The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria.

Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene-gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA-target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP .

Deep Learning for Discussion-Based Cross-Domain Performance Prediction of MOOC Learners Grouped by Language on FutureLearn.

Analysing learners' behaviours in MOOCs has been used to identify predictive features associated with positive outcomes in engagement and learning success. Early methods predominantly analysed numerical features of behaviours such as the page views, video views, and assessment grades. Analysing extracted numeric features using baseline machine learning algorithms performed well to predict the learners' future performance in MOOCs. We propose categorising learners by likely English language proficiency and extending the range of data to include the content of comment texts. We compare results to a model trained with a combined set of extracted features. Not all platforms provide this rich variety of data. We analysed a series of a FutureLearn language focused MOOCs. Our data were from discussions embedded into each lesson's content. Analysing whether we gained any additional insights, over 420,000 comments were used to train the algorithm. We created a method for identifying one's possible first language from their country. We found that using comments alone is a weaker predictive approach than using a combination including extracted features from learners' activities. Our study contributes to research on generalisability of learning algorithms. We replicated the method across different MOOCs-the performance varies on the model though it always remained over 50%. One of the deep learning architecture, Bidirectional LSTM, trained with discussions on the language learning 73% successfully predicted learners' performance on a different MOOC.

A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up.

DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences.

Two-tier combinatorial structure to integrate various gene co-expression networks of prostate cancer.

Advances in DNA sequencing technologies enable researchers to integrate various biological datasets in order to reveal hidden relations at the molecular level. In this study, we present a two-tiered combinatorial structure (TTCS) to integrate gene co-expression networks (GCNs) that are inferred from microarray gene expression, RNA-Seq and miRNA-target gene data. In the initial phase of TTCS, we derive GCNs by using gene network inference (GNI) algorithms for each dataset. In the first and second integration phases, we use straightforward methods: intersection, union and simple majority voting to combine GCNs. We use overlap, topological and biological analyses in performance evaluation and investigate the integration effects of GCNs separately for all phases. Our results prove that the first integration phase has limited contribution on performance. However, combining the biological datasets in the second phase significantly enhances the overlap and topological performance analyses.

Comparative proteogenomic analysis of right-sided colon cancer, left-sided colon cancer and rectal cancer reveals distinct mutational profiles.

Right-sided colon cancer (RCC) has worse prognosis compared to left-sided colon cancer (LCC) and rectal cancer. The reason for this difference in outcomes is not well understood. We performed comparative somatic and proteomic analyses of RCC, LCC and rectal cancers to understand the unique molecular features of each tumor sub-types. Utilizing a novel in silico clonal evolution algorithm, we identified common tumor-initiating events involving APC, KRAS and TP53 genes in RCC, LCC and rectal cancers. However, the individual role-played by each event, their order in tumor development and selection of downstream somatic alterations were distinct in all three anatomical locations. Some similarities were noted between LCC and rectal cancer. Hotspot mutation analysis identified a nonsense mutation, APC R1450* specific to RCC. In addition, we discovered new significantly mutated genes at each tumor location, Further in silico proteomic analysis, developed by our group, found distinct central or hub proteins with unique interactomes among each location. Our study revealed significant differences between RCC, LCC and rectal cancers not only at somatic but also at proteomic level that may have therapeutic relevance in these highly complex and heterogeneous tumors.

The effect of dutasteride on microscopic and macroscopic changes of testosterone replacement treatment on prostate tissue.

Testosterone replacement therapy has a growing interest in daily practice; however, debates on its safety for prostate cancer still continue. Dutasteride-a 5α-reductase inhibitor-was shown to be effective in preventing prostate cancer. We therefore aimed to evaluate the effect of testosterone replacement therapy and dutasteride treatment on prostate tissue in castrated rats. Rats were randomised in four groups after bilateral orchidectomy as follows: Group I received testosterone + dutasteride, Group II received only testosterone, Group III had no medical treatment, and Group IV was the control group. After 3 months, rats were sacrificed and laboratory and histopathological examinations were performed. In Groups I and II, prostate volume, T and DHT levels were significantly higher compared to Group III and controls. Groups I and II had also significantly greater preneoplastic histopathological signs; however, in intergroup analyses, Group I showed less premalignant changes compared to Group II. We concluded that dutasteride was effective when combined with testosterone therapy in preventing premalignant histopathological changes in prostate tissue. Further evidence is needed to confirm our findings.

Management of staghorn renal stones.

