Breast cancer ranks first in terms of mortality and incidence rates worldwide among women. The HER2+ molecular subtype is one of the most aggressive subtypes; its treatment includes neoadjuvant chemotherapy and the use of a HER2 antibody. Some patients develop resistance despite positive results obtained using this therapeutic strategy. Objective. To identify prognostic markers for treatment and survival in HER2+ patients. Methods. Patients treated with neoadjuvant chemotherapy were assigned to sensitive and resistant groups based on their treatment response. Differentially expressed genes (DEGs) were identified using RNA-seq analysis. KEGG pathway, gene ontology, and interactome analyses were performed for all DEGs. An enrichment analysis Gene set enrichment analysis was performed. All DEGs were analyzed for overall (OS) and disease-free survival (DFS). Results. A total of 94 DEGs were related to treatment resistance. Survival analysis showed that 12 genes (ATF6B, DHRS13, DIRAS1, ERAL1, GRIN2B, L1CAM, IRX3, PRTFDC1, PBX2, S100B, SLC9A3R2, and TNXB) were good predictors of disease-free survival, and eight genes (GNG4, IL22RA2, MICA, S100B, SERPINF2, HLA-A, DIRAS1, and TNXB) were good predictors of overall survival (OS). Conclusion: We highlighted a molecular expression signature that can differentiate the treatment response, overall survival, and DFS of patients with HER2+ breast cancer.
Background: A high mortality rate makes hepatocellular carcinoma (HCC) one of the most common types of cancer globally. 5-methylcytosine (m5C) is an epigenetic modification that contributes to the prognosis of several cancers, but its relevance to HCC remains unknown. We sought to determine if the m5C-related regulators had any diagnostic or prognostic value in HCC.
Methods: M5C regulatory genes were screened and compared between HCC and normal tissue from The Cancer Genome Atlas (TCGA)and Gene Expression Omnibus (GEO) databases. Least absolute shrinkage and selection operator method (LASSO) and univariate Cox regression analysis of differentially expressed genes were then performed to identify diagnostic markers. A LASSO prognostic model was constructed using M5C regulatory genes with prognostic values screened by TCGA expression data. HCC patients were stratified based on risk score, then clinical characteristics analysis and immune correlation analysis were performed for each subgroup, and the molecular functions of different subgroups were analyzed using both Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). The prognostic model was evaluated using univariate and multivariate Cox analyses as well as a nomogram. Molecular typing was performed according to m5C regulatory genes and immune checkpoint genes expression respectively, and clinical characterization and immune correlation analysis were performed for each subgroup.
Results: M5C regulatory genes are expressed differently in HCC patients with different clinical and pathological characteristics, and mutations in these genes are frequent. Based on five m5C regulators (NOP2, NSUN2, TET1, YBX1, and DNMT3B), we constructed a prognostic model with high predictive ability. The risk score was found to be an independent prognostic indicator. Additionally, risk scores can also be applied in subgroups with different clinical characteristics as prognostic indicators.
Conclusion: The study combined data from TCGA and GEO for the first time to reveal the genetic and prognostic significance of m5C-related regulators in HCC, which provides new directions for identifying predictive biomarkers and developing molecularly targeted therapies for HCC.