Peroxiredoxin I and II as novel therapeutic molecular targets in cervical cancer treatment through regulation of endoplasmic reticulum stress induced by bleomycin.

Cervical cancer, significantly affecting women worldwide, often involves treatment with bleomycin, an anticancer agent targeting breast, ovarian, and cervical cancers by generating reactive oxygen species (ROS) to induce cancer cell death. The Peroxiredoxin (PRDX) family, particularly PRDX1 and 2, plays a vital role in maintaining cellular balance by scavenging ROS, thus mitigating the damaging effects of bleomycin-induced mitochondrial and cellular oxidative stress. This process reduces endoplasmic reticulum (ER) stress and prevents cell apoptosis. However, reducing PRDX1 and 2 levels reverses their protective effect, increasing apoptosis. This research highlights the importance of PRDX1 and 2 in cervical cancer treatments with bleomycin, showing their potential to enhance treatment efficacy by managing ROS and ER stress and suggesting a therapeutic strategy for improving outcomes in cervical cancer treatment.

Bioinformatics Analysis of Novel Targets for Treating Cervical Cancer by Immunotherapy Based on Immune Escape.

BACKGROUND/AIM: Cervical cancer (CC) is a high-risk disease in women, and advanced CC can be difficult to treat even with surgery, radiotherapy, and chemotherapy. Hence, developing more effective treatment methods is imperative. Cancer cells undergo a renewal process to escape immune surveillance and then attack the immune system. However, the underlying mechanisms remain unclear. Currently, only one immunotherapy drug has been approved by the Food and Drug Administration for CC, thus indicating the need for and importance of identifying key targets related to immunotherapy. MATERIALS AND METHODS: Data on CC and normal cervical tissue samples were downloaded from the National Center for Biotechnology Information database. Transcriptome Analysis Console software was used to analyze differentially expressed genes (DEGs) in two sample groups. These DEGs were uploaded to the DAVID online analysis platform to analyze biological processes for which they were enriched. Finally, Cytoscape was used to map protein interaction and hub gene analyses. RESULTS: A total of 165 up-regulated and 362 down-regulated genes were identified. Among them, 13 hub genes were analyzed in a protein-protein interaction network using the Cytoscape software. The genes were screened out based on the betweenness centrality value and average degree of all nodes. The hub genes were as follows: ANXA1, APOE, AR, C1QC, CALML5, CD47, CTSZ, HSP90AA1, HSP90B1, NOD2, THY1, TLR4, and VIM. We identified the following 12 microRNAs (miRNAs) that target the hub genes: hsa-miR-2110, hsa-miR-92a-2-5p, hsa-miR-520d-5p, hsa-miR-4514, hsa-miR-4692, hsa-miR-499b-5p, hsa-miR-5011-5p, hsa-miR-6847-5p, hsa-miR-8054, hsa-miR-642a-5p, hsa-miR-940, and hsa-miR-6893-5p. CONCLUSION: Using bioinformatics, we identified potential miRNAs that regulated the cancer-related genes and long noncoding RNAs (lncRNAs) that regulated these miRNAs. We further elucidated the mutual regulation of mRNAs, miRNAs, and lncRNAs involved in CC occurrence and development. These findings may have major applications in the treatment of CC by immunotherapy and the development of drugs against CC.