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Article

Gene Expression Analysis of Autophagy Markers in Primary and Secondary Myelofibrosis

by
Marin Medugorac
1,
Katarina Marija Glick
2,
Ana Livun
2,3,
Marko Lucijanic
3,4,5,
Davor Galusic
6,7 and
Rajko Kusec
2,3,5,*
1
Division of Hematology, Department of Internal Medicine, University Hospital Centre Zagreb, 10000 Zagreb, Croatia
2
Division of Molecular Diagnostics and Genetics, Department of Laboratory Diagnostics, University Hospital Dubrava, 10000 Zagreb, Croatia
3
Department of Scientific Research and Translational Medicine, University Hospital Dubrava, 10000 Zagreb, Croatia
4
Division of Hematology, Department of Internal Medicine, University Hospital Dubrava, 10000 Zagreb, Croatia
5
School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
6
Division of Hematology, Department of Internal Medicine, University Hospital Centre Split, 21000 Split, Croatia
7
School of Medicine, University of Split, 21000 Split, Croatia
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2025, 14(7), 2333; https://doi.org/10.3390/jcm14072333
Submission received: 10 February 2025 / Revised: 19 March 2025 / Accepted: 25 March 2025 / Published: 28 March 2025
(This article belongs to the Special Issue Advancements and Challenges in Treating Chronic Myeloproliferative Neoplasms)
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Versions Notes

Abstract

:
Background/Objectives: According to previous research, the process of autophagy in myeloid neoplasms has proven to be ambivalent depending on the type and stage of the disease. The aim of our work was to investigate the mechanism of autophagy in patients with primary and secondary myelofibrosis. Methods: Based on the RT-PCR method, we retrospectively analyzed the expression of Beclin-1 and LC3B-II in bone marrow cells of patients with primary and secondary myelofibrosis (74 participants) compared to the control group which had patients with lymphoma in a localized stage without bone marrow infiltration (11 participants). Results: There was no statistically significant difference in the expression of Beclin-1 and LC3B-II between patients with primary and secondary myelofibrosis and control participants. Among patients with primary myelofibrosis, higher expression of LC3B-II was statistically significantly associated with lower DIPSS. Higher Beclin-1 expression was statistically significantly associated with better patient survival. Conclusions: Our results suggest that the upregulation of autophagy genes may be associated with favorable prognosis and survival of patients with myelofibrosis.

1. Introduction

Autophagy is an evolutionarily conserved mechanism of degradation of senescent organelles and macromolecules, essential for maintaining cell homeostasis [1,2]. Induction of autophagy occurs in states of increased energy needs and oxidative stress [1,2]. Autophagy is a sequential process that begins with nucleation (the formation of an isolation membrane or phagophore) and ends with the fusion of autophagosome with the lysosome [1,2]. Beclin-1 and LC3B-II (membrane-bound microtubule-associated protein 1 light chain 3 beta) are the most commonly used autophagy markers [1,2]. Different types of stress stimuli, including nutrient starvation [3], hypoxia [4], oxidative stress [5], misfolded protein accumulation [6], mitochondrial [7] and DNA damage [8], invasion by pathogens [9], mechanical stress [10] and psychological stress [11], can affect activation of autophagy. The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) is a key intracellular signaling pathway regulating the activation of autophagy [12]. Under normal cell conditions, a basal level of autophagy is necessary to maintain homeostasis and eliminate old or damaged proteins and organelles [13,14]. Moreover, autophagy is required to maintain unique properties of hematopoietic stem cells and balance processes of their self-renewal and differentiation [15,16]. However, regarding cancer biology, autophagy may play an ambiguous role, both promoting and suppressing processes that may aid in cancer survival or death [17].
Chronic myeloproliferative neoplasms (MPNs) are malignant diseases of hematopoietic stem cells, exerting indolent clinical behavior considering the risks of clonal progression into more aggressive myeloid malignancies [18]. Among three traditional BCR::ABL negative MPN entities, polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF), PMF exerts the highest tendency for progression and death [19]. It is characterized by neoplastic proliferation of megakaryocytes and granulocytes, fibrosis of the bone marrow, and extramedullary hematopoiesis in an enlarged spleen. In most patients, somatic mutations in the genes JAK2V617F, CALR or MPL are detected [20]. Secondary myelofibrosis (SMF) results from transformation of polycythemia vera (post-PV SMF), or essential thrombocythemia (post-ET SMF), and the clinical and laboratory characteristics of the disease are overall similar to PMF although some differences in specific risks may exist [21,22]. Previous research has established that the role of autophagy in the development of myeloid neoplasms is ambivalent, depending on the type and stage of disease and the use of certain drugs [23,24]. The aim of our research was to investigate autophagy by measuring the expression of the Beclin-1 and LC3B-II genes in the bone marrow cells of patients suffering from primary and secondary myelofibrosis and examine the correlation with clinical and hematological parameters.

