Development of pluripotent stem cell-derived epidermal organoids that generate effective extracellular vesicles in skin regeneration.

Cellular skin substitutes such as epidermal constructs have been developed for various applications, including wound healing and skin regeneration. These cellular models are mostly derived from primary cells such as keratinocytes and fibroblasts in a two-dimensional (2D) state, and further development of three-dimensional (3D) cultured organoids is needed to provide insight into the in vivo epidermal phenotype and physiology. Here, we report the development of epidermal organoids (EpiOs) generated from induced pluripotent stem cells (iPSCs) as a novel epidermal construct and its application as a source of secreted biomolecules recovered by extracellular vesicles (EVs) that can be utilized for cell-free therapy of regenerative medicine. Differentiated iPSC-derived epidermal organoids (iEpiOs) are easily cultured and expanded through multiple organoid passages, while retaining molecular and functional features similar to in vivo epidermis. These mature iEpiOs contain epidermal stem cell populations and retain the ability to further differentiate into other skin compartment lineages, such as hair follicle stem cells. By closely recapitulating the epidermal structure, iEpiOs are expected to provide a more relevant microenvironment to influence cellular processes and therapeutic response. Indeed, iEpiOs can generate high-performance EVs containing high levels of the angiogenic growth factor VEGF and miRNAs predicted to regulate cellular processes such as proliferation, migration, differentiation, and angiogenesis. These EVs contribute to target cell proliferation, migration, and angiogenesis, providing a promising therapeutic tool for in vivo wound healing. Overall, the newly developed iEpiOs strategy as an organoid-based approach provides a powerful model for studying basic and translational skin research and may also lead to future therapeutic applications using iEpiOs-secreted EVs.

CDKN2 expression is a potential biomarker for T cell exhaustion in hepatocellular carcinoma.

Hepatocellular Carcinoma (HCC), the predominant primary hepatic malignancy, is the prime contributor to mortality. Despite the availability of multiple surgical interventions, patient outcomes remain suboptimal. Immunotherapies have emerged as effective strategies for HCC treatment with multiple clinical advantages. However, their curative efficacy is not always satisfactory, limited by the dysfunctional T cell status. Thus, there is a pressing need to discover novel potential biomarkers indicative of T cell exhaustion (Tex) for personalized immunotherapies. One promising target is Cyclin-dependent kinase inhibitor 2 (CDKN2) gene, a key cell cycle regulator with aberrant expression in HCC. However, its specific involvement remains unclear. Herein, we assessed the potential of CDKN2 expression as a promising biomarker for HCC progression, particularly for exhausted T cells. Our transcriptome analysis of CDKN2 in HCC revealed its significant role involving in HCC development. Remarkably, single-cell transcriptomic analysis revealed a notable correlation between CDKN2 expression, particularly CDKN2A, and Tex markers, which was further validated by a human cohort study using human HCC tissue microarray, highlighting CDKN2 expression as a potential biomarker for Tex within the intricate landscape of HCC progression. These findings provide novel perspectives that hold promise for addressing the unmet therapeutic need within HCC treatment.

Inhibition of SIRT7 overcomes sorafenib acquired resistance by suppressing ERK1/2 phosphorylation via the DDX3X-mediated NLRP3 inflammasome in hepatocellular carcinoma.

AIMS: Sirtuin 7 (SIRT7) plays an important role in tumor development, and has been characterized as a potent regulator of cellular stress. However, the effect of SIRT7 on sorafenib acquired resistance remains unclear and a possible anti-tumor mechanism beyond this process in HCC has not been clarified. We examined the therapeutic potential of SIRT7 and determined whether it functions synergistically with sorafenib to overcome chemoresistance. METHODS: Cancer Genome Atlas-liver HCC data and unbiased gene set enrichment analyses were used to identify SIRT7 as a potential effector molecule in sorafenib acquired resistance. Two types of SIRT7 chemical inhibitors were developed to evaluate its therapeutic properties when synergized with sorafenib. Mass spectrometry was performed to discover a direct target of SIRT7, DDX3X, and DDX3X deacetylation levels and protein stability were explored. Moreover, an in vivo xenograft model was used to confirm anti-tumor effect of SIRT7 and DDX3X chemical inhibitors combined with sorafenib. RESULTS: SIRT7 inhibition mediated DDX3X depletion can re-sensitize acquired sorafenib resistance by disrupting NLRP3 inflammasome assembly, finally suppressing hyperactive ERK1/2 signaling in response to NLRP3 inflammasome-mediated IL-1ß inhibition. CONCLUSIONS: SIRT7 is responsible for sorafenib acquired resistance, and its inhibition would be beneficial when combined with sorafenib by suppressing hyperactive pro-cell survival ERK1/2 signaling.

