Anticancer peptide MCP-1 induces ferroptosis in liver cancer HCCLM3 cells by targeting FOXM1/ALOXE3 signal pathway

Anticancer peptide MCP-1 induces ferroptosis in liver cancer HCCLM3 cells by targeting FOXM1/ALOXE3 signal pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Anticancer peptide MCP-1 induces ferroptosis in liver cancer HCCLM3 cells by targeting FOXM1/ALOXE3 signal pathway Fanyue Zhu, Zhixian Shang, Shijie Jia, Yuhong Jiang, Miao Chang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4002517/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 May, 2024 Read the published version in International Journal of Peptide Research and Therapeutics → Version 1 posted 7 You are reading this latest preprint version Abstract FOXM1 is a crucial oncogenic transcription factor involved in almost all cancer hallmark pathways across all cancer types. Our previous work had found that FOXM1 targeted peptide P201 can strongly inhibit the growth of cancer cells including the liver cancer HCCLM3 cells. In addition, by RNA-seq of HCCLM3 cells treated with MCP-1, an anticancer peptide optimized from P201, ALOXE3, a key feature of ferroptosis was significantly elevated while FOXM1 was down-regulated, we wonder if the cell death of HCCLM3 induced by MCP-1 was associated with ferroptosis. Also, the relationship between FOXM1 and ferroptosis was less understood. Hence, in this study, we explore the effect of MCP-1 on ferroptosis and establish the associations among MCP-1, FOXM1 and ALOXE3 in HCCLM3 cells. The results showed that MCP-1 can significantly induce the elevated expression of ALOXE3, decreased content of GSH, down-regulation of GPX4 expression, increased contents of ROS and total iron in HCCLM3 cells. Also, ferrostatin-1, a specific inhibitor for ferroptosis, can reverse the cell death in HCCLM3 cells when co-administrated with MCP-1. TCGA database hepatocellular carcinoma gene expression analysis showed that FOXM1 was negative-related to ALOXE3 and further confirmed by the results of siRNA knockdown of FOXM1 in HCCLM3 cells. Moreover, the co-expressed genes analysis for FOXM1 and ALOXE3 revealed that many of them were closely involved in the regulation of ferroptosis. Taken together, we discovered and confirmed the induction of ferroptosis by MCP-1 in liver cancer HCCLM3 cells and primarily established the relationships among MCP-1, FOXM1 and ALOXE3. MCP-1 FOXM1 ALOXE3 Ferroptosis HCCLM3 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction FOXM1 is a crucial transcription factor belongs to the evolutionarily conserved forkhead box (FOX) superfamily. It has emerged as an important molecule implicated in the initiation, progression, metastasis, angiogenesis and drug resistance of cancers (Bella et al. 2014 ; Nandi et al. 2018 ). It is involved in almost all cancer hallmark pathways across all cancer types and proved to be the “Achilles’ heel” of cancer(Halasi and Gartel 2013 ; Raghuwanshi and Gartel 2023 ). The inhibition of this single transcription factor may induce programmed cell death (PCD) and should be possible to target multiple facets of tumorigenesis (Radhakrishnan and Gartel 2008 ), suggesting FOXM1 has great potential in anticancer therapy. Ferroptosis is a term coined by Stockwell and colleagues in 2012 and a newly recognized form of PCD, which is readily mechanistically and morphologically different from other forms of cell death such as apoptosis, necroptosis, and autophagic cell death (Dixon et al. 2012 ). It is defined as an iron-dependent, non-apoptotic type of cell death resulted from lipid peroxide accumulation and characterized by the increase of reactive oxygen species (ROS) (Dixon and Pratt 2023 ; Newton K et al. 2024 ). In ferroptosis, the mitochondrial respiratory chain promotes lipid peroxidation through cytochrome P450 reductase or arachidonate lipoxygenase (ALOX) (Kuang et al. 2020 ). The mammalian ALOX family consists of six members (ALOXE3, ALOX5, ALOX12, ALOX12B, ALOX15 and ALOX15B), which are responsible for ferroptosis through lipid peroxidation (Li et al. 2020 ), and that ROS formation catalyzed by the ALOX is a necessary step in ferroptosis (Yang et al. 2016 ). Accumulating evidence supports the remarkable potential of ferroptosis in cancer treatment and targeted induction of ferroptosis is an effective strategy for the treatment of cancers (Zhao et al. 2022 ). In addition, pharmacological induction of ferroptosis by bioactive compounds could overcome chemotherapeutic drug resistance (Wang et al. 2023 ), for example, targeting the STAT3-ferroptosis circuit can promote ferroptosis and restore sensitivity to chemotherapy (Ouyang et al. 2022 ). Knockdown of FOXM1 can downregulate the expression of ferroptosis-resistant genes and increase malonaldehyde (MDA) and ROS levels in cisplatin-resistant endometrial cancer cells (Peng et al. 2023 ). Nevertheless, the correlation between ferroptosis and FOXM1 was less reported. In particular, there was no report concerning the association between FOXM1 and ALOXE3. At present, some anticancer drugs such as cisplatin, lapatinib and sorafenib had been found to induce ferroptosis (Chen et al. 2021 ). In our previous work, we had found FOXM1 targeted peptide P201, which was obtained from the selection of phage random dodecapeptide library against the DNA binding domain of FOXM1 (FOXM1-DBD), can strongly inhibit the growth of cancer cells including liver cancer cells (Bi et al. 2017 ; Liu et al. 2017 ). In addition, by RNA-seq analysis of HCCLM3 cells treated with MCP-1, which was optimized from P201 in our lab, ALOXE3 was found to be significantly elevated. We wonder if the cell death of HCCLM3 induced by MCP-1 was associated with ferroptosis in addition to the common PCD. Also, the relationship between FOXM1 and ferroptosis was less understood while they were both involved in drug resistance, the latter is a bottleneck for cancer therapy. Additionally, there was no reports on the study of the correlation between FOXM1 and ALOXE3. Hence, in this work, we firstly confirmed the induction of ferroptosis by MCP-1 in HCCLM3 cells through analyzing the expression or contents of the key features for ferroptosis including ALOXE3, glutathione (GSH), glutathione peroxidase 4 (GPX4), ROS and total iron. Then, ferroptosis inhibitor was used to see if it can reverse the viability of the cells when co-administrated with MCP-1. Later, TCGA hepatocellular carcinoma (HCC) database was used to find if the expression of FOXM1 and ALOXE3 were correlated to the cancers and lifespans of the patients, in addition, the co-expressed genes for FOXM1 and ALOXE3 in HCC patients were also analyzed. Finally, siRNA knockdown of FOXM1 in HCCLM3 cells was employed to further confirm the correlations between FOXM1 and ALOXE3. 2. Material and method 2.1. Cell line and culture The human high metastatic hepatocellular carcinoma HCCLM3 cells (purchased from Bio Biotechnology, Chengdu, P.R. China) were maintained in DMEM high glucose medium (Gibco, USA) supplemented with 10% FBS (Gibco, USA) and antibiotics (100 U/ml of penicillin and 100 mg/ml of streptomycin, Biosharp, Beijing, P.R. China). The cells were cultured at 37 ℃ in a humidified atmosphere of 5% CO 2 . 2.2 Peptide design and synthesis The MCP-1 peptide was composed of three components: a cell-penetrating peptide 9-mer polyarginine (D-enantiomers) at the N-terminus, the dodecapeptide optimized from P201, and a (GS) 2 polypeptide linker between them for flexibility. It was chemically synthesized by Shanghai Qiangyao Biol. Com. (Shanghai, P.R. China). The purity was determined to be greater than 95% by HPLC. In preparation of peptide stock solution, the peptide was dissolved in DMSO at a concentration of 20 µg/µl. It was stored at -80℃ and diluted immediately before use. 2.3 qRT-PCR Cellular total RNA was isolated using Trizol reagent (CWBIO, Jiangsu, P. R. China) according to the manufacturer's instructions. Total RNA was reverse transcribed to cDNA with a reverse transcription kit (Accurate Biology, Hangzhou, P. R. China). qRT-PCR was conducted with a SYBR Green Master Mix Kit (Accurate Biology, Hangzhou, P. R. China) on the StepOne Plus instrument (Thermo, USA). The PCR cycling conditions were 94 ℃ for 3 min, followed by 40 cycles of 94 ℃ for 30 s, 60 ℃ for 20 s and 72 ℃ for 10 s. Each sample was conducted in triplicate. Relative gene expression levels were analyzed using comparative Ct methods where Ct was the cycle threshold number normalized to the internal control of GAPDH. Primers used for qRT-PCR were shown in Table 1 . Table 1 Designed primers used in qRT‒PCR Targeted Gene Forward Primer 5’-3’ Reverse Primer 5’-3’ FOMX1 ACCCAAACCAGCTATGATGCC TCTCCCGTTTCTGCTCGCAAA ALOXE3 ACAACACGCACTTTCTGTGC GGAGCTTGTAGATGGGGTGG GAPDH GCACCGTAATCGGACTCA ATGGTGGTGAAGACGCCAGT 2.4 Western blot Cell lysates were prepared with RIPA (Beyotime, Shanghai, P. R. China) and protein concentration was quantified with the BCA Protein Assay Kit (Beyotime, Shanghai, P. R. China). Subsequently, protein samples (20.0 µg) were separated by 12% SDS-PAGE and then transferred to a PVDF membrane (Immobilon-P, Beijing, P. R. China). After blocking with 5% skim milk in TBST for 2 h at room temperature, the membrane was incubated with primary antibodies [FOXM1 rabbit monoclonal antibody (1:1000, Abcam, UK); ALOXE3 rabbit polyclonal antibody (1:1000, Thermo Fisher, USA); GPX4 rabbit monoclonal antibody (1:1000, Abmart, Shanghai, P. R. China); GAPDH rabbit monoclonal antibody (1:6000, Proteintech, Beijing, P. R. China)] respectively for 1 h at room temperature and then overnight at 4 ℃. After washed three times with TBST, the membrane was incubated with peroxidase-conjugated anti-rabbit secondary antibody (1:8000, Proteintech, Wuhan, P. R. China) for 2 h at room temperature. The membrane was then washed again with TBST three times, immunoreactive bands were visualized by the enhanced chemiluminescence reagent (ECL, Beyotime, Shanghai, P. R. China) and detected by an iBright FL1500 detection system (Thermo Fischer, USA). GAPDH was used as the protein loading control. Densitometric analysis of each band was measured using ImageJ software for quantification. 2.5 CCK-8 assay The cells were seeded at a density of 2×10 3 cells/well into a 96-well plate and incubated at 37℃ with 5% CO 2 overnight. Then, they were co-administrated with various concentrations of ferrostatin-1 (Fer-1) (Adamas, Shanghai, P.R. China) and 15.0 µM MCP-1 for 24 h. After addition of 10 µl CCK-8 solution (Beyotime, Shanghai, P. R. China) to each well, the plate was incubated for another 1 h. The absorbance was measured at 450 nm by using a Synergy H1 microplate reader (Biotek, USA). The percentage of cell viability versus the concentration of Fer-1 and the peptide was then plotted. 