Activating the AMPK-p53 Axis by Mitochondrial Impairment: Unveiling a Novel Anti-Liver Cancer Mechanism of Sulfane Sulfur

Activating the AMPK-p53 Axis by Mitochondrial Impairment: Unveiling a Novel Anti-Liver Cancer Mechanism of Sulfane Sulfur | 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 Article Activating the AMPK-p53 Axis by Mitochondrial Impairment: Unveiling a Novel Anti-Liver Cancer Mechanism of Sulfane Sulfur Xue Zheng, Yuhua Luo, Rui Huo, Yiwen Wang, Youbang Chen, Mianrong Chen, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5228470/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, notoriously refractory to conventional chemotherapy. Historically, sulfane sulfur-based compounds have been explored for the treatment of HCC, but their efficacy has been underwhelming. We recently reported a novel sulfane sulfur donor, PSCP, which exhibited improved chemical stability and structural malleability. This study aimed to investigate the effects of PSCP on HCC and elucidate the underlying mechanisms. We utilized bioinformatics algorithms for clustering, function enrichment, feature screening and survival analysis on proteomic data from the Cancer Proteome Atlas (CPTAC) and transcriptomic data from the Cancer Genome Atlas (TCGA). The impact of PSCP on HCC were assessed in vitro and in vivo , focusing on the expression and activity of p53 and AMP-activated protein kinase (AMPK), as well as mitochondrial function. The molecular target of PSCP was identified using Autodock, and binding interactions were visually analyzed. Sulfur metabolism was found to be reprogrammed in HCC, with downregulation of sulfur-related pathways correlating with poor patient prognosis. PSCP treatment significantly inhibited HCC tumor growth in an allograft model, reduced cell viability and proliferation, and induced apoptosis. PSCP potently increased p53 expression and induced AMPK phosphorylation in SNU398 HCC cells. AMPK suppression diminished PSCP-induced p53 upregulation. PSCP also impaired mitochondrial function by inhibiting mitochondrial respiratory complex I. The supplementation of ATP significantly countered PSCP-induced SNU398 cell injury. Our findings suggest that the reprogramming of sulfur-related metabolic pathways is pivotal in HCC. PSCP presents as a promising therapeutic strategy by activating the mitochondrial-AMPK-p53 signaling axis. Biological sciences/Biochemistry Biological sciences/Cancer Metabolic reprogramming Hepatocellular carcinoma Sulfane sulfur Mitochondrial complex AMPK p53 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Liver cancer is a major contributor to global cancer-related mortality, with an anticipated incidence exceeding one million cases by 2025. Hepatocellular carcinoma (HCC), the most prevalent form of primary liver cancer, accounts for approximately 90% of all cases 1 . The standard treatment for HCC is radical surgery; however, the risk of postoperative recurrence is relatively high, and many patients are diagnosed at advanced stages, precluding surgical indications. This leads to a 5-year survival rate of less than 20% 2,3 . Chemotherapeutic options are limited due to HCC’s resistance to conventional drugs. Even with multi-targeted tyrosine kinase inhibitors like Lenvatinib, the emergence of acquired resistance is prevalent, and the prolongation of progression-free survival and overall survival remains extremely limited 1 , 4 . Hence, innovative therapies are urgently needed to improve the survival prospects of HCC patients. Since the 1950s, garlic extracts have demonstrated anticancer effects 5 . Subsequent research has consistently confirmed that the anticancer properties are attributed to the release of highly reactive sulfur atoms from polysulfides, also known as sulfane sulfurs 6 . Although sulfane sulfurs, like allicin and lycopene sulfanes, have previously been implicated in anticancer activity 7 , 8 , their precise mechanisms remain largely elusive. Furthermore, the existing sulfane sulfurs are prone to oxidation and decomposition, hindering drug development and clinical application 9 . We recently reported a novel sulfane sulfur donor, persulfided cysteine precursor (PSCP), which offers improved chemical stability and flexibility of structural modifications. Our previous work demonstrated the efficacy and specificity of PSCP in HCC cell line models 9 . However, the in vivo efficacy and the anticancer mechanisms of PSCP have not been thoroughly investigated. Metabolic reprogramming has been accepted as a hallmark of cancer 10 . The role of sulfur-containing amino acids, like methionine, has been extensively studied 11 – 13 . Nevertheless, the metabolic landscape of sulfur and its related amino acids in HCC tissues and their impact on patient survival not well-characterized. Mitochondria are central to the cellular metabolic network, serving as the primary site for nutrient oxidation and energy release 14 . They also play pivotal roles in governing cell cycle progression, differentiation, and programmed cell death 15 , 16 . Recent studies have highlighted abnormal mitochondrial metabolism as a defining feature of cancer 17 . Mitochondria in tumor cells exhibit significant metabolic heterogeneity compared to normal cells, which benefits rapid DNA replication, excessive proliferation, and drug resistance 18 , 19 . Consequently, targeting mitochondrial metabolism is a promising strategy in cancer therapy. However, the impact of PSCP on mitochondrial metabolism in HCC treatment has not been extensively explored. In this study, we wanted to confirm the in vivo efficacy of PSCP and to elucidate the mechanisms underlying its inhibitory effects on HCC growth. We characterized the sulfur metabolism landscape in HCC tumors and control liver tissues and analyzed the survival outcomes of HCC patients by integrating proteomic data from the CPTAC database with their survival status. Transcriptome sequencing, combined with gene set enrichment or variation analysis, was employed to reveal the molecular mechanisms behind PSCP’s anti-HCC effects. Results Impaired sulfur and related metabolism in HCC tissues portends a dismal prognosis for patients The application of cluster analysis to TCGA RNA-Seq data, coupled with UMAP dimensionality reduction, revealed a distinct clustering pattern among 374 HCC samples and 50 control samples (Fig. 1 A ). Furthermore, gene expression data were converted into phenotype score through the GSVA algorithm, with a specific focus on sulfur-related metabolic pathways. As shown in Fig. 1 B , the sulfur metabolism was not found to be clustered between HCC and control samples, nor within HCC samples. However, sulfur amino acid metabolism and the methionine metabolism leading to sulfur amino acid and related disorders, were markedly clustered within the HCC samples (Fig. 1 C and D ). Moreover, the downregulation of these phenotypic scores emerged as a powerful predictor of diminished survival rates among HCC patients (Fig. 1 E and F ). In addition to the aforementioned genomic analyses, we delved into the proteomic data of CPTAC. Protein expression data were similarly converted into phenotypic scores, and cluster analysis revealed a distinct segregation between HCC and para-cancerous tissues (Fig. 2 A ). The diverging bar chart highlighted the downregulation of several sulfur metabolism-related phenotypes within HCC tissues (Fig. 2 B ). To assess the impact of these phenotypes on HCC patient survival, we employed the Random Survival Forest algorithm. Three key phenotypes were distinguished based on their ranking in terms of importance: Trans-sulfuration, Sulfur metabolism, and Sulfur amino acid metabolism (Fig. 2 C-D ). Cox regression analysis and survival curves confirmed that deficiencies in these three phenotypes were critical predictors of poor survival outcomes in HCC patients (Fig. 2E ). Finally, ROC curves revealed that these metabolic defects (Trans-sulfuration, Sulfur metabolism, and Sulfur amino acid metabolism) served as robust diagnostic indicators for HCC, with all AUC values of 0.9 or higher (Fig. 2 F ). PSCP inhibits HCC tumor growth in vitro and in vivo To elucidate the consequences of impaired sulfur and related amino acid metabolism on the growth of HCC tumors, we investigated the effects of a recently synthesized sulfane sulfur donor, PSCP. Initially, we assessed the inhibitory impact of PSCP on tumor growth in an allograft model of H22 HCC cells, which were implanted subcutaneously into nude mice. PSCP was administered at varying concentrations via intraperitoneal injection. The results showed that PSCP dose-dependently suppressed the growth of transplanted tumors (Fig. 3 A ). Furthermore, in vitro experiments confirmed that treatment of SNU398 HCC cells with PSCP significantly reduced cell viability in a dose- and time-dependent manner (Fig. 3 B ). PSCP suppresses HCC cell proliferation by arresting cell cycle To reveal how PSCP inhibits the growth of HCC cells, we conducted transcriptomic sequencing and all genes-based enrichment analysis. The results of GSEA showed that the treatment with PSCP downregulated gene sets associated with cell cycle (Fig. 4 A ) and DNA replication pathways (Fig. 4 B ). Subsequently, we employed flow cytometry to assess cell cycle changes. SNU398 cells treated with PSCP for 24 h were harvested and stained with PI to examine cell cycle distribution. The data demonstrated that PSCP treatment increased the proportion of cells in the G0/G1 phase, indicating G0/G1 phase arrest due to the inhibition of DNA replication (Fig. 4 C-D ). To further confirm the impact on DNA replication, we performed an EdU incorporation assay. The assay showed that PSCP dose-dependently inhibited DNA replication within a concentration range of 100 to 400 μM (Fig. 4 E ). These findings collectively suggest that PSCP exerts its inhibitory effects on HCC cell proliferation by inducing cell cycle arrest at the G0/G1 phase. PSCP promotes apoptosis in HCC cells During the cell proliferation process, cells unable to complete DNA repair are typically eliminated via apoptosis. To ascertain whether PSCP induced apoptosis in HCC cells, we employed the TUNEL assay followed by fluorography. As depicted in Fig. 5 A , the number of TUNEL-positive cells exhibiting green fluorescence increased significantly in the PSCP treatment group. To further quantify apoptosis and necrosis, we conducted Annexin V-FITC/PI double staining in conjunction with flow cytometry. The analysis revealed that PSCP treatment dose-dependently elevated the ratios of apoptosis and necrosis within a concentration range of 100 to 400 μM (Fig. 5 B - C ). PSCP exerts its anti-HCC effects through p53 upregulation To delineate the molecular mechanisms by which PSCP inhibits proliferation but promotes apoptosis in HCC cells, we examined the enriched KEGG pathways using GSVA. The analysis revealed that PSCP treatment led to the upregulation of pathways involved in “ Tp53 Targets Apoptotic Up ” and “ Tp53 Regulates Transcription of Caspase Activators and Caspases ”, while downregulating “ Tp53 Regulates Transcription of DNA Repair Genes ” (Fig. 6 A ). As p53 is the protein encoded by TP53 gene, we sought to determine whether p53 played a role in the anti-HCC effects of PSCP. Western blot analysis showed that PSCP treatment significantly enhanced p53 protein levels in SNU398 HCC cells (Fig. 6 B ). To further elucidate the involvement of p53, we knocked down TP53 gene expression using RNA interference (Fig. 6 C ). This knockdown resulted in a reduction of the inhibitory effects of PSCP on cell viability and proliferation (Fig. 6 D ). Collectively, these data indicate that the upregulation of p53 by PSCP is a critical factor in its anti-HCC activity. Activation of AMPK is implicated in PSCP-induced p53 upregulation in HCC cells To delve into how PSCP upregulates p53 and exerts its anti-HCC effects, we screened the enriched KEGG pathways. Our analysis revealed a significant enrichment of AMPK-related metabolic pathways in SNU398 HCC cells treated with PSCP (Fig. 7 A ). Following intraperitoneal administration of PSCP in nude mice, AMPK activation was markedly induced, as evidenced by increased levels of phosphorylated AMPK (p-AMPK) in the allografts of mice treated with 20 or 40 mg/kg PSCP (Fig. 7 B ). In vitro experiments also demonstrated that PSCP treatment of SNU398 HCC cells was able to raise the ratio of p-AMPK to total AMPK (Fig. 7 C ). To further validate the role of AMPK in PSCP’s anti-HCC actions, we employed the AMPK inhibitor Compound C (Comp. C) (Fig. 7 D ). Treatment with Comp. C remarkably attenuated PSCP-induced p53 expression (Fig. 7 E ) and its anti-HCC effects (Fig. 7 F ). These findings suggest that AMPK activation is essential for the anti-HCC efficacy of PSCP. Moreover, metformin, a commonly utilized AMPK agonist in clinical practice, also inhibited cell viability, while its potency was notably less than that of PSCP (Fig. 7 G ). PSCP induces mitochondrial dysfunction in HCC Mitochondrial impairment is known to activate AMPK due to ATP depletion and consequent AMP or ADP generation. To clarity how PSCP activates AMPK in HCC, we first conducted a bioinformatic analysis using the CPTAC proteomic data. The GSVA results showed that gene sets related to mitochondrial gene transcription, translation and biogenesis were positively enriched in HCC (Fig. 8 A ). Moreover, the gene set for mitochondrial translation was among the top ten enriched gene sets in GSEA (Fig. 8 B and C ), indicating an upregulation of mitochondrial activity in HCC. Subsequently, we carried out an all genes-based