Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Adipocyte pyroptosis occurs in omental tumor microenvironment and is associated with chemoresistance of ovarian cancer.

BACKGROUND: Ovarian carcinoma (OC) is a fatal malignancy, with most patients experiencing recurrence and resistance to chemotherapy. In contrast to hematogenous metastasizing tumors, ovarian cancer cells disseminate within the peritoneal cavity, especially the omentum. Previously, we reported omental crown-like structure (CLS) number is associated with poor prognosis of advanced-stage OC. CLS that have pathologic features of a dead or dying adipocyte was surrounded by several macrophages is well known a histologic hallmark for inflammatory adipose tissue. In this study, we attempted to clarify the interaction between metastatic ovarian cancer cells and omental CLS, and to formulate a therapeutic strategy for advanced-stage ovarian cancer. METHODS: A three-cell (including OC cells, adipocytes and macrophages) coculture model was established to mimic the omental tumor microenvironment (TME) of ovarian cancer. Caspase-1 activity, ATP and free fatty acids (FFA) levels were detected by commercial kits. An adipocyte organoid model was established to assess macrophages migration and infiltration. In vitro and in vivo experiments were performed for functional assays and therapeutic effect evaluations. Clinical OC tissue samples were collected for immunochemistry stain and statistics analysis. RESULTS: In three-cell coculture model, OC cells-derived IL-6 and IL-8 could induce the occurrence of pyroptosis in omental adipocytes. The pyroptotic adipocytes release ATP to increase macrophage infiltration, release FFA into TME, uptake by OC cells to increase chemoresistance. From OC tumor samples study, we demonstrated patients with high gasdermin D (GSDMD) expression in omental adipocytes is highly correlated with chemoresistance and poor outcome in advanced-stage OC. In animal model, by pyroptosis inhibitor, DSF, effectively retarded tumor growth and prolonged mice survival. CONCLUSIONS: Omental adipocyte pyroptosis may contribute the chemoresistance in advanced stage OC. Omental adipocytes could release FFA and ATP through the GSDMD-mediate pyroptosis to induce chemoresistance and macrophages infiltration resulting the poor prognosis in advanced-stage OC. Inhibition of adipocyte pyroptosis may be a potential therapeutic modality in advanced-stage OC with omentum metastasis.

Clinimetric Properties of the "FIND-NEEDS" to Screen Geriatric Conditions.

INTRODUCTION: Comprehensive geriatric assessment (CGA) is used to thoroughly assess and identify complex healthcare problems among older adults. However, administration of CGA is time-consuming and labor intensive. A simple screening tool with the mnemonic "FIND-NEEDS" was developed to quickly identify common geriatric conditions. The present study was to evaluate the clinimetric properties of the FIND-NEEDS. METHODS: The participants comprised first-visiting older adults aged 65 years and above (and who were able to communicate by themselves or with the help of a caregiver) who were assessed (October to December, 2021) using the FIND-NEEDS and CGA at geriatric outpatient clinics of a tertiary, referred medical center. The FIND-NEEDS was examined for its criterion-related validity and compared with the CGA results. Two types of scoring (summed score and binary score) of FIND-NEEDS and CGA were analyzed using Spearman correlation, sensitivity and specificity, and area under receiver operating characteristic curve (AUC). RESULTS: The mean age of the 114 outpatients was 78.3 ± 7.6 years, and 79 (69.3%) were female. The internal consistency was excellent when using all FIND-NEEDS items, and was acceptable when using domain scores. Exploratory factor analysis showed that most of the FIND-NEEDS domain scores had factor loadings higher than 0.3. Intercorrelations of binary scores between domains of FIND-NEEDS and CGA showed most domains were moderately correlated. The overall correlation of summed scores between FIND-NEEDS and CGA was high. The FIND-NEEDS summed score was moderately correlated with CGA score (r = 0.494; p < 0.001), and the binary score showed excellent correlation (r = 0.944; p < 0.001). When using the CGA score as the gold standard, the FIND-NEEDS showed excellent AUC (0.950), sensitivity (1.00), and specificity (0.90). DISCUSSION/CONCLUSION: The present study demonstrated that the FIND-NEEDS had acceptable clinimetric properties to screen for geriatric problems among older adults. Further in-depth assessment and care plan can then be conducted afterwards.

