Thyroid cancer incidences in the United Arab Emirates: a retrospective study on association with age and gender [version 1; peer review: 1 approved]

Background: Thyroid cancer is the ninth most common malignancy worldwide, but the third most common malignancy in the United Arab Emirates (UAE) . To our knowledge, this is the first UAE nationwide study aimed at presenting incidence rates of thyroid cancer at the national level of UAE based upon data from the national cancer registry and GLOBOCAN. Methods: Between 2011 and 2017, a total of 2036 thyroid cancer cases from UAE patients were registered, of which 75.3% were female and 24.7% male patients. Results: The results showed 6.6% increase in thyroid cancer cases in the UAE from 2011 to 2017 (p < 0.001) with a rise of approximately 400 cases per year from 2011 to 2040. Age standardized rate calculations showed increase in prevalence from 1.18 in 2011 to 4.32 in 2017 but decreases in incidence from 1.05 in 2011 to 0.15 in 2017. This trend is confirmed by the predictive model showing increase in incidence from 0.15 in 2017 to 0.64 by 2040. Gender was shown to be significantly associated with thyroid cancer. The female to male ratio was significantly higher in Emirati patients (4.86:1) (p < 0.001) than expat patients (2.47:1) (p < 0.01). Interestingly, expat patients contributed to the majority of thyroid cancer cases despite having lower female to male ratio. The age at diagnosis was significantly associated with thyroid cancer (p = 0.03) with the highest frequency diagnosed at 35-39 years of age. Globally, data from the predictive model showed that Asia had the highest rate of increase per year and UAE the lowest. Conclusions: The slight increase in thyroid cancer prevalence and incidence, together with the different female to male ratio and diagnosis at younger age warrants further investigation at the molecular level from UAE thyroid cancer patients to elucidate the molecular basis of thyroid cancer

Thyroid cancer incidence in the United Arab Emirates: a retrospective study on association with age and gender [version 2; peer review: 2 approved, 1 approved with reservations]

Background: Thyroid cancer is the ninth most common malignancy worldwide, but the third most common malignancy in the United Arab Emirates (UAE). To our knowledge, this is the first UAE nationwide study aimed at presenting incidence rates of thyroid cancer at the national level of UAE based upon data from the national cancer registry and GLOBOCAN. Methods: Between 2011 and 2017, a total of 2036 thyroid cancer cases from UAE patients were registered, of which 75.3% were female and 24.7% male patients. Results: The results showed 6.6% increase in thyroid cancer cases in the UAE from 2011 to 2017 (p < 0.001) with a rise of approximately 400 cases per year from 2011 to 2040. Age standardized rate calculations showed increase in prevalence from 1.18 in 2011 to 4.32 in 2017 but decreases in incidence from 1.05 in 2011 to 0.15 in 2017. This trend is confirmed by the predictive model showing increase in incidence from 0.15 in 2017 to 0.64 by 2040. Gender was shown to be significantly associated with thyroid cancer. The female to male ratio was significantly higher in Emirati patients (4.86:1) (p < 0.001) than expat patients (2.47:1) (p < 0.01). Interestingly, expat patients contributed to the majority of thyroid cancer cases despite having lower female to male ratio. The age at diagnosis was significantly associated with thyroid cancer (p = 0.03) with the highest frequency diagnosed at 35-39 years of age. Globally, data from the predictive model showed that Asia had the highest rate of increase per year and UAE the lowest. Conclusions: The slight increase in thyroid cancer prevalence and incidence, together with the different female to male ratio and diagnosis at younger age warrants further investigation at the molecular level from UAE thyroid cancer patients to elucidate the molecular basis of thyroid cancer

Identifying Diagnostic and Prognostic targets for Papillary Thyroid Carcinoma through mining Gene Expression BIG Datasets using Adaptive Filtering and Advanced Bioinformatics Algorithms