enrichment analysis on the transcriptomic data from SNU398 HCC cells treated with PSCP and control. It was found that PSCP markedly suppressed gene sets involved in mitochondrial gene translation (Fig. 8 D ). Further analysis suggested that PSCP specifically repressed gene sets related to the assembly of mitochondrial complexes I, II and III (Fig. 8 E ). PSCP-induced mitochondrial dysfunction activates AMPK in HCC To ascertain the inhibitory effects of PSCP on mitochondrial function, we conducted a series of in vitro experiments. Initially, SNU398 HCC cells were exposed to PSCP for 24 h, and MMP was assessed using JC-1 staining followed by confocal fluorescence microscopy. The results showed that PSCP dose-dependently reduced the MMP levels, as evidenced by a decreased ratio of red to green fluorescence, within a concentration range of 100 to 400 μM (Fig. 9 A - B ). MMP generation relies on proton transport through mitochondrial complexes. Next, we performed an assay to measure mitochondrial complex I activity and found that PSCP significantly suppressed its activity (Fig. 9 C ). Additionally, we showed that PSCP treatment led to marked increases in the ratio of NADH to NAD + and the content of intracellular ROS (Fig. 9 D - E ), while decreasing the intracellular ATP levels (Fig. 9 F ). Importantly, the addition of ATP was able to rescue PSCP-induced cell damage (Fig. 9 G ). These data indicate that PSCP-induced AMPK activation is contingent upon the inhibition of mitochondrial complex I, which hampers proton transport and ATP synthesis. PSCP inhibits mitochondrial translocation of Ndus3, thereby inducing mitochondrial dysfunction in HCC To elucidate how PSCP disrupts mitochondrial complex I, we first observed the expression of NADH dehydrogenase [ubiquinone] iron-sulfur protein 3 (Ndus3), a critical iron-sulfur protein component of complex I. As shown in Fig. 10 A , PSCP remarkably reduced Ndus3 protein levels in SNU398 HCC cell mitochondria. Given the pivotal role of Ndus3 in the catalytic reaction of NADH, we compared the binding affinity of Ndus3 for NADH and PSCP, as well as its degradation products (PSCP-SSH and PSCP-Polysulfide) using molecular docking techniques. The most favorable docking models and corresponding binding energies were presented. In Fig. 10B, the binding energy between Ndus3 and NADH was determined to be -7.5 kcal/mol. The interaction was mediated by key residues, including GLY195, ARG186, TYR207, HIS196, TRP171, LEU184 and ASP201. Figure10C illustrated the interactions between PSCP and its degradation products with Ndus3. For PSCP, the binding energy was -3.2 kcal/mol, with GLY175 and ARG199 identified as key residues involved in the interaction. For PSCP-SSH, the binding energy was -3.1 kcal/mol, with PHE193, GLY195 and ARG186 identified as key residues. For PSCP-Polysulfide, the binding energy was -4.0 kcal/mol, with ARG140, ASN138 and ARG48 identified as key residues. These findings indicate that PSCP or its degradation products are likely to competitively bind to Ndus3, thereby impeding its mitochondrial translocation and catalytic function. Discussion In this study, we have provided compelling evidence that the metabolism of sulfur and related amino acids is significantly dysregulated in HCC, serving as a prognostic indicator for poor survival among HCC patients. The synthetic sulfane sulfur donor, PSCP, has proven to be an effective inhibitor of HCC growth, achieving this by promoting apoptosis and inhibiting proliferation. The activation of the AMPK-p53 axis, induced by mitochondrial impairment, is the underlying mechanism. Building upon transcriptomic findings in cell line 9 , the current proteomic analysis of patient-derived HCC specimens from the CPTAC database has revealed aberrant sulfur and related amino acid metabolisms in HCC. Metabolic reprogramming is well-recognized hallmark in cancer progression 10 , where altered metabolites of glucose or lipids fuel tumorigenesis, invasion, and metastasis 20 – 22 . The focus on amino acid metabolism has intensified; however, the present findings not only broaden the scope of the amino acid metabolic profile but also highlight that the dysregulation of sulfur and related amino acid metabolisms may serve as a predictor of poor survival in HCC patients. Further experiments illustrated the inhibitory effects of PSCP on HCC allograft growth in nude mice, which was corroborated by analogous findings with allicin, a different sulfane sulfur donor 23 . Our transcriptomic analysis elucidated the potential molecular mechanisms, by which PSCP impeded the cell cycle and DNA replication, confirmed through the in vitro assays. Also, PSCP was observed to substantially induce apoptosis in HCC cells. While there is conflicting evidence on the role of reactive sulfur species (RSS) in cancer, it is becoming increasingly clear that the effects are highly dependent on the cell types and the specific sulfur species 24 . PSCP, in particular, showed selective anti-HCC activity not observed with other cancer types, as per our earlier study 9 . Moreover, PSCP’s specificity was underscored by its lack of significant impact on non-tumor cells, like H9c2 cardiomyoblasts, raw-blue macrophages or immortalized hepatocyte (LO2 cells) 9 . These findings and reports suggest that metabolic reprogramming of sulfur and related amino acids is essential for PSCP to exert its anti-HCC effects, highlighting the importance of this metabolic dysregulation as a therapeutic target. The TP53 gene, a pivotal tumor suppressor, governs cell proliferation and apoptosis 25 . Our transcriptomic analysis revealed that PSCP upregulated TP53-induced transcriptional activity of Caspases and activators, consistent with the aforementioned pro-apoptotic effects. Additionally, we observed that PSCP downregulated MDM2, a negative regulator of p53 26,27 , which may be one of the reasons for the increased p53 protein. Such increase in p53 was crucial for PSCP’s anti-HCC activity, as it can arrest cell cycle, repair DNA and prime cells with damaged DNA to apoptosis. To gain insight into the mechanism behind the p53 upregulation, we also focused on AMPK. Our study demonstrated that PSCP promoted the phosphorylation of AMPK and the mRNA expression of its downstream genes. The inhibition of AMPK significantly diminished PSCP’s anti-HCC effects and p53 upregulation. These findings are consistent with previous reports 28 , 29 . Therefore, the activation of AMPK plays a significant role in PSCP’s anti-HCC mechanism, likely through the enhancement of p53 content. Finally, our investigation into the AMPK activation mechanism in PSCP-treated HCC cells revealed that PSCP initiated energy status-dependent AMPK dysregulation, typically induced by elevated AMP/ATP or ADP/ATP ratios 30 , 31 . The mitochondrial assay demonstrated that PSCP reduced mitochondrial membrane potential by inhibiting electron transport chain complex I, resulting in ATP synthesis impairment and AMPK activation, a pattern observed in many studies 30 , 31 . Theoretically, PSCP, as a sulfane sulfur donor, likely interacts with iron-sulfur proteins of electron transport chain, hindering their mitochondrial translocation and resultant ATP production. In line with this, a recent study indicates that activation of AMPK could boost T cells’ anti-tumor activity 32 . Of course, this speculation still requires further experimental validation. Conclusion This study has uncovered that sulfur and related amino acid metabolisms are disrupted in HCC, which correlates with poor patient outcomes. The synthetic sulfane sulfur donor, PSCP, efficiently inhibits HCC growth by triggering the mitochondrial-AMPK-p53 axis, thereby promoting apoptosis and suppressing proliferation. This research provides a theoretical foundation for the therapeutic application of sulfane sulfur in HCC treatment. Materials and methods Tumor transplantation in nude mice Four-week-old male BALB/c-nu nude mice (Production license: SCXK-SU-2020-0008) were acquired from Gempharma_tech Co., Ltd (Nanjing, China). The mice were housed in the Specific Pathogen Free (SPF) facility at the Laboratory Animal Center of Guangzhou Medical University (Guangzhou, China). All procedures were strictly adhered to the guidelines approved by the Animal Ethics Committee of Guangzhou Medical University (Approval Number: GY2020-099) and were conducted in full compliance with the ARRIVE guidelines. Following a one-week acclimatization period, the mice were inoculated subcutaneously with murine-derived H22 HCC cells (Procell Life Science & Technology Company, Wuhan, China). The tumor-bearing mice were randomly assigned to one of four groups: Control group, 10 mg/kg PSCP group, 20 mg/kg PSCP group, and 40 mg/kg PSCP. The growth of the allografts in the nude mice was monitored for three weeks. Starting from the 4th day post-inoculation, the mice received daily intraperitoneal injections of various doses of PSCP. After three weeks, all the mice were euthanized with 4 mg/10 g pentobarbital sodium. The euthanasia and anesthesia methods were conducted in accordance with the guidelines of the Animal Ethics Committee of Guangzhou Medical University. Lastly, the allografts were observed and measured. Cell culture SNU398 HCC cells were acquired from Luyuan Bode Biotechnology Co., Ltd (Beijing, China), and were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS; Gibco, USA) at 37°C in a humidified atmosphere of 5% CO 2 and 95% air. The cells were subcultured and harvested using 0.25% trypsin. Cell viability assay Cell viability was measured with the Cell Counting Kit-8 (CCK-8; Dojindo, Kumamoto, Japan). SNU398 HCC cells were seeded into 96-well culture plates at a density of 10,000 cells per well and treated accordingly. Subsequently, 100 µL of the CCK-8 working solution was added to each well, and the plates were incubated for 2 hours at 37°C. Absorbance ( A ) was measured at 450 nm using a microplate reader (Thermo Fisher Scientific Inc., USA). The percentage of cell viability was calculated according to the following formula: % Cell viability = [ A (Treatment group) - A (Blank)] / [ A (Control group) - A (Blank)] × 100. Whole transcriptome analysis Following treatment with control or 200 µM PSCP for 24 h, total RNA was extracted from SNU398 HCC cells using TRIzol reagent (Invitrogen, MD, USA). The transcriptome was analyzed by Biomarker Technologies (Guangzhou, China), with all procedures adhering to the protocols established by Oxford Nanopore Technologies (Oxford, UK). The analysis platform (BMK_Cloud) was used to conduct correlation analysis based on reference sequences and nanopore transcriptome sequencing data. The data were deposited in ArrayExpress ( https://www.ebi.ac.uk/fg/annotare/ , Accession number: E-MTAB-14578). Differential expression and functional enrichment analysis The transcriptome data from SNU398 HCC cells treated with control or PSCP were normalized and subjected to log2 transformation. Differential expression analysis was conducted using the limma R package 33 . The resulting log2 fold changes (log2FC) were then utilized for Gene Set Enrichment Analysis (GSEA) by referring to the MSigDB database ( https://www.gsea-msigdb.org/gsea/msigdb ). For gene sets with a more pronounced degree of enrichment, the GseaVis R package was applied to visually represent the gene distribution 34 . Transcriptomic data from HCC and para-carcinoma tissues were downloaded from The Cancer Genome Atlas (TCGA) database ( https://xenabrowser.net/datapages/ ) and analyzed using Uniform Manifold Approximation and Projection (UMAP). Proteomic data from HCC and para-carcinoma tissues were downloaded from the Clinical Proteomic Tumor Analysis Consortium (CPTAC, https://proteomics.cancer.gov/data-portal ) and clustered using t-distributed Stochastic Neighbor Embedding (t-SNE). Functional enrichment analysis was conducted via Gene Set Variation Analysis (GSVA). Protein expression matrices were transformed into GSVA score matrices using the GSVA R package, following the differential expression analysis procedure described above 35 . The generated t-values were used to create a diverging bar chart. Feature screening with randomized survival forests Randomized survival forest analysis was performed to identify sulfur-related phenotypes that influenced the survival of HCC patients 36 . Cox regression analysis was then applied to the top three phenotypes with the greatest impact on survival. Kaplan–Meier survival analysis was utilized to visualize the different survival of HCC patients between the up-regulated and down-regulated phenotypes. To evaluate the diagnostic potential of these three features, receiver operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC) was calculated accordingly. Cell cycle analysis SNU398 HCC cells were seeded into each well of 6-well culture plates, allowed to reach approximately 80% confluency, and then treated with varying concentrations of PSCP. Following digestion with EDTA-free trypsin, the cells were stained with a commercial propidium iodide (PI) dye kit (Biyotime, Shanghai, China). The staining reaction was carried out at room temperature for 30 min, and the proportion of cells at each stage of the cell cycle was determined using a flow cytometer (BD Biosciences, USA). Cell proliferation assay The EdU-incorporation assay was utilized to monitor cell proliferation with a BeyoClick™ EdU Cell Proliferation Kit (Biyotime, Shanghai, China). SNU398 HCC cells were seeded into 96-well culture plates, with 4 replicate wells per group. Following treatment with different concentrations of PSCP, the