Staghorn stones are large branching stones that fill part of all of the renal pelvis and renal calyces and they can be complete or partial depending on the level of occupancy of the collecting system. Although kidney stones are commoner in men, staghorn stones are less often reported in men compared to women and they are usually unilateral. Due to the significant morbidity and potential mortality attributed to staghorn stones, prompt assessment and treatment is mandatory. Conversely, conservative treatment has been shown to carry a mortality rate of 28% in 10-year period and 36% risk of developing significant renal impairment. Staghorn stones are, therefore, significant disease entity that should be managed aggressively and effectively. Generally, the gold standard treatment for staghorn stones is surgical with a view to achieve stone-free collecting system and preserve renal function. Percutaneous nephrolithotomy should be the recommended first-line treatment for staghorn stones. Other non-surgical options are usually considered in combination with surgery or as monotherapy only if patients are surgically unfit. The decision for optimal treatment of staghorn stones should be individualized according to the circumstances of the patient involved and in order to do so, a closer look at the advantages and disadvantages of each option is necessary.

Estimation of the proteomic cancer co-expression sub networks by using association estimators.

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.

Renal Behçet's disease: an update.

OBJECTIVE: The aims of this study are (1) to report 33 patients with Behçet's disease (BD) having various renal manifestations, and (2) to update current data using our patients and published papers about BD and renal manifestations. METHODS: The PubMed database was searched using the terms BD or Behçet's syndrome. We found reports of 94 patients (including ours) with BD and specific renal diseases (amyloidosis, 39; glomerulonephritis [GN], 37; renal vascular disease, 19; interstitial nephritis, 1). RESULTS: The presentation of renal disease was edema/nephrotic syndrome in 12 patients (36%). Renal disease was incidentally diagnosed by routine urine analysis and measurement of serum creatinine level in 20 patients (61%). Renal failure was present in 23 patients (70%) and 5 of them have had cyclosporine treatment. The frequency of renal disease among BD patients has been reported to vary from less than 1 to 29%. CONCLUSIONS: The clinical spectrum of renal BD shows a wide variation. Amyloidosis (AA type), GN, and macroscopic/microscopic vascular disease are the main causes of renal BD. Patients with vascular involvement have a high risk of amyloidosis and amyloidosis is the most common cause of renal failure in BD. Several types of glomerular lesions are seen in BD. Current treatment options for renal BD are not evidence based. Radiological vascular intervention combined with immunosuppressive drugs can be useful in selected cases. Routine urine analysis and measurement of serum creatinine level are needed for early diagnosis of renal BD.

Lemierre syndrome variant: Staphylococcus aureus associated with thrombosis of both the right internal jugular vein and the splenic vein after the exploration of a river cave.

BACKGROUND: Lemierre syndrome is a rare and potentially fatal entity characterized by septic emboli from thrombosis of the internal jugular vein after oropharyngeal infection. The etiologic agent is not always an anaerobic bacterium. We report a patient with a Lemierre syndrome variant who presented with thrombosis of both the right internal jugular vein and the splenic vein as well as septic pulmonary emboli caused by Staphylococcus aureus, which proved resistant to methicillin, amoxicillin, and ciprofloxacin. The patient was thought to have acquired the infection during the exploration of a river cave in Turkey 10 days before his admission to the hospital. Such caves are natural reservoirs of infectious microorganisms. METHODS: The patient had a physical examination, coagulation screening tests, tests of thrombin generation and fibrinolysis, coagulation inhibitors, activated protein C resistance, early and late serological tests (including autoantibodies), computed tomographic studies of the chest and abdomen, and bacterial examination of peripheral blood. Bacterial investigations were made on samples collected from the river cave. RESULTS: After having undergone a 6 week course of antimicrobial treatment and anticoagulant therapy, the patient recovered from the infection with no residual complications, and the signs and symptoms of Lemierre syndrome resolved. CONCLUSION: The results probably indicate an exposure of the patient to soil-based Staphylococcus aureus. This observation suggests the action of environmental factors that may contribute to the development of this rare syndrome.

Behçet's disease and renal failure.

BACKGROUND: The aims of this study were (i) to investigate the prevalence of Behçet's disease (BD) among dialysis patients in Turkey, (ii) to report the clinical characteristics of patients with BD and end-stage renal disease (ESRD), (iii) to evaluate the effect of ESRD on course and activity of BD and (iv) to analyse the published data about BD and renal failure. METHODS: A questionnaire investigating BD among dialysis patients was submitted to 350 dialysis centres and we obtained the data for 20 596 patients from 331 dialysis centres. We submitted a second questionnaire regarding clinical characteristics of the patients with BD and ESRD. The PubMed and Web of Science databases were used for the analysis of BD and renal failure. RESULTS: Fourteen patients with BD were determined and the prevalence of BD was 0.07% among 20 596 dialysis patients in Turkey. None of the patients has had a new manifestation of BD after initiation of haemodialysis treatment. The analysis of previous data about renal BD demonstrated 67 patients with renal failure. CONCLUSIONS: The most common cause of renal failure in BD is amyloidosis. Routine urine analysis and measurement of serum creatinine and blood urea nitrogen levels are needed for early diagnosis. Vascular access-related problems are common and the activity of BD appears to decrease in patients with ESRD after initiation of haemodialysis.