Hypermethylation of TMEM240 predicts poor hormone therapy response and disease progression in breast cancer.

BACKGROUND: Approximately 25% of patients with early-stage breast cancer experience cancer progression throughout the disease course. Alterations in TMEM240 in breast cancer were identified and investigated to monitor treatment response and disease progression. METHODS: Circulating methylated TMEM240 in the plasma of breast cancer patients was used to monitor treatment response and disease progression. The Cancer Genome Atlas (TCGA) data in Western countries and Illumina methylation arrays in Taiwanese breast cancer patients were used to identify novel hypermethylated CpG sites and genes related to poor hormone therapy response. Quantitative methylation-specific PCR (QMSP), real-time reverse transcription PCR, and immunohistochemical analyses were performed to measure DNA methylation and mRNA and protein expression levels in 394 samples from Taiwanese and Korean breast cancer patients. TMEM240 gene manipulation, viability, migration assays, RNA-seq, and MetaCore were performed to determine its biological functions and relationship to hormone drug treatment response in breast cancer cells. RESULTS: Aberrant methylated TMEM240 was identified in breast cancer patients with poor hormone therapy response using genome-wide methylation analysis in the Taiwan and TCGA breast cancer cohorts. A cell model showed that TMEM240, which is localized to the cell membrane and cytoplasm, represses breast cancer cell proliferation and migration and regulates the expression levels of enzymes involved in estrone and estradiol metabolism. TMEM240 protein expression was observed in normal breast tissues but was not detected in 88.2% (67/76) of breast tumors and in 90.0% (9/10) of metastatic tumors from breast cancer patients. QMSP revealed that in 54.5% (55/101) of Taiwanese breast cancer patients, the methylation level of TMEM240 was at least twofold higher in tumor tissues than in matched normal breast tissues. Patients with hypermethylation of TMEM240 had poor 10-year overall survival (p = 0.003) and poor treatment response, especially hormone therapy response (p < 0.001). Circulating methylated TMEM240 dramatically and gradually decreased and then diminished in patients without disease progression, whereas it returned and its levels in plasma rose again in patients with disease progression. Prediction of disease progression based on circulating methylated TMEM240 was found to have 87.5% sensitivity, 93.1% specificity, and 90.2% accuracy. CONCLUSIONS: Hypermethylation of TMEM240 is a potential biomarker for treatment response and disease progression monitoring in breast cancer.

Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion.

This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan-Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10-9, which is better than the former study (p-value = 5 × 10-7). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10-15, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.

Tissue microbiota in nasopharyngeal adenoid and its association with pneumococcal carriage.

The microbial colonization in the nasopharynx is a prerequisite for the onset of infectious diseases. For successful infection, pathogens should overcome host defenses as well as compete effectively with the resident microbiota. Hence, elucidating the richness and diversity of the microbiome at the site of pathogen colonization is pivotal. Here, we investigated the adenoidal tissue microbiota collected through adenoidectomy to evaluate the impact of Streptococcus pneumoniae. Prospectively, children with sleep-disordered breathing (SDB) and otitis media with effusion (OME) were enrolled. During adenoidectomy, the nasopharyngeal swab and adenoid tissues were collected to determine the pneumococcal carriage and tissue microbiota, using multiplex PCR and 16S ribosomal RNA (16S rRNA) pyrosequencing. A total of 66 pediatric patients comprising 38 children with SDB and 28 children with OME were enrolled. There was no difference between the bacterial cultures from the surface of the nasopharyngeal adenoid in the SDB and OME groups. Thirty-four samples (17 SDB and 17 OME) underwent 16S rRNA pyrosequencing and fulfilled the criteria for further analysis. The Shannon diversity index for the samples from the SDB patients was found to be higher than that observed for the samples from OME patients, although the difference was not significant (p = 0.095). The Shannon diversity index for the samples negative for the pneumococcal carriage was significantly higher than that for the samples positive for pneumococcal carriage (p = 0.038). Alloprevotella, Staphylococcus, Moraxella, and Neisseriaceae were significantly dominant in the samples positive for the pneumococcal carriage. Dialister was significantly less present in the adenoid tissue positive for the pneumococcal carriage. Streptococcus pneumoniae, one of the most common pathogens of the airway, significantly influences the composition and diversity of the microbiota in the nasopharyngeal adenoid. Thus, bacterial community analysis based on 16S rRNA pyrosequencing allows for better understanding of the relationship between the adenoidal microbial communities.

Platelet autophagic machinery involved in thrombosis through a novel linkage of AMPK-MTOR to sphingolipid metabolism.