2. Materials and Methods

2.1. Patients and the Methodology

The study included 74 participants in the test group and 11 in the control group. The bone marrow was sampled as part of the standard diagnostic algorithm with previously obtained informed consent of the participants and the approval of the Ethics Committee. The test group included patients with PMF or post-ET and post-PV SMF evaluated at the time of diagnosis in the Hematology department of University hospital Dubrava, Zagreb in period from 2007 to 2021. Diagnoses were reassessed according to the 2022 WHO and ICC criteria [18,25]. The control group included patients suffering from lymphoma in the localized stage, sampled at the time of diagnosis, before the introduction of specific therapies. The study samples were bone marrow aspirates which were informative of bone marrow morphology, obtained at the time of bone marrow biopsy procedure. Mononuclear cells were isolated from bone marrow aspirates using Histopaque (Sigma, St. Louis, MO, USA; density 1.077 g/mL) and preserved in liquid nitrogen using DMSO until needed. RNA was isolated from bone marrow cells using the QIAamp RNA Blood Mini Kit (Quiagen, Hilden, Germany, Cat. nr. 52304). After measuring the concentration on a Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), the RNA was reverse-transcribed into cDNA using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA, Cat. nr. 4368814) and amplified with Brilliant II QPCR High Rox Master Mix (Alphachrom, Agilent Technologies, Santa Clara, CA, USA, Cat. nr. 600805). The relative expressions of Beclin-1 (Thermo Fisher Scientific, TaqMan expression assay ID: Hs00186838_m1) and LC3B-II (Hs00797944_s1) were quantified by RT-PCR (real-time polymerase chain reaction) on an ABI Prism 7300 Sequence Detection System (Applied Biosystems). Expression was normalized by comparison with the GUSB (beta glucuronidase) gene (Thermo Fisher Scientific, TaqMan Assay ID: Hs99999908_m1). Relative expression (∆Ct, delta cycle threshold) was calculated as the difference between the mean Ct value of the endogenous control and the mean Ct value of the gene of interest (∆Ct = μCtGUSB − μCtGene of interest). Relative expressions are shown as the difference in cycle number (∆Ct; higher value corresponds to higher expression). No significant difference was present regarding the date of sample acquisition in either of the tested genes (CT and delta CT values). Complete blood count was obtained using the Advia 2120i counter (Siemens Medical Solutions Diagnostics Pte Ltd., Swords, Ireland). Biochemical parameters were determined using the AU5800 analyzer (Beckman Coulter, Tokyo, Japan) and original manufacturer reagents.

2.2. Statistical Methods

The normality of the distribution of numerical variables was tested with the Shapiro–Wilk test. Because most numerical variables did not have a normal distribution, numerical variables were presented as median and interquartile range (IQR) and were compared between groups using Mann–Whitney U test and the Kruskal–Wallis ANOVA test. Two numerical variables were compared with each other using Spearman’s correlation. Categorical variables were presented as frequencies and percentages and were compared between groups using the Chi-squared (χ2) test. Survival analysis was based on the Kaplan–Meier method, and the log-rank test was used. ROC curve analysis was used to select the optimal threshold value of the expression of individual genes for survival analysis. p values < 0.05 were considered statistically significant. In Table 1 and Table 2, significant associations are denoted with * if statistical significance was present when the variable of interest was dichotomized and ** if the variable of interest was treated as a continuous variable; p values in tables are reported for dichotomized context and may be denoted with ** even if above 0.05, hence implying there was a significant association in alternative analysis. No formal adjustments of the p values for multiple presented comparisons were made. The statistical program MedCalc version 20.110 (MedCalc Software Ltd., Ostend, Belgium) was used for all analyses.
Considering required sample size, we conducted a pre-study power analysis based on an assumption that a 10-fold increase in a specific gene relative expression would represent meaningful difference, which would correspond to ∆Ct difference of 3.32. Assuming a difference in ∆Ct value of 3.32, standard deviations of 3 in both groups, ratio of patients in subgroups of 7:1, type I error of 0.05, and 80% statistical power, it would be required to include a total of 61 patients (53 and 8 in specific subgroups) to detect statistically significant difference.

3. Results

Out of 74 participants in the test group, there were a total of 42 (56.8%) men and 32 (43.2%) women. The median age was 67 years (IQR 58–75). The majority of patients were JAK2V617F-mutated (42 (67.2%)). A total of 56 (75.7%) patients had PMF, 10 (13.5%) post-PV SMF, and 8 (10.8%) post-ET SMF. According to DIPSS (dynamic international prognostic scoring system), 9 (18.8%) PMF patients were at low risk, 25 (52.1%) at intermediate-1, 9 (18.8%) at intermediate-2, and 5 (10.4%) at high risk. According to Mysec-PM (prognostic model for secondary myelofibrosis), 1 (8.3%) SMF patient was at low risk, 4 (33.3%) were at intermediate-1, 4 (33.3%) at intermediate-2, and 3 (25%) at high risk. Median follow-up was 40 months. The 5-year survival rate was 44%. Out of 11 control participants, there were a total of 9 (81.8%) men and 2 (18.2%) women. The median age was 46 years (IQR 41–57). There was no statistically significant difference in Beclin-1 expression between patients with PMF, patients with SMF, and control group (p = 0.112, Figure 1).
When Beclin-1 expression in the test group was stratified on the median, higher expression was statistically significantly associated with lower absolute number of basophils and lymphocytes, lower percentage of circulatory blasts, lower RDW (red cell distribution width), higher platelet count, lower MPV (mean platelet volume), lower LDH (lactate dehydrogenase), and lower CRP (C-reactive protein) (p < 0.05 for all comparisons, Table 1). There was no statistically significant difference in Beclin-1 expression depending on age, sex, origin of myelofibrosis, grade of bone marrow fibrosis, JAK2 mutational status, nor DIPSS disease risk in PMF and Mysec-PM disease risk in SMF patients (p > 0.05 for all comparisons, Table 1).
There was no statistically significant difference in the expression of LC3B-II between patients with PMF, patients with SMF, and the control group (p = 0.561, Figure 2).
After stratification on the median, higher LC3B-II expression was statistically significantly associated with lower absolute monocyte and lymphocyte counts, lower LDH, lower serum iron level, lower transferrin saturation (TSAT), and lower ferritin concentration (p < 0.05 for all comparisons, Table 2). When it was analyzed as a continuous variable, higher LC3B-II expression was statistically significantly associated with lower DIPSS disease risk in PMF patients (Figure 3 and Table 2).
There was no statistically significant difference in LC3B-II expression depending on age, sex, origin of myelofibrosis, grade of bone marrow fibrosis, JAK2 mutational status, or Mysec-PM disease risk in SMF patients (p > 0.05 for all comparisons).
The optimal threshold values of the tested genes for survival analysis were determined by ROC curve analysis. Higher Beclin-1 expression (∆Ct > −1.9) was statistically significantly associated with better patient survival (p = 0.043, Figure 4a). LC3B-II expression divided by optimal threshold value of ∆Ct (>1.58) was not statistically significantly associated with patient survival (p = 0.303, Figure 4b), although the similar trend of favorable survival was observed.

