Computer-aided diagnostic system with automated deep learning method based on the AutoGluon framework improved the diagnostic accuracy of early esophageal cancer.

Background: There have been studies on the application of computer-aided diagnosis (CAD) in the endoscopic diagnosis of early esophageal cancer (EEC), but there is still a significant gap from clinical application. We developed an endoscopic CAD system for EEC based on the AutoGluon framework, aiming to explore the feasibility of automatic deep learning (DL) in clinical application. Methods: The endoscopic pictures of normal esophagus, esophagitis, and EEC were collected from The First Affiliated Hospital of Soochow University (September 2015 to December 2021) and the Norwegian HyperKvasir database. All images of non-cancerous esophageal lesions and EEC in this study were pathologically examined. There were three tasks: task A was normal vs. lesion classification under non-magnifying endoscopy (n=932 vs. 1,092); task B was non-cancer lesion vs. EEC classification under non-magnifying endoscopy (n=594 vs. 429); and task C was non-cancer lesion vs. EEC classification under magnifying endoscopy (n=505 vs. 824). In all classification tasks, we took 100 pictures as the verification set, and the rest comprised as the training set. The CAD system was established based on the AutoGluon framework. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior >15 years; junior <5 years). Model evaluation indicators included accuracy, recall rate, precision, F1 value, interpretation time, and the area under the receiver operating characteristic (ROC) curve (AUC). Results: In tasks A and B, the accuracies of medium-performance CAD and high-performance CAD were lower than those of junior doctors and senior doctors. In task C, the medium-performance and high-performance CAD accuracies were close to those of junior doctors and senior doctors. The high-performance CAD model outperformed the junior doctors in both task A (0.850 vs. 0.830) and task C (0.840 vs. 0.830) in sensitivity comparison, but there was still a large gap between high-performance CAD models and doctors in sensitivity comparison. In task A, with the aid of CAD pre-interpretation, the accuracy of junior and senior physicians were significantly improved (from 0.880 to 0.915 and from 0.920 to 0.945, respectively); the time spent on film reading was significantly shortened (junior: from 11.3 to 8.7 s; senior: from 6.7 to 5.5 s). In task C, with the aid of CAD pre-interpretation, the accuracy of junior and senior physicians were significantly improved (from 0.850 to 0.865 and from 0.915 to 0.935, respectively); the reading time was significantly shortened (junior: from 9.5 to 7.7 s; senior: from 5.6 to 3.0 s). Conclusions: The CAD system based on the AutoGluon framework can assist doctors to improve the diagnostic accuracy and reading time of EEC under endoscopy. This study reveals that automatic DL methods are promising in clinical application.

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Association of the fat mass index with hepatic steatosis and fibrosis: evidence from NHANES 2017-2018.

Limited population-based studies discuss the association between fat mass index (FMI) and the risk of liver diseases. This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the linkage between the FMI and liver conditions, specifically steatosis and fibrosis. The study leveraged data from NHANES's 2017-2018 cross-sectional study, employing an oversampling technique to deal with sample imbalance. Hepatic steatosis and fibrosis were identified by vibration-controlled transient elastography. Receiver operating curve was used to assess the relationship of anthropometric indicators, e.g., the FMI, body mass index (BMI), weight-adjusted-waist index (WWI), percentage of body fat (BF%), waist-to-hip ratio (WHR), and appendicular skeletal muscle index (ASMI), with hepatic steatosis and fibrosis. In this study, which included 2260 participants, multivariate logistic regression models, stratified analyses, restricted cubic spline (RCS), and sharp regression discontinuity analyses were utilized. The results indicated that the WHR and the FMI achieved the highest area under the curve for identifying hepatic steatosis and fibrosis, respectively (0.720 and 0.726). Notably, the FMI presented the highest adjusted odds ratio for both hepatic steatosis (6.40 [4.91-8.38], p = 2.34e-42) and fibrosis (6.06 [5.00, 7.37], p = 5.88e-74). Additionally, potential interaction effects were observed between the FMI and variables such as the family income-to-poverty ratio, smoking status, and hypertension, all of which correlated with the presence of liver fibrosis (p for interaction < 0.05). The RCS models further confirmed a significant positive correlation of the FMI with the controlled attenuation parameter and liver stiffness measurements. Overall, the findings underscore the strong link between the FMI and liver conditions, proposing the FMI as a potential straightforward marker for identifying liver diseases.

