Basal Metabolic Rate shapes the development and progression of hepatocellular carcinoma

Basal Metabolic Rate shapes the development and progression of hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Basal Metabolic Rate shapes the development and progression of hepatocellular carcinoma Sebastian Maciak, Diana Sawicka, Irena Kasacka, Lech Chyczewski, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6046205/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Cancer → Version 1 posted 10 You are reading this latest preprint version Abstract Background Among several cancer risk factors, the basal metabolism rate (BMR), which constitutes up to 70% of total energy expenditures in humans, may be causally linked with neoplasm development. As BMR reflects the mass of metabolically active organs, being the function of cell size and/or cell number, it may serve as a critical metabolic proxy of cancer susceptibility in the context of cell growth and cell size. Methods We examined the progression and rate of development of chemically induced hepatocellular carcinoma, using lines of mice divergently selected for high or low BMR and differing with respect to both the size of metabolically active organs and their cellular architecture. Results The high BMR mouse line developed hepatocellular carcinoma much faster and with a higher progression rate, accompanied by a considerable increase in liver size and hepatocyte enlargement, as compared to the low BMR mouse line. The HBMR mice also manifested an increased expression of metabolism- and cell size-related genes ( mTOR , PI3K , c-myc , but not IGF-1 ), with a simultaneous decrease in the activity of tumor suppressors ( p-53 , APC ) at the beginning of cancerogenic processes, promoting further neoplasm expansion. Conclusion Presented results suggest that genetically determined high BMR may additionally burden liver cells via changes in the action of specific genes, leading to higher tumorigenesis. BMR metabolism cell size mTOR hepatocellular carcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Liver cancer is one of the most common and fatal malignancies, ranking fourth leading cause of cancer-related mortality in the world, with extremely limited treatment options [ 1 – 3 ]. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for over 85% of all cases, characterized by a low survival rate and a high level of recurrence [ 1 , 4 , 5 ]. As with any other tumorigenic cell development, hepatocarcinogenesis is a multistep process involving the accumulation of genetic disruptions that ultimately lead to the malignant transformation of the hepatocytes [ 6 , 7 ]. To date, many risk factors for HCC have been identified, with most of them associated with the key specificity of the hepatic structure and function [ 2 , 3 ]. The liver is the major metabolically active internal organ [ 8 ], with its mass accounting per se for a significant proportion of the basal/resting metabolic rate (BMR) both in animals [ 9 ] and humans [ 10 ]. Thus, a mechanistic link between liver metabolism and whole-body metabolism may fit the association between increased BMR and risk for most cancers observed in humans [ 11 – 14 ]. It has been broadly demonstrated that genetically determined variation in metabolic rate exists, and its significant part at both inter- or intraspecific levels can be attributed to differences in cell size (CS; for review, see: [ 15 ]). Cell size is positively correlated with body mass and negatively correlated with BMR [ 15 ], which may directly impact many physiological traits, including cancer-related ones. First, cell size negatively affects cell division rate [e.g., 16,17]. Larger cells are usually characterized by a considerable increase in protein synthesis rate and shortened cell cycle rate [ 16 – 18 ], promoting aging, mutations, and cancer susceptibility with every cell multiplication. Secondly, the size of cells is one of the key factors determining cellular metabolism rate via costs of maintenance of membrane gradients and/or mitochondria number and activity [ 19 , 20 ]. Ion flux across membranes constitutes a significant burden for cellular metabolism, reaching up to 30% of its overall energy expenditures [ 21 ]. As the cell surface/volume ratio decreases in bigger cells, such metabolic costs might be reduced [ 20 ]. However, the size of cells can also be affected by the rate of cellular metabolism in specialized cells (i.e., hepatocytes) involved in the multitude of concurrent metabolic processes. Such cells must be large to avoid the limiting effect of molecular crowding [ 22 ]. Nevertheless, any changes in the size-related area for metabolic processes or intensity of those may affect the rate of cellular damage, ultimately influencing cancer susceptibility, especially in the so-called metabolically active organs like the liver, contributing to almost 1/5 of total BMR [ 8 ]. It is particularly interesting in the case of hepatocytes, as they exhibit cell size differentiation [ 23 ]. Moreover, potential changes in hepatocyte size may constitute an important predictor of chronic liver injury, including malignant transformations [ 23 – 25 ]. Thus, understanding the mechanisms underlying the contribution of cell-mediated metabolic rate in neoplasm initiation may be critical for future anticancer therapies. In order to tackle the problem, we test the impact of genetically determined differences in BMR and CS on susceptibility and progression of hepatocellular carcinoma. To do so, we used a customized animal model consisting of two lines of laboratory mice divergently selected for BMR. Beyond observed and stable through several generations, with over 50% difference in BMR (unmatched by any other model), proposed animals represent considerable heterogeneity in their cellular architecture (see: Animals and experimental setup ). For example, animals characterized by high BMR have substantially larger and more active hepatocytes, resulting in considerable liver expansion compared to low BMR mice [see: 22]. The proposed model is, therefore, uniquely suited to test the significance of metabolism-related factors in carcinogenesis. We challenged mice from the above-described lines with diethyl-nitrosamine (DEN) as a genotoxic agent, a standard preclinical model to test in vivo development and progression of liver cancer [ 6 , 26 ]. The advantage of DEN-induced models is the resemblance of gene expression patterns and induction of specific HCC markers, analogous to those observed during the injury-fibrosis-malignancy cycle in humans [ 6 ]. A comparison of tumor development and its progression in mice differing with respect to the BMR and cell size provides a robust model for investigating metabolism-related determinants of carcinogenesis. To understand the metabolic-linked mechanisms that potentially influence cancer susceptibility, analysis of the action of specific genes that underlie BMR capacity and cell size-related processes is required. From the molecular point of view, both CS and the rate of organismal metabolism depend mainly on extracellular growth factors, nutrient availability, and/or nuclear regulatory genes – all organized in specific signaling pathways. The first group of controllers comprises downstream effectors of growth hormones, with its primary action mediated by Insulin-like growth factor 1 (IGF-1), with its downstream partner -phosphatidylinositol-3 kinase (PI3K). The second group of regulators represented by the mTOR gene are found to be the central players in both insulin- and nutrient-induced cell size changes [i.e., 27,28]. Accordingly, the group of intracellular regulators of the cell size, centered around p53 and/or c-myc genes, act to coordinate proliferation and interject metabolic changes in cells through the regulation of energy metabolism and oxidative stress [ 29 , 30 ]. Dysregulation in the activity of the above representatives is broadly associated with cell aging and various types of cancer, including HCC [ 5 , 31 ], mostly marked with disruption in the action of the canonical Wnt/APC/β-catenin pathway [ 32 , 33 ]. Thus, we predict that the propensity for HCC development is causally linked with increased BMR through genetically determined overexpression of metabolism/cell size-related genes. Conversely, decreased levels of cellular metabolic processes may play a crucial role in lowering the cancer susceptibility of low BMR individuals. Methods Animals and experimental setup We analyzed the development and progression of chemically induced hepatocellular carcinoma (HCC) in 20-week-old Swiss-Webster males of laboratory mice ( Mus musculus ) from the 61st generation of a long-term selection experiment designed to generate two lines with divergent basal metabolic rates (respectively, the L-BMR and H-BMR line), as described previously [e.g., 22]. Briefly, depending on the breeding success, we maintain 30–35 families in each selected line in subsequent generations. Whenever possible, no less than three randomly chosen males and three females from each family are subjected to measurements of BMR. Animals characterized by highest (H-BMR line) or lowest (L-BMR line) body mass (BM)-corrected BMR are chosen as progenitors and always mated outside their families. Apart from the over 50% higher BMR, the H-BMR mice consume more food, have a higher mass of the small intestine, liver, kidneys, and heart [ 34 ], and by 0.6°C higher core body temperature at both 23°C and 30°C than mice with the low BMR [ 35 ]. The BMR difference between the L-BMR and H-BMR lines exceeds the BMR variation range observed in other laboratory mice lines [36, Fig. 3 ]. The selection experiment is carried out at the Department of Evolutionary and Physiological Ecology, Faculty of Biology, University of Bialystok. Following the completion of BMR measurements (see: SI), the H- and LBMR mice were randomly assigned to experimental (DEN) or control (CONT) groups. In total, we chemically induced liver cancer in 36 individuals per metabolic line and randomly culled 12 animals per line (if available; S1 Table) at the 15th, 35th, and 45th week of the experiment. As a control, on the first day of the experiment (day of injection, week 0), we sacrificed 8, then − 12, PBS-treated individuals on every consecutive time point from each of the selection lines to collect the material for experiment control (44 individuals per line in total, S1 Table). Such study design allowed us to track histological and genetic changes in investigated individuals and collect samples of sufficient sizes for in-experiment measurements and assays of statistical significance. At each specific time point, culled animals were anesthetized by intraperitoneal administration of a mixture of ketamine and xylazine at a dose of 50 and 5 mg/kg, respectively. The liver was dissected, dried on absorbent paper, weighed, and finally frozen in liquid nitrogen and stored at -80 ᵒ C until analyzed. Additionally, two pieces of liver (~ 50mg) with tumor (if observed) were taken for histological (4% buffered formaldehyde, Chempur, Poland) and genetic (RNA stabilization solution (RNA later, Invitrogen, Lithuania)) analyses. During experiments, animals were maintained individually in plastic cages at 23°C and 12d:12n photoperiod. Metabolic rate measurements Before experimental procedures, BMR was measured in all animals following the standard methodology (Książek et al. 2004). We used a positive-pressure open-circuit respirometry system with dried and warmed atmospheric air pushed through the Tygon tubes. The system sequentially monitored the oxygen consumption rate of mice placed individually in three 350 cm 3 chambers submerged in a water bath set at 32 ± 0.1°C (thermoneutral zone for mice). The outflow of the airstream was directed to a computer-controlled channel multiplexer, a part of a Sable Systems TR-1 oxygen analyzer (Henderson, NV, USA). The air was sampled at the rate of 75 mL min − 1 . Prior to passing through an oxygen sensor (S-3A/I Applied Electrochemistry, Pittsburgh, PA, USA), it was scrubbed off CO2 (Carboabsorb AS, BDH Laboratory Supplies, Lutterworth, UK) and moisture (Drierite, Drierite Co.LTD, Xenia, OH, USA). Each metabolic measurement trial lasted three hours, and oxygen concentrations in each chamber were recorded every second for two hours. Animals were fasted for six hours before BMR measurements. We defined BMR (mL O 2 h − 1 ± 0.0013) as the lowest rate of oxygen consumption that did not change for at least four minutes by more than 0.01%. Metabolic data were analyzed using Sable System DATACAN V software. Tumor induction and monitoring of progression We applied a two-step carcinogenesis model with additional administration of tumor promoter for HCC development [ 6 ]. At the starting point, animals assigned to experimental groups were intraperitoneally injected with a 50µl solution of N-nitroso diethylamine (DEN, Sigma Aldrich, St. Louis, MO, USA; 90 µg/kg) as a genotoxic reagent for hepatocellular carcinoma initiation. At the same time, the remaining CONT mice (60 individuals per line) received phosphate-buffered saline (PBS, Sigma-Aldrich) shot in an analogical manner. Fourteen days after DEN intoxication, treated animals were given drinking water with an addition of phenobarbital sodium salt for 15 weeks (PB, Amara, Cracow, Poland, 500 mg/L) as a hepatic adenoma promoter [ 6 ]. The drinking water containing PB was changed every three days (standard housing procedure), and intake was allowed ad libitum . The control group was receiving normal drinking water in an analogical manner. We observed no significant differences in the amount of drinking water between metabolic lines or experimental groups. Obtained liver samples were visually checked for any histopathological changes. Eventually, tumor parameters were measured using a digital caliper, and volume was calculated using a modified ellipsoidal formula: 0.52 x length x width 2 . To determine tumor progression in the obtained liver samples, we have adopted basic histopathological markers: number of atypical cells/1mm 2 , number of tumor foci/10mm 2 , number of cancer cells/1mm 2 , followed by β-catenin immunostaining for each histological slide, as a standard cancer-related diagnostic procedure (for details, see: SI). The size of hepatocytes was estimated in 30 randomly chosen cells per mouse in the pericentral region of the lobules by outlining the edges of cells (MultiScan software). Genes expression The total tissue RNA was isolated from the liver samples using a standard commercial kit (Total RNA mini plus, A&A Biotechnology, Poland). DNAse I treatment with a Clean-up RNA concentrator (A&A Biotechnology, Poland) to remove traces of DNA was applied to all samples. RNA purity was checked with NanoDrop 2000 (Thermo Fisher Scientific, Pittsburgh, PA, USA). All RNA samples were adjusted to the starting concentration of 50ng/µl. Primers-specific, high-capacity cDNA reverse transcription kit with oligo dT starters was used to convert RNA to cDNA (TranScriba, A&A Biotechnology, Poland). Relative expression of the genes of interest (i.e., p53, mTOR, IGF1, APC, c-myc, CTNNB1, and PIK3C) was assessed in two technical replicates by quantitative PCR (qPCR) on a StepOnePlus Real-Time PCR 96-well System (Applied Biosystems) with the use of SYBR Green RT PCR Mix (A&A Biotechnology, Poland). All analyses used the standard housekeeping β-actin gene as an endogenous reference. Sequences of the analyzed genes (i.e., p53, mTOR, IGF1, APC, c-myc, CTNNB1, PIK3C) and endogenous reference gene (β-actin) used for gene expression were as followed: p53, forward primer 5’-ATCACCTCACTGCAT-GGACG-3’, reverse primer 5’-GCCATAGTTGCCCTGGTAAG-3’; mTOR, forward primer 5’-GGGAGAACAGAAGATGGGTAAC-3’, reverse primer 5’-CTGAGTCCCTGCTGCA-AATA-3’; IGF1, forward primer 5’-TGGATGCTCTTCAGTTCGTG-3’, reverse primer 5’-GTCTTGGGCATGTCAGTGTG-3’, APC, forward primer 5’-GGCAGAAGTACAGATGA-TGCTC-3’, reverse primer 5’-GGATGCTT-GTTGGACTTGGT-3’; c-myc, forward primer 5’-CAACGTCTTGGAACGTCA-3’, reverse primer 5’-TCGTCTGCTTGAATGGACAG-3’; CTNNB1, forward primer 5’-GTTCGCCTTCATTATGGACTGCC-3’, reverse primer 5’-ATAGCACCCTGTTCCCGCAAA-3’; PIK3C, forward primer 5’-CCAGAGCAAGTCATT-GCTGA-3’, reverse primer 5’-TATGACCCAGAGGGATTTCG-3’ and β-actin, forward primer 5’-AGAGGGAAATCGTGCGTGAC-3’, reverse primer 5’-CAATAGTGATGAC-CTGGCCGT-3’. All primer combinations resulted in amplicons of 207, 399, 220, 183, 153, 145, and 341 bp, respectively, and 214 bp for β-actin, without a specific background band as visualized on an agarose gel. Primers were designed and checked using FastPCR (Primer Digital Ltd.). The primer mix has been tested to generate satisfactory qPCR data by using the following PCR program: pre-soaking 95°C for 2 min; denaturation: 95°C for 30 sec, annealing: 60°C for 1 min; extension 72°C for 45 sec. The amplification efficiencies of the target and reference genes were comparable. ΔCt values for each analyzed gene in experimental groups were corrected for untreated controls with average ΔCt as the calibrator, according to the 2 −ΔΔCt method. The data were presented as the fold change in gene expression normalized to a β-actin gene and relative to the untreated control groups. Assessment of total, mTOR, and β-catenin protein concentrations Liver samples, frozen in liquid nitrogen, were cut into ~ 30 mg pieces, rinsed thoroughly in ice-cold PBS to remove excess of blood, and weighed (± 0.01 mg) before homogenization. Each probe was homogenized in fresh, ice-cold 1 ml lysis buffer (Cell Biologics) with the addition of proteolysis inhibitor (Complete Mini Roche, France) at a 1:20 ratio, with the use of a tissue grinding tool (EURx). The resulting suspension was then sonicated with an ultrasonic cell disrupter (UP200S, Hielscher) and centrifuged (10 min, 10.000 x g, 4°C) with a supernatant collection for further biochemical assays. The concentration of total (µg/ml; BCA™, Pierce, Thermo