Liver-specific DICER1 syndrome model mice develop cystic liver tumors with defective primary cilia.

DICER1 syndrome is a tumor predisposition syndrome caused by familial genetic mutations in DICER1. Pathogenic variants of DICER1 have been discovered in many rare cancers, including cystic liver tumors. However, the molecular mechanisms underlying liver lesions induced by these variants remain unclear. In the present study, we sought to gain a better understanding of the pathogenesis of these variants by generating a mouse model of liver-specific DICER1 syndrome. The mouse model developed bile duct hyperplasia with fibrosis, similar to congenital hepatic fibrosis, as well as cystic liver tumors resembling those in Caroli's syndrome, intrahepatic cholangiocarcinoma, and hepatocellular carcinoma. Interestingly, the mouse model of DICER1 syndrome showed abnormal formation of primary cilia in the bile duct epithelium, which is a known cause of bile duct hyperplasia and cyst formation. These results indicated that DICER1 mutations contribute to cystic liver tumors by inducing defective primary cilia. The mouse model generated in this study will be useful for elucidating the potential mechanisms of tumorigenesis induced by DICER1 variants and for obtaining a comprehensive understanding of DICER1 syndrome. © 2024 The Pathological Society of Great Britain and Ireland.

Novel AI Combining CNN and SVM to Predict Colorectal Cancer Prognosis and Mutational Signatures from HE Images.

Reducing recurrence following radical resection of colon cancer without over- or under-treatment remains a challenge. Postoperative adjuvant chemotherapy (Adj) is currently administered based solely on pathological tumor, node, and metastasis (TNM) stage. However, prognosis can vary significantly among patients with the same disease stage. Therefore, novel classification systems in addition to the TNM are necessary to inform decision-making regarding postoperative treatment strategies, especially stage II and III disease, and to minimize overtreatment and undertreatment with Adj. We developed a prognostic prediction system for colorectal cancer by using a combined convolutional neural network (CNN) and support vector machine (SVM) approach to extract features from hematoxyling and eosin staining (HE) images. We combined the TNM and our AI-based classification system into a TNM-AI (mTNM-AI) classification system with high discriminative power for recurrence-free survival (RFS). Furthermore, the cancer cell population recognized by this system as low risk of recurrence exhibited the mutational signature SBS87 as a genetic phenotype. The novel AI-based classification system developed here is expected to play an important role in prognostic prediction and personalized treatment selection in oncology.

The membrane-linked adaptor FRS2ß fashions a cytokine-rich inflammatory microenvironment that promotes breast cancer carcinogenesis.

Although it is held that proinflammatory changes precede the onset of breast cancer, the underlying mechanisms remain obscure. Here, we demonstrate that FRS2ß, an adaptor protein expressed in a small subset of epithelial cells, triggers the proinflammatory changes that induce stroma in premalignant mammary tissues and is responsible for the disease onset. FRS2ß deficiency in mouse mammary tumor virus (MMTV)-ErbB2 mice markedly attenuated tumorigenesis. Importantly, tumor cells derived from MMTV-ErbB2 mice failed to generate tumors when grafted in the FRS2ß-deficient premalignant tissues. We found that colocalization of FRS2ß and the NEMO subunit of the IκB kinase complex in early endosomes led to activation of nuclear factor-κB (NF-κB), a master regulator of inflammation. Moreover, inhibition of the activities of the NF-κB-induced cytokines, CXC chemokine ligand 12 and insulin-like growth factor 1, abrogated tumorigenesis. Human breast cancer tissues that express higher levels of FRS2ß contain more stroma. The elucidation of the FRS2ß-NF-κB axis uncovers a molecular link between the proinflammatory changes and the disease onset.

Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.

PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction. METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation. RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction. CONCLUSION: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.

MiR-34a induces myofibroblast differentiation from renal fibroblasts.