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS: Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS: In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS: This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning.

BACKGROUND: Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem. METHODS: EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds. RESULTS: Dual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better. CONCLUSION: The proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study.

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model's favorable calibration and the decision curve analysis illustrated the model's value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.

Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study.

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan−Meier (K−M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K−M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies.

To explore the active constituents of Sedum aizoon L in the treatment of coronary heart disease based on network pharmacology and molecular docking methodology.

Background: There is a lack of effective drugs for the treatment of coronary heart disease (CHD). Sedum aizoon L (SL) has multiple effects, and there is no report on CHD in SL at present. The aim of this study is to explore the mechanisms of action of SL in the treatment of CHD based on network pharmacology and molecular docking technology. Methods: The targets and active ingredients of SL were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and CHD-related targets were obtained by searching GeneCards and DisGeNet databases. The intersection of LS active ingredient targets and CHD targets was used to construct a "drug-ingredient-disease-target" network using the Cytoscape software. The STRING database was used to construct a protein-protein interaction (PPI) network, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Key targets and core active ingredients were selected and molecular docking was performed using the AutoDock software. Results: According to the predicted results, a total of 134 corresponding target genes for LS, 12 active components, 1,704 CHD-related targets, and 52 intersecting targets were obtained. GO function and KEGG pathway analysis showed that the key targets were involved with signal transducer and activator of transcription 3 (STAT3), tumor protein p53 (TP53), and vascular endothelial growth factor A (VEGFA). The molecular docking results showed that the key targets bound to the important active ingredients in a stable conformation. The core active ingredients of LS in the treatment of CHD were determined to be ursolic acid, myricetin, and beta-sitosterol. Conclusions: SL may act on targets such as STAT3, TP53, and VEGFA through tumor necrosis factor (TNF) signaling pathway, interleukin 17A (IL-17A) signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and other related pathways, thereby playing a role in preventing and treating CHD.

A deafness-associated tRNA mutation caused pleiotropic effects on the m1G37 modification, processing, stability and aminoacylation of tRNAIle and mitochondrial translation.

Defects in the posttranscriptional modifications of mitochondrial tRNAs have been linked to human diseases, but their pathophysiology remains elusive. In this report, we investigated the molecular mechanism underlying a deafness-associated tRNAIle 4295A>G mutation affecting a highly conserved adenosine at position 37, 3' adjacent to the tRNA's anticodon. Primer extension and methylation activity assays revealed that the m.4295A>G mutation introduced a tRNA methyltransferase 5 (TRMT5)-catalyzed m1G37 modification of tRNAIle. Molecular dynamics simulations suggested that the m.4295A>G mutation affected tRNAIle structure and function, supported by increased melting temperature, conformational changes and instability of mutated tRNA. An in vitro processing experiment revealed that the m.4295A>G mutation reduced the 5' end processing efficiency of tRNAIle precursors, catalyzed by RNase P. We demonstrated that cybrid cell lines carrying the m.4295A>G mutation exhibited significant alterations in aminoacylation and steady-state levels of tRNAIle. The aberrant tRNA metabolism resulted in the impairment of mitochondrial translation, respiratory deficiency, decreasing membrane potentials and ATP production, increasing production of reactive oxygen species and promoting autophagy. These demonstrated the pleiotropic effects of m.4295A>G mutation on tRNAIle and mitochondrial functions. Our findings highlighted the essential role of deficient posttranscriptional modifications in the structure and function of tRNA and their pathogenic consequence of deafness.

Effectiveness of a Guided Internet- and Mobile-Based Intervention for Patients with Chronic Back Pain and Depression (WARD-BP): A Multicenter, Pragmatic Randomized Controlled Trial.