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.

Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vast number of existing variants and the existence of variants which are recognized as variant of uncertain significance (VUS). To tackle these challenges, we propose algorithms to assign a significance to each gene rather than each variant, describing its degree of pathogenicity. Since the interpreters identified most of the variants as VUS, most of the gene scores were identified as uncertain significance. To predict the uncertain significance scores, we design two matrix factorization-based models: the common latent space model uses genomics variant data as well as heterogeneous clinical data, while the single-matrix factorization model can be used when heterogeneous clinical data are unavailable. We have managed to show that the models successfully predict the uncertain significance scores with low error and high accuracy. Moreover, to evaluate the effectiveness of our novel input features, we train five different multi-label classifiers including a feedforward neural network with the same feature set and show they all achieve high accuracy as the main impact of our approach comes from the features. Availability: The source code is freely available at https://github.com/sabdollahi/CoLaSpSMFM.

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images.

BACKGROUND: Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. METHODS: The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics. RESULTS: We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox. CONCLUSIONS: A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

Comprehensive assessments of germline deletion structural variants reveal the association between prognostic MUC4 and CEP72 deletions and immune response gene expression in colorectal cancer patients.

BACKGROUND: Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. We used deletion structural variants (DSVs) generated from germline whole-genome sequencing (WGS) and DSV immune-related association tumor microenvironment (TME) to predict cancer risk and prognosis. METHODS: We investigated the contribution of germline DSVs to cancer susceptibility and prognosis by silicon and causal inference models. DSVs in germline WGS data were generated from the blood samples of 192 cancer and 499 non-cancer subjects. Clinical information, including family cancer history (FCH), was obtained from the National Cheng Kung University Hospital and Taiwan Biobank. Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. We used joint calling tools and an attention-weighted model to build the cancer risk predictive model and identify DSVs in familial cancer. The survival support vector machine (survival-SVM) was used to select prognostic DSVs. RESULTS: We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the attention-weighted model was 0.71. The 3 most frequent DSV genes observed in cancer patients were identified as ADCY9, AURKAPS1, and RAB3GAP2 (p < 0.05). The DSVs in SGSM2 and LHFPL3 were relevant to colorectal cancer. We found a higher incidence of FCH in cancer patients than in non-cancer subjects (p < 0.05). SMYD3 and NKD2DSV genes were associated with cancer patients with FCH (p < 0.05). We identified 65 immune-associated DSV markers for assessing cancer prognosis (p < 0.05). The functional protein of MUC4 DSV gene interacted with MAGE1 expression, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene affect the recurrence-free survival (RFS) of IFIT1 expression. CONCLUSIONS: We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using germline DSVs and immune gene expression datasets. Comprehensive assessments of germline DSVs can predict the cancer risk and clinical outcome of colon cancer patients.

Integration of an Intensive Care Unit Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds: Cluster-Randomized Controlled Trial.

BACKGROUND: Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). OBJECTIVE: i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs. METHODS: A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys. RESULTS: Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs. CONCLUSIONS: i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698.

PD-L1 Expression in High-Risk Early-Stage Colorectal Cancer-Its Clinical and Biological Significance in Immune Microenvironment.

Programmed death-ligand 1 (PD-L1) is an immune checkpoint molecule that can regulate immune responses in the tumor microenvironment (TME); however, the clinical applications of PD-L1 in early-stage colorectal cancer (CRC) remain unclear. In this study, we aimed to investigate the relationship between PD-L1 expression and survival outcome and explore its relevant immune responses in CRC. PD-L1 expression was evaluated by immunohistochemical staining to determine the tumor proportion score and combined positive score (CPS) in a Taiwanese CRC cohort. The oncomine immune response research assay was conducted for immune gene expression analyses. CRC datasets from the TCGA database were reappraised for PD-L1-associated gene enrichment analyses using GSEA. The high expression of PD-L1 (CPS ≥ 5) was associated with longer recurrence-free survival (p = 0.031) and was an independent prognostic factor as revealed by multivariate analysis. High PD-L1 expression was related to six immune-related gene signatures, and CXCL9 is the most significant overexpressed gene in differential analyses. High CXCL9 expression correlated with increased infiltration levels of immune cells in the TME, including CD8+ T lymphocytes and M1 macrophages. These findings suggest that high PD-L1 expression is a prognostic factor of early-stage CRC, and CXCL9 may play a key role in regulating PD-L1 expression.

