Comprehensive analyses of fatty acid metabolism-related lncRNA for ovarian cancer patients.

Ovarian cancer (OC) is a disease with difficult early diagnosis and treatment and poor prognosis. OC data profiles were downloaded from The Cancer Genome Atlas. Eight key fatty acid metabolism-related long non-coding RNAs (lncRNAs) were finally screened for building a risk scoring model by univariate/ multifactor and least absolute shrinkage and selection operator (LASSO) Cox regression. To make this risk scoring model more applicable to clinical work, we established a nomogram containing the clinical characteristics of OC patients after confirming that the model has good reliability and validity and the ability to distinguish patient prognosis. To further explore how these key lncRNAs are involved in OC progression, we explored their relationship with LUAD immune signatures and tumor drug resistance. The structure shows that the risk scoring model established based on these 8 fatty acid metabolism-related lncRNAs has good reliability and validity and can better predict the prognosis of patients with different risks of OC, and LINC00861in these key RNAs may be a hub gene that affects the progression of OC and closely related to the sensitivity of current OC chemotherapy drugs. In addition, combined with immune signature analysis, we found that patients in the high-risk group are in a state of immunosuppression, and Tfh cells may play an important role in it. We innovatively established a prognostic prediction model with excellent reliability and validity from the perspective of OC fatty acid metabolism reprogramming and lncRNA regulation and found new molecular/cellular targets for future OC treatment.

CCNE1 is a predictive and immunotherapeutic indicator in various cancers including UCEC: a pan-cancer analysis.

BACKGROUND: CCNE1 plays an important oncogenic role in several tumors, especially high-stage serous ovarian cancer and endometrial cancer. Nevertheless, the fundamental function of CCNE1 has not been explored in multiple cancers. Therefore, bioinformatics analyses of pan-cancer datasets were carried out to explore how CCNE1 regulates tumorigenesis. METHODS: A variety of online tools and cancer databases, including GEPIA2, SangerBox, LinkedOmics and cBioPortal, were applied to investigate the expression of CCNE1 across cancers. The pan-cancer datasets were used to search for links between CCNE1 expression and prognosis, DNA methylation, m6A level, genetic alterations, CCNE1-related genes, and tumor immunity. We verified that CCNE1 has biological functions in UCEC cell lines using CCK-8, EdU, and Transwell assays. RESULTS: In patients with different tumor types, a high mRNA expression level of CCNE1 was related to a poor prognosis. Genes related to CCNE1 were connected to the cell cycle, metabolism, and DNA damage repair, according to GO and KEGG enrichment analyses. Genetic alterations of CCNE1, including duplications and deep mutations, have been observed in various cancers. Immune analysis revealed that CCNE1 had a strong correlation with TMB, MSI, neoantigen, and ICP in a variety of tumor types, and this correlation may have an impact on the sensitivity of various cancers to immunotherapy. CCK-8, EdU and Transwell assays suggested that CCNE1 knockdown can suppress UCEC cell proliferation, migration and invasion. CONCLUSION: Our study demonstrated that CCNE1 is upregulated in multiple cancers in the TCGA database and may be a promising predictive biomarker for the immunotherapy response in some types of cancers. Moreover, CCNE1 knockdown can suppress the proliferation, migration and invasion of UCEC cells.