cells were labeled with the EdU working solution after a 2-hour incubation at 37°C. The cells were then fixed with 4% paraformaldehyde and treated with an endogenous peroxidase blocking solution. Next, the Click reaction solution and Streptavidin-HRP reaction solution were applied sequentially. Cell proliferation levels were quantified by measuring the absorbance ( A ) at 650 nm after development of a distinct color with a microplate reader (Thermo Fisher Scientific Inc., USA). Apoptosis detection SNU398 HCC cells were treated with 200 µM PSCP for 24 h. The induction of cellular apoptosis was visualized through the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay.The cell nuclei were labeled with 4′,6-diamidino-2-phenylindole (DAPI) (Biyotime, Shanghai, China). The cellular imaging was conducted using a confocal microscopy (Zeiss, Germany). To quantify apoptosis, a flow cytometric assay involving Annexin V/propidium iodide (PI) double staining was applied. A total of 500,000 cells were seeded into each well of a 6-well plate and treated with various concentrations of PSCP. Following digestion with EDTA-free trypsin, 80,000 cells were selected for analysis. These cells were incubated with 195 µL of Annexin V-FITC conjugate (Biyotime, Shanghai, China), followed by the sequential addition of 10 µL of PI dye. The apoptosis rate was subsequently analyzed using a flow cytometer (BD Biosciences, USA). Mitochondrial function assessment SNU398 HCC cells were treated with PSCP at concentrations ranging from 100 to 400 µM. The mitochondrial membrane potential (MMP) was observed with JC-1 staining (Dojindo, Kumamoto, Japan) followed by fluorography under a confocal microscopy (Zeiss, Germany). To assess mitochondrial activity, mitochondria were isolated and incubated with NADH as a substrate to measure the mitochondrial oxidation rate, which serves as an indicator of Complex I activity. The NADH/NAD + ratio was determined as follows: The cell lysate was divided into two portions. One portion was directly used to measure NADH content using a color development solution. For the other portion, NAD + was enzymatically converted to NADH. The total NADH content was then quantified as above, with the NADH content of the first portion subtracted to calculate the NAD + content. The intracellular ATP levels were detected using a luminescent ATP detection kit. Cell lysates were mixed with 0.25% trichloroacetic acid to precipitate proteins. Subsequently, the firefly luciferase buffer was added, and the luminescence intensity was measured with a fluorescent microplate reader (Thermo Fisher Scientific, USA). To assess the intracellular ROS content, the cell lysates were incubated with the highly sensitive fluorescent probe DCFH-DA. The emitted DCF fluorescence was then detected with the same fluorescent microplate reader. Western blot analysis SNU398 HCC cells were treated with PSCP for the designated duration and lysed on ice using RIPA buffer for 15 min. The extracted proteins were quantified with the bicinchoninic acid (BCA) assay. Subsequently, the total protein samples were separated by SDS-PAGE and then transferred onto PVDF membranes. Following blocking with 5% fat-free milk, the membranes were incubated with primary antibodies at 4°C with gentle agitation. After washing, the membranes were further incubated with the corresponding secondary antibodies. The protein bands were visualized using an Amersham Imager 680 multifunctional imager (GE, USA). The grayscale values were calculated using ImageJ software. Molecular docking and binding energy calculation Molecular docking and the calculation of binding energies were conducted using the AutoDock software suite. The visualization of the docking outcomes was achieved through the PyMOL software 37 . The structure of Ndus3 was obtained from the AlphaFold Protein Structure Database ( https://alphafold.com/ ), while the structure of NADH was retrieved from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov ). The structure of PSCP, along with its degradation products (PSCP-SSH and PSCP-Polysulfide), was constructed using the ChemDraw software. Statistical Analysis All data were presented as mean ± standard deviation (SD) and analyzed using GraphPad Prism 9.0 software (San Diego, USA). Statistical significance between groups was determined using one-way analysis of variance (ANOVA) followed by the Student-Newman–Keuls post-hoc test. A p-value less than 0.05 was considered statistically significant. Furthermore, R programming language was utilized for advanced statistical analyses, including clustering, function enrichment, feature screening and survival analysis. Declarations Competing interests: The authors affirm that the research presented in this study was conducted without the presence of any commercial or financial interests that could be interpreted as a potential conflict of interest. Approval statement: This study was approved by the Animal Ethics Committee of Guangzhou Medical University (Approval Number: GY2020-099). Accordance statement: All procedures were strictly adhered to the guidelines of the Animal Ethics Committee of Guangzhou Medical University. ARRIVE statement: All procedures were conducted in full compliance with the ARRIVE guidelines. Foundation: This study was funded by Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515010618, 2021A1515011365). Medical Research Fund Project of Guangdong (A2023184). Health Medical Research Project of Guangdong Foshan (20230087). Acknowledgment: We extend our gratitude to the editor and the reviewers for their meticulous and insightful contribution to this work. References Llovet, J. M. et al. Hepatocellular carcinoma. Nat. Rev. Dis. Primers . 7 , 6. https://doi.org/10.1038/s41572-020-00240-3 (2021). Yang, C. et al. Evolving therapeutic landscape of advanced hepatocellular carcinoma. Nat. Rev. Gastroenterol. Hepatol. 20 , 203–222. https://doi.org/10.1038/s41575-022-00704-9 (2023). Li, N. et al. Deficient DNASE1L3 facilitates neutrophil extracellular traps-induced invasion via cyclic GMP-AMP synthase and the non-canonical NF-kappaB pathway in diabetic hepatocellular carcinoma. Clin. Transl Immunol. 11 , e1386. https://doi.org/10.1002/cti2.1386 (2022). Philips, C. A. et al. Hepatocellular Carcinoma in 2021: An Exhaustive Update. Cureus 13, e19274. (2021). https://doi.org/10.7759/cureus.19274 Weisberger, A. S. & Pensky, J. Tumor-inhibiting effects derived from an active principle of garlic (Allium sativum). Science . 126 , 1112–1114. https://doi.org/10.1126/science.126.3283.1112-a (1957). Touloupakis, E. & Ghanotakis, D. F. Nutraceutical use of garlic sulfur-containing compounds. Adv. Exp. Med. Biol. 698 , 110–121. https://doi.org/10.1007/978-1-4419-7347-4_9 (2010). Zhou, Y. et al. Allicin in digestive system cancer: From biological effects to clinical treatment. Front. Pharmacol. 13 , 903259. https://doi.org/10.3389/fphar.2022.903259 (2022). Mirahmadi, M. et al. Potential inhibitory effect of lycopene on prostate cancer. Biomed. Pharmacother . 129 , 110459. https://doi.org/10.1016/j.biopha.2020.110459 (2020). Zhang, X. et al. Metabolic reprogramming of sulfur in hepatocellular carcinoma and sulfane sulfur-triggered anti-cancer strategy. Front. Pharmacol. 11 , 571143. https://doi.org/10.3389/fphar.2020.571143 (2020). Faubert, B., Solmonson, A. & DeBerardinis, R. J. Metabolic reprogramming and cancer progression. Science . 368 , eaaw5473. https://doi.org/10.1126/science.aaw5473 (2020). Wei, F. & Locasale, J. W. Methionine restriction and antitumor immunity. Trends cancer . 9 , 705–706. https://doi.org/10.1016/j.trecan.2023.07.008 (2023). Xue, Y. et al. Intermittent dietary methionine deprivation facilitates tumoral ferroptosis and synergizes with checkpoint blockade. Nat. Commun. 14 , 4758. https://doi.org/10.1038/s41467-023-40518-0 (2023). Hung, M. H. et al. Tumor methionine metabolism drives T-cell exhaustion in hepatocellular carcinoma. Nat. Commun. 12 , 1455. https://doi.org/10.1038/s41467-021-21804-1 (2021). Frattaruolo, L. et al. Targeting the mitochondrial metabolic network: A promising strategy in cancer treatment. Int. J. Mol. Sci. 21 , 6014. https://doi.org/10.3390/ijms21176014 (2020). Antico Arciuch, V. G., Elguero, M. E., Poderoso, J. J. & Carreras, M. C. Mitochondrial regulation of cell cycle and proliferation. Antioxid. Redox. Signal. 16 , 1150–1180. https://doi.org/10.1089/ars.2011.4085 (2012). Bock, F. J. & Tait, S. W. G. Mitochondria as multifaceted regulators of cell death. Nat. Rev. Mol. Cell. Biol. 21 , 85–100. https://doi.org/10.1038/s41580-019-0173-8 (2020). Liu, Y. E. & Shi, Y. F. Mitochondria as a target in cancer treatment. MedComm 1, 129–139. (2020). https://doi.org/10.4155/fmc-2017-0110 Bonekamp, N. A. et al. Small-molecule inhibitors of human mitochondrial DNA transcription. Nature . 588 , 712–716. https://doi.org/10.1038/s41586-020-03048-z (2020). Chen, L. et al. Mitochondrial heterogeneity in diseases. Signal. Transduct. Target. Ther. 8 , 311. https://doi.org/10.1038/s41392-023-01546-w (2023). Cassim, S., Raymond, V. A., Dehbidi-Assadzadeh, L., Lapierre, P. & Bilodeau, M. Metabolic reprogramming enables hepatocarcinoma cells to efficiently adapt and survive to a nutrient-restricted microenvironment. Cell. cycle (Georgetown Tex) . 17 , 903–916. https://doi.org/10.1080/15384101.2018.1460023 (2018). Shang, R. Z., Qu, S. B. & Wang, D. S. Reprogramming of glucose metabolism in hepatocellular carcinoma: Progress and prospects. World J. Gastroenterol. 22 , 9933–9943. https://doi.org/10.3748/wjg.v22.i45.9933 (2016). Sangineto, M. et al. Lipid metabolism in development and progression of hepatocellular carcinoma. Cancers (Basel) . 12 , 1419. https://doi.org/10.3390/cancers12061419 (2020). Chu, Y. L. et al. Allicin induces anti-human liver cancer cells through the p53 gene modulating apoptosis and autophagy. J. Agric. Food Chem. 61 , 9839–9848. https://doi.org/10.1021/jf403241s (2013). Zuhra, K., Tomé, C. S., Forte, E., Vicente, J. B. & Giuffrè, A. The multifaceted roles of sulfane sulfur species in cancer-associated processes. Biochim. Biophys. Acta Bioenerg . 1862 , 148338. https://doi.org/10.1016/j.bbabio.2020.148338 (2021). Hafner, A., Bulyk, M. L., Jambhekar, A. & Lahav, G. The multiple mechanisms that regulate p53 activity and cell fate. Nat. Rev. Mol. Cell. Biol. 20 , 199–210. https://doi.org/10.1038/s41580-019-0110-x (2019). Cao, H. et al. The role of MDM2-p53 axis dysfunction in the hepatocellular carcinoma transformation. Cell. Death Discov . 6 , 53. https://doi.org/10.1038/s41420-020-0287-y (2020). Konopleva, M. et al. MDM2 inhibition: an important step forward in cancer therapy. Leukemia . 34 , 2858–2874. https://doi.org/10.1038/s41375-020-0949-z (2020). He, G. et al. AMP-activated protein kinase induces p53 by phosphorylating MDMX and inhibiting its activity. Mol. Cell. Biol. 34 , 148–157. https://doi.org/10.1128/mcb.00670-13 (2014). Lee, C. W. et al. AMPK promotes p53 acetylation via phosphorylation and inactivation of SIRT1 in liver cancer cells. Cancer Res. 72 , 4394–4404. https://doi.org/10.1158/0008-5472.CAN-12-0429 (2012). Steinberg, G. R. & Hardie, D. G. New insights into activation and function of the AMPK. Nat. Rev. Mol. Cell. Biol. 24 , 255–272. https://doi.org/10.1038/s41580-022-00547-x (2023). Lin, S. C., Hardie, D. G. & AMPK Sensing glucose as well as cellular energy status. Cell Metabol. 27 , 299–313. https://doi.org/10.1016/j.cmet.2017.10.009 (2018). Mamedov, M. R. et al. CRISPR screens decode cancer cell pathways that trigger γδ T cell detection. Nature . 621 , 188–195. https://doi.org/10.1038/s41586-023-06482-x (2023). Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 , e47. https://doi.org/10.1093/nar/gkv007 (2015). Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550. (2005). https://doi.org/10.1073/pnas.0506580102 Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14 , 7. https://doi.org/10.1186/1471-2105-14-7 (2013). Pickett, K. L., Suresh, K., Campbell, K. R., Davis, S. & Juarez-Colunga, E. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. BMC Med. Res. Methodol. 21 , 216. https://doi.org/10.1186/s12874-021-01375-x (2021). Seeliger, D. & de Groot, B. L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des. 24 , 417–422. https://doi.org/10.1007/s10822-010-9352-6 (2010). Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Nov, 2024 Reviews received at journal 12 Nov, 2024 Reviewers agreed at journal 09 Nov, 2024 Reviews received at journal 08 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 28 Oct, 2024 Editor assigned by journal 28 Oct, 2024 Editor invited by journal 28 Oct, 2024 Submission checks completed at journal 28 Oct, 2024 First submitted to journal 08 Oct, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5228470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379054236,"identity":"42d4c060-5ba1-4430-88ca-299b602582fd","order_by":0,"name":"Xue Zheng","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Zheng","suffix":""},{"id":379054237,"identity":"739e6f1f-a360-4587-927b-e492ecc6685e","order_by":1,"name":"Yuhua Luo","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuhua","middleName":"","lastName":"Luo","suffix":""},{"id":379054238,"identity":"bc2046bd-b412-4262-9990-e99ac6d605de","order_by":2,"name":"Rui Huo","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Huo","suffix":""},{"id":379054239,"identity":"c0256d2d-240c-4a06-bf9c-e1bd561db233","order_by":3,"name":"Yiwen Wang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiwen","middleName":"","lastName":"Wang","suffix":""},{"id":379054240,"identity":"f96f1635-c0df-4642-8736-00af081afcd4","order_by":4,"name":"Youbang Chen","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youbang","middleName":"","lastName":"Chen","suffix":""},{"id":379054241,"identity":"5aca65d3-409c-4dbe-9e45-942d5d8547f8","order_by":5,"name":"Mianrong Chen","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mianrong","middleName":"","lastName":"Chen","suffix":""},{"id":379054242,"identity":"1d7d2b68-10b8-4797-ba91-ba469f9e7bd3","order_by":6,"name":"Qi Zhao","email":"","orcid":"","institution":"Lecong Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Zhao","suffix":""},{"id":379054243,"identity":"88e623cd-2310-427b-832a-d6e260e09fb4","order_by":7,"name":"Kexin Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kexin","middleName":"","lastName":"Li","suffix":""},{"id":379054247,"identity":"7aadb697-500c-415a-a0fe-fdf92e2795fd","order_by":8,"name":"Hanyi Zhang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hanyi","middleName":"","lastName":"Zhang","suffix":""},{"id":379054249,"identity":"2031250c-47a5-47c9-b5e2-2dfa365c02c4","order_by":9,"name":"Xiaotong Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Li","suffix":""},{"id":379054250,"identity":"12ed9aab-eb97-49c2-8f3a-2db120ce5ee2","order_by":10,"name":"Xiang Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":379054252,"identity":"4d94f35f-5df6-4758-831b-670ae569565a","order_by":11,"name":"Hui Zhang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":379054254,"identity":"5abe6c41-395a-4883-a42c-d4f2a8f9b44c","order_by":12,"name":"Zaopeng He","email":"","orcid":"","institution":"Lecong Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zaopeng","middleName":"","lastName":"He","suffix":""},{"id":379054255,"identity":"f92e2012-9857-402d-b4f4-47ac02dcf473","order_by":13,"name":"Li Huang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Huang","suffix":""},{"id":379054256,"identity":"238d9ced-5270-43c9-935e-fbc50c3a02e8","order_by":14,"name":"Chun-tao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYNCCCgYGAzCDjWgtZ0jWwthGihb+GcnPHn6dV2tvLnbGgOFD2WEG/tkN+LVI3EgzN5bddpzZcnaOAeOMc4cZJO4cwK/FQCLBTFpy2zE2g9s5Bsy8bYdBIoS0pH+TlpxzjAes5S9xWnLMJD821EiAtTASo0XizJsyaYZjBwwMbqcVHOw5l84jcYOAFv729G2SP2rq7A1uJ2988KPMWo5/BgEtDAIJDMw8DIfB7ANAzENAPciaAwyMPxjqCCscBaNgFIyCkQsAxhlBiL+4yEQAAAAASUVORK5CYII=","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chun-tao","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-10-09 01:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5228470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5228470/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-83530-0","type":"published","date":"2025-01-29T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71625403,"identity":"53d2a07e-3bf0-499b-9cd0-aef98433ba2c","added_by":"auto","created_at":"2024-12-17 08:48:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of sulfur and related metabolic pathways using TCGA transcriptomic data.\u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Cluster analysis of RNA expression of HCC (n=374) and control para-cancerous (n=50) tissues with the Umap dimensionality reduction algorithm. (\u003cstrong\u003eB-D\u003c/strong\u003e) The pathway of sulfur metabolism (B), sulfur amino acid metabolism (C), and methionine metabolism leading to sulfur amino acid and related disorders (D) were integrated into the Umap plot. (\u003cstrong\u003eE-F\u003c/strong\u003e) Cox regression analysis was conducted to assess the impact of each phenotype on the survival of HCC patients (n=187).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/f4db67a89e994d2fa96db90e.png"},{"id":71624633,"identity":"e8833b70-f3e5-4060-9457-323ebff4d336","added_by":"auto","created_at":"2024-12-17 08:40:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":367897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamination of sulfur and related metabolism using CPTAC proteomic data.\u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Protein expression of HCC (n=160) and control para-cancerous (n=160) tissues was analyzed using GSVA, followed by t-SNE clustering. (\u003cstrong\u003eB\u003c/strong\u003e) Utilizing the t-values generated by the limma algorithm, a diverging bar chart was constructed to depict the GSVA scores of sulfur-related metabolisms. (\u003cstrong\u003eC-D\u003c/strong\u003e) Random Survival Forestanalysis was performed to evaluate the effects of sulfur-related metabolism phenotypes on the survival of HCC patients. (\u003cstrong\u003eE\u003c/strong\u003e) Cox regression analysis was conducted to observe the impact of each phenotype group (Down 80 versus Up 80) on the survival of HCC patients. (\u003cstrong\u003eF\u003c/strong\u003e) ROC curves were plotted, and the AUC was calculated to evaluate the diagnostic efficacy for HCC.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/3c3dfa56e72eadea95a640e9.png"},{"id":71622989,"identity":"3416f19d-8ccd-4a5e-abc4-70c41ff6d6cd","added_by":"auto","created_at":"2024-12-17 08:32:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of PSCP on the growth of HCC in vivo and in vitro. \u003c/strong\u003eH22 HCC cells were subcutaneously implanted into nude mice. Four days post-implantation, mice were administered varying concentrations of PSCP intraperitoneally once daily for three weeks. (\u003cstrong\u003eA\u003c/strong\u003e) Upon sacrifice, the allografts from the mice were imaged, and their relative tumor volumes were measured. (\u003cstrong\u003eB\u003c/strong\u003e) SNU398 HCC cells were treated with increasing concentrations of PSCP for various time periods, and the cell viability was assessed using the CCK-8 assay. Data are expressed as mean ± SD (n=4-5). \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 \u003cem\u003evs\u003c/em\u003e. Control group. \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 \u003cem\u003evs\u003c/em\u003e. 24 h within the same group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/05b8f43d36e63122c4a96bac.png"},{"id":71622979,"identity":"bcd1e2a7-e765-4467-8cef-71a10f797706","added_by":"auto","created_at":"2024-12-17 08:32:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":392997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluence of PSCP on cell cycle progression and proliferation in HCC cells. \u003c/strong\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) The GSEA algorithm was used to identify enriched pathways related to cell cycle (A) and DNA replication (B) in PSCP-treated HCC cells. (\u003cstrong\u003eC-E\u003c/strong\u003e) Following a 24-h treatment with PSCP, cell cycle was analyzed using flow cytometry after PI staining (C and D), and cell proliferation was measured with the EdU incorporation assay (E). Data are presented as mean ± SD (n=4). *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01 \u003cem\u003evs.\u003c/em\u003e Control group.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/d548aba464c013e13ea5a4ea.png"},{"id":71622986,"identity":"820335c0-c5da-4107-baf4-e69369332f2a","added_by":"auto","created_at":"2024-12-17 08:32:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260239,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of PSCP on apoptosis in HCC cells. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) SNU398 HCC cells were exposed to 200 μM PSCP for 24 h, and the TUNEL assay followed by fluorography was utilized to assess apoptosis. (\u003cstrong\u003eB-C\u003c/strong\u003e) After treatment with PSCP at concentrations ranging from 100 to 400 μM for 48 h, SNU398 HCC cells were stained with Annexin V and PI, and then analyzed for apoptosis rate using flow cytometry. Data are represented as mean ± SD, n=5.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/080b99fa691513ee45835e77.png"},{"id":71622980,"identity":"1456111d-6bd5-48ff-9b58-341901a2d416","added_by":"auto","created_at":"2024-12-17 08:32:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":288306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibitory effects of PSCP on HCC were mediated through the p53 pathway \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) The enriched KEGG pathways related to p53 were identified using the GSVA algorithm in SNU398 HCC cells treated with PSCP. (\u003cstrong\u003eB\u003c/strong\u003e) The expression of p53 protein in SNU398 HCC cells exposed to 200 μM PSCP was measured using Western blotting. (\u003cstrong\u003eC\u003c/strong\u003e) RNA interference was conducted to reduce p53 expression in SNU398 HCC cells. \u003cstrong\u003e(D)\u003c/strong\u003e Scramble control and TP53 knockdown SNU398 HCC cells were treated with 0~400 μM PSCP for 48 h, cell viability and proliferation were assessed using the CCK-8 assay and the EdU incorporation assay, respectively. Data are expressed as mean ± SD, n=3-4.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/86f9ee94efbf07d56a304051.png"},{"id":71627069,"identity":"f419d9d0-317a-492d-8648-0d413d0e2bf5","added_by":"auto","created_at":"2024-12-17 08:56:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":309260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRoles of AMPK in PSCP-induced anti-HCC effects. (A)\u003c/strong\u003e The enriched KEGG pathways associated with AMPK were identified in PSCP-treated SNU398 HCC cells using the GSVA algorithm. (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) Proteins were extracted from the mouse allografts (B) or SNU398 HCC cells (C) treated with PSCP, and the levels of phosphorylated (p) and total AMPK were measured with Western blotting. (\u003cstrong\u003eD-E\u003c/strong\u003e) The effects of pretreatment with 2 μM Comp. C (an AMPK inhibitor) for 2 h on the phosphorylation of AMPK (D) and the upregulation of p53 (E) induced by 200 μM PSCP for 48 h were examined. (\u003cstrong\u003eF\u003c/strong\u003e) After SNU398 HCC cells were treated with PSCP in the absence or presence of Comp. Cpretreatment, cell viability was measured using the CCK-8 assay. \u003cstrong\u003e(G)\u003c/strong\u003e The effects of increasing concentrations of Metformin (an AMPK agonist) on SNU398 cell viability were assessed as described in (F). Data are expressed as mean ± SD, n=3-4.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/5311f57d8e009dbce58ee745.png"},{"id":71624637,"identity":"2662f088-e6df-4174-971b-409cdaace7de","added_by":"auto","created_at":"2024-12-17 08:40:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":607792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnhanced mitochondrial function in HCC and PSCP-induced mitochondrial impairment in HCC cells. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Using the t-values generated by the limma algorithm, a diverging bar chart was constructed to represent the GSVA scores of mitochondrial function. (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) The GSEA algorithm was applied to identify the top ten enriched pathways in HCC tissues (B) and the mitochondrial translation set was displayed (C). (\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e) Following treatment of SNU398 HCC cells with PSCP, the GSEA algorithm was employed to analyze the enriched gene sets related to mitochondrial function (D) and complex activity (E).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/45c1e70cf69e86a24ecd8335.png"},{"id":71622984,"identity":"b9c579ad-12b1-4acb-8b79-a408f9d0edba","added_by":"auto","created_at":"2024-12-17 08:32:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":467362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePSCP-induced impairment of mitochondrial function in HCC cells. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Following a 24-h treatment with 100 μM PSCP, MMP in SNU398 HCC cells was observed with JC-1 staining assay and fluorography. (\u003cstrong\u003eB\u003c/strong\u003e) The MMP levels were quantified by measuring the fluorescence ratio of red to green using Image J software. (\u003cstrong\u003eC-F\u003c/strong\u003e) After treatment with PSCP (100~400 μM) for 24 h, the activity of mitochondrial complex I (\u003cstrong\u003eC\u003c/strong\u003e), the NADH/NAD\u003csup\u003e+\u003c/sup\u003e ratio (\u003cstrong\u003eD\u003c/strong\u003e), intracellular ROS (\u003cstrong\u003eE\u003c/strong\u003e), and intracellular ATP content (\u003cstrong\u003eF\u003c/strong\u003e) were measured in SNU398 HCC cells. (\u003cstrong\u003eG\u003c/strong\u003e) After SNU398 HCC cells were treated with PSCP in the absence or presence of ATP, the viability was measured using the CCK-8 assay. Data are expressed as mean ± SD (n=4~5). *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01 \u003cem\u003evs. \u003c/em\u003eControl group.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/1fbec6763b2342062992ba83.png"},{"id":71625401,"identity":"2b456dc8-75de-454e-9bcc-8b6b010d5de4","added_by":"auto","created_at":"2024-12-17 08:48:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":414161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction of PSCP with mitochondrial complex I.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Following a 24-hour treatment of SNU398 HCC cells with 200 μM PSCP, the expression and mitochondrial translocation of Ndus3 were observed using confocal fluorescence microscopy. \u003cstrong\u003e(B)\u003c/strong\u003eThe interaction of NADH with the Ndus3 protein was analyzed using Autodock, and the visualization of the specific types of interactions was achieved through the PyMOL software. \u003cstrong\u003e(C)\u003c/strong\u003e The interaction of PSCP, as well as its degradation products (PSCP-SSH and PSCP-Polysulfide) with the Ndus3 protein was analyzed and visualized using the same methods as in (B).