BACKGROUND: Renal fibrosis is the common outcome of progressive kidney diseases. To avoid dialysis, the molecular mechanism of renal fibrosis must be explored further. MicroRNAs play key roles in renal fibrosis. MiR-34a is a transcriptional target of p53, which regulates the cell cycle and apoptosis. Previous studies demonstrated that miR-34a promotes renal fibrosis. However, the distinct roles of miR-34a in renal fibrosis have not been fully elucidated. Here, we identified the roles of miR-34a in renal fibrosis. METHOD: We first analyzed p53 and miR-34a expression in kidney tissues in s UUO (unilateral ureteral obstruction) mouse model. Then, to confirm the effects of miR-34a in vitro, we transfected a miR-34a mimic into a kidney fibroblast cell line (NRK-49F) and analyzed. RESULTS: We found that the expression of p53 and miR-34a was upregulated after UUO. Furthermore, after transfection of the miR-34a mimic into kidney fibroblasts, the expression of α-SMA was upregulated dramatically. In addition, α-SMA upregulation was greater upon transfection of the miR-34a mimic than upon treatment with TGF-ß1. Moreover, high expression of Acta2 was maintained despite sufficient removal of the miR-34a mimic by changing the medium 4 times during the 9-day culture. After transfection of the miR-34a mimic into kidney fibroblasts, we did not detect phospho-SMAD2/3 by immunoblotting analysis. CONCLUSION: Our study revealed that miR-34a induces myofibroblast differentiation from renal fibroblasts. Moreover, the miR-34a-induced upregulation of α-SMA was independent of the TGF-ß/SMAD signaling pathway. In conclusion, our study indicated that the p53/miR-34a axis promotes the development of renal fibrosis.

Survival Rate of 3-unit Fixed Partial Dentures Replacing First Molars: A Retrospective Cohort Study.

Fixed partial dentures (FPDs) made of 12% Au-Pd alloy are covered under Japan's national health insurance system. The survival rate of such 3-unit fixed FPDs remains unknown, however. The purpose of this retrospective study was to assess their survival rate in the replacement of first molars. A total of 140 FPDs were included, and the endpoint was removal of an FPD. During the observation period, 43 FPDs were removed. The FPD survival rate was 70.2% at 10 yr, 58.2% at 15 yr, and 42.1% at 20 yr according to the Kaplan-Meier method. The estimated mean survival period was 19.4 yr. The main reasons for FPD loss were dental caries (27.9%), separation from the abutment tooth (18.6%), and pulpitis (18.6%). Single factor analysis using the log-rank test showed that two factors influenced FPD survival: a smaller gonial angle and deep pockets around the abutment teeth. This effect was not statistically significant in either case, however (p>0.05). The present results suggest that the prevention of caries and of the separation of the dentures from the abutment teeth are important factors in the long-term survival of FPDs.

Order controls disordered droplets: structure-function relationships in C9ORF72-derived poly(PR).

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) have been thought as two distinct neurodegenerative diseases. However, recent genetic screening and careful investigations found the genetic and pathological overlap among these disorders. Hexanucleotide expansions in intron 1 of C9ORF72 are a leading cause of familial ALS and familial FTD. These expansions facilitate the repeat-associated non-ATG-initiated translation (RAN translation), producing five dipeptide repeat proteins (DRPs), including Arg-rich poly(PR: Pro-Arg) and poly(GR: Gly-Arg) peptides. Arg is a positively charged, highly polar amino acid that facilitates interactions with anionic molecules such as nucleic acids and acidic amino acids via electrostatic forces and aromatic amino acids via cation-π interaction, suggesting that Arg-rich DRPs underlie the pathophysiology of ALS via Arg-mediated molecular interactions. Arg-rich DRPs have also been reported to induce neurodegeneration in cellular and animal models via multiple mechanisms; however, it remains unclear why the Arg-rich DRPs exhibit such diverse toxic properties, because not all Arg-rich peptides are toxic. In this mini-review, we discuss the current understanding of the pathophysiology of Arg-rich C9ORF72 DRPs and introduce recent findings on the role of Arg distribution as a determinant of the toxicity and its contribution to the pathogenesis of ALS.

How can we interpret the relationship between liquid-liquid phase separation and amyotrophic lateral sclerosis?

