The potential therapeutic applications of CRISPR/Cas9 in colorectal cancer

The application of the CRISPR-associated nuclease 9 (Cas9) system in tumor studies has led to the discovery of several new treatment strategies for colorectal cancer (CRC), including the recognition of novel target genes, the construction of animal mass models, and the identification of genes related to chemotherapy resistance. CRISPR/Cas9 can be applied to genome therapy for CRC, particularly regarding molecular-targeted medicines and suppressors. This review summarizes some aspects of using CRISPR/Cas9 in treating CRC. Further in-depth and systematic research is required to fully realize the potential of CRISPR/Cas9 in CRC treatment and integrate it into clinical practice

The potential therapeutic applications of CRISPR/Cas9 in colorectal cancer

The application of the CRISPR-associated nuclease 9 (Cas9) system in tumor studies has led to the discovery of several new treatment strategies for colorectal cancer (CRC), including the recognition of novel target genes, the construction of animal mass models, and the identification of genes related to chemotherapy resistance. CRISPR/Cas9 can be applied to genome therapy for CRC, particularly regarding molecular-targeted medicines and suppressors. This review summarizes some aspects of using CRISPR/Cas9 in treating CRC. Further in-depth and systematic research is required to fully realize the potential of CRISPR/Cas9 in CRC treatment and integrate it into clinical practice

Genetic determinants of response to statins in cardiovascular diseases

Despite extensive efforts to identify patients with cardiovascular disease (CVD) who could most benefit from the treatment approach, patients vary in their benefit from therapy and propensity for adverse drug events. Genetic variability in individual responses to drugs (pharmacogenetics) is considered an essential determinant in responding to a drug. Thus, understanding these pharmacogenomic relationships has led to a substantial focus on mechanisms of disease and drug response. In turn, understanding the genomic and molecular bases of variables that might be involved in drug response is the main step in personalized medicine. There is a growing body of data evaluating drug-gene interactions in recent years, some of which have led to FDA recommendations and detection of markers to predict drug responses (e.g., genetic variant in VKORC1 and CYP2C9 genes for prediction of drug response in warfarin treatment). Also, statins are widely prescribed drugs for the prevention of CVD. Atorvastatin, fluvastatin, rosuvastatin, simvastatin, and lovastatin are the most common statins used to manage dyslipidemia. This review provides an overview of the current knowledge on the pharmacogenetics of statins, which are being used to treat cardiovascular diseases.</p

Breast cancer prediction using different machine learning methods applying multi factors

ObjectiveBreast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.MethodsThe population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated.ResultCompared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54).ConclusionBreast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.</p

The circadian clock as a potential biomarker and therapeutic target in pancreatic cancer

Pancreatic cancer (PC) has a very high mortality rate globally. Despite ongoing efforts, its prognosis has not improved significantly over the last two decades. Thus, further approaches for optimizing treatment are required. Various biological processes oscillate in a circadian rhythm and are regulated by an endogenous clock. The machinery controlling the circadian cycle is tightly coupled with the cell cycle and can interact with tumor suppressor genes/oncogenes; and can therefore potentially influence cancer progression. Understanding the detailed interactions may lead to the discovery of prognostic and diagnostic biomarkers and new potential targets for treatment. Here, we explain how the circadian system relates to the cell cycle, cancer, and tumor suppressor genes/oncogenes. Furthermore, we propose that circadian clock genes may be potential biomarkers for some cancers and review the current advances in the treatment of PC by targeting the circadian clock. Despite efforts to diagnose pancreatic cancer early, it still remains a cancer with poor prognosis and high mortality rates. While studies have shown the role of molecular clock disruption in tumor initiation, development, and therapy resistance, the role of circadian genes in pancreatic cancer pathogenesis is not yet fully understood and further studies are required to better understand the potential of circadian genes as biomarkers and therapeutic targets

Extracellular vesicles: emerging mediators of cell communication in gastrointestinal cancers exhibiting metabolic abnormalities

There is a complex interaction between pro-tumoural and anti-tumoural networks in the tumour microenvironment (TME). Throughout tumourigenesis, communication between malignant cells and various cells of the TME contributes to metabolic reprogramming. Tumour Dysregulation of metabolic pathways offer an evolutional advantage in the TME and enhance the tumour progression, invasiveness, and metastasis. Therefore, understanding these interactions within the TME is crucial for the development of innovative cancer treatments. Extracellular vesicles (EVs) serve as carriers of various materials that include microRNAs, proteins, and lipids that play a vital role in the communication between tumour cells and non-tumour cells. EVs are actively involved in the metabolic reprogramming process. This review summarized recent findings regarding the involvement of EVs in the metabolic reprogramming of various cells in the TME of gastrointestinal cancers. Additionally, we highlight identified microRNAs involved in the reprogramming process in this group of cancers and explained the abnormal tumour metabolism targeted by exosomal cargos as well as the novel potential therapeutic approaches.</p

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC

Preclinical tumor mouse models for studying esophageal cancer

Preclinical models are extensively employed in cancer research because they can be manipulated in terms of their environment, genome, molecular biology, organ systems, and physical activity to mimic human behavior and conditions. The progress made in in vivo cancer research has resulted in significant advancements, enabling the creation of spontaneous, metastatic, and humanized mouse models. Most recently, the remarkable and extensive developments in genetic engineering, particularly the utilization of CRISPR/Cas9, transposable elements, epigenome modifications, and liquid biopsies, have further facilitated the design and development of numerous mouse models for studying cancer. In this review, we have elucidated the production and usage of current mouse models, such as xenografts, chemical-induced models, and genetically engineered mouse models (GEMMs), for studying esophageal cancer. Additionally, we have briefly discussed various gene-editing tools that could potentially be employed in the future to create mouse models specifically for esophageal cancer research.</p

Association of a genetic variant in the adenosine triphosphate transmembrane glycoprotein and risk of pancreatic cancer

Background: Pancreatic cancer (PC) is one of the most aggressive neoplasms with a poor prognosis. The association of multidrug resistance genes, MDR1/ABCB1, with the poor outcomes of several malignancies has been reported, which can be explained at least in part by a reduction in the transportation of drugs and their metabolites. Here, we explore the association of a genetic variant, rs2032582, in the ABCB1 gene with the risk of developing PC.Methods: Seventy-five patients and 188 controls were recruited. DNA was extracted followed by genotyping using Taqman® real-time polymerase chain reaction-based method. Kaplan-Meier and Cox models analysis showed that there is no significant association between genetic models and overall survival (OS) (P=0.32).Results: The frequencies of AA, AC, and CC genotypes of the variant were 29.7%, 42.2%, and 28.1%, respectively in the PC group, while the frequencies of the genotypes were 32.4%, 54.8%, and 12.8%, respectively, in the control group. Individuals with the AA genotype had an increased risk of developing PC {e.g., dominant genetic model [CC versus AA + AC: OS ± standard deviation (SD): 28±5.8 versus 50.8±6.7 months] with odds ratio (OR) of 2.67 (CI =1.33–5.34, P=0.005)}.Conclusions: Our findings demonstrated the association of the ABCB1 variant with an increased risk of PC. Further studies in a larger sample size and multi-center setting are suggested to explore the prognostic value of emerging marker PC.</p