\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/8047b5866aca8b21dc36881f.png"},{"id":75351310,"identity":"5ed2e829-df2f-418a-b952-f53779052955","added_by":"auto","created_at":"2025-02-03 16:09:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4980352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/a8d9da01-aa02-44d2-9657-0e9a009f4d07.pdf"},{"id":71622978,"identity":"e8a66b80-e78c-4105-bf9a-278944c8382b","added_by":"auto","created_at":"2024-12-17 08:32:47","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":397592,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5228470/v1/91f5b559052acf24fb57dde9.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Activating the AMPK-p53 Axis by Mitochondrial Impairment: Unveiling a Novel Anti-Liver Cancer Mechanism of Sulfane Sulfur","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer is a major contributor to global cancer-related mortality, with an anticipated incidence exceeding one million cases by 2025. Hepatocellular carcinoma (HCC), the most prevalent form of primary liver cancer, accounts for approximately 90% of all cases \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The standard treatment for HCC is radical surgery; however, the risk of postoperative recurrence is relatively high, and many patients are diagnosed at advanced stages, precluding surgical indications. This leads to a 5-year survival rate of less than 20% \u003csup\u003e2,3\u003c/sup\u003e. Chemotherapeutic options are limited due to HCC\u0026rsquo;s resistance to conventional drugs. Even with multi-targeted tyrosine kinase inhibitors like Lenvatinib, the emergence of acquired resistance is prevalent, and the prolongation of progression-free survival and overall survival remains extremely limited \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Hence, innovative therapies are urgently needed to improve the survival prospects of HCC patients.\u003c/p\u003e \u003cp\u003eSince the 1950s, garlic extracts have demonstrated anticancer effects \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Subsequent research has consistently confirmed that the anticancer properties are attributed to the release of highly reactive sulfur atoms from polysulfides, also known as sulfane sulfurs \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Although sulfane sulfurs, like allicin and lycopene sulfanes, have previously been implicated in anticancer activity \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, their precise mechanisms remain largely elusive. Furthermore, the existing sulfane sulfurs are prone to oxidation and decomposition, hindering drug development and clinical application \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We recently reported a novel sulfane sulfur donor, persulfided cysteine precursor (PSCP), which offers improved chemical stability and flexibility of structural modifications. Our previous work demonstrated the efficacy and specificity of PSCP in HCC cell line models \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, the \u003cem\u003ein vivo\u003c/em\u003e efficacy and the anticancer mechanisms of PSCP have not been thoroughly investigated. Metabolic reprogramming has been accepted as a hallmark of cancer \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The role of sulfur-containing amino acids, like methionine, has been extensively studied \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the metabolic landscape of sulfur and its related amino acids in HCC tissues and their impact on patient survival not well-characterized.\u003c/p\u003e \u003cp\u003eMitochondria are central to the cellular metabolic network, serving as the primary site for nutrient oxidation and energy release \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. They also play pivotal roles in governing cell cycle progression, differentiation, and programmed cell death \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Recent studies have highlighted abnormal mitochondrial metabolism as a defining feature of cancer \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Mitochondria in tumor cells exhibit significant metabolic heterogeneity compared to normal cells, which benefits rapid DNA replication, excessive proliferation, and drug resistance \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Consequently, targeting mitochondrial metabolism is a promising strategy in cancer therapy. However, the impact of PSCP on mitochondrial metabolism in HCC treatment has not been extensively explored.\u003c/p\u003e \u003cp\u003eIn this study, we wanted to confirm the \u003cem\u003ein vivo\u003c/em\u003e efficacy of PSCP and to elucidate the mechanisms underlying its inhibitory effects on HCC growth. We characterized the sulfur metabolism landscape in HCC tumors and control liver tissues and analyzed the survival outcomes of HCC patients by integrating proteomic data from the CPTAC database with their survival status. Transcriptome sequencing, combined with gene set enrichment or variation analysis, was employed to reveal the molecular mechanisms behind PSCP\u0026rsquo;s anti-HCC effects.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eImpaired sulfur and related metabolism in HCC tissues portends a dismal prognosis for patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe application of cluster analysis to TCGA RNA-Seq data, coupled with UMAP dimensionality reduction, revealed a distinct clustering pattern among 374 HCC samples and 50 control samples (Fig. 1\u003cstrong\u003eA\u003c/strong\u003e). Furthermore, gene expression data were converted into phenotype score through the GSVA algorithm, with a specific focus on sulfur-related metabolic pathways. As shown in Fig. 1\u003cstrong\u003eB\u003c/strong\u003e, the sulfur metabolism was not found to be clustered between HCC and control samples, nor within HCC samples. However, sulfur amino acid metabolism and the methionine metabolism leading to sulfur amino acid and related disorders, were markedly clustered within the HCC samples (Fig. 1\u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eD\u003c/strong\u003e). Moreover, the downregulation of these phenotypic scores emerged as a powerful predictor of diminished survival rates among HCC patients (Fig. 1\u003cstrong\u003eE\u003c/strong\u003e and \u003cstrong\u003eF\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition to the aforementioned genomic analyses, we delved into the proteomic data of CPTAC. Protein expression data were similarly converted into phenotypic scores, and cluster analysis revealed a distinct segregation between HCC and para-cancerous tissues (Fig. 2\u003cstrong\u003eA\u003c/strong\u003e). The diverging bar chart highlighted the downregulation of several sulfur metabolism-related phenotypes within HCC tissues (Fig. 2\u003cstrong\u003eB\u003c/strong\u003e). To assess the impact of these phenotypes on HCC patient survival, we employed the Random Survival Forest algorithm. Three key phenotypes were distinguished based on their ranking in terms of importance: Trans-sulfuration, Sulfur metabolism, and Sulfur amino acid metabolism (Fig. 2\u003cstrong\u003eC-D\u003c/strong\u003e). Cox regression analysis and survival curves confirmed that deficiencies in these three phenotypes were critical predictors of poor survival outcomes in HCC patients (Fig. \u003cstrong\u003e2E\u003c/strong\u003e). Finally, ROC curves revealed that these metabolic defects (Trans-sulfuration, Sulfur metabolism, and Sulfur amino acid metabolism) served as robust diagnostic indicators for HCC, with all AUC values of 0.9 or higher (Fig. 2\u003cstrong\u003eF\u003c/strong\u003e). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP inhibits HCC tumor growth in vitro and in vivo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the consequences of impaired sulfur and related amino acid metabolism on the growth of HCC tumors, we investigated the effects of a recently synthesized sulfane sulfur donor, PSCP. Initially, we assessed the inhibitory impact of PSCP on tumor growth in an allograft model of H22 HCC cells, which were implanted subcutaneously into nude mice. PSCP was administered at varying concentrations via intraperitoneal injection. The results showed that PSCP dose-dependently suppressed the growth of transplanted tumors (Fig. 3\u003cstrong\u003eA\u003c/strong\u003e). Furthermore, \u003cem\u003ein vitro\u003c/em\u003e experiments confirmed that treatment of SNU398 HCC cells with PSCP significantly reduced cell viability in a dose- and time-dependent manner (Fig. 3\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP suppresses HCC cell proliferation by arresting cell cycle\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo reveal how PSCP inhibits the growth of HCC cells, we conducted transcriptomic sequencing and all genes-based enrichment analysis. The results of GSEA showed that the treatment with PSCP downregulated gene sets associated with cell cycle (Fig. 4\u003cstrong\u003eA\u003c/strong\u003e) and DNA replication pathways (Fig. 4\u003cstrong\u003eB\u003c/strong\u003e). Subsequently, we employed flow cytometry to assess cell cycle changes. SNU398 cells treated with PSCP for 24 h were harvested and stained with PI to examine cell cycle distribution. The data demonstrated that PSCP treatment increased the proportion of cells in the G0/G1 phase, indicating G0/G1 phase arrest due to the inhibition of DNA replication (Fig. 4\u003cstrong\u003eC-D\u003c/strong\u003e). To further confirm the impact on DNA replication, we performed an EdU incorporation assay. The assay showed that PSCP dose-dependently inhibited DNA replication within a concentration range of 100 to 400 \u0026mu;M (Fig. 4\u003cstrong\u003eE\u003c/strong\u003e). These findings collectively suggest that PSCP exerts its inhibitory effects on HCC cell proliferation by inducing cell cycle arrest at the G0/G1 phase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP promotes apoptosis in HCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the cell proliferation process, cells unable to complete DNA repair are typically eliminated via apoptosis. To ascertain whether PSCP induced apoptosis in HCC cells, we employed the TUNEL assay followed by fluorography. As depicted in\u0026nbsp;Fig.\u0026nbsp;5\u003cstrong\u003eA\u003c/strong\u003e, the number of TUNEL-positive cells exhibiting green fluorescence increased significantly in the PSCP treatment group. To further quantify apoptosis and\u0026nbsp;necrosis, we conducted Annexin V-FITC/PI double staining in conjunction with flow cytometry. The analysis revealed that PSCP treatment\u0026nbsp;dose-dependently elevated the\u0026nbsp;ratios\u0026nbsp;of\u0026nbsp;apoptosis and necrosis within a concentration range of 100 to 400 \u0026mu;M (Fig. 5\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP exerts its anti-HCC effects through p53 upregulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo delineate the molecular mechanisms by which PSCP inhibits proliferation but promotes apoptosis in HCC cells, we examined the enriched KEGG pathways using GSVA. The analysis revealed that PSCP treatment led to the upregulation of pathways involved in \u0026ldquo;\u003cem\u003eTp53 Targets Apoptotic Up\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eTp53 Regulates Transcription of Caspase Activators and Caspases\u003c/em\u003e\u0026rdquo;, while downregulating \u0026ldquo;\u003cem\u003eTp53 Regulates Transcription of DNA Repair Genes\u003c/em\u003e\u0026rdquo; (Fig. 6\u003cstrong\u003eA\u003c/strong\u003e). As p53 is the protein encoded by \u003cem\u003eTP53\u003c/em\u003e gene, we sought to determine whether p53 played a role in the anti-HCC effects of PSCP. Western blot analysis showed that PSCP treatment significantly enhanced p53 protein levels in SNU398 HCC cells (Fig. 6\u003cstrong\u003eB\u003c/strong\u003e). To further elucidate the involvement of p53, we knocked down \u003cem\u003eTP53\u003c/em\u003e gene expression using RNA interference (Fig. 6\u003cstrong\u003eC\u003c/strong\u003e). This knockdown resulted in a reduction of the inhibitory effects of PSCP on cell viability and proliferation (Fig. 6\u003cstrong\u003eD\u003c/strong\u003e). Collectively, these data indicate that the upregulation of p53 by PSCP is a critical factor in its anti-HCC activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eActivation of AMPK is implicated in PSCP-induced p53 upregulation in HCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo delve into how PSCP upregulates p53 and exerts its anti-HCC effects, we screened the enriched KEGG pathways. Our analysis revealed a significant enrichment of AMPK-related metabolic pathways in SNU398 HCC cells treated with PSCP (Fig. 7\u003cstrong\u003eA\u003c/strong\u003e).