One of the critical definitions of neurodegenerative diseases is the formation of insoluble intracellular inclusion body. These inclusions are found in various neurodegenerative diseases such as Alzheimer's disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, Parkinson's disease, and frontotemporal dementia (FTD). Each inclusion body contains disease-specific proteins and is also resistant to common detergent treatments. These aggregates are generally ubiquitinated and thus recognized as misfolded by the organism. They are observed in residual neurons at the affected sites in each disease, suggesting a contribution to disease pathogenesis. The molecular mechanisms for the formation of these inclusion bodies remain unclear. Some proteins, such as superoxide dismutase 1 (SOD1) mutant that causes familial ALS, are highly aggregative due to altered folding caused by point mutations. Still, the aggregates observed in neurodegenerative diseases contain wild-type proteins. In recent years, it has been reported that the proteins responsible for neurodegenerative diseases undergo liquid-liquid phase separation (LLPS). In particular, the ALS/FTD causative proteins such as TAR DNA-binding protein 43 kDa (TDP-43) and fused-in-sarcoma (FUS) undergo LLPS. LLPS increases the local concentration of these proteins, and these proteins eventually change their phase from liquid to solid (liquid-solid phase transition) due to abnormal folding during repetitive separation cycles into two phases and recovery to one phase. In addition to the inclusion body formation, sequestration of essential proteins into the LLPS droplets or changes in the LLPS status can directly impair neural functions and cause diseases. In this review, we will discuss the relationship between the LLPS observed in ALS causative proteins and the pathogenesis of the disease and outline potential therapeutic approaches.

Circulating cancer-associated extracellular vesicles as early detection and recurrence biomarkers for pancreatic cancer.

Early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for improving patient survival rates, and noninvasive biomarkers are urgently required to identify patients who are eligible for curative surgery. Here, we examined extracellular vesicles (EVs) from the serum of PDAC patients to determine their ability to detect early-stage disease. EV-associated proteins purified by ultracentrifugation and affinity columns underwent proteomic analysis to identify novel PDAC markers G protein-coupled receptor class C group 5 member C (GPRC5C) and epidermal growth factor receptor pathway substrate 8 (EPS8). To verify the potency of GPRC5C- or EPS8-positive EVs as PDAC biomarkers, we analyzed EVs from PDAC patient blood samples using ultracentrifugation in two different cohorts (a total of 54 PDAC patients, 32 healthy donors, and 22 pancreatitis patients) by immunoblotting. The combination of EV-associated GPRC5C and EPS8 had high accuracy, with area under the curve values of 0.922 and 0.946 for distinguishing early-stage PDAC patients from healthy controls in the two cohorts, respectively, and could detect PDAC patients who were negative for CA19-9. Moreover, we analyzed 30 samples taken at three time points from 10 PDAC patients who underwent surgery: before surgery, after surgery, and recurrence as an early-stage model. These proteins were detected in EVs derived from preoperative and recurrence samples. These results indicated that GPRC5C- or EPS8-positive EVs were biomarkers that have the potential to detect stage I early pancreatic cancer and small recurrent tumors detected by computed tomography.

Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features.

Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis; however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used; data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.

Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer.

BACKGROUND: The treatment strategies for colorectal cancer (CRC) must ensure a radical cure of cancer and prevent over/under treatment. Biopsy specimens used for the definitive diagnosis of T1 CRC were analyzed using artificial intelligence (AI) to construct a risk index for lymph node metastasis. METHODS: A total of 146 T1 CRC cases were analyzed. The specimens for analysis were mainly biopsy specimens, and in the absence of biopsy specimens, the mucosal layer of the surgical specimens was analyzed. The pathology slides for each case were digitally imaged, and the morphological features of cancer cell nuclei were extracted from the tissue images. First, statistical methods were used to analyze how well these features could predict lymph node metastasis risk. A lymph node metastasis risk model using AI was created based on these morphological features, and accuracy in test cases was verified. RESULTS: Each developed model could predict lymph node metastasis risk with a > 90% accuracy in each region of interest of the training cases. Lymph node metastasis risk was predicted with 81.8-86.3% accuracy for randomly validated cases, using a learning model with biopsy data. Moreover, no case with lymph node metastasis or lymph node risk was judged to have no risk using the same model. CONCLUSIONS: AI models suggest an association between biopsy specimens and lymph node metastases in T1 CRC and may contribute to increased accuracy of preoperative diagnosis.

