3. ROC curves of MVD, miR-106a, miR-340, miR-155, and miR-146a for predicting CML resistance after TKI treatment.

Table 4. Value of MVD and miRNA expression in predicting CML resistance after TKI treatment.

	AUC	95% CI	P	Sensitivity (%)	Specificity (%)	Cutoff value
MVD	0.757	0.682–0.832	<0.001	61.0	81.1	32.5
miR-106a	0.594	0.515–0.674	0.030	66.1	51.4	1.124
miR-155	0.849	0.797–0.901	<0.001	67.8	85.7	1.252
miR-146a	0.574	0.489–0.659	0.090	37.3	76.6	1.155
miR-340	0.886	0.834–0.937	<0.001	81.4	84.6	0.823

Note: MVD, microvessel density; ROC, Receiver Operator Characteristic; CML, chronic myelogenous leukemia; TKI: tyrosine kinase inhibitor; AUC, area under the curve; CI, confidence interval.

Survival analysis of patients without resistance to chronic myelogenous leukemia with different microvessel density and miRNA expression levels

Kaplan-Meier survival curves were established to analyze the correlation between relapse-free prognosis after 5 years of follow-up and MVD, miR-106a, miR-340, miR-155 and miR-146a expression levels. The results are shown in Figure 4. The 5-year resistance-free survival rate was higher in patients with low MVD (48.7%) than in patients with high MVD (10.4%) (P < 0.001) (Figure 4A); the 5-year resistance-free survival rate of patients was not statistically different between the miR-106a low expression group (21.6%) and the miR-106a high expression group (32.7%) (P = 0.145) (Figure 4B); the 5-year resistance-free survival rate of patients in the miR-340 low expression group (5.6%) was lower than that in the miR-340 high expression group (65.1%) (P < 0.001) (Figure 4C); as for miR-155, the low expression group (74.3%) presented a much higher 5-year resistance-free survival rate than the high group (9.3%) (P < 0.001) (Figure 4D).

Figure 4. Kaplan-Meier curves. Survival analysis curves for MVD (A), miR-106a (B), miR-340 (C), and miR-155 (D). MVD, microvessel density.

Multivariate logistic regression analysis for prognosis of patients with chronic myelogenous leukemia

As shown in Table 5, the results of multivariate logistic regression analysis showed that disease stage, risk level (high risk), high MVD, low miR-340 expression and high miR-155 expression were all independent risk factors affecting CML resistance after TKI treatment (P < 0.05). Notably, WBC, miR-106a and miR-146a expression levels did not correlate with prognostic CML resistance (P > 0.05).

Table 5. Multivariate logistic regression analysis of prognosis in CML patients.

	B	S.E.	Wald	P	OR	95% CI
						Lower limit	Upper limit
WBC	0.006	0.004	1.830	0.176	1.006	0.997	1.014
Disease stage	1.024	0.363	7.955	0.005	2.784	1.367	5.67
Risk level	0.913	0.349	6.821	0.009	2.491	1.256	4.94
MVD	1.764	0.577	9.359	0.002	5.835	1.885	18.065
MiR-340	−3.158	0.575	30.214	0.000	0.043	0.014	0.131
MiR-155	2.049	0.549	13.93	0.000	7.763	2.646	22.772
MiR-106a	0.897	0.579	2.4	0.121	2.453	0.788	7.631
MiR-146a	0.985	0.59	2.784	0.095	2.678	0.842	8.516

Note: B, regression coefficient; S.E., standard error; Wald, chi-square value; OR, odds ratio; CI, confidence interval; MVD, microvessel density.

Discussion

The incidence of CML, the most common type of myeloproliterative neoplasm, keeps growing in the world [1]. CML patients will develop resistance after TKI therapy such as imatinib mesylate. Imatinib resistance has been reported to be brought on by mutations in the BCR-ABL kinase domain (KD), but no mutations in BCR-ABL-KD are detected in many imatinib-resistant patients [25]. MiRNAs are short non-coding RNAs involved in controlling gene expression [25]. In order to investigate the role of miRNAs in TKI resistance in CML patients, we discussed the correlation of miR-106a, miR-340, miR-155 and miR-146a with TKI resistance in CML patients as well as their prediction in CML resistance. We finally revealed the predictive value of MVD, miR-155 and miR340 for CML resistance after TKI treatment.

