Microbiome and Metabolomics in Liver Cancer: Scientific Technology

Short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function

BACKGROUND

Hepatectomy is the first choice for treating liver cancer. However, inflammatory factors, released in response to pain stimulation, may suppress perioperative immune function and affect the prognosis of patients undergoing hepatectomies.

AIM

To determine the short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function.

METHODS

Clinical data from patients with liver cancer admitted to Suzhou Ninth People’s Hospital from January 2020 to December 2023 were retrospectively analyzed. Thirty-five patients underwent laparoscopic hepatectomy for liver cancer (liver cancer resection group) and 35 patients underwent medical image-guided microwave ablation (liver cancer ablation group). The short-term efficacy, complications, liver function, and immune function indices before and after treatment were compared between the two groups.

RESULTS

One month after treatment, 19 patients experienced complete remission (CR), 8 patients experienced partial remission (PR), 6 patients experienced stable disease (SD), and 2 patients experienced disease progression (PD) in the liver cancer resection group. In the liver cancer ablation group, 21 patients experienced CR, 9 patients experienced PR, 3 patients experienced SD, and 2 patients experienced PD. No significant differences in efficacy and complications were detected between the liver cancer ablation and liver cancer resection groups (P > 0.05). After treatment, total bilirubin (41.24 ± 7.35 vs 49.18 ± 8.64 μmol/L, P < 0.001), alanine aminotransferase (30.85 ± 6.23 vs 42.32 ± 7.56 U/L, P < 0.001), CD4⁺ (43.95 ± 5.72 vs 35.27 ± 5.56, P < 0.001), CD8⁺ (20.38 ± 3.91 vs 22.75 ± 4.62, P < 0.001), and CD4⁺/CD8⁺ (2.16 ± 0.39 vs 1.55 ± 0.32, P < 0.001) were significantly different between the liver cancer ablation and liver cancer resection groups.

CONCLUSION

The short-term efficacy and safety of microwave ablation and laparoscopic surgery for the treatment of liver cancer are similar, but liver function recovers quickly after microwave ablation, and microwave ablation may enhance immune function.

Characteristics of microbiome-derived metabolomics according to the progression of alcoholic liver disease

BACKGROUND AND AIM

The prevalence and severity of alcoholic liver disease (ALD) are increasing. The incidence of alcohol-related cirrhosis has risen up to 2.5%. This study aimed to identify novel metabolite mechanisms involved in the development of ALD in patients. The use of gut microbiome-derived metabolites is increasing in targeted therapies. Identifying metabolic compounds is challenging due to the complex patterns that have long-term effects on ALD. We investigated the specific metabolite signatures in ALD patients.

METHODS

This study included 247 patients (heathy control, HC: n = 62, alcoholic fatty liver, AFL; n = 25, alcoholic hepatitis, AH; n = 80, and alcoholic cirrhosis, AC, n = 80) identified, and stool samples were collected. 16S rRNA sequencing and metabolomics were performed with MiSeq sequencer and liquid chromatography coupled to time-of-flight-mass spectrometry (LC-TOF-MS), respectively. The untargeted metabolites in AFL, AH, and AC samples were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Metabolic network classifiers were used to predict the pathway expression of the AFL, AH, and AC stages.

RESULTS

The relative abundance of Proteobacteria was increased and the abundance of Bacteroides was decreased in ALD samples (p = 0.001) compared with that in HC samples. Fusobacteria levels were higher in AH samples (p = 0.0001) than in HC samples. Untargeted metabolomics was applied to quantitatively screen 103 metabolites from each stool sample. Indole-3-propionic acid levels are significantly lower in AH and AC (vs. HC, p = 0.001). Indole-3-lactic acid (ILA: p = 0.04) levels were increased in AC samples. AC group showed an increase in indole-3-lactic acid (vs. HC, p = 0.040) level. Compared with that in HC samples, the levels of short-chain fatty acids (SCFAs: acetic acid, butyric acid, propionic acid, iso-butyric acid, and iso-valeric acid) and bile acids (lithocholic acids) were significantly decreased in AC. The pathways of linoleic acid metabolism, indole compounds, histidine metabolism, fatty acid degradation, and glutamate metabolism were closely associated with ALD metabolism.

CONCLUSIONS

This study identified that microbial metabolic dysbiosis is associated with ALD-related metabolic dysfunction. The SCFAs, bile acids, and indole compounds were depleted during ALD progression.

CLINICAL TRIAL

Clinicaltrials.gov, number NCT04339725.

6,7-Dimethoxycoumarin, Gardenoside and Rhein combination improves non-alcoholic fatty liver disease in rats

ETHNOPHARMACOLOGICAL RELEVANCE

This study explores the potential therapeutic benefits of using a three-component DGR (composed of specific compounds) to target the NLRP3 inflammasome in the context of non-alcoholic fatty liver disease (NAFLD).

