OBJECTIVE: Ovarian cancer (OVC) is a common, aggressive, and heterogeneous malignancy, with a widely variable prognosis. With the advances of modern immunology, mast cells (MCs) have been shown to play a significant role in the prognosis of some malignant tumors. However, the role of mast cells in the prognosis of OVC is unknown.
METHODS: In this study, MC-associated prognostic genes (MRGs) were used to classify OVC from The Cancer Genome Atlas (TCGA)-OVC cohort. Genes were evaluated using univariate cox regression analysis. Twenty-nine prognostic gene signatures were identified using LASSO-COX analysis. COX regression models and principal component analysis (PCA) algorithms were used to construct MRG scores and individual MRGs patterns. External validation was performed in the TCGA-breast cancer (BRCA) and IMvigor210 cohorts. Immunity analysis based on MRGs was performed using CIBERSORT, and GSVA methods, and immunotherapy response was evaluated using the TIDE website.
RESULTS: Using TCGA-OVC data, we established a model for constructing MRG scores based on the twenty-nine identified prognostic gene signatures using the PCA algorithm. MRG scores were found to be strongly correlated with immune cell infiltration and were excellent predictors of prognosis in patients with OVC. Low MRG scores were associated with better prognosis and better response to immunotherapy and chemotherapy.
CONCLUSIONS: MC-related prognosis signature characterizes the immune landscape and predicts the prognosis of OVC. Understanding the correlation between MC-related gene signatures and immunotherapy and chemotherapy may improve the development of personalized clinical treatment strategies.

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-β, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.

A more accurate prognosis for non-small-cell lung cancer (NSCLC) patients could aid in the identification of patients at high risk for recurrence. Many NSCLC mRNA expression signatures claiming to be prognostic have been reported in the literature. The goal of this study was to identify the most promising mRNA prognostic signatures in NSCLC for further prospective clinical validation.
We carried out a systematic review and meta-analysis of published mRNA prognostic signatures for resected NSCLC. The prognostic performance of each signature was evaluated via a meta-analysis of 1927 early stage NSCLC patients collected from 15 studies using three evaluation metrics (hazard ratios, concordance scores, and time-dependent receiver-operating characteristic curves). The performance of each signature was then evaluated against 100 random signatures. The prognostic power independent of clinical risk factors was assessed by multivariate Cox models.
Through a literature search, we identified 42 lung cancer prognostic signatures derived from genome-wide expression profiling analysis. Based on meta-analysis, 25 signatures were prognostic for survival after adjusting for clinical risk factors and 18 signatures carried out significantly better than random signatures. When analyzing histology types separately, 17 signatures and 8 signatures are prognostic for adenocarcinoma and squamous cell lung cancer, respectively. Despite little overlap among published gene signatures, the top-performing signatures are highly concordant in predicted patient outcomes.
Based on this large-scale meta-analysis, we identified a set of mRNA expression prognostic signatures appropriate for further validation in prospective clinical studies.