HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F 1 scores (average F 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.

Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.

Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10-4). The pipeline can make precise lesion tracking with accuracies of 85.7% and 90.8% then finally yield the RECIST accuracies of 82.1% and 81.4% on the validation and test sets. Our proposed pipeline can provide precise and convenient RECIST outcome assessments and has the potential to aid clinicians with more efficient therapeutic decisions.

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints.

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

A novel cost function for nuclei segmentation and classification in imbalanced histopathology data-sets.

Cancer is a major global health problem, causing millions of deaths yearly. Histopathological analysis plays a crucial role in detecting and diagnosing various types of cancer, enabling an accurate diagnosis to inform targeted treatment planning, allowing for better cancer staging, and ultimately improving prognosis. We aim to detect cancer earlier, which can ultimately help reduce mortality rates and enhance patients' quality of life. However, detecting and classifying rare cells is a key challenge for pathologists and researchers. Many histopathological data-sets contain imbalanced data, with only a few instances of rare cells whose unique morphological structures can impede early diagnosis efforts. Our model, SPNet, a spatially aware convolutional neural network, addresses this problem by employing a spatial data balancing technique, enhancing the classification of rare nuclei by 21.8 %. Since nuclei often cluster and exhibit patterns of the same class, SPNet's novel cost function targets spatial regions, resulting in a 1.9 % increase in the F1 classification of rare class types within the CoNSeP dataset. When integrated with a ResNet50-SE encoder, SPNet increases the mean F1 score for classifying all nuclei in the CoNSeP dataset by 4.3 %, compared to the benchmark set by the state-of-the-art HoVer-Net model. The potential integration of SPNet into existing medical devices could allow us to streamline diagnostic processes and minimise false negatives.

Individualized red-cell transfusion strategy for non-cardiac surgery in adults: a randomized controlled trial.

BACKGROUND: Red-cell transfusion is critical for surgery during the peri-operative period; however, the transfusion threshold remains controversial mainly owing to the diversity among patients. The patient's medical status should be evaluated before making a transfusion decision. Herein, we developed an individualized transfusion strategy using the West-China-Liu's Score based on the physiology of oxygen delivery/consumption balance and designed an open-label, multicenter, randomized clinical trial to verify whether it reduced red cell requirement as compared with that associated with restrictive and liberal strategies safely and effectively, providing valid evidence for peri-operative transfusion. METHODS: Patients aged >14 years undergoing elective non-cardiac surgery with estimated blood loss > 1000 mL or 20% blood volume and hemoglobin concentration <10 g/dL were randomly assigned to an individualized strategy, a restrictive strategy following China's guideline or a liberal strategy with a transfusion threshold of hemoglobin concentration <9.5 g/dL. We evaluated two primary outcomes: the proportion of patients who received red blood cells (superiority test) and a composite of in-hospital complications and all-cause mortality by day 30 (non-inferiority test). RESULTS: We enrolled 1182 patients: 379, 419, and 384 received individualized, restrictive, and liberal strategies, respectively. Approximately 30.6% (116/379) of patients in the individualized strategy received a red-cell transfusion, less than 62.5% (262/419) in the restrictive strategy (absolute risk difference, 31.92%; 97.5% confidence interval [CI]: 24.42-39.42%; odds ratio, 3.78%; 97.5% CI: 2.70-5.30%; P <0.001), and 89.8% (345/384) in the liberal strategy (absolute risk difference, 59.24%; 97.5% CI: 52.91-65.57%; odds ratio, 20.06; 97.5% CI: 12.74-31.57; P <0.001). No statistically significant differences were found in the composite of in-hospital complications and mortality by day 30 among the three strategies. CONCLUSION: The individualized red-cell transfusion strategy using the West-China-Liu's Score reduced red-cell transfusion without increasing in-hospital complications and mortality by day 30 when compared with restrictive and liberal strategies in elective non-cardiac surgeries. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01597232.

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).

A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data.

Colorectal cancer is a highly heterogeneous disease. Tumor heterogeneity limits the efficacy of cancer treatment. Single-cell RNA-sequencing technology (scRNA-seq) is a powerful tool for studying cancer heterogeneity at cellular resolution. The sparsity, heterogeneous diversity, and fast-growing scale of scRNA-seq data pose challenges to the flexibility, accuracy, and computing efficiency of the differential expression (DE) methods. We proposed HEART (high-efficiency and robust test), a statistical combination test that can detect DE genes with various sources of differences beyond mean expression changes. To validate the performance of HEART, we compared HEART and the other six popular DE methods on various simulation datasets with different settings by two simulation data generation mechanisms. HEART had high accuracy ( F 1 score >0.75) and brilliant computational efficiency (less than 2 min) on multiple simulation datasets in various experimental settings. HEART performed well on DE genes detection for the PBMC68K dataset quantified by UMI counts and the human brain single-cell dataset quantified by read counts ( F 1 score = 0.79, 0.65). By applying HEART to the single-cell dataset of a colorectal cancer patient, we found several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, and VIM) associated with colorectal cancer metastasis and validated them on additional spatial transcriptomic data of other colorectal cancer patients.