Basal macroautophagy/autophagy has recently been found in anucleate platelets. Platelet autophagy is involved in platelet activation and thrombus formation. However, the mechanism underlying autophagy in anucleate platelets require further clarification. Our data revealed that LC3-II formation and SQSTM1/p62 degradation were noted in H2O2-activated human platelets, which could be blocked by 3-methyladenine and bafilomycin A1, indicating that platelet activation may cause platelet autophagy. AMPK phosphorylation and MTOR dephosphorylation were also detected, and block of AMPK activity by the AMPK inhibitor dorsomorphin reversed SQSTM1 degradation and LC3-II formation. Moreover, autophagosome formation was observed through transmission electron microscopy and deconvolution microscopy. These findings suggest that platelet autophagy was induced partly through the AMPK-MTOR pathway. In addition, increased LC3-II expression occurred only in H2O2-treated Atg5f/f platelets, but not in H2O2-treated atg5-/- platelets, suggesting that platelet autophagy occurs during platelet activation. atg5-/- platelets also exhibited a lower aggregation in response to agonists, and platelet-specific atg5-/- mice exhibited delayed thrombus formation in mesenteric microvessles and decreased mortality rate due to pulmonary thrombosis. Notably, metabolic analysis revealed that sphingolipid metabolism is involved in platelet activation, as evidenced by observed several altered metabolites, which could be reversed by dorsomorphin. Therefore, platelet autophagy and platelet activation are positively correlated, partly through the interconnected network of sphingolipid metabolism. In conclusion, this study for the first time demonstrated that AMPK-MTOR signaling could regulate platelet autophagy. A novel linkage between AMPK-MTOR and sphingolipid metabolism in anucleate platelet autophagy was also identified: platelet autophagy and platelet activation are positively correlated.Abbreviations: 3-MA: 3-methyladenine; A.C.D.: citric acid/sod. citrate/glucose; ADP: adenosine diphosphate; AKT: AKT serine/threonine kinase; AMPK: AMP-activated protein kinase; ANOVA: analysis of variance; ATG: autophagy-related; B4GALT/LacCS: beta-1,4-galactosyltransferase; Baf-A1: bafilomycin A1; BECN1: beclin 1; BHT: butylate hydrooxytoluene; BSA: bovine serum albumin; DAG: diacylglycerol; ECL: enhanced chemiluminescence; EDTA: ethylenediamine tetraacetic acid; ELISA: enzyme-linked immunosorbent assay; GALC/GCDase: galactosylceramidase; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GBA/GluSDase: glucosylceramidase beta; GPI: glycosylphosphatidylinositol; H2O2: hydrogen peroxide; HMDB: human metabolome database; HRP: horseradish peroxidase; IF: immunofluorescence; IgG: immunoglobulin G; KEGG: Kyoto Encyclopedia of Genes and Genomes; LAMP1: lysosomal associated membrane protein 1; LC-MS/MS: liquid chromatography-tandem mass spectrometry; mAb: monoclonal antibody; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MPV: mean platelet volume; MTOR: mechanistic target of rapamycin kinase; ox-LDL: oxidized low-density lipoprotein; pAb: polyclonal antibody; PC: phosphatidylcholine; PCR: polymerase chain reaction; PI3K: phosphoinositide 3-kinase; PLS-DA: partial least-squares discriminant analysis; PRP: platelet-rich plasma; Q-TOF: quadrupole-time of flight; RBC: red blood cell; ROS: reactive oxygen species; RPS6KB/p70S6K: ribosomal protein S6 kinase B; SDS: sodium dodecyl sulfate; S.E.M.: standard error of the mean; SEM: scanning electron microscopy; SGMS: sphingomyelin synthase; SM: sphingomyelin; SMPD/SMase: sphingomyelin phosphodiesterase; SQSTM1/p62: sequestosome 1; TEM: transmission electron microscopy; UGT8/CGT: UDP glycosyltransferase 8; UGCG/GCS: UDP-glucose ceramide glucosyltransferase; ULK1: unc-51 like autophagy activating kinase 1; UPLC: ultra-performance liquid chromatography; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PtdIns3P: phosphatidylinositol-3-phosphate; WBC: white blood cell; WT: wild type.

Classification of Changes in the Fecal Microbiota Associated with Colonic Adenomatous Polyps Using a Long-Read Sequencing Platform.