2.6 GSH determination The cells were seeded at a density of 3×10 5 cells/well into a 6-well plate and cultured overnight. On the next day, the cells were treated with indicated concentrations of MCP-1 and further incubated for 12 hours. Total protein was extracted and quantified. The intracellular GSH content was detected and calculated using a GSH detection kit (Elabscience, Wuhan, P. R. China) according to the manufacturer’s instructions. The amount of GSH was expressed as µmol of GSH/l solution. 2.7 Detection of iron content The cells were cultured and treated as that of GSH assay. The total iron content in cells was detected and calculated using a total iron colorimetry test kit (Elabscience, Wuhan, P. R. China) according to the manufacturer’s instructions. 2.8 ROS assay The cells were cultured and treated similar to that of GSH assay. For ROS assay, the cultured medium was replaced with serum-free DMEM medium containing DCFH-DA at 1000:1 (v/v) Beyotime, Shanghai, P. R. China) for 20 minutes. The relative level of intracellular fluorescence was quantified by flow cytometry (BD C6 Plus, Becton Dickinson, USA) and the data were analyzed using FlowJo software. 2.9 Bioinformatic analysis Bioinformatic analysis was performed based on a combination of R software, and web-based public bioinformatics tools. Liver hepatocellular carcinoma (LIHC) data were obtained from The Cancer Genome Atlas (TCGA) ( https://gdac.broadinstitute.org/ ), Kaplan-Meier Plotter ( http://kmplot.com/analysis/ ) and LinkedOmics databases ( https://linkedomics.org/login.php ) (Vasaikar et al. 2018 ). RNA-seq data (level 3) for LIHC tissue samples (n = 1109), patients’ clinical features (n = 425), and matching adjacent normal tissue samples (n = 50) were obtained from the TCGA dataset ( https://portal.gdc.cancer.gov ) and integrated using R software (stringr, tidyr, tibble, rstatix, ggprism). The survival curve was generated using the Kaplan-Meier method and compared by log-rank test. For the acquisition of co-expressed genes, LinkedOmics was used to calculate the Pearson’s correlation coefficient for each gene expression in the database (the coefficient is between − 1 and 1), then the orders of the positive or negative associated co-expressed genes were obtained. The Gene Set Enrichment Analysis (GSEA) algorithm were used for Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis and visualization. Enrichment results with FDR < 0.05, or adjusted p < 0.05, were considered statistically significant, and simulated for 1000 times. 2.10 siRNA transfection Two siRNAs designed to target human FOXM1 and one non-silencing control siRNA were purchased from GenePharma Com.(Shanghai, P. R. China). For siRNA transfection, the cells were seeded at a density of 5×10 4 cells/well into a 24-well plate and incubated at 37 ℃ with 5% CO 2 overnight. Transfection was in accordance with the manufacturer’s instructions (Polyplus, France). The final concentrations of siRNAs were 60 pmol/well. After incubation for 24 hours, the transfected cells were harvested for subsequent study. Table 2 siRNA used in this work Targeted Gene sense 5’-3’ Source Negative control (NC) UUCUCCGAACGUGUCACGUTT GenePharma siFOMX1#1 GCCAATCGTTCTCTGACAGAA Merk TRCN0000015546 siFOXM1#2 TGGGATCAAGATTATTAACCA (Madhi et al. 2022 ) 2.11 Statistical analysis GraphPad Prism 9 was used for all statistical analyses. The data were calculated by three independent experiments and presented as means ± standard deviation (SD), the differences between the two groups were analyzed using Student’s t test. The statistical significance was denoted as *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. 3. Results and discussions 3.1 MCP-1 induces ferroptosis in HCCLM3 cells To verify whether MCP-1 can really induce ferroptosis, we firstly confirmed our important finding in our previous transcriptomic sequencing analysis in which the expression of ALOXE3 as the key feature of ferroptosis was significantly upregulated by 5.3 times when treated with MCP-1 in HCCLM3 cells. As expected, both the mRNA and protein expression of ALOXE3 in HCCLM3 cells were significantly upregulated by 5.7 (p < 0.001) (Fig. 1 -a), and 2.9 times (p < 0.001) (Fig. 1 -b, c) respectively when treated with 10 µΜ of MCP-1 for 24 hours and analyzed by qRT-PCR and Western blot. They were consistent with our RNA-seq result and the fact that ferroptosis inducers such as the natural small molecule compound talaroconvolutin A can upregulate the expression of ALOXE3 and kill colorectal cancer cells in vitro and in vivo (Xia et al. 2020 ). Next, the other four important features of ferroptosis including total iron, ROS, GSH and GPX4 were determined. To our current knowledge, abnormal iron metabolism may accelerate the peroxidation of polyunsaturated fatty acids(PUFAs)in cells, it can not only directly catalyze the peroxidation of PUFAs in lipid membrane through the Fenton reaction to produce lipid peroxides but also participate in the synthesis of lipid peroxides as an important cofactor for lipid peroxidase ALOX (Xie et al. 2016 ; Lei et al. 2022 ). GSH-GPX4 system is one of the critical ferroptosis defense mechanism, while GPX4 belongs to the GPX protein family and is the only GPX member capable of converting phospholipid hydroperoxides to phospholipid alcohols (Hassannia et al. 2019 ), they are usually synthesized to resist the lethal accumulation of ROS and reduce its harm to cells. In mitochondria, inactivation of GPX4 weakens the antioxidant capacity of the cell and causes potent ferroptosis (Gan 2021 ). Therefore, when ferroptosis occurs, the total iron content and ROS will increase while the production of GSH and GPX4 will reduce. In this work, both the intracellular ROS and the total iron content were significantly increased while the intracellular GSH and GPX4 were markedly decreased when HCCLM3 cells were treated with 15 µΜ of MCP-1 for 12 hours (p < 0.001, Fig. 2 a-d). In addition, the changes of ROS, GSH, GPX4 and total iron were dose dependent. Moreover, Fer-1, a synthetic compound and specific inhibitor for ferroptosis, was used to see if it can reverse the cell death in HCCLM3 cells when treated with MCP-1. It was reported that Fer-1 can specifically inhibit ferroptosis but not cell death induced by other apoptosis-inducing agents (Dixon et al. 2012 ; Miotto et al. 2019 ). As expected, when HCCLM3 cells were co-administrated with Fer-1 and MCP-1, the cell viabilities were significantly elevated with the increasing concentration of Fer-1 from 2.5 to 5.0 µM (Fig. 2 e), indicating Fer-1 can partly reverse the cell death induced by MCP-1. Collectively, we can conclude that MCP-1 can induce ferroptosis in HCCLM3 cells. 3.2 Bioinformatics analysis supported that FOXM1 has close relations to ALOX3 in regulation of LIHC Since MCP-1 is a FOXM1 targeting peptide which can upregulate ALOXE3 and induce ferroptosis in HCCLM3 cells as convinced above, we proposed there is association between FOXM1 and ALOXE3. In order to confirm this hypothesis, transcriptome data of 374 LIHC samples (including 3 recurrent tumor samples) and 50 paired adjacent tissue controls were collected from TCGA database. Gene expression analysis were firstly performed to compare the differential expressions of FOXM1 and ALOXE3 in the two datasets. The results showed that the expression of FOXM1 in tumor samples was significantly higher than that of the controls (P < 0.001) (Fig. 3 a-b) as evidenced by many other researches (Barger et al. 2019 ; Wei et al. 2022 ). Also, the expression of ALOXE3 in LIHC tissues was significantly higher than that of the controls (P < 0.05) while its expression in recurrent tumors was significantly lower than that of the primary tumors (P < 0.001) (Fig. 3 c-d). This is reasonable, upregulation of ALOXE3 was really reported in HCC and colorectal cancer (Qin et al. 2021 ; Chen and Li 2022 ) while markedly down-regulated in human glioblastoma (GBM) (Yang et al. 2021 ). Next, the impact of the expression of FOXM1 and ALOXE3 on survival of LIHC patients was analyzed by the Kaplan‒Meier survival curves (Fig. 4 ). Expectedly, the patients with higher ALOXE3 expression (P < 0.001) and lower FOXM1 expression (P < 0.001) had higher survival probability and longer life time. Then, the co-expressed genes (Triska et al. 2017 ; van Dam et al. 2017 ) for FOXM1 and ALOXE3 in LIHC patients from LinkedOmics database were analyzed by Pearson’s correlation coefficient using R software. The top 50 genes with strong positive or negative correlation to FOXM1 or ALOXE3 were selected to construct the visual heatmap respectively (Fig. 5 ). Many genes related to the occurrence and development of liver cancer and ferroptosis were involved. For examples of ALOXE3 co-expressed genes, neuronal pentraxin 1 (NPTX1), a downstream target of the AKT pathway, which can inhibit proliferation and promote apoptosis in HCC (Zhao et al. 2019 ) was positively correlated with ALOXE3 expression. In contrast, the apolipoprotein (APOC1, APOE, APOC2, APOC3 and APOC4) and solute carrier protein (SLC38A4, SLC22A3 and SLC47A1) family members were negatively correlated with ALOXE3 expression. APOC1 has been proven to reduce ferroptosis in GBM cells. APOE can effectively inhibit ferroptosis by blocking ferritin autophagy (Hao et al. 2022 ; Zheng et al. 2022 ). The consumption of SLC47A1 can sensitize cells to ferroptosis induction (Belaidi et al. 2022 ). For FOXM1 co-expressed genes, the kinesin superfamily members (KIF18B, KIF4A, KIF23, KIF18A, KIFC1, KIF20A, KIF11, and KIF2C) were positively correlated with FOXM1. It was reported that KIF2C/4A/11/18B/20A/23 is associated with poor prognosis of HCC and can promote cell proliferation, and inhibiting KIF20A/NUAK1/PP1 involved in the KIF20a β/GPX4 pathway can induce cell ferroptosis (Lin et al. 2022 ). Finally, GO and KEGG analysis were used to decipher the involvement of the co-expressed genes including FOXM1 and ALOXE3 in the biological process and metabolic pathways of LIHC (Fig. 6 ). It was found in GO analysis that both ALOXE3 and FOXM1 were positively involved in the cell cycle G2/M phase transition and negatively involved in cell acetaldehyde metabolism, lipid modification, dicarboxylic acid metabolism, drug catabolism, and metabolism of benzene compounds. In KEGG pathway analysis, it was shown that both ALOXE3 and FOXM1 were negatively regulated in fatty acid metabolism and degradation pathway, drug metabolism and valine-leucine-isoleucine degradation. In addition, many terms possibly associated with ferroptosis such as lipid modification, lipid catabolic process, lipid homeostasis were enriched in the GO biological process, while fatty acid metabolism, fatty acid