Linear high-dimensional mediation models adjusting for confounders using propensity score method.

High-dimensional mediation analysis has been developed to study whether epigenetic phenotype in a high-dimensional data form would mediate the causal pathway of exposure to disease. However, most existing models are designed based on the assumption that there are no confounders between the exposure, the mediators, and the outcome. In practice, this assumption may not be feasible since high-dimensional mediation analysis (HIMA) tends to be observational where a randomized controlled trial (RCT) cannot be conducted for some economic or ethical reasons. Thus, to deal with the confounders in HIMA cases, we proposed three propensity score-related approaches named PSR (propensity score regression), PSW (propensity score weighting), and PSU (propensity score union) to adjust for the confounder bias in HIMA, and compared them with the traditional covariate regression method. The procedures mainly include four parts: calculating the propensity score, sure independence screening, MCP (minimax concave penalty) variable selection, and joint-significance testing. Simulation results show that the PSU model is the most recommended. Applying our models to the TCGA lung cancer dataset, we find that smoking may lead to lung disease through the mediation effect of some specific DNA-methylation sites, including site Cg24480765 in gene RP11-347H15.2 and site Cg22051776 in gene KLF3.

Particulate matter of air pollution may increase risk of kidney failure in IgA nephropathy.

IgA nephropathy (IgAN) is characterized by deposition of galactose-deficient IgA1 (Gd-IgA1) in glomerular mesangium associated with mucosal immune disorders. Since environmental pollution has been associated with the progression of chronic kidney disease in the general population, we specifically investigated the influence of exposure to fine particulate matter less than 2.5 µm in diameter (PM2.5) on IgAN progression. Patients with biopsy-proven primary IgAN were recruited from seven Chinese kidney centers. PM2.5 exposure from 1998 to 2016 was derived from satellite aerosol optical depth data and a total of 1,979 patients with IgAN, including 994 males were enrolled. The PM2.5 exposure levels for patients from different provinces varied but, in general, the PM2.5 exposure levels among patients from the north were higher than those among patients from the south. The severity of PM2.5 exposure in different regions was correlated with regional kidney failure burden. In addition, each 10 µg/m3 increase in annual average concentration of PM2.5 exposure before study entry (Hazard Ratio, 1.14; 95% confidence interval, 1.06-1.22) or time-varying PM2.5 exposure after study entry (1.10; 1.01-1.18) were associated with increased kidney failure risk after adjustment for age, gender, estimated glomerular filtration rate, urine protein, uric acid, hemoglobin, mean arterial pressure, Oxford classification, glucocorticoid and renin-angiotensin system blocker therapy. The associations were robust when the time period, risk factors of cardiovascular diseases or city size were further adjusted on the basis of the above model. Thus, our results suggest that PM2.5 is an independent risk factor for kidney failure in patients with IgAN, but these findings will require validation in more diverse populations and other geographic regions.

Eye-Preserving Therapies for Advanced Retinoblastoma: A Multicenter Cohort of 1678 Patients in China.

