Non-muscle-invasive bladder cancer (NMIBC) consists of transcriptional subtypes that are distinguishable from those of muscle-invasive cancer. We aimed to identify genetic signatures of NMIBC related to basal (K5/6) and luminal (K20) keratin expression. Based on immunohistochemical staining, papillary high-grade NMIBC was classified into K5/6-only (K5/6High-K20Low), K20-only (K5/6Low-K20High), double-high (K5/6High-K20High), and double-low (K5/6Low-K20Low) groups (n = 4 per group). Differentially expressed genes identified between each group using RNA sequencing were subjected to functional enrichment analyses. A public dataset was used for validation. Machine learning algorithms were implemented to predict our samples against UROMOL subtypes. Transcriptional investigation demonstrated that the K20-only group was enriched in the cell cycle, proliferation, and progression gene sets, and this result was also observed in the public dataset. The K5/6-only group was closely regulated by basal-type gene sets and showed activated invasive or adhesive functions. The double-high group was enriched in cell cycle arrest, macromolecule biosynthesis, and FGFR3 signaling. The double-low group moderately expressed genes related to cell cycle and macromolecule biosynthesis. All K20-only group tumors were classified as UROMOL “class 2” by the machine learning algorithms. K5/6 and K20 expression levels indicate the transcriptional subtypes of NMIBC. The K5/6Low-K20High expression is a marker of high-risk NMIBC.

BACKGROUND Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. OBJECTIVE In this study, we aimed to detect the early occurrence of AKI by applying the interpretable LSTM-based model on a hospital EHR-based time series in patients who took nephrotoxic drugs using a DRN. METHODS We conducted a multi-institutional retrospective cohort study of data from six hospitals using a DRN. For each institution, a patient-based dataset was constructed using five drugs for AKI, and the interpretable multi-variable long short-term memory (IMV-LSTM) model was used for training. This study employed propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using one-way analysis of variance. RESULTS This study analyzed 8,643 and 31,012 patients with and without AKI, respectively, across six hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median: 12 days), and acyclovir was the slowest compared to the other drugs (median: 23 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.80). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. CONCLUSIONS Early surveillance of AKI outbreaks can be achieved by applying the IMV-LSTM based on time series data through hospital electronic health records (EHR)-based DRNs. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

Hypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Nine hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into the following six groups. The group-specific genetic risk score was classified as high or low risk based on mRNA expression, and survival outcomes were evaluated. The risk scores in the Female reproductive group and Lung group were internally and externally validated. In the Female reproductive group, CDKN2A, FN1 and ITGA5, were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female reproductive group was enriched in terms related to cilia and skin, while the Lung group was enriched in terms related to cytokines and defense. This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.