degradation, peroxisome were enriched in the pathways of KEGG analysis for ALOXE3. All these results again convinced our primary conclusions that FOXM1 and ALOX3 has close correlations in regulation of LIHC. 3.3 FOXM1 knockdown can upregulate the expression of ALOXE3 To further verify the negative correlation between FOXM1 and ALOXE3, siRNA knockdown of FOXM1 was carried to see the expression patterns of FOXM1 and ALOXE3. Two siRNAs (#1 and #2) were designed to knockdown FOXM1 and their effects on the expression of FOXM1 and ALOXE3 were assayed by qRT-PCR and Western blot, respectively. It was shown that the efficiency of knocked down by siFOXM1#1 and #2 for the mRNA expression of FOXM1 was − 19.4% and − 78.0%, (P < 0.001) respectively (Fig. 7 -a), while ALOXE3 was upregulated by 3.1 and 2.5 times (P < 0.01) (Fig. 7 -b) respectively; Accordingly, the efficiency of knocked down by siFOXM1#1 and #2 for the protein expression of FOXM1 was − 40.5% and − 94.2% (P < 0.001) respectively (Fig. 7 -d), while ALOXE3 was upregulated by 1.93 and 1.28 times (P < 0.01) respectively (Fig. 7 -e). As FOXM1 transcriptionally regulates the expression of a plethora of genes involved in various cellular processes such as cell cycle, DNA damage response, senescence, apoptosis, migration, invasion, oxidative stress, and drug resistance, many downstream targets such as Cyclin B1, CDC25B, Cyclin A, BCL-2, MMP-2, MMP9, VEGFR were regulated by FOXM1 (Kalathil et al. 2020 ). The results suggested that FOXM1 is involved in the metabolism of ALOXE3, and ALOXE3 may also be a possible downstream target for FOXM1. It again confirmed our hypothesis that knockdown of FOXM1 can upregulate the expression of ALOXE3, leading to the ferroptosis of the cancer cells. Nevertheless, the relationship between FOXM1 and ALOXE3 needs to be further explored. Conclusions In summary, we demonstrated that MCP-1, a FOXM1 targeting peptide optimized from P201, can inhibit the expression of FOXM1 and induce ferroptosis in HCCLM3 cells by upregulating the expression of ALOXE3, total iron content and ROS, while downregulating the content of GSH and expression of GPX4. Meanwhile, it is the first report putting FOXM1 and ALOXE3 together. It should be noted that since FOXM1 can regulate a large number of target genes involved in various biological processes, the exact regulatory role of FOXM1 in ferroptosis and its association with ALOXE3 are still unclear and needed to be further studied. Declarations Author Contribution The draft paper was written by my graduate student FZ and revised by CM and Mrs YJ. The main work was finished by FZ. Coauthors of ZS, SJ, YJ, MC, AL and XH joined parts of the work and analyses. All authors reviewed the manuscript. Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 81872789); the Key Research and Development Project of Chengdu (No. 2018-YF05-00004-SN). References Barger CJ, Branick C, Chee L, Karpf AR (2019) Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer. Cancers (Basel) 11(2):251. https://doi.org/10.3390/cancers11020251 Belaidi AA, Masaldan S, Southon A, Kalinowski P, Acevedo K, Appukuttan AT, Portbury S, Lei P, Agarwal P, Leurgans SE et al (2022) Apolipoprotein E potently inhibits ferroptosis by blocking ferritinophagy. Mol Psychiatry 1–10. https://doi.org/10.1038/s41380-022-01568-w Bella L, Zona S, Nestal de Moraes G, Lam EW-F (2014) FOXM1: A key oncofoetal transcription factor in health and disease. Semin Cancer Biol 29:32–39. https://doi.org/10.1016/j.semcancer.2014.07.008 Bi Z, Liu W, Ding R, Wu Y, Dou R, Zhang W, Yuan X, Liu X, Xiong L, Guo Z, Mao C (2017) A novel peptide, 9R-P201, strongly inhibits the viability, proliferation and migration of liver cancer HepG2 cells and induces apoptosis by down-regulation of FoxM1 expression. Eur J Pharmacol 796:175–189. https://doi.org/10.1016/j.ejphar.2016.12.029 Chen X, Kang R, Kroemer G, Tang D (2021) Broadening horizons: the role of ferroptosis in cancer. Nat Rev Clin Oncol 18(5):280–296. https://doi.org/10.1038/s41571-020-00462-0 Chen Y, Li H (2022) Prognostic and Predictive Models for Left- and Right- Colorectal Cancer Patients: A Bioinformatics Analysis Based on Ferroptosis-Related Genes. Front Oncol 12:833834. https://doi.org/10.3389/fonc.2022.833834 van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP (2017) Gene co-expression analysis for functional classification and gene–disease predictions. Brief Bioinform 19(4):575–592. https://doi.org/10.1093/bib/bbw139 Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, Patel DN, Bauer AJ, Cantley AM, Yang WS et al (2012) Ferroptosis: An Iron-Dependent Form of Non-Apoptotic Cell Death. Cell 149(5):1060–1072. https://doi.org/10.1016/j.cell.2012.03.042 Dixon SJ, Pratt DA (2023) Ferroptosis: A flexible constellation of related biochemical mechanisms. Mol Cell 83(7):1030–1042. https://doi.org/10.1016/j.molcel.2023.03.005 Gan B (2021) Mitochondrial regulation of ferroptosis. J Cell Biol 220(9):202105043. https://doi.org/10.1083/jcb.202105043 Halasi M, Gartel AL (2013) Targeting FOXM1 in cancer. Biochem Pharmacol 85(5):644–652. https://doi.org/10.1016/j.bcp.2012.10.013 Hao X, Zheng Z, Liu H, Zhang Y, Kang J, Kong X, Rong D, Sun Guangshun S, Guoqiang, Liu L et al (2022) Inhibition of APOC1 promotes the transformation of M2 into M1 macrophages via the ferroptosis pathway and enhances anti-PD1 immunotherapy in hepatocellular carcinoma based on single-cell RNA sequencing. Redox Biol 56:102463. https://doi.org/10.1016/j.redox.2022.102463 Hassannia B, Vandenabeele P, Vanden Berghe T (2019) Targeting Ferroptosis to Iron Out Cancer. Cancer Cell 35(6):830–849. https://doi.org/10.1016/j.ccell.2019.04.002 Kalathil D, John S, Nair AS (2020) FOXM1 and Cancer: Faulty Cellular Signaling Derails Homeostasis. Front Oncol 10:626836. https://doi.org/10.3389/fonc.2020.626836 Kuang F, Liu J, Tang D, Kang R (2020) Oxidative Damage and Antioxidant Defense in Ferroptosis. Front Cell Dev Biol 8:586578. https://doi.org/10.3389/fcell.2020.586578 Lei G, Zhuang L, Gan B (2022) Targeting ferroptosis as a vulnerability in cancer. Nat Rev Cancer 22(7):381–396. https://doi.org/10.1038/s41568-022-00459-0 Li C, Zhang Y, Liu J, Kang R, Klionsky DJ, Tang D (2020) Mitochondrial DNA stress triggers autophagy-dependent ferroptotic death. Autophagy 17(4):948–960. https://doi.org/10.1080/15548627.2020.1739447 Lin Z, Liu J, Long F, Kang R, Kroemer G, Tang D, Yang M (2022) The lipid flippase SLC47A1 blocks metabolic vulnerability to ferroptosis. Nat Commun 13(1):7965. https://doi.org/10.1038/s41467-022-35707-2 Liu W, Ding R, Zhang Y, Mao C, Kang R, Meng J, Huang Q, Xiong L, Guo Z (2017) Transcriptome profiling analysis of differentially expressed mRNAs and lncRNAs in HepG2 cells treated with peptide 9R-P201. Biotechnol Lett 39(11):1639–1647. https://doi.org/10.1007/s10529-017-2407-1 Madhi H, Lee J, Choi YE, Li Y, Kim MH, Choi Y, Goh S (2022) FOXM1 Inhibition Enhances the Therapeutic Outcome of Lung Cancer Immunotherapy by Modulating PD-L1 Expression and Cell Proliferation. Adv Sci (Weinh) 9(29):2202702. https://doi.org/10.1002/advs.202202702 Miotto G, Rossetto M, Di Paolo ML, Orian L, Venerando R, Roveri A, Vučković A-M, Bosello Travain V, Zaccarin M, Zennaro L et al (2019) Insight into the mechanism of ferroptosis inhibition by ferrostatin-1. Redox Biol 28:101328. https://doi.org/10.1016/j.redox.2019.101328 Nandi D, Cheema PS, Jaiswal N, Nag A (2018) FoxM1: Repurposing an oncogene as a biomarker. Semin Cancer Biol 52(Pt 1):74–84. https://doi.org/10.1016/j.semcancer.2017.08.009 Ouyang S, Li H, Lou L, Huang Q, Zhang Z, Mo J, Li M, Lu J, Zhu K, Chu Y et al (2022) Inhibition of STAT3-ferroptosis negative regulatory axis suppresses tumor growth and alleviates chemoresistance in gastric cancer. Redox Biol 52:102317. https://doi.org/10.1016/j.redox.2022.102317 Peng M, Hu Q, Wu Z, Wang B, Wang C, Yu F (2023) Mutation of tp53 confers ferroptosis resistance in lung cancer through the foxm1/mef2c axis. Am J Pathol 193(10):1587–1602. https://doi.org/10.1016/j.ajpath.2023.05.003 Qin Y, Pei Z, Feng Z, Lin P, Wang S, Li Y, Huo F, Wang Q, Wang Z, Chen Z-N et al (2021) Oncogenic Activation of YAP Signaling Sensitizes Ferroptosis of Hepatocellular Carcinoma via ALOXE3-Mediated Lipid Peroxidation Accumulation. Front Cell Dev Biol 9:751593. https://doi.org/10.3389/fcell.2021.751593 Radhakrishnan SK, Gartel AL (2008) FOXM1: the Achilles’ heel of cancer? Nat Rev Cancer 8(3). https://doi.org/10.1038/nrc2223-c1 . c1; author reply c2 Raghuwanshi S, Gartel AL (2023) Small-molecule inhibitors targeting FOXM1: Current challenges and future perspectives in cancer treatments. Biochim Biophys Acta Rev Cancer 1878(6):189015. https://doi.org/10.1016/j.bbcan.2023.189015 Triska M, Ivliev A, Nikolsky Y, Tatarinova TV (2017) Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer. Methods Mol Biol 1613:291–310. https://doi.org/10.1007/978-1-4939-7027-8_11 Vasaikar SV, Straub P, Wang J, Zhang B (2018) LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 46(D1):D956–D963. https://doi.org/10.1093/nar/gkx1090 Wang Y, Wu X, Ren Z, Li Y, Zou W, Chen J, Wang H (2023) Overcoming cancer chemotherapy resistance by the induction of ferroptosis. Drug Resist Updat 66:100916. https://doi.org/10.1016/j.drup.2022.100916 Wei G, Yang X, Lu H, Zhang L, Wei Y, Li H, Zhu M, Zhou X (2022) Prognostic value and immunological role of FOXM1 in human solid tumors. Aging 14(22):9128–9148. https://doi.org/10.18632/aging.204394 Xia Y, Liu S, Li C, Ai Z, Shen W, Ren W, Yang X (2020) Discovery of a novel ferroptosis inducer-talaroconvolutin A-killing colorectal cancer cells in vitro and in vivo. Cell Death Dis 11(11):988. https://doi.org/10.1038/s41419-020-03194-2 Xie Y, Hou W, Song X, Yu Y, Huang J, Sun X, Kang R, Tang D (2016) Ferroptosis: process and function. Cell Death Differ 23(3):369–379. https://doi.org/10.1038/cdd.2015.158 Yang WS, Kim KJ, Gaschler MM, Patel M, Shchepinov MS, Stockwell BR (2016) Peroxidation of polyunsaturated fatty acids by lipoxygenases drives ferroptosis. Proc Natl Acad Sci U S A 113(34):E4966–E4975. https://doi.org/10.1073/pnas.1603244113 Yang X, Liu J, Wang C, Cheng KK-Y, Xu H, Li Q, Hua T, Jiang X, Sheng L, Mao J, Liu Z (2021) miR-18a promotes glioblastoma development by down-regulating ALOXE3-mediated ferroptotic and anti-migration activities. Oncogenesis 10(2):15. https://doi.org/10.1038/s41389-021-00304-3 Zhao L, Zhou X, Xie F, Zhang L, Yan H, Huang J, Zhang C, Zhou F, Chen J, Zhang L (2022) Ferroptosis in cancer and cancer immunotherapy. Cancer Commun (Lond) 42(2):88–116. https://doi.org/10.1002/cac2.12250 Zhao Y, Yu Y, Zhao W, You S, Feng M, Xie C, Chi X, Zhang Y, Wang X (2019) As a downstream target of the AKT pathway, NPTX1 inhibits proliferation and promotes apoptosis