Clinical and biochemical features of studied populations
We analyzed the metabolic composition of serum obtained from 249 Caucasian patients divided into a discovery set comprising patients with MAFLD-HCC with pre-diagnosed NASH (n=27), patients with alcohol- or viral-related HCC (AV-HCC) (n=32), and non-cancerous controls (n=137). These controls included 44 healthy individuals (35 healthy subjects reported previously18 and 9 bariatric surgery patients with NAS<3, and fibrosis score <2) and 93 morbidly obese MAFLD patients awaiting bariatric surgery (OB-MAFLD). Furthermore, we independently validated our findings in serum from 37 patients with MAFLD and in plasma from 16 patients with MAFLD-HCC. Importantly, all MAFLD-HCC patients had no prior history of viral hepatitis or excessive alcohol consumption. The clinical, pathological, and biochemical features of all patients are summarized in Table 1 and Supp. Fig. 1. We observed a significant difference in mean age (MAFLD-HCC patients were significantly older compared to CTRL, OB-MAFLD and AV-HCC), BMI (significantly higher in OB-MAFLD compared to CTRL, AV-HCC and MAFLD-HCC), as well as male:female ratio (significantly higher prevalence of HCC among males, but equal between MAFLD-HCC and AV-HCC). Nevertheless, unsupervised principal component analysis (PCA) showed that the metabolic profiles grouped independently of these covariates (Supp. Fig. 2). Additionally, the presence of underlying diabetes, cirrhosis, and level of fibrosis were not confounders. Further, the analysis of alpha-fetoprotein (AFP) or other liver biochemical features (alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase (AP), bilirubin, albumin and prothrombin activity) to follow the liver function showed extensive variability already in non-cancerous patients, but generally remained within the reference ranges (Supp. Fig. 1). As such, there markers presented limited potential for diagnosing MAFLD-HCC (Table 2). To investigate whether metabolomic changes in MAFLD-HCC are etiology-specific, we compared the profiles of MAFLD-HCC patients and 32 patients with AV-HCC. As such, the metabolite profiles of patients with AV-HCC showed significant overlap, but a clear separation from the profiles of patients with MAFLD-HCC (Supp. Fig. 2). Compared to patients with AV-HCC, MAFLD-HCC patients were older (p<0.05) and less likely to develop HCC on a cirrhotic background (respectively 90% compared to 30%, p=0.035). However, no significant differences were observed between MAFLD-HCC and AV-HCC in measurements of the liver function (AFP, AP, ALT, GGT, and bilirubin) or diabetes. Interestingly, tumors obtained from MAFLD-HCC patients were larger in size compared to tumors from patients with AV-HCC (p=0.0006) and more frequently displayed microvascular invasion (41% MAFLD-HCC compared to 20% AV-HCC, p=0.016).
MAFLD-HCC patients present a disparate serum metabolome
To establish a serum-based metabolomic landscape of MAFLD-HCC, we performed detailed metabolomics using a comprehensive library of 1,295 metabolites covering amino acids (AA), glycerophospholipids, fatty acyls, sterols, sphingolipids, and glycerolipids. In total, we detected 470 metabolites, of which 43 were excluded after correcting for age, gender, and BMI, before univariate and multivariate analyses.
As such, sparse partial least squares discriminant analysis (sPLS-DA) revealed that MAFLD-HCC patients metabolically are the most distinct group compared to controls (CTRL and OB-MAFLD) and patients with AV-HCC (Fig. 1A). Besides, MAFLD-HCC patients were the most dissimilar using unsupervised hierarchical clustering (Fig. 1B). Interestingly, AV-HCC and MAFLD-HCC subgroups showed low similarity and clustered far apart (Fig. 1A), suggesting that unique metabolic programs can be driven by different etiologies.
