Machine learning models for pancreatic cancer diagnosis based on microbiome markers from serum extracellular vesicles.

Pancreatic cancer (PC) is a fatal disease with an extremely low 5-year survival rate, mainly because of its poor detection rate in early stages. Given emerging evidence of the relationship between microbiota composition and diseases, this study aims to identify microbiome markers linked to the diagnosis of pancreatic cancer. We utilized extracellular vesicles (EVs) data obtained from blood samples of 38 pancreatic cancer patients and 51 health controls.

Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.

Background: High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.

The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study.

Background: Understanding the clinical course and pivotal time points of COVID-19 aggravation is critical for enhancing patient monitoring. This retrospective, multi-center cohort study aims to identify these significant time points and associate them with potential risk factors, leveraging data from a sizable cohort with mild-to-moderate symptoms upon admission.

Methods: This study included data from 1,696 COVID-19 patients with mild-to-moderate clinical severity upon admission across multiple hospitals in Daegu-Kyungpook Province (Daegu dataset) between February 18 and early March 2020 and 321 COVID-19 patients at Seoul Boramae Hospital (Boramae dataset) collected from February to July 2020.

Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record.

Background: As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient.

Objective: This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms.

Methods: The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed.

Comparison of survival prediction models for pancreatic cancer: Cox model versus machine learning models.

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation.

Long-Term Oncologic Outcomes for T2 Gallbladder Cancer According to the Type of Surgery Performed and the Optimal Timing for Sequential Extended Cholecystectomy.

Background: Sequential extended cholecystectomy (SEC) is currently recommended for T2 and higher gallbladder cancer (GBC) diagnosed after simple cholecystectomy (SC), but the value and timing of re-resection has not been fully studied. We evaluated the long-term oncologic outcomes of T2 GBC according to the type of surgery performed and investigated the optimal timing for SEC.

Methods: Patients diagnosed with T2 GBC who underwent SC, extended cholecystectomy (EC), or SEC between 2002 and 2017 were retrospectively reviewed.

Kernel-based hierarchical structural component models for pathway analysis.

Motivation: Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results.

Effects of government policies on the spread of COVID-19 worldwide.

The outbreak of novel COVID-19 disease elicited a wide range of anti-contagion and economic policies like school closure, income support, contact tracing, and so forth, in the mitigation and suppression of the spread of the SARS-CoV-2 virus. However, a systematic evaluation of these policies has not been made. Here, 17 implemented policies from the Oxford COVID-19 Government Response Tracker dataset employed in 90 countries from December 31, 2019, to August 31, 2020, were analyzed.

Identifying miRNA-mRNA Integration Set Associated With Survival Time.

In the "personalized medicine" era, one of the most difficult problems is identification of combined markers from different omics platforms. Many methods have been developed to identify candidate markers for each type of omics data, but few methods facilitate the identification of multiple markers on multi-omics platforms. microRNAs (miRNAs) is well known to affect only indirectly phenotypes by regulating mRNA expression and/or protein translation.

Multi-biomarker panel prediction model for diagnosis of pancreatic cancer.

Background/purpose: The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma.

Methods: Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9.

Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP).

Background/aims: Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database.

Methods: Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation.

CORRIGENDUM: Addition of data source: Diagnostic model for pancreatic cancer using a multi-biomarker panel.

[This corrects the article on p. 144 in vol. 100, PMID: 33748028.

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study.

Background: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness.

Objective: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission.

Methods: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea.

Development, validation, and comparison of a nomogram based on radiologic findings for predicting malignancy in intraductal papillary mucinous neoplasms of the pancreas: An international multicenter study.

Background: Although we previously proposed a nomogram to predict malignancy in intraductal papillary mucinous neoplasms (IPMN) and validated it in an external cohort, its application is challenging without data on tumor markers. Moreover, existing nomograms have not been compared. This study aimed to develop a nomogram based on radiologic findings and to compare its performance with previously proposed American and Korean/Japanese nomograms.