INTRODUCTION: There is neither strong evidence on effective treatments for patients with chronic back pain (CBP) and depressive disorder nor sufficiently available mental health care offers. OBJECTIVE: The aim is to assess the effectiveness of internet- and mobile-based interventions (IMI) as a scalable approach for treating depression in a routine care setting. METHODS: This is an observer-masked, multicenter, pragmatic randomized controlled trial with a randomization ratio of 1:1.Patients with CBP and diagnosed depressive disorder (mild to moderate severity) were recruited from 82 orthopedic rehabilitation clinics across Germany. The intervention group (IG) received a guided depression IMI tailored to CBP next to treatment-as-usual (TAU; including medication), while the control group (CG) received TAU. The primary outcome was observer-masked clinician-rated Hamilton depression severity (9-week follow-up). The secondary outcomes were: further depression outcomes, pain-related outcomes, health-related quality of life, and work capacity. Biostatistician blinded analyses using regression models were conducted by intention-to-treat and per protocol analysis. RESULTS: Between October 2015 and July 2017, we randomly assigned 210 participants (IG, n = 105; CG, n = 105), mostly with only a mild pain intensity but substantial pain disability. No statistically significant difference in depression severity between IG and CG was observed at the 9-week follow-up (ß = -0.19, 95% CI -0.43 to 0.05). Explorative secondary depression (4/9) and pain-related (4/6) outcomes were in part significant (p < 0.05). Health-related quality of life was significantly higher in the IG. No differences were found in work capacity. CONCLUSION: The results indicate that an IMI for patients with CBP and depression in a routine care setting has limited impact on depression. Benefits in pain and health-related outcomes suggest that an IMI might still be a useful measure to improve routine care.

Primary acral amelanotic melanoma: A rare case report.

The aim of the present study was to present a rare case of primary acral amelanotic malignant melanoma (AMM). A 61-year-old man developed an aggressive tumor in the front part of the sole of his left foot, which continued to increase in size for >1 year. The biopsy results revealed epidermis loss, ulcer formation, and the presence of abundant allotropic tumor cells throughout the dermis, with deeply stained nuclei, light reddish cytoplasm and visible multinucleated giant cells with heterogeneous nuclear division. The tumor cells exhibited partial formation of nests and bundled distribution, and there were no observed pigment particles. The diagnosis was confirmed as AMM based on the findings of the histopathological examination and immunohistochemical staining for Ki67 (+++), Melan-A (+++), human melanoma black 45 (+), CD20 (-), cytokeratin (CK)7 (-) and CK5/6 (-).

Effectiveness of a Guided Web-Based Self-help Intervention to Prevent Depression in Patients With Persistent Back Pain: The PROD-BP Randomized Clinical Trial.

Importance: Depression is a frequent comorbid condition in patients with persistent back pain and is associated with substantial adverse consequences, including the risk of developing opioid use disorders. Shifting the focus from depression treatment to preventing depression might be a viable way to reduce the disease burden. Objective: To evaluate the effectiveness of a web-based self-help intervention to reduce the incidence of major depressive episode (MDE) in patients with persistent back pain. Design, Setting, and Participants: Prevention of Depression in Back Pain Patients (PROD-BP) was a pragmatic, observer-blinded randomized clinical trial with a parallel design conducted in Germany. Eligible adults with a diagnosis of persistent back pain and subclinical depressive symptoms, but who were depression free, were recruited either on-site or after discharge from 82 orthopedic clinics between October 1, 2015, and July 31, 2017. All analyses were conducted according to the intention-to-treat principle from October 31, 2018, to April 30, 2019. Interventions: The intervention group received an e-coach-guided, web-based self-help intervention that was based on cognitive behavioral therapy and tailored to the needs of patients with persistent back pain. The intervention included 6 obligatory modules and 3 optional modules to be completed by participants as well as feedback from e-coaches. Both the intervention and control groups had unrestricted access to treatment as usual. Main Outcomes and Measures: Primary outcome was time to onset of an MDE over a 12-month period as assessed by blinded diagnostic raters using the Structured Clinical Interview for DSM-5. Secondary outcomes included depression severity, quality of life, pain intensity, pain-related disability, pain self-efficacy, work capacity, and user satisfaction assessed with a variety of instruments. Results: A total of 295 participants (mean [SD] age, 52.8 [7.7] years; 184 women [62.4%]) were recruited and randomized to either the intervention group (n = 149) or control group (n = 146). The intervention reduced the risk of MDE onset by 52% (hazard ratio, 0.48; 95% CI, 0.28-0.81; P < .001). Twenty-one participants (14.1%) in the intervention group and 41 participants (28.1%) in the control group experienced an MDE over the 12-month period. The number needed to treat to prevent 1 new case of MDE was 2.84 (95% CI, 1.79-9.44). Conclusions and Relevance: Results of this trial showed that among patients with persistent back pain, depression can be prevented by a guided web-based self-help intervention in addition to treatment as usual. This finding suggests that using a scalable digital approach to integrate psychological treatment into routine pain management is feasible. Trial Registration: German Clinical Trials Register Identifier: DRKS00007960.

