Background Platelet-related exosomes (PREs) are microparticles secreted by platelets into the bloodstream and are implicated in various cancer processes. This study aims to identify critical genes involved in Breast Cancer (BC)-associated PREs and to evaluate their role in cancer prognosis. PLA2G4A was identified as a key gene through the use of machine learning techniques and various genomic analyses, providing a foundation for precision medicine in BC treatment. Methods Download cancer-related data from databases such as UCSC Xena and ExMdb, use LASSO Cox regression and various machine learning algorithms to screen genes associated with BC survival, and perform functional and pathway enrichment analysis. The expression, immune relevance, diagnostic efficacy, and drug sensitivity of the PLA2G4A gene in pan-cancer and BC were specifically analyzed. The function of PLA2G4A in BC was validated through experiments, and its drug response and molecular docking were predicted using various databases and software tools. Results Machine learning methods and LASSO Cox regression were applied to analyze the relationship between gene expression and BC survival. PLA2G4A was identified as a key gene associated with cancer prognosis, supported by analyses of differential gene expression, survival outcomes, single nucleotide variations (SNVs), and copy number variations (CNVs). Biological pathway analyses through KEGG, GO, and GSEA highlighted PLA2G4A’s involvement in key cancer-related processes. In vitro studies, including cell scratch assays, Transwell migration assays, and EdU proliferation tests, demonstrated that overexpression of PLA2G4A inhibited the proliferation and migration of BC cells. Conclusions PLA2G4A plays a crucial role in the progression of BC, acting as a potential tumor-suppressor gene. The findings support its potential as a prognostic biomarker and further investigation is needed to explore its therapeutic potential in clinical settings.

Purpose: An increasing number of breast cancer (BC) patients choose prosthesis implantation after mastectomy, and the occurrence of breast implant illness (BII) has received increasing attention and the underlying molecular mechanisms have not been clearly elucidated. This study aimed to identify the crosstalk genes between BII and BC and explored their clinical value and molecular mechanism initially. Methods: We retrieved the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and identified the differentially expressed genes (DEG) as well as module genes using Limma and weighted gene co-expression network analysis (WGCNA). Enrichment analysis, the protein–protein interaction network (PPI), and machine learning algorithms were performed to explore the hub genes. We employed a nomogram and receiver operating characteristic curve to evaluate the diagnostic accuracy. Single-cell analysis disclosed variations in the expression of key genes across distinct cellular populations. The expression levels of the key genes were further confirmed in BC cell lines. Immunohistochemical analysis was utilized to examine protein levels from 25 patients with breast cancer undergoing prosthetic implant surgery. Ultimately, we deployed single-sample Gene Set Enrichment Analysis (ssGSEA) to scrutinize the immunological profiles between the normal and BC cohorts, as well as between the non-BII and BII groups. Results: WGCNA identified 1137 common genes, whereas DEG analysis found 541 overlapping genes in BII and BC. After constructing the PPI network, 17 key genes were selected, and three potential hub genes include KRT14, KIT, ALB were chosen for nomogram creation and diagnostic assessment through machine learning. The validation of these results was conducted by examining gene expression patterns in the validation dataset, breast cancer cell lines, and BII-BC patients. However, ssGSEA uncovered different immune cell infiltration patterns in BII and BC. Conclusion: We pinpointed shared three central genes include KRT14, KIT, ALB and molecular pathways common to BII and BC. Shedding light on the complex mechanisms underlying these conditions and suggesting potential targets for diagnostic and therapeutic strategies.

Background: Platelet-related exosomes (PREs) are microparticles secreted by platelets into the bloodstream and are implicated in various cancer processes. This study aims to identify critical genes involved in Breast Cancer (BC)-associated PREs and to evaluate their role in cancer prognosis. PLA2G4A was identified as a key gene through the use of machine learning techniques and various genomic analyses, providing a foundation for precision medicine in BC treatment. Methods: Download cancer-related data from databases such as UCSC Xena and ExMdb, use LASSO Cox regression and various machine learning algorithms to screen genes associated with BC survival, and perform functional and pathway enrichment analysis. The expression, immune relevance, diagnostic efficacy, and drug sensitivity of the PLA2G4A gene in pan-cancer and BC were specifically analyzed. The function of PLA2G4A in BC was validated through experiments, and its drug response and molecular docking were predicted using various databases and software tools. Results: Machine learning methods and LASSO Cox regression were applied to analyze the relationship between gene expression and BC survival. PLA2G4A was identified as a key gene associated with cancer prognosis, supported by analyses of differential gene expression, survival outcomes, single nucleotide variations (SNVs), and copy number variations (CNVs). Biological pathway analyses through KEGG, GO, and GSEA highlighted PLA2G4A’s involvement in key cancer-related processes. In vitro studies, including cell scratch assays, Transwell migration assays, and EdU proliferation tests, demonstrated that overexpression of PLA2G4A inhibited the proliferation and migration of BC cells. Conclusions: PLA2G4A plays a crucial role in the progression of BC, acting as a potential tumor-suppressor gene. The findings support its potential as a prognostic biomarker and further investigation is needed to explore its therapeutic potential in clinical settings.

Objective: The development of acquired endocrine resistance and reduced chemosensitivity in oestrogen receptor-positive (ER+) breast cancer presents significant challenges. Microtubule-based process-related genes (MBPRGs) play essential biological roles in the cell cycle and the development of migration. This study aimed to establish a novel prognostic signature based on MBPRGs to improve patient outcomes and offer additional treatment options for those with ER+ breast cancer.Methods: Clinical data along with relevant RNA information with ER+ breast cancer were sourced from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium. Consensus clustering was subsequently utilised to identify new molecular subgroups. Evaluations of the tumour immune microenvironment and immune status of these subgroups were performed via ESTIMATE, CIBERSORT, MCP, and ssGSEA. Additionally, functional analyses were conducted to investigate the underlying mechanisms involved. Prognostic risk models were developed via random forest, support vector machines and the least absolute shrinkage and selection operator algorithm. Single-cell analysis revealed differences in the expression levels of key genes among various cell types. Western blotting was used to measure protein levels in breast cancer cell lines. Immunohistochemical staining was used to assess protein expression in paraffin-embedded tissues, and Kaplan–Meier survival curves were generated to evaluate survival differences between the high- and low-expression groups of key genes. Transwell and cell viability assays were used to examine the biological functions of CHORDC1.Results: Two molecular subgroups with significantly different survival outcomes were identified. Longer survival was linked to a high immune score, low tumour purity, a greater presence of immune infiltrating cells, and an overall positive immune status. Risk models derived from MBPRGs exhibited strong potential for predicting survival in patients with ER+ breast cancer. Key genes had elevated protein levels in differentiated breast cancer cell lines, and elevated CHORDC1 expression was linked to a tendency towards a worse outcome in patients with ER+ breast cancer. Silencing CHORDC1 inhibited cell viability and invasion, reducing sensitivity to tamoxifen and paclitaxel in vitro.Conclusion: MBPRG expression is linked to the immune microenvironment and drug resistance in ER+ breast cancer patients, providing a reliable prognostic indicator for this group.