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Immunohistochemical (IHC) staining for CK5/6 and CK20 was reported to be correlated with the prognosis of early urothelial carcinoma in a way contrary to that of advanced tumors for unknown reasons. We aimed to characterize the gene expression profiles of subgroups of non-muscle-invasive papillary high-grade upper tract urothelial carcinoma (UTUC) classified by CK5/6 and CK20 expression levels: group 1 (CK5/6-high/CK20-low), group 2 (CK5/6-high/CK20-high), and group 3 (CK5/6-low/CK20-high). Expression of group 3 was predictive of worse prognosis of non-muscle-invasive papillary high-grade UTUC. Transcriptional analysis revealed 308 differentially expressed genes across the subgroups. Functional analyses of the genes identified cell adhesion as a common process differentially enriched in group 3 compared to the other groups, which could explain its high-risk phenotype. Late cell cycle/proliferation signatures were also enriched in group 3 and in some of the other groups, which may be used as a prognostic biomarker complementary to CK5/6 and CK20. Group 2, characterized by low levels of genes associated with mitogen-activated protein kinase and tumor necrosis factor signaling pathways, was hypothesized to represent the least cancerous subtype considering its normal urothelium-like IHC pattern. This study would facilitate the application of easily accessible prognostic biomarkers in practice.

We investigated the gene expression profiles of calcifications in breast cancer. Gene expression analysis of surgical specimen was performed using Affymetrix GeneChip® Human Gene 2.0 ST arrays in 168 breast cancer patients. The mammographic calcifications were reviewed by three radiologists and classified into three groups according to malignancy probability: breast cancers without suspicious calcifications; breast cancers with low-to-intermediate suspicious calcifications; and breast cancers with highly suspicious calcifications. To identify differentially expressed genes (DEGs) between these three groups, a one-way analysis of variance was performed with post hoc comparisons with Tukey’s honest significant difference test. To explore the biological significance of DEGs, we used DAVID for gene ontology analysis and BioLattice for clustering analysis. A total of 2551 genes showed differential expression among the three groups. ERBB2 genes are up-regulated in breast cancers with highly suspicious calcifications (fold change 2.474, p < 0.001). Gene ontology analysis revealed that the immune, defense and inflammatory responses were decreased in breast cancers with highly suspicious calcifications compared to breast cancers without suspicious calcifications (p from 10−23 to 10−8). The clustering analysis also demonstrated that the immune system is associated with mammographic calcifications (p < 0.001). Our study showed calcifications in breast cancers are associated with high levels of mRNA expression of ERBB2 and decreased immune system activity.

The combination of docetaxel, cisplatin, and fluorouracil (DCF) is highly synergistic in advanced gastric cancer. We aimed to explain these synergistic effects at the molecular level. Thus, we constructed a weighted correlation network using the differentially expressed genes between Stage I and IV gastric cancer based on The Cancer Genome Atlas (TCGA), and three modules were derived. Next, we investigated the correlation between the eigengene of the expression of the gene network modules and the chemotherapeutic drug response to DCF from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The three modules were associated with functions related to cell migration, angiogenesis, and the immune response. The eigengenes of the three modules had a high correlation with DCF (−0.41, −0.40, and −0.15). The eigengenes of the three modules tended to increase as the stage increased. Advanced gastric cancer was affected by the interaction the among modules with three functions, namely cell migration, angiogenesis, and the immune response, all of which are related to metastasis. The weighted correlation network analysis model proved the complementary effects of DCF at the molecular level and thus, could be used as a unique methodology to determine the optimal combination of chemotherapy drugs for patients with gastric cancer.Although its incidence is decreasing in some parts of the world, gastric cancer is still the fourth most common cancer worldwide 1,2 . It poses a critical clinical challenge and is associated with poor prognosis, because a high percentage of patients are diagnosed at an advanced stage in some areas and due to its relatively chemoresistant properties and limited treatment options. Metastatic spread occurs frequently in advanced gastric cancer, and it is one of the major causes of death 3,4 . Systemic chemotherapy, which involves a combination of various cytotoxic agents, constitutes the main type of treatment in the adjuvant or palliative setting for patients with metastatic or recurrent cancer 5-7 . Thus far, the choices regarding chemotherapeutic agents or their combinations have typically been determined according to the results from clinical trials. Therefore, the accurate prediction of the response to combination chemotherapy is labor intensive, time consuming, and expensive because of the explosion in the number of combinations available 8 . Accordingly, a new methodology for conducting a molecular-level analysis in order to understand the association between advanced gastric cancer and chemotherapy sensitivity is required.Despite the increasing knowledge about tumor biology and pharmacology, our understanding of combination chemotherapy is limited due to the complex factors involved, such as the gene-drug interactions and gene regulatory networks 9 . Nonetheless, various combinations of chemotherapeutic drugs have been extensively tested, because they can increase efficacy, lower dosages, and minimize drug resistance. The combination of docetaxel, cisplatin, and 5-fluorouracil (DCF) is one ...

Objective To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. Materials and Methods A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated. Results The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876–0.954), 0.813 (0.756–0.870), and 0.684 (0.616–0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840–0.928) and 0.833 (0.779–0.887) in the BSR and GR groups, respectively ( p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001). Conclusion AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.