Fisher Scientific, Rockfold, IL, USA), mTOR (pg/ml; Abcam SimpleStep ELISA, ab206311), and β-catenin (ng/ml; Abcam SimpleStep ELISA, ab275100) proteins in liver homogenates was determined with the use of commercial protein assay kits. All reagents were equilibrated to room temperature, and probes were diluted 1:5 (total proteins) or 1:10 (mTOR and β-catenin) before analysis. We used 150 µl of each standard and unknown samples in duplicates. Probes were read at 562 nm (total proteins) or 450nm (mTOR and β-catenin) wavelength with the use of a Varioskan Lux microplates reader (Thermo Fisher). Statistical analyses BMR was analyzed by means of ANCOVA with the line type (HBMR vs. LBMR) and treatment (CONT vs. DEN) as fixed factors and body mass as a covariate. The differences in the gene expression were determined using three-way ANOVA, with the line type, DEN treatment, and time point as fixed factors. The change in expression level at the consecutive time points in experimental groups compared to control was checked with the use of the Student t-test. Protein concentration and histopathological parameters were analyzed using three-way ANOVA, with line type, treatment, and time point as fixed factors. Post hoc comparisons were performed with a Fisher's LSD test. All statistical analyses were done with Statistica 13.3 software (StatSoft, Poland). P < 0.05 was considered statistically significant. Results An increase in Basal Metabolic Rate enlarges hepatocyte- and liver size The HBMR and LBMR mice differ significantly in terms of metabolic rate (Table 1 ). We observed over 60% differentiation in energy expenditures with the average BMR values of 70.54 ± 0.68 ml O 2 /h and 40.74 ± 0.68 ml O 2 /h in high and low lines, respectively (S2 Table). Notably, initial (week 0) and final body mass did not differ between the lines (F 1,158 =0.00; P = 0.99 and F 1,128 =2.89; P = 0.09, respectively). Table 1 The results of a three-way analysis of variance (ANOVA) for BMR, liver masses, hepatocyte size, major histopathological markers, and protein concentration in mice artificially selected for low or high basal metabolic rate and enforced to develop hepatocellular carcinoma. df – degrees of freedom, F – ratio of variances, NA - not applied; values below p ≤ 0.05 were considered statistically significant (bold). ANOVA Change direction in high BMR line df Line effect Treatment effect Time effect Line x Treatment Line x Time Treatment x Time BMR (ml O 2 /h) 157 F = 955.3 p < 0.001 NA NA NA NA NA increase Liver mass (g) 115 F = 98.79 p < 0.001 F = 40.08 p < 0.001 F = 6.09 P < 0.001 F = 6.24 p = 0.014 F = 1.16 p = 0.33 F = 8.11 p < 0.001 increase Hepatocyte size (µm 2 ) 115 F = 1171.3 p < 0.001 F = 155.5 p < 0.001 F = 15.0 p < 0.001 F = 5.88 p = 0.017 F = 0.98 p = 0.41 F = 4.54 p = 0.005 increase No. atypical cells 80 F = 4.84 p = 0.03 F = 548.5 p < 0.001 F = 100 p < 0.001 F = 4.84 p = 0.03 F = 8.13 p < 0.001 F = 100 p < 0.001 increase Tumor foci /10mm 2 80 F = 10.07 p = 0.002 F = 34.41 p < 0.001 F = 9.69 p < 0.001 F = 10.1 p = 0.002 F = 1.39 p = 0.25 F = 9.69 p < 0.001 increase Cancer cells 80 F = 5.29 p = 0.024 F = 34.97 p < 0.001 F = 13.3 p < 0.001 F = 5.29 p = 0.024 F = 0.86 p = 0.43 F = 13.3 p < 0.001 increase Total protein (mg/ml) 80 F = 53.1 p < 0.001 F = 18.8 p < 0.001 F = 2.49 p = 0.09 F = 0.07 p = 0.80 F = 2.82 p = 0.07 F = 3.51 p = 0.04 increase mTOR (pg/ml) 80 F = 8.45 p = 0.004 F = 21.20 p < 0.001 F = 0.32 p = 0.72 F = 0.04 p = 0.84 F = 2.75 p = 0.07 F = 3.11 p = 0.049 increase β-catenin (ng/ml) 80 F = 43.3 p < 0.001 F = 13.5 P < 0.001 F = 29.3 p < 0.001 F = 0.05 p = 0.82 F = 1.64 p = 0.20 F = 4.46 p = 0.014 increase The liver mass differed between metabolic lines, experimental groups, and the time of the experiment, with, in general, higher liver mass in the HBMR line and a significant increase of this organ in cancerogenic groups (Table 1 ). Line x treatment interaction was significant. Differences in liver size were directly and positively related to the hepatocyte size and its enlargement in DEN-treated individuals (Table 1 ). Change in BMR influences tumor development We visually observed a few liver tumors at the 45th week in four (out of eight) individuals originating from the HBMR line only, with the neoplasm's average volume ranging from 0.676 to 0.814 mm 3 . However, characteristic features of hepatocyte atypia were noticed in both lines in tumor-induced groups, increasing with the duration of the experiment, with the most significant number of atypical cells at the last time point- week 45 (Fig. 1 ; Table 1 ). Importantly, the process of cancerogenesis began much earlier and with greater intensity in the HBMR line, as we observed the first atypical cells in week 15th and an increase in the number of those at weeks 35th and 45th, while in the LBMR line, a significant increase was seen only at 45th week (Fig. 1 ; S3 and S4 Tables). The number of detected tumor foci per defined area and number of cancer cells was significantly higher in the HBMR group and increased during the experiment (Table 1 ). After 45 weeks following DEN genotoxic liver stimulation, the fully developed hepatocellular carcinoma was observed in half of the cases among HBMR mice (four individuals), while in the LBMR group, the pre-cancerous stage (atypical cells characteristic for tumor development) was detected only (Fig. 1 C, F). Our results strongly suggest, then, that individuals with genetically determined, high BMR are more prone to develop liver cancer. Protein concentration depends on the level of BMR The total protein concentration differed between metabolic lines and experimental groups, with the HBMR line characterized by significantly higher protein content in hepatocytes in both control and cancerogenic groups (Fig. 2 A; Table 1 ; see also S2 Table). Cancer induction increased hepatocyte protein content in both metabolic group types (Fig. 2 A) with a statistically significant treat x time interaction (Table 1 ). A similar pattern was observed in the case of mTOR. Its concentration differed between metabolic lines and treatment, but not with the time of the experiment (Table 1 , Fig. 2 B; see also S2 Table). A marked increase was seen in the amount of mTOR in the DEN-treated, high-metabolism individuals at weeks 15th and 45th, but with a slight decrease compared to the control group at week 35th (Fig. 2 B). Cancer-induced LBMR mice were characterized by an apparent increase in mTOR concentration at week 15th, followed by a decrease in the content of this protein at subsequent time points (Fig. 2 B; S4 Table). The concentration of β-catenin was considerably higher in the HBMR line (Table 1 ; Fig. 2 C; see also S2 Table). Moreover, we observed a general increase of catenin content in both cancer-induced animals and during the whole experiment, with significant treatment x time interaction (Table 1 ). Genes expression may differ with respect to BMR IGF-1/PI3K/mTOR as an insulin- and nutrient-induced metabolism regulators Ongoing liver cancerogenesis changed the expression of insulin-like growth factor 1 (IGF-1) through the analyzed time points, but differences between metabolic lines were insignificant (Fig. 3 ; Table 2 ). IGF-1 was downregulated at weeks 15th and 35th in tumor-induced individuals, especially in LBMR animals (Fig. 3 ; S5 Table). However, we observed marked overexpression of this gene at week 45th in both metabolic lines, suggesting increased intensity of tumor-associated processes. Table 2 The results of a three-way analysis of variance (ANOVA) for selected gene expression in mice artificially selected for low or high basal metabolic rate and enforced to develop hepatocellular carcinoma. df (degrees of freedom) = 101 in all cases. F – ratio of variances, NC stands for “no change” between metabolic lines; values below p ≤ 0.05 were considered statistically significant (bold). ANOVA Change direction in high BMR line Line effect Treatment effect Time effect Line x Treatment Line x Time Treatment x Time IGF-1 F = 0.06 p = 0.81 F = 9.31 p = 0.003 F = 126.5 p < 0.001 F = 0.06 p = 0.81 F = 0.45 p = 0.64 F = 126.5 p < 0.001 NC PI3K F = 5.49 p = 0.044 F = 96.42 p < 0.001 F = 41.67 p < 0.001 F = 5.49 p = 0.044 F = 1.91 p = 0.15 F = 41.60 p < 0.001 increase mTOR F = 5.86 p = 0.017 F = 26.79 p < 0.001 F = 15.28 p < 0.001 F = 5.61 p = 0.02 F = 8.81 p < 0.001 F = 15.06 p < 0.001 increase p53 F = 21.11 p < 0.001 F = 21.67 p < 0.001 F = 38.95 p < 0.001 F = 25.60 p < 0.001 F = 8.63 p < 0.001 F = 31.62 p < 0.001 decrease c-myc F = 7.14 p = 0.009 F = 60.32 p < 0.001 F = 11.91 p < 0.001 F = 10.33 p = 0.002 F = 2.69 p = 0.072 F = 14.32 p < 0.001 increase APC F = 2.17 p = 0.14 F = 72.55 p < 0.001 F = 2.31 p = 0.10 F = 0.27 p = 0.60 F = 0.56 p = 0.57 F = 17.18 p < 0.001 NC CTNNB1 F = 10.66 p = 0.001 F = 82.93 p < 0.001 F = 15.22 p < 0.001 F = 10.69 p = 0.001 F = 4.23 p = 0.017 F = 16.11 p < 0.001 increase PI3K expression differed between metabolic lines with significant treatment effect and a decrease in its amount during the experiment (Table 2 , Fig. 3 ). PI3K gene was overexpressed in DEN-treated individuals at week 15th and differed from control groups at week 35th in high BMR line-only (Fig. 3 ; S5 Table). The PI3K overexpression at weeks 15th and 35th was markedly different between metabolic lines but not at week 45th (Fig. 3 ). A similar pattern was observed for the mTOR gene. Its expression, in general, was considerably different between metabolic lines and throughout the experiment, with significant line x treatment interaction (Table 2 ). However, the line differences revealed by the t-test at weeks 15th and 35th were statistically insignificant (Fig. 3 ; S5 Table). mTOR gene expression slightly decreased in consecutive time points, with insignificantly higher overexpression in LBMR mice at week 15 compared to the HBMR group (Fig. 3 ). p53/c-myc as intracellular regulators of the cell size/cell division rate The expression of the p53 gene differed between metabolic lines and time points, with significant line x treatment interaction (Table 2 ). p53 was downregulated at week 15th in both metabolic lines (Fig. 4 ). However, in weeks 35 and 45, there was an increase in mRNA concentration for the p53 gene, especially seen in the HBMR line (Fig. 4 ; S5 Table). The high expression of the c-myc gene was observed throughout the whole experiment in tumor-induced HBMR individuals (Fig. 4 ). At the same time, in the LBMR group, its action was less expressed and seen only at weeks 15th and 35th (Fig. 4 ) with general, between-lines and time differences (Table 2 ). APC/CTNNB1 as a markers for tumorigenesis The tumor suppressor APC gene expression changed in response to HCC induction and differed between time points (Fig. 4 ; Table 2 ). However, the effect of the metabolic line in the case of APC expression was insignificant (Table 2 ). APC was considerably downregulated in cancer-induced groups at weeks 35th and 45th, with a marked decrease in HBMR mice at week 45th (Fig. 4 ; S5 Table). In LBMR animals, expression of this gene was reduced only at week 35th (Fig. 4 ), while APC gene expression increased at week 45th, which led to differences between metabolic lines (p = 0.01) at that time. The effects of metabolic line, treatment, and time of the experiment, with between effects interactions, were significant for the CTNNB1 gene (Table 2 ). There was higher expression of the CTNNB1 gene at weeks 15th and 45th (Fig. 4 ; S5 Table) and cellular accumulation of β-catenin in HBMR mice (see: S1-3 Figures), suggesting higher intensity of cancerogenic processes in the HBMR line. In the case of LBMR animals, we only observed a slight increase in CTNNB1 gene expression at week 15th (Fig. 4 ). Discussion The intensity of metabolic expenditures shapes the probability of cancer development Our results indicate that differentiation in organismal energy turnover translates directly into neoplasm susceptibility and tumor progression rate. HBMR individuals developed fully organized hepatocellular carcinoma, while in LBMR mice, the pre-cancerous stage was observed only (Fig. 2 ). Although the intensity of necrosis and apoptosis processes was comparable between metabolic lines during the experiment (S3 and S4 Tables), atypical and cancerogenic cells were detected much earlier in HBMR animals, with cytoplasm shift of β-catenin’s (S1-3 Figures), suggesting greater vulnerability of metabolically overloaded hepatocytes for neoplasm transformation. As the liver accounts for almost 20% of total BMR [ 8 ], an increase in metabolic rate via changes in liver size may stand for the fundamental mechanism underlying multiple cancer susceptibility. Recent theoretical studies based on Mendelian randomization showed that, in general, BMR is positively associated with most cancers [ 13 , 14 , 37 ]. Although Biro et al. [ 38 ] pointed out the net protective function of increased metabolism against cancer, our results showed that LBMR mice are more resistant to DEN intoxication, with a lower prevalence of cancerogenic changes than HBMR individuals (Fig. 1 ). Accordingly, another study based on this animal model revealed that tumor-induced development with human DLD-1 colorectal cancer cells was significantly promoted in HBMR mice [ 39 ]. Moreover, absolute neoplasm regression and lower total oxidative status were detected in the LBMR metabolic line after 36 days of trial [ 39 ]. In this manner, low metabolism inhibits tumor-related processes and protects against cancer. Lowered mass-specific metabolism also reduces the risk of postmenopausal breast cancer [ 40 ], acute complications after anticancer treatment [ 38 ], and general mortality [ 11 ]. Moreover, lowering energy expenditures via caloric restriction or intermittent fasting restores the balance for cellular repair mechanisms and counteracts metabolic-related disorders and neoplasm development [ 42 , 43 ]. In this manner, our studies provide new, direct empirical evidence that genetically determined high metabolism may translate into greater cancer susceptibility. Cell size and protein content The observed variation in BMR results mostly from differentiation in a mass of metabolically active organs, being the function of changes in cell size and/or cell number [ 15 , 20 , 34 ]. That is why cellular mechanisms regulating the functional size and adequate metabolic capacity of cells are critical to fully understanding the etiology of metabolism-related disorders, including cancer development. Here, we observed a considerable increase in hepatocyte size, resulting in whole liver enlargement in HBMR individuals (S2 Table). Metabolism-related increases in cell size result mainly from mRNA transcript abundance and improved concentration of proteome products, enabling enhanced physiological processes against molecular crowding [ 17 , 22 , 44 ]. Indeed, we noticed a significant increase in both total and mTOR protein concentration in hepatocytes of HBMR mice (Fig. 2 ). Moreover, due to HCC progression, there was a further increase in cellular protein concentrations and hepatocyte size, followed by concomitant enlargement in total liver mass in both metabolic lines. To date, it has been broadly demonstrated that cancer cells show extensive alterations in protein expression levels involved in the regulation of the cell size and metabolic pathways, which are drivers of their malignant transformation [ 45 – 47 ]. Changes in cell size are typically observed when the balance between the growth rate and cell division rate is altered. According to the main models for size homeostasis, larger cells need less time to reach the mitosis G1/S phase and thus tend to grow much slower, but their cell cycle is considerably faster [ 18 ]. Here, we can fairly assume that observed liver enlargement in a high BMR line (while body mass remains fixed between H- and L- lines) is achieved via an increase in cell size and number, thus requiring a faster rate of cell division in HBMR individuals. Such a conclusion is consistent with the common observation that cell cycle length and so-cell division rate are inversely proportional to the cell size [ 18 ]. However, direct measurements of the rate of cellular proliferation in the high vs. low BMR should be investigated further. A high division rate of big, metabolic active cells may result in cell exhaustion and a shortened lifespan. Enlargement in hepatocyte size may be linked with a multitude of atypical forms and advanced tumorigenesis (Fig. 1 ). Similarly, computational [ 48 ] and in vivo models suggest that high energy turnover accelerates cellular evolution toward cancer, particularly in bigger cells [ 49 , 50 ]. Thus, high BMR is linked with fast growth and cell cycle progression, burdening cells with metabolic by-products, including enhanced ROS concentration, protein degradation, and nucleic acid impairment. An increase in BMR correlates with the expression of metabolism-related genes IGF-1/PI3K/(Akt)/mTOR pathway The insulin-like growth factor IGF-1 via PI3K/(Akt)mTOR pathway constitutes a major intracellular signal transduction pathway involved in the control of cellular metabolism, cell growth, and proliferation [e.g., 2,5,51]. Although the IGF-1 expression seems multifaced during cancerogenesis, most studies point to its overexpression in various functional human cancers, including hepatocellular carcinoma [see: 52,53]. In the case of our study, IGF-1 was downregulated in both metabolic lines at the beginning of the carcinogenesis process, with an increase in its expression after 45 weeks of DEN intoxication, which may suggest advanced neoplasm development at the end of the experiment period. Simultaneously, we observed overexpression of IGF-1 downstream effectors, i.e., PI3K and mTOR, with mostly higher concentrations of those genes in the HBMR line. Although the change in gene expression itself can be ambiguous to activation of the specific signaling pathway, it