4. Discussion

Our research did not establish a statistically significant difference in the expression of Beclin-1 and LC3B-II in bone marrow cells of patients with primary and secondary myelofibrosis compared to the control group. When LC3B-II expression was analyzed as a continuous variable among patients with primary myelofibrosis, higher expression was statistically significantly associated with lower DIPSS risk. As another significant result, we highlight that higher expression of Beclin-1 was statistically significantly associated with better patient survival. To the best of our knowledge, this is the first time that expression of Beclin-1 and LC3B-II was analyzed by RT-PCR on mononuclear bone marrow cells of patients with primary and secondary myelofibrosis.
In a previously published work (Shi, Guanfang et al.), the expression of Beclin-1 and LC3B-II was quantified by RT-PCR and Western blot method on mononuclear cells from peripheral blood, and the results showed a lower expression of Beclin-1 and LC3B-II in patients with myelofibrosis compared to healthy subjects and patients with polycythemia vera and essential thrombocythemia [26], thus implying downregulation of the process of autophagy associated with the development of bone marrow fibrosis. As emphasized in the introduction, the process of autophagy is ambivalent in the etiopathogenesis of myeloid neoplasms, depending on the type of the disease and the use of certain drugs. In silico studies on patients suffering from myelodysplastic syndrome, acute myeloid leukemia, and chronic myeloproliferative neoplasms confirmed that the expression of genes involved in the mechanism of autophagy is reduced in the blasts of the patients compared to the granulocytes of healthy subjects [23]. The mentioned genes are located in the chromosome regions lost by deletion (CDRs, commonly deleted regions) [23]. Taking into account the fact that mitochondria are the main source of ROS (reactive oxygen species), dysfunctional mitophagy results in increased oxidative stress and DNA damage [23]. It is known that many drugs used in the treatment of patients with myeloid neoplasms lead to induction of autophagy. The difference is that some result in cytoreductive and the other in cytoprotective autophagy. For example, ATRA (all-trans retinoic acid) and ATO (arsenic trioxide), which form the backbone of the treatment of patients with acute promyelocytic leukemia, result in autophagy-induced proteolysis of the fusion oncoprotein PML-RARA (promyelocytic leukemia/retinoic acid receptor alpha), meaning they induce cytoreductive autophagy [24]. Contrary to the above, it was recently reported that ruxolitinib may lead to the induction of cytoprotective autophagy, and the concomitant use of autophagy inhibitors may increase the sensitivity of JAK2V617F-positive cells to ruxolitinib [27].
Autophagy is an essential process for the development and maintenance of healthy hematopoietic stem cells and becomes especially important for normal hematopoiesis under conditions of metabolic stress [16], such as the high inflammatory burden present in patients with myelofibrosis. This housekeeping role of the autophagy mechanisms is one of the limitations in developing specific therapies aimed directly at autophagy processes. In addition, discrepancies exist in the specific roles played by autophagy inside cancer cells, potentially acting both as a tumor suppressor and promoter [28]. Autophagy may provide an alternative mechanism of cell death in cancer cells that have escaped apoptosis, and its inhibition may be effective especially in BRAF-mutated cancer cells [29]. There are several drug classes actively targeting autophagy pathways in cancer that are being developed. These include PI3K inhibitors that can block autophagosome formation [30]. Chloroquine, hydroxychloroquine, and other lysosome-tropic agents can block lysosome acidification, in turn resulting in disruption in autophagosome degradation [31]. A similar result is achieved by inhibitors of autophagosome and lysosome fusion [32]. Delivery of antitumor drugs using nanoparticles was also shown to enhance autophagy [33]. Other mechanisms such as microRNAs inhibiting the expression of key autophagy components [34] and tumor vaccines based on autophagy targets have also been investigated [35]. Although the preclinical data suggest that autophagy inhibition may mitigate development of myelofibrosis in mice [36], to the best of our knowledge, there are no active ongoing trials of autophagy inhibitors in patients with myelofibrosis at the moment. It should be noted that our current results imply that the upregulation of autophagy might be beneficial in the context of myelofibrosis, warranting special considerations in design and the implementation of such treatment approaches.
Several more favorable clinical myelofibrosis features were significantly associated with the upregulation of Beclin-1 and LC3B-II in the current study. These include a lower absolute number of basophils and lymphocytes, lower percentage of circulatory blasts, lower RDW, higher platelet count, lower MPV, lower LDH and lower CRP for Beclin-1, lower absolute monocyte and lymphocyte counts, lower LDH, lower serum iron level, lower TSAT, lower ferritin concentration, and lower DIPSS risk score for LC3B-II. It seems that higher expression of autophagy genes is associated with a less proliferative disease phenotype, as judged by lower counts of leukocyte subtypes and lower LDH. Both higher basophil [37] and monocyte counts [38] have been associated with unfavorable survival in myelofibrosis patients, and the same was reported for higher ferritin [39], CRP [40] and LDH [41], RDW [42], and lower platelet count [43]. Thus, the tendency of improved long-term outcomes with higher expression of autophagy genes may be mediated by more favorable disease features. Also, the process of autophagy may play an important role outside of the context of diseased stem cells and may reflect on the biology of general metabolism [44], thrombosis [45], and cardiovascular health [46,47], which are important for long-term cardiovascular morbidity.
Limitations of our work are the retrospective study design, small sample size limiting the statistical power of analyses, as well as inability to investigate the contribution of exposure to different therapies over time that may have affected autophagy during the follow-up period. Since the study samples were bone marrow aspirates, some degree of dilution by peripheral blood could not be avoided due to technical limitations of the process and biological characteristics of the disease. We had no access to information on immature granulocyte count, besides the proportion of circulatory blasts. Multiple statistical comparisons are presented which may result in an inflated type I error rate. Due to the exploratory nature of the study, we did not attempt to control for this phenomenon, and no specific corrections were used for interpretation of obtained p values (to avoid increase in the type II error rates that may lead to further loss of statistical power). Thus, the current findings should be interpreted with “informal adjustments” by the readers themselves, without applying formal corrections to the raw findings, recognizing that a certain percentage of significant findings may occur by chance [48].
Considering future directions, it should be noted that autophagy inhibition that may suppress tumor clone have uncertain benefits regarding long-term effects in patients with myelofibrosis, since markers of autophagy upregulation seem to be associated with longer survival in these patients. Drugs such as metformin, which is known to initiate processes of autophagy [49], were recently reported to be potentially beneficial in preventing development of MPN [50]; however, they may not be able to revert the established bone marrow fibrosis [51]. Since long-term survival of patients with MPNs is determined by the occurrence of thrombotic events, the role of autophagy in the development and prevention of atherosclerosis and cardiovascular morbidity may also be important [52]. Also, any drugs aimed at inhibition of autophagy in malignant stem cell clones may also inadvertently affect this process in vascular tissues as well. The risk/benefit ratio of such an approach would probably depend on the aggressiveness of the malignant clone and the degree of cardiovascular burden, and would surely need to be tested in randomized controlled trials.

