FHL1 as a prognostic biomarker and therapeutic target in acute promyelocytic leukaemia

Abstract

Acute myeloid leukemia (AML) has a poor prognosis and high heterogeneity. Most cases of leukemias are caused by environmental factors interacting with the cell’s genetic material, but treatment is still dominated by cell cycle drugs. Therefore, there is an urgent need to find reliable biomarkers. Based on the Gene Expression Omnibus database, Kaplan–Meier survival analysis and univariate Cox regression analysis were used to select the genes that had the most significant influence on the prognosis of patients with AML. Quantitative real-time PCR and Western blot were used to assess the effects of small interfering RNA transfection and lentiviral interference on the gene's knockout and overexpression, respectively. These method were also used to confirm the expression levels of the FHL1 gene in the HL60 cell line compared to neutrophils.. Cell Counting Kit-8 and flow cytometry were used to detect the effect of high or low expression of FHL1 on cell viability and apoptosis under the influence of cytarabine and daunorubicin. FHL1 was found to be the most prognostic independent biomarker by GSE12417 screening and GSE37642 validation. FHL1 is highly expressed in AML, and knockdown of FHL1 can increase the sensitivity of AML cells to cytarabine and daunorubicin. FHL1 may play a role as a potential molecular marker and therapeutic target for predicting poor prognosis of AML and for direct treatment (chemotherapy).

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-01738-6.

Introduction

Acute myeloid leukemia (AML) is a cancer of myeloid blood cells characterized by clonal dilation of myeloid precursors at various stages of differentiation, resulting in dysplasia of normal blood cells and bone marrow failure [1]. It is the most common subtype of leukemia, with high heterogeneity, an annual global incidence of 3 in 100,000, high mortality, and poor prognosis [2, 3].

The etiology and pathogenesis are complex. Despite significant progress in this field, a complete understanding of the etiology remains elusive. Currently, it is believed that most AML is caused by the interaction between environmental factors and the genetic material of the cell. However, current treatment is still mainly cell cycle chemotherapy drugs such as trans-retinoic acid, arsenic in combination with anthracycline (daunorubicin), and cytarabine [4]. Although treated with traditional regimens, 40% of patients did not achieve complete response, and overall survival was low [4]. In addition, about 70% of patients who achieve remission after the first complete remission with induction therapy still relapse and develop refractory leukemia, resulting in high mortality [5, 6]. Hematopoietic stem cell transplantation can treat AML to the maximum extent, but because of insufficient donor availability, low matching degree, and financial considerations, the applicable population is limited [7]. Therefore, the medical community is eager to find new targets for AML and improve the prognosis of patients with AML.

With the expanding research on the pathogenesis of AML, new molecular targeted drugs have emerged, such as Bcl-2 inhibitors [8], FLT3 inhibitors [9], IDH1/2 inhibitors [10], and various monoclonal antibodies [11]. New targeted drugs are bringing the treatment of AML into a whole new era, especially with the advent of Bcl-2 inhibitors, and are milestone AML treatments for older adults or those not eligible for standard chemotherapy [12]. But our understanding of the prognosis of AML is still evolving. Moreover, with the development of gene chip and high-throughput sequencing technology, more studies have shown that gene signature has great potential as a biomarker in predicting cancer prognosis [13, 14].

Therefore, through bioinformatics, we identified the gene FHL1, which is most relevant to prognosis; it can serve as an independent factor for prognosis and is closely associated to poor prognosis. In addition, high expression of FHL1 in AML was found to reduce the sensitivity of conventional chemotherapy drugs such as cytarabine and daunorubicin in subsequent tests. Consequently,, it may represent a potential therapeutic target for predicting poor prognosis of AML and for direct treatment strategies (chemotherapy).

Materials and methods

Data collection

The data analyzed in this study were from two microarray datasets, GSE12417 and GSE37642, sequenced on the GPL96 platform. The reason why we choose GSE12417 is for it contains163 samples of bone marrow or peripheral blood mononuclear cells from adult patients with untreated acute myeloid leukemia. And we choose GSE37642 is for it contains 562 samples (140 HGU-133plus2; 422 HGU-133A; 422 HGU-133B) from adult patients with acute myeloid leukemia (AML). They were obtained from the Gene Expression Omnibus database, including the gene expression profile and corresponding clinical information. GSE12417 is the training set. In addition, to verify the differences in FHL1 gene expression between normal cells and AML, another dataset of coordinated RNA sequencing FPKM including Genotype-Tissue Expression, The Cancer Genome Atlas, and Target Queue was obtained from UCSC Toil.