suggests here, however, that the observed increase in the amount of the PI3K and mTOR occurred rather independently of extracellular growth factors, like IGF-1. For example, catalytic subunit p110 of PI3K may be efficiently stimulated by activation of RAS proteins (membrane GTPases) or substrates of insulin receptors in response to various nutritional signals [ 53 , 54 ]. Thus, the previously noted increase in mTOR expression in our animal model [ 55 ] points rather to nutrients-related regulation of the PI3K/mTOR cascade than the IGF signaling pathway, most probably as a result of considerably higher food consumption in HBMR individuals [ 34 ]. However, to fully recognize the activity of specific molecular pathways, further functional studies with, e.g., phosphorylation-based assays are needed. Dysregulation of the PI3K and its downstream mediators is one of the most frequent events in tumorigenesis, broadly linked to enhanced hepatocyte proliferation and an unfavorable prognosis in HCC development [ 5 ]. Excessive expression of the PI3K gene in HBMR mice may translate into more intense cancer-related processes in those organisms, increasing their susceptibility to developing malignant transformation. Heightened risk for HCC development in HBMR individuals may also be noted with elevated activity of the mTOR gene, with its all physiological and anatomical implications. mTOR kinase is a proficient regulator of cellular protein synthesis, connecting nutrient sensing to cell growth and cell proliferation [ 5 , 51 ]. Although mTOR function depends on stimulation from its downstream mediators like AKT or S6K, which were not examined in our study, the observed increase in hepatocyte size, protein content, and general liver expansion suggest that both higher mTOR gene expression and mTOR protein concentration fully activated its physiological pathway resulting in cell size enlargement. Increasing mTOR activity, however, seems a double-edged sword in cancer biology [ 56 ]. A positive correlation between protein content and cell volume [ 45 ] is observed, allowing the bypass of the molecular crowding associated with elevated levels of specific biomolecules required for metabolism-related processes [ 22 ]. On the other hand, boosted protein synthesis enables increased aerobic glycolysis (so-called Warburg effect), cytoskeletal rearrangement, and cell cycle progression, all being crucial hallmarks of cancer metabolism [ 5 , 12 , 45 , 47 ]. On the one hand, a higher BMR requires increased PI3K/(Akt)mTOR signaling pathway activity to process nutrient resources effectively. Simultaneously, such enhanced activity indirectly facilitates the background for cancer development. Although many mTOR inhibitors have been developed to treat cancer [ 57 ], their influence on BMR, cell size, and the mass of metabolically active organs remains unknown and should be investigated thoroughly. c-myc/p-53 expression Among the most critically important factors in controlling cell proliferation, growth, and overall cellular metabolism, c-myc and p53 genes play a pivotal role. Stimulated p53 triggers reactions resulting in cell cycle arrest and DNA-damage response [ 3 ]. In contrast, c-myc acts the opposite way, prompting DNA replication, cell cycle progression, and general anabolic processes followed by increased energy expenditures [ 30 , 58 ]. In our study, p53 was downregulated at the beginning of neoplasm development in both metabolic lines, with an apparent increase in its expression through the cancer progression in HBMR individuals. On the other hand, we observed enhanced activity of c-myc, especially in HBMR mice. Decreased activity of p53 during the first weeks of the cancerogenesis process in our trial may result in significantly elevated expression of both c-myc and mTOR genes. Physiologically, p53 concentration is maintained at low levels due to the amplification/overexpression of its specific inhibitors, especially in conditions of high energy turnover [ 31 , 59 , 60 ]. As the increase in BMR during the early stages of cancerogenesis was noticed in similar studies [e.g., 39], it may occur, most probably just through enhanced PI3K/mTOR and c-myc signaling, with simultaneous silencing of p53 gene expression. However, a further increase in p53 expression in HBMR individuals, along with intensive neoplasm development, may be induced via noted positive mTOR/c-myc – p53 loops [ 30 , 59 , 60 ]. Such regulation should, in turn, lower the activity of mTOR and c-myc, which we just observed in the case of our animal model. Such a scenario enables the balance between response to stresses or commitment to cell proliferation and survival, mediated by various p53 and mTOR pathways [ 60 ]. Nevertheless, the relationship between the pro-growth pathways and the stress response signaling is likely highly complicated and influenced by genetic (energetic properties of specific cell types) and environmental (food) factors. APC/CTNNB1 The β-catenin, encoded by the CTNNB1 gene, constitutes an evolutionarily conserved primary biomarker broadly used in hepatocellular carcinoma evaluation [ 32 , 33 ]. Its interacting pathway with its antagonist -the adenomatous polyposis coli (APC)- is pivotal in initiating and sustaining HCC development [ 33 , 61 , 62 ]. In our study, we observed significant upregulation of the CTNNB1 gene in HBMR individuals at the experiment's beginning and end, with simultaneous lowered expression of the APC gene in both metabolic lines. However, the enhanced activity of CTNNB1 was followed by a delayed increase in β-catenin concentration, as its amount was not altered in the first stage of HCC development (week 15th ) compared to control groups. Overexpression of CTNNB1 is mainly linked to APC methylation, resulting in its downregulation, which may markedly influence the initiation and progression of cancer [ 61 ]. Similarly, the inactivation of the APC gene in Apc Iox/Iox mutants or its mechanistic deletion induces HCC formation, hepatomegaly, and general mortality [ 33 , 63 ]. Although we observed a loss of APC gene expression, it was similar in both metabolic lines, except in the very last stage of our experiment (week 45th ). However, previous studies suggest that in mice, ablation of APC alone is not a key driver for hepatocarcinogenesis, and additional signals cooperating with activated β-catenin cascade are required [ 33 ]. Disruption in β-catenin is associated with transactivation of other (Wnt)/β-catenin target genes like c-myc [ 62 ]. Similarly, inactivation of the p53 pathway may cooperate with β-catenin signaling to elicit the tumor's development [ 63 ]. As we observed changes in the expression of both c-myc and p53 genes, it suggests that joint metabolism signaling pathways may play essential roles in cancer initiation and its further progression. Conclusions Genetically determined increase in BMR may constitute a considerable risk factor for hepatocellular carcinoma development. Genotoxic stimulation of hepatocytes in mice characterized by high BMR leads to boosted protein synthesis and cell growth via upregulation of PI3K/mTOR and β-catenin/c-myc signaling pathways, promoting faster and more intense neoplasm development. The results suggest that an increase in energy expenditures constitutes an additional burden for the cells, especially in metabolically active organs. However, changes in the activity of metabolism-related genes seem to be multifaceted, reflecting various biological (e.g., life histories, genetic variations) and/or environmental (e.g., nutrient availability) conditions influencing the metabolic properties of an organism. Such genetic adjustment may be particularly interesting in the context of BMR evolution. Today, the intriguing question arises -does the observed decrease in adjusted human basal energy expenditures observed for the last 100 years [ 64 ] stand for an evolutionary response against the ongoing increase in cancer-related mortality rate? If so, lowering BMR via the action of metabolism-related genes and/or proteins may be an important tool for future clinical strategies and pharmacological interventions against hepatocellular carcinoma. Abbreviations BMR basal metabolic rate CS cell size HCC hepatocellular carcinoma DEN N–nitroso diethylamine mTOR mammalian target of rapamycin IGF 1–insulin–like growth factor 1 PI3K phosphatidylinositol–3 kinase APC adenomatous polyposis coli CTNNB1 catenin beta–1 Declarations Ethics approval and consent to participate: The experiment design and animal use were currently approved by the Local Ethical Committee in Olsztyn (no. 10/2016). Consent for publication: Not applicable. Availability of data and materials: All data will be shared by the lead contact upon request after publication. Competing interests: The authors declare that they have no competing interests Funding: This research was supported by a grant from the Polish National Science Center, 2019/33/B/NZ8/01976, to S.M. Authors Contributions: S.M.conceptualization and funding acquisition, S.M., D.S. methods and research performance, S.M., D.S. biochemistry, S.M. genetics, I.K., S.M. immunohistochemistry, L.C., S.M. histopathology, S.M, M.K. formal analysis, S.M. writing - original draft, S.M., D.S, H.C., and M.K. review and editing. All authors have read and agreed to the published version of the manuscript. Acknowledgements: The authors would like to thank Prof. Izabela Święcicka from the Laboratory of Applied Microbiology, University of Bialystok, for providing the necessary space and equipment for the chosen molecular analysis. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics. Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. Bang J, Jun M, Lee S, Moon H, Ro SW. Targeting EGFR/PI3K/AKT/mTOR signaling in hepatocellular carcinoma. Pharmaceutics. 2023;15(8):2130. 10.3390/pharmaceutics15082130 . Choudhary HB, Mandlik SK, Mandlik DS. Role of p53 suppression in the pathogenesis of hepatocellular carcinoma. World J Gastrointest Pathophysiol. 2023;14(3):46–70. 10.4291/wjgp.v14.i3.46 . Lu G, Lin J, Song G, Chen M. Prognostic significance of CTNNB1 mutation in hepatocellular carcinoma: a systematic review and meta-analysis. Aging. 2023;15(18):9759–78. 10.18632/aging.205047 . Tian LY, Smit DJ, Jücker M. The role of PI3K/AKT/mTOR signaling in hepatocellular carcinoma metabolism. Int J Mol Sci. 2023;24(3):2652. 10.3390/ijms24032652 . Schulien I, Hasselblatt P. Diethylnitrosamine-induced liver tumorigenesis in mice. Met Cell Biol. 2021;163:137–52. Memon A, Pyao Y, Jung Y, Lee JI, Lee WK. A modified protocol of diethylnitrosamine administration in mice to model hepatocellular carcinoma. Int J Mol Sci. 2020;21(15):5461. 10.3390/ijms21155461 . Löffler MC, Betz MJ, Blondin DP, Augustin R, Sharma AK, Tseng YH, et al. Challenges in tackling energy expenditure as obesity therapy: From preclinical models to clinical application. Mol Metab. 2021;51:101237. 10.1016/j.molmet.2021.101237 . Konarzewski M, Diamond J. Evolution of basal metabolic rate and organ masses in laboratory mice. Evolution. 1995;49:1239–48. doi.org/10.1111/j.1558-5646.1995.tb04450.x . Javed F, He Q, Davidson LE, Thornton JC, Albu J, Boxt L, et al. Brain and high metabolic rate organ mass: contributions to resting energy expenditure beyond fat-free mass. Am J Clin Nutr. 2010;91(4):907–12. 10.3945/ajcn.2009.28512 . Ruggiero C, Metter EJ, Melenovsky V, Cherubini A, Najjar SS, Ble A, et al. High basal metabolic rate is a risk factor for mortality: the Baltimore longitudinal study of aging. J Gerontol Biol. 2008;63:698–706. Dang CV. Links between metabolism and cancer. Genes Dev. 2012;26:877–90. Kliemann N, Murphy N, Viallon V, Freisling H, Tsilidis KK, Rinaldi S, et al. Predicted basal metabolic rate and cancer risk in the European prospective investigation into cancer and nutrition. Int J Cancer. 2020;147:648–61. doi.org/10.1002/ijc.32753 . Ng JCM, Schooling CM. Effect of Basal Metabolic Rate on cancer: A mendelian randomization study. Front Genet. 2021;12:735541. 10.3389/fgene.2021.735541 . Maciak S. Cell size, body size and Peto's paradox. BMC Ecol Evol. 2022;22(1):142. 10.1186/s12862-022-02096-5 . Li Y, Barbash O, Diehl JA. Regulation of the cell cycle. In: Mendelsohn J, Gray JW, Howley PM, Israel MA, Thompson CB, editors. The molecular basis of cancer. Elsevier; 2015. pp. 165–78. Björklund M. Cell size homeostasis: Metabolic control of growth and cell division. Biochim Biophys Acta Mol Cell Res. 2019;66(3):409–17. 10.1016/j.bbamcr.2018.10.002 . Björklund M, Marguerat S, Editorial. Determinants of Cell Size. Front Cell Dev Biol. 2017;5:115. 10.3389/fcell.2017.00115 . Miettinen TP, Björklund M. Mitochondrial function and cell size: An allometric relationship. Trends Cell Biol. 2017;27(6):393–402. 10.1016/j.tcb.2017.02.006 . Kozłowski J, Konarzewski M, Czarnoleski M. Coevolution of body size and metabolic rate in vertebrates: a life-history perspective. Biol Rev. 2020;95:1393–417. https://doi.org/10.1111/brv.12615 . Rolfe DF, Brown GC. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol Rev. 1997;77:731–58. Maciak S, Bonda-Ostaszewska E, Czarnołęski M, Konarzewski M, Kozłowski J. Mice divergently selected for high and low basal metabolic rates evolved different cell size and organ mass. J Evol Biol. 2014;27:478–87. https://doi.org/10.1111/jeb.12306 . Ölander M, Wegler C, Flörkemeier I, Treyer A, Handin N, Pedersen JM, et al. Hepatocyte size fractionation allows dissection of human liver zonation. J Cell Physiol. 2021;236(8):5885–94. 10.1002/jcp.30273 . Caldwell S, Ikura Y, Dias D, Isomoto K, Yabu A, Moskaluk C, et al. Hepatocellular ballooning in NASH. J Hepatol. 2010;53(4):719–23. 10.1016/j.jhep.2010.04.031 . Niu C, Sheng Y, Zhu E, Ji L, Wang Z. Ferulic acid prevents liver injury induced by Diosbulbin B and its mechanism. Biosci Trends. 2016;10(5):386–91. 10.5582/bst.2016.01152 . Tolba R, Kraus T, Liedtke C, Schwarz M, Weiskirchen R. Diethylnitrosamine (DEN)-induced carcinogenic liver injury in mice. Lab Anim. 2015;49(1 Suppl):59–69. 10.1177/0023677215570086 . Cargnello M, Tcherkezian J, Roux PP. The expanding role of mTOR in cancer cell growth and proliferation. Mutagenesis. 2015;30(2):169–76. 10.1093/mutage/geu045 . Panwar V, Singh A, Bhatt M, Tonk RK, Azizov S, Raza AS, et al. Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Signal Transduct Target Ther. 2023;8(1):375. 10.1038/s41392-023-01608-z . Kunst C, Haderer M, Heckel S, Schlosser S, Müller M. The p53 family in hepatocellular carcinoma. Transl Cancer Res. 2016;5(6):632–8. 10.21037/tcr.2016.11.79 . Feng YC, Liu XY, Teng L, Ji Q, Wu Y, Li JM, et al. c-Myc inactivation of p53 through the pan-cancer lncRNA MILIP drives cancer pathogenesis. Nat Commun. 2020;11(1):4980. 10.1038/s41467-020-18735-8 . Rahadiani N, Stephanie M, Perkasa AG, Handjari DR, Krisnuhoni E. p53 expression is associated with tumor stage, grade and subtype in patients with hepatocellular carcinoma. Mol Clin Oncol. 2023;19(1):54. 10.3892/mco.2023.2650 . Chen L, Zhou Q, Liu J, Zhang W. CTNNB1 alternation is a potential biomarker for immunotherapy prognosis in patients with hepatocellular carcinoma. Front Immunol. 2021;12:759565. 10.3389/fimmu.2021.759565 . Zhang Y, Liang B, Song X, Wang H, Evert M, Zhou Y, et al. Loss of Apc cooperates with activated oncogenes to induce liver tumor formation in mice. Am J Pathol. 2021;191(5):930–46. 10.1016/j.ajpath.2021.01.010 . Konarzewski M, Książek A. Determinants of intra-specific variation in basal metabolic rate. J Comp Physiol B. 2013;183(1):27–41. 10.1007/s00360-012-0698-z . Buczyńska S, Książek A, Maciak S, Brzęk P, Hambly C, Speakman JR, Konarzewski M. Lactation at hot temperature: a test of heat dissipation limitation in mice divergently selected for BMR. Biol Lett 2025;in press. Gębczyński AK, Sadowska J, Konarzewski M. Differences in the range of thermoneutral zone between mouse strains: potential effects on translational research. Am J Physiol Regul Integr Comp Physiol. 2024;326:R91–9. Cornish AJ, Law PJ, Timofeeva M, Palin K, Farrington SM, Palles C, et al. Modifiable pathways for colorectal cancer: a mendelian randomisation analysis. Lancet Gastroenterol Hepatol. 2020;5:55–62. 10.1016/S2468-1253(19)30294-8 . Biro PA, Thomas F, Ujvari B, Beckmann C. A novel perspective suggesting high sustained energy expenditure may be net protective against cancer. Evol Med Public Health. 2022;10:170–6. doi.org/10.1093/emph/eoac012 . Sawicka D, Maciak S, Sadowska A, Sokołowska E, Gohal S, Guzińska-Ustymowicz K, et al. Metabolic rate and oxidative stress as a risk factors in the development of colorectal cancer. Int J Mol Sci. 2024;25:10713. 10.3390/ijms251910713 . Arthur RS, Mossavar-Rahmani Y, Prentice RL, Shadyab AH, Luo J, Sattari M, et al. The Association of Predicted Resting Energy Expenditure with Risk of Breast Cancer among Postmenopausal Women in the Women's Health Initiative Cohort. Cancer Prev Res (Phila). 2022;15(4):255–64. 10.1158/1940-6207.CAPR-21-0467 . Boudou-Rouquette P, de Moura A, Martinez-Tapia C, Serrano AG, Chahwakilian A, Jouinot A, et al. ELCAPA Study Energy expenditure profiles and the risk of early limiting toxicity in older patients with cancer: The ELCAPA-25 prospective cohort survey. Clin Nutr. 2022;41:1073–108. 10.1016/j.clnu.2022.02.016 . Mattson MP, Longo VD, Harvie M. Impact of intermittent fasting on health and disease processes. Ageing Res Rev. 2017;39:46–58. Dorling JL, van Vliet S, Huffman KM, Kraus WE, Bhapkar M, Pieper CF, et al. Effects of caloric restriction on human physiological, psychological, and behavioral outcomes: highlights from CALERIE phase 2. Nutr Rev. 2021;79(1):98–113. 10.1093/nutrit/nuaa085 . Miettinen TP, Pessa HK, Caldez MJ, Fuhrer T, Diril MK, Sauer U, et al. Identification of transcriptional and metabolic programs related to mammalian cell size. Curr Biol. 2014;24(6):598–608. 10.1016/j.cub.2014.01.071 . Dolfi SC, Chan LL-Y, Qiu J, Tedeschi PM, Bertino JR, Hirshfield KM, et al. The metabolic demands of cancer cells are coupled to their size and protein synthesis rates. Cancer Metab. 