Growth Patterns of Prostate Cancers Undetected by Prostate 3T Multiparametric MRI.

BACKGROUND/AIM: The multiparametric magnetic resonance imaging (mpMRI)-ultrasound (US) fusion targeted biopsy (TB) is a useful diagnostic device for men with suspected prostate cancer (PC) and can increase the detection rate for clinically significant PCs (csPC). However, few studies have shown pathological findings of undetectable csPCs on the prostate mpMRI. PATIENTS AND METHODS: This study investigated the growth patterns of csPC undetected in prostate mpMRI. The study enrolled 248 patients with suspected PCs and ≥PI-RADS 2 lesions, who then underwent mpMRI-US fusion TB and nearly prostate-mapping systematic biopsies (SB). A total 248 biopsies included 404 regions of interest in TB and 2976 mapping-regions in SB. RESULTS: The detection rates of csPC, defined as PC grade group (GG) ≥2, were 42% in TB and 44% in SB, and the highest detection rate was 50%, using both TB and SB. Approximately 79% of PI-RADS 3/4/5 with any PC showed csPC. A total 201 PI-RADS 3/4/5 lesions showed benign prostatic hyperplasia, lymphocytic prostatitis, or fibromuscular stroma only in the core tissues. Notably, 22 csPCs detected in SB but undetected in prostate mpMRI preferentially showed a pattern of mixed well-formed and fused PC glands. The other patterns including cribriform glands and poorly formed glands with intracytoplasmic vacuoles were also seen. Approximately 85% of the 22 csPCs showed tumor volume less than 50% of core tissues. CONCLUSION: Changes in prostatic stroma amounts, inflammation severity, tumor volume and growth patterns of PC glands affected the detectability of prostate mpMRI.

SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions about treatment strategies or the use of palliative care. (2) Methods: Gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected a total of 1187 RNA-seq data points from breast cancer patients (median age 58 years) in Fragments Per Kilobase Million (FPKM) format from the TCGA database. Among them, we selected 144 patients with date of death information to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to TPM to build the survival prediction model SaBrcada. After normalization and dimension raising, we used the differential gene expression data to test eight different deep learning architectures. Considering the effect of age on prognosis, we also performed a stratified random sampling test on all ages between the lower and upper quartiles of patient age, 48 and 69 years; (3) Results: Stratifying by age 61, the performance of SaBrcada built by GoogLeNet was improved to a highest accuracy of 0.798. We also built a free website tool to provide five predicted survival periods: within six months, six months to one year, one to three years, three to five years, or over five years, for clinician reference. (4) Conclusions: We built the prediction model, SaBrcada, and the website tool of the same name for breast cancer survival analysis. Through these models and tools, clinicians will be provided with survival interval information as a basis for formulating precision medicine.

NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches.

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.

GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System.

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance. GasPhos is available at http://predictor.nchu.edu.tw/GasPhos.

Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.

Information about the expression status of hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), and Her-2 is crucial in the management and prognosis of breast cancer. Therefore, the retrieval and analysis of hormone receptor expression characteristics in metastatic breast cancer may be valuable in breast cancer study. Herein, we report a text mining tool based on word/phrase matching that retrieves hormone receptor expression data of regional or distant metastatic breast cancer from pathology reports. It was tested on pathology reports at the China Medical University Hospital from 2013 to 2018. The tool showed specificities of 91.6% and 63.3% for the detection of regional lymph node metastasis and distant metastasis, respectively. Sensitivity in immunohistochemical study result extraction in these cases was 98.6% for distant metastasis and 78.3% for regional lymph node metastasis. Statistical analysis on these retrieved data showed significant difference s in PR and Her-2 expressions between regional and metastatic breast cancer, which is compatible with previous studies. In conclusion, our study shows that metastatic breast cancer hormone receptor expression characteristics can be retrieved by text mining. The algorithm designed in this study may be useful in future studies about text mining in pathology reports.

miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator.