Optimal DNA quality preservation process for comprehensive genomic testing of pediatric clinical autopsy formalin-fixed, paraffin-embedded tissues.

Clinical autopsies are performed to reveal the process of the disease that caused patient death and validate the diagnosis and treatment decisions. In pediatric clinical autopsy, the feedback provided to bereaved families has a considerable social impact; however, pediatric diseases are diverse, which makes it difficult to elucidate them. Therefore, it is necessary to employ molecular biology techniques in addition to conventional methods. Formalin-fixed, paraffin-embedded (FFPE) tissues are routinely prepared. However, clinical autopsy FFPE tissue processing is not standardized, and it is unclear whether DNA from such tissues can be used for comprehensive genomic analysis. In this study, we evaluated the DNA quality of FFPE tissues from 15 recent autopsy cases at a single-center children's hospital using quantitative polymerase chain reaction [PCR (Q129/Q41)] and nanoelectrophoresis (DNA integrity number (DIN)). Good quality DNA was obtained from every organ type excluding bone marrow within 6 days of formalin fixation. Prolonged proteinase K digestion (48 h > 24 h > 1 h) and thicker tissue sections (10 µm > 1 µm) improved Q129/Q41; however, 24 h fixed FFPE tissues showed better DNA quality. We propose an optimal and feasible workflow for storing short-term fixed FFPE tissues as DNA-preserved FFPE tissues for future comprehensive genomic searches.

The Patterning and Proportion of Charged Residues in the Arginine-Rich Mixed-Charge Domain Determine the Membrane-Less Organelle Targeted by the Protein.

Membrane-less organelles (MLOs) are formed by biomolecular liquid-liquid phase separation (LLPS). Proteins with charged low-complexity domains (LCDs) are prone to phase separation and localize to MLOs, but the mechanism underlying the distributions of such proteins to specific MLOs remains poorly understood. Recently, proteins with Arg-enriched mixed-charge domains (R-MCDs), primarily composed of R and Asp (D), were found to accumulate in nuclear speckles via LLPS. However, the process by which R-MCDs selectively incorporate into nuclear speckles is unknown. Here, we demonstrate that the patterning of charged amino acids and net charge determines the targeting of specific MLOs, including nuclear speckles and the nucleolus, by proteins. The redistribution of R and D residues from an alternately sequenced pattern to uneven blocky sequences caused a shift in R-MCD distribution from nuclear speckles to the nucleolus. In addition, the incorporation of basic residues in the R-MCDs promoted their localization to the MLOs and their apparent accumulation in the nucleolus. The R-MCD peptide with alternating amino acids did not undergo LLPS, whereas the blocky R-MCD peptide underwent LLPS with affinity to RNA, acidic poly-Glu, and the acidic nucleolar protein nucleophosmin, suggesting that the clustering of R residues helps avoid their neutralization by D residues and eventually induces R-MCD migration to the nucleolus. Therefore, the distribution of proteins to nuclear speckles requires the proximal positioning of D and R for the mutual neutralization of their charges.

Establishment of chemically oligomerizable TAR DNA-binding protein-43 which mimics amyotrophic lateral sclerosis pathology in mammalian cells.

