The inhibitory function of GDF11/BMP11 in liver cancer by inducing apoptosis and ROS–JNK pathway

Yonghui Zhang; Chen Wang; Jiaxin Li; Lishan Jin; Wenxuan Ding; Huan Liu; Na Zhou; Zhengni Ren; Junqi Zhang; Yong Wei; Lei Li; Lianhong Pan; Dan Liu

doi:10.1515/oncologie-2023-0001

Article Open Access

The inhibitory function of GDF11/BMP11 in liver cancer by inducing apoptosis and ROS–JNK pathway

Yonghui Zhang , Chen Wang , Jiaxin Li , Lishan Jin , Wenxuan Ding , Huan Liu , Na Zhou , Zhengni Ren , Junqi Zhang , Yong Wei , Lei Li , Lianhong Pan and Dan Liu

Published/Copyright: February 15, 2023

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From the journal Oncologie Volume 25 Issue 2

Abstract

Objectives

The inhibitory mechanism of growth differentiation factor 11 (GDF11) on liver cancer cells is unknown. Our study applied RNA-Seq to investigate the transcriptome results of liver cancer cells after GDF11 treatment, revealing the underlying molecule mechanisms of the inhibitory roles of GDF11 on liver cancer cells.

Methods

First, mRNA and protein expression levels of GDF11 were detected through the Oncomine database and tissue microassay. In vitro, Smad2/3 signaling was checked using Western blot in liver cancer cell lines (MHCC97-H and HCCLM3) after GDF11 treatment. The growth effect of GDF11 on liver cancer cells was investigated by microscopic observation and the Cell Counting Kit-8 experiment. The underlying mechanisms were explored by transcriptome experiments, flow cytometry, electron microscopy, and Western blot.

Results

GDF11 was reduced in human malignant liver tissues and cell lines compared to normal liver tissues and cell lines. GDF11 activated Smad2/3 signaling and decreased cell viability in liver cancer cell lines (MHCC97-H and HCCLM3). RNA-Seq analysis found that 39 genes were significantly changed, 9 genes were significantly downregulated, and 30 genes were significantly upregulated. GDF11 could affect apoptosis and ROS, and JNK signaling.

Conclusions

GDF11 may have anti-liver cancer effects by affecting Smad2/3 and inducing apoptosis through the ROS-JNK pathway.

Keywords: growth differentiation factor 11; JNK; liver cancer; ROS; transcriptome

Introduction

Liver cancer has a high incidence among digestive system malignant tumors. Based on the American Cancer Society 2021 statistics report, the incidence of liver cancer continues to increase, with a global death toll of 830,000 cases, ranking third in cancer-related deaths worldwide [1]. Despite therapeutic improvements in recent decades, the prognosis for primary liver cancer remains very poor, requiring the development of new, more effective drugs. The discovery of targeted drugs, especially the success of sorafenib, has opened a new chapter for liver cancer therapy. Many targeted drugs, such as atezolizumab, have been studied continually [2], [3], [4], [5]. However, these drugs are effective only for some patients and are not effective enough to meet clinical needs. It is necessary to explore more effective target drugs.

Growth differentiation factor 11 (GDF11) controls cell proliferation, death, and differentiation in various normal and pathological processes [6], [7], [8]. Furthermore, GDF11 may affect the body’s aging, breast cancer, and other types of cancer [9], [10], [11]. The mechanism of the GDF11 function includes binding type I/II receptors and activating the Smad2/Smad3 protein pathway [3, 12]. This study also detected the roles of GDF11 on Smad-related signaling proteins.

Apoptosis, a classical mode of cell death, is involved in many physiological and pathological processes, such as cancer [13]. Studies have indicated that apoptosis induction could treat various types of cancer, including liver cancer. Reactive oxygen species (ROS) and the mitogen-activated protein kinase (MAPK) signaling pathway are just a couple of the signaling pathways that play a role in regulating apoptosis [14, 15]. ROS are oxygen-containing reactive chemicals that are important in cell signaling and homeostasis. Serine-threonine protein kinases, such as MAPK, can be triggered by various substances, including cytokines, neurotransmitters, hormones, cell stress, and cell adhesion. Many investigators have proved that MAPK signal transduction activation was associated with excessive ROS production [16].