in hepatocellular carcinoma. Biosci Rep 39(6):BSR20181662. https://doi.org/10.1042/BSR20181662 Zheng X, Chen W, Yi J, Li W, Liu J, Fu W, Ren L, Li S, Ge B, Yang Y et al (2022) Apolipoprotein C1 promotes glioblastoma tumorigenesis by reducing KEAP1/NRF2 and CBS-regulated ferroptosis. Acta Pharmacol Sin 43(11):2977–2992. https://doi.org/10.1038/s41401-022-00917-3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 May, 2024 Read the published version in International Journal of Peptide Research and Therapeutics → Version 1 posted Editorial decision: Revision requested 02 Apr, 2024 Reviews received at journal 24 Mar, 2024 Reviewers agreed at journal 18 Mar, 2024 Reviewers invited by journal 07 Mar, 2024 Editor assigned by journal 01 Mar, 2024 Submission checks completed at journal 01 Mar, 2024 First submitted to journal 01 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4002517","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275877284,"identity":"c5c19821-48b4-4c6b-990b-e0110ab46871","order_by":0,"name":"Fanyue Zhu","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Fanyue","middleName":"","lastName":"Zhu","suffix":""},{"id":275877285,"identity":"a7a086ac-3a0b-4f3b-a5e2-0bf99d07c81a","order_by":1,"name":"Zhixian Shang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhixian","middleName":"","lastName":"Shang","suffix":""},{"id":275877286,"identity":"7e493ca6-0029-4a2c-a84b-8bfb3b35d894","order_by":2,"name":"Shijie Jia","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Shijie","middleName":"","lastName":"Jia","suffix":""},{"id":275877287,"identity":"48b32c57-d593-4eca-8308-262ae0677fe0","order_by":3,"name":"Yuhong Jiang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuhong","middleName":"","lastName":"Jiang","suffix":""},{"id":275877288,"identity":"bd711268-f33b-48a5-b196-b127539f90e8","order_by":4,"name":"Miao Chang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Chang","suffix":""},{"id":275877289,"identity":"da088510-971d-4cc0-b34f-e3e3cafdc151","order_by":5,"name":"Anping Liang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Anping","middleName":"","lastName":"Liang","suffix":""},{"id":275877290,"identity":"2473ab4e-a852-4610-90ce-e9276e656107","order_by":6,"name":"Xinyi Hua","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Hua","suffix":""},{"id":275877291,"identity":"7c24abcd-4458-4767-8231-affbf7eb41b0","order_by":7,"name":"Canquan Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYNCCCgYGxgYQg41oLWeAWtpI0gJWzkasFt32wwc/vJ1Xl9g8v8eA4UPZYQb+2Q34tZidSUuWnLvtcGJjG48B44xzhxkk7hwgoOVAjhkz77YDuSAtzLxthxkMJBIIaDn/BqhlTh1Ey1+itNwA2dLADNHCSJyWZ8mSc44drm9sSys42HMunUfiBkGHJR/88Kamztiw+fDGBz/KrOX4ZxDQAgY8QGzYwMBwAMomUos8cUpHwSgYBaNgJAIASmRDX9zozo4AAAAASUVORK5CYII=","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Canquan","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2024-03-01 08:04:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4002517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4002517/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10989-024-10614-w","type":"published","date":"2024-05-10T21:17:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51996799,"identity":"fe9df284-76e9-4e75-935f-b5587b7eb8eb","added_by":"auto","created_at":"2024-03-05 06:23:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":591296,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of ALOXE3 in HCCLM3 cells after treatment with 0 and 10μM of MCP-1 for 24 h hours. a: qRT-PCR assay of the relative mRNA expression; b, c: Western blot determination of the protein expression of ALOXE3. GAPDH was used as the internal control. ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/e4ab0c303a01e2d8c574c6e4.png"},{"id":51996800,"identity":"b9a238c4-5c43-4290-9e20-32f6726a5234","added_by":"auto","created_at":"2024-03-05 06:23:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1617707,"visible":true,"origin":"","legend":"\u003cp\u003eDeterminations of the ferroptosis related key features in HCCLM3 cells treated with 0, 5 and 15 μΜ of MCP-1 for 12 hours. a: Total iron content assay; b: Intracellular GSH assay; c-d: The expression of GPX4 was determined by Western blot; e: ferroptosis inhibitor ferrostatin-1(Fer-1, 0, 2.5, 5 µM) reversed the viability of HCCLM3 cells induced by MCP-1 (15µM); f: Fluorescence quantification of ROS by flow cytometry. *p \u0026lt; 0.05, ***p \u0026lt; 0.001 and ****p \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/f82417d56fa16ccf77d50050.png"},{"id":51996798,"identity":"e89465f4-1fbd-4449-8a3b-c7abd1dd5e1f","added_by":"auto","created_at":"2024-03-05 06:23:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256968,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of ALOXE3 and FOXM1 in normal and LIHC based cancer samples. a: Higher expression of FOXM1 was found in tumor samples as compared with the matched normal tissues; b: Higher expression of FOXM1 was found in primary solid tumor samples as compared with the matched solid normal tissues; c: Higher expression of ALOXE3 was found in tumor samples as compared with the matched normal tissues; d: Higher expression of ALOXE3 was found in primary solid tumor samples as compared with the recurrent solid tumor tissues. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/28519e1a7b1a1527278bbb87.png"},{"id":51996801,"identity":"06fd6084-156e-4034-84f8-c49781a52883","added_by":"auto","created_at":"2024-03-05 06:23:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114955,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier plots of survivalanalysis for LIHC samples with high and low expressionof FOXM1 and ALOXE3. a: The high and low expression of ALOXE3 (adjusted hazard ratio (HR), 0.53; 95% confidence interval [CI], 0.37 to 0.75); b: The high and low expression of FOXM1 (adjusted HR, 1.91; 95% CI, 1.33 to 2.74). The data were analyzed by logrank test, and logrank p\u0026lt;0.05 was statistically significant\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/249a65ce9225cf49e2e96385.png"},{"id":51996805,"identity":"5360524c-eee0-49e4-b2b5-c066b5a5c360","added_by":"auto","created_at":"2024-03-05 06:23:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6826835,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the top 50 different expressed genes with positive or negative correlations with ALOXE3 and FOXM1. a: The top 50 genes with strong positive correlation with ALOXE3; b: The top 50 genes with strong negative correlation with ALOXE3; c: The top 50 genes with strong positive correlation with FOXM1; d: The top 50 genes with strong negative correlation with FOXM1. Genes with higher expression were shown in red, while lower expressions were shown in blue\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/5facfee84b979007aa1c1ccb.png"},{"id":51996802,"identity":"fbd7cc96-bd7a-47ce-8547-91b7b1a301ea","added_by":"auto","created_at":"2024-03-05 06:23:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2010191,"visible":true,"origin":"","legend":"\u003cp\u003eGO biological process terms and KEGG function enrichment analyses associated with ALOXE3 and FOXM1 were shown in histogram. a, c: GO Biological process terms for FOXM1 and ALOXE3 respectively; b, d: KEGG enrichment analyses for FOXM1 and ALOXE3 respectively. GO and KEGG analyses were based on the differentially expressed genes (DEGs) (|log2FC| ≥ 1, FDR \u0026lt; 0.05) between different groups. Gene set enrichment analysis (GSEA) was used to analyze the biological process (GO_BP) and KEGG pathway. Normalized enrichment score（NES）value was used to determine the activation or inhibition of the pathway. NES\u0026gt;0 means that the gene set was enriched at the top of the list, and the gene expression was up-regulated. NES\u0026lt;0 means that the gene set was enriched at the bottom of the list, and the gene expression was down regulated\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/a3a15b99f41b7c60ec3d1dbe.png"},{"id":51997302,"identity":"d1b8435d-82fc-41af-a3aa-ea278a923752","added_by":"auto","created_at":"2024-03-05 06:31:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":170169,"visible":true,"origin":"","legend":"\u003cp\u003esiRNA knockdown of FOXM1 upregulated the expression of ALOXE3. a-b: mRNA expression of FOXM1 and ALOXE3 respectively when siRNA knockdown of FOXM1 and assayed by qRT-PCR; c: Western blot of FOXM1 and ALOXE3 when siRNA knockdown of FOXM1; d: Relative protein expression of FOXM1 when siRNA knockdown of FOXM1; e：Relative protein expression of ALOXE3 when siRNA knockdown of FOXM1. GAPDH was used as the protein loading control\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/1d0ff0feabd8ccaf55156120.png"},{"id":56488099,"identity":"20496619-f682-4a7b-b207-0be32aaf569b","added_by":"auto","created_at":"2024-05-14 21:29:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1730331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4002517/v1/2deaa024-2d0a-426f-b0b7-03c9e5300378.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anticancer peptide MCP-1 induces ferroptosis in liver cancer HCCLM3 cells by targeting FOXM1/ALOXE3 signal pathway","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFOXM1 is a crucial transcription factor belongs to the evolutionarily conserved forkhead box (FOX) superfamily. It has emerged as an important molecule implicated in the initiation, progression, metastasis, angiogenesis and drug resistance of cancers (Bella et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nandi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is involved in almost all cancer hallmark pathways across all cancer types and proved to be the \u0026ldquo;Achilles\u0026rsquo; heel\u0026rdquo; of cancer(Halasi and Gartel \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Raghuwanshi and Gartel \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The inhibition of this single transcription factor may induce programmed cell death (PCD) and should be possible to target multiple facets of tumorigenesis (Radhakrishnan and Gartel \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), suggesting FOXM1 has great potential in anticancer therapy.