Thyroid Cancer is the most common endocrine malignancy. Although the mortality rate of thyroid cancer is considered to be low, however the reoccurrence and persistence of the disease is still considered high. The most common type of thyroid cancer is papillary thyroid carcinoma consisting of >70% of all types of thyroid cancer. Thyroid cancer is heterogeneous and complex. BIG data in the form of publicly available gene expression (transcriptomics) datasets can provide valuable source to gain deeper understanding of complex diseases such as papillary thyroid carcinoma (PTC). In this study, we used a novel bioinformatics method based on adaptive filtering to reduce the number of genes expressed eliminating genes that are invariant across the various disease stages. In order to shed light on some of the mechanisms involved in PTC, the filtered genes were used in systematic pathway analysis searches across 20,500 annotated cellular pathways using modified Kolmogorov-Smirnov algorithm to identify the relevant differentially activated cellular pathways across the various stages of the disease. Our analysis from 95 PTC patient biopsies consisting of 41 normal, 28 nonaggressive and 26 metastatic papillary thyroid carcinoma revealed 2193 differential activated cellular pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. The key pathways for non-aggressive PTC includes calcium and potassium ion transport, hormone signaling pathways, protein tyrosine phosphatase activity and protein tyrosine kinase activity. The key pathways for metastatic PTC include growth, apoptosis, activation of MAPK activity and regulation of serine threonine kinase activity. The most frequent genes across the enriched pathways were KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B for non-aggressive PTC, and EGFR, PTK2B, KCNN4 and BCL2 for metastatic PTC. Survival analysis results showed that PTK2B, CACNA1D and BCL2 contributed to poor survival of PTC patients. The study identified insights into mechanisms of PTC

Integrative systematic review meta-analysis and bioinformatics identifies MicroRNA-21 and its target genes as biomarkers for colorectal adenocarcinoma

BACKGROUND: Advanced colorectal has poor survival and are difficult to treat. Therefore, there is an urgent need for biomarkers to diagnose this cancer at earlier manageable stages. Micro-RNAs (miRNAs) are amongst the most significant biomarkers that have shown promise in improving management and early detection of different types of cancers. However, since MiRNAs are non-coding, the main limitation of using them as biomarkers is that they do not have associated phenotype and therefore difficult to validate using other techniques. This makes it difficult to understand the mechanism of miRNA is disease initiation and progression, therefore any methodology that can provide semantics to miRNA expression would enhance the understanding of the role of miRNA in disease. METHODS: Here we report an integrative meta-analysis and bioinformatics methodology that showed microRNA21 and its associated target mRNA to be the most significant predictive biomarkers for colorectal adenoma and adenocarcinoma. After drawing key inferences by meta-analysis, the authors then developed a bioinformatics method to identify mir-21 gene targeting in a specific tissue using two different bioinformatics approaches; absolute GSEA (Gene Set Enrichment Analysis) and LIMMA (Linear Models for MicroArray data) to identify differentially expressed genes of miRNA-21. RESULTS: Results from GSEA intersection with mir-21 gene targets was a subset of longer gene list that was obtained from the GEO2R intersect. In our study, both of longer GEO2R gene target list and the more focused GSEA list established the fact that mir-21 target numerous functional pathways that are mostly interconnected. Our three steps bioinformatics approach identified ABCB1, HPGD, BCL2, TIAM1, TLR3, and PDCD4 as common targets for mir-21 in both of adenoma as well as adenocarcinoma suggesting they are biomarkers for early CRC. CONCLUSIONS: The approach in this study proposed combining the big data from the scientific literature together with novel bioinformatics to bring about a methodology that can be used to first identify which microRNAs are involved in a specific disease, and then to identify a panel of biomarkers derived from the microRNAs target genes, and from these target genes the functional significance of these microRNAs can be inferred providing better clinical value for the surgeon

Systems Immunology Analysis Reveals an Immunomodulatory Effect of Snail-p53 Binding on Neutrophil- and T Cell-Mediated Immunity in KRAS Mutant Non-Small Cell Lung Cancer