Low Expression of GLIS2 Gene Might Associate with Radiosensitivity of Gastric Cancer.

Human gene GLIS family zinc finger 2 (GLIS2) is a member of GLI-similar zinc finger protein family. Previous studies indicated GLIS2 gene involved in tumorigenesis mechanisms. However, the association between GLIS2 expression and radiosensitivity of gastric cancer has not been well understood. In this study, we used the gastric cancer database in TCGA, and significant association was observed between the low expression of GLIS2 and radiosensitivity of patients with gastric cancer. The adjusted HR values for radiotherapy were 0.162(0.035-0.756) and 0.089(0.014-0.564), with p values 0.021 and 0.010, respectively, in training and testing data, for these patients with low expression of GLIS2, while for patients with high expression of GLIS2, there was no significant survival difference between radiotherapy and nonradiotherapy groups. The adjusted HR were 0.676(0.288-1.586) and 0.508(0.178-1.450), with p values 0.368 and 0.206 in training and testing data, respectively. Further study showed that, for low expression patients, radiotherapy did not significantly increase new tumor event rate and disease progression rate, which partially supported our assumption. These results suggested that low expression of GLIS2 might significantly associate with the radiosensitivity of patients with gastric cancer. The GLIS2 gene might be a potential effective molecular marker of gastric cancer for precise radiotherapy.

Effectiveness and cost-effectiveness of a guided internet- and mobile-based depression intervention for individuals with chronic back pain: protocol of a multi-centre randomised controlled trial.

INTRODUCTION: Depression often co-occurs with chronic back pain (CBP). Internet and mobile-based interventions (IMIs) might be a promising approach for effectively treating depression in this patient group. In the present study, we will evaluate the effectiveness and cost-effectiveness of a guided depression IMI for individuals with CBP (eSano BackCare-D) integrated into orthopaedic healthcare. METHODS AND ANALYSIS: In this multicentre randomised controlled trial of parallel design, the groups eSano BackCare-D versus treatment as usual will be compared. 210 participants with CBP and diagnosed depression will be recruited subsequent to orthopaedic rehabilitation care. Assessments will be conducted prior to randomisation and 9 weeks (post-treatment) and 6 months after randomisation. The primary outcome is depression severity (Hamilton Rating Scale for Depression-17). Secondary outcomes are depression remission and response, health-related quality of life, pain intensity, pain-related disability, self-efficacy and work capacity. Demographic and medical variables as well as internet affinity, intervention adherence, intervention satisfaction and negative effects will also be assessed. Data will be analysed on an intention-to-treat basis with additional per-protocol analyses. Moreover, a cost-effectiveness and cost-utility analysis will be conducted from a societal perspective after 6 months. ETHICS AND DISSEMINATION: All procedures are approved by the ethics committee of the Albert-Ludwigs-University of Freiburg and the data security committee of the German Pension Insurance (Deutsche Rentenversicherung). The results will be published in peer-reviewed journals and presented on international conferences. TRIAL REGISTRATION NUMBER: DRKS00009272; Pre-results.

A time course-dependent metastatic gene expression signature predicts outcome in human metastatic melanomas.