\u003c/p\u003e \u003cp\u003eFerroptosis is a term coined by Stockwell and colleagues in 2012 and a newly recognized form of PCD, which is readily mechanistically and morphologically different from other forms of cell death such as apoptosis, necroptosis, and autophagic cell death (Dixon et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is defined as an iron-dependent, non-apoptotic type of cell death resulted from lipid peroxide accumulation and characterized by the increase of reactive oxygen species (ROS) (Dixon and Pratt \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Newton K et al. 2024 ). In ferroptosis, the mitochondrial respiratory chain promotes lipid peroxidation through cytochrome P450 reductase or arachidonate lipoxygenase (ALOX) (Kuang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The mammalian ALOX family consists of six members (ALOXE3, ALOX5, ALOX12, ALOX12B, ALOX15 and ALOX15B), which are responsible for ferroptosis through lipid peroxidation (Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and that ROS formation catalyzed by the ALOX is a necessary step in ferroptosis (Yang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accumulating evidence supports the remarkable potential of ferroptosis in cancer treatment and targeted induction of ferroptosis is an effective strategy for the treatment of cancers (Zhao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, pharmacological induction of ferroptosis by bioactive compounds could overcome chemotherapeutic drug resistance (Wang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), for example, targeting the STAT3-ferroptosis circuit can promote ferroptosis and restore sensitivity to chemotherapy (Ouyang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Knockdown of FOXM1 can downregulate the expression of ferroptosis-resistant genes and increase malonaldehyde (MDA) and ROS levels in cisplatin-resistant endometrial cancer cells (Peng et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, the correlation between ferroptosis and FOXM1 was less reported. In particular, there was no report concerning the association between FOXM1 and ALOXE3.\u003c/p\u003e \u003cp\u003eAt present, some anticancer drugs such as cisplatin, lapatinib and sorafenib had been found to induce ferroptosis (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our previous work, we had found FOXM1 targeted peptide P201, which was obtained from the selection of phage random dodecapeptide library against the DNA binding domain of FOXM1 (FOXM1-DBD), can strongly inhibit the growth of cancer cells including liver cancer cells (Bi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, by RNA-seq analysis of HCCLM3 cells treated with MCP-1, which was optimized from P201 in our lab, ALOXE3 was found to be significantly elevated. We wonder if the cell death of HCCLM3 induced by MCP-1 was associated with ferroptosis in addition to the common PCD. Also, the relationship between FOXM1 and ferroptosis was less understood while they were both involved in drug resistance, the latter is a bottleneck for cancer therapy. Additionally, there was no reports on the study of the correlation between FOXM1 and ALOXE3. Hence, in this work, we firstly confirmed the induction of ferroptosis by MCP-1 in HCCLM3 cells through analyzing the expression or contents of the key features for ferroptosis including ALOXE3, glutathione (GSH), glutathione peroxidase 4 (GPX4), ROS and total iron. Then, ferroptosis inhibitor was used to see if it can reverse the viability of the cells when co-administrated with MCP-1. Later, TCGA hepatocellular carcinoma (HCC) database was used to find if the expression of FOXM1 and ALOXE3 were correlated to the cancers and lifespans of the patients, in addition, the co-expressed genes for FOXM1 and ALOXE3 in HCC patients were also analyzed. Finally, siRNA knockdown of FOXM1 in HCCLM3 cells was employed to further confirm the correlations between FOXM1 and ALOXE3.\u003c/p\u003e"},{"header":"2. Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Cell line and culture\u003c/h2\u003e \u003cp\u003eThe human high metastatic hepatocellular carcinoma HCCLM3 cells (purchased from Bio Biotechnology, Chengdu, P.R. China) were maintained in DMEM high glucose medium (Gibco, USA) supplemented with 10% FBS (Gibco, USA) and antibiotics (100 U/ml of penicillin and 100 mg/ml of streptomycin, Biosharp, Beijing, P.R. China). The cells were cultured at 37 ℃ in a humidified atmosphere of 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Peptide design and synthesis\u003c/h2\u003e \u003cp\u003eThe MCP-1 peptide was composed of three components: a cell-penetrating peptide 9-mer polyarginine (D-enantiomers) at the N-terminus, the dodecapeptide optimized from P201, and a (GS)\u003csub\u003e2\u003c/sub\u003e polypeptide linker between them for flexibility. It was chemically synthesized by Shanghai Qiangyao Biol. Com. (Shanghai, P.R. China). The purity was determined to be greater than 95% by HPLC. In preparation of peptide stock solution, the peptide was dissolved in DMSO at a concentration of 20 \u0026micro;g/\u0026micro;l. It was stored at -80℃ and diluted immediately before use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 qRT-PCR\u003c/h2\u003e \u003cp\u003eCellular total RNA was isolated using Trizol reagent (CWBIO, Jiangsu, P. R. China) according to the manufacturer's instructions. Total RNA was reverse transcribed to cDNA with a reverse transcription kit (Accurate Biology, Hangzhou, P. R. China). qRT-PCR was conducted with a SYBR Green Master Mix Kit (Accurate Biology, Hangzhou, P. R. China) on the StepOne Plus instrument (Thermo, USA). The PCR cycling conditions were 94 ℃ for 3 min, followed by 40 cycles of 94 ℃ for 30 s, 60 ℃ for 20 s and 72 ℃ for 10 s. Each sample was conducted in triplicate. Relative gene expression levels were analyzed using comparative Ct methods where Ct was the cycle threshold number normalized to the internal control of GAPDH. Primers used for qRT-PCR were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDesigned primers used in qRT‒PCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer 5\u0026rsquo;-3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse Primer 5\u0026rsquo;-3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOMX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCCAAACCAGCTATGATGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCTCCCGTTTCTGCTCGCAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALOXE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACAACACGCACTTTCTGTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAGCTTGTAGATGGGGTGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACCGTAATCGGACTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATGGTGGTGAAGACGCCAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Western blot\u003c/h2\u003e \u003cp\u003eCell lysates were prepared with RIPA (Beyotime, Shanghai, P. R. China) and protein concentration was quantified with the BCA Protein Assay Kit (Beyotime, Shanghai, P. R. China). Subsequently, protein samples (20.0 \u0026micro;g) were separated by 12% SDS-PAGE and then transferred to a PVDF membrane (Immobilon-P, Beijing, P. R. China). After blocking with 5% skim milk in TBST for 2 h at room temperature, the membrane was incubated with primary antibodies [FOXM1 rabbit monoclonal antibody (1:1000, Abcam, UK); ALOXE3 rabbit polyclonal antibody (1:1000, Thermo Fisher, USA); GPX4 rabbit monoclonal antibody (1:1000, Abmart, Shanghai, P. R. China); GAPDH rabbit monoclonal antibody (1:6000, Proteintech, Beijing, P. R. China)] respectively for 1 h at room temperature and then overnight at 4 ℃. After washed three times with TBST, the membrane was incubated with peroxidase-conjugated anti-rabbit secondary antibody (1:8000, Proteintech, Wuhan, P. R. China) for 2 h at room temperature. The membrane was then washed again with TBST three times, immunoreactive bands were visualized by the enhanced chemiluminescence reagent (ECL, Beyotime, Shanghai, P. R. China) and detected by an iBright FL1500 detection system (Thermo Fischer, USA). GAPDH was used as the protein loading control. Densitometric analysis of each band was measured using ImageJ software for quantification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 CCK-8 assay\u003c/h2\u003e \u003cp\u003eThe cells were seeded at a density of 2\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells/well into a 96-well plate and incubated at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e overnight. Then, they were co-administrated with various concentrations of ferrostatin-1 (Fer-1) (Adamas, Shanghai, P.R. China) and 15.0 \u0026micro;M MCP-1 for 24 h. After addition of 10 \u0026micro;l CCK-8 solution (Beyotime, Shanghai, P. R. China) to each well, the plate was incubated for another 1 h. The absorbance was measured at 450 nm by using a Synergy H1 microplate reader (Biotek, USA). The percentage of cell viability versus the concentration of Fer-1 and the peptide was then plotted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 GSH determination\u003c/h2\u003e \u003cp\u003eThe cells were seeded at a density of 3\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well into a 6-well plate and cultured overnight. On the next day, the cells were treated with indicated concentrations of MCP-1 and further incubated for 12 hours. Total protein was extracted and quantified. The intracellular GSH content was detected and calculated using a GSH detection kit (Elabscience, Wuhan, P. R. China) according to the manufacturer\u0026rsquo;s instructions. The amount of GSH was expressed as \u0026micro;mol of GSH/l solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Detection of iron content\u003c/h2\u003e \u003cp\u003eThe cells were cultured and treated as that of GSH assay. The total iron content in cells was detected and calculated using a total iron colorimetry test kit (Elabscience, Wuhan, P. R. China) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 ROS assay\u003c/h2\u003e \u003cp\u003eThe cells were cultured and treated similar to that of GSH assay. For ROS assay, the cultured medium was replaced with serum-free DMEM medium containing DCFH-DA at 1000:1 (v/v) Beyotime, Shanghai, P. R. China) for 20 minutes. The relative level of intracellular fluorescence was quantified by flow cytometry (BD C6 Plus, Becton Dickinson, USA) and the data were analyzed using FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Bioinformatic analysis\u003c/h2\u003e \u003cp\u003eBioinformatic analysis was performed based on a combination of R software, and web-based public bioinformatics tools. Liver hepatocellular carcinoma (LIHC) data were obtained from The Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdac.broadinstitute.org/\u003c/span\u003e\u003cspan address=\"https://gdac.broadinstitute.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Kaplan-Meier Plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and LinkedOmics databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkedomics.org/login.php\u003c/span\u003e\u003cspan address=\"https://linkedomics.org/login.