Review of Current Treatment Intensification Strategies for Prostate Cancer Patients.

Prostate cancer (PCa) used to be one of the most common nondermatologic cancers in men that can be treated only with surgery. However, a revolutionary breakthrough came in the 1980s with the introduction of long-acting luteinizing hormone-releasing hormone (LHRH) agonists for the curative treatment of PCa. This paradigm shift contributed to the combined use of androgen deprivation therapy (ADT), chemotherapy, and radiotherapy for the treatment. The latest data highlight the use of treatment intensification (TI), i.e., combined use of radiotherapy (RT) and hormonal or drug treatments, for localized or locally advanced PCa. Indeed, the results of combined modality treatments have shown a reduction in disease-specific mortality and improved overall survival. Although TI seems promising, more research studies are warranted to confirm its efficacy. This review summarizes the latest available outcome results of pivotal trials and clinical studies on the efficacy of TI.

Network and Computational Drug Repurposing Analysis for c-Myc Inhibition in Burkitt Lymphoma.

BACKGROUND/AIM: The treatment rate of Burkitt lymphoma (BL) is still low in low-income countries and among elderly patients. The c-Myc dysregulation induced by mutations is one of the characteristics of BL. However, studies on the downstream signaling pathways of c-Myc are still lacking. This study aimed to identify the signaling pathways regulated by c-Myc. MATERIALS AND METHODS: Network and gene set analyses using c-Myc inhibition (i.e., c-Myc knock-down and c-Myc inhibitor treatment) transcriptome datasets for BL cell lines were performed to determine the pathways regulated by c-Myc. In addition, computational drug repurposing was used to identify drugs that can regulate c-Myc downstream signaling pathway. RESULTS: Computational drug repurposing revealed that the ERK/MAPK signaling pathway is regulated by c-Myc in BL and that this pathway can be modulated by vorinostat. Furthermore, in the pharmacogenomics database, vorinostat showed a cell viability half-maximal inhibitory concentration of less than 2 µM in the BL cell lines. CONCLUSION: The downstream signaling pathway regulated by c-Myc and the drug that can modulate this pathway is presented for the first time.

Characterization of the genomic alterations in poorly differentiated thyroid cancer.

Poorly differentiated thyroid carcinoma (PDTC) is a subtype of thyroid cancer that has a high rate of metastasis or recurrence and a relatively poor prognosis. However, there are few studies that have been conducted on PDTC at the whole protein-coding gene scale. Here, we performed genomic profiling of 15 patients with PDTC originated from follicular thyroid carcinoma using whole exome sequencing and also performed gene functional enrichment analysis of differentially expressed genes (DEGs) for three patients. Further, we investigated genetic variants associated with PDTC progression and the characteristics of clinical pathology. We revealed somatic genomic alterations in the RAF1, MAP2K2, and AKT2 genes that were not reported in previous studies. We confirmed frequent occurrences in the RAS gene in patients with PDTC; the genetic alterations were associated with the RAS-RAF-MEK-ERK/JNK, PI3K-AKT-mTOR signaling pathways, and the cell cycle. DEG analysis showed that immune response was lower in cancer tissues than in normal tissues. Through the association analysis of somatic mutations and the characteristics of clinical pathology from patients with PDTC, the somatic mutations of ABCA12, CLIP1, and ATP13A3 were significantly associated with a vascular invasion phenotype. By providing molecular genetic insight on PDTC, this study may contribute to the discovery of novel therapeutic target candidates.

