NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank.

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging.Genotype, clinical and demographic data of subjects (aged 60-73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

The largest genome-wide association study for breast cancer in Taiwanese Han population.

PURPOSE: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. METHODS: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. RESULTS: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively. CONCLUSION: In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.

Twnbiome: a public database of the healthy Taiwanese gut microbiome.

With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/ .

An Oral Microbial Biomarker for Early Detection of Recurrence of Oral Squamous Cell Carcinoma.

Changes in the oral microbiome are associated with oral squamous cell carcinoma (OSCC). Oral microbe-derived signatures have been utilized as markers of OSCC. However, the structure of the oral microbiome during OSCC recurrence and biomarkers for the prediction of OSCC recurrence remains unknown. To identify OSCC recurrence-associated microbial biomarkers for the prediction of OSCC recurrence, we performed 16S rRNA amplicon sequencing on 54 oral swab samples from OSCC patients. Differences in bacterial compositions were observed in patients with vs without recurrence. We found that Granulicatella, Peptostreptococcus, Campylobacter, Porphyromonas, Oribacterium, Actinomyces, Corynebacterium, Capnocytophaga, and Dialister were enriched in OSCC recurrence. Functional analysis of the oral microbiome showed altered functions associated with OSCC recurrence compared with nonrecurrence. A random forest prediction model was constructed with five microbial signatures including Leptotrichia trevisanii, Capnocytophaga sputigena, Capnocytophaga, Cardiobacterium, and Olsenella to discriminate OSCC recurrence from original OSCC (accuracy = 0.963). Moreover, we validated the prediction model in another independent cohort (46 OSCC patients), achieving an accuracy of 0.761. We compared the accuracy of the prediction of OSCC recurrence between the five microbial signatures and two clinicopathological parameters, including resection margin and lymph node counts. The results predicted by the model with five microbial signatures showed a higher accuracy than those based on the clinical outcomes from the two clinicopathological parameters. This study demonstrated the validity of using recurrence-related microbial biomarkers, a noninvasive and effective method for the prediction of OSCC recurrence. Our findings may contribute to the prognosis and treatment of OSCC recurrence.

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data.

OBJECTIVE: Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. METHODS: We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. RESULTS: We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. CONCLUSION: Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. STATEMENT OF SIGNIFICANCE: Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

Copy number variation of gasdermin D gene is associated with atrial fibrillation-related thromboembolic stroke.

AIMS: Atrial fibrillation (AF) is one of the major causes of ischaemic stroke. In addition to clinical risk evaluated by the CHA2DS2-VASC score, the impact of genetic factors on the risk of AF-related thromboembolic stroke has been largely unknown. We found several copy number variations (CNVs) in novel genes that were associated with thromboembolic stroke risk in our AF patients by genome-wide approach. Among them, the gasdermin D (GSDMD) gene was related to inflammation. We aimed to test whether GSDMD deletion was associated with AF-related stroke. METHODS AND RESULTS: A total of 400 patients with documented non-familial AF were selected, of which 100 patients were diagnosed with ischaemic stroke. The baseline characteristics of age, sex, valvular heart disease, coronary artery disease, heart failure, and CHA2DS2-VASc scores were not statistically different between cases and controls. We found that individuals who carried GSDMD homozygous deletion genotype had a higher risk for ischaemic stroke (odds ratio 2.195; 95% confidence interval, 1.24-3.90; P = 0.007), even adjusted by CHA2DS2-VASc scores. We also validated the association of GSDMD with AF stroke in a large Caucasian population (UK Biobank). CONCLUSION: We found a link between the homozygous deletion of the GSDMD gene and an increased risk of stroke in patients with AF. This may implicate the use of therapy targeting GSDMD in the prevention of ischaemic stroke for AF patients.

CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants.

BACKGROUND: Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants. RESULTS: CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case-control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT , installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats. CONCLUSIONS: Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.

16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences.

Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers' performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.

Regulatory mechanisms and function of hypoxia-induced long noncoding RNA NDRG1-OT1 in breast cancer cells.

