Tissue microarrays
The study has attained the approval of the Institutional Review Board. The informed consents were obtained from all subjects in the study. The tissue microarray (TMA) chips were obtained from Shanghai Zhuoli Biotechnology Company Ltd. Breast cancer tissues and matched adjacent tissues were obtained from 90 patients. Representative tumor areas were carefully selected. Formalin-fixed, paraffin-embedded core cylinders were punched and deposited into a recipient paraffin block. Ninety pairs (180 points) 4 μm-thick sections were cut and placed on charged Poly-L-Lysine-coated slides using a tissue-arraying instrument (Beecher Instruments®, Silver Spring, MD, USA).
Immunohistochemistry
Immunohistochemistry analyses were performed as we previously described.[14] In short, the tissue microarray was incubated overnight in a 59 °C oven for 60 minutes, dewaxed in xylene 10 minutes a time for 3 times, and dehydrated in a series of graded alcohols. To exhaust the endogenous peroxidase activity, the chips were treated with 3% hydrogen peroxide and NaN3 for 10 min. To unmask the epitopes, the chips were microwaved in 10 mM citrate buffer pH 6.0 for 1 minute. Rabbit polyclonal anti-human Siglec15 antibody (Abcam, # ab198684), the primary antibody, was diluted 1:50 in phosphate-buffered saline (PBS). The chips were incubated with diluted primary antibody overnight at 4°C. After PBS wash, the chips were incubated with anti-rabbit IgG secondary antibody at 37°C for 35 minutes. Horseradish peroxidase (HRP) conjugate was then applied to the chips. After washing, the chips were incubated with peroxidase substrate diaminobenzidine for 2 min and counterstained with hematoxylin.
Evaluation of immunohistochemical staining
The immunohistochemical staining scoring of Siglec-15 expression was defined as: 0, no staining in cells; 1+, weak and incomplete cytoplasmic staining in cells; 2+, an intermediate between 1 + and 3 + in cells; and 3+, diffuse cytoplasmic staining in more than 80% cells. The percentage of positive staining cells (0% to 100%) were also calculated.
Data acquisition and bioinformatics analysis
The clinical information and gene expression data of breast cancer patients and normal controls were downloaded from the official TCGA and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) websites. Boxplots were used to visualize expression differences for discrete variables. The RNA-Seq gene expression level 3 HTSeq-Counts data of breast cancer patients with breast invasive carcinoma (BRCA) and clinical data were retained and further analyzed.
The gene expression profiles of normal controls and breast cancer patients were downloaded from the official TCGA and METABRIC websites in September 2021. The expression of genes in breast invasive carcinoma (BRCA) were analyzed. Clinical characteristics from the corresponding patients were downloaded.
Evaluation of tumor-infiltrating immune cells (TIIC)
We used the CIBERSORT algorithm (http://cibersort.stanford.edu/) to calculate the tumor-infiltrating immune cell composition. [15] The TIIC immune cells included B cells, plasma cells, T cells, NK cells, macrophages, monocytes, mast cells, dendritic cells, neutrophils and eosinophils. Perl (The Perl Programming Language, version 5.28.1, http://www.perl.org) was conducted to convert IDs and group samples. The limma package of R (The R Project for Statistical Computing, version 3.5.3, https://www.r-project.org) was used to normalize the gene expression data. TIIC, P-value, root mean square error and Pearson's correlation coefficient were quantified for each sample. Furthermore, we applied CIBERSORT to calculate the P value for the deconvolution.
Statistical analysis
All statistical analyses were accomplished using R software (version 3.6.0, R Foundation). The expression of Siglec15 in patients dataset was evaluated. The chi-square test and Fisher’s exact test were applied to identify correlations between Siglec15 mRNA expression and the clinical features of breast cancer. The relationship between clinicopathological features and Siglec15 expression was analyzed with the Wilcoxon signed-rank test and logistic regression. The associations of clinical and pathological characteristics with overall survival in patients were assessed using Cox regression and the Kaplan-Meier method. The cut-off value of Siglec15 expression was determined by its median value. The correlations between TNM stage and immune cells were measured by the Wilcoxon test. The association between clinical follow-ups and immune cells and survival curves were evaluated by log-rank test and Kaplan-Meier analysis. Student's t-test was performed to examine the statistical relevance between two groups. P values <0.05 were considered statistically significant.