Amonafide Maleate

Hepatocellular Carcinoma (HCC) is the most common type of primary liver cancer, accounting for the majority of liver cancer cases. Hepatocellular Carcinoma not only exhibits high heterogeneity but also possesses an immune-suppressive tumor microenvironment that promotes tumor evasion, posing substantial difficulties for efficient therapy. Our aim is to utilize single-cell RNA transcriptome data to investigate the dynamic changes in the tumor microenvironment during the malignant progression of HCC, the communication among immune cells, and the marker genes associated with patient prognosis.

We constructed expression matrices from open single-cell RNA transcriptome data (GSE149614) of HCC patients (representing stages I-IV), establishing single-cell RNA transcriptional atlases for different stages of HCC progression. For each stage, we conducted cell subgroup analysis to identify cell types at each stage. Horizontally, we explored the dynamic changes of the same cell type across different stages, performing trajectory analysis and prognosis analysis. Vertically, we investigated pairwise comparisons of different stages of HCC progression, probing the dynamic alterations in tumor microenvironment immune cell signaling pathways. Finally, potential drugs for the treatment of HCC were predicted based on relevant genes.

As the HCC advances towards increased malignancy, there is a shift in the predominant composition of the tumor microenvironment, with a decline in the dominance of hepatic cells. Tumor-infiltrating immune cells migrate and accumulate within the tumor microenvironment, where T cells and myeloid cells display distinct patterns of change. Genes associated with cancer-associated fibroblasts (CAFs) and T cells are correlated with adverse patient outcomes. In the late stages of HCC, the tumor microenvironment is infiltrated by more myeloid-derived suppressor cells (MDSCs), and a prognostic model constructed based on genes related to myeloid cells can predict patient outcomes. Additionally, in the analysis of transcription factors, YY1 and MYC are found to be highly expressed. Cell communication analysis among tumor-infiltrating immune cells reveals significant differences in the main signaling pathways at different stages of HCC progression. Finally, drug sensitivity analysis based on key genes identifies Acetalax, Allopurinol, and Amonafide as potential candidates for HCC treatment.

SUMOylation, an important post-translational protein modification, plays a critical role in cancer development and immune processes. This study aimed to construct diagnostic and prognostic models for cervical cancer (CC) using SUMOylation-related genes (SRGs) and explore their implications for novel clinical therapies. We analyzed the expression profiles of SRGs in CC patients and identified 15 SRGs associated with CC occurrence. After the subsequent qPCR verification of 20 cases of cancer and adjacent tissues, 13 of the 15 SRGs were differentially expressed in cancer tissues. Additionally, we identified molecular markers associated with the prognosis and recurrence of CC patients, based on SRGs. Next, a SUMOScore, based on SRG expression patterns, was generated to stratify patients into different subgroups. The SUMOScore showed significant associations with the tumor microenvironment, immune function features, immune checkpoint expression, and immune evasion score in CC patients, highlighting the strong connection between SUMOylation factors and immune processes. In terms of immune therapy, our analysis identified specific chemotherapy drugs with higher sensitivity in the subgroups characterized by high and low SUMOScore, indicating potential treatment options. Furthermore, we conducted drug sensitivity analysis to evaluate the response of different patient subgroups to conventional chemotherapy drugs. Our findings revealed enrichment of immune-related pathways in the low-risk subgroup identified by the prognostic model. In conclusion, this study presents diagnostic and prognostic models based on SRGs, accompanied by a comprehensive index derived from SRGs expression patterns. These findings offer valuable insights for CC diagnosis, prognosis, treatment, and immune-related analysis.