One of the pathological hallmarks of amyotrophic lateral sclerosis (ALS) is mislocalized, cytosolic aggregation of TAR DNA-Binding Protein-43 (TDP-43). Not only TDP-43 per se is a causative gene of ALS but also mislocalization and aggregation of TDP-43 seems to be a common pathological change in both sporadic and familial ALS. The mechanism how nuclear TDP-43 transforms into cytosolic aggregates remains elusive, but recent studies using optogenetics have proposed that aberrant liquid-liquid phase separation (LLPS) of TDP-43 links to the aggregation process, leading to cytosolic distribution. Although LLPS plays an important role in the aggregate formation, there are still several technical problems in the optogenetic technique to be solved to progress further in vivo study. Here we report a chemically oligomerizable TDP-43 system. Oligomerization of TDP-43 was achieved by a small compound AP20187, and oligomerized TDP-43 underwent aggregate formation, followed by cytosolic mislocalization and induction of cell toxicity. The mislocalized TDP-43 co-aggregated with wt-TDP-43, Fused-in-sarcoma (FUS), TIA1 and sequestosome 1 (SQSTM1)/p62, mimicking ALS pathology. The chemically oligomerizable TDP-43 also revealed the roles of the N-terminal domain, RNA-recognition motif, nuclear export signal and low complexity domain in the aggregate formation and mislocalization of TDP-43. The aggregate-prone properties of TDP-43 were enhanced by a familial ALS-causative mutation. In conclusion, the chemically oligomerizable TDP-43 system could be useful to study the mechanisms underlying the droplet-aggregation phase transition and cytosolic mislocalization of TDP-43 in ALS and further study in vivo.

An improved macromolecular crowding sensor CRONOS for detection of crowding changes in membrane-less organelles under stressed conditions.

Membrane-less organelles (MLOs) formed by liquid-liquid phase separation (LLPS) play pivotal roles in biological processes. During LLPS, proteins and nucleotides are extremely condensed, resulting in changes in their conformation and biological functions. Disturbed LLPS homeostasis in MLOs is thought to associate with fatal diseases such as amyotrophic lateral sclerosis. Therefore, it is important to detect changes in the degree of crowding in MLOs. However, it has not been investigated well due to the lack of an appropriate method. To address this, we developed a genetically encoded macromolecular crowding sensor CRONOS (crowding sensor with mNeonGreen and mScarlet-I) that senses the degree of macromolecular crowding in MLOs using a fluorescence resonance energy transfer (FRET) system. CRONOS is a bright biosensor with a wide dynamic range and successfully detects changes in the macromolecular volume fraction in solution. By fusing to the scaffold protein of each MLO, we delivered CRONOS to MLO of interest and detected previously undescribed differences in the degree of crowding in each MLO. CRONOS also detected changes in the degree of macromolecular crowding in nucleolus induced by environmental stress or inhibition of transcription. These findings suggest that CRONOS can be a useful tool for the determination of molecular crowding and detection of pathological changes in MLOs in live cells.

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning.

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test); Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28); and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation; however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.

Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles.

PURPOSE: Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model. DESIGN: Cross-sectional study. PARTICIPANTS: Five hundred twelve eyes with diagnoses from among 17 intraocular diseases. METHODS: Aqueous humor samples were collected, and the concentrations of 28 immune mediators were determined using a cytometric bead array. Each immune mediator was ranked according to its importance using 5 machine learning algorithms. Stratified k-fold cross-validation was used in evaluation of algorithms with the dataset divided into training and test datasets. MAIN OUTCOME MEASURES: The algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristic curve, area under the precision-recall curve, and mean decrease in Gini index. RESULTS: Among the 5 machine learning models, random forest (RF) yielded the highest classification accuracy in multiclass differentiation of 17 intraocular diseases. The RF prediction models for vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma achieved the highest classification accuracy, precision, and recall. Random forest recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma with the top 5 F-scores. The 3 highest-ranking relevant immune mediators were interleukin (IL)-10, interferon-γ-inducible protein (IP)-10, and angiogenin for prediction of vitreoretinal lymphoma; monokine induced by interferon γ, interferon γ, and IP-10 for acute retinal necrosis; and IL-6, granulocyte colony-stimulating factor, and IL-8 for endophthalmitis. CONCLUSIONS: Random forest algorithms based on 28 immune mediators in aqueous humor successfully predicted the diagnosis of vitreoretinal lymphoma, acute retinal necrosis, and endophthalmitis. Overall, the findings of the present study contribute to increased knowledge on new biomarkers that potentially can facilitate diagnosis of intraocular diseases in the future.