BACKGROUND: The prognosis of patients with metastatic melanomas is extremely heterogeneous. Therefore, identifying high-risk subgroups by using innovative prediction models would help to improve selection of appropriate management options. METHODS: In this study, two datasets (GSE7929 and GSE7956) of mRNA expression microarray in an animal melanoma model were normalized by frozen Robust Multi-Array Analysis and then combined by the distance-weighted discrimination method to identify time course-dependent metastasis-related gene signatures by Biometric Research Branch-ArrayTools (BRB)-ArrayTools. Then two datasets (GSE8401 and GSE19234) of clinical melanoma samples with relevant clinical and survival data were used to validate the prognosis signature. RESULTS: A novel 192-gene set that varies significantly in parallel with the increasing of metastatic potentials was identified in the animal melanoma model. Further, this gene signature was validated to correlate with poor prognosis of human metastatic melanomas but not of primary melanomas in two independent datasets. Furthermore, multivariate Cox proportional hazards regression analyses demonstrated that the prognostic value of the 192-gene set is independent of the TNM stage and has higher areas under the receiver operating characteristic curve than stage information in both validation datasets. CONCLUSION: Our findings suggest that a time course-dependent metastasis-related gene expression signature is useful in predicting survival of malignant melanomas and might be useful in informing treatment decisions for these patients.

In-vivo transfection of pcDNA3.1-IGFBP7 inhibits melanoma growth in mice through apoptosis induction and VEGF downexpression.

BACKGROUND: Genome-wide RNA interference screening study revealed that loss of expression of insulin-like growth factor binding protein 7 (IGFBP7) is a critical step in development of a malignant melanoma (MM), and this secreted protein plays a central role in apoptosis of MM. In this study we constructed pcDNA3.1-IGFBP7 to obtain high expression of IGBPF7 and to inhibit the growth of MM in C57BL/6J mice. METHODS: pcDNA3.1-IGFBP7 was transfected into B16-F10 cell, the expression of IGFBP7 was detected by RT-PCR and western blot. The proliferations and apoptosis rates of transfected and control cells were measured by CCK8 and FCM, respectively. The tumorigenicity and tumor growth in both pcDNA3.1-IGFBP7 group and control groups were studied in C57BL/6J mice model. IGFBP7, caspase-3, and VEGF expressions in tumor tissue were measured by immunohistochemistry. Apoptosis of tumors were detected by TUNEL. RESULTS: We demonstrated this plasmid inhibited proliferation of B16-F10 melanoma cells efficiently in vivo, exploiting the high expression of IGFBP7. More importantly, in-vivo transfection of pcDNA3.1-IGFBP7 inhibited MM growth in C57BL/6J mice. The inhibition of MM growth was proved owing to apoptosis and reduced expression of VEGF induced by pcDNA3.1-IGFBP7. CONCLUSIONS: These results suggest a potential new clinical strategy for MM gene treatment.

The nongenomic effects of progesterone in repressing iNOS activation through P38MAPK pathways in gonococci-infected polymorphonuclear leukocytes and the clinical significance.

Progesterone has nongenomic effects on inducible nitric oxide synthase (iNOS), which is mediated by mitogen activated protein kinase (MAPK) pathways. This effect is supposed to have some potential association with asymptomatic gonococcal infections in women by immunological depression. In this study, polymorphonuclear leukocytes (PMNs) challenged by gonococci were used to study the nongenomic effects of progesterone. The activation of iNOS was assessed by measuring [(3)H] L-arginine converses to [(3)H] L-citrulline, and the activity of MAPK was detected by Western blot. It was found that the activity of iNOS and the yields of NO were enhanced significantly in gonococci-challenged PMNs compared with the controls (P<0.01). Progesterone could repress the activation of iNOS through P38MAPK pathway within PMNs (P<0.05), which could be blocked by SB203580 (P<0.01), but not by actinomycin D (P>0.05). It was also found subsequently that in the serum specimens collected from gonococci-infected but asymptomatic women, the progesterone level was higher than that in women with severe symptoms (P<0.01). Moreover, the yield of NO had an inverse correlation with progesterone. With these results it suggested that the rapid nongenomic effects of progesterone may inhibit iNOS activation and NO yields mediated by P38MAPK pathways, which were supposed to be concerned with asymptomatic women infected with gonococci.