2013;1:20. doi.org/10.1186/2049-3002-1-20 . Pessoa J, Martins M, Casimiro S, Pérez-Plasencia C, Shoshan-Barmatz V. Editorial: altered expression of proteins in cancer: function and potential therapeutic targets. Front Oncol. 2022;12:949139. 10.3389/fonc.2022.949139 . Liu H, Xie X, Wang B. Deep learning infers clinically relevant protein levels and drug response in breast cancer from unannotated pathology images. Breast Cancer. 2024;10:18. https://doi.org/10.1038/s41523-024-00620-y . Wu DJ, Aktipis A, Pepper JW. Energy oversupply to tissues: a single mechanism possibly underlying multiple cancer risk factors. Evol Med Public Health. 2019;1:9–16. doi.org/10.1093/emph/eoz004 . DeAngelis RA, Markiewski MM, Taub R, Lambris JD. A high-fat diet impairs liver regeneration in C57BL/6 mice through overexpression of the NF-κB inhibitor. IκBα Hepatol. 2005;42:1148–57. doi.org/10.1002/hep.20879 . Veteläinen R, Van Vliet AK, Van Gulik TM. Severe steatosis increases hepatocellular injury and impairs liver regeneration in a rat model of partial hepatectomy. Ann Surg. 2007;245:44–50. https://doi.org/10.1097/01.sla.0000225253.84501.0e . Sun EJ, Wankell M, Palamuthusingam P, McFarlane C, Hebbard L. Targeting the PI3K/Akt/mTOR pathway in hepatocellular carcinoma. Biomedicines. 2021;9(11):1639. 10.3390/biomedicines9111639 . Adamek A, Kasprzak A. Insulin-like growth factor (IGF) system in liver diseases. Int J Mol Sci. 2018;19(5):1308. 10.3390/ijms19051308 . Shan Y, Lu C, Wang J, Li M, Ye S, Wu S, et al. IGF-1 contributes to liver cancer development in diabetes patients by promoting autophagy. Ann Hepatol. 2022;27(4):100697. 10.1016/j.aohep.2022.100697 . Paskeh MDA, Ghadyani F, Hashemi M, Abbaspour A, Zabolian A, Javanshir S, et al. Biological impact and therapeutic perspective of targeting PI3K/Akt signaling in hepatocellular carcinoma: Promises and Challenges. Pharm Res. 2023;187:106553. doi.org/10.1016/j.phrs.2022.106553 . Maciak S, Sawicka D, Sadowska A, Prokopiuk S, Buczyńska S, Bartoszewicz M, et al. Low basal metabolic rate as a risk factor for development of insulin resistance and type 2 diabetes. BMJ Open Diabetes Res Care. 2020;8(1):e001381. 10.1136/bmjdrc-2020-001381 . Laplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell. 2012;149(2):274–93. 10.1016/j.cell.2012.03.017 . Hua H, Kong Q, Zhang H, Wang J, Luo T, Jiang Y. Targeting mTOR for cancer therapy. J Hematol Oncol. 2019;12:71. doi.org/10.1186/s13045-019-0754-1 . Zheng K, Cubero FJ, Nevzorova YA. c-MYC-making liver sick: Role of c-MYC in hepatic cell function, homeostasis and disease. Genes. 2017;8(4):123. 10.3390/genes8040123 . Hasty P, Sharp ZD, Curiel TJ, Campisi J. mTORC1 and p53: clash of the gods? Cell Cycle. 2013;2(1):20–5. 10.4161/cc.22912 . Cui D, Qu R, Liu D, Xiong X, Liang T, Zhao Y. The cross talk between p53 and mTOR pathways in response to physiological and genotoxic stresses. Front Cell Dev Biol. 2021;9:775507. 10.3389/fcell.2021.775507 . Zhang Y, Li B, Sun Q, Wu X, Chen Q, Li Z, et al. Association between APC promoter methylation and clinicopathological features of patients with hepatocellular carcinoma: a meta-analysis with PRISMA guideline. Transl Cancer Res. 2017;6(2):275–84. 10.21037/tcr.2017.03.71 . Javanmard D, Najafi M, Babaei MR, Hadi M, Niya K, Esghaei M, et al. Investigation of CTNNB1 gene mutations and expression in hepatocellular carcinoma and cirrhosis in association with hepatitis B virus infection. Infect Agents Cancer. 2020;15:37. doi.org/10.1186/s13027-020-00297-5 . Colnot S, Decaens T, Niwa-Kawakita M, Godard C, Hamard G, Kahn A, et al. Liver-targeted disruption of Apc in mice activates beta-catenin signaling and leads to hepatocellular carcinomas. Proc Natl Acad Sci USA. 2004;101(49):17216–21. 10.1073/pnas.0404761101 . Speakman J, de Jong JMA, Sinha S, Westerterp KR, Yamada Y, Sagayama H, et al. Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure. Nat Metabol. 2023;5:579–88. Additional Declarations No competing interests reported. Supplementary Files MaciakSIFigure.docx MaciakSupplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 19 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 22 Apr, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6046205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446956374,"identity":"48e5ded2-18a5-4ec1-87de-92b072556353","order_by":0,"name":"Sebastian Maciak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYJCCA0Aox8DAA2SyATE7kVqMEVqYibQnsYFoLbrthx8e+HGmLn3DjdwDDB/KDjOYE9JidibN4GDPjcO5G27kJTDOOHeYwbKZkJYDOQwHeD4cyN1wO8eAmbftMIPBYUJazr9hOPjnQ126AUjLX6K03MhhOMxzgzkBrIWROC3PDA7LnDlsOPP+u4SDPefSeQj75Xzy449vjtXJ8505e/DBjzJrOXP2BgJ6kMEBIOYxIEEDFJChZRSMglEwCoY5AADrU0we9kdZjwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bialystok","correspondingAuthor":true,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Maciak","suffix":""},{"id":446956375,"identity":"904e269a-0ddc-4f99-842d-34d4f9046d63","order_by":1,"name":"Diana Sawicka","email":"","orcid":"","institution":"Medical University of Bialystok","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Sawicka","suffix":""},{"id":446956376,"identity":"aa34d57c-3c43-4dad-9a4a-99f328b4ede6","order_by":2,"name":"Irena Kasacka","email":"","orcid":"","institution":"Medical University of Bialystok","correspondingAuthor":false,"prefix":"","firstName":"Irena","middleName":"","lastName":"Kasacka","suffix":""},{"id":446956377,"identity":"b7afcc0c-1320-4669-9d66-c76aab1e1eef","order_by":3,"name":"Lech Chyczewski","email":"","orcid":"","institution":"University of Medical Sciences in Bialystok","correspondingAuthor":false,"prefix":"","firstName":"Lech","middleName":"","lastName":"Chyczewski","suffix":""},{"id":446956378,"identity":"ce7f44cc-4696-4816-b3c0-903a1d27aff9","order_by":4,"name":"Halina Car","email":"","orcid":"","institution":"Medical University of Bialystok","correspondingAuthor":false,"prefix":"","firstName":"Halina","middleName":"","lastName":"Car","suffix":""},{"id":446956379,"identity":"aca6b173-4182-419f-8848-8e32258daaf8","order_by":5,"name":"Marek Konarzewski","email":"","orcid":"","institution":"University of Bialystok","correspondingAuthor":false,"prefix":"","firstName":"Marek","middleName":"","lastName":"Konarzewski","suffix":""}],"badges":[],"createdAt":"2025-02-17 09:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6046205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6046205/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14491-4","type":"published","date":"2025-07-01T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81326938,"identity":"59955a28-ce17-4946-86ef-71e91b22afe8","added_by":"auto","created_at":"2025-04-24 19:44:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":704483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLiver’s hepatocyte histopathology.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Representative photomicrographs of liver histopathology (H+E) of mice characterized by low (A, B, C) and high (D, E, F) basal metabolic rate (BMR) at chosen time points of hepatocellular carcinoma (HCC) development. (A, D) The liver of control mice (week 0) showed normal histology with a well-seen lobular structure. (B, C, E, F) HCC progression in the liver at the 35\u003csup\u003eth\u003c/sup\u003e and 45\u003csup\u003eth\u003c/sup\u003e week. Large numbers of hepatocytes with morphological markers for coagulative necrosis with homogenous, eosinophilic cytoplasm (pyramids) and apoptosis, characterized by shrunken cells with clear chromatin condensation or margination (thin arrows). Characteristic features of hepatocyte atypia can be observed, with cells differing considerably in size, forming sinusoid, round, or vesicular nuclei with distinct numbers of nucleoli (bold arrows). Proinflammatory cell influx (CI) and disturbance of the lobular structure are well seen in C, E photomicrographs, with few markers for focal necrosis (stars, E). Typical HCC picture (F) observed in the liver of high BMR individuals after 45 weeks of tumor development.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/77a9256517e2abfeeadf0f2c.jpg"},{"id":81327166,"identity":"fd91c843-437e-4d87-b122-805ce9969fc6","added_by":"auto","created_at":"2025-04-24 19:52:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":312126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProteins concentration.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe concentration of total (A), mTOR (B), and β-catenin (C) proteins in liver hepatocytes of mice selected for low and high basal metabolic rate (BMR), chemically enforced to develop hepatocellular carcinoma (DEN) in comparison to control groups (CONT) at 15\u003csup\u003eth\u003c/sup\u003e, 35\u003csup\u003eth\u003c/sup\u003e, and 45\u003csup\u003eth\u003c/sup\u003e week of genotoxic exposure. * - significance level for between lines comparison (HBMR \u003cem\u003evs\u003c/em\u003e. LBMR) revealed by ANOVA; ** - significance level for between-groups comparison among each metabolic line (CONT \u003cem\u003evs\u003c/em\u003e. DEN) revealed by t-test; Figure box charts labelled with different letters indicate significant differences between chosen time points in each treatment group at p≤0.05 as revealed by post-hoc NIR Fisher test.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/725d31c0bc12daa63d9deaf3.jpg"},{"id":81327163,"identity":"9749f89c-4bd7-4605-9dbe-ea2cf8806984","added_by":"auto","created_at":"2025-04-24 19:52:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExpression of IGF-1, PI3K, and mTOR genes.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLiver expression of representative genes for metabolism and cell size control in mice characterized by high (blue bars) and low (orange bars) basal metabolic rate (BMR) at different time points of hepatocellular carcinoma (HCC) development. IGF-1 (Insulin-like Growth Factor 1), PI3K (Phosphate Inozyto-3 Kinase), and mTOR (mammalian Target of Rapamycin). The results are presented as log2 fold change normalized to an endogenous reference (β-actin gene) and relative to the untreated control individuals after the Livak and Schmittgen (2001) method. * -indicates p\u0026lt;0.05 differences in cancer vs control group; NS -no differences; “p” values above the bars indicate significance level between metabolic lines at each time point with bold marks representing p≤0.05 values.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/8f0f1960691d0753c74a2136.jpg"},{"id":81327470,"identity":"a20ea489-b44f-4e2f-985e-3b9dac10c3c2","added_by":"auto","created_at":"2025-04-24 20:00:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":314943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExpression of p53, c-myc, APC, and CTNNB1 genes.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLiver expression of representative genes for tumor suppression (p53, APC) and proto-oncogenes markers (c-myc, CTNNB1) in mice characterized by high (blue bars) and low (orange bars) basal metabolic rate (BMR) at different time points of hepatocellular carcinoma (HCC) development. p53 (protein 53), c-myc (cellular MYC family), APC (Adenomatous Polyposis Coli), and CTNNB1 (Catenin Beta 1). The results are presented as log2 fold change normalized to an endogenous reference (β-actin gene) and relative to the untreated control individuals after the Livak and Schmittgen (2001) method. * -indicate p \u0026lt; 0.05 differences in cancer vs control group; NS -no differences; “p” values above the bars indicate significance level between metabolic lines at each time point with bold marks representing p≤0.05 values.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/5f141944fc3cf3d30e56ed51.jpg"},{"id":86179814,"identity":"0d259d3e-6c17-4f2d-b6e9-122913470080","added_by":"auto","created_at":"2025-07-07 16:19:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2939349,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/2f9193b2-afed-4229-83c7-4489a492200e.pdf"},{"id":81326952,"identity":"f27b1408-ab24-46c4-bab0-e40e07712690","added_by":"auto","created_at":"2025-04-24 19:44:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6958961,"visible":true,"origin":"","legend":"","description":"","filename":"MaciakSIFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/e12983eca3bf1daef632b698.docx"},{"id":81326940,"identity":"c645d244-9258-42d9-95cb-4aeb06941b0b","added_by":"auto","created_at":"2025-04-24 19:44:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32602,"visible":true,"origin":"","legend":"","description":"","filename":"MaciakSupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6046205/v1/aa7e3b2e42e58b2aed999c9a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Basal Metabolic Rate shapes the development and progression of hepatocellular carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eLiver cancer is one of the most common and fatal malignancies, ranking fourth leading cause of cancer-related mortality in the world, with extremely limited treatment options [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for over 85% of all cases, characterized by a low survival rate and a high level of recurrence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As with any other tumorigenic cell development, hepatocarcinogenesis is a multistep process involving the accumulation of genetic disruptions that ultimately lead to the malignant transformation of the hepatocytes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To date, many risk factors for HCC have been identified, with most of them associated with the key specificity of the hepatic structure and function [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The liver is the major metabolically active internal organ [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], with its mass accounting per se for a significant proportion of the basal/resting metabolic rate (BMR) both in animals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and humans [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, a mechanistic link between liver metabolism and whole-body metabolism may fit the association between increased BMR and risk for most cancers observed in humans [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt has been broadly demonstrated that genetically determined variation in metabolic rate exists, and its significant part at both inter- or intraspecific levels can be attributed to differences in cell size (CS; for review, see: [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]). Cell size is positively correlated with body mass and negatively correlated with BMR [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which may directly impact many physiological traits, including cancer-related ones. First, cell size negatively affects cell division rate [e.g., 16,17]. Larger cells are usually characterized by a considerable increase in protein synthesis rate and shortened cell cycle rate [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], promoting aging, mutations, and cancer susceptibility with every cell multiplication. Secondly, the size of cells is one of the key factors determining cellular metabolism rate via costs of maintenance of membrane gradients and/or mitochondria number and activity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Ion flux across membranes constitutes a significant burden for cellular metabolism, reaching up to 30% of its overall energy expenditures [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As the cell surface/volume ratio decreases in bigger cells, such metabolic costs might be reduced [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the size of cells can also be affected by the rate of cellular metabolism in specialized cells (i.e., hepatocytes) involved in the multitude of concurrent metabolic processes. Such cells must be large to avoid the limiting effect of molecular crowding [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, any changes in the size-related area for metabolic processes or intensity of those may affect the rate of cellular damage, ultimately influencing cancer susceptibility, especially in the so-called metabolically active organs like the liver, contributing to almost 1/5 of total BMR [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is particularly interesting in the case of hepatocytes, as they exhibit cell size differentiation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, potential changes in hepatocyte size may constitute an important predictor of chronic liver injury, including malignant transformations [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, understanding the mechanisms underlying the contribution of cell-mediated metabolic rate in neoplasm initiation may be critical for future anticancer therapies.\u003c/p\u003e \u003cp\u003eIn order to tackle the problem, we test the impact of genetically determined differences in BMR and CS on susceptibility and progression of hepatocellular carcinoma. To do so, we used a customized animal model consisting of two lines of laboratory mice divergently selected for BMR. Beyond observed and stable through several generations, with over 50% difference in BMR (unmatched by any other model), proposed animals represent considerable heterogeneity in their cellular architecture (see: \u003cem\u003eAnimals and experimental setup\u003c/em\u003e). For example, animals characterized by high BMR have substantially larger and more active hepatocytes, resulting in considerable liver expansion compared to low BMR mice [see: 22]. The proposed model is, therefore, uniquely suited to test the significance of metabolism-related factors in carcinogenesis.