Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.

Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-grade glioma (LGG) is a less severe. The prognosis for glioma remains poor even after various treatments, such as chemotherapy and immunotherapy. Cuproptosis is a newly defined form of programmed cell death, distinct from ferroptosis and apoptosis, primarily caused by the accumulation of the copper within cells. In this study, we compared the difference between the expression of cuproptosis-related genes in GBM and LGG, respectively, and conducted further analysis on the enrichment pathways of the exclusive expressed cuproptosis-related mRNAs in GBM and LGG. We established two prediction models for survival status using xgboost and random forest algorithms and applied the ROSE algorithm to balance the dataset to improve model performance.

Comparison of Doppler Imaging and Microvascular Imaging in Cervical Lymph Node Blood Flow Analysis.

Cervical lymph node metastasis is an important determinant of cancer stage and the selection of an appropriate treatment plan for patients with head and neck cancer. Therefore, metastatic cervical lymph nodes should be effectively differentiated from lymphoma, tuberculous lymphadenitis, and other benign lymphadenopathies. The aim of this work is to describe the performance of Doppler ultrasound and superb microvascular imaging (SMI) in evaluating blood flow information of cervical lymph nodes. In addition, the features of flow imaging in metastatic lymph nodes, lymphoma, and tuberculous lymphadenitis were described. Compared with Doppler ultrasound, SMI, the latest blood flow imaging technology, could detect more blood flow signals because the sensitivity, specificity, and accuracy of SMI in the diagnosis of cervical lymph node disease were higher. This article summarizes the value of Doppler ultrasound and SMI in evaluating cervical lymph node diseases and focuses on the diagnostic performance of SMI.

Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer.

In recent years, molecular and genetic research hotspots of gastric cancer have been investigated, including microRNAs, long noncoding RNAs (lncRNAs) and messenger RNA (mRNAs). The study on the role of lncRNAs may help to develop personalized treatment and identify potential prognostic biomarkers in gastric cancer. The RNA-seq and miRNA-seq data of gastric cancer were downloaded from the TCGA database. Differential analysis of RNA expression between gastric cancer samples and normal samples was performed using the edgeR package. The ceRNA regulatory network was visualized using Cytoscape. KEGG pathway analysis of mRNAs in the ceRNA network was performed using the clusterProfiler package. CIBERSORT was used to distinguish 22 immune cell types and the prognosis-related genes and immune cells were determined using Kaplan-Meier and Cox proportional hazard analyses. To estimate these nomograms, we used receiver operating characteristic and calibration curve studies. The ceRNA regulation network of gastric cancer was built in this study, and the genes in the network were analyzed for prognosis. A total of 980 lncRNAs were differentially expressed, of which 774 were upregulated and 206 were downregulated. A survival study identified 15 genes associated with gastric cancer prognosis, including VCAN-AS1, SERPINE1, AL139002.1, LINC00326, AC018781.1, C15orf54, hsa-miR-145. Monocytes and Neutrophils were associated with the survival rate of gastric cancer. Our research uncovers new ceRNA network for the detection, treatment, and monitoring of gastric cancer.

The role of pyroptosis-related lncRNA risk signature in ovarian cancer prognosis and immune system.

Ovarian cancer is a leading cause of death in females with gynecologic cancers. Pyroptosis is a relatively new discovered programmed cell death that is believed to be associated with inflammation. However, studies on pyroptosis-related lncRNAs in ovarian cancer are limited. In this study, we identified 29 pyroptosis-related genes and screened out 72 pyroptosis-related lncRNAs. Furthermore, the 72 lncRNAs were eliminated to 2 survival-related lncRNAs using Cox regression and Lasso regression to build an ovarian cancer prognostic prediction signature and were further validated on the test set. We adopted a riskscore from the two-gene signature, and the survival in low-risk group was higher than the high-risk group. Functional enrichment analysis indicated that the differentially expressed genes (DEGs) between two risk groups were associated with tumor immunity. This study implies that pyroptosis-related genes are closely related to tumor immunity and could be potential therapeutic factors for ovarian cancer treatment.

