Epithelial-mesenchymal transition and H2O2 signaling – a driver of disease progression and a vulnerability in cancers

Mutation-selective drugs constitute a great advancement in personalized anticancer treatment with increased quality of life and overall survival in cancers. However, the high adaptability and evasiveness of cancers can lead to disease progression and the development of drug resistance, which cause recurrence and metastasis. A common characteristic in advanced neoplastic cancers is the epithelial-mesenchymal transition (EMT) which is strongly interconnected with H2O2 signaling, increased motility and invasiveness. H2O2 relays its signal through the installation of oxidative posttranslational modifications on cysteines. The increased H2O2 levels that are associated with an EMT confer a heightened sensitivity towards the induction of ferroptosis as a recently discovered vulnerability.

Multi-Omics Analysis of the Therapeutic Value of MAL2 Based on Data Mining in Human Cancers

Recent studies have reported that T-cell differentiation protein 2 (MAL2) is an important regulator in cancers. Here, we downloaded data from multiple databases to analyze MAL2 expression and function in pan-cancers, especially in ovarian cancer (OC). Gene Expression Profiling Interactive Analysis (GEPIA) databases was used to examine MAL2 expression in 13 types of cancer. Kaplan–Meier plotter database was used to analyze the overall survival rate of MAL2 in pan-cancers. The Catalog of Somatic Mutations in Cancer (COSMIC), cBioPortal, and UCSC databases were used to examine MAL2 mutation in human cancers. Metascape, STRING, and GeneMANIA websites were used to explore MAL2 function in OC. Furthermore, ggplot2 package and ROC package were performed to analyze hub gene expression and undertake receiver operating characteristic (ROC) analysis. Drug sensitivity of MAL2 in OC was examined by the GSCALite database. In order to verify the results from databases above, real-time quantitative polymerase chain reaction (qRT-PCR) and western blotting were conducted to detect the expression of MAL2 in OC cells. CRISPR/Cas9 system was used to knockout the MAL2 gene in the OC cell lines HO8910 and OVCAR3, using specific guide RNA targeting the exons of MAL2. Then, we performed proliferation, colony formation, migration, and invasion assays to investigate the impact of MAL2 in OC cell lines in vivo and in vitro. Epithelial-mesenchymal transition (EMT)-associated biomarkers were significantly altered in vitro via western blotting and qRT-PCR. Taken together, we observed that MAL2 was remarkably dysregulated in multiple cancers and was related to patient overall survival (OS), mutation, and drug sensitivity. Furthermore, experimental results showed that MAL2 deletion negatively regulated the proliferation, migration, invasion, and EMT of OC, indicating that MAL2 is a novel oncogene that can activate EMT, significantly promote both the proliferation and migration of OC in vitro and in vivo, and provide new clues for treatment strategies.

Blood cholesterol-to-lymphocyte ratio as a novel prognostic marker to predict postoperative overall survival in patients with colorectal cancer

Background Lipid disequilibrium and systemic inflammation are reported to correlate with tumorigenesis and development of colorectal cancer (CRC). We construct the novel biomarker cholesterol-to-lymphocyte ratio (CLR) to reflect the synergistic effect of cholesterol metabolism and inflammation on CRC outcomes. This study aims to investigate the clinical significance of CLR and establish a prognostic model for CRC. Methods Our study retrospectively enrolled 223 CRC patients who underwent curative surgical resection. The Kaplan-Meier method was employed to estimate the overall survival (OS) rates, and the association between serological biomarkers and survival was assessed with a log-rank test. Cox proportional hazard regression was applied in the univariate and multivariate analyses to identify independent prognostic factors, which were then used to develop a predictive nomogram model for OS in CRC. The nomogram was evaluated by the C-index, receiver operator characteristic curve (ROC) analysis, and calibration plot. All cases were grouped into three stratifications according to the total risk points calculated from the nomogram, and the difference in OS between them was assessed with the Kaplan-Meier method. Results At the end of the study, death occurred in 47 (21%) cases. Patients with low CLR (< 3.23) had significantly prolonged survival (P < 0.001). Multivariate analyses revealed that N stage (P < 0.001), harvested lymph nodes (P = 0.021), and CLR (P = 0.005) were independent prognostic factors for OS and a prognostic nomogram was established based on these variables. The nomogram showed good calibration and predictive performance with a superior C-index than TNM stage (0.755 (0.719–0.791) vs. 0.663 (0.629–0.697), P = 0.001). Patients of different risk stratifications based on the total score of nomogram showed distinct survival (P < 0.001). Conclusions The nomogram based on CLR and other clinical features can be used as a potentially convenient and reliable tool in predicting survival in patients with CRC.

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.