\u003c/p\u003e \u003cp\u003eWe challenged mice from the above-described lines with diethyl-nitrosamine (DEN) as a genotoxic agent, a standard preclinical model to test in vivo development and progression of liver cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The advantage of DEN-induced models is the resemblance of gene expression patterns and induction of specific HCC markers, analogous to those observed during the injury-fibrosis-malignancy cycle in humans [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A comparison of tumor development and its progression in mice differing with respect to the BMR and cell size provides a robust model for investigating metabolism-related determinants of carcinogenesis.\u003c/p\u003e \u003cp\u003eTo understand the metabolic-linked mechanisms that potentially influence cancer susceptibility, analysis of the action of specific genes that underlie BMR capacity and cell size-related processes is required. From the molecular point of view, both CS and the rate of organismal metabolism depend mainly on extracellular growth factors, nutrient availability, and/or nuclear regulatory genes \u0026ndash; all organized in specific signaling pathways. The first group of controllers comprises downstream effectors of growth hormones, with its primary action mediated by Insulin-like growth factor 1 (IGF-1), with its downstream partner -phosphatidylinositol-3 kinase (PI3K). The second group of regulators represented by the mTOR gene are found to be the central players in both insulin- and nutrient-induced cell size changes [i.e., 27,28]. Accordingly, the group of intracellular regulators of the cell size, centered around p53 and/or c-myc genes, act to coordinate proliferation and interject metabolic changes in cells through the regulation of energy metabolism and oxidative stress [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Dysregulation in the activity of the above representatives is broadly associated with cell aging and various types of cancer, including HCC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], mostly marked with disruption in the action of the canonical Wnt/APC/β-catenin pathway [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Thus, we predict that the propensity for HCC development is causally linked with increased BMR through genetically determined overexpression of metabolism/cell size-related genes. Conversely, decreased levels of cellular metabolic processes may play a crucial role in lowering the cancer susceptibility of low BMR individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and experimental setup\u003c/h2\u003e \u003cp\u003eWe analyzed the development and progression of chemically induced hepatocellular carcinoma (HCC) in 20-week-old Swiss-Webster males of laboratory mice (\u003cem\u003eMus musculus\u003c/em\u003e)\u003c/p\u003e \u003cp\u003efrom the 61st generation of a long-term selection experiment designed to generate two lines with divergent basal metabolic rates (respectively, the L-BMR and H-BMR line), as described previously [e.g., 22]. Briefly, depending on the breeding success, we maintain 30\u0026ndash;35 families in each selected line in subsequent generations. Whenever possible, no less than three randomly chosen males and three females from each family are subjected to measurements of BMR. Animals characterized by highest (H-BMR line) or lowest (L-BMR line) body mass (BM)-corrected BMR are chosen as progenitors and always mated outside their families. Apart from the over 50% higher BMR, the H-BMR mice consume more food, have a higher mass of the small intestine, liver, kidneys, and heart [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and by 0.6\u0026deg;C higher core body temperature at both 23\u0026deg;C and 30\u0026deg;C than mice with the low BMR [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The BMR difference between the L-BMR and H-BMR lines exceeds the BMR variation range observed in other laboratory mice lines [36, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e]. The selection experiment is carried out at the Department of Evolutionary and Physiological Ecology, Faculty of Biology, University of Bialystok.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing the completion of BMR measurements (see: SI), the H- and LBMR mice were randomly assigned to experimental (DEN) or control (CONT) groups. In total, we chemically induced liver cancer in 36 individuals per metabolic line and randomly culled 12 animals per line (if available; S1 Table) at the 15th, 35th, and 45th week of the experiment. As a control, on the first day of the experiment (day of injection, week 0), we sacrificed 8, then \u0026minus;\u0026thinsp;12, PBS-treated individuals on every consecutive time point from each of the selection lines to collect the material for experiment control (44 individuals per line in total, S1 Table). Such study design allowed us to track histological and genetic changes in investigated individuals and collect samples of sufficient sizes for in-experiment measurements and assays of statistical significance.\u003c/p\u003e \u003cp\u003eAt each specific time point, culled animals were anesthetized by intraperitoneal administration of a mixture of ketamine and xylazine at a dose of 50 and 5 mg/kg, respectively. The liver was dissected, dried on absorbent paper, weighed, and finally frozen in liquid nitrogen and stored at -80\u003csup\u003eᵒ\u003c/sup\u003eC until analyzed. Additionally, two pieces of liver (~\u0026thinsp;50mg) with tumor (if observed) were taken for histological (4% buffered formaldehyde, Chempur, Poland) and genetic (RNA stabilization solution (RNA later, Invitrogen, Lithuania)) analyses.\u003c/p\u003e \u003cp\u003eDuring experiments, animals were maintained individually in plastic cages at 23\u0026deg;C and 12d:12n photoperiod.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetabolic rate measurements\u003c/h3\u003e\n\u003cp\u003eBefore experimental procedures, BMR was measured in all animals following the standard methodology (Książek et al. 2004). We used a positive-pressure open-circuit respirometry system with dried and warmed atmospheric air pushed through the Tygon tubes. The system sequentially monitored the oxygen consumption rate of mice placed individually in three 350 cm\u003csup\u003e3\u003c/sup\u003e chambers submerged in a water bath set at 32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026deg;C (thermoneutral zone for mice). The outflow of the airstream was directed to a computer-controlled channel multiplexer, a part of a Sable Systems TR-1 oxygen analyzer (Henderson, NV, USA). The air was sampled at the rate of 75 mL min \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Prior to passing through an oxygen sensor (S-3A/I Applied Electrochemistry, Pittsburgh, PA, USA), it was scrubbed off CO2 (Carboabsorb AS, BDH Laboratory Supplies, Lutterworth, UK) and moisture (Drierite, Drierite Co.LTD, Xenia, OH, USA). Each metabolic measurement trial lasted three hours, and oxygen concentrations in each chamber were recorded every second for two hours. Animals were fasted for six hours before BMR measurements. We defined BMR (mL O\u003csub\u003e2\u003c/sub\u003e h \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026plusmn; 0.0013) as the lowest rate of oxygen consumption that did not change for at least four minutes by more than 0.01%. Metabolic data were analyzed using Sable System DATACAN V software.\u003c/p\u003e\n\u003ch3\u003eTumor induction and monitoring of progression\u003c/h3\u003e\n\u003cp\u003eWe applied a two-step carcinogenesis model with additional administration of tumor promoter for HCC development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At the starting point, animals assigned to experimental groups were intraperitoneally injected with a 50\u0026micro;l solution of N-nitroso diethylamine (DEN, Sigma Aldrich, St. Louis, MO, USA; 90 \u0026micro;g/kg) as a genotoxic reagent for hepatocellular carcinoma initiation. At the same time, the remaining CONT mice (60 individuals per line) received phosphate-buffered saline (PBS, Sigma-Aldrich) shot in an analogical manner. Fourteen days after DEN intoxication, treated animals were given drinking water with an addition of phenobarbital sodium salt for 15 weeks (PB, Amara, Cracow, Poland, 500 mg/L) as a hepatic adenoma promoter [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The drinking water containing PB was changed every three days (standard housing procedure), and intake was allowed \u003cem\u003ead libitum\u003c/em\u003e. The control group was receiving normal drinking water in an analogical manner. We observed no significant differences in the amount of drinking water between metabolic lines or experimental groups.\u003c/p\u003e \u003cp\u003eObtained liver samples were visually checked for any histopathological changes. Eventually, tumor parameters were measured using a digital caliper, and volume was calculated using a modified ellipsoidal formula: 0.52 x length x width\u003csup\u003e2\u003c/sup\u003e. To determine tumor progression in the obtained liver samples, we have adopted basic histopathological markers: number of atypical cells/1mm\u003csup\u003e2\u003c/sup\u003e, number of tumor foci/10mm\u003csup\u003e2\u003c/sup\u003e, number of cancer cells/1mm\u003csup\u003e2\u003c/sup\u003e, followed by β-catenin immunostaining for each histological slide, as a standard cancer-related diagnostic procedure (for details, see: SI). The size of hepatocytes was estimated in 30 randomly chosen cells per mouse in the pericentral region of the lobules by outlining the edges of cells (MultiScan software).\u003c/p\u003e\n\u003ch3\u003eGenes expression\u003c/h3\u003e\n\u003cp\u003eThe total tissue RNA was isolated from the liver samples using a standard commercial kit (Total RNA mini plus, A\u0026amp;A Biotechnology, Poland). DNAse I treatment with a Clean-up RNA concentrator (A\u0026amp;A Biotechnology, Poland) to remove traces of DNA was applied to all samples. RNA purity was checked with NanoDrop 2000 (Thermo Fisher Scientific, Pittsburgh, PA, USA). All RNA samples were adjusted to the starting concentration of 50ng/\u0026micro;l. Primers-specific, high-capacity cDNA reverse transcription kit with oligo dT starters was used to convert RNA to cDNA (TranScriba, A\u0026amp;A Biotechnology, Poland). Relative expression of the genes of interest (i.e., p53, mTOR, IGF1, APC, c-myc, CTNNB1, and PIK3C) was assessed in two technical replicates by quantitative PCR (qPCR) on a StepOnePlus Real-Time PCR 96-well System (Applied Biosystems) with the use of SYBR Green RT PCR Mix (A\u0026amp;A Biotechnology, Poland). All analyses used the standard housekeeping \u003cem\u003eβ-actin\u003c/em\u003e gene as an endogenous reference. Sequences of the analyzed genes (i.e., p53, mTOR, IGF1, APC, c-myc, CTNNB1, PIK3C) and endogenous reference gene (β-actin) used for gene expression were as followed: p53, forward primer 5\u0026rsquo;-ATCACCTCACTGCAT-GGACG-3\u0026rsquo;, reverse primer 5\u0026rsquo;-GCCATAGTTGCCCTGGTAAG-3\u0026rsquo;; mTOR, forward primer 5\u0026rsquo;-GGGAGAACAGAAGATGGGTAAC-3\u0026rsquo;, reverse primer 5\u0026rsquo;-CTGAGTCCCTGCTGCA-AATA-3\u0026rsquo;; IGF1, forward primer 5\u0026rsquo;-TGGATGCTCTTCAGTTCGTG-3\u0026rsquo;, reverse primer 5\u0026rsquo;-GTCTTGGGCATGTCAGTGTG-3\u0026rsquo;, APC, forward primer 5\u0026rsquo;-GGCAGAAGTACAGATGA-TGCTC-3\u0026rsquo;, reverse primer 5\u0026rsquo;-GGATGCTT-GTTGGACTTGGT-3\u0026rsquo;; c-myc, forward primer 5\u0026rsquo;-CAACGTCTTGGAACGTCA-3\u0026rsquo;, reverse primer 5\u0026rsquo;-TCGTCTGCTTGAATGGACAG-3\u0026rsquo;; CTNNB1, forward primer 5\u0026rsquo;-GTTCGCCTTCATTATGGACTGCC-3\u0026rsquo;, reverse primer 5\u0026rsquo;-ATAGCACCCTGTTCCCGCAAA-3\u0026rsquo;; PIK3C, forward primer 5\u0026rsquo;-CCAGAGCAAGTCATT-GCTGA-3\u0026rsquo;, reverse primer 5\u0026rsquo;-TATGACCCAGAGGGATTTCG-3\u0026rsquo; and β-actin, forward primer 5\u0026rsquo;-AGAGGGAAATCGTGCGTGAC-3\u0026rsquo;, reverse primer 5\u0026rsquo;-CAATAGTGATGAC-CTGGCCGT-3\u0026rsquo;. All primer combinations resulted in amplicons of 207, 399, 220, 183, 153, 145, and 341 bp, respectively, and 214 bp for β-actin, without a specific background band as visualized on an agarose gel. Primers were designed and checked using FastPCR (Primer Digital Ltd.). The primer mix has been tested to generate satisfactory qPCR data by using the following PCR program: pre-soaking 95\u0026deg;C for 2 min; denaturation: 95\u0026deg;C for 30 sec, annealing: 60\u0026deg;C for 1 min; extension 72\u0026deg;C for 45 sec. The amplification efficiencies of the target and reference genes were comparable. ΔCt values for each analyzed gene in experimental groups were corrected for untreated controls with average ΔCt as the calibrator, according to the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The data were presented as the fold change in gene expression normalized to a \u003cem\u003eβ-actin\u003c/em\u003e gene and relative to the untreated control groups.\u003c/p\u003e\n\u003ch3\u003eAssessment of total, mTOR, and β-catenin protein concentrations\u003c/h3\u003e\n\u003cp\u003eLiver samples, frozen in liquid nitrogen, were cut into ~\u0026thinsp;30 mg pieces, rinsed thoroughly in ice-cold PBS to remove excess of blood, and weighed (\u0026plusmn;\u0026thinsp;0.01 mg) before homogenization. Each probe was homogenized in fresh, ice-cold 1 ml lysis buffer (Cell Biologics) with the addition of proteolysis inhibitor (Complete Mini Roche, France) at a 1:20 ratio, with the use of a tissue grinding tool (EURx). The resulting suspension was then sonicated with an ultrasonic cell disrupter (UP200S, Hielscher) and centrifuged (10 min, 10.000 x g, 4\u0026deg;C) with a supernatant collection for further biochemical assays.\u003c/p\u003e \u003cp\u003eThe concentration of total (\u0026micro;g/ml; BCA\u0026trade;, Pierce, Thermo Fisher Scientific, Rockfold, IL, USA), mTOR (pg/ml; Abcam SimpleStep ELISA, ab206311), and β-catenin (ng/ml; Abcam SimpleStep ELISA, ab275100) proteins in liver homogenates was determined with the use of commercial protein assay kits. All reagents were equilibrated to room temperature, and probes were diluted 1:5 (total proteins) or 1:10 (mTOR and β-catenin) before analysis. We used 150 \u0026micro;l of each standard and unknown samples in duplicates. Probes were read at 562 nm (total proteins) or 450nm (mTOR and β-catenin) wavelength with the use of a Varioskan Lux microplates reader (Thermo Fisher).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eBMR was analyzed by means of ANCOVA with the line type (HBMR vs. LBMR) and treatment (CONT vs. DEN) as fixed factors and body mass as a covariate. The differences in the gene expression were determined using three-way ANOVA, with the line type, DEN treatment, and time point as fixed factors. The change in expression level at the consecutive time points in experimental groups compared to control was checked with the use of the Student t-test.\u003c/p\u003e \u003cp\u003eProtein concentration and histopathological parameters were analyzed using three-way ANOVA, with line type, treatment, and time point as fixed factors. Post hoc comparisons were performed with a Fisher's LSD test. All statistical analyses were done with \u003cem\u003eStatistica\u003c/em\u003e 13.3 software (StatSoft, Poland). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAn increase in Basal Metabolic Rate enlarges hepatocyte- and liver size\u003c/h2\u003e \u003cp\u003eThe HBMR and LBMR mice differ significantly in terms of metabolic rate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We observed over 60% differentiation in energy expenditures with the average BMR values of 70.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 ml O\u003csub\u003e2\u003c/sub\u003e/h and 40.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 ml O\u003csub\u003e2\u003c/sub\u003e/h in high and low lines, respectively (S2 Table). Notably, initial (week 0) and final body mass did not differ between the lines (F\u003csub\u003e1,158\u003c/sub\u003e=0.00; P\u0026thinsp;=\u0026thinsp;0.99 and F\u003csub\u003e1,128\u003c/sub\u003e=2.89; P\u0026thinsp;=\u0026thinsp;0.09, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of a three-way analysis of variance (ANOVA) for BMR, liver masses, hepatocyte size, major histopathological markers, and protein concentration in mice artificially selected for low or high basal metabolic rate and enforced to develop hepatocellular carcinoma. df \u0026ndash; degrees of freedom, F \u0026ndash; ratio of variances, NA - not applied; values below p\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant (bold).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c14\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eANOVA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eChange direction in high BMR line\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLine effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTreatment effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eTime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eLine x Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eLine x Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eTreatment x Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMR\u003c/p\u003e \u003cp\u003e(ml O\u003csub\u003e2\u003c/sub\u003e/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;955.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver mass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;98.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;40.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocyte size (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1171.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;155.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. atypical cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;548.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor foci\u003c/p\u003e \u003cp\u003e/10mm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;34.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;34.