To the Editor: Breast cancer is one of the malignant diseases that cause death in women and is a severe threat to women's health. With the progress of medical treatment, there are many methods to treat breast cancer, such as drug therapy and hormone therapy. Among them, molecular targeted therapy has dramatically improved the treatment effect of breast cancer. Therefore, it is vital to find important molecular markers. Breast cancer can be divided into four subtypes: triple-negative (TN), lumA, lumB, and HER2+. HER2+ breast cancer accounts for 15–20% of breast cancers, with a higher grade, a more aggressive phenotype, and a worse prognosis.
The main objective of this study was to screen for key genes that appear only in the HER2+ subtype of breast cancer relative to the other three subtypes, including basal, lumA, and lumB. Screening for these key genes can help determine how HER2+ subtype differs from other subtypes in terms of pathogenesis, then treat them in a more targeted manner.
The dataset was obtained from the The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and included 47 HER2+ samples, 87 basal samples, 291 lumA samples, 118 lumB samples, and 97 samples from normal tissues. The clinical features of HER2+ samples are shown in [Supplementary Table 1, https://links.lww.com/CM9/B268]. Differential gene analysis was done for each of the four subtypes vs. normal samples using the limma package in the R language. According to the screening condition |logFC| >2.0, P <0.05, a total of 2063 differentially expressed genes were obtained from HER2+ samples, of which 897 genes were upregulated, and 1166 genes were downregulated [Figure 1A]. The numbers of differential genes obtained for basal, lumA, and lumB under the same screening conditions were 2093, 1365, and 2084, respectively [Figure 1B].
Weighted correlation network analysis (WGCNA) of HER2+ differential genes was done, and four modules were obtained. Among them, the turquoise module was the most correlated with HER2+, with a correlation of 0.94. The turquoise module was a key co-expression module with a gene count of 760 [Figure 1C].
Protein-protein interaction networks (PPIs) were constructed for key genes of key modules and visualized using Cytoscape software. The top 100 key genes were screened using the MCC algorithm of the CytoHubba plugin [Figure 1D].
Gene Ontology (GO) analysis revealed that key genes were mainly enriched in ion transmembrane transport, collagen-containing extracellular matrix, transporter complex, and extracellular matrix structural constituent. The main Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were neuroactive ligand-receptor interaction and cytokine-cytokine receptor interaction [Figure 1E].
The HER2+ differential genes were compared with basal, lumA, and lumB, respectively, to screen key genes that distinguish HER2+ subtypes from other subtypes. As a result, DRD4, UTS2, and GLP1R were obtained as key genes present only in the HER2+ subtype [Figure 1B]. DRD4 and UTS2 were upregulated, while GLP1R was downregulated.
The expression values of DRD4, UTS2, and GLP1R were compared in the four subtypes and the normal samples, respectively. The results showed that the expression of UTS2 was significantly higher in HER2+ subtype than in other subtypes and normal samples. In contrast, the expression values of GLP1R in HER2+ were lower than those in other subtypes and normal samples [Figure 1F].
The PROGgeneV2 (http://genomics.jefferson.edu/proggene/) database was used to see the impact of UTS2, DRD4, and GLP1R expression on the prognosis of HER2+ patients. The data used for survival analysis was GSE6130 from the Gene Expression Omnibus (GEO) database. The clinical features of GSE6130 are shown in Supplementary Table 2, https://links.lww.com/CM9/B268. Results showed that patients with low DRD4 and GLP1R expression had more down survival times than those with high expression, while patients with high UTS2 expression had even lower survival rates [Figure 1G, H]. Similarly, looking at the effect of UTS2, DRD4, and GLP1R on survival in breast cancer patients through the Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/) database yielded the same results as above [Figure 1G, H].
The above results suggest that UTS2 is a key gene associated with HER2+ prognosis. The accuracy of the results was further verified by using the Human Protein Atlas (HPA) database (http://www.proteinatlas.org/) to view its expression in normal breast tissue and breast cancer tissue, which showed that the protein expression level of UTS2 was significantly higher in breast cancer than in normal tissue [Figure 1I].
Finally, clinical information of HER2+ patients, including stage and age, combined with UTS2 expression values were used to construct Cox regression models to predict patient survival at two years and four years. UTS2 was divided into two groups of high and low expression using median values, with zero representing low expression and one representing high expression. The results revealed that patients with low UTS2 expression had a lower score and a better prognosis [Figure 1J]. Also, the accuracy of the outcome prediction showed that the results were reliable [Figure 1K].
All statistical analyses were performed with R software (version 3.6.2, Institute for Statistics and Mathematics, Vienna, Austria;https://www.r-project.org/). Continuous variables are represented using means, and categorical variables are represented using counts. Kaplan–Meier survival rates were compared between groups using the log-rank statistic. The centerpiece of the HPA is its unique antibody collection for mapping the entire human proteome by immunohistochemistry and immunocytochemistry. Multivariate analysis was used to construct a nomogram predicting patient survival. Calibration plots were also used to determine the nomogram's prognostic value. P <0.05 indicated a statistically significant difference.
The above analysis shows that UTS2 is the key gene that distinguishes HER2+ from other subtypes and that high expression of UTS2 leads to poorer patient prognosis. UTS2 (urotensin 2) has three (splice variants), 251 orthologues, and 1 Ensembl protein family member. It is a protein coding gene. Diseases associated with UTS2 include portal hypertension and congestive heart failure. Among its related pathways are RET signaling and signaling by GPCR. Gene Ontology (GO) annotations related to this gene include signaling receptor binding and hormone activity. Chen et al constructed an immunogenetic model of colon cancer using genes such as UTS2 better to predict patient survival at 5 years. Wang et al used multivariate Cox proportional hazards analysis to construct a prognostic model of immunogenetic correlates, including eight genes such as UTS2, which better predicted patient survival. UTS2, an immune-related gene, is highly associated with the prognosis of HER2+ breast cancer and is expected to play an important role in immunotherapy of HER2+.
Conflicts of interest
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