Data_Sheet_1_Bioinformatic Identification of miR-622 Key Target Genes and Experimental Validation of the miR-622-RNF8 Axis in Breast Cancer.PDF

Breast cancer is the leading cause of cancer-associated deaths among females. In recent decades, microRNAs (miRNAs), a type of short non-coding RNA that regulates gene expression at the post-transcription level, have been reported to participate in the regulation of many hub genes associated with tumorigenesis, tumor progression, and metastasis. However, the precise mechanism by which miRNAs regulate breast cancer metastasis remains poorly discussed, which limits the opportunity for the development of novel, effective therapeutic targets. Here, we aimed to determine the miR-622-related principal regulatory mechanism in cancer. First, we found that miR-622 was significantly related to a poor prognosis in various cancers. By utilizing an integrated miRNA prediction process, we identified 77 promising targets and constructed a protein-protein interaction network. Furthermore, enrichment analyses, including GO and KEGG pathway analyses, were performed to determine the potential function of miR-622, which revealed regulation networks and potential functions of miR-622. Then, we identified a key cluster comprised of six hub genes in the protein-protein interaction network. These genes were further chosen for pan-cancer expression, prognostic and predictive marker analyses based on the TCGA and GEO datasets to mine the potential clinical values of these hub genes. To further validate our bioinformatic results, the regulatory axis of miR-622 and RNF8, one of the hub genes recently reported to promote breast cancer cell EMT process and breast cancer metastasis, was selected as in vitro proof of concept. In vitro, we demonstrated the direct regulation of RNF8 by miR-622 and found that the predicted miR-622-RNF8 axis could regulate RNF8-induced epithelial-mesenchymal transition, cell migration, and cell viability. These results were further demonstrated with rescue experiments. We established a closed-loop miRNA-target-phenotype research model that integrated the bioinformatic analysis of the miRNA target genes and experimental validation of the identified key miRNA-target-phenotype axis. We not only identified the hub target genes of miR-622 in silico but also revealed the regulatory mechanism of miR-622 in breast cancer cell EMT process, viability, and migration in vitro for the first time.</p

Spatial Confinement of Single-Drop System to Enhance Aggregation-Induced Emission for Detection of MicroRNAs

Due to high incidence, poor prognosis, and easy transformation into pancreatic cancer (PC) with high mortality, early diagnosis and prevention of acute pancreatitis (AP) have become significant research focuses. In this work, we proposed a magnetic single-drop microextraction (SDME) system with spatial confinement to enhance the aggregation-induced emission (AIE) effect for simultaneous fluorescence detection of miRNA-155 (associated with AP) and miRNA-196a (associated with PC). The target miRNAs were selectively recognized by the hairpin probe and triggered the DNA amplification reaction; then, the DNA strands with two independent probes of G-quadruplex/TAIN and Cy5 were constructed on the surfaces of the magnetic beads. The SDME process, in which a drop containing the fluorescence probes was formed at the tip of the magnetic microextraction rod rapidly within 10 s, was performed by magnetic extraction. In this way, G-quadruplex/TAIN was enriched owing to the spatial confinement of the single-drop system, and the fluorescence signal given off (by G-quadruplex/TAIN) was highly enhanced (AIE effect). This was detected directly by fluorescence spectrophotometry. The approach achieved low limits of detection of 2.1 aM for miRNA-196a and 8.1 aM for miRNA-155 and wide linear ranges from 10 aM to 10 nM for miRNA-196a and from 25 aM to 10 nM for miRNA-155. This novel method was applied to the fluorescence detection of miRNAs in human serum samples. High relative recoveries from 95.6% to 104.8% were obtained

Additional file 10 of A functional reference map of the RNF8 interactome in cancer

Additional file 10. Figure S10. The survival analysis of hub proteins. The heatmap of the pan-cancer OS rate of 11 hub proteins by Kaplan-Meier survival analysis based on TCGA samples by GEPIA. A log rank p <0.05 was considered to indicate a statistically significant difference and are framed in red (positively correlated) or blue (negatively correlated)

Additional file 4 of A functional reference map of the RNF8 interactome in cancer

Additional file 4. Figure S4. The PPI network using RNF8 and identified interactome

Additional file 16 of A functional reference map of the RNF8 interactome in cancer

Additional file 16. Table S2. Hub genes list identified from online tools and LC-MS-based identification

Additional file 15 of A functional reference map of the RNF8 interactome in cancer

Additional file 15. Table S1. 218 proteins identified by intersection of two dataset

Additional file 9 of A functional reference map of the RNF8 interactome in cancer

Additional file 9. Figure S9. Upsetplot and Venn diagrame showing the intersection between five PPI database-generated RNF8-interacting proteins

Supplementary Figures 1 and 2 from RNF8 Promotes Epithelial–Mesenchymal Transition in Lung Cancer Cells via Stabilization of Slug

Figure S1 shows the expression of RNF8 in different pathological types of lung cancers tissues. Figure S2 shows the overexpression of RNF8-C406S cannot improve the migration potential of A549 cells.</p

Additional file 7 of A functional reference map of the RNF8 interactome in cancer

Additional file 7. Figure S7. Wikipathway analysis of RNF8 in cancers Group1

Additional file 6 of A functional reference map of the RNF8 interactome in cancer

Additional file 6. Figure S6. RNF8 abundance-based classification of cancer samples