AIM OF THE STUDY

To assess the impact of a three-component DGR on NAFLD, specifically examining its effects on liver lipid accumulation, inflammation, and the diversity of intestinal microbial communities.

METHODS

NAFLD was induced in 8-week-old Sprague Dawley rats by feeding them a high-fat emulsion diet every morning for 8 consecutive weeks. Oral administration of DGR or its constituent equivalents in the afternoon. The pharmacological effects of DGR were evaluated using H&E, ORO and ELISA methods to determine the changes in serum and liver tissue indexes of rat-models. Immunohistochemical staining and Western blot were used to assess the interaction between DGR, NLRP3 and IL-1β.

RESULTS

The induction of NAFLD resulted in elevated hepatic triglycerides (TG), total cholesterol (TC), and free fatty acids (FFA). However, these alterations were ameliorated upon administration of DGR. It is noteworthy that DGR exhibited superior efficacy in comparison to its constituent compounds, manifesting augmented antioxidant activity, diminished hepatic damage, and the attenuation of pro-inflammatory factors. Both DGR and its individual monomeric constituents exhibited the capacity to attenuate the activation of the NLRP3 inflammasome in the liver, leading to an amelioration of the pathological characteristics associated with NAFLD. An analysis of the intestinal flora unveiled an elevated abundance of p_Firmicutes (1.1-fold), p_Cyanobacteria (5.76-fold), and p_Verrucomicrobia (5.2-fold), accompanied by a heightened p_Firmicutes to p_Bacteroidetes ratio (5.49-fold).

CONCLUSIONS

In the non-alcoholic fatty liver disease (NAFLD) rat model, the concurrent administration of three-component DGR effectively regulated lipid deposition, suppressed liver inflammation, and restored balance in the intestinal flora, thereby improving NAFLD pathology. These findings propose a promising therapeutic strategy for NAFLD, centered on inhibiting the NLRP3 inflammasome through the use of the three-component DGR.

Purine salvage-associated metabolites as biomarkers for early diagnosis of esophageal squamous cell carcinoma: a diagnostic model-based study

Esophageal squamous cell carcinoma (ESCC) remains an important health concern in developing countries. Patients with advanced ESCC have a poor prognosis and survival rate, and achieving early diagnosis remains a challenge. Metabolic biomarkers are gradually gaining attention as early diagnostic biomarkers. Hence, this multicenter study comprehensively evaluated metabolism dysregulation in ESCC through an integrated research strategy to identify key metabolite biomarkers of ESCC. First, the metabolic profiles were examined in tissue and serum samples from the discovery cohort (n = 162; ESCC patients, n = 81; healthy volunteers, n = 81), and ESCC tissue-induced metabolite alterations were observed in the serum. Afterward, RNA sequencing of tissue samples (n = 46) was performed, followed by an integrated analysis of metabolomics and transcriptomics. The potential biomarkers for ESCC were further identified by censoring gene-metabolite regulatory networks. The diagnostic value of the identified biomarkers was validated in a validation cohort (n = 220), and the biological function was verified. A total of 457 dysregulated metabolites were identified in the serum, of which 36 were induced by tumor tissues. The integrated analyses revealed significant alterations in the purine salvage pathway, wherein the abundance of hypoxanthine/xanthine exhibited a positive correlation with HPRT1 expression and tumor size. A diagnostic model was developed using two purine salvage-associated metabolites. This model could accurately discriminate patients with ESCC from normal individuals, with an area under the curve (AUC) (95% confidence interval (CI): 0.680-0.843) of 0.765 in the external cohort. Hypoxanthine and HPRT1 exerted a synergistic effect in terms of promoting ESCC progression. These findings are anticipated to provide valuable support in developing novel diagnostic approaches for early ESCC and enhance our comprehension of the metabolic mechanisms underlying this disease.

The role of microbiomes in gastrointestinal cancers: new insights

Gastrointestinal (GI) cancers constitute more than 33% of new cancer cases worldwide and pose a considerable burden on public health. There exists a growing body of evidence that has systematically recorded an upward trajectory in GI malignancies within the last 5 to 10 years, thus presenting a formidable menace to the health of the human population. The perturbations in GI microbiota may have a noteworthy influence on the advancement of GI cancers; however, the precise mechanisms behind this association are still not comprehensively understood. Some bacteria have been observed to support cancer development, while others seem to provide a safeguard against it. Recent studies have indicated that alterations in the composition and abundance of microbiomes could be associated with the progression of various GI cancers, such as colorectal, gastric, hepatic, and esophageal cancers. Within this comprehensive analysis, we examine the significance of microbiomes, particularly those located in the intestines, in GI cancers. Furthermore, we explore the impact of microbiomes on various treatment modalities for GI cancer, including chemotherapy, immunotherapy, and radiotherapy. Additionally, we delve into the intricate mechanisms through which intestinal microbes influence the efficacy of GI cancer treatments.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Identification of a prognostic evaluator from glutamine metabolic heterogeneity studies within and between tissues in hepatocellular carcinoma

Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues. Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes. Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC. Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.