Hypoxia is a classic feature of the tumor microenvironment that has profound effects on cancer progression and is tightly associated with poor prognosis. Long noncoding RNAs (lncRNAs), a component of the noncoding genome, have been increasingly investigated due to their diverse roles in tumorigenesis. Previously, a hypoxia-induced lncRNA, NDRG1-OT1, was identified in MCF-7 breast cancer cells using next-generation sequencing. However, the regulatory mechanisms of NDRG1-OT1 remain elusive. Therefore, the purpose of this study was to investigate the regulatory mechanisms and functional roles of NDRG1-OT1 in breast cancer cells. Expression profiling of NDRG1-OT1 revealed that it was upregulated under hypoxia in different breast cancer cells. Overexpression and knockdown of HIF-1α up- and downregulated NDRG1-OT1, respectively. Luciferase reporter assays and chromatin immunoprecipitation assays validated that HIF-1α transcriptionally activated NDRG1-OT1 by binding to its promoter (-1773 to -1769 and -647 to -643 bp). Next, to investigate whether NDRG1-OT1 could function as a miRNA sponge, results of in silico analysis, expression profiling of predicted miRNAs, and RNA immunoprecipitation assays indicated that NDRG1-OT1 could act as a miRNA sponge of miR-875-3p. In vitro and in vivo functional assays showed that NDRG1-OT1 could promote tumor growth and migration. Lastly, a small peptide (66 a.a.) translated from NDRG1-OT1 was identified. In summary, our findings revealed novel regulatory mechanisms of NDRG1-OT1 by HIF-1α and upon miR-875-3p. Also, NDRG1-OT1 promoted the malignancy of breast cancer cells and encoded a small peptide.

Identification of Genetic Variations in the NAD-Related Pathways for Patients with Major Depressive Disorder: A Case-Control Study in Taiwan.

Background and Objectives: Nicotinamide adenine dinucleotide (NAD) is an important coenzyme in various physiological processes, including sirtuins (SIRTs) and kynurenine pathway (KP). Previous studies have shown that lower NAD levels can be indicative of increased risks of cancer and psychiatric disorders. However, there has been no prior study exploring the link between NAD homeostasis and psychiatric disorders from a genetic perspective. Therefore, we aimed to investigate the association of genetic polymorphism in the pathways of NAD biosynthesis with major depressive disorder (MDD). Methods: A total of 317 patients were included in the case group and were compared with sex-matched control group of 1268 participants (1:4 ratio) from Taiwan Biobank (TWB). All subjects in the control group were over 65 years old, which is well past the average age of onset of MDD. Genomic DNA extracted from patients' blood buffy coat was analyzed using the Affymetrix TWB array. Full-model tests were conducted for the analysis of single nucleotide polymorphism (SNPs) in all candidate genes. We focused on genes within the NAD-related candidate pathways, including 15 in KP, 12 in nicotinate metabolism, 7 in SIRTs, and 19 in aldehyde dehydrogenases (ALDHs). A total of 508 SNPs were analyzed in this study. After significant SNPs were determined, 5000 genome-wide max(T) permutations were performed in Plink. Finally, we built a predictive model with logistic regression and assessed the interactions of SNPs with the haplotype association tests. Results: We found three SNPs that were significantly associated with MDD in our NAD-related candidate pathways, one within the KP (rs12622574 in ACMSD) and two within the nicotinate metabolism (rs28532698 in BST1 and rs3733593 in CD38). The observed association with MDD was significant in the dominant model of inheritance with marital status, education level, and body mass index (BMI) adjusted as covariates. Lastly, in haplotype analysis, the three associated SNPs consisted of one haploblock in ACMSD, four haploblocks in BST1, and two haploblocks in CD38. Conclusions: This study provides the first evidence that genetic variations involved in NAD homeostasis in the KP and nicotinate metabolism may be associated with the occurrence of MDD.

Uremic Toxin-Producing Bacteroides Species Prevail in the Gut Microbiota of Taiwanese CKD Patients: An Analysis Using the New Taiwan Microbiome Baseline.