The causal relationship of colorectal cancer on schizophrenia: A Mendelian randomization study.

Comorbidities associated with psychiatric disorders often occur in patients with cancer. A causal effect of schizophrenia on cancer was observed using Mendelian randomization (MR) analysis. However, the causal effect of colorectal cancer on schizophrenia has not been studied using MR analysis. Therefore, we performed MR analysis to investigate the causal effects of colorectal cancer on schizophrenia. We performed "two-sample summary-data Mendelian randomization" using publicly available genome-wide association studies data to investigate the causal relationship between colorectal cancer (as exposure) and schizophrenia (as outcome). The inverse variance weighted method was used to calculate causal estimates. In 2 TSMR analyses, we reported that the odds ratios for schizophrenia per log odds increase in colorectal cancer risk were 6.48 (95% confidential interval [CI] of OR 1.75-24.03; P = .005) and 9.62 × 106 (95% CI of OR 1.13-8.22 × 1013; P = .048). Pleiotropic tests and sensitivity analysis demonstrated minimal horizontal pleiotropy and robustness of the causal relationship. We provide evidence for a causal relationship between the incidence of colorectal cancer and the development of schizophrenia through TSMR analysis.

Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol.

Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA (torsion-angle and implicit-solvation-optimized simulated annealing) that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and dihedral angle restraints. We compared the initial NMR structures with the TrioSA-refined structures and found significant improvements in structural quality. In particular, we observed a reduction in both the maximum and number of NOE (nuclear Overhauser effect) violations, indicating better agreement with experimental NMR data. TrioSA improved geometric validation metrics of NMR protein structure, including backbone accuracy and the secondary structure ratio. We evaluated the contribution of each refinement element and found that the torsional angle potential played a significant role in improving the geometric validation metrics. In addition, we investigated protein-ligand docking to determine if TrioSA can improve biological outcomes. TrioSA structures exhibited better binding prediction compared to the initial NMR structures. This study suggests that further development and research in computational refinement methods could improve biomolecular NMR structural determination.

Significance of 8-OHdG Expression as a Predictor of Survival in Colorectal Cancer.

The incidence of colorectal cancer (CRC) is increasing worldwide. 8-hydroxy-2'-deoxyguanosine (8-OHdG), one of the most prevalent DNA alterations, is known to be upregulated in several carcinomas; however, 8-OHdG has not been used to predict the prognosis of patients with CRC. We aimed to determine 8-OHdG levels in patients with CRC using immunohistochemistry and conducted a survival analysis according to the pathological stage. The 5-year event-free survival (EFS) and disease-specific survival (DSS) hazard ratios (HRs) of the low 8-OHdG subgroup were 1.41 (95% confidence interval (CI): 1.01-1.98, p = 0.04) and 1.60 (95% CI: 1.12-2.28, p = 0.01), respectively. When tumor node metastasis (TNM) staging and 8-OHdG expression were combined, the 5-year EFS and DSS HRs of patients with CRC with low 8-OHdG expression cancer at the same TNM stage (stage Ⅲ/Ⅳ) were 1.51 (95% CI: 1.02-2.22, p = 0.04) and 1.64 (95% CI: 1.09-2.48, p = 0.02), respectively, compared to those with high 8-OHdG expression cancer, indicating a poor prognosis. Therefore, low 8-OHdG expression is a significant predictive factor for 5-year EFS and DSS in patients with CRC, and it can serve as an essential biomarker of CRC.

Clinicopathologic Significance of Heat Shock Protein 60 as a Survival Predictor in Colorectal Cancer.

