Glycosylation profiling of triple-negative breast cancer: clinical and immune correlations and identification of LMAN1L as a biomarker and therapeutic target

Abstract

Introduction: Breast cancer (BC) is the most prevalent malignant tumor in women, with triple-negative breast cancer (TNBC) showing the poorest prognosis among all subtypes. Glycosylation is increasingly recognized as a critical biomarker in the tumor microenvironment, particularly in BC. However, the glycosylation-related genes associated with TNBC have not yet been defined. Additionally, their characteristics and relationship with prognosis have not been deeply investigated.

Methods: Transcriptomic analyses were used to identify a glycosylation-related signature (GRS) associated with TNBC prognosis. A machine learning-based prediction model was constructed and validated across multiple independent datasets. The model's predictive capability was extended to evaluate the prognosis of TNBC individuals, tumor immune microenvironment and immunotherapy response. LMAN1L (Lectin, Mannose Binding 1 Like) was identified as a novel prognostic marker in TNBC, and its biological effects were validated through experimental assays.

Results: The GRS showed significant prognostic relevance for TNBC patients. The risk model effectively predicted molecular features, including immune cell infiltration and potential responses to immunotherapy. Experimental validation confirmed LMAN1L as a novel glycosylation-related prognostic gene, with low expression significantly inhibiting TNBC cell proliferation and migration.

Discussion: Our GRS risk model demonstrates robust predictive capability for TNBC prognosis and immunotherapy response. This model offers a promising strategy for personalized treatment and improved clinical outcomes in TNBC.

Keywords: TNBC; glycosylation; machine learning; prognosis; tumor immune microenvironment.

Figure 1

Construction of a glycosylation-related signature. (A) Study flow chart. (B) Venn diagram showing the intersection of prognosis-related genes (PRG), differentially expressed genes (DEG), and glycosylation-related genes (GRG). (C) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the glycosylation-related signature (GRS).

Figure 2

Construction of a GRS risk model via machine learning algorithms. (A) 101 prediction models were evaluated using the leave-one-out cross-validation (LOOCV) framework, with the concordance index (C-index) calculated for each model across all validation datasets. (B) The optimal λ value in the SCANB cohort was identified at the minimum partial likelihood deviance. (C) Lasso coefficients were derived for the most informative prognostic genes. (D) Error rate and (E) variable importance were assessed using random survival forest (RSF) analysis. Kaplan-Meier survival curves for overall survival (OS) were stratified by GRS risk groups in (F) SCANB, (G) TCGA-TNBC, (H) GSE103091, (I) SCANB-Basal and (J) SCANB-Non-Basal cohorts.

Figure 3

Association of GRS risk model with clinical characteristics in TNBC patients. Circos plots showing survival and pathological differences between two risk groups in the (A) SCANB, (B) GSE103091, and (C) TCGA-TNBC cohorts (Chi-square test). (D) GO and (E) KEGG analysis of differentially expressed genes between high- and low-risk groups. (F-I) GO and KEGG terms enriched by differentially expressed genes using GSEA analysis between the two risk groups.

Figure 4

Correlation of immune microenvironment with GRS risk model. (A) Thermogram displaying relationships between GRS risk groups (top 20% samples) and tumor immune microenvironment components based on CIBERSORT, XCELL, TIMER, and ESTIMATE analyses (Wilcoxon test). The representative images show the variations in pathological HE staining between the (B) high- and (C) low-risk groups. (D) Boxplot illustrating the association between GRS risk groups and the mRNA expression levels of various immune-related markers (Wilcoxon test). (E) Histogram depicting differences in tumor mutation burden (TMB) between risk groups (Wilcoxon test). Waterfall plots of genetic alterations in common mutant genes for the (F) high- and (G) low-risk groups in the SCANB cohort. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 5

Predictive value of GRS risk model for immunotherapy and identification of potential therapeutic agents. (A) Prediction of immunotherapy responses between two GRS risk groups in the SCANB cohort. (B) Histogram showing differences in GRS scores across immune phenotypes in the IMvigor210 cohort (Kruskal-Wallis test). (C, D) Histograms displaying variations in PD-L1 expression across immune cell (IC) and tumor cell (TC) subsets in the IMvigor210 cohort (Wilcoxon test). (E) Stacked histogram of anti-PD-L1 responsiveness between GRS risk groups in the IMvigor210 cohort. Kaplan-Meier survival curves for GRS risk groups in the IMvigor210 cohort (F) and Liu’s cohort (G). (H) Histogram of area under the curve (AUC) values between high- and low-risk groups from the GDSC dataset (Wilcoxon test). (I) Boxplot showing variations in IC50 values between risk groups from the GDSC dataset (Wilcoxon test). (J) Venn diagram showing compounds with significant differences in both AUC and IC50 values in the GDSC dataset. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 6

Validation of mRNA and protein expression in GRS risk model. (A) The expression of 21 gene mRNA in TNBC cell lines experimented by RT-qPCR analysis: MAN1A1, LMAN1L, DERL3, CHST1, HS6ST3, FUT2, BCAN, XXYLT1, VEGFB, FUT7, ALG3, CHST7, BGN, MFNG, GCNT2, GALNT13, GAS2, B4GALNT4, SRD5A3, NEU4, and SCGB1A1. (B) Protein expression in breast cancer and normal tissues validated by the immunohistochemistry analysis of the HPA database. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

Figure 7

LMAN1L promotes the malignant characteristics of TNBC. (A) RT-qPCR analyzing the knockdown efficiency of LMAN1L in LM2 cells and SUM159PT cells. (B) The level of overall intracellular O-GlcNAcylation in LM2 cells and SUM159PT cells following treatment with siRNA sequences (siNC, siLMAN1L#1, siLMAN1L#2). (C) Cell proliferation assays using CCK-8 in LM2 cells and SUM159PT cells with LMAN1L knockdown. (D) Representative images (scale bar 0.5cm) and quantification (E, F) of plate colony formation assay. (G) Representative images (scale bar 100μm) and quantification (H, I) of transwell migration assay in LM2 cells and SUM159PT cells with LMAN1L knockdown. (J) Representative images and quantification (K, L) of EdU assay in LM2 cells and SUM159PT cells with LMAN1L knockdown. EdU (red) and Hoechst 33342 (blue). (M) Representative images of immunofluorescence assay in TNBC patient samples grouped based on LMAN1L protein expression. LMAN1L (red), DAPI (blue), CD3 (white), CD19 (green), and CD68 (yellow). ***P < 0.001.