Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Genetic variants causing underlying pharmacogenetic and disease phenotypes have been used as the basis for clinical decision-making. However, due to the lack of standards for next-generation sequencing (NGS) pipelines, reproducing genetic variants among institutions is still difficult. The aim of this study is to show how many important variants for clinical decisions can be individually detected using different pipelines. Genetic variants were derived from 105 breast cancer patient target DNA sequences via three different variant-calling pipelines. HaplotypeCaller, Mutect2 tumor-only mode in the Genome Analysis ToolKit (GATK), and VarScan were used in variant calling from the sequence read data processed by the same NGS preprocessing tools using Variant Effect Predictor. GATK HaplotypeCaller, VarScan, and MuTect2 found 25,130, 16,972, and 4232 variants, comprising 1491, 1400, and 321 annotated variants with ClinVar significance, respectively. The average number of ClinVar significant variants in the patients was 769.43, 16.50% of the variants were detected by only one variant caller. Despite variants with significant impact on clinical decision-making, the detected variants are different for each algorithm. To utilize genetic variants in the clinical field, a strict standard for NGS pipelines is essential.

Background: As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised. Objective: The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information. Methods: RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be accurately predicted from RNA expression data. Performance measurement of the prediction models was based on the accuracy and area under the receiver operating characteristic curve. We selected five cancer types (breast carcinoma, kidney renal clear cell carcinoma, head and neck squamous cell carcinoma, low-grade glioma, and lung adenocarcinoma) with large samples sizes to verify whether predictive accuracy would differ between them. We also validated the efficacy of our four machine learning models in analyzing normal samples from 593 cancer patients. Results: In most samples, personal information with high genetic relevance, such as gender and cancer type, could be predicted from RNA expression data alone. The prediction accuracies for gender and cancer type, which were the best models, were 0.93-0.99 and 0.78-0.94, respectively. Other aspects of personal information, such as age, race, and cancer stage, were difficult to predict from RNA expression data, with accuracies ranging from 0.0026-0.29, 0.76-0.96, and 0.45-0.79, respectively. Among the tested machine learning methods, the highest predictive accuracy was obtained using the support vector machine algorithm (mean accuracy 0.77), while the lowest accuracy was obtained using the random forest method (mean accuracy 0.65). Gender and race were predicted more accurately than other variables in the samples. On average, the accuracy of cancer stage prediction ranged between 0.71-0.67, while the age prediction accuracy ranged between 0.18-0.23 for the five cancer types. Conclusions: We attempted to predict patient information using RNA expression data. We found that some identifiers could be predicted, but most others could not. This study showed that personal information available from RNA expression data is limited and this information cannot be used to identify specific patients.

Background/Objectives: Malignant ascites frequently arises in advanced cancers with peritoneal metastasis and is associated with poor outcomes. Known mechanisms include lymphatic obstruction by tumor cells, increased vascular permeability, and sodium retention via the renin–angiotensin–aldosterone system; however, the pathogenesis remains not fully understood. We investigated whether gut and bladder microbiomes correlate with malignant ascites development or progression and whether the immune microenvironment in ascitic fluid is altered. Methods: We enrolled 66 histologically confirmed cancer patients, dividing them into malignant ascites (n = 20) and non-ascites (n = 46) groups. Stool, urine, and ascitic fluid samples were analyzed using 16S rRNA next-generation sequencing. Immune cell subsets in ascitic fluid were characterized using flow cytometry. Results: In 19 of the 20 malignant ascites samples, the bacterial load was too low for reliable 16S rRNA sequencing, suggesting that malignant ascites is largely sterile. The overall gut microbiome diversity did not differ significantly by ascites status, although a trend emerged in patients with peritoneal metastasis, including the enrichment of class Clostridia and Gammaproteobacteria. Bladder microbiome analysis also showed no significant differences in ascites or metastasis status. Flow cytometry revealed reduced T-cell (CD3+, CD4+, CD8+) and NK cell (CD56+) populations compared to data from cirrhotic ascites. Conclusions: Malignant ascites exhibit minimal bacterial biomass, making comprehensive microbiome analysis challenging. Although no major global changes were noted in gut and bladder microbiomes, specific taxa were linked to peritoneal metastasis. These findings highlight an immunosuppressive ascitic environment and suggest that larger-scale or multi-omics approaches may help elucidate the role of microbiota in malignant ascites.

BACKGROUND As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised. OBJECTIVE The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information. METHODS RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be accurately predicted from RNA expression data. Performance measurement of the prediction models was based on the accuracy and area under the receiver operating characteristic curve. We selected five cancer types (breast carcinoma, kidney renal clear cell carcinoma, head and neck squamous cell carcinoma, low-grade glioma, and lung adenocarcinoma) with large samples sizes to verify whether predictive accuracy would differ between them. We also validated the efficacy of our four machine learning models in analyzing normal samples from 593 cancer patients. RESULTS In most samples, personal information with high genetic relevance, such as gender and cancer type, could be predicted from RNA expression data alone. The prediction accuracies for gender and cancer type, which were the best models, were 0.93-0.99 and 0.78-0.94, respectively. Other aspects of personal information, such as age, race, and cancer stage, were difficult to predict from RNA expression data, with accuracies ranging from 0.0026-0.29, 0.76-0.96, and 0.45-0.79, respectively. Among the tested machine learning methods, the highest predictive accuracy was obtained using the support vector machine algorithm (mean accuracy 0.77), while the lowest accuracy was obtained using the random forest method (mean accuracy 0.65). Gender and race were predicted more accurately than other variables in the samples. On average, the accuracy of cancer stage prediction ranged between 0.71-0.67, while the age prediction accuracy ranged between 0.18-0.23 for the five cancer types. CONCLUSIONS We attempted to predict patient information using RNA expression data. We found that some identifiers could be predicted, but most others could not. This study showed that personal information available from RNA expression data is limited and this information cannot be used to identify specific patients.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).