The role of heat shock protein 60 (HSP60), a mitochondrial chaperone, in tumor progression or its anti-tumor effects remains controversial. This study aimed to confirm the possibility of using HSP60 as a prognostic marker in patients with colorectal cancer (CRC), considering TNM classification for precise prediction. HSP60 expression increased with differentiation and p53 mutations in patients. However, compared to patients with high HSP60 expression, patients with low HSP60 expression had event-free survival and disease-specific survival hazard ratios (HRs) of 1.42 and 1.69, respectively. Moreover, when the survival rate was analyzed by combining TNM classification and HSP60 expression, the prognosis was poor, particularly when HSP60 expression was low in the late/advanced stage. This pattern was also observed with HSP family D member 1, HSPD1, the gene that encodes HSP60. Low HSPD1 expression was linked to lower overall survival and relapse-free survival rates, with HRs of 1.80 and 1.87, respectively. When TNM classification and HSPD1 expression were considered, CRC patients with low HSPD1 expression and advanced malignancy had a poorer prognosis than those with high HSPD1 expression. Thus, HSPD1/HSP60 can be a useful biomarker for a sophisticated survival prediction in late- and advanced-stage CRC, allowing the design of individualized treatment strategies.

Urban dust particles disrupt mitotic progression by dysregulating Aurora kinase B-related functions.

Particulate matter (PM), a major component of outdoor air pollution, damages DNA and increases the risk of cancer. Although the harmful effects of PM at the genomic level are known, the detailed mechanism by which PM affects chromosomal stability remains unclear. In this study, we investigated the novel effects of PM on mitotic progression and identified the underlying mechanisms. Gene set enrichment analysis of lung cancer patients residing in countries with high PM concentrations revealed the downregulation of genes associated with mitosis and mitotic structures. We also showed that exposure of lung cancer cells in vitro to urban dust particles (UDPs) inhibits cell proliferation through a prolonged M phase. The mitotic spindles in UDP-treated cells were hyperstabilized, and the number of centrioles increased. The rate of ingression of the cleavage furrow and actin clearance from the polar cortex was reduced significantly. The defects in mitotic progression were attributed to inactivation of Aurora B at kinetochore during early mitosis, and spindle midzone and midbody during late mitosis. While previous studies demonstrated possible links between PM and mitosis, they did not specifically identify the dysregulation of spatiotemporal dynamics of mitotic proteins and structures (e.g., microtubules, centrosomes, cleavage furrow, and equatorial and polar cortex), which results in the accumulation of chromosomal instability, ultimately contributing to carcinogenicity. The data highlight the novel scientific problem of PM-induced mitotic disruption. Additionally, we introduce a practical visual method for assessing the genotoxic outcomes of airborne pollutants, which has implications for future environmental and public health research.

miRDM-rfGA: Genetic algorithm-based identification of a miRNA set for detecting type 2 diabetes.

BACKGROUND: Type 2 diabetes mellitus (T2DM) affects approximately 451 million adults globally. In this study, we identified the optimal combination of marker candidates for detecting T2DM using miRNA-Seq data from 95 samples including T2DM and healthy individuals. METHODS: We utilized the genetic algorithm (GA) in the discovery of an optimal miRNA biomarker set. We discovered miRNA subsets consisting of three miRNAs for detecting T2DM by random forest-based GA (miRDM-rfGA) as a feature selection algorithm and created six GA parameter settings and three settings using traditional feature selection methods (F-test and Lasso). We then evaluated the prediction performance to detect T2DM in the miRNA subsets derived from each setting. RESULTS: The miRNA subset in setting 5 using miRDM-rfGA performed the best in detecting T2DM (mean AUROC = 0.92). Target mRNA identification and functional enrichment analysis of the best miRNA subset (hsa-miR-125b-5p, hsa-miR-7-5p, and hsa-let-7b-5p) validated that this combination was involved in T2DM. We also confirmed that the targeted genes were negatively correlated with the clinical variables related to T2DM in the BxD mouse genetic reference population database. CONCLUSIONS: Using GA in miRNA-Seq data, we identified the optimal miRNA biomarker set for T2DM detection. GA can be a useful tool for biomarker discovery and drug-target identification.

A performance evaluation of drug response prediction models for individual drugs.

Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC50]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R2 ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R2 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.

Mild exposure to fine particulate matter promotes angiogenesis in non-small cell lung carcinoma.