Sequential and co-occurring DNA damage response genetic mutations impact survival in stage III colorectal cancer patients receiving adjuvant oxaliplatin-based chemotherapy.

BACKGROUND: Certain sequences of genomic mutations can lead to cancer formation and affect treatment outcomes and drug resistance. We constructed a cancer evolutionary tree using bulk-targeted deep sequencing to explore the impact of sequential and co-occurring somatic mutations on patients with stage III colorectal cancer (CRC). METHODS: A total of 108 stage III CRC patients from National Cheng Kung University Hospital (NCKUH) were recruited for this study between Jan. 2014 and Jan. 2019. Clinical information and tumor-targeted deep sequencing data were collected. Phylogenetic trees were reconstructed for evolutionary trajectories. We used a machine learning model for survival analysis. RESULTS: Six sequential somatic mutations stratified patients into seven subgroups based on survival. Patients carrying sequential germline followed by DNA damage response-related ATM or BRCA2 somatic mutations or non-TP53, APC somatic mutations had a better outcome than those without such mutations. The 4-year recurrence-free survival (RFS) probability was 88% in the low-risk group (G1) and 46% in the high-risk group (G2) (log-rank p-value 2e-05). The predictive efficacy by the area under the curve (AUC) was 0.73, 0.7, 0.797, and 0.88 at 2, 4, 6, and 8 years, respectively. The mutation status of mismatch repair (MMR) genes was not associated with RFS. Different genomic features were found between the groups. The orders of APC, KRAS and APC, BRCA2 sequential somatic mutations were associated with clinical outcomes. The occurrence of somatic mutations in BRCA2, such as TP53 somatic mutations, affected recurrence-free survival. CONCLUSIONS: According to the evolution model, DNA damage response (DDR)-related ATM or BRCA2 somatic mutations are promising biomarkers for assessing the response of stage III CRC patients to oxaliplatin-based chemotherapy. The sequential order and co-occurring DDR somatic mutations are associated with recurrence-free survival.

The Important Role of Ion Transport System in Cervical Cancer.

Cervical cancer is a significant gynecological cancer and causes cancer-related deaths worldwide. Human papillomavirus (HPV) is implicated in the etiology of cervical malignancy. However, much evidence indicates that HPV infection is a necessary but not sufficient cause in cervical carcinogenesis. Therefore, the cellular pathophysiology of cervical cancer is worthy of study. This review summarizes the recent findings concerning the ion transport processes involved in cell volume regulation and intracellular Ca2+ homeostasis of epithelial cells and how these transport systems are themselves regulated by the tumor microenvironment. For cell volume regulation, we focused on the volume-sensitive Cl- channels and K+-Cl- cotransporter (KCC) family, important regulators for ionic and osmotic homeostasis of epithelial cells. Regarding intracellular Ca2+ homeostasis, the Ca2+ store sensor STIM molecules and plasma membrane Ca2+ channel Orai proteins, the predominant Ca2+ entry mechanism in epithelial cells, are discussed. Furthermore, we evaluate the potential of these membrane ion transport systems as diagnostic biomarkers and pharmacological interventions and highlight the challenges.

CD40 Pathway and IL-2 Expression Mediate the Differential Outcome of Colorectal Cancer Patients with Different CSF1R c.1085 Genotypes.

Colony-stimulating factor 1 receptor (CSF-1R) acts as the receptor for colony stimulating factor 1, a cytokine that controls the production, differentiation, and function of macrophages. Prior studies showed cancer patients harboring germline CSF1R c.1085A>G genetic variant had better survival. Here, primary tumor samples from a stage III colorectal cancer (CRC) cohort were analyzed by a targeted gene expression assay containing 395 immune-related genes to study the immune mechanism underlying the different outcomes. CRC patients with CSF1R c.1085 genotype A_G had a better disease-free and overall survival than those with CSF1R genotype A_A. Compared to the group of patients without CSF1R variant, higher CD40LG expression, a surface marker of T cells, was found in the tumor tissues of patients with CSF1R c.1085 variant. In parallel with the higher CD40LG gene expression, immunofluorescent staining also showed more CD3+CD40L+ T cell infiltrates in tumors with CSF1R c.1085 genotype A_G. Moreover, higher IL-2 expression, known to be regulated by CD40 pathway, was also observed in tumors with CSF1R c.1085 genotype A_G than genotype A_A. Higher IL-2 expression generated by the interaction of CD40 ligand and CD40 between T cells and macrophages with CSF1R c.1085A>G variant is the potential mechanism explaining the different outcomes.