\u0026nbsp;Following intraperitoneal administration of PSCP in nude mice, AMPK activation was markedly induced, as evidenced by increased levels of phosphorylated AMPK (p-AMPK) in the\u0026nbsp;allografts\u0026nbsp;of mice\u0026nbsp;treated with\u0026nbsp;20 or 40 mg/kg PSCP (Fig. 7\u003cstrong\u003eB\u003c/strong\u003e). \u003cem\u003eIn vitro\u003c/em\u003e experiments also demonstrated that PSCP treatment of SNU398 HCC cells was able to raise the ratio of p-AMPK to total AMPK (Fig. 7\u003cstrong\u003eC\u003c/strong\u003e). To further validate the role of AMPK in PSCP\u0026rsquo;s anti-HCC actions, we employed the AMPK inhibitor Compound C (Comp. C) (Fig. 7\u003cstrong\u003eD\u003c/strong\u003e). Treatment with Comp. C remarkably attenuated PSCP-induced p53 expression (Fig. 7\u003cstrong\u003eE\u003c/strong\u003e) and its anti-HCC effects (Fig. 7\u003cstrong\u003eF\u003c/strong\u003e). These findings suggest that AMPK activation is essential for the anti-HCC efficacy of PSCP. Moreover, metformin, a commonly utilized AMPK agonist in clinical practice, also inhibited cell viability, while its potency was notably less than that of PSCP (Fig. 7\u003cstrong\u003eG\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP induces mitochondrial dysfunction in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMitochondrial impairment is known to activate AMPK due to ATP\u0026nbsp;depletion and consequent AMP or ADP generation. To clarity how PSCP activates AMPK in HCC, we first conducted a bioinformatic analysis using the CPTAC proteomic data. The GSVA results showed that gene sets related to mitochondrial gene transcription, translation and biogenesis were positively enriched in HCC\u0026nbsp;(Fig. 8\u003cstrong\u003eA\u003c/strong\u003e). Moreover, the gene set for mitochondrial translation was among the top ten enriched gene sets in GSEA\u0026nbsp;(Fig. 8\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e),\u0026nbsp;indicating an upregulation of mitochondrial activity in HCC. Subsequently, we carried out an\u0026nbsp;all genes-based enrichment analysis\u0026nbsp;on the transcriptomic data from SNU398 HCC cells treated with PSCP and control. It was found that PSCP markedly suppressed gene sets involved in mitochondrial gene translation (Fig. 8\u003cstrong\u003eD\u003c/strong\u003e). Further analysis suggested that PSCP specifically repressed gene sets related to the assembly of mitochondrial complexes I, II and III (Fig. 8\u003cstrong\u003eE\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP-induced mitochondrial dysfunction activates AMPK in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ascertain the inhibitory effects of PSCP on mitochondrial function, we conducted a series of \u003cem\u003ein vitro\u003c/em\u003e experiments. Initially, SNU398 HCC cells were exposed to PSCP for 24 h, and MMP was assessed using JC-1 staining followed by confocal fluorescence microscopy. The results showed that PSCP dose-dependently reduced the MMP levels, as evidenced by a decreased ratio of red to green fluorescence, within a concentration range of 100 to 400 \u0026mu;M (Fig. 9\u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eB\u003c/strong\u003e). MMP generation relies on proton transport through mitochondrial complexes. Next, we performed an assay to measure mitochondrial complex I activity and found that PSCP significantly suppressed its activity (Fig. 9\u003cstrong\u003eC\u003c/strong\u003e). Additionally, we showed that PSCP treatment led to marked increases in the ratio of NADH to NAD\u003csup\u003e+\u003c/sup\u003e and the content of intracellular ROS (Fig. 9\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e), while decreasing the intracellular ATP levels (Fig. 9\u003cstrong\u003eF\u003c/strong\u003e).\u0026nbsp;Importantly, the addition of ATP was able to rescue PSCP-induced cell damage (Fig. 9\u003cstrong\u003eG\u003c/strong\u003e). These data indicate that PSCP-induced AMPK activation is contingent upon the inhibition of mitochondrial complex I, which hampers proton transport and ATP synthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSCP inhibits mitochondrial translocation of Ndus3, thereby inducing mitochondrial dysfunction in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate how PSCP disrupts mitochondrial complex I, we first observed the expression of NADH dehydrogenase [ubiquinone] iron-sulfur protein 3 (Ndus3), a critical iron-sulfur protein component of complex I. As shown in Fig. 10\u003cstrong\u003eA\u003c/strong\u003e, PSCP remarkably reduced Ndus3 protein levels in SNU398 HCC cell mitochondria.\u003c/p\u003e\n\u003cp\u003eGiven the pivotal role of Ndus3 in the catalytic reaction of NADH, we compared the binding affinity of Ndus3 for NADH and PSCP, as well as its degradation products (PSCP-SSH and PSCP-Polysulfide) using molecular docking techniques. The most favorable docking models and corresponding binding energies were presented. In Fig. 10B, the binding energy between Ndus3 and NADH was determined to be -7.5 kcal/mol. The interaction was mediated by key residues, including GLY195, ARG186, TYR207, HIS196, TRP171, LEU184 and ASP201. Figure10C illustrated the interactions between PSCP and its degradation products with Ndus3. For PSCP, the binding energy was -3.2 kcal/mol, with GLY175 and ARG199 identified as key residues involved in the interaction. For PSCP-SSH, the binding energy was -3.1 kcal/mol, with PHE193, GLY195 and ARG186 identified as key residues. For PSCP-Polysulfide, the binding energy was -4.0 kcal/mol, with ARG140, ASN138 and ARG48 identified as key residues. These findings indicate that PSCP or its degradation products are likely to competitively bind to Ndus3, thereby impeding its mitochondrial translocation and catalytic function.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we have provided compelling evidence that the metabolism of sulfur and related amino acids is significantly dysregulated in HCC, serving as a prognostic indicator for poor survival among HCC patients. The synthetic sulfane sulfur donor, PSCP, has proven to be an effective inhibitor of HCC growth, achieving this by promoting apoptosis and inhibiting proliferation. The activation of the AMPK-p53 axis, induced by mitochondrial impairment, is the underlying mechanism.\u003c/p\u003e \u003cp\u003eBuilding upon transcriptomic findings in cell line \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, the current proteomic analysis of patient-derived HCC specimens from the CPTAC database has revealed aberrant sulfur and related amino acid metabolisms in HCC. Metabolic reprogramming is well-recognized hallmark in cancer progression \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, where altered metabolites of glucose or lipids fuel tumorigenesis, invasion, and metastasis \u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The focus on amino acid metabolism has intensified; however, the present findings not only broaden the scope of the amino acid metabolic profile but also highlight that the dysregulation of sulfur and related amino acid metabolisms may serve as a predictor of poor survival in HCC patients.\u003c/p\u003e \u003cp\u003eFurther experiments illustrated the inhibitory effects of PSCP on HCC allograft growth in nude mice, which was corroborated by analogous findings with allicin, a different sulfane sulfur donor \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our transcriptomic analysis elucidated the potential molecular mechanisms, by which PSCP impeded the cell cycle and DNA replication, confirmed through the \u003cem\u003ein vitro\u003c/em\u003e assays. Also, PSCP was observed to substantially induce apoptosis in HCC cells. While there is conflicting evidence on the role of reactive sulfur species (RSS) in cancer, it is becoming increasingly clear that the effects are highly dependent on the cell types and the specific sulfur species \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. PSCP, in particular, showed selective anti-HCC activity not observed with other cancer types, as per our earlier study \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, PSCP\u0026rsquo;s specificity was underscored by its lack of significant impact on non-tumor cells, like H9c2 cardiomyoblasts, raw-blue macrophages or immortalized hepatocyte (LO2 cells) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These findings and reports suggest that metabolic reprogramming of sulfur and related amino acids is essential for PSCP to exert its anti-HCC effects, highlighting the importance of this metabolic dysregulation as a therapeutic target.\u003c/p\u003e \u003cp\u003eThe TP53 gene, a pivotal tumor suppressor, governs cell proliferation and apoptosis \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our transcriptomic analysis revealed that PSCP upregulated TP53-induced transcriptional activity of Caspases and activators, consistent with the aforementioned pro-apoptotic effects. Additionally, we observed that PSCP downregulated MDM2, a negative regulator of p53 \u003csup\u003e26,27\u003c/sup\u003e, which may be one of the reasons for the increased p53 protein. Such increase in p53 was crucial for PSCP\u0026rsquo;s anti-HCC activity, as it can arrest cell cycle, repair DNA and prime cells with damaged DNA to apoptosis. To gain insight into the mechanism behind the p53 upregulation, we also focused on AMPK. Our study demonstrated that PSCP promoted the phosphorylation of AMPK and the mRNA expression of its downstream genes. The inhibition of AMPK significantly diminished PSCP\u0026rsquo;s anti-HCC effects and p53 upregulation. These findings are consistent with previous reports \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, the activation of AMPK plays a significant role in PSCP\u0026rsquo;s anti-HCC mechanism, likely through the enhancement of p53 content.\u003c/p\u003e \u003cp\u003eFinally, our investigation into the AMPK activation mechanism in PSCP-treated HCC cells revealed that PSCP initiated energy status-dependent AMPK dysregulation, typically induced by elevated AMP/ATP or ADP/ATP ratios \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The mitochondrial assay demonstrated that PSCP reduced mitochondrial membrane potential by inhibiting electron transport chain complex I, resulting in ATP synthesis impairment and AMPK activation, a pattern observed in many studies \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Theoretically, PSCP, as a sulfane sulfur donor, likely interacts with iron-sulfur proteins of electron transport chain, hindering their mitochondrial translocation and resultant ATP production. In line with this, a recent study indicates that activation of AMPK could boost T cells\u0026rsquo; anti-tumor activity \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Of course, this speculation still requires further experimental validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has uncovered that sulfur and related amino acid metabolisms are disrupted in HCC, which correlates with poor patient outcomes. The synthetic sulfane sulfur donor, PSCP, efficiently inhibits HCC growth by triggering the mitochondrial-AMPK-p53 axis, thereby promoting apoptosis and suppressing proliferation. This research provides a theoretical foundation for the therapeutic application of sulfane sulfur in HCC treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTumor transplantation in nude mice\u003c/h2\u003e \u003cp\u003eFour-week-old male BALB/c-nu nude mice (Production license: SCXK-SU-2020-0008) were acquired from Gempharma_tech Co., Ltd (Nanjing, China). The mice were housed in the Specific Pathogen Free (SPF) facility at the Laboratory Animal Center of Guangzhou Medical University (Guangzhou, China). All procedures were strictly adhered to the guidelines approved by the Animal Ethics Committee of Guangzhou Medical University (Approval Number: GY2020-099) and were conducted in full compliance with the ARRIVE guidelines. Following a one-week acclimatization period, the mice were inoculated subcutaneously with murine-derived H22 HCC cells (Procell Life Science \u0026amp; Technology Company, Wuhan, China). The tumor-bearing mice were randomly assigned to one of four groups: Control group, 10 mg/kg PSCP group, 20 mg/kg PSCP group, and 40 mg/kg PSCP. The growth of the allografts in the nude mice was monitored for three weeks. Starting from the 4th day post-inoculation, the mice received daily intraperitoneal injections of various doses of PSCP. After three weeks, all the mice were euthanized with 4 mg/10 g pentobarbital sodium. The euthanasia and anesthesia methods were conducted in accordance with the guidelines of the Animal Ethics Committee of Guangzhou Medical University. Lastly, the allografts were observed and measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eSNU398 HCC cells were acquired from Luyuan Bode Biotechnology Co., Ltd (Beijing, China), and were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS; Gibco, USA) at 37\u0026deg;C in a humidified atmosphere of 5% CO\u003csub\u003e2\u003c/sub\u003e and 95% air. The cells were subcultured and harvested using 0.25% trypsin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCell viability assay\u003c/h2\u003e \u003cp\u003eCell viability was measured with the Cell Counting Kit-8 (CCK-8; Dojindo, Kumamoto, Japan). SNU398 HCC cells were seeded into 96-well culture plates at a density of 10,000 cells per well and treated accordingly. Subsequently, 100 \u0026micro;L of the CCK-8 working solution was added to each well, and the plates were incubated for 2 hours at 37\u0026deg;C. Absorbance (\u003cem\u003eA\u003c/em\u003e) was measured at 450 nm using a microplate reader (Thermo Fisher Scientific Inc., USA). The percentage of cell viability was calculated according to the following formula: % Cell viability = [\u003cem\u003eA\u003c/em\u003e (Treatment group) - \u003cem\u003eA\u003c/em\u003e (Blank)] / [\u003cem\u003eA\u003c/em\u003e (Control group) - \u003cem\u003eA\u003c/em\u003e (Blank)] \u0026times; 100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWhole transcriptome analysis\u003c/h2\u003e \u003cp\u003eFollowing treatment with control or 200 \u0026micro;M PSCP for 24 h, total RNA was extracted from SNU398 HCC cells using TRIzol reagent (Invitrogen, MD, USA). The transcriptome was analyzed by Biomarker Technologies (Guangzhou, China), with all procedures adhering to the protocols established by Oxford Nanopore Technologies (Oxford, UK). The analysis platform (BMK_Cloud) was used to conduct correlation analysis based on reference sequences and nanopore transcriptome sequencing data. The data were deposited in ArrayExpress (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/fg/annotare/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/fg/annotare/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accession number: E-MTAB-14578).