Abnormal energy metabolism, as one of the important hallmarks of cancer, was induced by multiple carcinogenic factors and tumor-specific microenvironments. It comprises aerobic glycolysis, de novo lipid biosynthesis, and glutamine-dependent anaplerosis. Considering that metabolic reprogramming provides various nutrients for tumor survival and development, it has been considered a potential target for cancer therapy. Cannabinoids have been shown to exhibit a variety of anticancer activities by unclear mechanisms. This paper first reviews the recent progress of related signaling pathways (reactive oxygen species (ROS), AMP-activated protein kinase (AMPK), mitogen-activated protein kinases (MAPK), phosphoinositide 3-kinase (PI3K), hypoxia-inducible factor-1alpha (HIF-1α), and p53) mediating the reprogramming of cancer metabolism (including glucose metabolism, lipid metabolism, and amino acid metabolism). Then we comprehensively explore the latest discoveries and possible mechanisms of the anticancer effects of cannabinoids through the regulation of the above-mentioned related signaling pathways, to provide new targets and insights for cancer prevention and treatment.

Curcumin, a natural polyphenol derived from turmeric, functions as a potent exogenous antioxidant and exhibits a range of benefits in the prevention and management of metabolic diseases. Despite its extremely low systemic bioavailability, curcumin demonstrates significant bioactivity in vivo, a phenomenon likely attributable to its accumulation in the intestines and subsequent modulation of systemic oxidative stress and inflammation. This article systematically reviews the comprehensive regulatory effects of curcumin on systemic metabolic networks—including glucose metabolism, amino acid metabolism, lipid metabolism, and mitochondrial metabolism—and explores their molecular basis, particularly how curcumin facilitates systemic metabolic improvements by alleviating oxidative stress and interacting with inflammation. Preclinical studies indicate that curcumin accumulates in the intestines, where it remodels the microbiota through prebiotic effects, enhances barrier integrity, and reduces endotoxin influx—all of which are critical drivers of systemic oxidative stress and inflammation. Consequently, curcumin improves insulin resistance, hyperglycemia, and dyslipidemia across multiple organs (liver, muscle, adipose) by activating antioxidant defense systems (e.g., Nrf2), enhancing mitochondrial respiratory function (via PGC-1α/AMPK), and suppressing pro-inflammatory pathways (e.g., NF-κB). Clinical trials have corroborated these effects, demonstrating that curcumin supplementation significantly enhances glycemic control, lipid profiles, adipokine levels, and markers of oxidative stress and inflammation in patients with obesity, type 2 diabetes, and non-alcoholic fatty liver disease. Therefore, curcumin emerges as a promising multi-target therapeutic agent against metabolic diseases through its systemic antioxidant and anti-inflammatory networks. Future research should prioritize addressing its bioavailability limitations and validating its efficacy through large-scale trials to translate this natural antioxidant into a precision medicine strategy for metabolic disorders.

Metabolic reprogramming is a fundamental hallmark and a key driver of malignant tumors. By reshaping glucose, lipid, and amino acid metabolism, as well as mitochondrial function, it sustains the abnormal proliferation and survival of tumor cells, making it a crucial target for anti-tumor therapy. Curcumin, a natural multi-target compound, exhibits unique advantages in intervening in tumor metabolic reprogramming due to its low toxicity and broad-spectrum regulatory properties. In various tumor models, it can directly modulate the activity of key glycolytic enzymes, such as hexokinase 2, lactate dehydrogenase A, and pyruvate kinase M2, as well as transporters like glucose transporter 1. Furthermore, it inhibits the expression of proteins related to lipid metabolism, including fatty acid synthase and stearoyl-CoA desaturase 1, while also intervening in amino acid metabolic networks, such as glutaminase and branched-chain amino acid transaminase. Additionally, curcumin targets mitochondrial function and reactive oxygen species balance, creating multi-dimensional intervention effects through various pathways, including the induction of ferroptosis by regulating the SLC7A11/GPX4 axis and modulating gut microbiota metabolism. Its mechanism of action involves the synergistic regulation of key signaling pathways, including phosphoinositide 3-kinase/Akt, NF-κB, AMP-activated protein kinase, and hypoxia-inducible factor-1alpha. Furthermore, its specific effect profile demonstrates significant dependency on cell type and tumor model. This article systematically reviews the regulatory effects of curcumin on these critical metabolic processes and pathways in tumor metabolic reprogramming, revealing its molecular mechanisms in disrupting tumor growth and progression by targeting energy and biosynthetic metabolism. These findings provide a significant theoretical foundation and a preclinical research perspective for the development of natural antitumor drugs based on metabolic regulation, as well as for optimizing combination therapy strategies.

