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

Molecular mechanisms of MAZ targeting up-regulation of NDUFS3 expression to promote malignant progression in melanoma

Myc-associated Zinc-finger Protein (MAZ) has been implicated in the malignant progression of various tumors. However, its expression and functional relationship of MAZ in melanoma have not been previously investigated. This study confirms elevated expression of MAZ in melanoma, correlating with poor patient prognosis. Furthermore, our findings demonstrate that MAZ enhances melanoma progression by promoting proliferation, migration and invasion. It is worth noting that we found that MAZ can target and regulate the transcription of NADH dehydrogenase [ubiquinone] iron-sulfur protein 3 (NDUFS3), a core subunit of mitochondrial complex I, to enhance mitochondrial metabolism and thus promote malignant progression of melanoma. Predictive modeling indicates that the co-expression of MAZ and NDUFS3 could serve as a potential prognostic marker for melanoma patients.

The Value of H2BC12 for Predicting Poor Survival Outcomes in Patients With WHO Grade II and III Gliomas

Purpose: Glioma is a common primary malignant brain tumor. Grade II (GII) gliomas are prone to develop into anaplastic grade III (GIII) gliomas, which indicate a higher malignancy and poorer survival outcome. This study aimed to satisfy the increasing demand for novel sensitive biomarkers and potential therapeutic targets in the treatment of GII and GIII gliomas.

Methods: A TCGA dataset was used to investigate the expression of H2BC12 mRNA in GII and GIII gliomas and its relation to clinical pathologic characteristics. Glioma tissues were collected to verify results from the TCGA dataset, and H2BC12 mRNA was detected by RT-qPCR. ROC analysis was employed to evaluate the classification power for GII and GIII. The significance of H2BC12 mRNA GII and GIII gliomas was also investigated. In addition, H2BC12 expression-related pathways were enriched by gene set enrichment analysis (GSEA). DNA methylation level and mutation of H2BC12 were analyzed by the UALCAN and CBioPortal databases, respectively.

Results: Based on the sample data from multiple databases and RT-qPCR, higher expression of H2BC12 mRNA was found in GII and GIII glioma tissue compared to normal tissue, which was consistent with a trend with our clinical specimen. H2BC12 mRNA had a better power in distinguishing between GII and GIII and yielded an AUC of 0.706 with a sensitivity of 76.9% and specificity of 81.8%. Meanwhile, high H2BC12 levels were associated with IDH status, 1p/19q codeletion, primary therapy outcome, and the histological type of gliomas. Moreover, the overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) of GII glioma patients with higher levels of H2BC12 were shorter than those of patients with lower levels as well as GIII patients. In the multivariate analysis, a high H2BC12 level was an independent predictor for poor survival outcomes of gliomas. The Wnt or PI3K-AKT signaling pathways, DNA repair, cellular senescence, and DNA double-strand break repair were differentially activated in phenotypes that were positively associated with H2BC12. H2BC12 DNA methylation was high in TP53 nonmutant patients, and no H2BC12 mutation was observed in gliomas patients.

Conclusion: H2BC12 is a promising biomarker for the diagnosis and prognosis of patients with WHO grade II and III gliomas.

Breast Cancer Classification Based on Tumor Budding and Stem Cell-Related Signatures Facilitate Prognosis Evaluation

Background

Tumor budding (TB) is emerging as a prognostic factor in multiple cancers. Likewise, the stemness of cancer cells also plays a vital role in cancer progression. However, nearly no research has focused on the interaction of TB and tumor stemness in cancer.

Methods

Tissue microarrays including 229 cases of invasive breast cancer (BC) were established and subjected to pan-cytokeratin immunohistochemical staining to evaluate molecular expression. Univariate and multivariate analyses were applied to identify prognostic factors of BC, and the Chi-square test was used for comparison of categorical variables.

Results

High-grade TB was significantly associated with T stage, lymph node metastasis, tumor node metastasis (TNM) stage, epithelial-mesenchymal transition, and poor disease-free survival (DFS) of BC patients. We also found that the prognostic value of TB varied widely among different subtypes and subgroups. Cox regression analysis then showed that TB grade was an independent prognostic factor. Moreover, cancer stem cell (CSC) markers CD44 and ALDH1A1 were significantly higher in high-grade TB tumors. Consequently, patients were classified into high CSC score subgroup and low CSC score subgroups. Further research found that CSC scores correlated with clinicopathological features and DFS of BC patients. Based on TB grade and CSC scores, we classified BC patients into TB_low-CSCs_low (type I), TB_low-CSCs_high (type II), TB_high-CSCs_low (type III), and TB_high-CSCs_high (type IV) subgroups. Survival analysis showed that patients in the type I subgroup had the best DFS, whereas those in the type IV subgroup had the worst DFS. Finally, a TB-CSC-based nomogram for use in BC was established. The nomogram was well calibrated to predict the probability of 5-year DFS, and the C-index was 0.837. Finally, the area under the curve value for the nomogram (0.892) was higher than that of the TNM staging system (0.713).

Conclusion

The combination of TB grade with CSC score improves the prognostic evaluation of BC patients. A novel nomogram containing TB grade and CSC score provides doctors with a candidate tool to guide the individualized treatment of cancer patients.

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.

Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas

Glioma is the most common primary brain tumor with poor prognosis and high mortality. The purpose of this study was to use the epigenetic signature to predict prognosis and evaluate the degree of immune infiltration in gliomas. We integrated gene expression profiles and DNA methylation data of lower-grade glioma and glioblastoma to explore epigenetic differences and associated differences in biological function. Cox regression and lasso analysis were used to develop an epigenetic signature based on eight DNA methylation sites to predict prognosis of glioma patients. Kaplan–Meier analysis showed that the overall survival time of high- and low-risk groups was significantly separated, and ROC analysis verified that the model had great predictive ability. In addition, we constructed a nomogram based on age, sex, 1p/19q status, glioma type, and risk score. The epigenetic signature was obviously associated with tumor purity, immune checkpoints, and tumor-immune infiltrating cells (CD8+ T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, activated NK cells, monocytes, and activated mast cells) and thus, it may find application as a guide for the evaluation of immune infiltration or in treatment decisions in immunotherapy.

Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma

Background

Cancer stem cells (CSCs) are crucial to the malignant behaviour and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established or reported in PDAC.

Methods

A signature was developed and validated in seven independent PDAC datasets. The MTAB-6134 cohort was used as the training set, while one local Chinese cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and their predictive performance was evaluated by Kaplan–Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics of the gene signature.

Results

A robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It classified patients into high-risk and low-risk groups. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) than low-risk patients. Calibration curves and Cox regression analysis demonstrated powerful predictive performance. ROC curves showed the better survival prediction by this model than other models. Functional analysis revealed a positive association between risk score and CSC markers. These results had cross-dataset compatibility.

Impact

This signature could help further improve the current TNM staging system and provide data for the development of novel personalized therapeutic strategies in the future.