Treatment monitoring of colorectal cancer by integrated analysis of plasma concentration and sequencing of circulating tumor DNA.

Circulating cell-free DNA (cfDNA) analysis is an important tool for cancer monitoring. The patient-specific mutations identified in colorectal cancer (CRC) tissues are usually used to design the cfDNA analysis. Despite high specificity in predicting relapse, the sensitivity in most studies is around 40-50%. To improve this weakness, we designed a cfDNA panel according to the CRC genomic landscape and recurrent-specific mutations. The pathological variants in cfDNA samples from 60 CRC patients were studied by a next-generation sequencing (NGS) method incorporating the dual molecular barcode. Interestingly, patients in the disease positive group had a significantly higher cfDNA concentration than those in the disease negative group. Based on receiver operating characteristic analysis, the cfDNA concentration of 7 ng/mL was selected into the analytical workflow. The sensitivity in determining the disease status was 72.4%, which represented a considerable improvement on prior studies, and the specificity remained high at 80.6%. Compared to standard imaging and laboratory studies, earlier detection of residual disease and clinical benefits were shown on two cases by this cfDNA assay. We conclude this integrative framework of cfDNA analytical pipeline with a satisfactory sensitivity and specificity could be used in postoperative CRC surveillance.

Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.

BACKGROUND: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. OBJECTIVE: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. METHODS: This observational study on the emerging COVID-19 pandemic included 643 patients. An "AI trilogy" of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. RESULTS: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). CONCLUSIONS: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

The distinct role of STIM1 and STIM2 in the regulation of store-operated Ca2+ entry and cellular function.

Stromal interaction molecules STIM1 and STIM2 are endoplasmic reticulum (ER) Ca2+ sensors that initiate store-operated Ca 2+ entry (SOCE). The roles of STIM1-mediated SOCE in cancer biology have been highlighted in different types of cancer, but that of STIM2 is unknown. By the model of cervical cancer, here we focus on the cooperative regulation of SOCE by STIM proteins and their distinct roles in cellular function. Immunofluorescent stainings of surgical specimens of cervical cancer show that STIM1 and STIM2 are abundant in tumor tissues, but STIM1 is the major ER Ca 2+ sensor identified in the invasive front of cancer tissues. STIM1 or STIM2 overexpression in cervical cancer SiHa cells induces an upregulated SOCE. Regarding cellular function, STIM1 and STIM2 are necessary for cell proliferation, whereas STIM1 is the dominant ER Ca 2+ sensor involved in cell migration. During SOCE, STIM1 is aggregated and translocated towards the Orai1-containing plasma membrane in association with the microtubule plus-end binding protein EB1. In contrast, STIM2 is constitutively aggregated without significant trafficking or association with microtubules. These results show the distinct role of STIM1 and STIM2 in SOCE and cellular function of cervical cancer cells.

The store-operated Ca(2+) entry-mediated signaling is important for cancer spread.

Tumor cell migration and invasion are essential steps in the metastatic cascade that has great impact on patient outcomes. Spatial and temporal organization of Ca(2+) signaling regulates the multiple aspects of migration machinery, including cytoskeletal reorganization, traction force generation, and focal adhesion dynamics. Stromal interaction molecules (STIM) and Orai proteins, recently identified as critical constituents of store-operated Ca(2+) entry (SOCE), have been implicated in cancer cell migration and tumor metastasis. The clinical significance of STIM proteins and Orai Ca(2+) channels in tumor progression and their diagnostic and prognostic potentials have also been demonstrated in different types of cancers. Here we review the recent advances in understanding the important roles and regulatory mechanisms of STIM/Orai-mediated SOCE in cancer spread. The clinical implications and the emergence as a selective target for cancer therapeutics are also discussed. This article is part of a Special Issue entitled: Calcium and Cell Fate. Guest Editors: Jacques Haiech, Claus Heizmann, Joachim Krebs, Thierry Capiod and Olivier Mignen.