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe transcriptome data from SNU398 HCC cells treated with control or PSCP were normalized and subjected to log2 transformation. Differential expression analysis was conducted using the limma R package \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The resulting log2 fold changes (log2FC) were then utilized for Gene Set Enrichment Analysis (GSEA) by referring to the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For gene sets with a more pronounced degree of enrichment, the GseaVis R package was applied to visually represent the gene distribution \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTranscriptomic data from HCC and para-carcinoma tissues were downloaded from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and analyzed using Uniform Manifold Approximation and Projection (UMAP). Proteomic data from HCC and para-carcinoma tissues were downloaded from the Clinical Proteomic Tumor Analysis Consortium (CPTAC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proteomics.cancer.gov/data-portal\u003c/span\u003e\u003cspan address=\"https://proteomics.cancer.gov/data-portal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and clustered using t-distributed Stochastic Neighbor Embedding (t-SNE). Functional enrichment analysis was conducted via Gene Set Variation Analysis (GSVA). Protein expression matrices were transformed into GSVA score matrices using the GSVA R package, following the differential expression analysis procedure described above \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The generated t-values were used to create a diverging bar chart.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFeature screening with randomized survival forests\u003c/h2\u003e \u003cp\u003eRandomized survival forest analysis was performed to identify sulfur-related phenotypes that influenced the survival of HCC patients \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Cox regression analysis was then applied to the top three phenotypes with the greatest impact on survival. Kaplan\u0026ndash;Meier survival analysis was utilized to visualize the different survival of HCC patients between the up-regulated and down-regulated phenotypes. To evaluate the diagnostic potential of these three features, receiver operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC) was calculated accordingly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCell cycle analysis\u003c/h2\u003e \u003cp\u003eSNU398 HCC cells were seeded into each well of 6-well culture plates, allowed to reach approximately 80% confluency, and then treated with varying concentrations of PSCP. Following digestion with EDTA-free trypsin, the cells were stained with a commercial propidium iodide (PI) dye kit (Biyotime, Shanghai, China). The staining reaction was carried out at room temperature for 30 min, and the proportion of cells at each stage of the cell cycle was determined using a flow cytometer (BD Biosciences, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation assay\u003c/h2\u003e \u003cp\u003eThe EdU-incorporation assay was utilized to monitor cell proliferation with a BeyoClick\u0026trade; EdU Cell Proliferation Kit (Biyotime, Shanghai, China). SNU398 HCC cells were seeded into 96-well culture plates, with 4 replicate wells per group. Following treatment with different concentrations of PSCP, the cells were labeled with the EdU working solution after a 2-hour incubation at 37\u0026deg;C. The cells were then fixed with 4% paraformaldehyde and treated with an endogenous peroxidase blocking solution. Next, the Click reaction solution and Streptavidin-HRP reaction solution were applied sequentially. Cell proliferation levels were quantified by measuring the absorbance (\u003cem\u003eA\u003c/em\u003e) at 650 nm after development of a distinct color with a microplate reader (Thermo Fisher Scientific Inc., USA).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eApoptosis detection\u003c/h2\u003e \u003cp\u003eSNU398 HCC cells were treated with 200 \u0026micro;M PSCP for 24 h. The induction of cellular apoptosis was visualized through the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay.The cell nuclei were labeled with 4\u0026prime;,6-diamidino-2-phenylindole (DAPI) (Biyotime, Shanghai, China). The cellular imaging was conducted using a confocal microscopy (Zeiss, Germany). To quantify apoptosis, a flow cytometric assay involving Annexin V/propidium iodide (PI) double staining was applied. A total of 500,000 cells were seeded into each well of a 6-well plate and treated with various concentrations of PSCP. Following digestion with EDTA-free trypsin, 80,000 cells were selected for analysis. These cells were incubated with 195 \u0026micro;L of Annexin V-FITC conjugate (Biyotime, Shanghai, China), followed by the sequential addition of 10 \u0026micro;L of PI dye. The apoptosis rate was subsequently analyzed using a flow cytometer (BD Biosciences, USA).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMitochondrial function assessment\u003c/h2\u003e \u003cp\u003eSNU398 HCC cells were treated with PSCP at concentrations ranging from 100 to 400 \u0026micro;M. The mitochondrial membrane potential (MMP) was observed with JC-1 staining (Dojindo, Kumamoto, Japan) followed by fluorography under a confocal microscopy (Zeiss, Germany). To assess mitochondrial activity, mitochondria were isolated and incubated with NADH as a substrate to measure the mitochondrial oxidation rate, which serves as an indicator of Complex I activity. The NADH/NAD\u003csup\u003e+\u003c/sup\u003e ratio was determined as follows: The cell lysate was divided into two portions. One portion was directly used to measure NADH content using a color development solution. For the other portion, NAD\u003csup\u003e+\u003c/sup\u003e was enzymatically converted to NADH. The total NADH content was then quantified as above, with the NADH content of the first portion subtracted to calculate the NAD\u003csup\u003e+\u003c/sup\u003e content.\u003c/p\u003e \u003cp\u003eThe intracellular ATP levels were detected using a luminescent ATP detection kit. Cell lysates were mixed with 0.25% trichloroacetic acid to precipitate proteins. Subsequently, the firefly luciferase buffer was added, and the luminescence intensity was measured with a fluorescent microplate reader (Thermo Fisher Scientific, USA). To assess the intracellular ROS content, the cell lysates were incubated with the highly sensitive fluorescent probe DCFH-DA. The emitted DCF fluorescence was then detected with the same fluorescent microplate reader.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eSNU398 HCC cells were treated with PSCP for the designated duration and lysed on ice using RIPA buffer for 15 min. The extracted proteins were quantified with the bicinchoninic acid (BCA) assay. Subsequently, the total protein samples were separated by SDS-PAGE and then transferred onto PVDF membranes. Following blocking with 5% fat-free milk, the membranes were incubated with primary antibodies at 4\u0026deg;C with gentle agitation. After washing, the membranes were further incubated with the corresponding secondary antibodies. The protein bands were visualized using an Amersham Imager 680 multifunctional imager (GE, USA). The grayscale values were calculated using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eMolecular docking and binding energy calculation\u003c/h2\u003e \u003cp\u003eMolecular docking and the calculation of binding energies were conducted using the AutoDock software suite. The visualization of the docking outcomes was achieved through the PyMOL software \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The structure of Ndus3 was obtained from the AlphaFold Protein Structure Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.com/\u003c/span\u003e\u003cspan address=\"https://alphafold.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the structure of NADH was retrieved from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The structure of PSCP, along with its degradation products (PSCP-SSH and PSCP-Polysulfide), was constructed using the ChemDraw software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and analyzed using GraphPad Prism 9.0 software (San Diego, USA). Statistical significance between groups was determined using one-way analysis of variance (ANOVA) followed by the Student-Newman\u0026ndash;Keuls post-hoc test. A p-value less than 0.05 was considered statistically significant. Furthermore, R programming language was utilized for advanced statistical analyses, including clustering, function enrichment, feature screening and survival analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors affirm that the research presented in this study was conducted without the presence of any commercial or financial interests that could be interpreted as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval statement:\u003c/strong\u003e This study was approved by the Animal Ethics Committee of Guangzhou Medical University (Approval Number: GY2020-099).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccordance statement:\u0026nbsp;\u003c/strong\u003eAll procedures were strictly adhered to the guidelines of the Animal Ethics Committee of Guangzhou Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARRIVE statement:\u003c/strong\u003e All procedures were conducted in full compliance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFoundation:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study was funded by Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515010618, 2021A1515011365). Medical Research Fund Project of Guangdong (A2023184). Health Medical Research Project of Guangdong Foshan (20230087).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe extend our gratitude to the editor and the reviewers for their meticulous and insightful contribution to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLlovet, J. M. et al. Hepatocellular carcinoma. \u003cem\u003eNat. Rev. Dis. Primers\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41572-020-00240-3\u003c/span\u003e\u003cspan address=\"10.1038/s41572-020-00240-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, C. et al. Evolving therapeutic landscape of advanced hepatocellular carcinoma. \u003cem\u003eNat. Rev. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 203\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41575-022-00704-9\u003c/span\u003e\u003cspan address=\"10.1038/s41575-022-00704-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, N. et al. Deficient DNASE1L3 facilitates neutrophil extracellular traps-induced invasion via cyclic GMP-AMP synthase and the non-canonical NF-kappaB pathway in diabetic hepatocellular carcinoma. \u003cem\u003eClin. Transl Immunol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, e1386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cti2.1386\u003c/span\u003e\u003cspan address=\"10.1002/cti2.1386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilips, C. A. et al. Hepatocellular Carcinoma in 2021: An Exhaustive Update. \u003cem\u003eCureus\u003c/em\u003e 13, e19274. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.19274\u003c/span\u003e\u003cspan address=\"10.7759/cureus.19274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeisberger, A. S. \u0026amp; Pensky, J. Tumor-inhibiting effects derived from an active principle of garlic (Allium sativum). \u003cem\u003eScience\u003c/em\u003e. \u003cb\u003e126\u003c/b\u003e, 1112\u0026ndash;1114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.126.3283.1112-a\u003c/span\u003e\u003cspan address=\"10.1126/science.126.3283.1112-a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1957).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTouloupakis, E. \u0026amp; Ghanotakis, D. F. Nutraceutical use of garlic sulfur-containing compounds. \u003cem\u003eAdv. Exp. Med. Biol.\u003c/em\u003e \u003cb\u003e698\u003c/b\u003e, 110\u0026ndash;121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4419-7347-4_9\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4419-7347-4_9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y. et al. Allicin in digestive system cancer: From biological effects to clinical treatment. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 903259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2022.903259\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2022.903259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirahmadi, M. et al. Potential inhibitory effect of lycopene on prostate cancer. \u003cem\u003eBiomed. Pharmacother\u003c/em\u003e. \u003cb\u003e129\u003c/b\u003e, 110459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biopha.2020.110459\u003c/span\u003e\u003cspan address=\"10.1016/j.biopha.2020.110459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X. et al. Metabolic reprogramming of sulfur in hepatocellular carcinoma and sulfane sulfur-triggered anti-cancer strategy. