Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.

Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.

Background and Objectives: This study aims to evaluate the association between the use of oral isotretinoin and menstrual irregularities in acne patients with previously regular menstrual cycles. Materials and Methods: A prospective observational study was conducted on 58,599 female patients aged 14 to 36 at King Abdullah University Hospital in Irbid, Jordan. The patients were followed for a period of 4.5 to 8 months during treatment and for 2 months post-treatment. Menstrual cycle changes were documented, and statistical analysis was performed to identify any significant associations. Results: A total of 111 (37.1%) patients, who were previously known to have regular menstrual cycles, complained of menstrual changes while using oral isotretinoin. Ninety-nine of those patients who complained of menstrual changes had their cycles back to normal post-treatment. There is a significant difference in the total accumulative dose between those with changes in menses and those without; p-value [0.008]. The most common change that occurred was amenorrhea (p < 0.001), followed by oligomenorrhea and menorrhagia (p < 0.001 and p = 0.050, respectively). The duration of treatment was a significant predictor of menstrual irregularities, with an odds ratio (OR) of 5.106 (95% CI: 1.371–19.020, p = 0.015), indicating a higher likelihood of menstrual changes with increased treatment duration. The total accumulative dose was also significantly associated with menstrual irregularities (OR = 0.964; 95% CI: 0.939–0.990; p = 0.006). Additionally, a family history of PCOS significantly increased the odds of menstrual irregularities (OR = 3.783; 95% CI: 1.314–10.892; p = 0.014). Conclusions: The study identified that 37.1% of the participants experienced changes in their menstrual cycles while undergoing isotretinoin therapy, with the vast majority (89.2%) returning to normal within two months post-treatment. Our logistic regression analysis pinpointed the duration of isotretinoin treatment, the total accumulative dose, and a family history of PCOS as significant predictors of menstrual irregularities.