Screening and identification of prognostic genes

Kaplan–Meier survival analysis and univariate Cox regression analysis were used to screen all genes in the GSE12417 dataset. Based on P < 0.05 in both analyses, the gene with the most significant prognostic value (the largest area under the curve) was selected as the biomarker for subsequent analysis based on receiver operating characteristic analysis. We then assessed the clinical relevance of the biomarker. Based on French–American–British (FAB) classification and patient age, we created a box chart to visualize the expression distribution of biomarkers, and we evaluated the clinical correlation by univariate and multivariate Cox regression analysis. Kaplan–Meier survival analysis and clinical relevance assessment were validated with GSE37642.

Screening and functional identification of differentially expressed genes

The GSE12417 dataset was divided into high- and low-expression groups according to the median expression value of the biomarkers with the most significant prognosis. The R package limma was used for difference analysis between the two groups, with screening criteria of |log2 (fold change) |≥ 1.5 and false discovery rate < 0.05 [15]. Volcano plots and heat maps were used to visualize gene expression patterns. Then, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and correlation analyses to find the action pathways of the differentially expressed genes and the correlation between them.

Gene set enrichment analysis

To reveal the potential pathway related to FHL1 expression, we used gene set enrichment analysis (GSEA) to identify the enrichment items in the GSE12417 dataset. GSEA generated an ordered list based on the correlation between all genes and FHL1 expression, indicating significant differences between high- and low-FHL1 groups [16], arranged 1000 times for each analysis. The enrichment pathways were sequenced with a P value and a normalized enrichment score. P < 0.1 was considered statistically significant.

Cell culture and gene silencing and overexpression

The M3 cell line HL60 from AML was used in this study. The cell lines were cultured in RPMI 1640 medium containing 10% fetal bovine serum at 37 °C and 5% CO₂. Logarithmic growth cells were used for further experiments.

To determine whether there are differences in FHL1 expression between normal cells and AML, we first analyzed the expression values of FHL1 gene extracted from 337 whole blood data and 417 AML data obtained from UCSC Toil, then we used quantitative real-time PCR (qRT-PCR) and Western blot experiments to further verify the expression of FHL1 in neutrophils and HL60 cell lines.

First, HL60 cells were transfected with human FHL1-targeted small interfering RNA (siRNA) and negative control siRNA, designed, and synthesized by RiboBio Co. (Guangzhou, China) to silence the FHL1 gene. According to the manufacturer’s instructions, siRNA was transfected with 50 nM (1 × 10⁵ cells/mL). Western blot was performed 48 h later to assess the transfection efficiency. The sequence of siRNA was as follows: si-FHL1: GGGAAGAAGTATGTGCAAA. Second, FHL1-overexpressed plasmid pcDNA3.1-FHL1 and pcDNA 3.1-vector (NC), constructed by Fenghui Biotech (Hunan, China), were transfected into HL60 cells with Lipofectamine 2000. Western blot was performed 48 h later to assess the transfection efficiency.

qRT-PCR and Western blot

The effects of silencing and overexpression were evaluated by qRT-PCR and Western blot. Total RNA was extracted from cultured cells with TRIzol and then reverse transcribed to complementary DNA in accordance with the manufacturer’s protocol (DP419; Beijing Tian Gen Biochemical Technology Co., Ltd., Beijing, China). The fluorescence intensity of TB Green in the reaction solution was detected, and the target gene was accurately quantified. β-Tubulin was used to standardize RNA expression. The results were calculated via the threshold cycle (2^−ΔΔCt) method. Primers were designed and synthesized by Invitrogen (Shanghai, China), and the sequence was as follows: FHL1-human-qF 5′TCTGGCTCTGGAGCTAATTTGG3′;FHL1-human-qR5′TGGCAGTCAAACTTCTCCGC3′. In Western blot, the proteins were isolated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred to a 0.45-m polyvinylidene difluoride membrane. After the membrane was closed with 5% bovine serum albumin, the primary antibodies were used. Rabbit anti-FHL1 (1:1000; Proteintech, Wuhan, China) and rabbit anti-β-tubulin (1:5000; Abcam, Cambridge, UK) were incubated overnight at 4 °C. After washing the polyvinylidene difluoride membrane with Tris-buffered saline/Polysorbate 20 3 times (5 min/wash), we incubated the membrane with sheep anti-rabbit secondary antibody at room temperature for 2 h. Then the membrane was washed and developed to detect the protein expression level.