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (mg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emTOR\u003c/p\u003e \u003cp\u003e(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;21.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-catenin\u003c/p\u003e \u003cp\u003e(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe liver mass differed between metabolic lines, experimental groups, and the time of the experiment, with, in general, higher liver mass in the HBMR line and a significant increase of this organ in cancerogenic groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Line x treatment interaction was significant. Differences in liver size were directly and positively related to the hepatocyte size and its enlargement in DEN-treated individuals (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChange in BMR influences tumor development\u003c/h2\u003e \u003cp\u003eWe visually observed a few liver tumors at the 45th week in four (out of eight) individuals originating from the HBMR line only, with the neoplasm's average volume ranging from 0.676 to 0.814 mm\u003csup\u003e3\u003c/sup\u003e. However, characteristic features of hepatocyte atypia were noticed in both lines in tumor-induced groups, increasing with the duration of the experiment, with the most significant number of atypical cells at the last time point- week 45 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Importantly, the process of cancerogenesis began much earlier and with greater intensity in the HBMR line, as we observed the first atypical cells in week 15th and an increase in the number of those at weeks 35th and 45th, while in the LBMR line, a significant increase was seen only at 45th week (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; S3 and S4 Tables). The number of detected tumor foci per defined area and number of cancer cells was significantly higher in the HBMR group and increased during the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After 45 weeks following DEN genotoxic liver stimulation, the fully developed hepatocellular carcinoma was observed in half of the cases among HBMR mice (four individuals), while in the LBMR group, the pre-cancerous stage (atypical cells characteristic for tumor development) was detected only (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, F). Our results strongly suggest, then, that individuals with genetically determined, high BMR are more prone to develop liver cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProtein \u003cem\u003econcentration depends on the level of BMR\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe total protein concentration differed between metabolic lines and experimental groups, with the HBMR line characterized by significantly higher protein content in hepatocytes in both control and cancerogenic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; see also S2 Table). Cancer induction increased hepatocyte protein content in both metabolic group types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) with a statistically significant treat x time interaction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA similar pattern was observed in the case of mTOR. Its concentration differed between metabolic lines and treatment, but not with the time of the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; see also S2 Table). A marked increase was seen in the amount of mTOR in the DEN-treated, high-metabolism individuals at weeks 15th and 45th, but with a slight decrease compared to the control group at week 35th (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Cancer-induced LBMR mice were characterized by an apparent increase in mTOR concentration at week 15th, followed by a decrease in the content of this protein at subsequent time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; S4 Table).\u003c/p\u003e \u003cp\u003eThe concentration of β-catenin was considerably higher in the HBMR line (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; see also S2 Table). Moreover, we observed a general increase of catenin content in both cancer-induced animals and during the whole experiment, with significant treatment x time interaction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenes expression may differ with respect to BMR\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eIGF-1/PI3K/mTOR as an insulin- and nutrient-induced metabolism regulators\u003c/h2\u003e \u003cp\u003eOngoing liver cancerogenesis changed the expression of insulin-like growth factor 1 (IGF-1) through the analyzed time points, but differences between metabolic lines were insignificant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). IGF-1 was downregulated at weeks 15th and 35th in tumor-induced individuals, especially in LBMR animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e; S5 Table). However, we observed marked overexpression of this gene at week 45th in both metabolic lines, suggesting increased intensity of tumor-associated processes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of a three-way analysis of variance (ANOVA) for selected gene expression in mice artificially selected for low or high basal metabolic rate and enforced to develop hepatocellular carcinoma. df (degrees of freedom)\u0026thinsp;=\u0026thinsp;101 in all cases. F \u0026ndash; ratio of variances, NC stands for \u0026ldquo;no change\u0026rdquo; between metabolic lines; values below p\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant (bold).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eANOVA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eChange direction in high BMR line\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLine effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTreatment effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTime effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLine x Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eLine x Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eTreatment x Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIGF-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;126.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;126.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePI3K\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;96.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;41.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;41.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emTOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;26.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;15.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;15.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;21.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;38.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;25.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;31.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003edecrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ec-myc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;60.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;72.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;82.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePI3K expression differed between metabolic lines with significant treatment effect and a decrease in its amount during the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PI3K gene was overexpressed in DEN-treated individuals at week 15th and differed from control groups at week 35th in high BMR line-only (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e; S5 Table). The PI3K overexpression at weeks 15th and 35th was markedly different between metabolic lines but not at week 45th (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A similar pattern was observed for the mTOR gene. Its expression, in general, was considerably different between metabolic lines and throughout the experiment, with significant line x treatment interaction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the line differences revealed by the t-test at weeks 15th and 35th were statistically insignificant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e; S5 Table). mTOR gene expression slightly decreased in consecutive time points, with insignificantly higher overexpression in LBMR mice at week 15 compared to the HBMR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ep53/c-myc as intracellular regulators of the cell size/cell division rate\u003c/h2\u003e \u003cp\u003eThe expression of the p53 gene differed between metabolic lines and time points, with significant line x treatment interaction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). p53 was downregulated at week 15th in both metabolic lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, in weeks 35 and 45, there was an increase in mRNA concentration for \u003cem\u003ethe p53\u003c/em\u003e gene, especially seen in the HBMR line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; S5 Table). The high expression of the c-myc gene was observed throughout the whole experiment in tumor-induced HBMR individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At the same time, in the LBMR group, its action was less expressed and seen only at weeks 15th and 35th (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) with general, between-lines and time differences (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAPC/CTNNB1 as a markers for tumorigenesis\u003c/h2\u003e \u003cp\u003eThe tumor suppressor APC gene expression changed in response to HCC induction and differed between time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the effect of the metabolic line in the case of APC expression was insignificant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). APC was considerably downregulated in cancer-induced groups at weeks 35th and 45th, with a marked decrease in HBMR mice at week 45th (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; S5 Table). In LBMR animals, expression of this gene was reduced only at week 35th (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), while APC gene expression increased at week 45th, which led to differences between metabolic lines (p\u0026thinsp;=\u0026thinsp;0.01) at that time.\u003c/p\u003e \u003cp\u003eThe effects of metabolic line, treatment, and time of the experiment, with between effects interactions, were significant for the CTNNB1 gene (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was higher expression of the CTNNB1 gene at weeks 15th and 45th (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; S5 Table) and cellular accumulation of β-catenin in HBMR mice (see: S1-3 Figures), suggesting higher intensity of cancerogenic processes in the HBMR line. In the case of LBMR animals, we only observed a slight increase in CTNNB1 gene expression at week 15th (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe intensity of metabolic expenditures shapes the probability of cancer development\u003c/h2\u003e \u003cp\u003eOur results indicate that differentiation in organismal energy turnover translates directly into neoplasm susceptibility and tumor progression rate. HBMR individuals developed fully organized hepatocellular carcinoma, while in LBMR mice, the pre-cancerous stage was observed only (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although the intensity of necrosis and apoptosis processes was comparable between metabolic lines during the experiment (S3 and S4 Tables), atypical and cancerogenic cells were detected much earlier in HBMR animals, with cytoplasm shift of β-catenin\u0026rsquo;s (S1-3 Figures), suggesting greater vulnerability of metabolically overloaded hepatocytes for neoplasm transformation.\u003c/p\u003e \u003cp\u003eAs the liver accounts for almost 20% of total BMR [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], an increase in metabolic rate via changes in liver size may stand for the fundamental mechanism underlying multiple cancer susceptibility. Recent theoretical studies based on Mendelian randomization showed that, in general, BMR is positively associated with most cancers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although Biro et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] pointed out the net protective function of increased metabolism against cancer, our results showed that LBMR mice are more resistant to DEN intoxication, with a lower prevalence of cancerogenic changes than HBMR individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Accordingly, another study based on this animal model revealed that tumor-induced development with human DLD-1 colorectal cancer cells was significantly promoted in HBMR mice [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, absolute neoplasm regression and lower total oxidative status were detected in the LBMR metabolic line after 36 days of trial [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this manner, low metabolism inhibits tumor-related processes and protects against cancer. Lowered mass-specific metabolism also reduces the risk of postmenopausal breast cancer [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], acute complications after anticancer treatment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and general mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, lowering energy expenditures via caloric restriction or intermittent fasting restores the balance for cellular repair mechanisms and counteracts metabolic-related disorders and neoplasm development [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this manner, our studies provide new, direct empirical evidence that genetically determined high metabolism may translate into greater cancer susceptibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCell size and protein content\u003c/h2\u003e \u003cp\u003eThe observed variation in BMR results mostly from differentiation in a mass of metabolically active organs, being the function of changes in cell size and/or cell number [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. That is why cellular mechanisms regulating the functional size and adequate metabolic capacity of cells are critical to fully understanding the etiology of metabolism-related disorders, including cancer development. Here, we observed a considerable increase in hepatocyte size, resulting in whole liver enlargement in HBMR individuals (S2 Table). Metabolism-related increases in cell size result mainly from mRNA transcript abundance and improved concentration of proteome products, enabling enhanced physiological processes against molecular crowding [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Indeed, we noticed a significant increase in both total and mTOR protein concentration in hepatocytes of HBMR mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, due to HCC progression, there was a further increase in cellular protein concentrations and hepatocyte size, followed by concomitant enlargement in total liver mass in both metabolic lines. To date, it has been broadly demonstrated that cancer cells show extensive alterations in protein expression levels involved in the regulation of the cell size and metabolic pathways, which are drivers of their malignant transformation [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChanges in cell size are typically observed when the balance between the growth rate and cell division rate is altered. According to the main models for size homeostasis, larger cells need less time to reach the mitosis G1/S phase and thus tend to grow much slower, but their cell cycle is considerably faster [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Here, we can fairly assume that observed liver enlargement in a high BMR line (while body mass remains fixed between H- and L- lines) is achieved \u003cem\u003evia\u003c/em\u003e an increase in cell size and number, thus requiring a faster rate of cell division in HBMR individuals. Such a conclusion is consistent with the common observation that cell cycle length and so-cell division rate are inversely proportional to the cell size [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, direct measurements of the rate of cellular proliferation in the high vs. low BMR should be investigated further. A high division rate of big, metabolic active cells may result in cell exhaustion and a shortened lifespan. Enlargement in hepatocyte size may be linked with a multitude of atypical forms and advanced tumorigenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, computational [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and in vivo models suggest that high energy turnover accelerates cellular evolution toward cancer, particularly in bigger cells [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Thus, high BMR is linked with fast growth and cell cycle progression, burdening cells with metabolic by-products, including enhanced ROS concentration, protein degradation, and nucleic acid impairment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAn increase in BMR correlates with the expression of metabolism-related genes\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eIGF-1/PI3K/(Akt)/mTOR pathway\u003c/h2\u003e \u003cp\u003eThe insulin-like growth factor IGF-1 \u003cem\u003evia\u003c/em\u003e PI3K/(Akt)mTOR pathway constitutes a major intracellular signal transduction pathway involved in the control of cellular metabolism, cell growth, and proliferation [e.g., 2,5,51]. Although the IGF-1 expression seems multifaced during cancerogenesis, most studies point to its overexpression in various functional human cancers, including hepatocellular carcinoma [see: 52,53]. In the case of our study, IGF-1 was downregulated in both metabolic lines at the beginning of the carcinogenesis process, with an increase in its expression after 45 weeks of DEN intoxication, which may suggest advanced neoplasm development at the end of the experiment period. Simultaneously, we observed overexpression of IGF-1 downstream effectors, i.e., PI3K and mTOR, with mostly higher concentrations of those genes in the HBMR line. Although the change in gene expression itself can be ambiguous to activation of the specific signaling pathway, it suggests here, however, that the observed increase in the amount of the PI3K and mTOR occurred rather independently of extracellular growth factors, like IGF-1. For example, catalytic subunit p110 of PI3K may be efficiently stimulated by activation of RAS proteins (membrane GTPases) or substrates of insulin receptors in response to various nutritional signals [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Thus, the previously noted increase in mTOR expression in our animal model [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] points rather to nutrients-related regulation of the PI3K/mTOR cascade than the IGF signaling pathway, most probably as a result of considerably higher food consumption in HBMR individuals [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, to fully recognize the activity of specific molecular pathways, further functional studies with, e.g., phosphorylation-based assays are needed.