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Vasaikar et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). RNA-seq data (level 3) for LIHC tissue samples (n\u0026thinsp;=\u0026thinsp;1109), patients\u0026rsquo; clinical features (n\u0026thinsp;=\u0026thinsp;425), and matching adjacent normal tissue samples (n\u0026thinsp;=\u0026thinsp;50) were obtained from the TCGA dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and integrated using R software (stringr, tidyr, tibble, rstatix, ggprism). The survival curve was generated using the Kaplan-Meier method and compared by log-rank test. For the acquisition of co-expressed genes, LinkedOmics was used to calculate the Pearson\u0026rsquo;s correlation coefficient for each gene expression in the database (the coefficient is between \u0026minus;\u0026thinsp;1 and 1), then the orders of the positive or negative associated co-expressed genes were obtained. The Gene Set Enrichment Analysis (GSEA) algorithm were used for Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis and visualization. Enrichment results with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, or adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, were considered statistically significant, and simulated for 1000 times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 siRNA transfection\u003c/h2\u003e \u003cp\u003eTwo siRNAs designed to target human FOXM1 and one non-silencing control siRNA were purchased from GenePharma Com.(Shanghai, P. R. China). For siRNA transfection, the cells were seeded at a density of 5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/well into a 24-well plate and incubated at 37 ℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e overnight. Transfection was in accordance with the manufacturer\u0026rsquo;s instructions (Polyplus, France). The final concentrations of siRNAs were 60 pmol/well. After incubation for 24 hours, the transfected cells were harvested for subsequent study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esiRNA used in this work\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esense 5\u0026rsquo;-3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative control (NC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUUCUCCGAACGUGUCACGUTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenePharma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiFOMX1#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCAATCGTTCTCTGACAGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMerk TRCN0000015546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiFOXM1#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGGATCAAGATTATTAACCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Madhi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Statistical analysis\u003c/h2\u003e \u003cp\u003eGraphPad Prism 9 was used for all statistical analyses. The data were calculated by three independent experiments and presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), the differences between the two groups were analyzed using Student\u0026rsquo;s t test. The statistical significance was denoted as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 MCP-1 induces ferroptosis in HCCLM3 cells\u003c/h2\u003e \u003cp\u003eTo verify whether MCP-1 can really induce ferroptosis, we firstly confirmed our important finding in our previous transcriptomic sequencing analysis in which the expression of ALOXE3 as the key feature of ferroptosis was significantly upregulated by 5.3 times when treated with MCP-1 in HCCLM3 cells. As expected, both the mRNA and protein expression of ALOXE3 in HCCLM3 cells were significantly upregulated by 5.7 (p \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-a), and 2.9 times (p \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-b, c) respectively when treated with 10 µΜ of MCP-1 for 24 hours and analyzed by qRT-PCR and Western blot. They were consistent with our RNA-seq result and the fact that ferroptosis inducers such as the natural small molecule compound talaroconvolutin A can upregulate the expression of ALOXE3 and kill colorectal cancer cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e (Xia et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Next, the other four important features of ferroptosis including total iron, ROS, GSH and GPX4 were determined. To our current knowledge, abnormal iron metabolism may accelerate the peroxidation of polyunsaturated fatty acids(PUFAs)in cells, it can not only directly catalyze the peroxidation of PUFAs in lipid membrane through the Fenton reaction to produce lipid peroxides but also participate in the synthesis of lipid peroxides as an important cofactor for lipid peroxidase ALOX (Xie et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lei et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). GSH-GPX4 system is one of the critical ferroptosis defense mechanism, while GPX4 belongs to the GPX protein family and is the only GPX member capable of converting phospholipid hydroperoxides to phospholipid alcohols (Hassannia et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), they are usually synthesized to resist the lethal accumulation of ROS and reduce its harm to cells. In mitochondria, inactivation of GPX4 weakens the antioxidant capacity of the cell and causes potent ferroptosis (Gan \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, when ferroptosis occurs, the total iron content and ROS will increase while the production of GSH and GPX4 will reduce. In this work, both the intracellular ROS and the total iron content were significantly increased while the intracellular GSH and GPX4 were markedly decreased when HCCLM3 cells were treated with 15 µΜ of MCP-1 for 12 hours (p \u0026lt; 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d). In addition, the changes of ROS, GSH, GPX4 and total iron were dose dependent. Moreover, Fer-1, a synthetic compound and specific inhibitor for ferroptosis, was used to see if it can reverse the cell death in HCCLM3 cells when treated with MCP-1. It was reported that Fer-1 can specifically inhibit ferroptosis but not cell death induced by other apoptosis-inducing agents (Dixon et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Miotto et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As expected, when HCCLM3 cells were co-administrated with Fer-1 and MCP-1, the cell viabilities were significantly elevated with the increasing concentration of Fer-1 from 2.5 to 5.0 µM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), indicating Fer-1 can partly reverse the cell death induced by MCP-1. Collectively, we can conclude that MCP-1 can induce ferroptosis in HCCLM3 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Bioinformatics analysis supported that FOXM1 has close relations to ALOX3 in regulation of LIHC\u003c/h2\u003e \u003cp\u003eSince MCP-1 is a FOXM1 targeting peptide which can upregulate ALOXE3 and induce ferroptosis in HCCLM3 cells as convinced above, we proposed there is association between FOXM1 and ALOXE3. In order to confirm this hypothesis, transcriptome data of 374 LIHC samples (including 3 recurrent tumor samples) and 50 paired adjacent tissue controls were collected from TCGA database. Gene expression analysis were firstly performed to compare the differential expressions of FOXM1 and ALOXE3 in the two datasets. The results showed that the expression of FOXM1 in tumor samples was significantly higher than that of the controls (P \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b) as evidenced by many other researches (Barger et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, the expression of ALOXE3 in LIHC tissues was significantly higher than that of the controls (P \u0026lt; 0.05) while its expression in recurrent tumors was significantly lower than that of the primary tumors (P \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d). This is reasonable, upregulation of ALOXE3 was really reported in HCC and colorectal cancer (Qin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen and Li \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) while markedly down-regulated in human glioblastoma (GBM) (Yang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Next, the impact of the expression of FOXM1 and ALOXE3 on survival of LIHC patients was analyzed by the Kaplan‒Meier survival curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Expectedly, the patients with higher ALOXE3 expression (P \u0026lt; 0.001) and lower FOXM1 expression (P \u0026lt; 0.001) had higher survival probability and longer life time. Then, the co-expressed genes (Triska et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; van Dam et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for FOXM1 and ALOXE3 in LIHC patients from LinkedOmics database were analyzed by Pearson’s correlation coefficient using R software. The top 50 genes with strong positive or negative correlation to FOXM1 or ALOXE3 were selected to construct the visual heatmap respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Many genes related to the occurrence and development of liver cancer and ferroptosis were involved. For examples of ALOXE3 co-expressed genes, neuronal pentraxin 1 (NPTX1), a downstream target of the AKT pathway, which can inhibit proliferation and promote apoptosis in HCC (Zhao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was positively correlated with ALOXE3 expression. In contrast, the apolipoprotein (APOC1, APOE, APOC2, APOC3 and APOC4) and solute carrier protein (SLC38A4, SLC22A3 and SLC47A1) family members were negatively correlated with ALOXE3 expression. APOC1 has been proven to reduce ferroptosis in GBM cells. APOE can effectively inhibit ferroptosis by blocking ferritin autophagy (Hao et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The consumption of SLC47A1 can sensitize cells to ferroptosis induction (Belaidi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For FOXM1 co-expressed genes, the kinesin superfamily members (KIF18B, KIF4A, KIF23, KIF18A, KIFC1, KIF20A, KIF11, and KIF2C) were positively correlated with FOXM1. It was reported that KIF2C/4A/11/18B/20A/23 is associated with poor prognosis of HCC and can promote cell proliferation, and inhibiting KIF20A/NUAK1/PP1 involved in the KIF20a β/GPX4 pathway can induce cell ferroptosis (Lin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Finally, GO and KEGG analysis were used to decipher the involvement of the co-expressed genes including FOXM1 and ALOXE3 in the biological process and metabolic pathways of LIHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). It was found in GO analysis that both ALOXE3 and FOXM1 were positively involved in the cell cycle G2/M phase transition and negatively involved in cell acetaldehyde metabolism, lipid modification, dicarboxylic acid metabolism, drug catabolism, and metabolism of benzene compounds. In KEGG pathway analysis, it was shown