Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers

Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.

New Strategies in Diagnosis and Treatments for Brain Tumors

In general, cancer is one of the most frequent causes of death [...].

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45 years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45 years-old) had four-fold more basal tumors and worst progression-free survival (median of 101 months), compared with the 45-65 years group (median of 168 months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

Reclassification of TCGA Diffuse Glioma Profiles Linked to Transcriptomic, Epigenetic, Genomic and Clinical Data, According to the 2021 WHO CNS Tumor Classification

In 2021, the fifth edition of the WHO classification of tumors of the central nervous system (WHO CNS5) was published. Molecular features of tumors were directly incorporated into the diagnostic decision tree, thus affecting both the typing and staging of the tumor. It has changed the traditional approach, based solely on histopathological classification. The Cancer Genome Atlas project (TCGA) is one of the main sources of molecular information about gliomas, including clinically annotated transcriptomic and genomic profiles. Although TCGA itself has played a pivotal role in developing the WHO CNS5 classification, its proprietary databases still retain outdated diagnoses which frequently appear incorrect and misleading according to the WHO CNS5 standards. We aimed to define the up-to-date annotations for gliomas from TCGA's database that other scientists can use in their research. Based on WHO CNS5 guidelines, we developed an algorithm for the reclassification of TCGA glioma samples by molecular features. We updated tumor type and diagnosis for 828 out of a total of 1122 TCGA glioma cases, after which available transcriptomic and methylation data showed clustering features more consistent with the updated grouping. We also observed better stratification by overall survival for the updated diagnoses, yet WHO grade 3 IDH-mutant oligodendrogliomas and astrocytomas are still indistinguishable. We also detected altered performance in the previous diagnostic transcriptomic molecular biomarkers (expression of SPRY1, CRNDE and FREM2 genes and FREM2 molecular pathway) and prognostic gene signature (FN1, ITGA5, OSMR, and NGFR) after reclassification. Thus, we conclude that further efforts are needed to reconsider glioma molecular biomarkers.

Molecular markers related to patient outcome in patients with IDH-mutant astrocytomas grade 2 to 4: A systematic review

BACKGROUND

Grading and classification of IDH-mutant astrocytomas has shifted from solely histology towards histology combined with molecular diagnostics. In this systematic review, we give an overview of all currently known clinically relevant molecular markers within IDH-mutant astrocytomas grade 2 to 4.

METHODS

A literature search was performed in five electronic databases for English original papers on patient outcome with respect to a molecular marker as determined by DNA/RNA sequencing, micro-arrays, or DNA methylation profiling in IDH-mutant astrocytomas grade 2 to 4. Papers were included if molecular diagnostics were performed on tumour tissue of at least 15 IDH-mutant astrocytoma patients, and if the investigated molecular markers were not limited to the diagnostic markers MGMT, ATRX, TERT, and/or TP53.

RESULTS

The literature search identified 4508 unique articles, published between August 2012 and December 2021, of which ultimately 44 articles were included. Numerous molecular markers from these papers were significantly correlated to patient outcome. The associations between patient outcome and non-canonical IDH mutations, PI3K mutations, high expression of MSH2, high expression of RAD18, homozygous deletion of CDKN2A/B, amplification of PDGFRA, copy number neutral loss of chromosomal arm 17p, loss of chromosomal arm 19q, the G-CIMP-low DNA methylation cluster, high total CNV, and high tumour mutation burden were confirmed in multiple studies.

CONCLUSIONS

Multiple genetic and epigenetic markers are associated with survival in IDH-mutant astrocytoma patients. Commonly affected are the RB signalling pathway, the RTK-PI3K-mTOR signalling pathway, genomic stability markers, and (epigenetic) gene regulation.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.

Prognostic Features of the Tumor Immune Microenvironment in Glioma and Their Clinical Applications: Analysis of Multiple Cohorts

Glioma is the most common malignant tumor of the central nervous system. Tumor purity is a source of important prognostic factor for glioma patients, showing the key roles of the microenvironment in glioma prognosis. In this study, we systematically screened functional characterization related to the tumor immune microenvironment and constructed a risk model named Glioma MicroEnvironment Functional Signature (GMEFS) based on eight cohorts. The prognostic value of the GMEFS model was also verified in another two glioma cohorts, glioblastoma (GBM) and low-grade glioma (LGG) cohorts, from The Cancer Genome Atlas (TCGA). Nomograms were established in the training and testing cohorts to validate the clinical use of this model. Furthermore, the relationships between the risk score, intrinsic molecular subtypes, tumor purity, and tumor-infiltrating immune cell abundance were also evaluated. Meanwhile, the performance of the GMEFS model in glioma formation and glioma recurrence was systematically analyzed based on 16 glioma cohorts from the Gene Expression Omnibus (GEO) database. Based on multiple-cohort integrated analysis, risk subpathway signatures were identified, and a drug–subpathway association network was further constructed to explore candidate therapy target regions. Three subpathways derived from Focal adhesion (path: 04510) were identified and contained known targets including platelet derived growth factor receptor alpha (PDGFRA), epidermal growth factor receptor (EGFR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). In conclusion, the novel functional signatures identified in this study could serve as a robust prognostic biomarker, and this study provided a framework to identify candidate therapeutic target regions, which further guide glioma patients’ clinical decision.