Combinatorial antitumor effects of amino acids and epigenetic modulations in hepatocellular carcinoma cell lines

UPLC-Q-TOF-MS-based unbiased serum metabolomics investigation of cholangiocarcinoma

Objective

Cholangiocarcinoma (CCA) is a highly aggressive malignancy, and early diagnosis remains challenging. Metabolic biomarkers are increasingly recognized as promising tools for the early detection of cancer. However, a comprehensive exploration of metabolic alterations in CCA, especially from a global metabolic perspective, has yet to be fully realized. To identify reliable metabolic markers for the early diagnosis of CCA and to explore its potential pathogenesis through an in-depth analysis of global metabolism.

Methods

Serum samples from 30 CCA patients and 31 healthy individuals were analyzed using an unbiased UPLC-Q-TOF-MS based metabolomics approach. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were applied to identify potential biomarkers. High-resolution MS/MS and available standards were used to further confirm the identified metabolites. A systematic metabolic pathway analysis was conducted to interpret the biological roles of these biomarkers and explore their relevance to CCA progression.

Results

A total of 25 marker metabolites were identified, including lysophosphatidylcholines (LysoPCs), phosphatidylcholines (PCs), organic acids, sphinganine, and ketoleucine. These metabolites effectively distinguished CCA patients from healthy controls, with an AUC of 0.995 for increased biomarkers and 0.992 for decreased biomarkers in positive mode. In negative mode, the AUC for increased and decreased biomarkers was 0.899 and 0.976, respectively. The metabolic pathway analysis revealed critical biological functions linked to these biomarkers, offering insights into the molecular mechanisms underlying CCA initiation and progression.

Conclusion

This study identifies novel metabolic biomarkers for the early diagnosis of CCA and provides a deeper understanding of the metabolic alterations associated with the disease. These findings could contribute to the development of diagnostic strategies and therapeutic interventions for CCA.

Gene Selection of Methionine-Dependent Melanoma and Independent Melanoma by Variable Selection Using Tensor Decomposition

Methionine is an essential amino acid. Dietary methionine restriction is associated with decreased tumor growth in preclinical studies and extended lifespans in animal models. The mechanism by which methionine restriction inhibits tumor growth while sparing normal cells is not fully understood. In this study, we applied tensor decomposition-based feature extraction for gene selection from the gene expression profiles of two cell lines of RNA sequencing. We compared two human melanoma cell lines, A101D and MeWo. A101D is a typical cancer cell line that exhibits methionine dependence. MeWo is a methionine-independent cell line. We used the application on R, TDbasedUFE, to perform an enrichment analysis of the selected gene set. Consequently, concordance with existing research on the differences between methionine-dependent melanoma and methionine-independent melanoma was confirmed. Targeting methionine metabolism is considered a promising strategy for treating melanoma and other cancers.

Double-Edge Effects of Leucine on Cancer Cells

Leucine is an essential amino acid that cannot be produced endogenously in the human body and therefore needs to be obtained from dietary sources. Leucine plays a pivotal role in stimulating muscle protein synthesis, along with isoleucine and valine, as the group of branched-chain amino acids, making them one of the most popular dietary supplements for athletes and gym-goers. The individual effects of leucine, however, have not been fully clarified, as most of the studies so far have focused on the grouped effects of branched-chain amino acids. In recent years, leucine and its metabolites have been shown to stimulate muscle protein synthesis mainly via the mammalian target of the rapamycin complex 1 signaling pathway, thereby improving muscle atrophy in cancer cachexia. Interestingly, cancer research suggests that leucine may have either anti-cancer or pro-tumorigenic effects. In the current manuscript, we aim to review leucine's roles in muscle protein synthesis, tumor suppression, and tumor progression, specifically summarizing the molecular mechanisms of leucine's action. The role of leucine is controversial in hepatocellular carcinoma, whereas its pro-tumorigenic effects have been demonstrated in breast and pancreatic cancers. In summary, leucine being used as nutritional supplement for athletes needs more attention, as its pro-oncogenic effects may have been identified by recent studies. Anti-cancer or pro-tumorigenic effects of leucine in various cancers should be further investigated to achieve clear conclusions.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence

Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite's individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model's predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.

