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BackgroundOvarian cancer (OC) is one of the most lethal gynecological cancer globally. Serous ovarian cancer (SOC) is most common pathological type of ovarian cancer. Growing evidence suggests that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, we aimed to develop a transcription factors-related prognostic signature to better predict the survival in patients with SOC.Materials and methodsThe TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. Lasso cox regression and consensus clustering analysis were utilized to construct a novel TFs related signature and cluster model, respectively. Genomic alternative analysis was used to elaborate on the association between the TFs related prognostic signature and genomic aberrations. GSEA analysis was applied to identify the biological functional difference between high risk-score group and low-risk score group.ResultsA 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score, and cluster models were effective at accurately distinguishing the prognosis of SOC. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. Immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups; indicating that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients.ConclusionsIn conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

Our previous studied indicated that eukaryotic translation initiation factor 3a (eIF3a) increases the sensitive of platinum-based chemotherapy in lung cancer. MiRNAs play an important role in lung carcinogenesis and drug response. In this study, we aimed to identify potential endogenous miRNAs that inhibit eIF3a expression and determine their influence of this inhibition on cisplatin resistance. Using bioinformatics analysis prediction and confirmation with dual-luciferase reporter assays, we found that miRNA-488 inhibited eIF3a expression by directly binding to the 3’UTR of eIF3a. In addition, the overexpression of miRNA-488 inhibited cell migration and invasion in A549 cells, and also inhibited cell proliferation, cell cycle progression by elevated P27 expression. Compared to the parental cell line, A549/cisplatin (DDP) resistant cells exhibited a higher level of miRNA-488. Moreover, we found that miRNA-488 was associated with cisplatin resistance in three NSCLC cells (A549, H1299 and SK-MES-1). The mechanism of miRNA-488 induced cisplatin resistance was that miRNA-488 activated nucleotide excision repair (NER) by increasing the expression of Replication Protein A (RPA) 14 and Xeroderma pigmentosum group C (XPC). In conclusion, our results demonstrated that miRNA-488 is a tumor suppressor miRNA that acts by targeting eIF3a. Moreover, miRNA-488 also participates in eIF3a mediated cisplatin resistance in NSCLC cells.