Background: Xenobiotic metabolism plays an important role in the progression of colon cancer; however, little is known about its related biomarkers. This study sought to construct a prognostic model related to xenobiotic metabolism in colon cancer, and further reveal the characteristics of tumor immune microenvironment based on the prognostic model. Methods: Transcriptome data of 41 normal colon tissues and 473 colon tumor tissues and the clinical features of 452 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database.Data on xenobiotic metabolism genes (XMGs) were obtained from the hallmark xenobiotic metabolism set of the Molecular Signatures Database (MSigDB) and articles. Additionally, data on differential XMGs in colon cancer were acquired for a functional enrichment analysis by R software. An XMG prognostic model was constructed by a Cox regression analysis, and evaluated using Kaplan-Meier survival curves, risk curves, receiver operating characteristic (ROC) curves, and an independent prognostic analysis in a training cohort and validation cohort. Moreover, tumor immune infiltration and negative regulatory immune genes of cancer-immunity cycle (CIC), including immune checkpoints and immune cytokines, were further analyzed between low-and high-risk groups in both the training and validation cohorts. Differences with P value <0.05 were interpreted as statistically significant.Results: A total of 126 differential XMGs were distinguished in the colon cancer data set, which were mainly enriched in the metabolism pathways of drugs and nutrients. There were 5 optimized genes (i.e., CYP2W1, GSTM1, TGFB2, MPP2, and ACOX1) used to construct the prognosis model, which effectively predicted prognosis and had good ROC curves. Between low-and high-risk groups, there were significant differences in abundance for T cells CD4 memory resting and T cells regulatory (Tregs), and expression of PDCD1, LAG3, NOS3, TGFB1, and ICAM1 in the training cohort and validation cohort. Conclusions: The XMGs in the prognostic model have a good prediction effect on the prognosis of colon cancer patients. The T cells CD4 memory resting, and Tregs, immune checkpoints PDCD1 and LAG3, and CIC negative regulatory immune cytokines NOS3, TGFB1, and ICAM1 are closely associated with xenobiotic metabolism.

No abstract

Esophageal cancer (EC) is one of the most common digestive system malignancies in the world. The combined modality treatment of EC is usually surgery and radiation therapy, however, its clinical efficacy for advanced patients is relatively limited. Ferroptosis, a new type of iron-dependent programmed cell death, is different from apoptosis, necrosis and autophagy. In recent years, many studies have further enlightened that ferroptosis plays an essential role in the occurrence, development and metastasis of tumors. Targeting ferroptosis stimulates a new direction for further exploration of oncologic treatment regimens. Furthermore, ferroptosis has a critical role in the immune microenvironment of tumors. This paper reviews the mechanism of ferroptosis and the ferroptosis research progress in the treatment of EC. We further elaborate the interaction between ferroptosis and immunotherapy, and the related mechanisms of ferroptosis participation in the immunotherapy of EC, so as to provide new directions and ideas for the treatment of EC.

Background: G-quadruplexes are molecular switches regulating gene transcription. c-MYC and hypoxiainducible factor 1-alpha (HIF1α) play important roles in cell proliferation, apoptosis, and metabolic regulation in colon cancer. Whether berberine can regulate metabolism by interacting with c-MYC and HIF1α G-quadruplexes in colon cancer needs to be explored. Methods: The binding mode of berberine with c-MYC and HIF1α G-quadruplexes were explored by ultraviolet and visible absorption spectroscopy and fluorescence spectroscopy. Circular dichroism (CD) spectroscopy was performed to evaluate the effects of berberine on the stability of c-MYC and HIF1α G-quadruplexes. After different concentrations of berberine acting on HCT116 cells for 24 h, cell proliferation and apoptosis were detected by MTT assay and flow cytometry; quantitative real-time polymerase chain reaction and western blot were performed to detect mRNA and protein expression of c-MYC and HIF1α; transcriptome sequencing was used to analyze the metabolic pathways. For the effects of berberine on colon cancer mouse model with dose of 50 mg•kg −1 for 14 days, tumor growth were monitored, hematoxylin and eosin staining and immunofluorescence staining were performed to analyze histopathology and protein expression of c-MYC and HIF1α, central carbon metabolism was detected in tumor tissues. Results: The binding ability of berberine with c-MYC G-quadruplex was different to that of berberine with HIF1α G-quadruplex. Both binding modes involved π-π stacking. The stoichiometric ratios were 1:1, 1:3, and 3:1 for berberine with c-MYC G-quadruplex and only 1:1 for berberine with HIF1α G-quadruplex. Temperature had a greater effect on the binding of berberine to c-MYC G-quadruplex. Berberine could improve the thermal stability of both c-MYC and HIF1α G-quadruplexes. Berberine inhibited the gene transcription and protein expression of c-MYC and HIF1α in colon cancer HCT116 cells. In vivo, berberine delayed tumor progression and inhibited the protein expression of c-MYC and HIF1α. Twelve differential metabolites such as decreased adenosine triphosphate were obtained, indicating that berberine could regulate the metabolic pathways of the tricarboxylic acid (TCA) cycle and glycolysis/gluconeogenesis, among others. Conclusions: Berberine may inhibit colon cancer by regulating the TCA cycle and glycolysis/ gluconeogenesis based on the interaction with c-MYC and HIF1α G-quadruplexes.

No abstract