Cancer as the Disintegration of Robustness: Population-Level Variance in Gene Expression Identifies Key Differences Between Tobacco- and HPV-Associated Oropharyngeal Carcinogenesis

A differential diagnosis between uterine leiomyoma and leiomyosarcoma using transcriptome analysis

BACKGROUND

The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, 17 genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), random forest (RF), and gradient boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using GeneMANIA. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets).

RESULTS

We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed sensitivity 0.88, 0.77 (8/9, 7/9) while the specificity 1.0 (8/8, 8/8) in two test data set supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample.

CONCLUSION

The transcriptome-based classifier accurately distinguished uterine leiomyosarcoma from leiomyoma. Our method can be helpful in clinical practice through the biopsy of sample in advance of surgery. Identification of leiomyosarcoma let the doctor avoid of laparoscopic surgery, thus it minimizes un-wanted tumor spread.

Targeted microRNA Profiling in Gastric Cancer with Clinical Assessement

Although several microRNAs (miRNAs) have been associated with gastric cancer there is still the need for identification of stable and validated biomarkers. The purpose of this study was to determine the alterations of a specific set of miRNA levels in gastric adenocarcinoma tissues to identify and validate gastric cancer-specific miRNAs using paired normal and tumor samples in an independent patient cohort. Gastric adenocarcinoma and normal stomach tissue samples of 20 patients who underwent surgery for gastric cancer were studied. The miRNA expression profiling was performed for eight miRNAs in a total of 40 tissue samples using quantitative reverse transcription polymerase chain reaction (RT-qPCR). Six out of these eight miRNAs, namely, miR-375-3p, hsamiR-129-5p, miR-196a-5p, miR-376c-3p, miR-34c-5p and miR-767-5p, were significantly underexpressed in malignant tissues of our cohort. Furthermore, the expression of miR-662 although not significantly different between normal and tumor tissues, was inversely associated with age (r = −0.440, p = 0.049). The levels of miR-129-3p and miR34c-5p were correlated with an increase in the number of metastatic lymph nodes (r = 0.470, p = 0.036; r = 0.510, p = 0.020), while and miR-376c-3p levels were negatively associated with smoking (p = 0.043). In addition, we found that the variability of miRNA expression in cancerous tissues was lower than that in normal tissues. Alterations in miRNA expression in gastric adenocarcinoma tissues in comparison to healthy tissues of each individual serves for identification of consistent biomarkers that can be used for development of diagnostic tools for gastric cancer.

Association of miR-1247-5p expression with clinicopathological parameters and prognosis in breast cancer

Our study aimed to clarify the correlation between miR‐1247‐5p expression and clinicopathological parameters and survival of patients with breast cancer (BC). We evaluated the expression level of miR‐1247‐5p in 224 formalin‐fixed, paraffin‐embedded specimens (112 BC and matched cancer free tissues) by quantitative real‐time reverse transcriptase polymerase chain reaction (qRT‐PCR). miR‐1247‐5p expression in BC tissues was found to be decreased compared with matched normal tissues (P < 0.01). Additionally, low miR‐1247‐5p expression in BC tissues was significantly associated with the advanced TNM stage (P = 0.007), lymph node metastasis (P = 0.015), poorer pathological differentiation (P = 0.005) and molecular subtype (P = 0.027). The patients in the low miR‐1247‐5p group had a shorter disease‐free survival and overall survival than those in the high miR‐1247‐5p group (P < 0.01). Furthermore, the univariate and the multivariate analyses showed that miR‐1247‐5p expression was an independent predictor of overall survival (P < 0.01). Our study showed that miR‐1247‐5p was related to the biological behaviour of breast tumour and prognosis of patients with BC. miR‐1247‐5p could be a novel tumour suppressor and act as a potential biomarker and therapeutic agent for breast carcinoma.

Comparative testing of HPV L1 protein monoclonal antibody panel for the detection of HPV in cervical exfoliated cells

AIMS

To determine the value of a monoclonal antibody panel against a C-terminal conserved sequence polypeptide of human papillomavirus (HPV) L1 (a major capsid protein) for the detection of HPV in cervical exfoliated cells, as well as the potential of this antibody panel to be developed into an assay kit for the clinical screening of cervical cancer.

METHODS

Cervical exfoliated cells were collected at a gynecology clinic. One part of each sample was sent to the Department of Pathology for HPV genotyping, and the other part was sent to the Department of Pathology for cytologic testing and then to the laboratory for immunological histological chemistry (IHC) assay in which an HPV L1 C-terminal conserved sequence polypeptide-induced mouse monoclonal antibody panel was used to detect HPV L1.

RESULTS

Cervical cell samples were collected from 514 patients at the gynecology clinic; of these, 339 samples were sent for HPV genotyping, and 220 were HPV positive (64.90%, 220/339). Moreover, the duplicate samples from these 339 patients were sent for IHC assay, and 229 samples were positive (67.55%, 229/339). The IHC result was concordant with that obtained by HPV genotyping (Kappa = 0.743, p < 0.001).

CONCLUSION

This study showed that use of the HPV L1 C-terminal conserved sequence polypeptide-induced mouse monoclonal antibody panel was of great value for the detection of HPV in cervical cells; the resulting detection rate was comparable to that obtained using the commercial HPV genotyping kit that is currently in use in clinical practice.

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.