Molecular Research in Human Microbiome

Recent evidence has shown that the human microbiome is associated with a wide range of diseases, from non-neoplastic to tumourigenesis, including cancer, inflammation, intestinal damage, etc [...].

Recent advances in understanding brain cancer metabolomics: a review

Regardless of the significant progress made in surgical techniques and adjuvant therapies, brain tumors are a major contributor to cancer-related morbidity and mortality in both pediatric and adult populations. Gliomas represent a significant proportion of cerebral neoplasms, exhibiting diverse levels of malignancy. The etiology and mechanisms of resistance of this malignancy are inadequately comprehended, and the optimization of patient diagnosis and prognosis is a challenge due to the diversity of the disease and the restricted availability of therapeutic options. Metabolomics refers to the comprehensive analysis of endogenous and exogenous small molecules, both in a targeted and untargeted manner, that enables the characterization of an individual's phenotype and offers valuable insights into cellular activity, particularly in the context of cancer biology, including brain tumor biology. Metabolomics has garnered attention in current years due to its potential to facilitate comprehension of the dynamic spatiotemporal regulatory network of enzymes and metabolites that enables cancer cells to adapt to their environment and foster the development of tumors. Metabolic changes are widely acknowledged as a significant characteristic for tracking the advancement of diseases, treatment efficacy, and identifying novel molecular targets for successful medical management. Metabolomics has emerged as an exciting area for personalized medicine and drug discovery, utilizing advanced analytical techniques such as nuclear magnetic resonance spectroscopy (MRS) and mass spectrometry (MS) to achieve high-throughput analysis. This review examines and highlights the latest developments in MRS, MS, and other technologies in studying human brain tumor metabolomics.

A metabolomics approach to the validation of predictive metabolites and phenotypic expression in non-alcoholic fatty liver disease

AIMS

Nonalcoholic fatty liver disease (NAFLD) is becoming more common and severe. Individuals with NAFLD have an altered composition of gut- microbial metabolites. We used metabolomics profiling to identify microbial metabolites that could indicate gut-liver metabolic severity. Noninvasive biomarkers are required for NAFLD, especially for patients at high risk of disease progression.

MAIN METHODS

We compared the stool metabolomes, untargeted metabolomics, and clinical data of 80 patients. Patients with nonalcoholic fatty liver (NAFL: n = 16), nonalcoholic steatohepatitis (NASH: n = 26), and cirrhosis (n = 19) and healthy control individuals (HC: n = 19) were enrolled. The identified metabolites in NAFLD were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography coupled to time-of-flight-mass spectrometry (LC-TOF-MS) were used to analyze metabolites.

KEY FINDINGS

Untargeted metabolomics was used to identify and quantify 103 metabolites. Principal component analysis (PCA) was used to assess the metabolic discrimination of NAFL, NASH, and cirrhosis. Short-chain fatty acids (SCFA) levels were significantly lower in NAFLD patients, including those of acetate (p = 0.03), butyrate (p = 0.0008), and propionate. The stool cholic acid (p = 0.001) level was significantly increased in NAFLD patients. Palmitoylcarnitine and l-carnitine levels were significantly increased in NASH and cirrhosis patients. The phenotypic expression of these metabolites was linked to β-oxidation.

SIGNIFICANCE

We demonstrated a distinct metabolome profile in NAFLD patients with NAFL, NASH, and cirrhosis. We also discovered that the expression of certain metabolites and metabolic pathways was linked to NAFLD.

Primary Liver Cancers: Connecting the Dots of Cellular Studies and Epidemiology with Metabolomics

Liver cancers are rising worldwide. Between molecular and epidemiological studies, a research gap has emerged which might be amenable to the technique of metabolomics. This review investigates the current understanding of liver cancer's trends, etiology and its correlates with existing literature for hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA) and hepatoblastoma (HB). Among additional factors, the literature reports dysfunction in the tricarboxylic acid metabolism, primarily for HB and HCC, and point mutations and signaling for CCA. All cases require further investigation of upstream and downstream events. All liver cancers reported dysfunction in the WNT/β-catenin and P13K/AKT/mTOR pathways as well as changes in FGFR. Metabolites of IHD1, IDH2, miRNA, purine, Q10, lipids, phosphatidylcholine, phosphatidylethanolamine, acylcarnitine, 2-HG and propionyl-CoA emerged as crucial and there was an attempt to elucidate the WNT/β-catenin and P13K/AKT/mTOR pathways metabolomically.