Unique metabolomic profile of MAFLD-HCC patients
To investigate differences in the metabolic profiles between MAFLD-HCC patients and the other groups, we performed a series of pair-wise tests. As such, orthogonal partial least squares discriminant analysis (oPLS-DA) showed substantial separation of MAFLD-HCC patients from CTRL (R2X=0.178, R2Y=0.751, Q2=0.723) (Fig. 2A). The differential expression analysis identified 274 significantly different (false-discover rate (FDR) corrected p<0.05) metabolites (DEMs), among which 152 metabolites were depleted and 122 metabolites were increased (Fig. 2B, Supp. Table 1A). Next, we performed pathway overrepresentation analysis of the depleted metabolites using integrated molecular pathway level analysis (IMPaLA)20 and identified linoleic acid metabolism and G protein-coupled receptor (GPCR) signaling as the most impaired networks (Supp. Table 1B). Contrary, cholesterol synthesis, membrane fluidity and trafficking, and glycerophospholipid metabolism were among the most upregulated pathways (Supp. Table 1C). In addition to relate individual metabolites to processes, we compared unique classes of metabolites with the same chemical characteristics. As such, we defined a unique depletion of acylcarnitines (AC), sterol lipids (ST), and fatty acids (FA; especially oxidized fatty acids (oxFA) and omega-6 FA) in MAFLD-HCC, while saturated triglycerides (TG) were upregulated (Fig. 2C, Supp. Fig. 3). Furthermore, we utilized Bioinformatics Methodology For Pathway Analysis (BioPAN)21 for lipid pathway enrichment analysis. We observed a significant activation of reactions converting sphingomyelins (SM) to ceramides (Cer), a process catalyzed by sphingomyelin phosphodiesterases (SMPD2 and SMPD3), as well as phosphatidylcholines (PC) to diglycerides (DG), which is catalyzed by the sphingomyelin synthases (SGMS1 and SGMS2) (Supp. Fig. 4A). Similarly, significant alterations were revealed in the activity of FA desaturases and elongases with specific activation of fatty acid desaturase 1 (FADS1) and impairment in FADS2, stearoyl-CoA desaturase 1 (SCD1) and elongation of very long chain fatty acid (ELOVL) elongases (ELOVL2, and ELOVL5) (Supp. Fig. 6A).
Next, we compared OB-MAFLD and MAFLD-HCC patients. Indeed, oPLS-DA analysis showed moderate separation (R2X=0.173, R2Y=0.715, Q2=0.697) (Fig. 2D). The differential expression analysis identified 316 DEMs with 154 metabolites upregulated and 162 downregulated (Fig, 2E, Supp. Table 2A). The overrepresentation analysis identified cholesterol and BA metabolism, aminoacyl-tRNA biosynthesis, as well as protein and glucose metabolism as significantly upregulated in MAFLD-HCC compared to OB-MAFLD. Contrary, FA biosynthesis and GPCR signaling were downregulated (Supp. Table 2B-C). Furthermore, OB-MAFLD significantly differed from MAFLD-HCC in specific classes of metabolites. As such, AA, BA, Cer, TG, PC, and phosphatidylethanolamines (PE) were all significantly upregulated in MAFLD-HCC compared to OB-MAFLD. Contrary, FA and AC were reduced in MAFLD-HCC (Fig. 2C, Supp. Fig. 3). Moreover, the pathway enrichment analysis identified two reaction paths (SM®Cer and DG®PE) as the most dynamic, whereas the opposite direction (Cer®SM and PE®PC®DG) was repressed (Supp. Fig. 4B). Moreover, ELOVL3 and ELOVL6 presented an increased activity, whereas ELOVL2, SCD1, and SCD3 were suppressed (Supp. Fig. 5B). Contrary, reactions catalyzed by FADS1, FADS2, and ELOVL5 displayed a mixed activity.
Lastly, we compared the metabolomes of MAFLD-HCC to HCCs with alcohol and/or viral etiology (AV-HCC). The oPLS-DA model showed moderate separation (R2X=0.171, R2Y=0.739, Q2=0.694) (Fig. 2F). As expected, the AV-HCC group showed greater heterogeneity compared to MAFLD-HCC, which partly can be due to the mixed etiology. Differential expression analysis identified 257 DEMs (125 downregulated and 132 upregulated) in MAFLD-HCC compared to AV-HCC (Fig. 2G, Supp. Table 3A), showing an overall impairment in the free FA (FFA) biosynthesis. Contrary, insulin resistance, glycerophospholipid, and choline metabolism were upregulated in MAFLD-HCC (Supp. Table 3B-C). Next, we compared metabolic classes between MAFLD-HCC and AV-HCC patients and observed significant depletion of AC, BA, FA, ST, lysophosphatidylethanolamines (LPE), and phosphatidylinositols (PI) in MAFLD-HCC. Contrary, cholesteryl esters (ChoE), SM, Cer, and TG were all increased in MAFLD-HCC compared to AV-HCC (Fig. 2C, Supp. Fig. 3). Moreover, lipid pathway enrichment analysis showed an elevated reaction activity in the path SM®Cer (Supp. Fig. 4C) with the enzyme activities of FADS2, ELOVL5, and ELOVL2 significantly reduced (Supp. Fig. 5C).