5. Conclusions

The results of our research suggest that the upregulation of autophagy genes may be associated with favorable prognosis and survival of patients with myelofibrosis.

Author Contributions

Conceptualization, M.M. and R.K.; methodology, M.L. and D.G.; software, M.L.; validation, R.K., A.L. and K.M.G.; formal analysis, M.L. and D.G.; investigation, K.M.G.; resources, R.K.; data curation, M.L. and D.G.; writing—original draft preparation, M.M.; writing—review and editing, M.L.; visualization, M.M.; supervision, R.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of University Hospital Dubrava (20 December 2017).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Glick, D.; Barth, S.; Macleod, K.F. Autophagy: Cellular and molecular mechanisms. J. Pathol. 2010, 221, 3–12. [Google Scholar] [CrossRef]
  2. Lim, S.M.; Mohamad Hanif, E.A.; Chin, S.F. Is targeting autophagy mechanism in cancer a good approach? The possible double-edge sword effect. Cell Biosci. 2021, 11, 56. [Google Scholar] [CrossRef]
  3. Kroemer, G.; Mariño, G.; Levine, B. Autophagy and the integrated stress response. Mol. Cell 2010, 40, 280–293. [Google Scholar] [CrossRef] [PubMed]
  4. Bellot, G.; Garcia-Medina, R.; Gounon, P.; Chiche, J.; Roux, D.; Pouysségur, J.; Mazure, N.M. Hypoxia-induced autophagy is mediated through hypoxia-inducible factor induction of BNIP3 and BNIP3L via their BH3 domains. Mol. Cell Biol. 2009, 29, 2570–2581. [Google Scholar] [CrossRef]
  5. Harding, H.P.; Zhang, Y.; Zeng, H.; Novoa, I.; Lu, P.D.; Calfon, M.; Sadri, N.; Yun, C.; Popko, B.; Paules, R.; et al. An integrated stress response regulates amino acid metabolism and resistance to oxidative stress. Mol. Cell 2003, 11, 619–633. [Google Scholar] [CrossRef] [PubMed]
  6. Bhadauriya, P.; Onkar, A.; Nagarajan, K.; Karuppusamy, K.A.; Ganesh, S.; Agarwal, S. Glycogen synthase is required for heat shock-mediated autophagy induction in neuronal cells. Biol. Open 2025, 14, BIO061605. [Google Scholar] [CrossRef]
  7. Ryter, S.W.; Choi, A.M. Autophagy: An Integral Component of the Mammalian Stress Response. J. Biochem. Pharmacol. Res. 2013, 1, 176–188. [Google Scholar]
  8. Pietrocola, F.; Izzo, V.; Niso-Santano, M.; Vacchelli, E.; Galluzzi, L.; Maiuri, M.C.; Kroemer, G. Regulation of autophagy by stress-responsive transcription factors. Semin. Cancer Biol. 2013, 23, 310–322. [Google Scholar] [CrossRef]
  9. Jo, E.K.; Yuk, J.M.; Shin, D.M.; Sasakawa, C. Roles of autophagy in elimination of intracellular bacterial pathogens. Front. Immunol. 2013, 4, 97. [Google Scholar] [CrossRef]
  10. Di-Luoffo, M.; Schmitter, C.; Barrere, E.C.; Therville, N.; Chaouki, M.; D’Angelo, R.; Arcucci, S.; Thibault, B.; Delarue, M.; Guillermet-Guibert, J. Mechanical compressive forces increase PI3K output signaling in breast and pancreatic cancer cells. Life Sci. Alliance 2025, 8, e202402854. [Google Scholar] [CrossRef]
  11. Puri, D.; Subramanyam, D. Stress—(self) eating: Epigenetic regulation of autophagy in response to psychological stress. FEBS J. 2019, 286, 2447–2460. [Google Scholar] [CrossRef] [PubMed]
  12. Wu, N.; Zhu, Y.; Xu, X.; Zhu, Y.; Song, Y.; Pang, L.; Chen, Z. The anti-tumor effects of dual PI3K/mTOR inhibitor BEZ235 and histone deacetylase inhibitor Trichostatin A on inducing autophagy in esophageal squamous cell carcinoma. J. Cancer 2018, 9, 987–997. [Google Scholar] [CrossRef] [PubMed]
  13. Yu, L.; Chen, Y.; Tooze, S.A. Autophagy pathway: Cellular and molecular mechanisms. Autophagy 2018, 14, 207–215. [Google Scholar] [CrossRef] [PubMed]
  14. Mizushima, N. Autophagy: Process and function. Genes. Dev. 2007, 21, 2861–2873. [Google Scholar] [CrossRef]
  15. Phadwal, K.; Watson, A.S.; Simon, A.K. Tightrope act: Autophagy in stem cell renewal, differentiation, proliferation, and aging. Cell Mol. Life Sci. 2013, 70, 89–103. [Google Scholar] [CrossRef]
  16. Ho, T.T.; Warr, M.R.; Adelman, E.R.; Lansinger, O.M.; Flach, J.; Verovskaya, E.V.; Figueroa, M.E.; Passegué, E. Autophagy maintains the metabolism and function of young and old stem cells. Nature 2017, 543, 205–210. [Google Scholar] [CrossRef]
  17. Yun, C.W.; Lee, S.H. The Roles of Autophagy in Cancer. Int. J. Mol. Sci. 2018, 19, 3466. [Google Scholar] [CrossRef] [PubMed]
  18. Arber, D.A.; Orazi, A.; Hasserjian, R.P.; Borowitz, M.J.; Calvo, K.R.; Kvasnicka, H.-M.; Wang, S.A.; Bagg, A.; Barbui, T.; Branford, S.; et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: Integrating morphologic, clinical, and genomic data. Blood 2022, 140, 1200–1228. [Google Scholar] [CrossRef]
  19. Hultcrantz, M.; Kristinsson, S.Y.; Andersson, T.M.; Landgren, O.; Eloranta, S.; Derolf, A.R.; Dickman, P.W.; Bjorkholm, M. Patterns of survival among patients with myeloproliferative neoplasms diagnosed in Sweden from 1973 to 2008: A population-based study. J. Clin. Oncol. 2012, 30, 2995–3001. [Google Scholar] [CrossRef]