PURPOSE: This study attempted to estimate the impact of eye-preserving therapies for the long-term prognosis of patients with advanced retinoblastoma with regard to overall survival and ocular salvage. DESIGN: Retrospective cohort study covering all 31 provinces (38 retinoblastoma treating centers) of mainland China. PARTICIPANTS: One thousand six hundred seventy-eight patients diagnosed with group D or E retinoblastoma from January 2006 through May 2016. METHODS: Chart review was performed. The patients were divided into primary enucleation and eye-preserving groups, and they were followed up for survival status. The impact of initial treatment on survival was evaluated by Cox analyses. MAIN OUTCOME MEASURES: Overall survival and final eye preservation. RESULTS: After a median follow-up of 43.9 months, 196 patients (12%) died, and the 5-year overall survival was 86%. In total, the eyeball preservation rate was 48%. In this cohort, 1172 patients (70%) had unilateral retinoblastoma, whereas 506 patients (30%) had bilateral disease. For patients with unilateral disease, 570 eyes (49%) underwent primary enucleation, and 602 patients (51%) received eye-preserving therapies initially. During the follow-up (median, 45.6 months), 59 patients (10%) from the primary enucleation group and 56 patients (9.3%) from the eye-preserving group died. Multivariate Cox analyses indicated no significant difference in overall survival between the 2 groups (hazard ratio [HR], 1.25; 95% confidence interval [CI], 0.85-1.84; P = 0.250). For patients with bilateral disease, 95 eyes (19%) underwent primary enucleation, and 411 patients (81%) received eye-preserving therapies initially. During the follow-up (median, 40.1 months), 12 patients (13%) from the primary enucleation group and 69 patients (17%) from the eye-preserving group died. For bilateral retinoblastoma with the worse eye classified as group E, patients undergoing primary enucleation exhibited better overall survival (HR, 2.35; 95% CI, 1.10-5.01; P = 0.027); however, this survival advantage was not evident until passing 22.6 months after initial diagnosis. CONCLUSIONS: Eye-preserving therapies have been used widely for advanced retinoblastoma in China. Patients with bilateral disease whose worse eye was classified as group E and who initially underwent eye-preserving therapies exhibited a worse overall survival. The choice of primary treatment for advanced retinoblastoma should be weighed carefully.

Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring.

Investigation of the genetic basis of traits or clinical outcomes heavily relies on identifying relevant variables in molecular data. However, characteristics such as high dimensionality and complex correlation structures of these data hinder the development of related methods, resulting in the inclusion of false positives and negatives. We developed a variable importance measure method, termed the ECAR scores, that evaluates the importance of variables in the dataset. Based on this score, ranking and selection of variables can be achieved simultaneously. Unlike most current approaches, the ECAR scores aim to rank the influential variables as high as possible while maintaining the grouping property, instead of selecting the ones that are merely predictive. The ECAR scores' performance is tested and compared to other methods on simulated, semi-synthetic, and real datasets. Results showed that the ECAR scores improve the CAR scores in terms of accuracy of variable selection and high-rank variables' predictive power. It also outperforms other classic methods such as lasso and stability selection when there is a high degree of correlation among influential variables. As an application, we used the ECAR scores to analyze genes associated with forced expiratory volume in the first second in patients with lung cancer and reported six associated genes.

Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data.

BACKGROUND: Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients' multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. METHODS: Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model . RESULTS: The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors' consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors' consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model's generalization ability. CONCLUSIONS: We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.

High-Dimensional Mediation Analysis With Confounders in Survival Models.

Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019-1.2167) and 1.1388 (95% CI: 1.1339-1.1438), respectively.

High-Dimensional Mediation Analysis Based on Additive Hazards Model for Survival Data.

Mediation analysis has been extensively used to identify potential pathways between exposure and outcome. However, the analytical methods of high-dimensional mediation analysis for survival data are still yet to be promoted, especially for non-Cox model approaches. We propose a procedure including "two-step" variable selection and indirect effect estimation for the additive hazards model with high-dimensional mediators. We first apply sure independence screening and smoothly clipped absolute deviation regularization to select mediators. Then we use the Sobel test and the BH method for indirect effect hypothesis testing. Simulation results demonstrate its good performance with a higher true-positive rate and accuracy, as well as a lower false-positive rate. We apply the proposed procedure to analyze DNA methylation markers mediating smoking and survival time of lung cancer patients in a TCGA (The Cancer Genome Atlas) cohort study. The real data application identifies four mediate CpGs, three of which are newly found.

del Nido cardioplegia in surgery for aortic root disease: a historically controlled study.

BACKGROUND: To determine the safely and effectively of del Nido cardioplegia (DNC) in surgery for aortic root disease, with mild hypothermic cardiopulmonary bypass (CPB). METHODS: From July to December 2017, all patients undergoing the surgery for aortic root disease (total aortic root replacement, valve-sparing aortic root replacement and replacement of aortic valve plus ascending aorta), with mild hypothermic CPB, were retrospectively reviewed at our institution. Patients were divided into two groups based on the type of cardioplegia: the classical blood cardioplegia (CBC group) and del Nido cardioplegia (DNC group). Demographics, operative details, perioperative data and postoperative complications were recorded and compared. A propensity score matching was performed in this study. RESULTS: The preoperative data in DNC group were similar to CBC group. The volume of ultrafiltration was lower in DNC than CBC group (2,053.49±806.62 DNC vs. 2,666.00±967.14 CBC, P=0.001), when matched. The use of temporary pacemaker was more in DNC group (n=20, 46.5%, P=0.023), and the rate of automatic heart resuscitating was higher in the CBC group (92.0% vs. 72.1% DNC group, P=0.024, unmatched).There were no differences in in-hospital mortality, troponin T (mean 0.66 ng/mL for CBC group vs. 0.49 ng/mL for DNC group, P=0.152), left ventricular ejection fraction (mean 58.37% for CBC group vs. 60.07% for DNC group, P=0.395) or other postoperative complications between two groups, after matching. In subgroup analysis, the ultrafiltration volume was lower in DNC than CBC group (1,932.26±749.39 DNC vs. 2,640.00±996.24 CBC, P=0.004), when ACC time less than or equal to 90 minutes. The apache score was better in DNC group (4.75±3.41, P=0.041), when ACC time greater than 90 min. There were no statistical significances in other characteristics between groups. CONCLUSIONS: DNC is safe and effective for surgery for aortic root disease, not inferior to the CBC.