Comprehensive analysis of lncRNA-mediated ceRNA networkfor hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a high-burden cancer. The molecular mechanism of HCC has not been fully elucidated. Notably, current research has revealed a significant function for long non-coding RNAs (lncRNAs) in the prognosis of patients with HCC. Here, this study aims to construct a regulated lncRNA-mediated ceRNA network and find biological targets for the treatment of HCC. Methods: Based on the RNA expression patterns from the TCGA, we did an analysis to determine which genes were expressed differently between liver tumor tissues and noncancerous tissues. Then, using bioinformatic tools, we built a lncRNA-miRNA-mRNA ceRNA network and used GO and KEGG functional analyses on the DEmRNAs connected to ceRNA networks. The main lncRNAs in the subnetwork were chosen, and we next looked at the relationships between these lncRNAs and the clinical characteristics of patients with HCC. The prognosis-related genes and immune cells were identified using Kaplan-Meier and Cox proportional hazard analyses, and CIBERSORT was utilized to separate the 22 immune cell types. CCK8 assay was performed to measure cell viability in HCC cells after lncRNA HOTTIP modulation. Results: Differentially expressed mRNA and lncRNAs in HCC and paracancerous tissues were identified. There are 245 lncRNAs, 126 miRNAs, and 1980 mRNAs that are expressed differently in liver tumour tissues than in noncancerous cells. Function analysis showed that mRNAs in ceRNA network were significantly enriched in G1/S transition of mototiv cell cycle, positive regulation of cell cycle process, hepatocellular carcinoma, and cancer related pathways. CD8 T cells and T follicular helper cells had a favourable link with a 0.65 correlation coefficient. Additionally, there was a strong correlation between Eosinophils, activated NK cells, and B memory cells. Strikingly, depletion of lncRNA HOTTIP inhibited viability of HCC cells. In addition, miR-205 upregulation suppressed viability of HCC cells, while miR-205 downregulation repressed viability of HCC cells. Notably, miR-205 depletion rescued HOTTIP depletion-mediated suppression of cell viability in HCC. Conclusion: A ceRNA network was created by examining the lncRNA, miRNA, and mRNA expression profiles of liver tumours from the TCGA database. LncRNA HOTTIP promoted cell viability via inhibition of miR-205 in HCC cells.

Expression profiles of m6A RNA methylation regulators, PD-L1 and immune infiltrates in gastric cancer.

Gastric cancer is the fourth most frequent cancer and has a high death rate. Immunotherapy represented by PD-1 has brought hope for the treatment of advanced gastric cancer. Methylation of the m6A genes is linked to the onset and progression of numerous cancers, but there are few studies on gastric cancer. The main purpose of this study aims to analyze the relationship between m6A RNA methylation regulators, PD-L1, prognosis and tumor immune microenvironment (TIME) in gastric cancer. The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases were used to acquire transcriptomic data and clinical information from gastric cancer patients. The changes in m6A regulator expression levels in gastric cancer tissues and normal tissues were studied. Consensus clustering analysis was used to separate gastric cancer samples into two categories. We employed Least Absolute Shrinkage, Selection Operator (LASSO) Cox regression analysis, Gene Set Enrichment Analysis (GSEA), and cBioPortal to analyze the m6A regulators, PD-L1 and TIME in gastric cancer. In gastric cancer tissues, the majority of m6A regulatory factors are considerably overexpressed. Two gastric cancer subgroups (Cluster1/2) based on consensus clustering of 21 m6A regulators. PD-L1 and PD-1 expression levels were significantly higher in gastric cancer tissues, and they were significantly linked with METTL3, WTAP, HNRNPD, ZC3H7B, METTL14, FTO, PCIF1, HNRNPC, YTHDF1 and YTDHF2. Cluster1 showed a large increase in resting memory CD4+ T cells, regulatory T cells, naïve B cells, active NK cells, and resting Mast cells. Cluster1 and Cluster2 were shown to be involved in numerous critical signaling pathways, including base excision repair, cell cycle, nucleotide excision repair, RNA degradation, and spliceosome pathways. Gastric cancer RiskScores based on prognostic factors have been found as independent prognostic indicators. The amount of tumor-infiltrating immune cells is dynamically affected by changes in the copy number of m6A methylation regulators associated with TIME.