Downregulation of the Coiled-Coil Domain Containing 80 and Its Perspective Mechanisms in Ovarian Carcinoma: A Comprehensive Study

Abstract

Introduction. We aimed to explore the downregulation of the coiled-coil domain containing 80 (CCDC80) and its underlying molecular mechanisms in ovarian carcinoma (OVCA). Materials/Methods. Immunohistochemical staining was performed to confirm the expression status of CCDC80 protein. Combining the data from in-house tissue microarrays and high-throughput datasets, we identified the expression level of CCDC80 in OVCA. We utilized cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between CCDC80 and the tumor microenvironment (TME) landscape in OVCA. Pathway enrichment, function annotation, and transcription factor (TFs) exploration were conducted to study the latent molecular mechanisms. Moreover, the cell line data in the Genomics of Drug Sensitivity in Cancer (GDSC) database was used to discover the relationship between CCDC80 and drug sensitivity. Results. An integrated standard mean difference (SMD) of −0.919 (95% CI: −1.515–0.324, P = 0.002 ) identified the downregulation of CCDC80 in OVCA based on 1048 samples, and the sROC ( AUC = 0.76 ) showed a moderate discriminatory ability of CCDC80 in OVCA. The fraction of infiltrating naive B cells showed significant differences between the high- and low-CCDC80 expression groups. Also, CCDC80-related genes are enriched in the Ras signaling pathway and metabolic of lipid. Nuclear receptor subfamily three group C member 1 (NR3C1) may be an upstream TF of CCDC80, and CCDC80 may be related to the sensitivity of mitocycin C and nilotinib. Conclusion. CCDC80 was downregulated in OVCA and may play a role as a tumor suppressor in OVCA.

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“…Furthermore, True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN) were obtained to summarize the results of diagnostic analysis and calculate the SEN, SPEC, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR). Finally, the summary receiver operating characteristic (SROC) curve was drawn to obtain the area under the curve (AUC) of SROC according to previous reports ( Gao et al, 2021 ; He et al, 2021 ; Huang W. Y. et al, 2021 ; Liang et al, 2021a , b ).…”

A cohesin‐associated gene score may predict immune checkpoint blockade in hepatocellular carcinoma

“…The immune scores, stromal scores, and tumor purity of the HCC patients were calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissue using the Expression data (ESTIMATE) algorithm [ 22 , 23 ]. The infiltration levels of 22 immune cells were quantified in each HCC patient using the Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm [ 24 , 25 , 26 , 27 , 28 ]. The tumor microenvironment (TME) of different CAG patterns was compared using either the Wilcoxon test or the ANOVA.…”

Downregulated Dual-Specificity Protein Phosphatase 1 in Ovarian Carcinoma: A Comprehensive Study With Multiple Methods

“…The scores of positive cells in visual field followed the criteria: 0%-5% (0 point), 6%-25% (1 point), 26%-50% (2 point), 51%-75% (3 point) and >75% (4 point). Finally, we calculated the IHC staining score via multiplying the intensity score and positive cells score [33,34].…”

“…Moreover, we calculated the SMD of every gene with a meta package of R and regarded the genes with p < 0.05 as differently expressed genes (DEGs) in OVCA. We then took the intersection genes of CEGs and DEGs to perform enrichment analysis [34]. We used the R package clusterProfiler to conduct GO term annotation and KEGG pathway enrichment analysis [40].…”