  20. Tremblay, D.; Yacoub, A.; Hoffman, R. Overview of Myeloproliferative Neoplasms: History, Pathogenesis, Diagnostic Criteria, and Complications. Hematol. Oncol. Clin. N. Am. 2021, 35, 159–176. [Google Scholar] [CrossRef]
  21. Passamonti, F.; Giorgino, T.; Mora, B.; Guglielmelli, P.; Rumi, E.; Maffioli, M.; Rambaldi, A.; Caramella, M.; Komrokji, R.; Gotlib, J.; et al. A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis. Leukemia 2017, 31, 2726–2731. [Google Scholar] [CrossRef]
  22. Lucijanic, M.; Krecak, I.; Soric, E.; Sabljic, A.; Galusic, D.; Holik, H.; Perisa, V.; Moric Peric, M.; Zekanovic, I.; Kusec, R. Patients with post polycythemia vera myelofibrosis might experience increased thrombotic risk in comparison to primary and post essential thrombocythemia myelofibrosis. Leuk. Res. 2022, 119, 106905. [Google Scholar] [CrossRef] [PubMed]
  23. Watson, A.S.; Mortensen, M.; Simon, A.K. Autophagy in the pathogenesis of myelodysplastic syndrome and acute myeloid leukemia. Cell Cycle 2011, 10, 1719–1725. [Google Scholar] [CrossRef]
  24. Auberger, P.; Puissant, A. Autophagy, a key mechanism of oncogenesis and resistance in leukemia. Blood 2017, 129, 547–552. [Google Scholar] [CrossRef] [PubMed]
  25. Khoury, J.D.; Solary, E.; Abla, O.; Akkari, Y.; Alaggio, R.; Apperley, J.F.; Bejar, R.; Berti, E.; Busque, L.; Chan, J.K.C.; et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia 2022, 36, 1703–1719. [Google Scholar] [CrossRef]
  26. Shi, G.; Kodali, S.; Wong, C.; Gotlieb, V.K.; Wang, J.-C. Defective Autophagy Process and Increased ROS Formation in Primary Myelofibrosis. Blood 2018, 132, 3069. [Google Scholar] [CrossRef]
  27. Courdy, C.; Platteeuw, L.; Ducau, C.; De Araujo, I.; Boet, E.; Sahal, A.; Saland, E.; Edmond, V.; Tavitian, S.; Bertoli, S.; et al. Targeting PP2A-dependent autophagy enhances sensitivity to ruxolitinib in JAK2(V617F) myeloproliferative neoplasms. Blood Cancer J. 2023, 13, 106. [Google Scholar] [CrossRef] [PubMed]
  28. Jogalekar, M.P.; Veerabathini, A.; Gangadaran, P. Recent developments in autophagy-targeted therapies in cancer. Exp. Biol. Med. 2021, 246, 207–212. [Google Scholar] [CrossRef]
  29. Foth, M.; McMahon, M. Autophagy Inhibition in BRAF-Driven Cancers. Cancers 2021, 13, 3498. [Google Scholar] [CrossRef]
  30. Kocaturk, N.M.; Akkoc, Y.; Kig, C.; Bayraktar, O.; Gozuacik, D.; Kutlu, O. Autophagy as a molecular target for cancer treatment. Eur. J. Pharm. Sci. 2019, 134, 116–137. [Google Scholar] [CrossRef]
  31. Levy, J.M.M.; Towers, C.G.; Thorburn, A. Targeting autophagy in cancer. Nat. Rev. Cancer 2017, 17, 528–542. [Google Scholar] [CrossRef] [PubMed]
  32. Cheong, H.; Lu, C.; Lindsten, T.; Thompson, C.B. Therapeutic targets in cancer cell metabolism and autophagy. Nat. Biotechnol. 2012, 30, 671–678. [Google Scholar] [CrossRef]
  33. Ning, F.; Yang, Z.; Xu, L.; Sun, Y. Targeted tumor therapy by autophagy of nanoparticles. Future Oncol. 2020, 16, 793–803. [Google Scholar] [CrossRef] [PubMed]
  34. Nuñez-Olvera, S.I.; Gallardo-Rincón, D.; Puente-Rivera, J.; Salinas-Vera, Y.M.; Marchat, L.A.; Morales-Villegas, R.; López-Camarillo, C. Autophagy Machinery as a Promising Therapeutic Target in Endometrial Cancer. Front. Oncol. 2019, 9, 1326. [Google Scholar] [CrossRef]
  35. Pan, H.; Chen, L.; Xu, Y.; Han, W.; Lou, F.; Fei, W.; Liu, S.; Jing, Z.; Sui, X. Autophagy-associated immune responses and cancer immunotherapy. Oncotarget 2016, 7, 21235–21246. [Google Scholar] [CrossRef]
  36. Becker, I.C.; Barrachina, M.N.; Lykins, J.; Camacho, V.; Stone, A.P.; Chua, B.A.; Signer, R.A.J.; Machlus, K.R.; Whiteheart, S.W.; Roweth, H.G.; et al. Inhibition of RhoA-mediated secretory autophagy in megakaryocytes mitigates myelofibrosis in mice. bioRxiv 2024. [Google Scholar] [CrossRef]
  37. Lucijanic, M.; Livun, A.; Stoos-Veic, T.; Pejsa, V.; Jaksic, O.; Cicic, D.; Lucijanic, J.; Romic, Z.; Orehovec, B.; Aralica, G.; et al. High absolute basophil count is a powerful independent predictor of inferior overall survival in patients with primary myelofibrosis. Hematology 2018, 23, 201–207. [Google Scholar] [CrossRef]
  38. Elliott, M.A.; Verstovsek, S.; Dingli, D.; Schwager, S.M.; Mesa, R.A.; Li, C.Y.; Tefferi, A. Monocytosis is an adverse prognostic factor for survival in younger patients with primary myelofibrosis. Leuk. Res. 2007, 31, 1503–1509. [Google Scholar] [CrossRef]
  39. Lucijanic, M.; Prka, Z.; Pejsa, V.; Stoos-Veic, T.; Lucijanic, J.; Kusec, R. Prognostic implications of low transferrin saturation in patients with primary myelofibrosis. Leuk. Res. 2018, 66, 89–95. [Google Scholar] [CrossRef]
