A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling.

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

Remodeling of the Gut Microbiota in Colorectal Cancer and its Association with Obesity.

The considerable burden of colorectal cancer and the increasing prevalence in young adults emphasizes the necessity of understanding its underlying mechanisms and risk factors as well as providing more effective treatments. There is growing evidence of a positive relationship between obesity and colorectal cancer. Furthermore, the prominent role of gut microbiota dysbiosis in colorectal carcinogenesis is becoming more evident. Sequencing studies demonstrate an altered composition and ecology of intestinal microorganisms in both colorectal cancer and obese patients and have pinpointed some specific bacteria as the key role players. The purpose of this review is to provide a general outlook of how gut microbiota may impact the initiation and promotion of colorectal cancer and describes probable links between gut microbiota and obesity. We also provide evidence about targeting the microbiota as an intervention strategy for both ameliorating the risk of cancer and augmenting the therapy efficacy.

Metabolic Pathways Regulating Colorectal Cancer: A Potential Therapeutic Approach.

Colorectal cancer (CRC) is one of the most prevalent cancers globally. Despite recent progress in identifying etiologies and molecular genetics as well as new therapeutic approaches, the clinical outcome of current CRC therapies remains poor. This fact highlights the importance of further understanding underlying mechanisms involved in colorectal tumor initiation and progression. Abnormal metabolic alterations offer an evolutional advantage for CRC tumor cells and enhance their aggressive phenotype. Therefore, dysregulation of cellular metabolism is intricately associated with colorectal tumorigenesis. This review summarizes recent findings regarding the CRC-related changes in cellular metabolic pathways such as glycolysis, tricarboxylic acid cycle, fatty acid oxidation, and mitochondrial metabolism. We describe the oncogenic signaling pathways associated with metabolic dysregulation during malignant transformation and tumor progression. Given the crucial role of metabolic pathway alterations in the pathogenesis of CRC, we provide an overview of novel pharmacological strategies for the treatment of CRC by targeting metabolic and signaling pathways.

Association of a Genetic Variant in the Cyclin-Dependent Kinase Inhibitor 2B with Risk of Pancreatic Cancer.

Background: Pancreatic cancer (PC) is among the most aggressive tumors with a poor prognosis, indicating the need for the identification of a novel prognostic biomarker for risk stratifications. Recent genome-wide association studies have demonstrated common genetic variants in a region on chromosome 9p21 associated with an increased risk of different malignancies. Methods: In the present study, we explore the possible relationship between genetic variant, rs10811661, and gene expression of CDKN2B in 75 pancreatic cancer patients, and 188 healthy individuals. DNAs were extracted and genotyping and gene expression were performed by TaqMan real-time PCR and RT-PCR, respectively. Logistic regression was used to assess the association between risk and genotypes, while the significant prognostic variables in the univariate analysis were included in multivariate analyses. Results: The patients with PDAC had a higher frequency of a TT genotype for rs10811661 than the control group. Also, PDAC patients with dominant genetic model, (TT + TC), was associated with increased risk of developing PDAC (OR= 14.71, 95% CI [1.96-110.35], p= 0.009). Moreover, patients with CC genotype had a higher expression of CDKN2B, in comparison with TT genotype. Conclusion: Our findings demonstrated that CDKN2A/B was associated with the risk of developing PDAC, supporting further investigations in the larger and multicenter setting to validate the potential value of this gene as an emerging marker for PDAC.