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Infection is among the most common causes of death in patients with acute myeloid leukemia (AML) after chemotherapy. The anti-tumor effect of the intestinal microbiota in patients with AML is increasingly being recognized. Tigecycline, a broad-spectrum antibiotics, plays a vital role in the anti-infection treatment of AML patients with neutropenia and accompanying infections. Previously, this group reported that long-term use of tigecycline caused coagulation dysfunction in patients with hematological malignancies, increasing the risk of casualties. RNA sequencing was performed on CHO cells before and after tigecycline treatment. Further, the combined analysis of AML prognostic differentially expressed genes revealed 13 genes affected by tigecycline and closely related to AML prognosis. These genes were used for modeling analysis, and the results showed that the prepared model significantly improved the prognostic prediction efficiency for AML patients. The model also explored the correlation between prognosis score and immune cells infiltrating tumors and immune therapy targets. Moreover, 16S sequencing was performed on fecal samples from AML patients before and after tigecycline treatment. The results revealed that tigecycline significantly altered the distribution of intestinal microbiota in AML patients - These changes in microbiota are related to chemotherapy resistance. This study emphasizes the importance of intestinal microbiota in AML prognosis. Thus, the findings of this study show that the long-term use of antibiotics can not only cause dysbiosis of the intestinal microbiota but also indirectly affect the sensitivity of chemotherapy drugs, affecting the prognosis of AML patients.

Background: Cuprotosis is a recently discovered copper-dependent cell death mechanism that relies on mitochondrial respiration. However, the role of cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) and their prognostic significances remain unknown.Methods: Based on the recently published CRGs, the LASSO Cox regression analysis was applied to construct a CRGs risk model using the gene expression data from the International Cancer Genome Consortium as a training set, followed by validation with datasets from The Cancer Genome Atlas and the Gene Expression Omnibus (GSE14520). Functional enrichment analysis of the CRGs was performed by single-sample gene set enrichment analysis.Results: Five of the 13 previously published CRGs were identified to be associated with prognosis in HCC. Kaplan-Meier analysis suggested that patients with high-risk scores have a shorter overall survival time than patients with low-risk scores. ROC curves indicated that the average AUC was more than 0.7, even at 4 years, and at least 0.5 at 5 years. Moreover, addition of this CRG risk score can significantly improve the efficiency of predicting overall survival compared to using traditional factors alone. Functional analysis demonstrated increased presence of Treg cells in patients with high-risk scores, suggesting a suppressed immune state in these patients. Finally, we point to the possibility that novel immunotherapies such as inhibitors of PDCD1, TIGIT, IDO1, CD274, CTLA4, and LAG3 may have potential benefits in high-risk patients.Conclusion: We constructed a better prognostic model for liver cancer by using CRGs. The CRG risk score established in this study can serve as a potentially valuable tool for predicting clinical outcome of patients with HCC.