Roles of fatty acid metabolism in tumourigenesis: Beyond providing nutrition (Review)

Combining fecal microbiome and metabolomics reveals diagnostic biomarkers for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most predominant subtypes of esophageal cancer. The characteristics of the gut microbiome and its metabolites from patients with ESCC have not been adequately studied and discussed. In this study, 40 fecal samples (20 from ESCC patients and 20 from healthy controls) were analyzed by 16S rRNA gene sequencing and untargeted metabolomics. The data sets were analyzed individually and synthesized using various bioinformatics methods. Alpha and beta diversity indicated significant differences in microbial diversity and abundance between ESCC and healthy control feces. At the genus level, the abundance of Phascolarctobacterium, Sutterella, and Streptococcus was significantly increased in ESCC. At the genus level, linear discriminant analysis effect size identified two biomarkers: Bacteroides_stercoris and Prevotella_copri. Untargeted metabolomics analysis revealed 307 differential metabolites between ESCC and healthy control feces, with indoles and derivatives, tropane alkaloids, lipids, and lipid-like molecules in higher relative abundance in ESCC feces than in healthy control feces. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that unsaturated fatty acids (FAs), ascorbate and aldarate metabolism, and hypoxia-inducible factor 1 signaling pathway were significantly associated with differential metabolite. Phenylethanolamine and despropionyl p-fluoro fentanyl could be used as reliable biomarkers to differentiate ESCC from healthy control. The correlation analysis showed that Prevotella may be involved in the synthesis of fatty acyl, carboxylic acids and derivatives, benzenes and substituted derivatives, organic oxygenates, and indoles and derivatives as metabolites. Fusicatenibacter and Lachnospira may be involved in the degradation of indoles and derivatives. Alistipes, Agathobacter, and Parabacteroides may be involved in the synthesis of indoles and derivatives with strong contributions. There is an intricate relationship between the gut microbiome and the levels of several metabolites (e.g., fatty acyls, carboxylic acids and derivatives, indoles, and derivatives). Microbial-associated metabolites can be used as diagnostic biomarkers in therapeutic exploration. Further analysis revealed that Prevotella, Alistipes, Agathobacter, and Parabacteroides might promote ESCC by regulating the synthesis of indoles and their derivatives. The results of this study provide favorable evidence for the early diagnosis of ESCC and subsequent individualized treatment and targeted interventions.IMPORTANCEWe describe for the first time the differences in fecal microbiome composition and metabolites between patients with esophageal squamous cell carcinoma (ESCC) and healthy controls by 16S rRNA gene sequencing and untargeted metabolomics. The results of this study provide a favorable basis for the early diagnosis of ESCC and subsequent targeted interventional therapy.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Clinical Significance of Carnitine in the Treatment of Cancer: From Traffic to the Regulation

Metabolic reprogramming is a common hallmark of cancer cells. Cancer cells exhibit metabolic flexibility to maintain high proliferation and survival rates. In other words, adaptation of cellular demand is essential for tumorigenesis, since a diverse supply of nutrients is required to accommodate tumor growth and progression. Diversity of carbon substrates fueling cancer cells indicate metabolic heterogeneity, even in tumors sharing the same clinical diagnosis. In addition to the alteration of glucose and amino acid metabolism in cancer cells, there is evidence that cancer cells can alter lipid metabolism. Some tumors rely on fatty acid oxidation (FAO) as the primary energy source; hence, cancer cells overexpress the enzymes involved in FAO. Carnitine is an essential cofactor in the lipid metabolic pathways. It is crucial in facilitating the transport of long-chain fatty acids into the mitochondria for β-oxidation. This role and others played by carnitine, especially its antioxidant function in cellular processes, emphasize the fine regulation of carnitine traffic within tissues and subcellular compartments. The biological activity of carnitine is orchestrated by specific membrane transporters that mediate the transfer of carnitine and its derivatives across the cell membrane. The concerted function of carnitine transporters creates a collaborative network that is relevant to metabolic reprogramming in cancer cells. Here, the molecular mechanisms relevant to the role and expression of carnitine transporters are discussed, providing insights into cancer treatment.