We detected GDF11 levels in liver cancer patients in the present work and performed correlation analysis. In vitro hepatoma cell lines were treated with GDF11, and a transcriptome test was carried out to explore the probable mechanism of GDF11 in liver cancer. The findings of this study lay the groundwork for further enhancing GDF11’s effects on liver disease.

Materials and methods

Agents

From Shanghai Outdo Biotech Co., Ltd., 15 human malignant liver cancer samples and comparable surrounding non-cancerous liver samples were acquired (Shanghai, China). We bought fetal bovine serum from Natocor-Industria Biológica (Cordoba, Argentina). L-O₂, MHCC97-H, and HCCLM3 cells were provided by Shanghai Zhongqiaoxinzhou Biotech (Shanghai, China). Recombinant GDF11 was bought from PeproTech (New Jersey, United States). Anti-GDF11 was bought from Abcam Technology (Cambridge, UK). Anti-p-Smad3 (Ser423/425), anti-Smad3, anti-p-Smad2 (Ser465/467), anti-Smad2, anti-Smad2/3, cleaved caspase-3, cleaved PARP, and Bcl-2 were bought from Cell Signaling Technology (Beverly, MA, United States). Anti-actin was bought from ZSGB-BIO (Beijing, China). Smad3 inhibitor SIS3 was bought from Sigma Aldrich (Missouri, United States). Cell Counting Kit-8 (CCK8) was bought from Dojindo Laboratories (Kumamoto, Japan). Cell cycle assay was bought from KeyGEN BioTECH (Jiangsu, China). Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) apoptosis assay kit was bought from NeoBioscience (Beijing, China). TRNzol was bought from TianGen Biotech Co., Ltd (Beijing, China). Bax was bought from Santa Cruz Biotechnology (California, United States). N-Acetylcysteine (NAC) was bought from MilliporeSigma (Darmstadt, Germany). SP600125 was bought from MedChemExpress LLC (New Jersey, United States).

Cell observation under a microscope

MHCC97-H and HCCLM3 cells of the human liver tumor were cultured with GDF11 (50 or 100 ng/ml). The growth of the cells was photographed by a microscope.

CCK8 cell proliferation assay

MHCC97-H and HCCLM3 cells were grown to a 90% confluence. MHCC97-H and HCCLM3 cells were grown into 96-well culture plates, and each group with six multiple wells of 5 × 10³ cells. After inoculation, the cells were placed at 37 °C, 5% CO_2, for 24 h. In the drug administration group, GDF11 was added and incubated for 24, 48, and 72 h, and 10 μl of CCK8 solution was added at 37 °C for 4 h. The optical density (OD) value was measured by a microplate tester for data analysis at 450 nm [17].

The transcriptome assay

MHCC97-H cells of the human liver tumor were treated with GDF11, the cells were lysed with TRNzol lysate, and the samples were stored at −80 °C. Shanghai Personalbio Biotechnology Co., Ltd. performed transcriptome sequencing. After RNA extraction, purification, and database development, second-generation sequencing was employed. On these libraries, double-terminal sequencing was carried out. The high-quality sequences from the filtered original off-machine data were matched to those from the human reference genome. Each gene’s expression was estimated according to the comparison results, and then expression difference, enrichment, and cluster analysis were performed. Datasets were from the Sequence Read Archive database (https://www.ncbi.nlm.nih.gov/sra/).

Flow cytometry analysis

The logarithmic phase cells were seeded in six-well plates with 2 ml of media containing GDF11 (50 and 100 ng/ml). After the drug incubation, the cells were collected and determined by flow cytometry analysis. Cells were digested with trypsin for the cell cycle assay (KeyGEN BioTECH, Jiangsu). The supernatant was centrifuged and discarded, and the cells were washed 2–3 times with Phosphate Buffer Saline (PBS). A small amount of PBS was added to the cell precipitation, and the cells were re-suspended. The suspended cells were added to 75% precooled ethanol at 4 °C for fixation and gently and evenly beaten. The cells were incubated at 4 °C for 24 h and washed twice with PBS. RNase A and PI (1:9) were added to re-suspend the cells and incubated at room temperature (RT) for 1 h in the dark. Finally, cell cycles were determined by flow cytometry.