that both ALOXE3 and FOXM1 were negatively regulated in fatty acid metabolism and degradation pathway, drug metabolism and valine-leucine-isoleucine degradation. In addition, many terms possibly associated with ferroptosis such as lipid modification, lipid catabolic process, lipid homeostasis were enriched in the GO biological process, while fatty acid metabolism, fatty acid degradation, peroxisome were enriched in the pathways of KEGG analysis for ALOXE3. All these results again convinced our primary conclusions that FOXM1 and ALOX3 has close correlations in regulation of LIHC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 FOXM1 knockdown can upregulate the expression of ALOXE3\u003c/h2\u003e \u003cp\u003eTo further verify the negative correlation between FOXM1 and ALOXE3, siRNA knockdown of FOXM1 was carried to see the expression patterns of FOXM1 and ALOXE3. Two siRNAs (#1 and #2) were designed to knockdown FOXM1 and their effects on the expression of FOXM1 and ALOXE3 were assayed by qRT-PCR and Western blot, respectively. It was shown that the efficiency of knocked down by siFOXM1#1 and #2 for the mRNA expression of FOXM1 was − 19.4% and − 78.0%, (P \u0026lt; 0.001) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-a), while ALOXE3 was upregulated by 3.1 and 2.5 times (P \u0026lt; 0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-b) respectively; Accordingly, the efficiency of knocked down by siFOXM1#1 and #2 for the protein expression of FOXM1 was − 40.5% and − 94.2% (P \u0026lt; 0.001) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-d), while ALOXE3 was upregulated by 1.93 and 1.28 times (P \u0026lt; 0.01) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e-e). As FOXM1 transcriptionally regulates the expression of a plethora of genes involved in various cellular processes such as cell cycle, DNA damage response, senescence, apoptosis, migration, invasion, oxidative stress, and drug resistance, many downstream targets such as Cyclin B1, CDC25B, Cyclin A, BCL-2, MMP-2, MMP9, VEGFR were regulated by FOXM1 (Kalathil et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results suggested that FOXM1 is involved in the metabolism of ALOXE3, and ALOXE3 may also be a possible downstream target for FOXM1. It again confirmed our hypothesis that knockdown of FOXM1 can upregulate the expression of ALOXE3, leading to the ferroptosis of the cancer cells. Nevertheless, the relationship between FOXM1 and ALOXE3 needs to be further explored.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we demonstrated that MCP-1, a FOXM1 targeting peptide optimized from P201, can inhibit the expression of FOXM1 and induce ferroptosis in HCCLM3 cells by upregulating the expression of ALOXE3, total iron content and ROS, while downregulating the content of GSH and expression of GPX4. Meanwhile, it is the first report putting FOXM1 and ALOXE3 together. It should be noted that since FOXM1 can regulate a large number of target genes involved in various biological processes, the exact regulatory role of FOXM1 in ferroptosis and its association with ALOXE3 are still unclear and needed to be further studied.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe draft paper was written by my graduate student FZ and revised by CM and Mrs YJ. The main work was finished by FZ. Coauthors of ZS, SJ, YJ, MC, AL and XH joined parts of the work and analyses. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 81872789); the Key Research and Development Project of Chengdu (No. 2018-YF05-00004-SN).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarger CJ, Branick C, Chee L, Karpf AR (2019) Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer. Cancers (Basel) 11(2):251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers11020251\u003c/span\u003e\u003cspan address=\"10.3390/cancers11020251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelaidi AA, Masaldan S, Southon A, Kalinowski P, Acevedo K, Appukuttan AT, Portbury S, Lei P, Agarwal P, Leurgans SE et al (2022) Apolipoprotein E potently inhibits ferroptosis by blocking ferritinophagy. Mol Psychiatry 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41380-022-01568-w\u003c/span\u003e\u003cspan address=\"10.1038/s41380-022-01568-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBella L, Zona S, Nestal de Moraes G, Lam EW-F (2014) FOXM1: A key oncofoetal transcription factor in health and disease. Semin Cancer Biol 29:32\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.semcancer.2014.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.semcancer.2014.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi Z, Liu W, Ding R, Wu Y, Dou R, Zhang W, Yuan X, Liu X, Xiong L, Guo Z, Mao C (2017) A novel peptide, 9R-P201, strongly inhibits the viability, proliferation and migration of liver cancer HepG2 cells and induces apoptosis by down-regulation of FoxM1 expression. Eur J Pharmacol 796:175\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejphar.2016.12.029\u003c/span\u003e\u003cspan address=\"10.1016/j.ejphar.2016.12.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Kang R, Kroemer G, Tang D (2021) Broadening horizons: the role of ferroptosis in cancer. Nat Rev Clin Oncol 18(5):280\u0026ndash;296. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41571-020-00462-0\u003c/span\u003e\u003cspan address=\"10.1038/s41571-020-00462-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Li H (2022) Prognostic and Predictive Models for Left- and Right- Colorectal Cancer Patients: A Bioinformatics Analysis Based on Ferroptosis-Related Genes. Front Oncol 12:833834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2022.833834\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.833834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dam S, V\u0026otilde;sa U, van der Graaf A, Franke L, de Magalh\u0026atilde;es JP (2017) Gene co-expression analysis for functional classification and gene\u0026ndash;disease predictions. Brief Bioinform 19(4):575\u0026ndash;592. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bib/bbw139\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbw139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, Patel DN, Bauer AJ, Cantley AM, Yang WS et al (2012) Ferroptosis: An Iron-Dependent Form of Non-Apoptotic Cell Death. Cell 149(5):1060\u0026ndash;1072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2012.03.042\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2012.03.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon SJ, Pratt DA (2023) Ferroptosis: A flexible constellation of related biochemical mechanisms. Mol Cell 83(7):1030\u0026ndash;1042. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molcel.2023.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.molcel.2023.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan B (2021) Mitochondrial regulation of ferroptosis. J Cell Biol 220(9):202105043. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1083/jcb.202105043\u003c/span\u003e\u003cspan address=\"10.1083/jcb.202105043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalasi M, Gartel AL (2013) Targeting FOXM1 in cancer. Biochem Pharmacol 85(5):644\u0026ndash;652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bcp.2012.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.bcp.2012.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao X, Zheng Z, Liu H, Zhang Y, Kang J, Kong X, Rong D, Sun Guangshun S, Guoqiang, Liu L et al (2022) Inhibition of APOC1 promotes the transformation of M2 into M1 macrophages via the ferroptosis pathway and enhances anti-PD1 immunotherapy in hepatocellular carcinoma based on single-cell RNA sequencing. Redox Biol 56:102463. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2022.102463\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2022.102463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassannia B, Vandenabeele P, Vanden Berghe T (2019) Targeting Ferroptosis to Iron Out Cancer. Cancer Cell 35(6):830\u0026ndash;849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ccell.2019.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2019.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalathil D, John S, Nair AS (2020) FOXM1 and Cancer: Faulty Cellular Signaling Derails Homeostasis. Front Oncol 10:626836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2020.626836\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2020.626836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuang F, Liu J, Tang D, Kang R (2020) Oxidative Damage and Antioxidant Defense in Ferroptosis. Front Cell Dev Biol 8:586578. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2020.586578\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2020.586578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei G, Zhuang L, Gan B (2022) Targeting ferroptosis as a vulnerability in cancer. Nat Rev Cancer 22(7):381\u0026ndash;396. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41568-022-00459-0\u003c/span\u003e\u003cspan address=\"10.1038/s41568-022-00459-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Zhang Y, Liu J, Kang R, Klionsky DJ, Tang D (2020) Mitochondrial DNA stress triggers autophagy-dependent ferroptotic death. Autophagy 17(4):948\u0026ndash;960. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15548627.2020.1739447\u003c/span\u003e\u003cspan address=\"10.1080/15548627.2020.1739447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Z, Liu J, Long F, Kang R, Kroemer G, Tang D, Yang M (2022) The lipid flippase SLC47A1 blocks metabolic vulnerability to ferroptosis. Nat Commun 13(1):7965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-35707-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-35707-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu W, Ding R, Zhang Y, Mao C, Kang R, Meng J, Huang Q, Xiong L, Guo Z (2017) Transcriptome profiling analysis of differentially expressed mRNAs and lncRNAs in HepG2 cells treated with peptide 9R-P201. Biotechnol Lett 39(11):1639\u0026ndash;1647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10529-017-2407-1\u003c/span\u003e\u003cspan address=\"10.1007/s10529-017-2407-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadhi H, Lee J, Choi YE, Li Y, Kim MH, Choi Y, Goh S (2022) FOXM1 Inhibition Enhances the Therapeutic Outcome of Lung Cancer Immunotherapy by Modulating PD-L1 Expression and Cell Proliferation. Adv Sci (Weinh) 9(29):2202702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/advs.202202702\u003c/span\u003e\u003cspan address=\"10.1002/advs.202202702\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiotto G, Rossetto M, Di Paolo ML, Orian L, Venerando R, Roveri A, Vučković A-M, Bosello Travain V, Zaccarin M, Zennaro L et al (2019) Insight into the mechanism of ferroptosis inhibition by ferrostatin-1. Redox Biol 28:101328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2019.101328\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2019.101328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandi D, Cheema PS, Jaiswal N, Nag A (2018) FoxM1: Repurposing an oncogene as a biomarker. Semin Cancer Biol 52(Pt 1):74\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.semcancer.2017.08.009\u003c/span\u003e\u003cspan address=\"10.1016/j.semcancer.2017.08.