  40. Lucijanic, M.; Galusic, D.; Krecak, I.; Sedinic, M.; Soric, E.; Holik, H.; Perisa, V.; Moric Peric, M.; Zekanovic, I.; Stoos-Veic, T.; et al. C reactive protein to albumin ratio as prognostic marker in primary and secondary myelofibrosis. Leuk. Lymphoma 2020, 61, 2969–2974. [Google Scholar] [CrossRef]
  41. Shah, S.; Mudireddy, M.; Hanson, C.A.; Ketterling, R.P.; Gangat, N.; Pardanani, A.; Tefferi, A. Marked elevation of serum lactate dehydrogenase in primary myelofibrosis: Clinical and prognostic correlates. Blood Cancer J. 2017, 7, 657. [Google Scholar] [CrossRef] [PubMed]
  42. Lucijanic, M.; Pejsa, V.; Jaksic, O.; Mitrovic, Z.; Tomasovic-Loncaric, C.; Stoos-Veic, T.; Prka, Z.; Pirsic, M.; Haris, V.; Vasilj, T.; et al. The Degree of Anisocytosis Predicts Survival in Patients with Primary Myelofibrosis. Acta Haematol. 2016, 136, 98–100. [Google Scholar] [CrossRef]
  43. Kuykendall, A.T.; Mo, Q.; Sallman, D.A.; Ali, N.A.; Chan, O.; Yun, S.; Sweet, K.L.; Padron, E.; Lancet, J.E.; Komrokji, R.S. Disease-related thrombocytopenia in myelofibrosis is defined by distinct genetic etiologies and is associated with unique prognostic correlates. Cancer 2022, 128, 3495–3501. [Google Scholar] [CrossRef]
  44. Rabinowitz, J.D.; White, E. Autophagy and metabolism. Science 2010, 330, 1344–1348. [Google Scholar] [CrossRef] [PubMed]
  45. Schwertz, H.; Middleton, E.A. Autophagy and its consequences for platelet biology. Thromb. Res. 2023, 231, 170–181. [Google Scholar] [CrossRef] [PubMed]
  46. Bielawska, M.; Warszyńska, M.; Stefańska, M.; Błyszczuk, P. Autophagy in Heart Failure: Insights into Mechanisms and Therapeutic Implications. J. Cardiovasc. Dev. Dis. 2023, 10, 352. [Google Scholar] [CrossRef]
  47. Shao, B.Z.; Han, B.Z.; Zeng, Y.X.; Su, D.F.; Liu, C. The roles of macrophage autophagy in atherosclerosis. Acta Pharmacol. Sin. 2016, 37, 150–156. [Google Scholar] [CrossRef]
  48. Saville, D.J. Multiple Comparison Procedures: The Practical Solution. Am. Stat. 1990, 44, 174–180. [Google Scholar] [CrossRef]
  49. Lu, G.; Wu, Z.; Shang, J.; Xie, Z.; Chen, C.; Zhang, C. The effects of metformin on autophagy. Biomed. Pharmacother. 2021, 137, 111286. [Google Scholar] [CrossRef]
  50. Lee, M.H.; Hobbs, G.S. Metformin for MPN: Teaching an old drug new tricks. Blood Adv. 2024, 8, 4476–4477. [Google Scholar] [CrossRef]
  51. Campos, P.D.M.; Pagnano, K.B.; Mancuso, R.I.; Della Via, F.I.; Tinoco, Â.C.; Assis-Mendonça, G.R.; Freitas, L.L.L.; Traina, F.; Olalla Saad, S.T. Final Results of the Fibromet Trial: An Open Label Phase II Study to Evaluate Metformin Effects on Bone Marrow Fibrosis and Disease Progression in Primary Myelofibrosis Patients. Blood 2021, 138, 2584. [Google Scholar] [CrossRef]
  52. Hassanpour, M.; Rahbarghazi, R.; Nouri, M.; Aghamohammadzadeh, N.; Safaei, N.; Ahmadi, M. Role of autophagy in atherosclerosis: Foe or friend? J. Inflamm. 2019, 16, 8. [Google Scholar] [CrossRef]
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Figure 1. Beclin-1 expression in patients with PMF, patients with SMF, and control group.
Figure 1. Beclin-1 expression in patients with PMF, patients with SMF, and control group.
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Figure 2. LC3B-II expression in patients with PMF, patients with SMF, and control subjects.
Figure 2. LC3B-II expression in patients with PMF, patients with SMF, and control subjects.
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Figure 3. LC3B-II expression depending on DIPSS risk category in PMF patients (1 = low risk, 2 = intermediate-1 risk, 3 = intermediate-2 risk, 4 = high risk).
Figure 3. LC3B-II expression depending on DIPSS risk category in PMF patients (1 = low risk, 2 = intermediate-1 risk, 3 = intermediate-2 risk, 4 = high risk).
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Figure 4. (a) Correlation of Beclin-1 expression with overall survival; (b) correlation of LC3B-II expression with overall survival.
Figure 4. (a) Correlation of Beclin-1 expression with overall survival; (b) correlation of LC3B-II expression with overall survival.
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Table 1. Clinical characteristics of patients with myelofibrosis stratified according to Beclin-1 expression divided on the median (higher ∆CtBeclin-1 = higher expression).
Table 1. Clinical characteristics of patients with myelofibrosis stratified according to Beclin-1 expression divided on the median (higher ∆CtBeclin-1 = higher expression).
∆CtBeclin-1 ≥ −1.76∆CtBeclin-1 < −1.76p
Age (years)66 IQR (58–77)67 IQR (58–73.25)0.659
Sex
Male22/37 (59.5%)20/37 (54.1%)
Female15/37 (40.5%)17/37 (45.9%)0.639
Origin of myelofibrosis
PMF27/37 (73%)29/37 (78.4%)
Post-PV SMF5/37 (13.5%)5/37 (13.5%)
Post-ET SMF5/37 (13.5%)3/37 (8.1%)0.751
Grade of fibrosis of bone marrow
0-I13/29 (44.8%)12/33 (36.4%)
II-III16/29 (55.2%)21/33 (63.6%)0.498
DIPSS (PMF) 0.176
Low6/21 (28.6%)3/27 (11.1%)
Intermediate-112/21 (57.1%)13/27 (48.1%)