Annexin V-FITC/PI apoptosis assay kit was bought from NeoBioscience (Beijing). The cells were cultured in a medium containing GDF11 for 48 h and washed twice with precooled PBS, and 5 × 10⁵ cells were suspended in 200 μl of binding buffer. Moreover, 195 μl of binding buffer and 5 μl of Annexin V-FITC solution were mixed. Then cells were placed in the dark at RT for 10 min. After the mixture was cleaned with PBS, PI was added. The results were determined by flow cytometry [18].

The ROS Assay Kit was obtained from Beyotime Biotechnology (Shanghai) for cell ROS detection. A final concentration of 10 mM Dichlorodihydrofluorescein diacetate (DCFH-DA) was yielded by serum-free medium dilution at 1:1,000. The cells were suspended in diluted DCFH-DA at 10⁸/ml and incubated at 37 °C for 20 min. The medium was shaken every 3–5 min for complete contact between the probe and the cells. The cells were washed 3 times with serum-free medium to remove redundant DCFH-DA completely. The results were determined by flow cytometry [19].

Transmission electron microscopy

The cells were incubated with GDF11 for 48 h, collected using trypsin, and then centrifuged at 1,000 r/min for 5 min. The cells were fixed with 1 ml of 2.5% glutaraldehyde fixative solution for 1 h and then with 1% OsO4 for 1 h. The sample was subjected to ethanol dehydration, epoxy resin embedding, slicing, and staining with uranyl acetate and lead citrate. Ultrathin sections were detected under a JEM-1200EX transmission electron microscope.

Western blot

Human liver cancer cell lines (MHCC97-H and HCCLM3), before incubation with GDF11, were lysed, and the supernatant was collected. The supernatant, Radio Immunoprecipitation Assay (RIPA), lysis buffer, and protein loading buffer were successively added and mixed, and then the proteins were denatured in a thermostatic metal bath. Different glue and concentrated glue were configured according to the formula. Electrophoresis, membrane transfer, and sealing were carried out successively. Polyvinylidene fluoride (PVDF) membrane was placed in primary antibody solution for incubation at 4 °C overnight treatment. After using Tris-buffered saline and Tween-20 (TBST) to clean the membrane, the secondary antibody was added. The enhanced chemiluminescence (ECL) method was performed. The optical density (OD) of the protein bands was detected by Bio-Rad Image Lab Software. β-Actin was the internal reference [20, 21].

Data analysis

Data are expressed as mean ± SEM. The Student’s t-test or one-way ANOVA and then the Holm-Šidák test was used to determine significance. The statistical analysis was conducted by GraphPad Prism 5 (GraphPad Software, Inc., California, United States). p<0.05 was considered a statistically significant difference.

Results

The decreased expression of GDF11 in liver cancer and the activation of the Smad2/3 signaling pathway in hepatoma cell lines

First, we referred to the Oncomine database to preliminarily estimate GDF11 expression decline in liver cancer (Figure 1A). Then, GDF11 was detected in 15 paired samples of liver cancer patients. The immunohistochemistry study revealed that GDF11 expression decreased in human malignant liver tissues than in the neighboring healthy tissues (Figure 1B). Compared with normal liver cell lines, GDF11 protein decreased in hepatoma cell lines (Figure 1C, D). In the meantime, we also analyzed the differences in characteristics, including gender, age, and differentiation. There was no significant difference, as displayed in Table 1.

Figure 1:

The decreased expression of GDF11 in liver cancer and the activation of Smad2/3 signaling in hepatoma cell lines. (A) Oncomine data shows GDF11 expression in normal vs. liver cancer (p=0.01, Student’s t-test), n=19. (B) GDF11 protein expression in 15 paired samples of cancerous and normal liver tissues in Chinese liver cancer patients, n=15. Scale bar, 200 μm. (C, D) GDF11 protein expression in normal and cancer cell lines, n=3. (E) GDF11 protein expression was detected using WB after GDF11 (50 and 100 ng/ml) incubation for 15 min in hepatoma cell lines. (F) p-Smad2/3 was tested after GDF11 treatment in hepatoma cell lines. (G) The Smad3 protein expression was detected after SIS3 incubation. The internal reference was β-actin.

Table 1:

The relationship between GDF11 expression and clinicopathological characteristics in a tissue microarray of 15 liver cancer patients.

