Development of a network visualization and analysis system for malignant tumors based on transcriptome data

Shiny technology has developed rapidly in recent years, as an R package for developing interactive app, through which we can package the written R code into a web app, which can not only save user time, but also accelerate the development of the speed of user-end communication, analyze the transcriptome data of related malignant tumors, and construct a ceRNA network diagram of desired malignant tumors. The code utilizing shiny technology package can facilitate users to map the ceRNA network associated with malignant tumors only through screen operation, significantly improving the efficiency and accuracy of clinical decision support in primary hospitals

Non-coding RNA model improves prognostic prediction in patients with nephroblastoma

Background. Nephroblastoma (Wilms tumor) is a common abdominal malignancy in children, ranking second among abdominal malignancies in children, but the pathogenesis is still unclear, and further research on their molecular mechanisms is needed. Method. We obtained lncRNA expression and clinical data from the TARGET database of the GDC data portal of the American Cancer Institute. Effective patient samples were determined based on gene differential expression analysis and clinical data screening. The risk calculation model was established by univariate and multivariate Cox regression analysis, after which the samples were divided into training group and test group to predict the prognosis of patients with nephroblastoma, and then the independent validation on gender was carried out for all samples. Finally, the corresponding target genes of lncRNA were predicted for functional enrichment analysis to explore the enrichment of genes and the interaction between them. Result. 125 valid samples were identified after screening 136 samples. After experimental analysis, five significant lncRNAs (AC00423

Design and implementation of cancer patient survival prediction model based on ensemble learning method

To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-coding RNA in the transcriptome that has a significant impact on clinical prognosis survival analysis was initially screened. Then use a random forest ensemble learning algorithm to train it to get a preliminary model. Finally, based on the optimized random survival forest model, the Cox regression model was once again integrated, and the risk values of the two were integrated. The RCCT (Random-Cox Combined to Survival) method was proposed to provide clinical decision-makers certain reference values

Stimulation of tumoricidal immunity via bacteriotherapy inhibits glioblastoma relapse

Abstract Glioblastoma multiforme (GBM) is a highly aggressive brain tumor characterized by invasive behavior and a compromised immune response, presenting treatment challenges. Surgical debulking of GBM fails to address its highly infiltrative nature, leaving neoplastic satellites in an environment characterized by impaired immune surveillance, ultimately paving the way for tumor recurrence. Tracking and eradicating residual GBM cells by boosting antitumor immunity is critical for preventing postoperative relapse, but effective immunotherapeutic strategies remain elusive. Here, we report a cavity-injectable bacterium-hydrogel superstructure that targets GBM satellites around the cavity, triggers GBM pyroptosis, and initiates innate and adaptive immune responses, which prevent postoperative GBM relapse in male mice. The immunostimulatory Salmonella delivery vehicles (SDVs) engineered from attenuated Salmonella typhimurium (VNP20009) seek and attack GBM cells. Salmonella lysis-inducing nanocapsules (SLINs), designed to trigger autolysis, are tethered to the SDVs, eliciting antitumor immune response through the intracellular release of bacterial components. Furthermore, SDVs and SLINs administration via intracavitary injection of the ATP-responsive hydrogel can recruit phagocytes and promote antigen presentation, initiating an adaptive immune response. Therefore, our work offers a local bacteriotherapy for stimulating anti-GBM immunity, with potential applicability for patients facing malignancies at a high risk of recurrence