Key Molecules of Fatty Acid Metabolism in Gastric Cancer

Fatty acid metabolism is closely linked to the progression of gastric cancer (GC), a very aggressive and life-threatening tumor. This study examines linked molecules, such as Sterol Regulatory Element-Binding Protein 1 (SREBP1), ATP Citrate Lyase (ACLY), Acetyl-CoA Synthases (ACSs), Acetyl-CoA Carboxylase (ACC), Fatty Acid Synthase (FASN), Stearoyl-CoA Desaturase 1 (SCD1), CD36, Fatty Acid Binding Proteins (FABPs), and Carnitine palmitoyltransferase 1 (CPT1), as well as their latest studies and findings in gastric cancer to unveil its core mechanism. The major enzymes of fatty acid de novo synthesis are ACLY, ACSs, ACC, FASN, and SCD1, while SREBP1 is the upstream molecule of fatty acid anabolism. Fatty acid absorption is mediated by CD36 and FABPs, and fatty acid catabolism is mediated by CPT1. If at all possible, we will discover novel links between fatty acid metabolism and a prospective gastric cancer target.

MS4A1 as a Potential Independent Prognostic Factor of Breast Cancer Related to Lipid Metabolism and Immune Microenvironment Based on TCGA Database Analysis

Background

Lipid metabolism has been proved to be related to the prognosis of breast cancer patients in previous studies, and the tumor immune microenvironment (TIME) plays an important role in tumorigenesis and development, but the dynamic regulation of these is still a challenge.

Material/Methods

This study used lipid metabolism-related pathways to score the gene expression of 980 breast cancer patients in the TCGA database. We used 4 pathways in HALLMARK related to lipid metabolism to score the genes in the database. The differentially expressed genes (DEGs) were further analyzed through survival analysis and Cox regression analysis, and MS4A1, which is associated with better prognosis, was finally determined to be a predictor. In-depth analysis found that MS4A1 was negatively correlated with patient age, clinical stage, tumor size, and distant metastasis. In the MS4A1 high-expression group, most genes were enriched in immune-related pathways, and CIBERSORT analysis found that MS4A1 expression was positively correlated with the abundance of 10 kinds of immune cells, such as CD8⁺T cells, which are related to the active immune status.

Results

Our results suggest that MS4A1 expression can indicate the situation of lipid metabolism in breast cancer patients and reflect the status of the immune microenvironment.

Conclusions

MS4A1 has the potential to be an independent indicator of prognosis. Since the expression of MS4A1 is also related to the immune checkpoint mutation burden, detecting its expression level can also provide guidance for choosing treatment options.

Cancer as a Metabolic Disorder

Cancer has long been considered a genetic disease characterized by a myriad of mutations that drive cancer progression. Recent accumulating evidence indicates that the dysregulated metabolism in cancer cells is more than a hallmark of cancer but may be the underlying cause of the tumor. Most of the well-characterized oncogenes or tumor suppressor genes function to sustain the altered metabolic state in cancer. Here, we review evidence supporting the altered metabolic state in cancer including key alterations in glucose, glutamine, and fatty acid metabolism. Unlike genetic alterations that do not occur in all cancer types, metabolic alterations are more common among cancer subtypes and across cancers. Recognizing cancer as a metabolic disorder could unravel key diagnostic and treatments markers that can impact approaches used in cancer management.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes in Gastric Cancer

Background: Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism–related genes in GC patients.

Materials and Methods: Two GC datasets from the Gene Expression Atlas, GSE62254 (n = 300) and GSE26942 (n = 217), were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes was evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors.

