Relationship between HLA-DRB1 gene polymorphism and breast cancer: A protocol for systematic review and meta-analysis

Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population

Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual's genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.

A prognostic model related to necrotizing apoptosis of breast cancer based on biorthogonal constrained depth semi-supervised nonnegative matrix decomposition and single-cell sequencing analysis

Breast cancer (BC) is one of the most common malignant tumours in women, and its prognosis is poor. The prognosis of BC patients can be improved by immunotherapy. However, due to the heterogeneity of BC, the identification of new biomarkers is urgently needed to improve the prognosis of BC patients. Necrotic apoptosis has been shown to play an essential role in many cancers. First, this study proposed a novel clustering algorithm called biorthogonal constrained depth semisupervised nonnegative matrix factorization (DO-DSNMF). The DO-DSNMF algorithm added multilayer nonlinear transformation to the coefficient matrix obtained after decomposition, which was used to mine the nonlinear relationship between samples. In addition, we also added orthogonal constraints on the basis matrix and coefficient matrix to reduce the influence of redundant features and samples on the results. We applied the DO-DSNMF algorithm and analysed the differences in survival and immunity between the subtypes. Then, we used prognosis analysis to construct the prognosis model. Finally, we analysed single cells using single-cell sequencing (scRNA-seq) data from the GSE75688 dataset in the GEO database. We identified two BC subtypes based on the BC transcriptome data in the TCGA database. Immune infiltration analysis showed that the necrotizing apoptosis-related genes of BC were related to various immune cells and immune functions. Necrotizing apoptosis was found to play a role in BC progression and immunity. The role of prognosis-related NRGs in BC was also verified by cell experiments. This study proposed a novel clustering algorithm to analyse BC subtypes and constructed an NRG prognostic model for BC. The prognosis and immune landscape of BC patients were evaluated by this model. The cell experiment supported its role in BC, which provides a potential therapeutic target for the treatment of BC.

HLA-DRB1: A new potential prognostic factor and therapeutic target of cutaneous melanoma and an indicator of tumor microenvironment remodeling

Cutaneous melanoma (CM) is the most common skin cancer and one of the most aggressive cancers and its incidence has risen dramatically over the past few decades. The tumor microenvironment (TME) plays a crucial role in the occurrence and development of cutaneous melanoma. Nevertheless, the dynamics modulation of the immune and stromal components in the TME is not fully understood. In this study, 471 CM samples were obtained from TCGA database, and the ratio of tumor-infiltrating immune cells (TICs) in the TME were estimated using the ESTIMATE algorithms and CIBERSORT computational method. The differently expressed genes (DEGs) were applied to GO and KEGG function enrichment analysis, establishment of protein-protein interaction (PPI) network and univariate Cox regression analysis. Subsequently, we identified a predictive factor: HLA-DRB1 (major histocompatibility complex, class II, DR beta 1) by the intersection analysis of the hub genes of PPI network and the genes associated with the prognosis of the CM patients obtained by univariate Cox regression analysis. Correlation analysis and survival analysis showed that the expression level of HLA-DRB1 was negatively correlated with the Stage of the patients while positively correlated with the survival, prognosis and TME of melanoma. The GEPIA web server and the representative immunohistochemical images of HLA-DRB1 in the normal skin tissue and melanoma tissue from the Human Protein Atlas (HPA) database were applied to validate the expression level of HLA-DRB1. CIBERSORT analysis for the ratio of TICs indicated that 9 types of TICs were positively correlated with the expression level of HLA-DRB1 and only 4 types of TICs were negatively correlated with the expression level of HLA-DRB1. These results suggested that the expression level of HLA-DRB1 may be related to the immune activity of the TME and may affect the prognosis of CM patients by changing the status of the TME.