Glutamine metabolic reprogramming in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a lethal disease with limited management strategies and poor prognosis. Metabolism alternations have been frequently unveiled in HCC, including glutamine metabolic reprogramming. The components of glutamine metabolism, such as glutamine synthetase, glutamate dehydrogenase, glutaminase, metabolites, and metabolite transporters, are validated to be potential biomarkers of HCC. Increased glutamine consumption is confirmed in HCC, which fuels proliferation by elevated glutamate dehydrogenase or upstream signals. Glutamine metabolism also serves as a nitrogen source for amino acid or nucleotide anabolism. In addition, more glutamine converts to glutathione as an antioxidant in HCC to protect HCC cells from oxidative stress. Moreover, glutamine metabolic reprogramming activates the mTORC signaling pathway to support tumor cell proliferation. Glutamine metabolism targeting therapy includes glutamine deprivation, related enzyme inhibitors, and transporters inhibitors. Together, glutamine metabolic reprogramming plays a pivotal role in HCC identification, proliferation, and progression.

Exosome-like Nanovesicles Derived from the Mucilage of Pinctada Martensii Exhibit Antitumor Activity against 143B Osteosarcoma Cells

Osteosarcoma is prone to metastasis and has a low long-term survival rate. The drug treatment of osteosarcoma, side effects of treatment drugs, and prognosis of patients with lung metastasis continue to present significant challenges, and the efficacy of drugs used in the treatment of osteosarcoma remains low. The development of new therapeutic drugs is urgently needed. In this study, we successfully isolated Pinctada martensii mucilage exosome-like nanovesicles (PMMENs). Our findings demonstrated that PMMENs inhibited the viability and proliferation of 143B cells, induced apoptosis, and inhibited cell proliferation by suppressing the activation of the ERK1/2 and Wnt signaling pathways. Furthermore, PMMENs inhibited cell migration and invasion by downregulating N-cadherin, vimentin, and matrix metalloprotease-2 protein expression levels. Transcriptomic and metabolomic analyses revealed that differential genes were co-enriched with differential metabolites in cancer signaling pathways. These results suggest that PMMENs may exert anti-tumor activity by targeting the ERK1/2 and Wnt signaling pathways. Moreover, tumor xenograft model experiments showed that PMMENs can inhibit the growth of osteosarcoma in mice. Thus, PMMENs may be a potential anti-osteosarcoma drug.

Global regulatory factor AaLaeA upregulates the production of antitumor substances in the endophytic fungus Alternaria alstroemeria

The global regulatory factor LaeA has been shown to be involved in the biosynthesis of secondary metabolites in various fungi. In a previous work, we isolated an endophytic fungus from Artemisia annua, and its extract had a significant inhibitory effect on the A549 cancer cell line. Phylogenetic analysis further identified the strain as Alternaria alstroemeria. Overexpression of AalaeA gene resulted in significantly increased antitumor activity of this strain's extract. The 3-(4, 5- dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide assay results showed that the inhibition rate of the AalaeA^OE29 mutant extract on A549 cancer cells was significantly higher than that of the WT extract, as the IC₅₀ decreased from 195.0 to 107.4 μg/ml, and the total apoptosis rate was enhanced. Overexpression of the AalaeA gene significantly increased the contents of myricetin, geraniol, ergosterol, and 18 other antitumor compounds as determined by metabolomic analysis. Transcriptomic analysis revealed significant changes in 95 genes in the mutant strain, including polyketide synthases, nonribosomal peptide synthases, cytochrome P450s, glycosyltransferases, acetyl-CoA acetyltransferases, and others. These results suggested that AaLaeA mediated the antitumor activity of the metabolites in A. alstroemeria by regulating multiple metabolic pathways.