Characteristics	n	GDF11	Low expression	p-Value
Characteristics	n	High expression	Low expression	p-Value
Gender				n
Male	12	8	4
Female	3	2	1
Age, years				n
≤60	8	4	4
>60	7	6	1
Differentiation				n
Well/moderate	12	8	4
Poor	3	2	1

We cultured hepatoma cell lines in vitro, which were incubated with recombinant GDF11 cytokine. Recombinant GDF11 can increase GDF11 in both hepatoma cell lines (Figures 1E and S1A). GDF11, a member of the TGF-β family, can increase phosphorylated Smad2 and Smad3 to activate the Smad2/3 signaling (Figures 1F and S1B, C). Smad3 inhibitor SIS3 can effectively decrease the Smad3 expression (Figures 1G and S1D). The above results confirm the effectiveness of recombinant GDF11.

GDF11 incubation decreases cell number and cell viability in hepatoma cell lines in a cell cycle-independent manner

Microscopic observation showed that GDF11 decreased the numbers and changed the morphology of the hepatic carcinoma cells dose-dependent (Figure 2A, B). CCK8 assay demonstrated that GDF11 reduced the cell viability at 50 and 100 ng/ml (Figure 2C).

Figure 2:

GDF11 can inhibit cell growth and viability but does not affect the cell cycle of hepatoma cell lines. (A, B) The cell numbers were counted after GDF11 (50 and 100 ng/ml) incubation in hepatoma cell lines for 48 h, n=8. Scale bar, 200 μm. (C) MHCC97-H and HCCLM3 cells were treated with GDF11 (50 and 100 ng/ml) for 24, 48, and 72 h. The cell viability was detected using CCK8 assay (p=0.01, Student’s t-test), n=12 (D, E) The effect of GDF11 on the cell cycle in MHCC97-H and HCCLM3 cells. CCK8, cell counting Kit-8.

As most anticancer drugs kill cancer cells by inhibiting the cell cycle, the effect of GDF11 in hepatoma cell lines was detected. The cell cycles of MHCC97-H and HCCLM3 cells were analyzed by flow cytometry after GDF11 treatment. However, GDF11 did not significantly affect the cell cycle after GDF11 treatment (Figure 2D, E). Therefore, how does GDF11 work in hepatoma cell lines?

Confirmation that apoptotic signaling pathway participates in the inhibitory function of GDF11

Gene expression was analyzed for differences by using DESeq. The following are screening methods for differentially expressed genes: fold of differential expression as |log2FoldChange| > 1, significance p<0.05. Partial difference analysis results are shown in Table 2.

Table 2:

Differentially expressed genes (part).

Upregulation			Downregulation
Gene name	Log2 fold change	p-Value	Gene name	Log2 fold change	p-Value
AMIGO2	1.224601541	2.09759E-08	SPINK5	−1.215570253	1.28812E-08
SERPINE1	1.027690688	2.19208E-07	MAPK4	−1.175936389	0.002773369
SERPINB5	1.376963214	4.41669E-06	ALDH1A1	−4.681028508	0.012086203
SPINK1	1.164319515	1.10827E-05	RFLNB	−1.548273299	0.020892673
HIST2H4A	3.034361551	3.69551E-05	CES1	-Inf	0.023213031
NKAIN4	1.677622693	0.000135288	MAN1A1	−2.08492545	0.039552287
NAV1	1.498763523	0.000179104	C7orf25	−4.302516885	0.039790396
MARCH4	1.382815941	0.000355389	AKR1B10	−4.302516885	0.039790396
ADAMTS10	1.301446578	0.001458958	FBXO10	−1.787943712	0.049296911

Volcano maps of differential expression genes were drawn by using the R language ggplots2 software. The volcano map displays the distribution of genes, gene expression multiple differences, and significant results (Figure 3A, B). After the GDF11 treatment, 39 genes changed: 30 genes were upregulated (p<0.05), and 9 genes were downregulated (p<0.05).

