Identification of a DNA Repair-Related Multigene Signature as a Novel Prognostic Predictor of Glioblastoma

Specific and shared biological functions of PARP2 - is PARP2 really a lil' brother of PARP1?

PARP2, that belongs to the family of ADP-ribosyl transferase enzymes (ART), is a discovery of the millennium, as it was identified in 1999. Although PARP2 was described initially as a DNA repair factor, it is now evident that PARP2 partakes in the regulation or execution of multiple biological processes as inflammation, carcinogenesis and cancer progression, metabolism or oxidative stress-related diseases. Hereby, we review the involvement of PARP2 in these processes with the aim of understanding which processes are specific for PARP2, but not for other members of the ART family. A better understanding of the specific functions of PARP2 in all of these biological processes is crucial for the development of new PARP-centred selective therapies.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

FFPE samples from cavitational ultrasonic surgical aspirates are suitable for RNA profiling of gliomas

During surgical procedures for gliomas, tissue material obtained from cavitational ultrasonic surgical aspirators (CUSAs) is generally discarded but can actually exceed the amount and quality of certain tumour core resections (TCRs). Despite reports indicating the suitability of CUSA-derived material for diagnosis and research, its use is still marginal. We extended these conclusions to formalin-fixed, paraffin-embedded (FFPE) samples, the most common format for archival tumour tissue in anatomical pathology departments, by conducting for the first time RNA-seq analysis in CUSA aspirates. We compared the molecular diagnosis of somatic mutations used in the clinical routine and the gene expression profiles of fixed solid material from CUSA aspirates and TCRs from the same patients in selected gliomas encompassing grades II to IV. Despite the characteristic heterogeneity of gliomas, we found substantial similarities between the corresponding aspirates and TCRs that included transcriptional signatures associated with glioma subtypes. Based on these results, we confirmed that CUSA-fixed biomaterials from glioma surgeries are appropriate for downstream applications and biomarkers screening.

Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients

Low-grade gliomas (LGGs) are a highly heterogeneous group of slow-growing, lethal, diffusive brain tumors. Temozolomide (TMZ) is a frequently used primary chemotherapeutic agent for LGGs. Currently there is no consensus as to the optimal biomarkers to predict the efficacy of TMZ, which calls for decision-making for each patient while considering molecular profiles. Low-grade glioma data sets were retrieved from The Cancer Genome Atlas. Cox regression and survival analyses were applied to identify clinical features significantly associated with survival. Subsequently, Ordinal logistic regression, co-expression, and Cox regression analyses were applied to identify genes that correlate significantly with response rate, disease-free survival, and overall survival of patients receiving TMZ as primary therapy. Finally, gene expression and methylation analyses were exploited to explain the mechanism between these gene expression and TMZ efficacy in LGG patients. Overall survival was significantly correlated with age, Karnofsky Performance Status score, and histological grade, but not with IDH1 mutation status. Using 3 distinct efficacy end points, regression and co-expression analyses further identified a novel 4-gene signature of ASPM, CCNB1, EXO1, and KIF23 which negatively correlated with response to TMZ therapy. In addition, expression of the 4-gene signature was associated with those of genes involved in homologous recombination. Finally, expression and methylation profiling identified a largely unknown olfactory receptor OR51F2 as potential mediator of the roles of the 4-gene signature in reducing TMZ efficacy. Taken together, these findings propose the 4-gene signature as a novel panel of efficacy predictors of TMZ therapy, as well as potential downstream mechanisms, including homologous recombination, OR51F2, and DNA methylation independent of MGMT.

Transcriptomic Profiling Identifies a DNA Repair-Related Signature as a Novel Prognostic Marker in Lower Grade Gliomas

BACKGROUND

Gliomas are the most common and malignant intracranial tumors. The standard therapy is surgical resection combined with radiotherapy and chemotherapy. However, the emergence of radioresistance and chemoresistance, which is largely due to DNA damage repair, limits the therapeutic efficacy. Therefore, we identified a high-efficiency DNA damage repair-related risk signature as a predictor for prognosis in lower grade glioma.

METHODS

The signature was developed and validated in two independent datasets of the Chinese Glioma Genome Atlas (172 samples) and The Cancer Genome Atlas (451 samples). The time-dependent ROC curve, Cox regression, Nomogram, and Kaplan-Meier analyses were performed to evaluate the prognostic performance of the risk signature. The Metascape and IHC staining were performed to reveal the potential biological mechanism. GraphPad prism, SPSS, and R language were used for statistical analysis and graphical work.

RESULTS

This signature could distinguish the prognosis of patients, and patients with high-risk scores exhibited short survival time. The time-dependent ROC curve, Cox regression, and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with the IDH mutation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell cycle and DNA repair. These mechanisms were confirmed in patient samples.

CONCLUSIONS

The DNA damage repair-related signature was an independent and powerful prognostic biomarker in lower grade glioma.

IMPACT

The signature may potentially improve risk stratification of patients and provide a more accurate assessment of personalized treatment in clinic.