Taken together, the serum of MAFLD-HCC patients is characterized by a significant depletion of FA reflective of a significantly lower FA biosynthesis with decreased FA desaturase and elongase activities. A depletion in both AC and ST with concurrent higher TG and Cer abundance is suggestive of a unique metabolic reprogramming in MAFLD-HCC patients. The altered SM:Cer ratio could be the result of an increased activity in the enzymes SMPD2 and SMPD3 or reduced activity of SGMS1, SGMS2, CERT1 in MAFLD-HCC patients. A simplified association between the lipid classes and their deregulation in MAFLD-HCC is presented in Fig. 2H.
Diagnostic potential of serum metabolomics
Serum metabolomics has been successfully used as a diagnostic tool to discriminate liver diseases17,18. Here, we investigated the potential of distinguishing MAFLD-HCC not only from healthy individuals and MAFLD patients, but also from AV-HCC. Thus, to generate a predictive metabolite signature, we first used receiver operating characteristic (ROC) curves and calculated area under the curve (AUC) for each metabolite as a contrast test between MAFLD-HCC and the respective comparative groups (CTRL, OB-MAFLD, and AV-HCC). As such, 89 metabolites presented an AUC>0.75 distinguishing MAFLD-HCC patients from the other control patients (healthy or disease). Among the metabolites in the DEM signature, 14 metabolites presented a superior AUC>0.9 in all contrast tests (Table 2). These metabolites and their fold change compared to CTRL are presented in Fig. 3A. Importantly, these 14 metabolites individually present AUCs markedly superior to alpha-fetoprotein (AFP: AUCMAFLD-HCC vs CTRL=0.791 and AUCMAFLD-HCC vs AV-HCC=0.614) and all biochemical measurements for the liver function (ALT: AUCMAFLD-HCC vs CTRL=0.776, AUCMAFLD-HCC vs MAFLD=0.733, and AUCMAFLD-HCC vs AV-HCC=0.570 or GGT: AUCMAFLD-HCC vs CTRL= 0.927, AUCMAFLD-HCC vs MAFLD=0.865, and AUCMAFLD-HCC vs AV-HCC=0.562), discriminating MAFLD-HCCs from all controls and HCCs with a different etiology.
Next, we assessed if a combination of the 14 metabolites would increase the diagnostic potential. As such, we employed support vector machine (SVM) modeling and determined that a panel of 5 metabolites yielded the optimal predictive accuracy (Supp. Fig. 6A). Indeed, the model based on the 5 metabolites reached an AUC>0.98 (Fig. 3B) for any of the contrasts (compared to all controls and AV-HCC) with a predictive accuracy greater than 90% (Fig. 3C). Importantly, the accuracy of the diagnostic panel was confirmed in the validation set and a model performance with an AUC of 0.91, including matching MAFLD-HCC patients according to BMI (Supp. Fig. 6B).
Validation and quantification of diagnostic metabolites
To reinforce the clinical relevance of the metabolite panel in the diagnosis of MAFLD-HCC patients, we wanted to validate and quantify the abundance of each metabolite. Also, we established their reference concentration range. Among the 14 metabolites with the greatest diagnostic value, only 10 of them have commercial standards available. Also, each metabolite needs to be detected within the linear range, which allows for absolute quantification. As such, we established the abundance of metabolites in the validation set (Fig. 4A). All metabolites except PC(0:0/22:5) showed a similar trend as in the discovery set, but overall with great variability. Moreover, we utilized the quantification to generate linear regression models estimating the concentration levels in the discovery set. The linear dependence and estimated concentrations are presented in Supp. Table 4. Lastly, among the validated metabolites in the panel, we selected an optimal set (linoleic acid, osbond acid, monounsaturated fatty acid MUFA (14:1n-5trans), and PC(18:2/0:0)) to build the MAFLD-HCC Diagnostic Score (MHDS). The MHDS is dependent on the absolute concentration level of each metabolite in the score. The MHDS performed with an AUC>0.75 for any given contrast (Fig. 4B-C). Finally, in the combined (discovery and validation) cohort, we established the odds ratio and relative risk for the MHDS (at cut-off value 0) based on the clinical and biochemical features. Taken together, the MDHS>0 was predictive of MAFLD-HCC with the highest odds ratio OR=97.55 (95% CI=16.40 to 999.7) (Fig. 4D) and relative risk RR=1.67 (95% CI=1.45 to 1.99) (Fig. 4E).