Figure 3:

Human hepatic carcinoma cells’ ability to undergo cell apoptosis in response to GDF11 therapy and functional enrichment analysis of differentially expressed genes (A) MHCC97-H cells were treated with GDF11 (100 ng/ml). The two dotted lines are the threshold of the twofold difference. The dotted line indicates the p-value=0.05 threshold. The red dots represent the upregulated genes, the blue dots represent the downregulated genes, and the gray dots represent the non-differentially expressed genes. (B) Statistical chart of expression difference analysis results. Red represents upregulated genes, and blue represents downregulated genes. (C) Cluster analysis showing differential gene expression in MHCC97-H cells without or with GDF11 treatment for 48 h (D) KEGG analysis of genes in MHCC97-H cells. The top 10 biological process terms based on fold enrichment are shown. (E) WB of cell apoptosis-related protein levels at 48 h in MHCC97-H cells. β-Actin was used as the control. (F, G) Effect of EA on cell apoptosis progression (early and late apoptosis) by flow cytometry in MHCC97-H cells, n=3. (H) Transmission electron microscopy of MHCC97-H and HCCLM3 cells treated with GDF11.

KEGG enrichment analyses were performed for differential expression genes. The top 10 pathways were selected for illustration, and the results are displayed in Figure 3C, D. These differential genes were enriched in pathways, including the apoptosis pathway, which is reportedly involved in cell death [22].

Cell apoptosis is one of the major processes of cell death. We measured the apoptosis-related proteins following GDF11 treatment to confirm the effect of GDF11 on apoptosis. The WB data demonstrated that GDF11 affected the expression of apoptosis-related proteins, including cleaved PARP, cleaved caspase-3, Bcl-2, and Bax (Figures 3E, S1E). GDF11 was discovered to induce late apoptosis using flow cytometry’s Annexin V-FITC/PI apoptosis test (Figure 3F, G). The transmission electron microscopy results indicated GDF11-induced apoptosis morphology changes, including chromatin pyknosis, cell membrane bubbling, cell shrinkage, and apoptotic bodies (Figure 3H).

Apoptotic signaling pathway is mediated via ROS–JNK signaling

It has been shown that ROS-JNK signaling involves in apoptosis [23, 24]. We detected the ROS level of MHCC97-H and HCCLM3 cells by flow cytometry after GDF11 treatment. The results indicated that GDF11 could increase ROS levels dose-dependent (Figure 4A, B). In the meantime, MAPK signaling was detected using WB. MAPKs include ERK, p38, and JNK. Figures 4C, S1F show that GDF11 can significantly enhance p-JNK. ROS inhibitor NAC and JNK inhibitor SP600125 can reverse the inhibition of GDF11 in human hepatic carcinoma cells (Figure 4D, E). The results indicated that ROS-JNK signaling participates in the GDF11-induced apoptosis pathway.

Figure 4:

GDF11 activates ROS-JNK signaling (A) MHCC97-H cells were treated with GDF11 (50 and 100 ng/ml). The ROS level was detected using flow cytometry. (B) The statistical data of ROS level. (C) WB of MAPK at 48 h in MHCC97-H cells. β-Actin was the control (D) MHCC97-H cells were incubated with ROS inhibitor NAC in advance for 30 min. Then, MHCC97-H cells were treated with GDF11 (100 ng/ml) for 48 h. The cell viability was detected using CCK8 assay, n=6. (E) MHCC97-H cells were incubated with JNK inhibitor SP600125 in advance for 30 min. Then, MHCC97-H cells were treated with GDF11 (100 ng/ml) for 48 h. The cell viability was detected using CCK8 assay, n=6. ROS, ROS; MAPK, mitogen-activated protein kinase; NAC, N-acetylcysteine; CCK8, cell counting Kit-8.

In conclusion, GDF11 may have anti-liver cancer effects by affecting Smad2/3 and inducing apoptosis through the ROS-JNK pathway.

Discussion

GDF11 was first observed in the incisor pulp of rats, and GDF11 protein exists in the form of a protein precursor with no activity of homologous dimer at the initial stage of secretion [25]. GDF11 induces downstream Smad2/3 by binding activin II receptors ActR II A and ActR II B. The phosphorylation of protein Smad2/3 has a function in the pathophysiological process [26].

