Identification of potential vinorelbine-associated prognostic genes in breast cancer through integrative bioinformatics and experimental validation
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Background The mechanisms underlying the occurrence and development of breast cancer (BC) is complex. Vinorelbine-related genes (Vino-RGs) may play important roles in the treatment of BC, but their specific mechanisms remain unclear. We aimed to explore vinorelbine-related prognostic genes and their mechanisms for BC treatment. Methods BC-related data were collected from public databases. Differentially expressed genes (DEGs) were screened based on TCGA database, and candidate genes were gained by intersecting with Vino-RGs. Prognostic genes were determined using Cox regression and machine learning algorithms. A random survival forest (RSF) model was built and evaluated, followed by construction and evaluation of a nomogram. Enrichment analysis, and immune microenvironment analysis were carried out. Single-cell analysis was used to further explore the development mechanism of BC. Finally, RT-qPCR was conducted to explore the expression of prognostic genes. Results TUBA1C, BRCA1, TGFB1, TUBA1B, XRCC1, PTGS2, IL7, and TUBB2B were screened as prognostic genes for BC. The constructed RSF model and nomogram showed good predictive accuracy for the prognosis of BC patients. Multiple pathways related to BC progression were identified. Immune microenvironment analysis revealed the correlations between the risk score and immune cells and the poor prognosis for BC patients with lower tumor microenvironment (TME) scores and immune phenotype score (IPS) in high risk group (HRG). AZD1332_1
Abstract
Background The mechanisms underlying the occurrence and development of breast cancer (BC) is complex. Vinorelbine-related genes (Vino-RGs) may play important roles in the treatment of BC, but their specific mechanisms remain unclear. We aimed to explore vinorelbine-related prognostic genes and their mechanisms for BC treatment. Methods BC-related data were collected from public databases. Differentially expressed genes (DEGs) were screened based on TCGA database, and candidate genes were gained by intersecting with Vino-RGs. Prognostic genes were determined using Cox regression and machine learning algorithms. A random survival forest (RSF) model was built and evaluated, followed by construction and evaluation of a nomogram. Enrichment analysis, and immune microenvironment analysis were carried out. Single-cell analysis was used to further explore the development mechanism of BC. Finally, RT-qPCR was conducted to explore the expression of prognostic genes. Results TUBA1C, BRCA1, TGFB1, TUBA1B, XRCC1, PTGS2, IL7, and TUBB2B were screened as prognostic genes for BC. The constructed RSF model and nomogram showed good predictive accuracy for the prognosis of BC patients. Multiple pathways related to BC progression were identified. Immune microenvironment analysis revealed the correlations between the risk score and immune cells and the poor prognosis for BC patients with lower tumor microenvironment (TME) scores and immune phenotype score (IPS) in high risk group (HRG). AZD1332_1463, BMS.754807_2171, mitoxantrone_1810, and nutlin.3a…._1047 had significantly positive correlations with the risk score. Macrophages were identified as crucial cells for BC development. Macrophages had active communication with other cells, and the expression of prognostic genes differed in the whole differentiation of macrophages. RT-qPCR analysis revealed significantly higher expressions of TGFB1 and BRCA1 in BC samples compared to BC-VLB+PD samples, while IL7 and PTGS2 expressions were significantly lower in BC samples. Conclusion This study identified TUBA1C, BRCA1, TGFB1, TUBA1B, XRCC1, PTGS2, IL7, and TUBB2B as prognostic genes for breast cancer that are potentially associated with vinorelbine treatment, offering actionable biomarkers for individualized prognosis assessment and guiding patient stratification for vinorelbine-based combination therapies in clinical practice.