A novel fatty acid metabolism-related signature identifies features of the tumor microenvironment and predicts clinical outcome in acute myeloid leukemia

Fatty acid metabolism affects hepatocellular carcinoma progression via the PPAR-γ signaling pathway and fatty acid β-oxidation

PURPOSE

This study aimed to explore novel targets for hepatocellular carcinoma (HCC) treatment by investigating the role of fatty acid metabolism.

METHODS

RNA-seq and clinical data of HCC were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatic analyses were employed to identify differentially expressed genes (DEGs) related to prognosis. A signature was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to classify HCC patients from the TCGA database into low-risk and high-risk groups. The predictive performance of the signature was evaluated through principal components analysis (PCA), Kaplan Meier (KM) survival analysis, receiver operating characteristics (ROC) curves, nomogram, genetic mutations, drug sensitivity analysis, immunological correlation analysis, and enrichment analysis. Single-cell maps were constructed to illustrate the distribution of core genes. Immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blot were employed to verify the expression of core genes. The function of one core gene was validated through a series of in vitro assays, including cell viability, colony formation, wound healing, trans-well migration, and invasion assays. The results were analyzed in the context of relevant signaling pathways.

RESULTS

Bioinformatic analyses identified 15 FAMGs that were related to prognosis. A 4-gene signature was constructed, and patients were divided into high- and low-risk groups according to the signature. The high-risk group exhibited a poorer prognosis compared to the low-risk group in both the training (P < 0.001) and validation (P = 0.020) sets. Furthermore, the risk score was identified as an independent predictor of OS (P < 0.001, HR = 8.005). The incorporation of the risk score and clinicopathologic features into a nomogram enabled the effective prediction of patient prognosis. The model was able to effectively predict the immune microenvironment, drug sensitivity to chemotherapy, and gene mutation for each group. Single-cell maps demonstrated that FAMGs in the model were distributed in tumor cells. Enrichment analyses revealed that the cell cycle, fatty acid β oxidation and PPAR signaling pathways were the most significant pathways. Among the four key prognostically related FAMGs, Spermine Synthase (SMS) was selected and validated as a potential oncogene affecting cell cycle, PPAR-γ signaling pathway and fatty acid β oxidation in HCC.

CONCLUSIONS

The risk characteristics based on FAMGs could serve as independent prognostic indicators for predicting HCC prognosis and could also serve as evaluation criteria for gene mutations, immunity, and chemotherapy drug therapy in HCC patients. Meanwhile, targeted fatty acid metabolism could be used to treat HCC through related signaling pathways.

Metabolic vulnerabilities in cancer: A new therapeutic strategy

Cancer metabolism is now a key area for therapeutic intervention, targeting unique metabolic reprogramming crucial for tumor growth and survival. This article reviews the therapeutic potential of addressing metabolic vulnerabilities through glycolysis and glutaminase inhibitors, which disrupt cancer cell metabolism. Challenges such as tumor heterogeneity and adaptive resistance are discussed, with strategies including personalized medicine and predictive biomarkers to enhance treatment efficacy. Additionally, integrating diet and lifestyle changes with metabolic targeting underscores a holistic approach to improving therapy outcomes. The article also examines the benefits of incorporating these strategies into standard care, highlighting the potential for more tailored, safer treatments. In conclusion, exploiting metabolic vulnerabilities promises a new era in oncology, positioning metabolic targeting at the forefront of personalized cancer therapy and transforming patient care.