GDF11 regulates cell proliferation, apoptosis, and differentiation and can reverse aging to a certain extent. It is also associated with many tumor types. For example, GDF11 overexpression can inhibit liver cancer tumors and enhance the sensitivity of cells to cisplatin in vitro. To some extent, it decreases the formation of tumor microvessels, blocks the nutritional supply and invasion pathways of tumors, and inhibits tumor growth and metastasis [27]. GDF11 overexpression can decrease the protein expression level of BRCA1, weaken the DNA repair ability of triple-negative breast cancer through the BRCA1 signaling pathway, and then increase the sensitivity of triple-negative breast cancer to cisplatin [10], indicating that GDF11 has a role in various cancer. Our study found that the mRNA and GDF11 decreased in human malignant liver tissues compared to neighboring healthy tissues by the Oncomine database and tissue microassay. The results were in agreement with the results between normal cell lines and hepatoma cell lines. In vitro, hepatoma cell lines MHCC97-H and HCCLM3 were incubated by recombinant GDF11 cytokine. GDF11 activated Smad2/3 signaling, and GDF11 inhibited cell growth by microscopic observation and CCK8 assay. Therefore, GDF11 is expected to be a valuable predictive biomarker for evaluating cancer patients’ disease severity, stage, and prognosis and has research significance.

Many anticancer drugs have a role in cell cycle arrest, such as vincristine and colchicine [28, 29]. Our study investigated the effect of GDF11 on the hepatic carcinoma cell cycle. However, GDF11 does not affect the cell cycle phase, indicating that inhibition of GDF11 on hepatic carcinoma cells is not through the cell cycle.

To find the mechanism of the GDF11 function, we performed transcriptome experiments. After GDF11 treatment, GDF11 changed the expression of genes and affected the pathways, including apoptosis. The results were verified by flow cytometry and WB.

Apoptosis, a programmed cell death, is a non-inflammatory and active death process in which cells maintain homeostasis under the induction of specific signals [30], [31], [32]. The abnormality of the apoptosis signaling pathway, the loss of apoptosis signaling, or the enhancement of the anti-apoptosis signaling shift will lead to various pathological changes, such as cancer occurrence and metastasis and chemotherapy resistance [33]. Therefore, targeting specific factors of the apoptosis pathway has become an important strategy for developing antitumor drugs.

At present, apoptosis can be generally divided into three signaling pathways: death receptor, endogenous mitochondria, and exogenous endoplasmic reticulum [34]. Death receptors are usually located at the cellular surface and belong to the tumor necrosis factor receptor superfamily. Today, the known death receptors mainly include Fas (Apo-1 and CD95), TNFR1 (DR1), trailr1 (DR4), trailr2 (DR5), and DR3 (apo-3 and trap) [35, 36]. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily. Many investigations have shown that TRAIL can selectively induce tumor cell apoptosis with a little toxic effect on normal tissue cells [37]. Therefore, TRAIL is expected to become an ideal antitumor drug. B-cell lymphoma 2 (Bcl-2) family proteins mediate mitochondrial apoptosis by regulating mitochondrial outer membrane permeabilization (MOMP), thereby activating downstream caspase cascades to execute apoptosis [38]. Bcl-2 family proteins include two categories: one is to inhibit apoptosis (Bcl-2 and BCL XL), and the other is to promote apoptosis (bad, bid, and Bak) [39]. In tumor treatment, there are two strategies to overcome anti-apoptotic Bcl-2 members: antisense oligonucleotides, which can significantly downregulate Bcl-2 protein. Both in vivo and in vitro studies have indicated antitumor activity and increased sensitivity of cells to chemotherapy [40]. Therefore, we also detected the apoptosis-related proteins by WB to verify that GDF11 induced apoptosis.

ROS are a normal by-product of oxygen metabolism [41]. Many highly reactive oxygen molecules will be produced in enzymatic reactions centered on mitochondrial metabolism. ROS are the upstream regulatory molecules of many cellular signaling pathways, and changes in their level will affect the activation and closure of multiple signaling pathways. ROS in the normal body are maintained at a stable level due to the regulation of excessive antioxidants by the enzyme system, which leads to normal cell proliferation and cell transmission. Overproduction of ROS can induce cell apoptosis, cell cycle arrest, and activation and closure of a series of signaling pathways. Increased levels of ROS induce apoptosis of cancer cells, and triggering cancer cell death is a new idea for cancer treatment [42].