Detection of cell viability and apoptosis after treatment with cytosine arabinoside and daunorubicin

Cytarabine and daunorubicin were dissolved in dimethyl sulfoxide and stored at –20°, both purchased from Shenggong Bioengineering Technology Limited (Shanghai, China). HL60 cells with knockdown and overexpression of FHL1 were treated with cytosine arabinoside at 100 nM, 200 nM, and 500 nM and daunorubicin at 0.1 nM, 0.2 nM, 0.5 nM, 1 nM, and 2 nM, respectively, to determine whether expressions of the FHL1 gene in HL60 cell lines differed in drug sensitivity. Cell activity was detected by Cell Counting Kit-8 (CCK8). HL60 cells at a density of 10⁴/100 µL were spread on a 96-well plate and treated with cytarabine and daunorubicin at different concentrations for 24 h. After 10 µL of CCK8 reagent was added to each cell, the cells were incubated for 4 h. The absorbance at 450 nm was measured with a microplate reader, and the cell viability was calculated. Cell viability (%) = (A Treatment group – A blank)/(A Control group – A blank) × 100. Cell apoptosis was detected with an Annexin V‐FITC/PI Apoptosis Kit (Sangon, China). HL60 cells at a density of 10⁴/100 µL were inoculated into 24-well plates and treated with cytarabine and daunorubicin at different concentrations for 24 h. Annexin V-FITC/PI (5 μL) was added to 100 µL of cell suspension and incubated at room temperature in the dark for 10 min, then cell apoptosis was detected by flow cytometry.

Statistical analysis

R software version 3.6.1 was used for bioinformatic analysis, and GraphPad Prism version 8 was used to display the following experimental results. Wilcoxon rank-sum and Kruskal–Wallis tests were used to evaluate the differences between the groups. Unless otherwise specified, P < 0.05 was considered statistically significant.

Results

FHL1 can be used as an independent prognostic biomarker

After Kaplan–Meier survival analysis, univariate Cox regression and receiver operating characteristic analysis were performed on the genes in the GSE12417 dataset, and FHL1 was identified as the gene most significant associated with patient prognosis (Supplementary Figure S1). Patients with high expression of the FHL1 gene had a poor prognosis, which was also the case in validation set GSE37642 (Fig. 1A, B). Expression of the FHL1 gene varied according to FAB type and age (Fig. 1C, D). In FAB type, the expression of FHL1 was the lowest in the M3 type, which is why we plan to use the M3 type HL60 cell line for future experimental verification. In both datasets, FHL1 gene indicators were significant in both univariate and multivariate Cox regression analyses, sugesting that FHL1 can serve as an independent prognostic factor for AML (Fig. 1E).

Fig. 1.

Identification of independent prognostic factors for acute myeloid leukemia (AML). Based on the influence of FHL1 expression on the overall survival of patients with AML in the Gene Expression Omnibusdataset, GSE12417 is the training set and GSE37642 is the verification set (A, B). The association of FHL1 expression with clinicopathological features, including differences in the distribution of FBA typing and age (C, D), and univariate and multivariate Cox analyses for these factors (E)

Differentially expressed genes were highly correlated with FHL1

According to the median expression of the FHL1 gene, the GSE12417 dataset was divided into a high-expression group (n = 81) and a low-expression group (n = 81), and a total of 20 differentially expressed messenger RNAs were identified between the two groups (Fig. 2A, B). Most of the differentially expressed genes showed high correlation (Fig. 2E). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed the role of these differentially expressed genes in hematopoietic cell lineage, homotypic cell − cell adhesion, regulation of G2/M transition of the mitotic cell cycle, positive regulation of the tumor necrosis factor biosynthetic process, protein kinase C binding, ubiquitin binding, serine-type endopeptidase inhibitor activity, ubiquitin-like protein binding, and other pathways (Fig. 2C, D). These findings suggest that these genes may be involved in the occurrence and development of AML.

Fig. 2.