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 571143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2020.571143\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2020.571143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaubert, B., Solmonson, A. \u0026amp; DeBerardinis, R. J. Metabolic reprogramming and cancer progression. \u003cem\u003eScience\u003c/em\u003e. \u003cb\u003e368\u003c/b\u003e, eaaw5473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aaw5473\u003c/span\u003e\u003cspan address=\"10.1126/science.aaw5473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei, F. \u0026amp; Locasale, J. W. Methionine restriction and antitumor immunity. \u003cem\u003eTrends cancer\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 705\u0026ndash;706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trecan.2023.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.trecan.2023.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue, Y. et al. Intermittent dietary methionine deprivation facilitates tumoral ferroptosis and synergizes with checkpoint blockade. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 4758. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-023-40518-0\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-40518-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHung, M. H. et al. Tumor methionine metabolism drives T-cell exhaustion in hepatocellular carcinoma. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-021-21804-1\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-21804-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrattaruolo, L. et al. Targeting the mitochondrial metabolic network: A promising strategy in cancer treatment. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 6014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21176014\u003c/span\u003e\u003cspan address=\"10.3390/ijms21176014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntico Arciuch, V. G., Elguero, M. E., Poderoso, J. J. \u0026amp; Carreras, M. C. Mitochondrial regulation of cell cycle and proliferation. \u003cem\u003eAntioxid. Redox. Signal.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1150\u0026ndash;1180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/ars.2011.4085\u003c/span\u003e\u003cspan address=\"10.1089/ars.2011.4085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBock, F. J. \u0026amp; Tait, S. W. G. Mitochondria as multifaceted regulators of cell death. \u003cem\u003eNat. Rev. Mol. Cell. Biol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 85\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41580-019-0173-8\u003c/span\u003e\u003cspan address=\"10.1038/s41580-019-0173-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Y. E. \u0026amp; Shi, Y. F. Mitochondria as a target in cancer treatment. \u003cem\u003eMedComm\u003c/em\u003e 1, 129\u0026ndash;139. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4155/fmc-2017-0110\u003c/span\u003e\u003cspan address=\"10.4155/fmc-2017-0110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonekamp, N. A. et al. Small-molecule inhibitors of human mitochondrial DNA transcription. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e588\u003c/b\u003e, 712\u0026ndash;716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-020-03048-z\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-03048-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L. et al. Mitochondrial heterogeneity in diseases. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-023-01546-w\u003c/span\u003e\u003cspan address=\"10.1038/s41392-023-01546-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCassim, S., Raymond, V. A., Dehbidi-Assadzadeh, L., Lapierre, P. \u0026amp; Bilodeau, M. Metabolic reprogramming enables hepatocarcinoma cells to efficiently adapt and survive to a nutrient-restricted microenvironment. \u003cem\u003eCell. cycle (Georgetown Tex)\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, 903\u0026ndash;916. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15384101.2018.1460023\u003c/span\u003e\u003cspan address=\"10.1080/15384101.2018.1460023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang, R. Z., Qu, S. B. \u0026amp; Wang, D. S. Reprogramming of glucose metabolism in hepatocellular carcinoma: Progress and prospects. \u003cem\u003eWorld J. Gastroenterol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 9933\u0026ndash;9943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3748/wjg.v22.i45.9933\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v22.i45.9933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSangineto, M. et al. Lipid metabolism in development and progression of hepatocellular carcinoma. \u003cem\u003eCancers (Basel)\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 1419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers12061419\u003c/span\u003e\u003cspan address=\"10.3390/cancers12061419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu, Y. L. et al. Allicin induces anti-human liver cancer cells through the p53 gene modulating apoptosis and autophagy. \u003cem\u003eJ. Agric. Food Chem.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 9839\u0026ndash;9848. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf403241s\u003c/span\u003e\u003cspan address=\"10.1021/jf403241s\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuhra, K., Tom\u0026eacute;, C. S., Forte, E., Vicente, J. B. \u0026amp; Giuffr\u0026egrave;, A. The multifaceted roles of sulfane sulfur species in cancer-associated processes. \u003cem\u003eBiochim. Biophys. Acta Bioenerg\u003c/em\u003e. \u003cb\u003e1862\u003c/b\u003e, 148338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbabio.2020.148338\u003c/span\u003e\u003cspan address=\"10.1016/j.bbabio.2020.148338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHafner, A., Bulyk, M. L., Jambhekar, A. \u0026amp; Lahav, G. The multiple mechanisms that regulate p53 activity and cell fate. \u003cem\u003eNat. Rev. Mol. Cell. Biol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 199\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41580-019-0110-x\u003c/span\u003e\u003cspan address=\"10.1038/s41580-019-0110-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, H. et al. The role of MDM2-p53 axis dysfunction in the hepatocellular carcinoma transformation. \u003cem\u003eCell. Death Discov\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41420-020-0287-y\u003c/span\u003e\u003cspan address=\"10.1038/s41420-020-0287-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonopleva, M. et al. MDM2 inhibition: an important step forward in cancer therapy. \u003cem\u003eLeukemia\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e, 2858\u0026ndash;2874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41375-020-0949-z\u003c/span\u003e\u003cspan address=\"10.1038/s41375-020-0949-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, G. et al. AMP-activated protein kinase induces p53 by phosphorylating MDMX and inhibiting its activity. \u003cem\u003eMol. Cell. Biol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 148\u0026ndash;157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mcb.00670-13\u003c/span\u003e\u003cspan address=\"10.1128/mcb.00670-13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, C. W. et al. AMPK promotes p53 acetylation via phosphorylation and inactivation of SIRT1 in liver cancer cells. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 4394\u0026ndash;4404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/0008-5472.CAN-12-0429\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-12-0429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinberg, G. R. \u0026amp; Hardie, D. G. New insights into activation and function of the AMPK. \u003cem\u003eNat. Rev. Mol. Cell. Biol.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 255\u0026ndash;272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41580-022-00547-x\u003c/span\u003e\u003cspan address=\"10.1038/s41580-022-00547-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, S. C., Hardie, D. G. \u0026amp; AMPK Sensing glucose as well as cellular energy status. \u003cem\u003eCell Metabol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 299\u0026ndash;313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2017.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2017.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMamedov, M. R. et al. CRISPR screens decode cancer cell pathways that trigger γδ T cell detection. \u003cem\u003eNature\u003c/em\u003e. \u003cb\u003e621\u003c/b\u003e, 188\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-023-06482-x\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-06482-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, e47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc. Natl. Acad. Sci. USA\u003c/em\u003e 102, 15545\u0026ndash;15550. (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann, S., Castelo, R. \u0026amp; Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-14-7\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-14-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePickett, K. L., Suresh, K., Campbell, K. R., Davis, S. \u0026amp; Juarez-Colunga, E. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. \u003cem\u003eBMC Med. Res. Methodol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12874-021-01375-x\u003c/span\u003e\u003cspan address=\"10.1186/s12874-021-01375-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeeliger, D. \u0026amp; de Groot, B. L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. \u003cem\u003eJ. Comput. Aided Mol. Des.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 417\u0026ndash;422. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10822-010-9352-6\u003c/span\u003e\u003cspan address=\"10.1007/s10822-010-9352-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic reprogramming, Hepatocellular carcinoma, Sulfane sulfur, Mitochondrial complex, AMPK, p53","lastPublishedDoi":"10.21203/rs.3.rs-5228470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5228470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, notoriously refractory to conventional chemotherapy. Historically, sulfane sulfur-based compounds have been explored for the treatment of HCC, but their efficacy has been underwhelming. We recently reported a novel sulfane sulfur donor, PSCP, which exhibited improved chemical stability and structural malleability. This study aimed to investigate the effects of PSCP on HCC and elucidate the underlying mechanisms. We utilized bioinformatics algorithms for clustering, function enrichment, feature screening and survival analysis on proteomic data from the Cancer Proteome Atlas (CPTAC) and transcriptomic data from the Cancer Genome Atlas (TCGA). The impact of PSCP on HCC were assessed \u003cem\u003ein vitro\u003c/em\u003eand \u003cem\u003ein vivo\u003c/em\u003e, focusing on the expression and activity of p53 and AMP-activated protein kinase (AMPK), as well as mitochondrial function. The molecular target of PSCP was identified using Autodock, and binding interactions were visually analyzed. Sulfur metabolism was found to be reprogrammed in HCC, with downregulation of sulfur-related pathways correlating with poor patient prognosis. PSCP treatment significantly inhibited HCC tumor growth in an allograft model, reduced cell viability and proliferation, and induced apoptosis. PSCP potently increased p53 expression and induced AMPK phosphorylation in SNU398 HCC cells. AMPK suppression diminished PSCP-induced p53 upregulation. PSCP also impaired mitochondrial function by inhibiting mitochondrial respiratory complex I. The supplementation of ATP significantly countered PSCP-induced SNU398 cell injury. Our findings suggest that the reprogramming of sulfur-related metabolic pathways is pivotal in HCC. PSCP presents as a promising therapeutic strategy by activating the mitochondrial-AMPK-p53 signaling axis.\u003c/p\u003e","manuscriptTitle":"Activating the AMPK-p53 Axis by Mitochondrial Impairment: Unveiling a Novel Anti-Liver Cancer Mechanism of Sulfane Sulfur","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 08:32:42","doi":"10.21203/rs.3.rs-5228470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-17T12:08:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-12T12:39:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280830996647139759618396001142419253903","date":"2024-11-09T13:20:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-09T03:17:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60775219799519303053668198167280675699","date":"2024-10-29T15:46:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-29T02:11:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-29T02:05:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-29T01:53:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-28T05:16:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-09T01:51:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f6dd830-1af5-48d4-85eb-a30e3f6e00d4","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40362868,"name":"Biological sciences/Biochemistry"},{"id":40362869,"name":"Biological sciences/Cancer"}],"tags":[],"updatedAt":"2025-02-03T16:02:36+00:00","versionOfRecord":{"articleIdentity":"rs-5228470","link":"https://doi.org/10.1038/s41598-024-83530-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-29 15:57:33","publishedOnDateReadable":"January 29th, 2025"},"versionCreatedAt":"2024-12-17 08:32:42","video":"","vorDoi":"10.1038/s41598-024-83530-0","vorDoiUrl":"https://doi.org/10.1038/s41598-024-83530-0","workflowStages":[]},"version":"v1","identity":"rs-5228470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5228470","identity":"rs-5228470","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}