Pancreatic adenocarcinoma (PAAD) is a fatal malignant tumor of the digestive system, and immunotherapy has currently emerged as a key therapeutic approach for treating PAAD, with its efficacy closely linked to T-cell subsets and the tumor immune microenvironment. However, reliable predictive markers to guide clinical immunotherapy for PAAD are not available. We analyzed the single-cell RNA sequencing (scRNA-seq) data focused on PAAD from the GeneExpressionOmnibus (GEO) database. Then, the information from the Cancer Genome Atlas (TCGA) database was integrated to develop and validate a prognostic risk model derived from T-cell marker genes. Subsequently, the correlation between these risk models and the effectiveness of immunotherapy was explored. Analysis of scRNA-seq data uncovered six T-cell subtypes and 1837 T-cell differentially expressed genes (DEGs). Combining these data with the TCGA dataset, we constructed a T-cell prognostic risk model containing 16 DEGs, which can effectively predict patient survival and immunotherapy outcomes. We have found that patients in the low-risk group had better prognostic outcomes, increased immune cell infiltration, and signs of immune activation compared to those in the high-risk group. Additionally, analysis of tumor mutation burden showed higher mutation rates in patients with PAAD in the high-risk group. Risk scores with immune checkpoint gene expression and drug sensitivity analysis provide patients with multiple therapeutic targets and drug options. Our study constructed a prognostic risk model for PAAD patients based on T-cell marker genes, providing valuable insights into predicting patient prognosis and the effectiveness of immunotherapy.

B-cell acute lymphoblastic leukemia (B-ALL) is characterized by the abnormal proliferation of B-lineage lymphocytes in the bone marrow (BM). The roles of immune cells within the BM microenvironment remain incompletely understood. Single-cell RNA sequencing (scRNA-seq) provides the potential for groundbreaking insights into the pathogenesis of B-ALL. In this study, scRNA-seq was conducted on BM samples from 17 B-ALL patients (B-ALL cohorts) and 13 healthy controls (HCs). Bioinformatics analyses, including clustering, differential expression, pathway analysis, and gene set variation analysis, systematically identified immune cell types and assessed T-cell prognostic and metabolic heterogeneity. A metabolic-feature-based machine learning model was developed for B-ALL subtyping. Furthermore, T-cell–monocyte interactions, transcription factor (TF) activity, and drug enrichment analyses were performed to identify therapeutic targets. The results indicated significant increases in Pro-B cells, alongside decreases in B cells, NK cells, monocytes, and plasmacytoid dendritic cells (pDCs) among B-ALL patients, suggesting immune dysfunction. Clinical prognosis correlated significantly with the distribution of T-cell subsets. Metabolic heterogeneity categorized patients into four distinct groups (A–D), all exhibiting enhanced major histocompatibility class I (MHC-I)-mediated intercellular communication. The metabolic-based machine learning model achieved precise classification of B-ALL groups. Analysis of TF activity underscored the critical roles of MYC, STAT3, and TCF7 within the B-ALL immunometabolic network. Drug targeting studies revealed that dorlimomab aritox and palbociclib specifically target dysregulation in ribosomal and CDK4/6 pathways, offering novel therapeutic avenues. This study elucidates immunometabolic dysregulation in B-ALL, characterized by altered cellular composition, metabolic disturbances, and abnormal cellular interactions. Key TFs were identified, and targeted drug profiles were established, demonstrating the significant clinical potential of integrating immunological mechanisms with metabolic regulation for the treatment of B-ALL.

Chemotherapy and immune checkpoint inhibitors (ICIs) are widely utilized in cancer treatment, exerting not only direct cytotoxic effects on tumor cells but also significantly reshaping the systemic immune status of patients, particularly the composition and function of the T cell repertoire. This review begins with the analysis of the widespread depletion and dysfunction of early T cell subsets in cancer patients, particularly naïve T cells (Tn) and stem-like memory T cells (Tscm). Afterward, it details how chemotherapy and ICIs differentially regulate T cell homeostasis, including their ability to induce T cell exhaustion and differentiation skewing, as well as their ability to stimulate immunity and remodel the tumor microenvironment. On this basis, it is shown that the quality of T cell memory phenotypes is central in determining the in vivo expansion and persistence of chimeric antigen receptor T (CAR-T) cells. To overcome the challenges of classical autologous CAR-T therapy, which stems from raw material quality and in vitro manufacturing bottlenecks, we focus on transforming chemotherapy and ICIs from traditional treatment modalities into beneficial “host preconditioning” regimens aimed at optimizing the baseline state of the endogenous T cell repertoire. All in all, this transformation of traditional therapies into host preconditioning strategies, along with recent advances in in vivo CAR-T approaches, provides theoretical grounds and translatable clues to develop next-generation cellular immunotherapy.