\u003c/p\u003e \u003cp\u003eDysregulation of the PI3K and its downstream mediators is one of the most frequent events in tumorigenesis, broadly linked to enhanced hepatocyte proliferation and an unfavorable prognosis in HCC development [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Excessive expression of the PI3K gene in HBMR mice may translate into more intense cancer-related processes in those organisms, increasing their susceptibility to developing malignant transformation. Heightened risk for HCC development in HBMR individuals may also be noted with elevated activity of the mTOR gene, with its all physiological and anatomical implications. mTOR kinase is a proficient regulator of cellular protein synthesis, connecting nutrient sensing to cell growth and cell proliferation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Although mTOR function depends on stimulation from its downstream mediators like AKT or S6K, which were not examined in our study, the observed increase in hepatocyte size, protein content, and general liver expansion suggest that both higher mTOR gene expression and mTOR protein concentration fully activated its physiological pathway resulting in cell size enlargement. Increasing mTOR activity, however, seems a double-edged sword in cancer biology [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. A positive correlation between protein content and cell volume [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] is observed, allowing the bypass of the molecular crowding associated with elevated levels of specific biomolecules required for metabolism-related processes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, boosted protein synthesis enables increased aerobic glycolysis (so-called Warburg effect), cytoskeletal rearrangement, and cell cycle progression, all being crucial hallmarks of cancer metabolism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. On the one hand, a higher BMR requires increased PI3K/(Akt)mTOR signaling pathway activity to process nutrient resources effectively. Simultaneously, such enhanced activity indirectly facilitates the background for cancer development. Although many mTOR inhibitors have been developed to treat cancer [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], their influence on BMR, cell size, and the mass of metabolically active organs remains unknown and should be investigated thoroughly.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ec-myc/p-53 expression\u003c/h2\u003e \u003cp\u003eAmong the most critically important factors in controlling cell proliferation, growth, and overall cellular metabolism, c-myc and p53 genes play a pivotal role. Stimulated p53 triggers reactions resulting in cell cycle arrest and DNA-damage response [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In contrast, c-myc acts the opposite way, prompting DNA replication, cell cycle progression, and general anabolic processes followed by increased energy expenditures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In our study, p53 was downregulated at the beginning of neoplasm development in both metabolic lines, with an apparent increase in its expression through the cancer progression in HBMR individuals. On the other hand, we observed enhanced activity of c-myc, especially in HBMR mice. Decreased activity of p53 during the first weeks of the cancerogenesis process in our trial may result in significantly elevated expression of both c-myc and mTOR genes. Physiologically, p53 concentration is maintained at low levels due to the amplification/overexpression of its specific inhibitors, especially in conditions of high energy turnover [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. As the increase in BMR during the early stages of cancerogenesis was noticed in similar studies [e.g., 39], it may occur, most probably just through enhanced PI3K/mTOR and c-myc signaling, with simultaneous silencing of p53 gene expression. However, a further increase in p53 expression in HBMR individuals, along with intensive neoplasm development, may be induced via noted positive mTOR/c-myc \u0026ndash; p53 loops [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Such regulation should, in turn, lower the activity of mTOR and c-myc, which we just observed in the case of our animal model. Such a scenario enables the balance between response to stresses or commitment to cell proliferation and survival, mediated by various p53 and mTOR pathways [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Nevertheless, the relationship between the pro-growth pathways and the stress response signaling is likely highly complicated and influenced by genetic (energetic properties of specific cell types) and environmental (food) factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eAPC/CTNNB1\u003c/h2\u003e \u003cp\u003eThe β-catenin, encoded by the CTNNB1 gene, constitutes an evolutionarily conserved primary biomarker broadly used in hepatocellular carcinoma evaluation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Its interacting pathway with its antagonist -the adenomatous polyposis coli (APC)- is pivotal in initiating and sustaining HCC development [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In our study, we observed significant upregulation of the CTNNB1 gene in HBMR individuals at the experiment's beginning and end, with simultaneous lowered expression of the APC gene in both metabolic lines. However, the enhanced activity of CTNNB1 was followed by a delayed increase in β-catenin concentration, as its amount was not altered in the first stage of HCC development (week 15th ) compared to control groups. Overexpression of CTNNB1 is mainly linked to APC methylation, resulting in its downregulation, which may markedly influence the initiation and progression of cancer [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Similarly, the inactivation of the APC gene in Apc\u003csup\u003eIox/Iox\u003c/sup\u003e mutants or its mechanistic deletion induces HCC formation, hepatomegaly, and general mortality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Although we observed a loss of APC gene expression, it was similar in both metabolic lines, except in the very last stage of our experiment (week 45th ). However, previous studies suggest that in mice, ablation of APC alone is not a key driver for hepatocarcinogenesis, and additional signals cooperating with activated β-catenin cascade are required [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Disruption in β-catenin is associated with transactivation of other (Wnt)/β-catenin target genes like c-myc [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Similarly, inactivation of the p53 pathway may cooperate with β-catenin signaling to elicit the tumor's development [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. As we observed changes in the expression of both c-myc and p53 genes, it suggests that joint metabolism signaling pathways may play essential roles in cancer initiation and its further progression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eGenetically determined increase in BMR may constitute a considerable risk factor for hepatocellular carcinoma development. Genotoxic stimulation of hepatocytes in mice characterized by high BMR leads to boosted protein synthesis and cell growth via upregulation of PI3K/mTOR and β-catenin/c-myc signaling pathways, promoting faster and more intense neoplasm development. The results suggest that an increase in energy expenditures constitutes an additional burden for the cells, especially in metabolically active organs. However, changes in the activity of metabolism-related genes seem to be multifaceted, reflecting various biological (e.g., life histories, genetic variations) and/or environmental (e.g., nutrient availability) conditions influencing the metabolic properties of an organism. Such genetic adjustment may be particularly interesting in the context of BMR evolution. Today, the intriguing question arises -does the observed decrease in adjusted human basal energy expenditures observed for the last 100 years [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] stand for an evolutionary response against the ongoing increase in cancer-related mortality rate? If so, lowering BMR via the action of metabolism-related genes and/or proteins may be an important tool for future clinical strategies and pharmacological interventions against hepatocellular carcinoma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebasal metabolic rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecell size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN\u0026ndash;nitroso diethylamine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emTOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emammalian target of rapamycin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1\u0026ndash;insulin\u0026ndash;like growth factor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI3K\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ephosphatidylinositol\u0026ndash;3 kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadenomatous polyposis coli\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTNNB1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecatenin beta\u0026ndash;1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment design and animal use were currently approved by the Local Ethical Committee in Olsztyn (no. 10/2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data will be shared by the lead contact upon request after publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant from the\u0026nbsp;Polish National Science Center, 2019/33/B/NZ8/01976, to S.M.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.M.conceptualization and funding acquisition, S.M., D.S. methods and research performance, S.M., D.S. biochemistry, S.M. genetics, I.K., S.M. immunohistochemistry, L.C., S.M. histopathology, S.M, M.K. formal analysis, S.M. writing - original draft, S.M., D.S, H.C., and M.K. review and editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Prof. Izabela Święcicka from the Laboratory of Applied Microbiology, University of Bialystok, for providing the necessary space and equipment for the chosen molecular analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics. Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBang J, Jun M, Lee S, Moon H, Ro SW. Targeting EGFR/PI3K/AKT/mTOR signaling in hepatocellular carcinoma. Pharmaceutics. 2023;15(8):2130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/pharmaceutics15082130\u003c/span\u003e\u003cspan address=\"10.3390/pharmaceutics15082130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhary HB, Mandlik SK, Mandlik DS. Role of p53 suppression in the pathogenesis of hepatocellular carcinoma. World J Gastrointest Pathophysiol. 2023;14(3):46\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4291/wjgp.v14.i3.46\u003c/span\u003e\u003cspan address=\"10.4291/wjgp.v14.i3.46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu G, Lin J, Song G, Chen M. Prognostic significance of CTNNB1 mutation in hepatocellular carcinoma: a systematic review and meta-analysis. Aging. 2023;15(18):9759\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/aging.205047\u003c/span\u003e\u003cspan address=\"10.18632/aging.205047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian LY, Smit DJ, J\u0026uuml;cker M. The role of PI3K/AKT/mTOR signaling in hepatocellular carcinoma metabolism. Int J Mol Sci. 2023;24(3):2652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24032652\u003c/span\u003e\u003cspan address=\"10.3390/ijms24032652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulien I, Hasselblatt P. Diethylnitrosamine-induced liver tumorigenesis in mice. Met Cell Biol. 2021;163:137\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMemon A, Pyao Y, Jung Y, Lee JI, Lee WK. A modified protocol of diethylnitrosamine administration in mice to model hepatocellular carcinoma. Int J Mol Sci. 2020;21(15):5461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21155461\u003c/span\u003e\u003cspan address=\"10.3390/ijms21155461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026ouml;ffler MC, Betz MJ, Blondin DP, Augustin R, Sharma AK, Tseng YH, et al. Challenges in tackling energy expenditure as obesity therapy: From preclinical models to clinical application. Mol Metab. 2021;51:101237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.molmet.2021.101237\u003c/span\u003e\u003cspan address=\"10.1016/j.molmet.2021.101237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonarzewski M, Diamond J. Evolution of basal metabolic rate and organ masses in laboratory mice. Evolution. 1995;49:1239\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1558-5646.1995.tb04450.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1558-5646.1995.tb04450.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaved F, He Q, Davidson LE, Thornton JC, Albu J, Boxt L, et al. Brain and high metabolic rate organ mass: contributions to resting energy expenditure beyond fat-free mass. Am J Clin Nutr. 2010;91(4):907\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3945/ajcn.2009.28512\u003c/span\u003e\u003cspan address=\"10.3945/ajcn.2009.28512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuggiero C, Metter EJ, Melenovsky V, Cherubini A, Najjar SS, Ble A, et al. High basal metabolic rate is a risk factor for mortality: the Baltimore longitudinal study of aging. J Gerontol Biol. 2008;63:698\u0026ndash;706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang CV. Links between metabolism and cancer. Genes Dev. 2012;26:877\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKliemann N, Murphy N, Viallon V, Freisling H, Tsilidis KK, Rinaldi S, et al. Predicted basal metabolic rate and cancer risk in the European prospective investigation into cancer and nutrition. Int J Cancer. 2020;147:648\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1002/ijc.32753\u003c/span\u003e\u003cspan address=\"10.1002/ijc.32753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg JCM, Schooling CM. Effect of Basal Metabolic Rate on cancer: A mendelian randomization study. Front Genet. 2021;12:735541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2021.735541\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2021.735541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaciak S. Cell size, body size and Peto's paradox. BMC Ecol Evol. 2022;22(1):142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12862-022-02096-5\u003c/span\u003e\u003cspan address=\"10.1186/s12862-022-02096-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Barbash O, Diehl JA. Regulation of the cell cycle. In: Mendelsohn J, Gray JW, Howley PM, Israel MA, Thompson CB, editors. The molecular basis of cancer. Elsevier; 2015. pp. 165\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026ouml;rklund M. Cell size homeostasis: Metabolic control of growth and cell division. Biochim Biophys Acta Mol Cell Res. 2019;66(3):409\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbamcr.2018.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.bbamcr.2018.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026ouml;rklund M, Marguerat S, Editorial. Determinants of Cell Size. Front Cell Dev Biol. 2017;5:115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2017.00115\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2017.00115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiettinen TP, Bj\u0026ouml;rklund M. Mitochondrial function and cell size: An allometric relationship. Trends Cell Biol. 2017;27(6):393\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tcb.2017.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tcb.2017.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozłowski J, Konarzewski M, Czarnoleski M. Coevolution of body size and metabolic rate in vertebrates: a life-history perspective. Biol Rev. 2020;95:1393\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/brv.12615\u003c/span\u003e\u003cspan address=\"10.1111/brv.12615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRolfe DF, Brown GC. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol Rev. 1997;77:731\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaciak S, Bonda-Ostaszewska E, Czarnołęski M, Konarzewski M, Kozłowski J. Mice divergently selected for high and low basal metabolic rates evolved different cell size and organ mass. J Evol Biol. 2014;27:478\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jeb.12306\u003c/span\u003e\u003cspan address=\"10.1111/jeb.12306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;lander M, Wegler C, Fl\u0026ouml;rkemeier I, Treyer A, Handin N, Pedersen JM, et al. Hepatocyte size fractionation allows dissection of human liver zonation. J Cell Physiol. 