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuyang S, Li H, Lou L, Huang Q, Zhang Z, Mo J, Li M, Lu J, Zhu K, Chu Y et al (2022) Inhibition of STAT3-ferroptosis negative regulatory axis suppresses tumor growth and alleviates chemoresistance in gastric cancer. Redox Biol 52:102317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2022.102317\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2022.102317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng M, Hu Q, Wu Z, Wang B, Wang C, Yu F (2023) Mutation of tp53 confers ferroptosis resistance in lung cancer through the foxm1/mef2c axis. Am J Pathol 193(10):1587\u0026ndash;1602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajpath.2023.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ajpath.2023.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin Y, Pei Z, Feng Z, Lin P, Wang S, Li Y, Huo F, Wang Q, Wang Z, Chen Z-N et al (2021) Oncogenic Activation of YAP Signaling Sensitizes Ferroptosis of Hepatocellular Carcinoma via ALOXE3-Mediated Lipid Peroxidation Accumulation. Front Cell Dev Biol 9:751593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.751593\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.751593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadhakrishnan SK, Gartel AL (2008) FOXM1: the Achilles\u0026rsquo; heel of cancer? Nat Rev Cancer 8(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrc2223-c1\u003c/span\u003e\u003cspan address=\"10.1038/nrc2223-c1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. c1; author reply c2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghuwanshi S, Gartel AL (2023) Small-molecule inhibitors targeting FOXM1: Current challenges and future perspectives in cancer treatments. Biochim Biophys Acta Rev Cancer 1878(6):189015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbcan.2023.189015\u003c/span\u003e\u003cspan address=\"10.1016/j.bbcan.2023.189015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTriska M, Ivliev A, Nikolsky Y, Tatarinova TV (2017) Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer. Methods Mol Biol 1613:291\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4939-7027-8_11\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4939-7027-8_11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasaikar SV, Straub P, Wang J, Zhang B (2018) LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 46(D1):D956\u0026ndash;D963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkx1090\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkx1090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Wu X, Ren Z, Li Y, Zou W, Chen J, Wang H (2023) Overcoming cancer chemotherapy resistance by the induction of ferroptosis. Drug Resist Updat 66:100916. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drup.2022.100916\u003c/span\u003e\u003cspan address=\"10.1016/j.drup.2022.100916\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei G, Yang X, Lu H, Zhang L, Wei Y, Li H, Zhu M, Zhou X (2022) Prognostic value and immunological role of FOXM1 in human solid tumors. Aging 14(22):9128\u0026ndash;9148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.204394\u003c/span\u003e\u003cspan address=\"10.18632/aging.204394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Y, Liu S, Li C, Ai Z, Shen W, Ren W, Yang X (2020) Discovery of a novel ferroptosis inducer-talaroconvolutin A-killing colorectal cancer cells in vitro and in vivo. Cell Death Dis 11(11):988. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41419-020-03194-2\u003c/span\u003e\u003cspan address=\"10.1038/s41419-020-03194-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Hou W, Song X, Yu Y, Huang J, Sun X, Kang R, Tang D (2016) Ferroptosis: process and function. Cell Death Differ 23(3):369\u0026ndash;379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/cdd.2015.158\u003c/span\u003e\u003cspan address=\"10.1038/cdd.2015.158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang WS, Kim KJ, Gaschler MM, Patel M, Shchepinov MS, Stockwell BR (2016) Peroxidation of polyunsaturated fatty acids by lipoxygenases drives ferroptosis. Proc Natl Acad Sci U S A 113(34):E4966\u0026ndash;E4975. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1603244113\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1603244113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Liu J, Wang C, Cheng KK-Y, Xu H, Li Q, Hua T, Jiang X, Sheng L, Mao J, Liu Z (2021) miR-18a promotes glioblastoma development by down-regulating ALOXE3-mediated ferroptotic and anti-migration activities. Oncogenesis 10(2):15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41389-021-00304-3\u003c/span\u003e\u003cspan address=\"10.1038/s41389-021-00304-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao L, Zhou X, Xie F, Zhang L, Yan H, Huang J, Zhang C, Zhou F, Chen J, Zhang L (2022) Ferroptosis in cancer and cancer immunotherapy. Cancer Commun (Lond) 42(2):88\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cac2.12250\u003c/span\u003e\u003cspan address=\"10.1002/cac2.12250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Yu Y, Zhao W, You S, Feng M, Xie C, Chi X, Zhang Y, Wang X (2019) As a downstream target of the AKT pathway, NPTX1 inhibits proliferation and promotes apoptosis in hepatocellular carcinoma. Biosci Rep 39(6):BSR20181662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1042/BSR20181662\u003c/span\u003e\u003cspan address=\"10.1042/BSR20181662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng X, Chen W, Yi J, Li W, Liu J, Fu W, Ren L, Li S, Ge B, Yang Y et al (2022) Apolipoprotein C1 promotes glioblastoma tumorigenesis by reducing KEAP1/NRF2 and CBS-regulated ferroptosis. Acta Pharmacol Sin 43(11):2977\u0026ndash;2992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41401-022-00917-3\u003c/span\u003e\u003cspan address=\"10.1038/s41401-022-00917-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-peptide-research-and-therapeutics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijpr","sideBox":"Learn more about [International Journal of Peptide Research and Therapeutics](http://link.springer.com/journal/10989)","snPcode":"10989","submissionUrl":"https://submission.nature.com/new-submission/10989/3","title":"International Journal of Peptide Research and Therapeutics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"MCP-1, FOXM1, ALOXE3, Ferroptosis, HCCLM3","lastPublishedDoi":"10.21203/rs.3.rs-4002517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4002517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFOXM1 is a crucial oncogenic transcription factor involved in almost all cancer hallmark pathways across all cancer types. Our previous work had found that FOXM1 targeted peptide P201 can strongly inhibit the growth of cancer cells including the liver cancer HCCLM3 cells. In addition, by RNA-seq of HCCLM3 cells treated with MCP-1, an anticancer peptide optimized from P201, ALOXE3, a key feature of ferroptosis was significantly elevated while FOXM1 was down-regulated, we wonder if the cell death of HCCLM3 induced by MCP-1 was associated with ferroptosis. Also, the relationship between FOXM1 and ferroptosis was less understood. Hence, in this study, we explore the effect of MCP-1 on ferroptosis and establish the associations among MCP-1, FOXM1 and ALOXE3 in HCCLM3 cells. The results showed that MCP-1 can significantly induce the elevated expression of ALOXE3, decreased content of GSH, down-regulation of GPX4 expression, increased contents of ROS and total iron in HCCLM3 cells. Also, ferrostatin-1, a specific inhibitor for ferroptosis, can reverse the cell death in HCCLM3 cells when co-administrated with MCP-1. TCGA database hepatocellular carcinoma gene expression analysis showed that FOXM1 was negative-related to ALOXE3 and further confirmed by the results of siRNA knockdown of FOXM1 in HCCLM3 cells. Moreover, the co-expressed genes analysis for FOXM1 and ALOXE3 revealed that many of them were closely involved in the regulation of ferroptosis. Taken together, we discovered and confirmed the induction of ferroptosis by MCP-1 in liver cancer HCCLM3 cells and primarily established the relationships among MCP-1, FOXM1 and ALOXE3.\u003c/p\u003e","manuscriptTitle":"Anticancer peptide MCP-1 induces ferroptosis in liver cancer HCCLM3 cells by targeting FOXM1/ALOXE3 signal pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 06:23:04","doi":"10.21203/rs.3.rs-4002517/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-02T07:09:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-24T06:05:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c8da56c9-795e-4937-aef9-26da4287120f","date":"2024-03-19T03:58:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-07T10:23:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-01T19:41:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-01T19:41:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Peptide Research and Therapeutics","date":"2024-03-01T07:37:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-peptide-research-and-therapeutics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijpr","sideBox":"Learn more about [International Journal of Peptide Research and Therapeutics](http://link.springer.com/journal/10989)","snPcode":"10989","submissionUrl":"https://submission.nature.com/new-submission/10989/3","title":"International Journal of Peptide Research and Therapeutics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9c7472ee-42e8-4d01-97be-344812130a4d","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T21:20:59+00:00","versionOfRecord":{"articleIdentity":"rs-4002517","link":"https://doi.org/10.1007/s10989-024-10614-w","journal":{"identity":"international-journal-of-peptide-research-and-therapeutics","isVorOnly":false,"title":"International Journal of Peptide Research and Therapeutics"},"publishedOn":"2024-05-10 21:17:38","publishedOnDateReadable":"May 10th, 2024"},"versionCreatedAt":"2024-03-05 06:23:04","video":"","vorDoi":"10.1007/s10989-024-10614-w","vorDoiUrl":"https://doi.org/10.1007/s10989-024-10614-w","workflowStages":[]},"version":"v1","identity":"rs-4002517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4002517","identity":"rs-4002517","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}