Microbiome Markers of Pancreatic Cancer Based on Bacteria-Derived Extracellular Vesicles Acquired from Blood Samples: A Retrospective Propensity Score Matching Analysis.

Novel biomarkers for early diagnosis of pancreatic cancer (PC) are necessary to improve prognosis. We aimed to discover candidate biomarkers by identifying compositional differences of microbiome between patients with PC (n = 38) and healthy controls (n = 52), using microbial extracellular vesicles (EVs) acquired from blood samples. Composition analysis was performed using 16S rRNA gene analysis and bacteria-derived EVs.

Diagnostic model for pancreatic cancer using a multi-biomarker panel.

Purpose: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage.

A comparative study on the unified model based multifactor dimensionality reduction methods for identifying gene-gene interactions associated with the survival phenotype.

Background: For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely employed to reduce multi-levels of gene-gene interactions into high- or low-risk groups using a binary attribute. For the survival phenotype, the Cox-MDR method has been proposed using a martingale residual of a Cox model since Surv-MDR was first proposed using a log-rank test statistic. Recently, the KM-MDR method was proposed using the Kaplan-Meier median survival time as a classifier.

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study.

Gene-Gene Interaction Analysis for the Survival Phenotype Based on the Kaplan-Meier Median Estimate.

In this study, we propose a simple and computationally efficient method based on the multifactor dimensional reduction algorithm to identify gene-gene interactions associated with the survival phenotype. The proposed method, referred to as KM-MDR, uses the Kaplan-Meier median survival time as a classifier. The KM-MDR method classifies multilocus genotypes into a binary attribute for high- or low-risk groups using median survival time and replaces balanced accuracy with log-rank test statistics as a score to determine the best model.

Dopaminergic Depletion, β-Amyloid Burden, and Cognition in Lewy Body Disease.

Objective: We aimed to determine the association between striatal dopaminergic depletion, cerebral β-amyloid deposition, and cognitive dysfunction in Lewy body disease (LBD).

Methods: This cross-sectional study recruited 48 LBD patients (30 with dementia, 18 with mild cognitive impairment) and 15 control subjects from a university-based hospital. We measured the striatal dopamine transporter (DAT) activity and regional β-amyloid burden using N-(3-[ F]fluoropropyl)-2β-carbon ethoxy-3β-(4-iodophenyl) nortropane (FP-CIT) positron emission tomography (PET) and F-florbetaben (FBB) PET, respectively.

Review of statistical methods for survival analysis using genomic data.

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed.

HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data.

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways.

Inhibition of IL-13 and IL-13Rα2 Expression by IL-32θ in Human Monocytic Cells Requires PKCδ and STAT3 Association.

Interleukin (IL)-32θ, a newly identified IL-32 isoform, has been reported to exert pro-inflammatory effects through the association with protein kinase C delta (PKCδ). In this study, we further examined the effects of IL-32θ on IL-13 and IL-13Rα2 expression and the related mechanism in THP-1 cells. Upon stimulating IL-32θ-expressing and non-expressing cells with phorbol 12-myristate 13-acetate (PMA), the previous microarray analysis showed that IL-13Rα2 and IL-13 mRNA expression were significantly decreased by IL-32θ.

Analysis of significant protein abundance from multiple reaction-monitoring data.

Background: Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA.

Unified Cox model based multifactor dimensionality reduction method for gene-gene interaction analysis of the survival phenotype.

Background: One strategy for addressing missing heritability in genome-wide association study is gene-gene interaction analysis, which, unlike a single gene approach, involves high-dimensionality. The multifactor dimensionality reduction method (MDR) has been widely applied to reduce multi-levels of genotypes into high or low risk groups. The Cox-MDR method has been proposed to detect gene-gene interactions associated with the survival phenotype by using the martingale residuals from a Cox model.