Fine particulate matter (PM2.5) is associated with public health problems worldwide. Especially, PM2.5 induces epigenetic and microenvironmental changes in lung cancer. Angiogenesis is important for the development and growth of cancer and is mediated by angiogenic factors, including vascular endothelial growth factor. However, the effects of mild PM2.5 exposure on angiogenesis in lung cancer remain unclear. In this study, we examined angiogenic effects using relatively lower concentrations of PM2.5 than in other studies and found that PM2.5 increased angiogenic activities in both endothelial cells and non-small cell lung carcinoma cells. PM2.5 also promoted the growth and angiogenesis of lung cancer via the induction of hypoxia-inducible factor-1α (HIF-1α) in a xenograft mouse tumor model. Angiogenic factors, including vascular endothelial growth factor (VEGF), were highly expressed in lung cancer patients in countries with high PM2.5 levels in the atmosphere, and high expression of VEGF in lung cancer patients lowered the survival rate. Collectively, these results provide new insight into the mechanisms by which mild exposure to PM2.5 is involved in HIF-1α-mediated angiogenesis in lung cancer patients.

Determining the Factors Predicting the Response to Anti-HER2 Therapy in HER2-Positive Breast Cancer Patients.

PURPOSE: We aimed to identify the differently expressed genes or related pathways associated with good responses to anti-HER2 therapy and to suggest a model for predicting drug response in neoadjuvant systemic therapy with trastuzumab in HER2-positive breast cancer patients. METHODS: This study was retrospectively analyzed from consecutively collected patient data. We recruited 64 women with breast cancer and categorized them into 3 groups: complete response (CR), partial response (PR), and drug resistance (DR). The final number of patients in the study was 20. RNA from 20 core needle biopsy paraffin-embedded tissues and 4 cultured cell lines (SKBR3 and BT474 breast cancer parent cells and cultured resistant cells) was extracted, reverse transcribed, and subjected to GeneChip array analysis. The obtained data were analyzed using Gene Ontology, Kyoto Gene and Genome Encyclopedia, Database for Annotation, Visualization and Integrated Discovery. RESULTS: In total, 6,656 genes differentially expressed between trastuzumab-susceptible and trastuzumab-resistant cell lines were identified. Among these, 3,224 were upregulated and 3,432 were downregulated. Expression changes in 34 genes in several pathways were found to be related to the response to trastuzumab-containing treatment in HER2-type breast cancer, interfering with adhesion to other cells or tissues (focal adhesion) and regulating extracellular matrix interactions and phagosome action. Thus, decreased tumor invasiveness and enhanced drug effects might be the mechanisms explaining the better drug response in the CR group. CONCLUSIONS: This multigene assay-based study provides insights into breast cancer signaling and possible predictions of therapeutic response to targeted therapies such as trastuzumab.

Clinical Significance of Combined Epithelial-Mesenchymal Transition Markers Expression and Role of Rac1 in Hepatocellular Carcinoma.

Epithelial-mesenchymal transition (EMT) has been implicated in cancer progression, invasion, and metastasis. We aimed to evaluate the correlations between clinicopathological characteristics and EMT markers in patients with hepatocellular carcinoma (HCC) who underwent surgical resection and to identify the key regulator in EMT process. Fresh-frozen HCC tissues and adjacent nontumor liver tissues from 30 patients who underwent surgical resection were provided by the Gachon University Gil Medical Center Bio Bank. Human HCC cell lines, Hep3B, SNU449, and Huh7 cells were transfected with Rac1 siRNA and exposed to hypoxic conditions. The combined EMT markers expression (down-expression of E-cadherin and overexpression of p21-activated kinases 1 (PAK1)/Snail) by Western blot in HCC tissues when compared to adjacent nontumor liver tissues was significantly associated with macrovascular invasion (p = 0.021), microvascular invasion (p = 0.001), large tumor size (p = 0.021), and advanced tumor stage (p = 0.015). Patients with combined EMT markers expression showed early recurrence and poor overall survival. In vitro studies showed that Rac1 knockdown decreased the expression of EMT markers including PAK1 and Snail in hypoxia-induced Hep3B cells and suppressed the migration and invasion of hypoxia-induced HCC cells. Rac1 may be a potential therapeutic target for inhibition of EMT process through the inhibition of PAK1 and Snail in HCC.

Oncogenic signaling pathways and hallmarks of cancer in Korean patients with acral melanoma.

