IJS Oncology

Exploring artificial neural network combined with laser-induced auto-fluorescence technology for noninvasive in vivo upper gastrointestinal tract cancer early diagnosis In this study, a laser-induced auto-fluorescence (LIAF) system combined with the artificial neural network (ANN) algorithm is developed for early detection of human upper gastrointestinal tract carcinoma in vivo, through investigating the LIAF spectrum characteristics of the normal mucosa layer and the changes concerning an abnormal surface. Of the 44 participating patients, 41 underwent biopsy at the abnormal surface area at endoscopy. The ANN is employed to differentiate the LIAF data obtained from the normal and carcinoma patients (according to biopsy pathology diagnosis). The LIAF spectrum between 500 and 700 nm is selected and normalized. One data point is selected every 10 nm. A feed-forward back-propagation network with 2 hidden layers is constructed and trained. To evaluate the performance of ANN, 10 normal and 10 carcinoma data sets are tested with the trained ANN. 100% of the carcinoma data are very close to −1 (desired), 80% of the normal surface is very close to 1 (desired), and 20% return values around −0.28. Previous works on this type of ANN suggested a threshold of −0.5. As a result, all normal data are successful and the carcinoma cases are accurately classified and diagnosed. In conclusion, the LIAF technology combined with ANN diagnosis is more accurate.

Identification of key genes and their functions in palbociclib-resistant breast carcinoma by using bioinformatics analysis Background: Palbociclib resistance is a significant problem in breast carcinoma, and its underlying molecular mechanisms remain poorly understood. This study aims to elucidate the molecular mechanisms of palbociclib resistance and to identify the key genes and pathways mediating progesterone resistance in breast cancer (BC). Methods: Gene dataset GSE117743 was downloaded from the Gene Expression Omnibus (GEO) database, which included 3 palbociclib-resistant and 3 palbociclib-sensitive BC cell lines. Then, we calculated the differentially expressed genes (DEGs) by using R software. Gene ontology and Enriched pathway analysis of genes we identified were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction network was performed according to Metascape, String, and Cytoscape software. Results: In total, 447 DEGs were selected, which consisted of 67 upregulated and 380 downregulated genes. According to gene ontology annotation, DEGs were associated with cytoplasm, signal transduction, and protein binding. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that genes enriched in certain tumor pathways, including IL-17 signaling pathways and Herpes simplex infection signaling pathways. Also, certain hub genes were highlighted after constructed and analyzed the protein-protein interaction network, including α-2A adrenergic receptor, cytochrome P450 subfamily IIR polypeptide, Cystathionine β-synthase, nucleotide-binding oligomerization domain-containing, erythropoietin-producing hepatocellular receptor A2 and adrenomedullin, which may be related with BC prognosis. A total of 4 of 6 hub genes had a significant relationship with the overall survival (P<0.05). Conclusions: Using microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of progesterone resistance. We offered several possible mechanisms of progesterone resistance and identified therapeutic and prognostic targets of palbociclib resistance in BC.