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to predict miRNA-target interactions, but variable results among the different prediction tools have caused confusion for users. To solve this problem, we developed miRgo, an application that integrates many of these tools. To train the prediction model, extreme values and median values from four different data combinations, which were obtained via an energy distribution function, were used to find the most representative dataset. Support vector machines were used to integrate 11 prediction tools, and numerous feature types used in these tools were classified into six categories-binding energy, scoring function, evolution evidence, binding type, sequence property, and structure-to simplify feature selection. In addition, a novel evaluation indicator, the Chu-Hsieh-Liang (CHL) index, was developed to improve the prediction power in positive data for feature selection. miRgo achieved better results than all other prediction tools in evaluation by an independent testing set and by its subset of functionally important genes. The tool is available at http://predictor.nchu.edu.tw/miRgo.

Analysis of Hormone Receptor Status in Primary and Recurrent Breast Cancer Via Data Mining Pathology Reports.

BACKGROUND: Hormone receptors of breast cancer, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2), are important prognostic factors for breast cancer. OBJECTIVE: The current study aimed to develop a method to retrieve the statistics of hormone receptor expression status, documented in pathology reports, given their importance in research for primary and recurrent breast cancer, and quality management of pathology laboratories. METHOD: A two-stage text mining approach via regular expression-based word/phrase matching, was developed to retrieve the data. RESULTS: The method achieved a sensitivity of 98.8%, 98.7% and 98.4% for extraction of ER, PR, and Her-2 results. The hormone expression status from 3679 primary and 44 recurrent breast cancer cases was successfully retrieved with the method. Statistical analysis of these data showed that the recurrent disease had a significantly lower positivity rate for ER (54.5% vs 76.5%, p=0.001278) than primary breast cancer and a higher positivity rate for Her-2 (48.8% vs 16.2%, p=9.79e-8). These results corroborated the previous literature. CONCLUSION: Text mining on pathology reports using the developed method may benefit research of primary and recurrent breast cancer.

KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection.

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy-maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.

Proteome Profiling of Urinary Exosomes Identifies Alpha 1-Antitrypsin and H2B1K as Diagnostic and Prognostic Biomarkers for Urothelial Carcinoma.

MALDI-TOF spectrometry has not been used for urinary exosome analysis. We used it for determining UC biomarkers. From 2012 to 2015, we enrolled 129 consecutive patients with UC and 62 participants without UC. Exosomes from their urine were isolated, and analyzed through MALDI-TOF spectrometry. Immunohistochemical (IHC) analysis of another 122 UC and 26 non-UC tissues was conducted to verify the discovered biomarkers. Two peaks at m/z 5593 (fragmented peptide of alpha-1-antitrypsin; sensitivity, 50.4%; specificity, 96.9%) and m/z 5947 (fragmented peptide of histone H2B1K sensitivity, 62.0%; specificity, 92.3%) were identified as UC diagnosis exosome biomarkers. UC patients with detectable histone H2B1K showed 2.29- and 3.11-fold increased risks of recurrence and progression, respectively, compared with those with nondetectable histone H2B1K. Verification results of IHC staining revealed significantly higher expression of alpha 1-antitrypsin (p = 0.038) and H2B1K (p = 0.005) in UC tissues than in normal tissues. The expression of alpha 1-antitrypsin and H2B1K in UC tissues was significantly correlated with UC grades (p < 0.05). Urinary exosome proteins alpha 1-antitrypsin and histone H2B1K, which are identified through MALDI-TOF analysis, could facilitate rapid diagnosis and prognosis of UC.

Solitary renal metastasis of esophageal squamous cell carcinoma mimicking primary renal neoplasm - A case report and literature review.

Solitary renal metastasis of esophageal cancer is rare clinically, with only 14 cases being reported in the literature. The authors here report a case of a 53-year-old man with a metachronous hypopharyngeal and esophageal squamous cell carcinoma who developed a solitary renal metastasis after complete chemoradiotherapy for esophageal cancer, and subsequently received a left nephrectomy. The metastatic esophageal cancer was indistinguishable from primary renal neoplasm in the computed tomography but showed the histopathologic characteristic of esophageal cancer in directly invading the renal artery, and the tumor spreading in the kidney. The patient died of pneumonia two months after diagnosis. Among the previous 14 reported cases, 12 occurred in Asians, and their overall survival time ranges from two months to nine years after nephrectomy, either with or without adjuvant chemotherapy. Accordingly, a solitary renal mass in patients with a history of esophageal cancer is warranted to differentiate metastatic esophageal cancer from primary renal neoplasm, and a reliable therapy needs to be planned early for improving the patient's chance of survival.