High expression of SLC27A2 predicts unfavorable prognosis and promotes inhibitory immune infiltration in acute lymphoblastic leukemia

Solute carrier family 27 member 2 (SLC27A2) is involved in fatty acid metabolism in tumors and represents a prospective target for cancer therapy. However, the role and mechanism of action of SLC27A2 in acute lymphoblastic leukemia (ALL) remain unclear. In this study, we aimed to explore the intrinsic associations between SLC27A2 and ALL and evaluate the prognostic significance, biological functions, and correlation with immune infiltration. We used the transcriptome and clinical data from the TARGET dataset. Differentially expressed genes (DEGs) in the SLC27A2 low- and high-expression groups were analyzed for prognostic implications and functional enrichment. Furthermore, we analyzed the relationship between SLC27A2 gene expression and immune cell infiltration using the ESTIMATE method, which was evaluated using the TIGER platform. Finally, we knocked down SLC27A2 in the Jurkat ALL cell line and conducted cell proliferation, western blotting, flow cytometry, and CCK-8 assays to elucidate the biological function of SLC27A2 in ALL. Patients with ALL who have higher expression levels of SLC27A2 have poorer overall survival and event-free survival. According to gene set enrichment analysis, the DEGs were primarily enriched with immune system processes and the PI3K-Akt signaling pathway. There was an inverse relationship between SLC27A2 expression and immune cell invasion, suggesting involvement of the former in tumor immune evasion. In vitro experiments showed that knockdown of SLC27A2 inhibited cell proliferation and protein expression and altered the Akt pathway, with a reduced proportion of B cells. In conclusion, SLC27A2 plays a vital role in the development of ALL.

Crosstalk between autophagy and metabolism: implications for cell survival in acute myeloid leukemia

Acute myeloid leukemia (AML), a prevalent form of leukemia in adults, is often characterized by low response rates to chemotherapy, high recurrence rates, and unfavorable prognosis. A critical barrier in managing refractory or recurrent AML is the resistance to chemotherapy. Increasing evidence indicates that tumor cell metabolism plays a crucial role in AML progression, survival, metastasis, and treatment resistance. Autophagy, an essential regulator of cellular energy metabolism, is increasingly recognized for its role in the metabolic reprogramming of AML. Autophagy sustains leukemia cells during chemotherapy by not only providing energy but also facilitating rapid proliferation through the supply of essential components such as amino acids and nucleotides. Conversely, the metabolic state of AML cells can influence the activity of autophagy. Their mutual coordination helps maintain intrinsic cellular homeostasis, which is a significant contributor to chemotherapy resistance in leukemia cells. This review explores the recent advancements in understanding the interaction between autophagy and metabolism in AML cells, emphasizing their roles in cell survival and drug resistance. A comprehensive understanding of the interplay between autophagy and leukemia cell metabolism can shed light on leukemia cell survival strategies, particularly under adverse conditions such as chemotherapy. This insight may also pave the way for innovative targeted treatment strategies.

A novel prognostic classification integrating lipid metabolism and immune co-related genes in acute myeloid leukemia

Background

As a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.

Methods

First, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.

Results

Eight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.

Conclusions

This study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.

A Novel Fatty Acid Metabolism-Associated Risk Model for Prognosis Prediction in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults, with an unfavourable outcome and a high rate of recurrence due to its heterogeneity. Dysregulation of fatty acid metabolism plays a crucial role in the development of several tumours. However, the value of fatty acid metabolism (FAM) in the progression of AML remains unclear. In this study, we obtained RNA sequencing and corresponding clinicopathological information from the TCGA and GEO databases. Univariate Cox regression analysis and subsequent LASSO Cox regression analysis were utilized to identify prognostic FAM-related genes and develop a potential prognostic risk model. Kaplan-Meier analysis was used for prognostic significances. We also performed ROC curve to illustrate that the risk model in prognostic prediction has good performance. Moreover, significant differences in immune infiltration landscape were found between high-risk and low-risk groups using ESTIMATE and CIBERSOT algorithms. In the end, differential expressed genes (DEGs) were analyzed by gene set enrichment analysis (GSEA) to preliminarily explore the possible signaling pathways related to the prognosis of FAM and AML. The results of our study may provide potential prognostic biomarkers and therapeutic targets for AML patients, which is conducive to individualized precision therapy.