2021;236(8):5885\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcp.30273\u003c/span\u003e\u003cspan address=\"10.1002/jcp.30273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaldwell S, Ikura Y, Dias D, Isomoto K, Yabu A, Moskaluk C, et al. Hepatocellular ballooning in NASH. J Hepatol. 2010;53(4):719\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2010.04.031\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2010.04.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu C, Sheng Y, Zhu E, Ji L, Wang Z. Ferulic acid prevents liver injury induced by Diosbulbin B and its mechanism. Biosci Trends. 2016;10(5):386\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5582/bst.2016.01152\u003c/span\u003e\u003cspan address=\"10.5582/bst.2016.01152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolba R, Kraus T, Liedtke C, Schwarz M, Weiskirchen R. Diethylnitrosamine (DEN)-induced carcinogenic liver injury in mice. Lab Anim. 2015;49(1 Suppl):59\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0023677215570086\u003c/span\u003e\u003cspan address=\"10.1177/0023677215570086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCargnello M, Tcherkezian J, Roux PP. The expanding role of mTOR in cancer cell growth and proliferation. Mutagenesis. 2015;30(2):169\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/mutage/geu045\u003c/span\u003e\u003cspan address=\"10.1093/mutage/geu045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanwar V, Singh A, Bhatt M, Tonk RK, Azizov S, Raza AS, et al. Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Signal Transduct Target Ther. 2023;8(1):375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-023-01608-z\u003c/span\u003e\u003cspan address=\"10.1038/s41392-023-01608-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunst C, Haderer M, Heckel S, Schlosser S, M\u0026uuml;ller M. The p53 family in hepatocellular carcinoma. Transl Cancer Res. 2016;5(6):632\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/tcr.2016.11.79\u003c/span\u003e\u003cspan address=\"10.21037/tcr.2016.11.79\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng YC, Liu XY, Teng L, Ji Q, Wu Y, Li JM, et al. c-Myc inactivation of p53 through the pan-cancer lncRNA MILIP drives cancer pathogenesis. Nat Commun. 2020;11(1):4980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-020-18735-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-18735-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahadiani N, Stephanie M, Perkasa AG, Handjari DR, Krisnuhoni E. p53 expression is associated with tumor stage, grade and subtype in patients with hepatocellular carcinoma. Mol Clin Oncol. 2023;19(1):54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/mco.2023.2650\u003c/span\u003e\u003cspan address=\"10.3892/mco.2023.2650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Zhou Q, Liu J, Zhang W. CTNNB1 alternation is a potential biomarker for immunotherapy prognosis in patients with hepatocellular carcinoma. Front Immunol. 2021;12:759565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2021.759565\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.759565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Liang B, Song X, Wang H, Evert M, Zhou Y, et al. Loss of Apc cooperates with activated oncogenes to induce liver tumor formation in mice. Am J Pathol. 2021;191(5):930\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajpath.2021.01.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ajpath.2021.01.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonarzewski M, Książek A. Determinants of intra-specific variation in basal metabolic rate. J Comp Physiol B. 2013;183(1):27\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00360-012-0698-z\u003c/span\u003e\u003cspan address=\"10.1007/s00360-012-0698-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuczyńska S, Książek A, Maciak S, Brzęk P, Hambly C, Speakman JR, Konarzewski M. Lactation at hot temperature: a test of heat dissipation limitation in mice divergently selected for BMR. Biol Lett 2025;in press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGębczyński AK, Sadowska J, Konarzewski M. Differences in the range of thermoneutral zone between mouse strains: potential effects on translational research. Am J Physiol Regul Integr Comp Physiol. 2024;326:R91\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCornish AJ, Law PJ, Timofeeva M, Palin K, Farrington SM, Palles C, et al. Modifiable pathways for colorectal cancer: a mendelian randomisation analysis. Lancet Gastroenterol Hepatol. 2020;5:55\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2468-1253(19)30294-8\u003c/span\u003e\u003cspan address=\"10.1016/S2468-1253(19)30294-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiro PA, Thomas F, Ujvari B, Beckmann C. A novel perspective suggesting high sustained energy expenditure may be net protective against cancer. Evol Med Public Health. 2022;10:170\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/emph/eoac012\u003c/span\u003e\u003cspan address=\"10.1093/emph/eoac012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawicka D, Maciak S, Sadowska A, Sokołowska E, Gohal S, Guzińska-Ustymowicz K, et al. Metabolic rate and oxidative stress as a risk factors in the development of colorectal cancer. Int J Mol Sci. 2024;25:10713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms251910713\u003c/span\u003e\u003cspan address=\"10.3390/ijms251910713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArthur RS, Mossavar-Rahmani Y, Prentice RL, Shadyab AH, Luo J, Sattari M, et al. The Association of Predicted Resting Energy Expenditure with Risk of Breast Cancer among Postmenopausal Women in the Women's Health Initiative Cohort. Cancer Prev Res (Phila). 2022;15(4):255\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1940-6207.CAPR-21-0467\u003c/span\u003e\u003cspan address=\"10.1158/1940-6207.CAPR-21-0467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudou-Rouquette P, de Moura A, Martinez-Tapia C, Serrano AG, Chahwakilian A, Jouinot A, et al. ELCAPA Study Energy expenditure profiles and the risk of early limiting toxicity in older patients with cancer: The ELCAPA-25 prospective cohort survey. Clin Nutr. 2022;41:1073\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2022.02.016\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2022.02.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattson MP, Longo VD, Harvie M. Impact of intermittent fasting on health and disease processes. Ageing Res Rev. 2017;39:46\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDorling JL, van Vliet S, Huffman KM, Kraus WE, Bhapkar M, Pieper CF, et al. Effects of caloric restriction on human physiological, psychological, and behavioral outcomes: highlights from CALERIE phase 2. Nutr Rev. 2021;79(1):98\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nutrit/nuaa085\u003c/span\u003e\u003cspan address=\"10.1093/nutrit/nuaa085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiettinen TP, Pessa HK, Caldez MJ, Fuhrer T, Diril MK, Sauer U, et al. Identification of transcriptional and metabolic programs related to mammalian cell size. Curr Biol. 2014;24(6):598\u0026ndash;608. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cub.2014.01.071\u003c/span\u003e\u003cspan address=\"10.1016/j.cub.2014.01.071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolfi SC, Chan LL-Y, Qiu J, Tedeschi PM, Bertino JR, Hirshfield KM, et al. The metabolic demands of cancer cells are coupled to their size and protein synthesis rates. Cancer Metab. 2013;1:20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1186/2049-3002-1-20\u003c/span\u003e\u003cspan address=\"10.1186/2049-3002-1-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePessoa J, Martins M, Casimiro S, P\u0026eacute;rez-Plasencia C, Shoshan-Barmatz V. Editorial: altered expression of proteins in cancer: function and potential therapeutic targets. Front Oncol. 2022;12:949139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2022.949139\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.949139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Xie X, Wang B. Deep learning infers clinically relevant protein levels and drug response in breast cancer from unannotated pathology images. Breast Cancer. 2024;10:18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41523-024-00620-y\u003c/span\u003e\u003cspan address=\"10.1038/s41523-024-00620-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu DJ, Aktipis A, Pepper JW. Energy oversupply to tissues: a single mechanism possibly underlying multiple cancer risk factors. Evol Med Public Health. 2019;1:9\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/emph/eoz004\u003c/span\u003e\u003cspan address=\"10.1093/emph/eoz004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeAngelis RA, Markiewski MM, Taub R, Lambris JD. A high-fat diet impairs liver regeneration in C57BL/6 mice through overexpression of the NF-κB inhibitor. IκBα Hepatol. 2005;42:1148\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1002/hep.20879\u003c/span\u003e\u003cspan address=\"10.1002/hep.20879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVetel\u0026auml;inen R, Van Vliet AK, Van Gulik TM. Severe steatosis increases hepatocellular injury and impairs liver regeneration in a rat model of partial hepatectomy. Ann Surg. 2007;245:44\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.sla.0000225253.84501.0e\u003c/span\u003e\u003cspan address=\"10.1097/01.sla.0000225253.84501.0e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun EJ, Wankell M, Palamuthusingam P, McFarlane C, Hebbard L. Targeting the PI3K/Akt/mTOR pathway in hepatocellular carcinoma. Biomedicines. 2021;9(11):1639. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines9111639\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines9111639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdamek A, Kasprzak A. Insulin-like growth factor (IGF) system in liver diseases. Int J Mol Sci. 2018;19(5):1308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms19051308\u003c/span\u003e\u003cspan address=\"10.3390/ijms19051308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan Y, Lu C, Wang J, Li M, Ye S, Wu S, et al. IGF-1 contributes to liver cancer development in diabetes patients by promoting autophagy. Ann Hepatol. 2022;27(4):100697. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.aohep.2022.100697\u003c/span\u003e\u003cspan address=\"10.1016/j.aohep.2022.100697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaskeh MDA, Ghadyani F, Hashemi M, Abbaspour A, Zabolian A, Javanshir S, et al. Biological impact and therapeutic perspective of targeting PI3K/Akt signaling in hepatocellular carcinoma: Promises and Challenges. Pharm Res. 2023;187:106553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.phrs.2022.106553\u003c/span\u003e\u003cspan address=\"10.1016/j.phrs.2022.106553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaciak S, Sawicka D, Sadowska A, Prokopiuk S, Buczyńska S, Bartoszewicz M, et al. Low basal metabolic rate as a risk factor for development of insulin resistance and type 2 diabetes. BMJ Open Diabetes Res Care. 2020;8(1):e001381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjdrc-2020-001381\u003c/span\u003e\u003cspan address=\"10.1136/bmjdrc-2020-001381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell. 2012;149(2):274\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2012.03.017\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2012.03.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua H, Kong Q, Zhang H, Wang J, Luo T, Jiang Y. Targeting mTOR for cancer therapy. J Hematol Oncol. 2019;12:71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1186/s13045-019-0754-1\u003c/span\u003e\u003cspan address=\"10.1186/s13045-019-0754-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng K, Cubero FJ, Nevzorova YA. c-MYC-making liver sick: Role of c-MYC in hepatic cell function, homeostasis and disease. Genes. 2017;8(4):123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes8040123\u003c/span\u003e\u003cspan address=\"10.3390/genes8040123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasty P, Sharp ZD, Curiel TJ, Campisi J. mTORC1 and p53: clash of the gods? Cell Cycle. 2013;2(1):20\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4161/cc.22912\u003c/span\u003e\u003cspan address=\"10.4161/cc.22912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui D, Qu R, Liu D, Xiong X, Liang T, Zhao Y. The cross talk between p53 and mTOR pathways in response to physiological and genotoxic stresses. Front Cell Dev Biol. 2021;9:775507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2021.775507\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.775507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Li B, Sun Q, Wu X, Chen Q, Li Z, et al. Association between APC promoter methylation and clinicopathological features of patients with hepatocellular carcinoma: a meta-analysis with PRISMA guideline. Transl Cancer Res. 2017;6(2):275\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/tcr.2017.03.71\u003c/span\u003e\u003cspan address=\"10.21037/tcr.2017.03.71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavanmard D, Najafi M, Babaei MR, Hadi M, Niya K, Esghaei M, et al. Investigation of CTNNB1 gene mutations and expression in hepatocellular carcinoma and cirrhosis in association with hepatitis B virus infection. Infect Agents Cancer. 2020;15:37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1186/s13027-020-00297-5\u003c/span\u003e\u003cspan address=\"10.1186/s13027-020-00297-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColnot S, Decaens T, Niwa-Kawakita M, Godard C, Hamard G, Kahn A, et al. Liver-targeted disruption of Apc in mice activates beta-catenin signaling and leads to hepatocellular carcinomas. Proc Natl Acad Sci USA. 2004;101(49):17216\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0404761101\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0404761101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeakman J, de Jong JMA, Sinha S, Westerterp KR, Yamada Y, Sagayama H, et al. Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure. Nat Metabol. 2023;5:579\u0026ndash;88.\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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BMR, metabolism, cell size, mTOR, hepatocellular carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-6046205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6046205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAmong several cancer risk factors, the basal metabolism rate (BMR), which constitutes up to 70% of total energy expenditures in humans, may be causally linked with neoplasm development. As BMR reflects the mass of metabolically active organs, being the function of cell size and/or cell number, it may serve as a critical metabolic proxy of cancer susceptibility in the context of cell growth and cell size.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examined the progression and rate of development of chemically induced hepatocellular carcinoma, using lines of mice divergently selected for high or low BMR and differing with respect to both the size of metabolically active organs and their cellular architecture.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe high BMR mouse line developed hepatocellular carcinoma much faster and with a higher progression rate, accompanied by a considerable increase in liver size and hepatocyte enlargement, as compared to the low BMR mouse line. The HBMR mice also manifested an increased expression of metabolism- and cell size-related genes (\u003cem\u003emTOR\u003c/em\u003e, \u003cem\u003ePI3K\u003c/em\u003e, \u003cem\u003ec-myc\u003c/em\u003e, but not \u003cem\u003eIGF-1\u003c/em\u003e), with a simultaneous decrease in the activity of tumor suppressors (\u003cem\u003ep-53\u003c/em\u003e, \u003cem\u003eAPC\u003c/em\u003e) at the beginning of cancerogenic processes, promoting further neoplasm expansion.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePresented results suggest that genetically determined high BMR may additionally burden liver cells via changes in the action of specific genes, leading to higher tumorigenesis.\u003c/p\u003e","manuscriptTitle":"Basal Metabolic Rate shapes the development and progression of hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 19:44:13","doi":"10.21203/rs.3.rs-6046205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T09:56:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T08:38:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T21:29:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134607014248552320480391050206388007276","date":"2025-05-07T07:29:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T08:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281707782990012279259823864761653720592","date":"2025-04-23T13:36:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187706711537683060666491052015109892432","date":"2025-04-23T07:39:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T07:26:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T22:27:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-04-22T10:41:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2f140c6-5d3e-4439-983a-bdc02a40a838","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:12:04+00:00","versionOfRecord":{"articleIdentity":"rs-6046205","link":"https://doi.org/10.1186/s12885-025-14491-4","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-07-01 15:58:17","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-24 19:44:13","video":"","vorDoi":"10.1186/s12885-025-14491-4","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14491-4","workflowStages":[]},"version":"v1","identity":"rs-6046205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6046205","identity":"rs-6046205","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}