Transforming RNA-Seq gene expression to track cancer progression in the multi-stage early to advanced-stage cancer development.

BACKGROUND: Cancer progression can be tracked by gene expression changes that occur throughout early-stage to advanced-stage cancer development. The accumulated genetic changes can be detected when gene expression levels in advanced-stage are less variable but show high variability in early-stage. Normalizing advanced-stage expression samples with early-stage and clustering of the normalized expression samples can reveal cancers with similar or different progression and provide insight into clinical and phenotypic patterns of patient samples within the same cancer. OBJECTIVE: This study aims to investigate cancer progression through RNA-Seq expression profiles across the multi-stage process of cancer development. METHODS: RNA-sequenced gene expression of Diffuse Large B-cell Lymphoma, Lung cancer, Liver cancer, Cervical cancer, and Testicular cancer were downloaded from the UCSC Xena database. Advanced-stage samples were normalized with early-stage samples to consider heterogeneity differences in the multi-stage cancer progression. WGCNA was used to build a gene network and categorized normalized genes into different modules. A gene set enrichment analysis selected key gene modules related to cancer. The diagnostic capacity of the modules was evaluated after hierarchical clustering. RESULTS: Unnormalized RNA-Seq gene expression failed to segregate advanced-stage samples based on selected cancer cohorts. Normalization with early-stage revealed the true heterogeneous gene expression that accumulates across the multi-stage cancer progression, this resulted in well segregated cancer samples. Cancer-specific pathways were enriched in the normalized WGCNA modules. The normalization method was further able to stratify patient samples based on phenotypic and clinical information. Additionally, the method allowed for patient survival analysis, with the Cox regression model selecting gene MAP4K1 in cervical cancer and Kaplan-Meier confirming that upregulation is favourable. CONCLUSION: The application of the normalization method further enhanced the accuracy of clustering of cancer samples based on how they progressed. Additionally, genes responsible for cancer progression were discovered.

Upregulation of FHL1, SPNS3, and MPZL2 predicts poor prognosis in pediatric acute myeloid leukemia patients with FLT3-ITD mutation.

Chromosomal translocations and gene mutations are characteristics of the genomic profile of acute myeloid leukemia (AML). We aim to identify a gene signature associated with poor prognosis in AML patients with FLT3-ITD compared to AML patients with NPM1/CEBPA mutations. RNA-sequencing (RNA-Seq) count data were downloaded from the UCSC Xena browser. Samples were grouped by their mutation status into high and low-risk groups. Differential gene expression (DGE), machine learning (ML) and survival analyses were performed. A total of 471 differentially expressed genes (DEGs) were identified, of which 16 DEGs were used as features for the prediction of mutation status. An accuracy of 92% was obtained from the ML model. FHL1, SPNS3, and MPZL2 were found to be associated with overall survival in FLT3-ITD samples. FLT3-ITD mutation confers an indicative gene expression profile different from NPM1/CEBPA mutation, and the expression of FHL1, SPSN3, and MPZL2 can serve as prognostic indicators of unfavorable disease.

Predicting amplification of MYCN using CpG methylation biomarkers in neuroblastoma.

Background: Neuroblastoma is the most common extracranial solid tumor in childhood. Amplification of MYCN in neuroblastoma is a predictor of poor prognosis. Materials and methods: DNA methylation data from the TARGET data matrix were stratified into MYCN amplified and non-amplified groups. Differential methylation analysis, clustering, recursive feature elimination (RFE), machine learning (ML), Cox regression analysis and Kaplan-Meier estimates were performed. Results and Conclusion: 663 CpGs were differentially methylated between the two groups. A total of 25 CpGs were selected by RFE for clustering and ML, and a 100% clustering accuracy was obtained. ML validation on three external datasets produced high accuracy scores of 100%, 97% and 93%. Eight survival-associated CpGs were also identified. Therapeutic interventions may need to be targeted to patient subgroups.

Lay abstract Neuroblastoma is the most common extracranial solid tumor in childhood. Elevated levels of the MYCN protein in neuroblastoma is a predictor of poor prognosis. It is the most relevant prognostic factor in neuroblastoma and predicting MYCN gene amplification (which leads to increased gene expression and more protein) from epigenetic data rather than genetic testing might be useful in the oncology clinic. This study was designed to identify a DNA methylation (epigenetic) signature that can be used to diagnose MYCN amplification without actually testing for the gene. The authors also aimed to correlate this DNA methylation signature with patient survival and poorer prognosis. Based on statistical and computational methods applied to DNA methylation data for neuroblastoma, signatures that are predictive of MYCN amplification and poor prognosis were found, which clinicians can use for early patient diagnosis and selection of the best therapies for patients at high risk.