Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm.

OBJECTIVE: Thyroid Cancer (TC) is the most frequent endocrine malignancy neoplasm. It is the sixth cause of cancer in women worldwide. The treatment process could be expedited by identifying the controlling molecular mechanisms at the early and late stages, which can contribute to the acceleration of treatment schemes and the improvement of patient survival outcomes. In this work, we study the significant mRNAs through Machine Learning Algorithms in both the early and late stages of Papillary Thyroid Cancer (PTC). METHOD: During the course of our study, we investigated various methods and techniques to obtain suitable results. The sequence of procedures we followed included organizing data, using nested cross-validation, data cleaning, and normalization at the initial stage. Next, to apply feature selection, a t-test and binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) were chosen to be employed. Later on, during the analysis stage, the discriminative power of the selected features was evaluated using machine learning and deep learning algorithms. Finally, we considered the selected features and utilized Association Rule Mining algorithm to identify the most important ones for improving the decoding of dominant molecular mechanisms in PTC through its early and late stages. RESULT: The SVM classifier was able to distinguish between early and late-stage categories with an accuracy of 83.5% and an AUC of 0.78 based on the identified mRNAs. The most significant genes associated with the early and late stages of PTC were identified as (e.g., ZNF518B, DTD2, CCAR1) and (e.g., lnc-DNAJB6-7:7, RP11-484D2.3, MSL3P1), respectively. CONCLUSION: Current study reveals a clear picture of the potential candidate genes that could play a major role not only in the early stage, but also throughout the late one. Hence, the findings could be of help to identify therapeutic targets for more effective PTC drug developments.

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method.

Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.

Main drivers of diabetes pharmaceuticals expenditures: evidence from OECD countries and Iran.

Purpose: This study aimed to identify the impact of prominent drivers on drug expenditure for diabetes. Method: Following the examination of previous studies, this study identified possible factors contributing to diabetes pharmaceutical expenditures. The explanatory variables for the study were the median population age, access to innovative drugs, GDP per capita, prevalence, price, and consumption of diabetes drugs. Then, to estimate the per capita expenditure among diabetic patients, this study developed the panel data model and two time-series regression models for OECD countries and Iran, respectively. Results: In the panel data regression model, R2 was 0.43. The influence of the age, prevalence, consumption volume and GDP per capita coefficients were + 1.79, + 0.704, + 3.86057, + 0.00054, respectively. Also, the probability level of all variables was less than 0.05. In Iran's comparative time-series regression model, R2 was 0.9, and the only significant influence coefficient was the age (ß=+0.91). In the another model for Iran, R2 was 0.99, the influence coefficient of age was + 0.249, the prevalence was + 0.131, innovation was + 0.029, and the price was + 0.00054; all the probability levels were less than 0.05. Conclusion: Pharmaceutical per capita expenditure is affected by several factors. These factors are not the same in various counties. Passing a judgment on drug utilization only based on pharmaceutical per capita expenditure cannot be perfect. Also, judging whether the per capita drug expenditure in one country is desirable without attention to the affecting factors and only relying on the value of utilized medicines is defective.

Correlation of rs35753505 polymorphism in Neuregulin 1 gene with psychopathology and intelligence of people with schizophrenia.

BACKGROUND AND AIM: Schizophrenia is one of the most severe psychiatric disorders. About 0.5 to 1% of the world's population suffers from this non-Mendelian disorder. Environmental and genetic factors seem to be involved in this disorder. In this article, we investigate the alleles and genotypic correlation of mononucleotide rs35753505 polymorphism of Neuregulin 1 (NRG1), one of the selected genes of schizophrenia, with psychopathology and intelligence. MATERIALS AND METHODS: 102 independent and 98 healthy patients participated in this study. DNA was extracted by the salting out method and the polymorphism (rs35753505) were amplified by polymerase chain reaction (PCR). Sanger sequencing was performed on PCR products. Allele frequency analysis was performed using COCAPHASE software, and genotype analysis was performed using Clump22 software. RESULTS: According to our study's statistical findings, all case samples from the three categories of men, women, and overall participants significantly differed from the control group in terms of the prevalence of allele C and the CC risk genotype. The rs35753505 polymorphism significantly raised Positive and Negative Syndrome Scale (PANSS) test results, according to a correlation analysis between the two variables. However, this polymorphism led to a significant decrease in overall intelligence in case samples compared to control samples. CONCLUSION: In this study, it seems that the rs35753505 polymorphism of NRG1 gene has a significant role in the sample of patients with schizophrenia in Iran and also in psychopathology and intelligence disorders.

Investigation and confirmation of differentially expressed miRNAs, as well as target gene prediction in papillary thyroid cancer, with a special emphasis on the autophagy signaling pathway.

Papillary thyroid carcinoma (PTC) is the most common endocrine cancer. However, the role of biomechanics in the development and progression of PTC is obscure. The microarray dataset GSE104005 was examined to identify important microRNAs (miRNAs or miRs) and their probable roles in the carcinogenesis of PTC. The gene expression omnibus (GEO) database was used to obtain the data. R was used to access the differentially expressed miRNAs (DEMs) and genes (DEGs). The multiMiR software was used to predict DEM targets. To validate the top DEMs and DEGs, thirty tissue samples were obtained from PTC patients who had their thyroids removed and compared with 30 normal samples. The total RNA content of the tumor and corresponding non-tumoral adjacent samples were purified and converted to cDNA. Expression levels of top dysregulated miRNAs and their target and predicted DEG were evaluated using the RT-qPCR method. miR-182 and miR-183 were top upregulated miRs and miR-30d was the most downregulated miR among DEMs. Furthermore, FOXO1 which was shown to be targeted by aforementioned miRNAs, was the most downregulated genes among other DEGs. 10 hub nodes were detected by PPI construction. PTEN was the hub node with highest score. The in vitro gene expression analysis was also showed the same expression pattern in tissues. Significant increase in miR-182-5p and miR-183-5p expressions, as well as a significant decrease in FOXO1 and miR-30d-5p expressions, suggest that PTC cells may tend to preserve their autophagy capability.

Upregulation of miR-142 in papillary thyroid carcinoma tissues: a report based on in silico and in vitro analysis.

Papillary thyroid carcinoma (PTC) accounts for approximately 80% of all human thyroid malignancies. Recently, there has been a dramatic rise in the prevalence of thyroid cancer all over the globe. Through analysis of the GEO database, GSE104005, the authors of the current research were able to determine the differential expression of microRNAs (DEMs) as well as their target genes. Real-time PCR was used on a total of 40 samples, 40 of which were from PTC samples and 40 from normal tissues, in order to validate the discovered DEMs and the genes. Gene Ontology (GO) categories were identified, and KEGG was used to conduct pathway enrichment analysis. The multiMiR R package was used to predict target genes of DEMs. Mir-142 was found to be overexpressed in PTC samples, as compared to normal tissues, and this was validated by the identification and validation. In addition, metal ion binding and the cellular response to metal ions were identified as essential pathways in the carcinogenesis of PTC. This demonstrates their significance in the development of this malignancy. The results of our research will serve as the foundation for further research in the area of miRNA-based cancer treatment.