Seeking new surgical predictors of mesh exposure after transvaginal mesh repair.

INTRODUCTION AND HYPOTHESIS: The purpose of this study was to explore new preventable risk factors for mesh exposure. METHODS: A retrospective review of 92 consecutive patients treated with transvaginal mesh (TVM) in the urogynecological unit of our university hospital. An analysis of perioperative predictors was conducted in patients after vaginal repairs using a type 1 mesh. Mesh complications were recorded according to International Urogynecological Association (IUGA) definitions. Mesh-exposure-free durations were calculated by using the Kaplan-Meier method and compared between different closure techniques using log-rank test. Hazard ratios (HR) of predictors for mesh exposure were estimated by univariate and multivariate analyses using Cox proportional hazards regression models. RESULTS: The median surveillance interval was 24.1 months. Two late occurrences were found beyond 1 year post operation. No statistically significant correlation was observed between mesh exposure and concomitant hysterectomy. Exposure risks were significantly higher in patients with interrupted whole-layer closure in univariate analysis. In the multivariate analysis, hematoma [HR 5.42, 95 % confidence interval (CI) 1.26-23.35, P = 0.024), Prolift mesh (HR 5.52, 95 % CI 1.15-26.53, P = 0.033), and interrupted whole-layer closure (HR 7.02, 95 % CI 1.62-30.53, P = 0.009) were the strongest predictors of mesh exposure. CONCLUSION: Findings indicate the risks of mesh exposure and reoperation may be prevented by avoiding hematoma, large amount of mesh, or interrupted whole-layer closure in TVM surgeries. If these risk factors are prevented, hysterectomy may not be a relative contraindication for TVM use. We also provide evidence regarding mesh exposure and the necessity for more than 1 year of follow-up and preoperative counselling.

Mechanosensitive store-operated calcium entry regulates the formation of cell polarity.

Ca(2+) -mediated formation of cell polarity is essential for directional migration which plays an important role in physiological and pathological processes in organisms. To examine the critical role of store-operated Ca(2+) entry, which is the major form of extracellular Ca(2+) influx in non-excitable cells, in the formation of cell polarity, we employed human bone osteosarcoma U2OS cells, which exhibit distinct morphological polarity during directional migration. Our analyses showed that Ca(2+) was concentrated at the rear end of cells and that extracellular Ca(2+) influx was important for cell polarization. Inhibition of store-operated Ca(2+) entry using specific inhibitors disrupted the formation of cell polarity in a dose-dependent manner. Moreover, the channelosomal components caveolin-1, TRPC1, and Orai1 were concentrated at the rear end of polarized cells. Knockdown of TRPC1 or a TRPC inhibitor, but not knockdown of Orai1, reduced cell polarization. Furthermore, disruption of lipid rafts or overexpression of caveolin-1 contributed to the downregulation of cell polarity. On the other hand, we also found that cell polarity, store-operated Ca(2+) entry activity, and cell stiffness were markedly decreased by low substrate rigidity, which may be caused by the disorganization of actin filaments and microtubules that occurs while regulating the activity of the mechanosensitive TRPC1 channel.

The ER Ca²âº sensor STIM1 regulates actomyosin contractility of migratory cells.