Intermediate-22/21 (9.5%)7/27 (25.9%)
High1/21 (4.8%)4/27 (14.8%)
Mysec-PM (SMF) 0.771
Low1/8 (12.5%)0/4 (0%)
Intermediate-13/8 (37.5%)1/4 (25%)
Intermediate-22/8 (25%)2/4 (50%)
High2/8 (25%)1/4 (25%)
JAK2V617F-positive22/29 (75.9%)19/32 (59.4%)0.171
WBC (×109/L; n.v. 3.4–9.7)11.1 IQR (6.9–15.8)11.5 IQR (8.3–16)0.530
Monocytes (×109/L; n.v. 0.12–0.84)0.5 IQR (0.3–0.6)0.5 IQR (0.35–0.85)0.291
Basophils (×109/L; n.v. 0–0.06)0.1 IQR (0.07–0.17)0.2 IQR (0.1–0.3)0.032 *,**
Lymphocytes (×109/L; n.v. 1.19–3.35)1.3 IQR (1–1.7)1.8 IQR (1.45–2.75)0.012 *,**
Circulatory blasts (%, n.v. <1)0 IQR (0–1)2 IQR (0–5)0.013 *,**
Hemoglobin (g/L, n.v. 119–157)125 IQR (108–142)111 IQR (97–122)0.093
RDW (%, n.v. 9.0–15.0)18.5 IQR (15.5–20.8)19.6 IQR (17.78–21.7)0.050 *
Platelets (×109/L; n.v. 158–424)574 IQR (297–758)342 IQR (181–550)0.021 *
MPV (fL; n.v. 6.8–10.4)8.8 IQR (8.1–9.7)9.5 IQR (8.75–10.8)0.024 *,**
LDH (U/L; n.v. <241)359 IQR (234–485)546.5 IQR (342.75–887.25)0.004 *,**
CRP (mg/L; n.v. <5)2.6 IQR (1.2–3.6)4.3 IQR (2.18–13.4)0.032 *
* statistically significant at level p < 0.05, Beclin-1 expression stratified on the median. ** statistically significant at level p < 0.05, Beclin-1 expression analyzed as continuous variable. Abbreviations: ∆Ct—delta cycle threshold, IQR—interquartile range, PMF—primary myelofibrosis, post-PV SMF—post-polycythemia vera secondary myelofibrosis, post-ET SMF—post-essential thrombocythemia secondary myelofibrosis, DIPSS—dynamic international prognostic scoring system, Mysec-PM—prognostic model for secondary myelofibrosis, JAK2—Janus kinase 2, WBC—white blood cell count, n.v.—normal value, RDW—red cell distribution width, MPV—mean platelet volume, LDH—lactate dehydrogenase, CRP—C-reactive protein.
Table 2. Clinical characteristics of patients with myelofibrosis stratified according to LC3B-II expression divided on the median (higher ∆CtLC3B-II = higher expression).
Table 2. Clinical characteristics of patients with myelofibrosis stratified according to LC3B-II expression divided on the median (higher ∆CtLC3B-II = higher expression).
∆CtLC3B-II ≥ 1.43∆CtLC3B-II < 1.43p
Age (years)64 IQR (58–77)68 IQR (59.5–74.25)0.842
Sex
Male20/37 (54.1%)22/37 (59.5%)
Female17/37 (45.9%)15/37 (40.5%)0.639
Origin of myelofibrosis
PMF27/37 (73%)29/37 (78.4%)
Post-PV SMF7/37 (18.9%)3/37 (8.1%)
Post-ET SMF3/37 (8.1%)5/37 (13.5%)0.337
Grade of fibrosis of bone marrow
0–I14/30 (46.7%)11/32 (34.4%)
II–III16/30 (53.3%)21/32 (65.6%)0.324
DIPSS (PMF) 0.068 **
Low7/22 (31.8%)2/26 (7.7%)
Intermediate-112/22 (54.5%)13/26 (50%)
Intermediate-22/22 (9.1%)7/26 (26.9%)
High1/22 (4.5%)4/26 (15.4%)
Mysec-PM (SMF) 0.560
Low0/7 (0%)1/5 (20%)
Intermediate-13/7 (42.9%)1/5 (20%)
Intermediate-22/7 (28.6%)2/5 (40%)
High2/7 (28.6%)1/5 (20%)
JAK2V617F-positive23/30 (76.7%)18/31 (58.1%)0.122
WBC (×109/L; n.v. 3.4–9.7)10.3 IQR (7.35–15.05)13.1 IQR (8.23–17.85)0.130
Monocytes (×109/L; n.v. 0.12–0.84)0.4 IQR (0.3–0.6)0.6 IQR (0.4–0.9)0.021 *,**
Basophils (×109/L; n.v. 0–0.06)0.1 IQR (0.02–0.2)0.2 IQR (0.1–0.25)0.060 **
Lymphocytes (×109/L; n.v. 1.19–3.35)1.3 IQR (1–1.6)1.8 IQR (1.5–2.8)0.004 *,**
Circulatory blasts (%, n.v. <1)0 IQR (0–1.33)0 IQR (0–3.65)0.320
Hemoglobin (g/L, n.v. 119–157)121 IQR (108–132)116 IQR (97–127)0.223
RDW (%, n.v. 9.0–15.0)18.7 IQR (16.93–20.5)19.6 IQR (17.15–22.35)0.207
Platelets (×109/L; n.v. 158–424)546.5 IQR (281.25–683.5)351.5 IQR (179.25–570.25)0.128
MPV (fL; n.v. 6.8–10.4)9.1 IQR (8.15–9.7)9.3 IQR (8.43–10.68)0.336
LDH (U/L; n.v. <241)375.5 IQR (252.75–556.6)515 IQR (341.5–744.5)0.045 *,**
CRP (mg/L; n.v. <5)3.2 IQR (1.65–10.3)3.5 IQR (1.3–8.55)0.960
Fe (μmol/L; n.v. 11–32)12 IQR (9.75–14)15.9 IQR (11.9–18.4)0.016 *,**
TIBC (μmol/L; n.v. 49–72)55.6 IQR (48.5–58.45)50.2 IQR (46.3–53.25)0.092 **
TSAT (%, n.v. >20)21.7 IQR (13.8–28.38)30.2 IQR (24.18–35.85)0.013 *,**
Ferritin (μg/L; n.v. 10–120)83 IQR (18–208)232.5 IQR (177.75–352.25)0.027 *,**
* statistically significant at level p < 0.05, LC3B-II expression stratified on the median. ** statistically significant at level p < 0.05, LC3B-II expression analyzed as continuous variable. Abbreviations: ∆Ct—delta cycle threshold, LC3B-II—membrane-bound microtubule-associated protein 1 light chain 3 beta, IQR—interquartile range, PMF—primary myelofibrosis, post-PV SMF—post-polycythemia vera secondary myelofibrosis, post-ET SMF—post-essential thrombocythemia secondary myelofibrosis, DIPSS—dynamic international prognostic scoring system, Mysec-PM—prognostic model for secondary myelofibrosis, JAK2—Janus kinase 2, WBC—white blood cell count, n.v.—normal value, RDW—red cell distribution width, MPV—mean platelet volume, LDH—lactate dehydrogenase, CRP—C-reactive protein, TIBC—total iron binding capacity, TSAT—transferrin saturation.
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MDPI and ACS Style