Acral melanoma (AM), a rare subtype of cutaneous melanoma, shows higher incidence in Asians, including Koreans, than in Caucasians. However, the genetic modification associated with AM in Koreans is not well known and has not been comprehensively investigated in terms of oncogenic signaling, and hallmarks of cancer. We performed whole-exome and RNA sequencing for Korean patients with AM and acquired the genetic alterations and gene expression profiles. KIT alterations (previously known to be recurrent alterations in AM) and CDK4/CCND1 copy number amplifications were identified in the patients. Genetic and transcriptomic alterations in patients with AM were functionally converge to the hallmarks of cancer and oncogenic pathways, including 'proliferative signal persistence', 'apoptotic resistance', and 'activation of invasion and metastasis', despite the heterogeneous somatic mutation profiles of Korean patients with AM. This study may provide a molecular understanding for therapeutic strategy for AM.

Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance.

A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods.

Association Between DPP4 Inhibitor Use and the Incidence of Cirrhosis, ESRD, and Some Cancers in Patients With Diabetes.

CONTEXT: There are relatively few data on noncardiovascular (non-CV) long-term clinical outcomes of dipeptidyl peptidase 4 inhibitor (DPP4i) treatment. OBJECTIVE: We aimed to evaluate some non-CV effects of DPP4is in patients with diabetes. METHODS: Based on data from the National Health Insurance Service database in Korea (2007-2018), we conducted 3 pairwise comparisons of metformin-combined antidiabetic therapies in adult patients with diabetes: DPP4is vs (1) all other oral antidiabetic agents, (2) sulfonylureas/glinides, and (3) thiazolidinediones (TZDs). Major outcomes were liver cirrhosis, end-stage renal disease (ESRD), and cancers in the liver, kidney, and pancreas. Adjusted hazard ratios (HRs) and 95% CIs for the outcomes were estimated using an adjusted Cox model. RESULTS: Of the 747 124 patients included, 628 217 had received DPP4i therapy for a mean duration of 33.8 ± 25.0 months. Compared with TZD therapy, DPP4i therapy was associated with higher adjusted HRs [95% CIs] for liver cirrhosis (1.267 [1.108-1.449]), ESRD (1.596 [1.139-2.236]), liver cancer (1.117 [1.011-1.235]), and pancreatic cancer (1.158 [1.040-1.290]). Furthermore, apart from liver cirrhosis, a higher risk of each of these outcomes was associated with DPP4i use than with non-DPP4i use. The higher adjusted HRs associated with DPP4i use further increased when patients with long-term exposure to DPP4is were analyzed. CONCLUSION: DPP4i therapy in patients with diabetes was associated with a higher risk of liver cirrhosis and cancer, ESRD, and pancreatic cancer than TZD therapy and, except for liver cirrhosis, the risk of these outcomes was greater with DPP4i treatment than with non-DPP4i treatment.

High Expression of PRNP Predicts Poor Prognosis in Korean Patients with Gastric Cancer.

Gastric cancer (GC) has the highest occurrence and fourth-highest mortality rate of all cancers in Korea. Although survival rates are improving with the development of diagnosis and treatment methods, the five-year survival rate for stage 4 GC in Korea remains <10%. Therefore, it is important to identify candidate prognostic factors for predicting poor prognosis. PRNP is a gene encoding the prion protein PrP, which has been noted for its role in the nervous system and is known to be upregulated in various cancers and associated with both cell proliferation and metastasis. However, the value of PRNP as a prognostic factor for Korean GC patients remains unclear. Here, we analyzed the relationship between PRNP expression and survival in three independent datasets for Korean patients with GC as well as the TCGA-STAD dataset. Survival analysis indicates that high levels of PRNP expression are associated with poor overall survival of patients with GC. Gene set enrichment analysis showed that PRNP is associated with epithelial mesenchymal transition and Hedgehog signaling. In addition, proliferation of GC cell lines was inhibited after siRNA-mediated knockdown of PRNP. In conclusion, our study suggests a potential role for PRNP as a candidate prognostic factor for patients with GC.