Table1_A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies.XLSX

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.</p

DataSheet1_A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies.PDF

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.</p

Table3_A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies.XLSX

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.</p

Targeting Cell Membranes, Depleting ROS by Dithiane and Thioketal-Containing Polymers with Pendant Cholesterols Delivering Necrostatin‑1 for Glaucoma Treatment

Glaucoma is the leading cause of irreversible blindness worldwide, characterized by progressive vision loss due to the selective damage to retinal ganglion cells (RGCs) and their axons. Oxidative stress is generally believed as one key factor of RGCs death. Recently, necroptosis was identified to play a key role in glaucomatous injury. Therefore, depletion of reactive oxygen species (ROS) and inhibition of necroptosis in RGCs has become one of treatment strategies for glaucoma. However, existing drugs without efficient drug enter into the retina and have controlled release due to a short drug retention. Herein, we designed a glaucomatous microenvironment-responsive drug carrier polymer, which is characterized by the presence of thioketal bonds and 1,4-dithiane unit in the main chain for depleting ROS as well as the pendant cholesterols for targeting cell membranes. This polymer was adopted to encapsulate an inhibitor of necroptosis, necrostatin-1, into nanoparticles (designated as NP1). NP1 with superior biosafety could scavenge ROS in RGCs both in vitro and in vivo of an acute pathological glaucomatous injury model. Further, NP1 was found to effectively inhibit the upregulation of the necroptosis pathway, reducing the death of RGCs. The findings in this study exemplified the use of nanomaterials as potential strategies to treat glaucoma

Conjugation of Benzylvanillin and Benzimidazole Structure Improves DNA Binding with Enhanced Antileukemic Properties

<div><p>Benzyl-o-vanillin and benzimidazole nucleus serve as important pharmacophore in drug discovery. The benzyl vanillin (2-(benzyloxy)-3-methoxybenzaldehyde) compound shows anti-proliferative activity in HL60 leukemia cancer cells and can effect cell cycle progression at G2/M phase. Its apoptosis activity was due to disruption of mitochondrial functioning. In this study, we have studied a series of compounds consisting of benzyl vanillin and benzimidazole structures. We hypothesize that by fusing these two structures we can produce compounds that have better anticancer activity with improved specificity particularly towards the leukemia cell line. Here we explored the anticancer activity of three compounds namely 2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2MP, N-1-(2-benzyloxy-3-methoxybenzyl)-2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2XP, and (R) and (S)-1-(2-benzyloxy-3-methoxyphenyl)-2, 2, 2-trichloroethyl benzenesulfonate, 3BS and compared their activity to 2-benzyloxy-3-methoxybenzaldehyde, (Bn1), the parent compound. 2XP and 3BS induces cell death of U937 leukemic cell line through DNA fragmentation that lead to the intrinsic caspase 9 activation. DNA binding study primarily by the equilibrium binding titration assay followed by the Viscosity study reveal the DNA binding through groove region with intrinsic binding constant 7.39 µM/bp and 6.86 µM/bp for 3BS and 2XP respectively. 2XP and 3BS showed strong DNA binding activity by the UV titration method with the computational drug modeling showed that both 2XP and 3BS failed to form any electrostatic linkages except via hydrophobic interaction through the minor groove region of the nucleic acid. The benzylvanillin alone (Bn1) has weak anticancer activity even after it was combined with the benzimidazole (2MP), but after addition of another benzylvanillin structure (2XP), stronger activity was observed. Also, the combination of benzylvanillin with benzenesulfonate (3BS) significantly improved the anticancer activity of Bn1. The present study provides a new insight of benzyl vanillin derivatives as potential anti-leukemic agent.</p> </div

Half maximal inhibitory concentration (IC₅₀) values of various fractions of <i>E. longifolia</i> root methanolic extract on K-562 cell line.

<p>Half maximal inhibitory concentration (IC₅₀) values of various fractions of <i>E. longifolia</i> root methanolic extract on K-562 cell line.</p

The activity on caspase activation enzymes after 2XP, 3BS and betulinic acid exposure.

<p>(i) The fluorescence intensity of caspase enzymes ratio on U937 cells after exposure to 2XP, 3BS and betulinic acid (as a positive control) at different time intervals. caspase 3&7 with significant difference between the treatments and the control group at 5hrs, 7hrs and 12hrs incubation period. p<0.01, (ii) comparison caspase 8 & 9 during the maximum peak period with significant difference between the caspases, p<0.0001 and within the treatment, p=0.047, (iii) caspase 8 with significant difference between the treatments and the control (DMSO) at 3hrs and 5hrs of incubation period. p<0.01, (iv) caspase 9 with significant difference among the treatments at 3hrs, 5hrs, 7hrs and 9hrs of incubation period. p<0.01. Note: The results for 3BS and betulinic acid are not displayed for 7hrs and 9 hrs as their respective caspase 3 activation occurs at 5 hours.</p

Effect of TAF273 on the size and histological appearance of subcutaneous tumor induced by injecting the K-562 cells in nude mice.

<p>(A) Gross appearance of tumors in the control mice. (B) Gross appearance of tumors in TAF273-treated mice. (<b>C</b>): An H&E-stained tumor section (original magnification of 40×) of the control group is composed of compact sheet of aggressively proliferating viable tumor cells (VC), abundance of blood vessels (BV), and the presence of mitotic figures (MC). (<b>D</b>): The tumor section (original magnification of 40×) of TAF273 (50 mg/kg) IP- treatment revealed notable changes in tumor histology, as significant loss of compact arrangement of viable tumor cells (VC), with less number of blood vessels (BV), abundance of apoptotic cells (AC) surrounded by necrotic regions (NC) and absence of mitotic figures. (<b>E</b>): Graphical comparison of the mean apoptotic cells/microscopic field (control vs TAF273). (<b>F</b>): Graphical comparison of the mean necrotic areas (control vs TAF273) as calculated by using imageJ softwere. Values are presented as mean ± SD, (<i>n</i> = 4).</p

Expression changes of apoptosis and cell cycle related genes in K562 cells.

<p>Expression levels of RNA were analyzed based on the Ct value. (A): Up-regulated genes in apoptotic pathway; (B): Down-regulated genes in apoptotic pathway; (C): Up-regulated genes in cell cycle pathway; (D): Down-regulated genes in cell cycle pathway.</p

Cell Morphology of a Human Leukemic cancer cell lines (U937) after different treatment.

<p>(i) 50μM 2XP, (ii) 50μM 3BS, (iii) 50μM betulinic acid, (iv) 1% DMSO and (v) medium alone. Arrows indicate area of apoptosis. Figure 3(vi) shows the DNA fragmentation of nucleic acid extracted from U937 after treating with 20, 60 and 100 µM of 2XP and 3BS as well as betulinic acid (positive control) and DMSO (negative control).</p