A 4-gene prognostic index for enhancing acute myeloid leukaemia survival prediction.

Despite advancements in utilizing genetic markers to enhance acute myeloid leukaemia (AML) outcome prediction, significant disease heterogeneity persists, hindering clinical management. To refine survival predictions, we assessed the transcriptome of non-acute promyelocytic leukaemia chemotherapy-treated AML patients from five cohorts (n = 975). This led to the identification of a 4-gene prognostic index (4-PI) comprising CYP2E1, DHCR7, IL2RA and SQLE. The 4-PI effectively stratified patients into risk categories, with the high 4-PI group exhibiting TP53 mutations and cholesterol biosynthesis signatures. Single-cell RNA sequencing revealed enrichment for leukaemia stem cell signatures in high 4-PI cells. Validation across three cohorts (n = 671), including one with childhood AML, demonstrated the reproducibility and clinical utility of the 4-PI, even using cost-effective techniques like real-time quantitative polymerase chain reaction. Comparative analysis with 56 established prognostic indexes revealed the superior performance of the 4-PI, highlighting its potential to enhance AML risk stratification. Finally, the 4-PI demonstrated to be potential marker to reclassified patients from the intermediate ELN2017 category to the adverse category. In conclusion, the 4-PI emerges as a robust and straightforward prognostic tool to improve survival prediction in AML patients.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients.

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox ß regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-ß pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Impact of mini-driver genes in the prognosis and tumor features of colorectal cancer samples: a novel perspective to support current biomarkers.

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its development is associated with the gains and/or losses of genetic material, which leads to the emergence of main driver genes with higher mutational frequency. In addition, there are other genes with mutations that have weak tumor-promoting effects, known as mini-drivers, which could aggravate the development of oncogenesis when they occur together. The aim of our work was to use computer analysis to explore the survival impact, frequency, and incidence of mutations of possible mini-driver genes to be used for the prognosis of CRC. Methods: We retrieved data from three sources of CRC samples using the cBioPortal platform and analyzed the mutational frequency to exclude genes with driver features and those mutated in less than 5% of the original cohort. We also observed that the mutational profile of these mini-driver candidates is associated with variations in the expression levels. The candidate genes obtained were subjected to Kaplan-Meier curve analysis, making a comparison between mutated and wild-type samples for each gene using a p-value threshold of 0.01. Results: After gene filtering by mutational frequency, we obtained 159 genes of which 60 were associated with a high accumulation of total somatic mutations with Log2 (fold change) > 2 and p values < 10-5. In addition, these genes were enriched to oncogenic pathways such as epithelium-mesenchymal transition, hsa-miR-218-5p downregulation, and extracellular matrix organization. Our analysis identified five genes with possible implications as mini-drivers: DOCK3, FN1, PAPPA2, DNAH11, and FBN2. Furthermore, we evaluated a combined classification where CRC patients with at least one mutation in any of these genes were separated from the main cohort obtaining a p-value < 0.001 in the evaluation of CRC prognosis. Conclusion: Our study suggests that the identification and incorporation of mini-driver genes in addition to known driver genes could enhance the accuracy of prognostic biomarkers for CRC.

High ME1 Expression Is a Molecular Predictor of Post-Transplant Survival of Patients with Acute Myeloid Leukemia.

Several laboratory and clinical variables have been reported to be associated with the outcome of intensive chemotherapy for acute myeloid leukemia (AML), but only a few have been tested in the context of hematopoietic stem cell transplant (HSCT). This study aimed to identify genes whose expression of AML at diagnosis were associated with survival after HSCT. For this purpose, three publicly available adult AML cohorts (TCGA, BeatAML, and HOVON), whose patients were treated with intensive chemotherapy and then subjected to allogeneic or autologous HSCT, were included in this study. After whole transcriptome analysis, we identified ME1 as the only gene whose high expression was associated with shorter survival in patients subjected to HSCT. In addition, the inclusion of ME1 expression was able to improve the European LeukemiaNet risk stratification. Pathways related to lipid biosynthesis, mainly fatty acids, and cholesterol were positively correlated with ME1 expression. Furthermore, ME1 expression was associated with an M2 macrophage-enriched microenvironment, mature AML blasts hierarchy, and oxidative phosphorylation metabolism. Therefore, ME1 expression can be used as biomarker of poor response to HSCT in AML.