Rationale and Objective: Gut microbiota have been targeted by alternative therapies for non-communicable diseases. We examined the gut microbiota of a healthy Taiwanese population, identified various bacterial drivers in different demographics, and compared them with dialysis patients to associate kidney disease progression with changes in gut microbiota. Study Design: This was a cross-sectional cohort study. Settings and Participants: Fecal samples were obtained from 119 healthy Taiwanese volunteers, and 16S rRNA sequencing was done on the V3-V4 regions to identify the bacterial enterotypes. Twenty-six samples from the above cohort were compared with fecal samples from 22 peritoneal dialysis and 16 hemodialysis patients to identify species-level bacterial biomarkers in the dysbiotic gut of chronic kidney disease (CKD) patients. Results: Specific bacterial species were identified pertaining to different demographics such as gender, age, BMI, physical activity, and sleeping habits. Dialysis patients had a significant difference in gut microbiome composition compared to healthy controls. The most abundant genus identified in CKD patients was Bacteroides, and at the species level hemodialysis patients showed significant abundance in B. ovatus, B. caccae, B. uniformis, and peritoneal dialysis patients showed higher abundance in Blautia producta (p ≤ 0.05) than the control group. Pathways pertaining to the production of uremic toxins were enriched in CKD patients. The abundance of the bacterial species depended on the type of dialysis treatment. Conclusion: This study characterizes the healthy gut microbiome of a Taiwanese population in terms of various demographics. In a case-control examination, the results showed the alteration in gut microbiota in CKD patients corresponding to different dialysis treatments. Also, this study identified the bacterial species abundant in CKD patients and their possible role in complicating the patients' condition.

The Gut Microbiome, Seleno-Compounds, and Acute Myocardial Infarction.

Background: Gut microbiome alterations might be considered a metabolic disorder. However, the relationship between the microbiome and acute myocardial infarction (AMI) has not been properly validated. Methods: The feces of 44 subjects (AMI: 19; control: 25) were collected for fecal genomic DNA extraction. The variable region V3−V4 of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. The metabolite amounts were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways. Results: The bacteria were more enriched in the AMI group both in the observed operational taxonomic units (OTUs) and faith phylogenetic diversity (PD) (p-value = 0.01 and <0.001 with 95% CI, individually). The Selenomonadales were less enriched in the AMI group at the family, genus, and species levels (all linear discriminant analysis (LDA) scores > 2). Seleno-compounds were more abundant in the AMI group at the family, genus, and species levels (all LDA scores > 2). Conclusions: This is the first study to demonstrate the association of Selenomonadales and seleno-compounds with the occurrence of AMI. Our findings provide an opportunity to identify a novel approach to prevent and treat AMI.

To compare the performance of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences.

BACKGROUND: Taxonomic assignment is a vital step in the analytic pipeline of bacterial 16S ribosomal RNA (rRNA) sequencing. Over the past decade, most research in this field used next-generation sequencing technology to target V3∼V4 regions to analyze bacterial composition. However, focusing on only one or two hypervariable regions limited the taxonomic resolution to the species level. In recent years, third-generation sequencing technology has allowed researchers to easily access full-length prokaryotic 16S sequences and presented an opportunity to attain greater taxonomic depth. However, the accuracy of current taxonomic classifiers in analyzing 16S full-length sequence analysis remains unclear. OBJECTIVE: The purpose of this study is to compare the accuracy of several widely-used 16S sequence classifiers and to indicate the most suitable 16S training dataset for each classifier. METHODS: Both curated 16S full-length sequences and cross-validation datasets were used to validate the performance of seven classifiers, including QIIME2, mothur, SINTAX, SPINGO, Ribosomal Database Project (RDP), IDTAXA, and Kraken2. Different sequence training datasets, such as SILVA, Greengenes, and RDP, were used to train the classification models. RESULTS: The accuracy of each classifier to the species levels were illustrated. According to the experimental results, using RDP sequences as the training data, SINTAX and SPINGO provided the highest accuracy, and were recommended for the task of classifying prokaryotic 16S full-length rRNA sequences. CONCLUSION: The performance of the classifiers was affected by sequence training datasets. Therefore, different classifiers should use the most suitable 16S training data to improve the accuracy and taxonomy resolution in the taxonomic assignment.

An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease.

(1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed. (2) Methods: We collected ECG data from a multi-center retrospective cohort with patients of significant CAD documented by invasive coronary angiography and control patients in Taiwan from 1 January 2018 to 31 December 2020. (3) Results: We trained convolutional neural networks (CNN) models to identify patients with significant CAD (>70% stenosis), using the 12,954 ECG from 2303 patients with CAD and 2090 ECG from 1053 patients without CAD. The Marco-average area under the ROC curve (AUC) for detecting CAD was 0.869 for image input CNN model. For detecting individual coronary artery obstruction, the AUC was 0.885 for left anterior descending artery, 0.776 for right coronary artery, and 0.816 for left circumflex artery obstruction, and 1.0 for no coronary artery obstruction. Marco-average AUC increased up to 0.973 if ECG had features of myocardial ischemia. (4) Conclusions: We for the first time show that using the AI-enhanced CNN model to read standard 12-lead ECG permits ECG to serve as a powerful screening tool to identify significant CAD and localize the coronary obstruction. It could be easily implemented in health check-ups with asymptomatic patients and identifying high-risk patients for future coronary events.