Survival Rate of Removable Partial Dentures with Mandibular Bilateral Free End Saddle: A Retrospective Study.

The purpose of this study was to investigate the survival of removable partial dentures with a mandibular bilateral free end saddle (BFES) and abutment teeth in a clinical setting. Only mandibular dentures with a BFES were included (10 or fewer present teeth, and fewer than 4 occlusal units). The endpoints were replacement of denture and loss of abutment teeth. A total of 128 dentures and 595 abutment teeth were analyzed. Nineteen dentures had to be replaced during the observation period (mean duration: 11.4±6.9 years; range: 3 to 36 years). According to Kaplan-Meier analysis, the survival rate was 93.2% at 10 years and 68.6% at 20 years. The estimated mean survival period was 27.8 years. Single-factor analysis using the log-rank test showed that no factor investigated had a significant influence. The main reason for denture replacement was loss of abutment teeth (47.4%). The survival rate of the abutment teeth was 91.3% at 10 years and 77.3% at 20 years. The analysis revealed 4 significant risk factors: male sex (hazard ratio [HR]: 1.78); premolars (HR: 1.67); a lower number of abutment teeth (HR: 3.24); and history of endodontic treatment (HR: 2.79). The removable partial dentures with a mandibular BFES in this study lasted over 20 years, and their survival was influenced by loss of abutment teeth. Dentures are used continuously over long periods of time and should therefore be designed to allow easy adjustment when abutment teeth are lost.

miR-27a ameliorates chemoresistance of breast cancer cells by disruption of reactive oxygen species homeostasis and impairment of autophagy.

In patients with breast cancer, primary chemotherapy often fails due to survival of chemoresistant breast cancer stem cells (BCSCs) which results in recurrence and metastasis of the tumor. However, the factors determining the chemoresistance of BCSCs have remained to be investigated. Here, we profiled a series of differentially expressed microRNAs (miRNAs) between parental adherent breast cancer cells and BCSC-mimicking mammosphere-derived cancer cells, and identified hsa-miR-27a as a negative regulator for survival and chemoresistance of BCSCs. In the mammosphere, we found that the expression of hsa-miR-27a was downregulated, and ectopic overexpression of hsa-miR-27a reduced both number and size of mammospheres. In addition, overexpression of hsa-miR-27a sensitized breast cancer cells to anticancer drugs by downregulation of genes essential for detoxification of reactive oxygen species (ROS) and impairment of autophagy. Therefore, enhancing the hsa-miR-27a signaling pathway can be a potential therapeutic modality for breast cancer.

Differences in drug sensitivity between two-dimensional and three-dimensional culture systems in triple-negative breast cancer cell lines.

Three-dimensional (3D) culture reflects tumor biology complexities compared with two-dimensional (2D) culture. Thus, 3D culture has attracted attention in cell biology studies including drug sensitivity tests. Herein, we investigated differences in anticancer drug sensitivities between 2D and 3D culture systems in triple-negative breast cancer (TNBC) cell lines. Thirteen TNBC cell lines were maintained in 2D and 3D cultures for 3 days before drug exposure. Cell morphology in the 3D culture was examined by phase-contrast microscopy. Sensitivities to epirubicin (EPI), cisplatin (CDDP), and docetaxel (DTX) were investigated by cell viability assay in both cultures and compared. The IC50s of all 3 drugs were significantly higher in the 3D culture than in the 2D culture in most cell lines. Those were correlated between the 2D and 3D cultures in EPI (R = 0.555) and CDDP (R = 0.955), but not in DTX (R = 0.221). Round spheroid-forming cells were more resistant to agents than grape-like types. In conclusion, 3D culture was more resistant to all 3 drugs than 2D culture in most TNBC cell lines. Sensitivity to CDDP was highly correlated between the 2D and 3D cultures, but not to DTX. 2D culture may be acceptable for sensitivity test for DNA-damaging agents.