In ovarian carcinogenesis and progression, long non-coding RNAs (lncRNAs) have been shown to have a role, although the underlying processes remain a mystery. By modulating the degree of autophagy in ovarian cancer cells, we sought to learn more about the function lncRNA HOXA11-AS plays in the development of ovarian cancer. The expression of HOXA11-AS in ovarian normal cells and ovarian cancer cell lines was measured using R package and qRT-PCR. Ovarian cancer cells expressed HOXA11-AS substantially higher than normal cells, while cisplatin-resistant cells expressed HOXA11-AS significantly higher than ovarian cancer cells. Next, we studied the prognostic data of HOXA11-AS in ovarian cancer in the Tissue Cancer Genome Atlas (TCGA). In the next step, lentiviral transfection of ovarian cancer cells A2780, OVCAR3, and A2780/DDP (cisplatin-resistant) were performed, and HOXA11-AS knockdown was found to significantly inhibit cell viability, migration, and invasion of A2780 and OVCAR3 cells, and promote apoptosis by CCK-8 assay, transwell assay, cell cycle, and apoptosis assay, and promoted the sensitivity of A2780/DDP cells to cisplatin. It has been shown by the western blot test that HOXA11-AS knockdown increases the amount of cellular autophagy in cells. In contrast, adding the autophagy inhibitor 3-methyladenine (3-MA) to HOXA11-AS cells knocked down in vivo reduced its anti-tumor properties. As a whole, this study found that HOXA11-AS knockdown increased the expression of autophagy-related proteins and improved cisplatin sensitivity, decreased ovarian cancer cell proliferation, and promoted cell apoptosis. This study provides new insights into the role of HOXA11-AS in ovarian cancer regulation.

Cuproptosis (copper-ion-dependent cell death) is an unprogrammed cell death, and intracellular copper accumulation, causing copper homeostasis imbalance and then leading to increased intracellular toxicity, which can affect the rate of cancer cell growth and proliferation. This study aimed to create a newly cuproptosis-related lncRNA signature that can be used to predict survival and immunotherapy in patients with cervical cancer, but also to predict prognosis in patients treated with radiotherapy and may play a role in predicting radiosensitivity. First of all, we found lncRNAs associated with cuproptosis between cervical cancer tumor tissues and normal tissues. By LASSO-Cox analysis, overlapping lncRNAs were then used to construct lncRNA signatures associated with cuproptosis, which can be used to predict the prognosis of patients, especially the prognosis of radiotherapy patients, ROC curves and PCA analysis based on cuprotosis-related lncRNA signature and clinical signatures were developed and demonstrated to have good predictive potential. In addition, differences in immune cell subset infiltration and differences in immune checkpoint expression between high-risk and low-risk score groups were analyzed, and we investigated the relationship between this signature and tumor mutation burden. In summary, we constructed a lncRNA prediction signature associated with cuproptosis. This has important clinical implications, including improving the predictive value of cervical cancer patients and providing a biomarker for cervical cancer.

Among gynecological malignancies, ovarian cancer has the highest mortality rate and has sparked widespread interest in studying the mechanisms underlying ovarian cancer development. Based on TCGA and GEO databases, we investigated the highly expressed autophagy-related genes that determine patient prognosis using limma differential expression and Kaplan-Meier survival analyses. The biological processes associated with these genes were also predicted using GO/KEGG functional enrichment analysis. CCK-8, cell scratch, and transwell assays were used to investigate the effects of PXN on the proliferation, migration, and invasion abilities of ovarian cancer cells. Transmission electron microscopy was used to observe the autophagosomes. The expression of autophagy proteins and the PI3K/Akt/mTOR and p110β/Vps34/Beclin1 pathway proteins in ovarian cancer cells was detected using western blot; autophagy protein expression was further detected and localized using cellular immunofluorescence. A total of 724 autophagy-related genes were found to be overexpressed in ovarian -cancer tissues, with high expression of PEX3, PXN, and RB1 associated with poor prognosis in patients (p < .05). PXN activates and regulates signaling pathways related to cellular autophagy, ubiquitination, lysosomes, PI3K-Akt, and mTOR. Autophagosomes were observed in all cell groups. The increase in PXN gene expression promoted the proliferation, migration, and invasion of ovarian cancer cells, increased the expression of SQSTM1/p62 protein, decreased LC3II/LC3Ⅰ, inhibited the phosphorylation of Akt and mTOR proteins, and suppressed the expression of PI3K(p110β) and Beclin1 proteins. The decrease in PXN expression also confirmed these changes. Thus, PXN is highly expressed during ovarian cancer and is associated with poor patient prognosis. It may promote ovarian cancer cell proliferation, migration, and invasion by inhibiting cellular autophagy via suppression of the p110β/Vps34/Beclin1 pathway.

Background: Ovarian cancer (OC) is an important cause of gynecologic cancer-related mortality worldwide. Exosomal miR-1825 and its target gene CLEC5A have been shown to have a significant association with tumorigenesis in other cancers. Methods: Exosomal miR-1825 expression and its ability in overall survival(OS) prediction were determined using GEO and TCGA data. Target genes of miR-1825 were searched in five prediction databases, and differentially expressed prognostic genes were identified. We performed GO and KEGG enrichment analyses. The ability of CLEC5A in OS prediction was assessed using univariate and multivariate Cox regression and Kaplan-Meier curves. Immunohistochemistry was applied to validate the CLEC5A expression pattern in OC. The immune cell landscape was compared using the CIBERSORT algorithm, and the results were validated in a GEO cohort. Finally, the predicted IC50 of five common chemotherapy agents was compared. Results: MiR-1825 was elevated in exosomes derived from OC cells and served as a tumor suppressor. The CLEC5A gene was confirmed as a target of miR-1825, whose upregulation was correlated with a poor prognosis. M2 macrophage infiltration was significantly enhanced in CLEC5A high expression group, and T follicular helper cell infiltration was reduced in it. The predicted IC50 for cisplatin and doxorubicin was higher in CLEC5A high expression group, and that for docetaxel, gemcitabine, and paclitaxel was lower. Conclusion: MiR-1825 may promote OC progression by increasing CLEC5A expression through exosome-mediated efflux from tumor cells and could be a promising biomarker for OC.

Background：Ovarian cancer has a high mortality rate, which has major contributing factors including advanced detection and recurrence susceptibility. For patients with ovarian cancer, optimal predictive models for therapy progression and medication sensitivity are currently lacking. Different types of programmed cell death (PCD) can predict prognosis and medication susceptibility in ovarian cancer and play a significant role in tumor growth. Materials and methods: By analyzing 12 PCD patterns (apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and novel regulated cell death by alkaliptosis), Cox multifactorial regression analysis was used to screen a gene set of 20 genes that can each individually affect the prognosis of ovarian cancer. The gene set was then typed and modeled. The model was further validated and additional risk scoring was done. To learn more about the IC50 link between high and low-risk scores and chemotherapeutic drugs, the relationship between high and low-risk scores and the immune microenvironment was examined. Results: The two examined subtypes of ovarian cancer have different key biological activities, according to the results of typing genes that can independently affect ovarian cancer survival, which combined with clinical characteristics to create a nomogram map with good predictive performance. Additionally, patients with ovarian cancer in the high-risk category were sensitive to Dasatinib, and the type was related to immune checkpoint genes and important tumor microenvironment components. Conclusion: In short, by combining several cell death patterns, we created a new staging model that can precisely predict the clinical outcome and medication sensitivity of ovarian cancer patients.