The extended concurrent genes signature for disease-free survival in breast cancer.

BACKGROUND/PURPOSE: Previously we had identified concurrent genes, which highlighted the interplay between copy number variation (CNV) and differential gene expression (GE) for Han Chinese breast cancers. The merit of the approach is to discovery biomarkers not identifiable by conventional GE only data, for which phenotype-correlation or gene variability is the criteria of gene selection. MATERIALS AND METHODS: Thirty-one comparative genomic hybridization (CGH) and 83 GE microarrays were performed, with 29 breast cancers assayed from both platforms. Potential targets were revealed by Genomic Identification of Significant Targets in Cancer (GISTIC) from CGH arrays. Concurrent genes and genes with significant GISTIC scores were used to derive the extended concurrent genes signature, which was consensus from leading edge analysis across all studies and a supervised partial least square (PLS) regression predictive model of disease-free survival was constructed. RESULTS: There were 1584 concurrent genes from 29 samples with both CGH and GE microarrays. Enriched concurrent genes sets for disease-free survival were identified independently from 83 GE arrays and another one with Han Chinese origin as well as three studies of Western origin. For five studies with disease-free survival follow up, prognostic discrepancy was observed between predicted high-risk and low-risk group patients. CONCLUSION: We concluded that through parallel analyses of CGH and GE microarrays, the proposed extended concurrent gene expression signature can identify biomarkers with prognostic values.

Extracellular domain of semaphorin 5A serves a tumor­suppressing role by activating interferon signaling pathways in lung adenocarcinoma cells.

Semaphorin 5A (SEMA5A), which was originally identified as an axon guidance molecule in the nervous system, has been subsequently identified as a prognostic biomarker for lung cancer in nonsmoking women. SEMA5A acts as a tumor suppressor by inhibiting the proliferation and migration of lung cancer cells. However, the regulatory mechanism of SEMA5A is not clear. Therefore, the purpose of the present study was to explore the roles of different domains of SEMA5A in its tumor­suppressive effects in lung adenocarcinoma cell lines. First, it was revealed that overexpression of full length SEMA5A or its extracellular domain significantly inhibited the proliferation and migration of both A549 and H1299 cells using MTT, colony formation and gap closure assays. Next, microarray analyses were performed to identify genes regulated by different domains of SEMA5A. Among the differentially expressed genes, the most significant function of these genes that were enriched was the 'Interferon Signaling' pathway according to Ingenuity Pathway Analysis. The activation of the 'Interferon Signaling' pathway was validated by reverse transcription­quantitative PCR and western blotting. In summary, the present study demonstrated that the extracellular domain of SEMA5A could upregulate genes in interferon signaling pathways, resulting in suppressive effects in lung adenocarcinoma cells.

Novel Tumor-Specific Antigens for Immunotherapy Identified From Multi-omics Profiling in Thymic Carcinomas.

Thymic carcinoma (TC) is the most aggressive thymic epithelial neoplasm. TC patients with microsatellite instability, whole-genome doubling, or alternative tumor-specific antigens from gene fusion are most likely to benefit from immunotherapies. However, due to the rarity of this disease, how to prioritize the putative biomarkers and what constitutes an optimal treatment regimen remains largely unknown. Therefore, we integrated genomic and transcriptomic analyses from TC patients and revealed that frameshift indels in KMT2C and CYLD frequently produce neoantigens. Moreover, a median of 3 fusion-derived neoantigens was predicted across affected patients, especially the CATSPERB-TC2N neoantigens that were recurrently predicted in TC patients. Lastly, potentially actionable alterations with early levels of evidence were uncovered and could be used for designing clinical trials. In summary, this study shed light on our understanding of tumorigenesis and presented new avenues for molecular characterization and immunotherapy in TC.

Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images.

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.