High-dimensional mediation analysis in survival models.

Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation.

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery.

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Single-dose del Nido cardioplegia used in adult minimally invasive valve surgery.

BACKGROUND: To analyze the protective effect of single-dose del Nido cardioplegia (DNC) in adult minimally invasive valve surgery. METHODS: From January to December 2017, 165 consecutive adult patients who underwent minimally invasive valve surgery by the same team of surgeons were divided into two cohorts based on the type of cardioplegia administered during surgery: (I) single-dose DNC (DNC group (n=76, male 41, female 35) used in patients from May to December, 2017 and (II) intermittent standard 4:1 blood cardioplegia based on St.Thomas solution (SBC group, n=89, male 45, female 44) used in patients from January to April, 2017. Preoperative baseline demographics, preoperative comorbidities, operative variables, postoperative complications, and patient outcomes were collected and compared between the two groups. RESULTS: Preoperative characteristics were shown to be similar between the two groups before and after propensity matching. Patients in the DNC group required a significantly lower volume of cardioplegia. The volume of ultrafiltration in the DNC group was substantially higher than that in the SBC group. The spontaneous return of heartbeat rate in the DNC group was considerably higher than that in the SBC group (97.0% vs. 78.8%, P=0.006). The Euroscore II in the DNC group was markedly lower than that in the SBC group (2.00 vs. 3.00, P<0.05). The level of blood urea nitrogen (BUN) in the DNC group was significantly lower than that in the SBC group (6.20 vs. 6.95, P<0.05). There were no differences in surgery procedure, cross-clamp time, bypass time, Apache score, troponin T (cTnT), brain natriuretic peptide (BNP), liver and renal function, postoperative complications or patient outcomes between two groups. Regression analysis showed that cTnT increased with the prolongation of myocardial ischemia time, and was closely related to the type of operation, but had no significant correlation with the type of cardioplegia. CONCLUSIONS: In our initial experience, single-dose DNC in adult minimally invasive valve surgery in which the cross-clamp time was mostly less than 90 min, achieved equivalent myocardial protection and clinical outcomes when compared with standard whole blood cardioplegia. In addition, single-dose DNC made the minimally invasive valve surgery procedure progress in a smoother and more convenient fashion.

Pathway-based biomarker identification with crosstalk analysis for robust prognosis prediction in hepatocellular carcinoma.

BACKGROUND: Although many prognostic single-gene (SG) lists have been identified in cancer research, application of these features is hampered due to poor robustness and performance on independent datasets. Pathway-based approaches have thus emerged which embed biological knowledge to yield reproducible features. METHODS: Pathifier estimates pathways deregulation score (PDS) to represent the extent of pathway deregulation based on expression data, and most of its applications treat pathways as independent without addressing the effect of gene overlap between pathway pairs which we refer to as crosstalk. Here, we propose a novel procedure based on Pathifier methodology, which for the first time has been utilized with crosstalk accommodated to identify disease-specific features to predict prognosis in patients with hepatocellular carcinoma (HCC). FINDINGS: With the cohort (N = 355) of HCC patients from The Cancer Genome Atlas (TCGA), cross validation (CV) revealed that PDSs identified were more robust and accurate than the SG features by deep learning (DL)-based approach. When validated on external HCC datasets, these features outperformed the SGs consistently. INTERPRETATION: On average, we provide 10.2% improvement of prediction accuracy. Importantly, governing genes in these features provide valuable insight into the cancer hallmarks of HCC. We develop an R package PATHcrosstalk (available from GitHub https://github.com/fabotao/PATHcrosstalk) with which users can discover pathways of interest with crosstalk effect considered.