Sadia Nawab

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

The dietary composition has been approved to be strongly associated with the risk of colorectal cancer (CRC), one of the most serious malignancies worldwide, through regulating the gut microbiota structure, thereby influencing the homeostasis of colonic epithelial cells by producing carcinogens, i.e., ammonia or antitumor metabolites, like butyrate. Though butyrate-producing Fusobacterium nucleatum has been considered a potential tumor driver associated with chemotherapy resistance and poor prognosis in CRC, it was more frequently identified in the gut microbiota of healthy individuals rather than CRC tumor tissues. First, within the concentration range tested, the fermentation broth of F. nucleatum exhibited no significant effects on Caco-2 and NCM460 cells viability except for a notable up-regulation of the expression of TLR4 (30.70%, p < 0.0001) and Myc (47.67%, p = 0.021) and genes encoding proinflammatory cytokines including IL1B (197.57%, p < 0.0001), IL6 (1704.51%, p < 0.0001), and IL8 (897.05%, p < 0.0001) in Caco-2 cells exclusively. Although no marked effects of polydextrose or fibersol-2 on the growth of F. nucleatum, Caco-2 and NCM460 cells were observed, once culture media supplemented with polydextrose or fibersol-2, the corresponding fermentation broths of F. nucleatum significantly inhibited the growth of Caco-2 cells up to 48.90% (p = 0.0003, 72 h, 10%) and 52.96% (p = 0.0002, 72 h, 10%), respectively in a dose-dependent manner. These two kinds of fibers considerably promoted butyrate production of F. nucleatum up to 205.67% (p < 0.0001, 6% polydextrose at 24 h) and 153.46% (p = 0.0002, 6% fibersol-2 at 12 h), which explained why and how the fermentation broths of F. nucleatum cultured with fibers suppressing the growth of Caco-2 cells. Above findings indicated that dietary fiber determined F. nucleatum to be a carcinogenic or antitumor bacterium, and F. nucleatum played an important role in the association between the dietary composition, primarily the content of dietary fibers, and the risk of CRC.

Cholangiocarcinoma (CCA) involves various epithelial tumors historically linked with poor prognosis because of its aggressive sickness course, delayed diagnosis, and limited efficacy of typical chemotherapy in its advanced stages. In-depth molecular profiling has exposed a varied scenery of genomic alterations as CCA’s oncogenic drivers. Previous studies have mainly focused on commonly occurring TP53 and KRAS alterations, but there is limited research conducted to explore other vital genes involved in CCA. We retrieved data from The Cancer Genome Atlas (TCGA) to hunt for additional CCA targets and plotted a mutational landscape, identifying key genes and their frequently expressed variants. Next, we performed a survival analysis for all of the top genes to shortlist the ones with better significance. Among those genes, we observed that MUC5B has the most significant p-value of 0.0061. Finally, we chose two missense mutations at different positions in the vicinity of MUC5B N and C terminal domains. These mutations were further subjected to molecular dynamics (MD) simulation, which revealed noticeable impacts on the protein structure. Our study not only reveals one of the highly mutated genes with enhanced significance in CCA but also gives insights into the influence of its variants. We believe these findings are a good asset for understanding CCA from genomics and structural biology perspectives.

using nanomolar concentrations. Therefore, the current study considers Tecovirimat as a reference compound for a machine learning-based guided screening to scan bioactive compounds from the DrugBank with similar chemical features or moieties as the Tecovirimat to inhibit the MPXV E8L surface binding protein. We used AlphaFold2 to model the E8L's 3D structure, followed by the conformational activity investigation of shortlisted drugs through computational structural biology approaches, including molecular docking and molecular dynamics simulations. As a result, we have shortlisted five drugs named ABX-1431, Alflutinib, Avacopan, Caspitant, and Darapalib that effectively engage the MPXV surface binding protein. Furthermore, the affinity of the proposed drugs is relatively higher than the Tecovirimat by having higher docking scores, establishing more hydrogen and hydrophobic bonds, engaging key residues in the target's structure, and exhibiting stable molecular dynamics.Communicated by Ramaswamy H. Sarma.