The MAPK pathway regulates signaling pathways, proliferation, angiogenesis, invasion and migration, apoptosis, and other physiological processes in cells [43]. This family is further divided into p38, JNK, and ERK subgroups according to their functions and characteristics. p38 MAPK is a major regulator of physiological and pathological signaling in cells. JNK (JNK1, JNK2, and JNK3) regulate cell proliferation, differentiation, death, and apoptosis. The ERK signaling pathway is also composed of five subtypes (ERK1-ERK5), which is the key to maintaining cell homeostasis. ERK has hundreds of substrates involved in cell proliferation, differentiation, apoptosis, and survival. In our study, GDF11 can activate JNK signaling.

We hypothesize that GDF11 suppressed tumor growth by regulating the ROS-JNK axis in hepatoma cell lines (Figure 5).

Figure 5:

Diagram of the mechanism by which GDF11 inhibits cell proliferation and tumor progression in human hepatocellular carcinoma cells.

Conclusions

We found that GDF11 activated the Smad2/3 signaling in hepatoma cell lines and inhibited cell viability. GDF11’s transcription and translation were down-regulated in liver cancer tissues compared to normal tissues. The probable mechanism is through the ROS-JNK-dependent apoptosis pathway. These data indicate that GDF11 is probably a neotype tumor biomarker in patients with hepatocarcinoma.

Corresponding authors: Lianhong Pan and Dan Liu, Chongqing Key Laboratory of Development and Utilization of Genuine Medicinal Materials in Three Gorges Reservoir Area, Chongqing Three Gorges Medical College, Chongqing, P. R. China, E-mail: 20131902023@cqu.edu.cn (L. Pan), liudan@cqtgmc.edu.cn (D. Liu)

Funding source: the Chongqing Key Disciplines of Traditional Chinese Medicine (Basic Theory of Traditional Chinese Medicine) Construction Project

Award Identifier / Grant number: No.16, 2021

Funding source: the Natural Science Research Program of Chongqing Three Gorges Medical College

Award Identifier / Grant number: Nos. 2018xzz01 and 2019XZZ004

Funding source: the Chongqing Natural Science Foundation of Chongqing Science and Technology Bureau

Award Identifier / Grant number: No. cstc2019jcyj-msxmX0607

Funding source: the Science and Technology Research Program of Chongqing Municipal Education Commission

Award Identifier / Grant number: Nos. KJZD-K201902701, KJQN201802702, KJQN202002716

Funding source: the Chongqing Talents Plan Project

Award Identifier / Grant number: No. cstc2022ycjh-bgzxm0226

Funding source: the Chongqing University Innovation Research Group

Award Identifier / Grant number: No. CXQT20030

Funding source: Chongqing Three Gorges Medical College young and Middle-aged Top-notch Talent Project

Award Identifier / Grant number: None

Funding source: the Bayu Scholars Program

Award Identifier / Grant number: None

Research funding: This work was supported by the grants from the Science and Technology Research Program of Chongqing Municipal Education Commission (Nos. KJZD-K201902701, KJQN201802702, KJQN202002716), the Chongqing Natural Science Foundation of Chongqing Science and Technology Bureau (No. cstc2019jcyj-msxmX0607), the Natural Science Research Program of Chongqing Three Gorges Medical College (Nos. 2018xzz01 and 2019XZZ004), the Chongqing Key Disciplines of Traditional Chinese Medicine (Basic Theory of Traditional Chinese Medicine ) Construction Project (No.16, 2021), the Bayu Scholars Program, Chongqing Three Gorges Medical College young and Middle-aged Top-notch Talent Project, the Chongqing University Innovation Research Group (No. CXQT20030), and the Chongqing Talents Plan Project (No. cstc2022ycjh-bgzxm0226).
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. The authors confirm contribution to the paper as follows: study conception and design: Lianhong Pan and Dan Liu; data collection: Yonghui Zhang, Chen Wang, Jiaxin Li, Lishan Jin, Wenxuan Ding, Huan Liu, Na Zhou, Zhengni Ren, Junqi Zhang and Lei Li; analysis and interpretation of results: Yong Wei and Yonghui Zhang; draft manuscript preparation: Lianhong Pan and Dan Liu.
Competing interests: Authors state no conflict of interest.
Informed consent: This article does not contain any studies with human participants.
Ethical approval: This article does not contain any studies with human participants and animal experiments.

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Received: 2022-10-23

Accepted: 2023-01-25

Published Online: 2023-02-15

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/oncologie-2023-0001

Keywords for this article

growth differentiation factor 11; JNK; liver cancer; ROS; transcriptome

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