Difference analysis and pathway analysis of FHL1 high-and low-expression groups based on the GSE12417 dataset.Volcano maps (A) and heat maps (B) were used to visualize differentially expressed genes in the high-and low-FHL1-expression groups, with red representing high or upregulated genes and green representing low or downregulated genes.Pathway analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomesof differentially expressed genes (C, D).Correlation analysis between differential genes (E)

FHL1 gene was highly expressed in AML, and its targeted intervention increased the cytotoxic effects of cytosine arabinoside and daunorubicin

There were differences between 337 whole blood samples of the FHL1 gene and 417 AML samples. We compared the FHL1 expression among silenced FHL1 cells, overexpressed FHL1 cells, and normal HL60 cells and normal PMN cells (Fig. 3B). We found that FHL1 expression in HL60 cells was increased compared to normal PMN cells (Fig. 3A). We performed western-blot (Fig. 3C) and RT-PCR (Fig. 3D) experiments to compare their changes in gene expression products and gene expression. After discovering that the FHL1 gene in HL60 cells was highly expressed compared to other cells, we then investigated the toxic effects of cytarabine and erythromycin on HL60 cell lines. Interventions targeting FHL1 increased the cytotoxic effects of cytarabine and daunorubicin, decreased cell activity, and promoted apoptosis (Figs. 4 and 5). Therefore, the FHL1 gene may contribute to the prognostic assessment of AML and become a potential therapeutic target.

Fig. 3.

A FHL1 gene differential expression was analyzed in 337 whole blood RNA sequencing data from Genotype-Tissue Expressionand 379 acute myeloid leukemia samples. B Silencing and overexpression of FHL1 gene siRNA in HL60 cells(Si-RNA and OV-FHL1), expression of FHL1 protein in normal human peripheral blood neutrophils (PMN), emptyvector-transfected group(Con), and β-Tubulin as an internal reference protein. C Western Blot grayscale value quantification. *P < 0.05;**P < 0.01;***P < 0.001, the difference was statistically significant. D The mRNA expression level of FHL1 was detected by qRT-PCR

Fig. 4.

A Effect of FHL1 on the percentage of apoptosis of HL60 cell lines under Ara-C 500 nM and DNR 2 nM treatment. B Cell Counting Kit-8 was used to detect the effect of Ara-C and DNRdifferent concentrations on cell proliferation underthe silencedFHL1. C Cell Counting Kit-8 was used to detect the effect of Ara-C and DNR different concentrations on cell proliferation underthe overexpressed FHL1

Fig. 5.

Study of the effect of FHL1 on apoptosis of HL60 cell lines under drug treatment.The apoptosis of cells treated with different concentrations of Ara-C (A) and DNR (B) was detected under the silenced and overexpressed FHL1.Under the treatment of 100 nM Ara-C, the percentage of apoptosis cells ofsilenced FHL1 increased by 2.42% compared with the cells of overexpressing FHL1.Under the treatment of 200 nM Ara-C, the percentage of apoptosis cells ofsilenced FHL1 increased by 10.97% compared with the cells of overexpressing FHL1.Under the treatment of 500 nMAra-C, the percentage of apoptosis cells ofsilenced FHL1 increased by 18.52% compared with the cells of overexpressing FHL1.Under the treatment of 0.2nM DNR, the percentage of apoptosis cells ofsilenced FHL1 increased by 2.07% compared with the cells of overexpressing FHL1.Under the treatment of 1nM DNR, the percentage of apoptosis cells ofsilenced FHL1 increased by 2.66% compared with the cells of overexpressing FHL1.Under the treatment of 2nM DNR, the percentage of apoptosis cells ofsilenced FHL1 increased by 8.55% compared with the cells of overexpressing FHL1

Gene set enrichment analysis

To identify the differentially activated signaling pathways in AML, we conducted a gene set enrichment analysis focusing on FHL1. We selected the signaling pathways that showed the most significant enrichment based on the normalized enrichment score. The enriched pathways included the Wnt signaling pathway, Notch signaling pathway, calcium signaling pathway, and phosphatidylinositol 3-kinase signaling pathway, as well as processes such as skin cell differentiation, smooth muscle cell differentiation, endothelial cell, and progenitor cell differentiation during vascular development, protein transport, small cell lung cancer, and chronic myeloid leukemia (Fig. 6).

Fig. 6.