Medugorac, M.; Glick, K.M.; Livun, A.; Lucijanic, M.; Galusic, D.; Kusec, R. Gene Expression Analysis of Autophagy Markers in Primary and Secondary Myelofibrosis. J. Clin. Med. 2025, 14, 2333. https://doi.org/10.3390/jcm14072333

AMA Style

Medugorac M, Glick KM, Livun A, Lucijanic M, Galusic D, Kusec R. Gene Expression Analysis of Autophagy Markers in Primary and Secondary Myelofibrosis. Journal of Clinical Medicine. 2025; 14(7):2333. https://doi.org/10.3390/jcm14072333

Chicago/Turabian Style

Medugorac, Marin, Katarina Marija Glick, Ana Livun, Marko Lucijanic, Davor Galusic, and Rajko Kusec. 2025. "Gene Expression Analysis of Autophagy Markers in Primary and Secondary Myelofibrosis" Journal of Clinical Medicine 14, no. 7: 2333. https://doi.org/10.3390/jcm14072333

APA Style

Medugorac, M., Glick, K. M., Livun, A., Lucijanic, M., Galusic, D., & Kusec, R. (2025). Gene Expression Analysis of Autophagy Markers in Primary and Secondary Myelofibrosis. Journal of Clinical Medicine, 14(7), 2333. https://doi.org/10.3390/jcm14072333

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