ABSTRACT
PURPOSE: HER2-positive breast cancer (BC) accounts for 20-30% of all BC subtypes and is linked to poor prognosis. Trastuzumab (Tz), a humanized anti-HER2 monoclonal antibody, is a first-line treatment for HER2-positive breast cancer which faces resistance challenges. This study aimed to identify the biomarkers driving trastuzumab resistance. METHODS: Differential expression analysis of genes and proteins between trastuzumab-sensitive (TS) and trastuzumab-resistant (TR) cells was conducted using RNA-seq and iTRAQ. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used to study their functions. The prognostic significance and protein levels of ARFIP2 and MSN were evaluated using online tools and immunohistochemistry. Sensitivity of MSN and ARFIP2 to other therapies was assessed using public pharmacogenomics databases and the R language. RESULTS: Five genes were up-regulated, and nine genes were down-regulated in TR cells at both transcriptional and protein levels. Low ARFIP2 and high MSN expression linked to poor BC prognosis. MSN increased and ARFIP2 decreased in TR patients, correlating with shorter OS. MSN negatively impacted fulvestrant and immunotherapy sensitivity, while ARFIP2 had a positive impact. CONCLUSION: Our findings suggest that MSN and ARFIP2 could serve as promising biomarkers for predicting response to Tz, offering valuable insights for future research in the identification of diagnostic and therapeutic targets for BC patients with Tz resistance.
ABSTRACT
BACKGROUND: Peritoneal dialysis (PD) helps prevent lethal complications of end-stage renal disease (ESRD). However, the clinical outcomes are affected by PD-related complications. We investigated metabolic biomarkers to estimate the clinical outcomes of PD and identify patients at high risk of downstream complications and recurrent/relapsing infections. METHODS: Metabolites of normal control and ESRD patient were compared via an untargeted metabolomic analysis. Potential metabolic biomarkers were selected and quantified using a multiple reaction monitoring-based target metabolite detection method. A nomogram was built to predict the clinical outcomes of PD patients using clinical features and potential metabolic biomarkers with the least absolute shrinkage and selection operator Cox regression model. RESULTS: Twenty-five endogenous metabolites were identified and analyzed. ESRD-poor clinical outcome-related metabolic modules were constructed. Adenine, isoleucine, tyramine, xanthosine, phenylacetyl-L-glutamine, and cholic acid were investigated using the weighted gene correlation network analysis blue module. Potential metabolic biomarkers were differentially expressed between the NC and ESRD groups and the poor and good clinical outcomes of PD groups. A 3-metabolite fingerprint classifier of isoleucine, cholic acid, and adenine was included in a nomogram predicting the clinical outcomes of PD. CONCLUSION: Metabolic variations can predict the clinical outcomes of PD in ESRD patients.