The serum lipidome landscape reflects the progression to MAFLD-HCC
We investigated whether the serum lipidome reflects the progressive nature of MAFLD-HCC development. To that end, we first performed pair-wise contrasts between each group (Fig. 5A) to establish the trajectory of disease progression. Then, we employed a pattern hunter approach with Pearson correlation to designate the metabolic rearrangements in the development of the disease (Fig. 5B). A total of 412 lipids were detected in the discovery and validation MAFLD samples. As such, we independently compared OB-MAFLD (bariatric surgery) and MAFLD (non-bariatric surgery) patients with healthy CTRLs, resulting in 257 DEMs distinguishing OB-MAFLD from CTRLs (Fig. 5A). Likewise, in MAFLD, we defined a total of 266 DEMs compared to the CTRL group (Fig. 5B), suggesting that both OB-MAFLD and MAFLD patients experience a significant metabolic rearrangement compared to healthy individuals. Indeed, a total of 138 metabolites were significantly different from CTRL and shared similar directionality, showing a significant depletion of AC, ChoE, and LPC (Fig. 5C). Contrary, DG and TG are among metabolites progressively upregulated in MAFLD patient groups. Interestingly, omega-6 FA and Cer were among deregulated metabolite classes.
Consequently, we next compared OB-MAFLD and MAFLD patients with MAFLD-HCC detecting 297 and 136 DEMs, respectively (Fig. 5D-E). As such, we noticed a second significant metabolic shift from MAFLD to MAFLD-HCC. Notably, OB-MAFLD samples were significantly different, suggesting that the disease progression is CTRL→OB-MAFLD→MAFLD→MAFLD-HCC. Following this disease progression, we defined a total of 83 metabolites significantly different from MAFLD-HCC that shared the same directionality. This metabolic shift included an increase in PC and BA levels as well as depletion of AC, FA, and SM levels (Fig. 5F). Interestingly, TG were higher in the serum of MAFLD-HCC when compared to OB-MAFLD (patients awaiting bariatric surgery), but diminished when compared to MAFLD patients, who were significantly leaner.
To further explore the OB-MAFLD and MAFLD differences, we directly compared these groups. First, the MAFLD patients were significantly older and have a lower BMI compared to OB-MAFLD (Supp. Fig. 1). Also, 76% of MAFLD patients were classified as NASH (biopsy proven) compared to only 12% among OB-MAFLD (Table 1, Fisher’s test p<0.0001). As such, we detected 323 DEMs reflecting a complete change in the lipidome landscape of these patient groups (Supp. Fig. 7). FA, AC, and ST metabolites were significantly diminished in MAFLD compared to OB-MAFLD. Contrary, SM, Cer, DG, TG, PE, and PC were all significantly higher in MAFLD. Interestingly, phosphatidylethanolamine N-methyltransferase (PEMT), the only enzyme catalyzing the reaction chain PE→PC→DG, and choline/ethanolamine phosphotransferase 1 (CEPT1), catalyzing the conversion of DG to PE are the only enzymes deregulated between the major lipid subclasses. As expected, we also observed significant differences in the FA reaction chain (Supp. Fig. 8).
Finally, we used pattern hunting to identify metabolites significantly associated with the full progression from healthy individuals to HCC (CTRL→OB-MAFLD→MAFLD→MAFLD-HCC axis) (Fig. 5G). As such, we found a total of 169 lipids progressively altered (FDR p<0.05, r>0.3; 100 DEMs negatively, and 69 positively) following this axis (Supp. Table 5). The negatively correlated metabolites include AC, ChoE, PUFA, LPC, and ST subclasses. Among the metabolites positively correlated, we found an overrepresentation of TG (47 out of 69 metabolites). Importantly, 18 TG significantly correlate with increasing NAS and fibrosis scores in OB-MAFLD patients, suggesting their importance in the progressive deterioration of the liver (Fig. 5H). Lastly, we tested whether any of these metabolites correlated with tumor size, recurrence, microvascular invasion or liver cirrhosis in MAFLD-HCC patients, however, none of the metabolites reached statistical significance. This suggests that these metabolites are associated with MAFLD-HCC, but not directly involved the progression axis.