Stromal interaction molecule 1 (STIM1) is an endoplasmic reticulum (ER) Ca(2+) sensor that triggers the store-operated Ca(2+) entry (SOCE). The clinical relevance of STIM1 has been highlighted in breast and cervical cancer, but the molecular mechanism by which STIM1 promotes cancer progression remains unclear. This study explores the regulatory mechanisms by which STIM1-dependent Ca(2+) signaling controls cancer cell migration. Three different SOCE inhibitors, SKF96365, 2-APB and YM-58483, significantly inhibited cervical cancer cell migration to a similar extent to that of STIM1 silencing. In contrast, STIM1 overexpression significantly enhanced cervical cancer cell migration. Live cell confocal images and three-dimensional tomograms showed that STIM1 formed aggregates and translocated towards the plasma membranes of migratory cells, and this was accompanied by increasing cytosolic Ca(2+) spikes. STIM1 silencing also inhibited the recruitment and association of active focal adhesion kinase (pTyr397-FAK) and talin at focal adhesions, indicating the blockade of force transduction from integrin signaling. Epidermal growth factor-induced phosphorylation of myosin II regulatory light chains was abolished by STIM1 knockdown and SOCE inhibition. Dual immunostaining of activated myosin II (pSer19-MLC) and actin revealed that actomyosin formation depended on STIM1-mediated Ca(2+) entry. Most importantly, STIM1 expression levels as well as SOCE activity controlled the generation of cell contractile force, as measured by the microfabricated post-array-detector system. These results highlight the unique role of STIM1-dependent Ca(2+) signaling in controlling cell migration by the regulation of actomyosin reorganization in conjunction with enhanced contractile forces.

Glycosylation regulates the function and membrane localization of KCC4.

Glycosylation plays a role in regulating many biological activities, including protein folding and cell surface expression of biomolecules. However, the importance of glycosylation for KCC4 function has not previously been demonstrated. Site-directed mutagenesis was performed on the four putative extracellular N-linked glycosylation sites of KCC4 to determine the role of these sites in KCC4 half-life, cell surface expression, and transporter activity, as well as in KCC4-dependent tumor formation. We showed that triple (N312/331/344/Q) and quadruple (N312/331/344/360/Q) mutations of N-linked glycosylation sites disrupt the N-linked glycosylation of KCC4, resulting in the accumulation of KCC4, predominantly in the endoplasmic reticulum (ER) and not at the cell surface. Further investigation indicated that mutations of the central two (N331/344/Q) N-linked glycosylation sites inhibit the membrane trafficking of KCC4. Our data suggest that the glycan moieties at the N331 and N344 sites were Endo H-resistant, complex-form structures, and that the N312 and N360 sites were Endo H-sensitive, high mannose-containing structures. Under hypotonic stress conditions, the ability to adapt to changes in intracellular chloride ion concentrations and RVD (regulatory volume decrease) activities were less efficient in cells containing the deglycosylated form of KCC4 that were not expressed at the cell surface. Deglycosylated forms of KCC4 also demonstrated decreased tumor formation and lung colonization in mouse xenografts. The difference in glycan complexity may account for the differential impact of each branch on the biological effects of KCC4. We propose that glycosylation is essential for the surface expression, stabilization, and bioactivity of KCC4.

Calcium store sensor stromal-interaction molecule 1-dependent signaling plays an important role in cervical cancer growth, migration, and angiogenesis.

Store-operated Ca(2+) entry (SOCE) is the principal Ca(2+) entry mechanism in nonexcitable cells. Stromal-interaction molecule 1 (STIM1) is an endoplasmic reticulum Ca(2+) sensor that triggers SOCE activation. However, the role of STIM1 in regulating cancer progression remains controversial and its clinical relevance is unclear. Here we show that STIM1-dependent signaling is important for cervical cancer cell proliferation, migration, and angiogenesis. STIM1 overexpression in tumor tissue is noted in 71% cases of early-stage cervical cancer. In tumor tissues, the level of STIM1 expression is significantly associated with the risk of metastasis and survival. EGF-stimulated cancer cell migration requires STIM1 expression and EGF increases the interaction between STIM1 and Orai1 in juxta-membrane areas, and thus induces Ca(2+) influx. STIM1 involves the activation of Ca(2+)-regulated protease calpain, as well as Ca(2+)-regulated cytoplasmic kinase Pyk2, which regulate the focal-adhesion dynamics of migratory cervical cancer cells. Because of an increase of p21 protein levels and a decrease of Cdc25C protein levels, STIM1-silencing in cervical cancer cells significantly inhibits cell proliferation by arresting the cell cycle at the S and G2/M phases. STIM1 also regulates the production of VEGF in cervical cancer cells. Interference with STIM1 expression or blockade of SOCE activity inhibits tumor angiogenesis and growth in animal models, confirming the crucial role of STIM1-mediated Ca(2+) influx in aggravating tumor development in vivo. These results make STIM1-dependent signaling an attractive target for therapeutic intervention.