The microbiota is the community of microorganisms that colonizes the oral cavity, respiratory tract, and gut of multicellular organisms. The microbiota exerts manifold physiological and pathological impacts on the organism it inhabits. A growing body of attention is being paid to host-microbiota interplay, which is highly relevant to the development of carcinogenesis. Adenomatous polyps are considered a common hallmark of colorectal cancer, the second leading cause of carcinogenesis-mediated death worldwide. In this study, we examined the relevance between targeted operational taxonomic units and colonic polyps using short- and long-read sequencing platforms. The gut microbiota was assessed in 132 clinical subjects, including 53 healthy participants, 36 patients with occult blood in the gut, and 43 cases with adenomatous polyps. An elevation in the relative abundance of Klebsiella pneumonia, Fusobacterium varium, and Fusobacterium mortiferum was identified in patients with adenomatous polyps compared with the other groups using long-read sequencing workflow. In contrast, the relatively high abundances of Blautia luti, Bacteroides plebeius, and Prevotella copri were characterized in the healthy groups. The diversities in gut microbiota communities were similar in all recruited samples. These results indicated that alterations in gut microbiota were characteristic of participants with adenomatous polyps, which might be relevant to the further development of CRC. These findings provide a potential contribution to the early prediction and interception of CRC occurrence.

Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication.

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.

Targeted sequencing of cancer-related genes in nasopharyngeal carcinoma identifies mutations in the TGF-ß pathway.

Approximately, 25% of nasopharyngeal carcinoma (NPC) patients develop recurrent disease. NPC may involve relatively few genomic alterations compared to other cancers due to its association with Epstein-Barr virus (EBV). We envisioned that in-depth sequencing of tumor tissues might provide new insights into the genetic alterations of this cancer. Thirty-three NPC paired tumor/adjacent normal or peripheral blood mononuclear cell samples were deep-sequenced (>1000×) with respect to a panel of 409 cancer-related genes. Newly identified mutations and its correlation with clinical outcomes were evaluated. Profiling of somatic mutations and copy number variations (CNV) in NPC tumors identified alterations in RTK/RAS/PI3K, NOTCH, DNA repair, chromatin remodeling, cell cycle, NF-κB, and TGF-ß pathways. In addition, patients harbored CNV among 409 cancer-related genes and missense mutations in TGF-ß/SMAD signaling were associated with poor overall survival and poor recurrence-free survival, respectively. The CNV events were correlated with plasma EBV copies, while mutations in TGFBR2 and SMAD4 abrogate SMAD-dependent TGF-ß signaling. Functional analysis revealed that the new TGFBR2 kinase domain mutants were incapable of transducing the signal, leading to failure of phosphorylation of SMAD2/3 and activation of downstream TGF-ß-mediated cell growth arrest. This study provides evidence supporting CNV and dysregulated TGF-ß signaling contributes to exacerbating the NPC pathogenesis.

Using the Post-traumatic Stress Disorder (PTSD) Checklist for DSM-5 to Screen for PTSD in the Chinese Context: A Pilot Study in a Psychiatric Sample.

Purpose: Post-traumatic stress disorder (PTSD) is a common mental health problem but it is often unrecognized in health care and social service settings. Reliable and valid measures are important for practitioners to screen for PTSD in the Chinese context. This study developed and pilot tested a Chinese version of the PTSD Checklist for DSM-5 (PCL-5). Methods: The reliability and validity of the Chinese version of the PCL-5 were evaluated in a sample of N = 56 adult psychiatric patients in Taiwan. Online methods were used to facilitate the research process. Results: The Chinese version of the PCL-5 is internally consistent and is associated with trauma exposure and other mental health constructs. Participants with clinically diagnosed DSM-5 PTSD scored significantly higher on the PCL-5 than those without PTSD. The PCL-5 also had an acceptable discrimination performance in this sample. Discussion: Although the initial findings suggest that the PCL-5 is reliable and valid in this pilot study, caution should be taken when interpreting the results. The Chinese version of the PCL-5 may be a promising screening tool, but further psychometric evaluation is necessary.

CMPD: cancer mutant proteome database.

Whole-exome sequencing, which centres on the protein coding regions of disease/cancer associated genes, represents the most cost-effective method to-date for deciphering the association between genetic alterations and diseases. Large-scale whole exome/genome sequencing projects have been launched by various institutions, such as NCI, Broad Institute and TCGA, to provide a comprehensive catalogue of coding variants in diverse tissue samples and cell lines. Further functional and clinical interrogation of these sequence variations must rely on extensive cross-platforms integration of sequencing information and a proteome database that explicitly and comprehensively archives the corresponding mutated peptide sequences. While such data resource is a critical for the mass spectrometry-based proteomic analysis of exomic variants, no database is currently available for the collection of mutant protein sequences that correspond to recent large-scale genomic data. To address this issue and serve as bridge to integrate genomic and proteomics datasets, CMPD (http://cgbc.cgu.edu.tw/cmpd) collected over 2 millions genetic alterations, which not only facilitates the confirmation and examination of potential cancer biomarkers but also provides an invaluable resource for translational medicine research and opportunities to identify mutated proteins encoded by mutated genes.