Immunomodulation and chronic inflammation are important mechanisms utilized by cancer cells to evade the immune defense and promote tumor progression. Therefore, various efforts were focused on the development of approaches to reprogram the immune response to increase the immune detection of cancer cells and enhance patient response to various types of therapy. A number of regulatory proteins were investigated and proposed as potential targets for immunomodulatory therapeutic approaches including p53 and Snail. In this study, we investigated the immunomodulatory effect of disrupting Snail-p53 binding induced by the oncogenic KRAS to suppress p53 signaling. We analyzed the transcriptomic profile mediated by Snail-p53 binding inhibitor GN25 in non-small cell lung cancer cells (A549) using Next generation whole RNA-sequencing. Notably, we observed a significant enrichment in transcripts involved in immune response pathways especially those contributing to neutrophil (IL8) and T-cell mediated immunity (BCL6, and CD81). Moreover, transcripts associated with NF-κB signaling were also enriched which may play an important role in the immunomodulatory effect of Snail-p53 binding. Further analysis revealed that the immune expression signature of GN25 overlaps with the signature of other therapeutic compounds known to exhibit immunomodulatory effects validating the immunomodulatory potential of targeting Snail-p53 binding. The effects of GN25 on the immune response pathways suggest that targeting Snail-p53 binding might be a potentially effective therapeutic strategy

Colorectal cancer: from epidemiology to current treatment

Colorectal cancer (CRC) was the second most frequent cancer in Europe in 2004, responsible for 13% (376,400) of all incident cancer cases. It is also the second most frequent cause of cancer mortality in Europe, with 11.9% (203,700) annual deaths. When localized, CRC is often a curable disease, but the overall prognosis is determined by the extent of local and particularly metastatic tumour spread. The disease outlook is relatively poor, because advanced disease is a significant cause of worldwide cancer-related mortality. Thus, estimated 5-year survival rates range from nearly 90% in stage I disease (Dukes’ A) to less than 10% in patients with metastatic disease (Dukes’ D). Comprehensive cancer care in the 21st century is dependent on a multidisciplinary approach to patients with malignant disease. Large bowel cancer is no exception, as there is increasing clinical trial data supporting multimodal treatment for both localized and advanced tumours. This review will focus on important aspects in CRC including the latest treatment strategies (chemotherapy, radiotherapy and the targeted therapies)

Immunohistochemical Assessment of TNFAIP3/A20 Expression Correlates With Early Tumorigenesis in Breast Cancer

BACKGROUND/AIM: Limited data exist on the expression pattern of TNFAIP3/A20, as assayed by immunohistochemistry (IHC), in breast cancer tissues. This study aimed to assess A20 expression pattern in breast cancer. Materials and Methods: The expression of A20 was analysed using IHC in 50 breast cancer cases retrieved from the Sharjah Breast Cancer Center at the University Hospital Sharjah, United Arab Emirates. Omics survival data were also used to analyse its association with survival in endocrine-treated subgroups. Results: A20 expression in breast cancer tissues was 'tumor-specific', and as compared to normal tissue areas, its expression was associated with both intensity and extent in early grade 1 (p<0.0001) in all molecular subtypes. In addition, using omics survival data from a cohort of 3,520 breast cancer patients, we showed that A20 overexpression associated with lower overall survival rate in the endocrine treated subgroups [hazard ratio (HR)=2.14, 95%CI=1.61-2.82, p<0.0001]. Conclusion: A20 can serve as a biomarker for early diagnosis of breast cancers

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area&#x2019;s worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease&#x2019;s onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems

Expression of carbonic anhydrase IX suggests poor outcome in rectal cancer

The aim of the study is to assess the value of carbonic anhydrase isozyme IX (CA IX) expression as a predictor of disease-free survival (DFS) and disease-specific survival (DSS) in rectal cancer treated by preoperative radio- or chemoradiotherapy or surgery only. Archival tumour samples from 166 patients were analysed for CA IX expression by three different evaluations: positive/negative, proportion of positivity and staining intensity. The results of immunohistochemical analysis were confirmed by demonstrating CA IX protein in western blotting analysis. Forty-four percent of the operative samples were CA IX positive, of these 34% had weak and 66% moderate/strong staining intensity. In univariate survival analysis, intensity of CA IX expression was a predictor of DFS (P=0.003) and DSS (P=0.034), both being markedly longer in tumours with negative or weakly positive staining. In multivariate Cox model, number of metastatic lymph nodes and CA IX intensity were the only independent predictors of DFS. Carbonic anhydrase isozyme IX intensity was the only independent predictor of DSS, with HR=9.2 for dying of disease with moderate-intense CA IX expression as compared with CA IX-negative/weak cases. Negative/weak CA IX staining intensity is an independent predictor of longer DFS and DSS in rectal cancer