ADCdb: the database of antibody-drug conjugates.

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Transformer with difference convolutional network for lightweight universal boundary detection.

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer. The TDCN structure consists of three parts: convolution, transformer, and head function. First, a convolution network fused with edge operators is used to extract multiscale difference features. These pixel difference features are then fed to the hierarchical transformer as tokens. Considering the intrinsic characteristics of the boundary detection task, a new boundary-aware self-attention structure was designed in the transformer to provide inductive bias. By incorporating the proposed attention loss function, it introduces the direction of the boundary as strongly supervised information to improve the detection ability of the model. Finally, several head functions with multiscale feature inputs were trained using a bidirectional additive strategy. In the experiments, the proposed method achieved competitive performance on multiple public datasets with fewer model parameters. A single model was obtained to realize universal prediction even for different datasets without retraining, demonstrating the effectiveness of the method. The code is available at https://github.com/neulmc/TDCN.

Caffeic acid phenethyl ester suppresses metastasis of breast cancer cells by inactivating FGFR1 via MD2.

BACKGROUND: Tumor metastasis is the main cause of death for breast cancer patients. Caffeic acid phenethyl ester (CAPE) has strong anti-tumor effects with very low toxicity and may be a potential candidate drug. However, the anti-metastatic effect and molecular mechanism of CAPE on breast cancer need more research. METHODS: MCF-7 and MDA-MB-231 breast cancer cells were used here. Wound healing and Transwell assay were used for migration and invasion detection. Western blot and RT-qPCR were carried out for the epithelial-to-myofibroblast transformation (EMT) process investigation. Western blot and immunofluorescence were performed for fibroblast growth factor receptor1 (FGFR1) phosphorylation and nuclear transfer detection. Co-immunoprecipitation was used for the FGFR1/myeloid differentiation protein2 (MD2) complex investigation. RESULTS: Our results suggested that CAPE blocks the migration, invasion, and EMT process of breast cancer cells. Mechanistically, CAPE inhibits FGFR1 phosphorylation and nuclear transfer while overexpression of FGFR1 reduces the anti-metastasis effect of CAPE. Further, we found that FGFR1 is bound to MD2, and silencing MD2 inhibits FGFR1 phosphorylation and nuclear transfer as well as cell migration and invasion. CONCLUSION: This study illustrated that CAPE restrained FGFR1 activation and nuclear transfer through MD2/FGFR1 complex inhibition and showed good inhibitory effects on the metastasis of breast cancer cells.

Identification and validation of the microRNAs and hub genes for pancreatic ductal adenocarcinoma by an integrated bioinformatic analysis.

Background: In the progression of pancreatic ductal adenocarcinoma (PDAC), aberrant micro RNAs (miRNAs) expression plays a crucial role. This study sought to identify and validate the key miRNAs and potential target genes involved in PDAC. A bioinformatic analysis was conducted to determine their potential use as biomarkers and therapeutic targets. Methods: Gene profiling data sets (GSE41372 and GSE32688) were retrieved from the Gene Expression Omnibus database. Differentially expressed miRNAs (DEMs) with a P value <0.05, and |fold change| >2 was identified. The prognostic value of the DEMs was accessed using the online server Kaplan-Meier plotter. Further, gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed using DAVID 6.7. The protein-protein interaction analyses were conducted with STRING, and miRNA-hub gene networks were constructed using Cytoscape software. The PDAC cells were transfected with miRNA inhibitors or mimics. Cell Counting Kit-8 assays and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining were used to examine cell proliferation and apoptosis, respectively. Wound-healing assays were performed to evaluate cell migration. Results: Three DEMs (hsa-miR-21-5p, hsa-miR-135b-5p, and hsa-miR-222-3p) were identified. High expression levels of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p predicted poor overall survival in PDAC patients. The pathway analysis revealed that the predicted target genes of the DEMs were closely related to several signaling pathways (including 'pathways in cancer', 'miRNAs in cancer', 'platinum drug resistance', 'lipid and atherosclerosis', and 'MAPK signaling pathway'). The MYC proto-oncogene (MYC), phosphate and tensin homolog gene (PTEN), poly(ADP-ribose) polymerase 1 (PARP1), von Hippel-Lindau (VHL), and fork head box p3 (FOXP3) were identified as potential target genes. The inhibition of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p expression decreased cell proliferation. The overexpression of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p facilitated PDAC cell migration. Conclusions: This study constructed the miRNA-hub gene network, which provides novel insights into the PDAC progression. Although further research is required, our results offer clues for new potential prognostic markers and therapeutic targets of PDAC.