Vanno: a visualization-aided variant annotation tool.

Next-generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost-effectiveness. With the ongoing discovery of disease-associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease-associated NGS data, we developed Vanno, a Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

ChIPseek, a web-based analysis tool for ChIP data.

BACKGROUND: Chromatin is a dynamic but highly regulated structure. DNA-binding proteins such as transcription factors, epigenetic and chromatin modifiers are responsible for regulating specific gene expression pattern and may result in different phenotypes. To reveal the identity of the proteins associated with the specific region on DNA, chromatin immunoprecipitation (ChIP) is the most widely used technique. ChIP assay followed by next generation sequencing (ChIP-seq) or microarray (ChIP-chip) is often used to study patterns of protein-binding profiles in different cell types and in cancer samples on a genome-wide scale. However, only a limited number of bioinformatics tools are available for ChIP datasets analysis. RESULTS: We present ChIPseek, a web-based tool for ChIP data analysis providing summary statistics in graphs and offering several commonly demanded analyses. ChIPseek can provide statistical summary of the dataset including histogram of peak length distribution, histogram of distances to the nearest transcription start site (TSS), and pie chart (or bar chart) of genomic locations for users to have a comprehensive view on the dataset for further analysis. For examining the potential functions of peaks, ChIPseek provides peak annotation, visualization of peak genomic location, motif identification, sequence extraction, and comparison between datasets. Beyond that, ChIPseek also offers users the flexibility to filter peaks and re-analyze the filtered subset of peaks. ChIPseek supports 20 different genome assemblies for 12 model organisms including human, mouse, rat, worm, fly, frog, zebrafish, chicken, yeast, fission yeast, Arabidopsis, and rice. We use demo datasets to demonstrate the usage and intuitive user interface of ChIPseek. CONCLUSIONS: ChIPseek provides a user-friendly interface for biologists to analyze large-scale ChIP data without requiring any programing skills. All the results and figures produced by ChIPseek can be downloaded for further analysis. The analysis tools built into ChIPseek, especially the ones for selecting and examine a subset of peaks from ChIP data, provides invaluable helps for exploring the high through-put data from either ChIP-seq or ChIP-chip. ChIPseek is freely available at http://chipseek.cgu.edu.tw.

CPAP: Cancer Panel Analysis Pipeline.

Targeted sequencing using next-generation sequencing technologies is currently being rapidly adopted for clinical sequencing and cancer marker tests. However, no existing bioinformatics tool is available for the analysis and visualization of multiple targeted sequencing datasets. In the present study, we use cancer panel targeted sequencing datasets generated by the Life Technologies Ion Personal Genome Machine Sequencer as an example to illustrate how to develop an automated pipeline for the comparative analyses of multiple datasets. Cancer Panel Analysis Pipeline (CPAP) uses standard output files from variant calling software to generate a distribution map of SNPs among all of the samples in a circular diagram generated by Circos. The diagram is hyperlinked to a dynamic HTML table that allows the users to identify target SNPs by using different filters. CPAP also integrates additional information about the identified SNPs by linking to an integrated SQL database compiled from SNP-related databases, including dbSNP, 1000 Genomes Project, COSMIC, and dbNSFP. CPAP only takes 17 min to complete a comparative analysis of 500 datasets. CPAP not only provides an automated platform for the analysis of multiple cancer panel datasets but can also serve as a model for any customized targeted sequencing project.

Demoralization syndrome among cancer outpatients in Taiwan.

GOALS: This paper aims to explore characteristics of demoralization syndrome as well as the relationship between demoralization syndrome and psychosocial issues as seen through examinations of cancer outpatients in Taiwan. MATERIALS AND METHODS: Outpatients with different cancer types were enrolled in this study. The Demoralization Scale Mandarin Version (DS-MV), Patient Health Questionnaire, Beck Hopelessness Scale, and McGill Quality of Life Questionnaire-Taiwan Version were used as instruments. All data were analyzed using SPSS 18.0. RESULTS: Among the 234 patients studied (97 men and 223 women), the majority had cervical cancer (29.1%), followed by breast cancer (26.5%) and head and neck cancer (24.3%). The mean score of DS-MV was 31.05 (SD 14.87). The results of ANOVA analysis showed a significant effect of occupation F(4.209) = 7.145 (p < 0.001), cancer diagnosis F(7.206) = 3.795 (p < 0.001), and treatment F(8.206) = 3.553 (p < 0.001) on DS-MV. CONCLUSIONS: Demoralization syndrome was found to be related to psychosocial issues, different cancer types, and treatments. Further studies are recommended to better understand causes and impacts of demoralization in the quality of life and care of cancer patients.