Results: A risk predictive score model was established based on the expression of 19 lipid metabolism–related genes (LPL, IPMK, PLCB3, CDIPT, PIK3CA, DPM2, PIGZ, GPD2, GPX3, LTC4S, CYP1A2, GALC, SGMS1, SMPD2, SMPD3, FUT6, ST3GAL1, B4GALNT1, and ACADS). The time-dependent ROC analysis revealed that the risk predictive score model was stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A higher risk score was associated with more aggressive features, including a higher tumor grade, a more advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the high–risk and low–risk score groups. A nomogram containing patients’ age, tumor stage, adjuvant chemotherapy, and the risk predictive score could accurately predict the survival probability of patients at 1, 3, and 5 years.

Conclusion: A novel 19-gene risk predictive score model was developed based on the lipid metabolism–related genes, which could be a potential prognostic indicator and therapeutic target of GC.

Lipid Metabolism and Tumor Antigen Presentation

Tumors always evade immune surveillance and block T cell activation in a poorly immunogenic and immunosuppressive environment. Cancer cells and immune cells exhibit metabolic reprogramming in the tumor microenvironment (TME), which intimately links immune cell function and edits tumor immunology. In addition to glucose metabolism, amino acid and lipid metabolism also provide the materials for biological processes crucial in cancer biology and pathology. Furthermore, lipid metabolism is synergistically or negatively involved in the interactions between tumors and the microenvironment and contributes to the regulation of immune cells. Antigen processing and presentation as the initiation of adaptive immune response play a critical role in antitumor immunity. Therefore, a relationship exists between antigen-presenting cells and lipid metabolism in TME. This chapter introduces the updated understandings of lipid metabolism of tumor antigen-presenting cells and describes new directions in the manipulation of immune responses for cancer treatment.

Berberine Suppressed Tumor Growth through Regulating Fatty Acid Metabolism and Triggering Cell Apoptosis via Targeting FABPs

Aim

To further investigate the mechanism behind the antitumor properties of berberine regarding lipid metabolism.

Methods

Cell viability, proliferation, and apoptosis assays were performed to determine the antigrowth effects of berberine in vitro. Ectopic xenograft models in Balb/c nude mice were established to determine the antitumor effects of berberine in vivo.

Results

Berberine inhibited cell viability and proliferation of MGC803 human gastric cancer cell lines in a time- and dose-dependent manner. Berberine induced apoptosis of MGC803 and increased the apoptotic rate with higher doses. Berberine induced the accumulation of fatty acid of MGC803 and suppressed the protein expression of FABPs and PPARα. The FABP inhibitor BMS309403 recapitulated the effects of berberine on MGC803 cells. In the xenograft model, berberine significantly decreased the tumor volume and tumor weight and induced apoptosis in tumor tissues. Berberine significantly elevated the fatty acid content and inhibited the expression of FABPs and PPARα in the MGC803 xenograft models.

Conclusion

Berberine exerted anticancer effects on human gastric cancer both in vitro and in vivo by inducing apoptosis, which was due to the reduced protein expression of FABPs and the accumulation of fatty acid.

Targeting Cellular Metabolism Modulates Head and Neck Oncogenesis

Considering the great energy and biomass demand for cell survival, cancer cells exhibit unique metabolic signatures compared to normal cells. Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent neoplasms worldwide. Recent findings have shown that environmental challenges, as well as intrinsic metabolic manipulations, could modulate HNSCC experimentally and serve as clinic prognostic indicators, suggesting that a better understanding of dynamic metabolic changes during HNSCC development could be of great benefit for developing adjuvant anti-cancer schemes other than conventional therapies. However, the following questions are still poorly understood: (i) how does metabolic reprogramming occur during HNSCC development? (ii) how does the tumorous milieu contribute to HNSCC tumourigenesis? and (iii) at the molecular level, how do various metabolic cues interact with each other to control the oncogenicity and therapeutic sensitivity of HNSCC? In this review article, the regulatory roles of different metabolic pathways in HNSCC and its microenvironment in controlling the malignancy are therefore discussed in the hope of providing a systemic overview regarding what we knew and how cancer metabolism could be translated for the development of anti-cancer therapeutic reagents.