In this study, 234 patients with CML who received TKI treatment were statistically analyzed, of which 59 cases were in the resistance group and 175 ones in the non-resistance group. There were substantial differences between the two groups in regards disease stage and risk level. The prognosis of patients with different disease stages is different. The therapeutic response to TKI was evaluated, there were 172 cases in the Optimum response and 62 cases in the Warning/Failure group. There were no significant differences in age, gender, PLT, risk level, ELTS score and therapeutic drugs between CML patients in the Optimum response group and the Warning/Failure group. There were significant differences in WBC and disease stage between the Optimum response and the Warning/Failure group. For example, TKIs are safe and well-tolerated for patients with advanced CML before and after allogeneic hematopoietic stem cell transplantation (HSCT), and they may improve the prognosis of this aggressive disease [26]. Although most CML patients have normal quality of life and survival time after TKI treatment, some patients progress to the accelerated phase (AP) and blast phase (BP) with poor outcomes [27]. Although the prognosis of patients present with de novo CML-AP is almost similar to that of CML-CP patients, patients who have developed from CML-CP and experienced relevant treatment should receive second- or third-generation TKIs and strongly consider allogeneic HSCT [27]. Similarly, patients transformed from CML-BP have a particularly poor prognosis, with median survival typically less than one year; some of them can obtain long-term survival if the combination of potent TKIs (such as ponatinib) and allogeneic HSCT is applied [27]. These discoveries of new drugs and new combination therapies are collected as an Arsenal for the treatment of advanced CML. The results of this study suggest that patients receiving TKI treatment in the chronic stage are less likely to experience CML resistance. The likelihood of CML resistance will therefore be decreased in individuals with chronic CML who are low risk. Angiogenesis plays a key role in hematologic malignancies. MVD is considered an indicator of malignant potential of the tumors and possesses the ability to predict tumor resistance [28]. For instance, MVD was useful in predicting drug resistance in patients with squamous cell cervical cancer stage II who were younger than 40 years old, and a higher MVD is associated with disease resistance [29]. The relationship between MVD and CML resistance hasn't been examined in any studies, though. Our study identified a significant difference in MVD between the resistance and non-resistance groups, and high MVD was strongly connected to postoperative CML resistance. The AUC of MVD for prediction of CML resistance was 0.757, indicating that MVD has some predictive value for prognostic CML resistance.

Research on the relationship between miRNAs and CML resistance is lacking. In this case, we conducted such research and discovered that there was a significant difference in the expression levels of miR-106a, miR-340, miR-155, and miR-146a between the resistance and non-resistance groups. The correlation analysis showed that MVD was positively correlated with miR-155 level, negatively correlated with miR-340 level, and not correlated with the other two miRNAs. For the ROC curve, miR-340 and miR-155 had AUCs of 0.886 and 0.849, respectively. More importantly, the AUC of miR-340 is closer to 1, suggesting its better predictive accuracy.

MiR-340 is aberrantly expressed in a variety of human tumors, such as breast cancer [30], colorectal cancer [31], and gastric cancer [32]. It can also serve as a potential therapeutic target for gliomas [33]. In non-small cell lung cancer tissues, MiR-340 is markedly underexpressed, and this underexpression is strongly correlated with a poor prognosis [34]. Specifically, a survival analysis of 136 patients with non-small cell lung cancer showed that low miR-340 expression was one of the contributors to poor 5-year overall survival [34]. In recent studies, knockdown of Circ-CHI3L1.2 has been discovered to modulate cisplatin resistance of osteosarcoma cells via the miR-340-5p-LPAATβ axis [35]; SOX2 mediates cisplatin resistance in small cell lung cancer by down-regulating the expression of hsa-miR-340-5p [36]. To date, no studies have been done on the correlation between miR-340 and CML resistance. Our study disclosed that patients with low miR-340 expression were more likely to have CML resistance after TKI treatment than those with high miR-340 expression, which may be attributed to the antitumor efficacy of miR-340.

MiR-155 is implicated in the regulation of key genes related to myelopoiesis [37] and is up-regulated in several lymphomas. In the study of Rokah et al. [38], miR-155 was found to be abnormally down-regulated in CML patients compared to healthy controls. Patients who have recently been diagnosed with CML have lower levels of miR-155 expression in their peripheral blood cells [39]. It follows that low miR-155 expression may be linked to the development of CML. However, in our findings, high miR-155 expression was a risk factor for CML resistance. This opposite result may be related to the disease process. The role of miR-155 in CML still needs further validation. Overall, it also implies that miRNAs have a variety of roles in CML. In addition, our study uncovered that MVD, miR-340 and miR-155 levels were strongly correlated with the 5-year resistance-free survival of patients. Concretely speaking, low miR-340 expression, high miR-155 expression and high MVD may lead to CML resistance after TKI treatment, which finally lowered the survival rates of patients.

Although this study demonstrated the predictive value of high MVD, low miR-340 expression and high miR-155 expression in CML resistance after TKI treatment, it still has certain limitations due to its small sample size and single center. Large sample sizes may be required for future analysis. Additionally, the specific mechanisms of action of MVD, miR-340 and miR-155, which may serve as therapeutic targets for CML, need to be further validated in animal models.

Conclusion

MVD, miR-340 and miR-155 are independent risk factors affecting CML resistance after TKI treatment, which have predictive value for postoperative CML resistance. Patients with high MVD, low miR-340 expression and high miR-155 expression are more likely to experience CML resistance after TKI treatment. In order to optimize TKI treatment outcomes, it is necessary to identify patients with the above conditions earlier before disease progression.

Authors’ contributions

Yi-gang Guo and Xue-lian Feng designed the study. Lu-lu Zhang, Ping Hu, Zhang-zhi Li and Rui-bo Zhang collated the data, carried out data analyses and produced the initial draft of the manuscript. Xi Lv and Qiong Yi contributed to drafting the manuscript. All authors have read and approved the final submitted manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this study are included within the article.

Additional information

Funding

This study is supported by 2022 Academy-level scientific research projects of Taihe Hospital (project number: 2022JJXM066).