Gene set enrichment analysis based on the FHL1 gene in the GSE12417 dataset showed a significant enrichment pathway

Discussion

AML is a malignant disease with a largely unchanged treatment options and poor prognosis despite decades of research. Therefore, there is an urgent need to identify reliable prognostic biomarkers to improve the clinical treatment and management of AML. In this study, we identified the single gene, FHL1, which was most significantly associated with the prognosis of AML based on the GSE12417 dataset and further verified in other datasets. In addition, multivariate Cox regression analysis confirmed that FHL1 was independent of age and FAB typing, making it a potential independent prognostic marker.

FHL1 encodes four and a half LIM domain protein 1, which consists of four and a half highly conserved LIM domains and participates in in many cellular processes [17]. FHL1 acts as a biomarker in a variety of diseases, including cancer, and plays a role in disease development. FHL1 is crutial for bone and myocardial growth, and its difunction can lead to a range of muscular dystrophy–like muscle diseases and cardiovascular diseases [18–20].

Moreover, a large number of studies have demonstrated that the downregulation of FHL1 is altered in a variety of tumors and is involved in the occurrence and development of these diseases, including lung [21], gastric [22, 23], and breast, kidney, and prostate cancers [24]. Notably, as a tumor-suppressor gene on the X chromosome, FHL1 may be completely inactivated by a single gene or epigenetic abnormality on an active allele, which is inactivated by DNA methylation and has a high risk of being affected by environment [25–27]. Through data analysis and experimental verification (comparing HL60 cells with neutrophils), we discoverd that its expression was upregulated in AML. This suggests that elevated FHL1 levels may serve as an indicator of poor prognosis.

Previous reports have shown that FHL1 acts through various mechanisms, including the inhibition of tumor cell growth through the tumor growth factor-β-like signaling pathway and negative regulation of the G2/M transition mitotic cell cycle [28, 29]. Using GSEA analysis, we identified that FHL1 is involved in cell differentiation, the regulation of pathways (such as Notch and Wnt signaling pathway), protein transfer, and development of chronic myeloid leukemia. Pathway analysis of differential expressed genes based on high and low FHL1 expression revealed that these differential genes were also involved in the regulation of the mitotic cycle, hematopoietic cell lineage and tumor necrosis factor. These studies underscore the significance of FHL1.

Given the important role of FHL1, several studies have focused on its involvement in AML. Fu et al. [28] demonstrated that FHL1 is a powerful prognostic candidate and potential therapeutic target of AML. Heuser et al. [29] also observed increased FHL1 expression in patients with drug-resistant AML. Consequently, we conducted a more in-depth exploration of the role of FHL1 in AML, employing an optimized and improved methodology. Firstly, we chose different cell lines. We found that the expression of FHL1 was lowest in the M3 type, leading us to conduct subsequent experiments using the HL60 cell line. Secondly, we experimentally confirmed that the expression of FHL1 in HL60 cells was higher than that of neutrophils. Thirdly, we analyzed the role of the FHL1 gene in the two drugs. Studies involving the knockout and overexpression of the FHL1 gene have indicated that elevated FHL1 expression is associated with poor clinical outcomes in AML patients undergoing induction chemotherapy with cytarabine and daunorubicin.

Therefore, FHL1 can serve as an independent biomarker for AML, with high expression levels suggesting poor survival rates and responsiveness to chemotherapy. Knocking out FHL1 increased the sensitivity of AML cells to cytarabine and daunorubicin. Consequently, FHL1 may be a candidate for both a prognostic biomarker and a therapeutic target for AML.

Author contributions

Suggestions for this study from Prof. Xiaoli Zheng and Dr. Bo Luo are gratefully acknowledged. Xiaoli Zheng, Dr. Bo Luo, and Wei Li wrote the main manuscript text. Yingyu Mao prepared Fig. 1. Jingyuan Zeng and Shuang He prepared Figs. 2, 3, 4 and 5. Qulian Guo prepared Fig. 6. We thank Nan Hu for the statistical suggestions. All authors reviewed the manuscript.

Funding

This study was supported by Luzhou Government andSouthwest Medical University Strategic Cooperation Project [Grant no. 2019LZXNYDZ01], Sichuan Science and Technology Program (2022YFS0622), Key R&D Program of Sichuan Provincial Department of Science and Technology (Major Science and Technology Project) (2020YFSY0030).

Data availability 

The data that support the findings of this study are available on request from the corresponding author, [Xiaoli Zheng]，upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.