Integration of Bulk and Single-Cell Transcriptomics Reveals BCL2L14 as a Novel IGKC+ T Cell-Associated Therapeutic Target in Breast Cancer

Abstract

Background: The tumor microenvironment and biomarkers play a pivotal role in breast cancer research, yet there remains a pressing need for effective biomarkers. This study focuses on identifying a novel IGKC+ T Cell subpopulation and its related biomarkers to pave the way for innovative targeted therapies and improved clinical outcomes.

Methods: We first performed single-cell RNA sequencing (scRNA-seq) analysis to characterize immune cell heterogeneity within the tumor microenvironment, leading to the identification of series cell subpopulation. Then, by performing univariate analysis to correlate cell proportions with patient prognosis, we identified a novel IGKC+ T cell subpopulation. Next, we applied bulk RNA-seq deconvolution algorithms to estimate the abundance of this subpopulation across breast cancer cohorts. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify genes associated with the IGKC+ T cell population. To pinpoint key regulatory genes, we applied machine learning algorithms. Based on the hub genes identified, we constructed a prognostic risk model and developed a nomogram to aid clinical decision-making. Immune infiltration patterns were further assessed in high- vs low-risk groups defined by the model. Finally, functional validation was performed through overexpression of BCL2L14 in vitro, and downstream signaling pathways were examined.

Results: We identified the novel IGKC+ T cell subpopulation and core genes. Machine learning pinpointed BCL2L14, IGHD, MAPT-AS1, NT5DC4, and TNIP3 as key regulators of breast cancer progression in this subpopulation. The model stratified patients into high- and low-risk groups, with high-risk patients showing worse prognosis and weaker immune infiltration. Overexpression of BCL2L14 was experimentally demonstrated to accelerate breast cancer progression, linked to enhanced phosphorylation of the NF-κB pathway.

Conclusion: Our results underscore BCL2L14 as a potential driver within the novel T-cell subpopulation and a critical biomarker for breast cancer diagnosis. These findings provide a basis for developing advanced diagnostic tools and targeted therapies, which may ultimately enhance patient prognosis.

Keywords: Mendelian randomization; bioinformatics; breast cancer; scPagwas; single cell sequencing.

Figure 1

Single-cell analysis of breast cancer tissues.(A)The violin plot shows the sample QC metrics, including nFeature_RNA (number of genes per cell), nCount_RNA (total RNA per cell), percent.mt (proportion of mitochondrial genes) and percent.rb (proportion of ribosomal genes). These metrics help identify potentially low-quality cells. (B)Correlations between different QC indicators were plotted.(C) Heat map was drawn to show the effect of cell clustering under the setting resolution.(D) UMAP (left) and t-sne (right) analyses were performed to reduce the dimensionality of the data and visualize the data. (E)The bar chart shows the cell types enriched in different samples. (F) Bubble plots show manually annotated genes significantly differentially expressed in different cell subsets.

Figure 2

BayesPrism deconvolution correlates cell proportions to clinical data.(A) The quality control process of BayesPrism removed noise. (B) GWAS data were combined to identify trait-related genes. (C) GWAS data were combined to identify trait-related cell subsets. (D)Volcano map of differentially expressed genes in TCGA-BRCA cohort. (E) The consistency of gene expression across different types of genes. (F) The KM survival curves demonstrated significant survival differences between indicated high and low cells.

Figure 3

Analysis of clinical traits based on cell subsets.(A and B) Transcriptional differences between high and low groups of convolutional cell populations as well as estimate immune scores were specified. (C–F) TIDE immunotherapy analysis of specified convolutional cells. (G)The differences in drug sensitivity of the convolutional cells are specified, and the top 20 drugs are shown. (H) SNV differences for the convolutional cells are specified. The significance levels are indicated as follows: ***P <0.001, ****P <0.001.

Figure 4

Coregene identification of IGKC+ T cells.(A) In the left graph, the horizontal line indicates that the threshold value is 0.9. (B) Cluster dendrogram of the WGCNA analysis. (C) Module-trait heatmap showing that the MEpurple module was closely related to the IGKC+ T cells. (D) Venn plot showing the intersecting types of genes. (E-M) The KM survival curve showed the genes with significant survival differences in hubgenes.

Figure 5

SMR reveal disease-causing genes. (A–F) The risky genes locus for BRCA.

Figure 6

Development and validation of prognostic models.(A) Visualization of LASSO regression in the TCGA-BRCA cohort. (B)The optimal λ was obtained when the partial likelihood deviance reached the minimum value. Regression coefficients of 6 genes obtained in Cox regression. (C)Sankey diagram was used to visualize the correspondence between patient subgroups across the two models. (D) Presentation of group risk scores based on PRGcluster. (E) Presentation of group risk scores based on geneCluster. (F) Construction of the nomogram based on the TIRS and clinical characteristics, including age, stage, risk score, N, and T. (G-I) Kaplan–Meier curves of OS according to the TIRS in the TCGA training set, TCGA internal validation set, and GEO cohort, based on the Log rank test. (J-L) ROC curves showing the specificity and sensitivity of TIRS in predicting 1, 3, and 5-year OS in the TCGA training set, TCGA internal validation set, and GEO cohort.

Figure 7

The Immunophenotypes and Therapeutic Analysis Based on Core Gene Models.(A) Pathway analysis based on risk score grouping. (B) Differential analysis of immune infiltrating cells based on risk score grouping. (C) Expression levels of model genes in different immune cells. (D) Expression differences in immune scores between high and low-risk groups. (E) Differences in risk scores in microsatellite instability. The significance levels are indicated as follows: *P <0.05, **P <0.01, ***P <0.001.

Figure 8

BCL2L14 upregulates the NF-κB signaling pathway to promote the proliferation and migration of breast cancer cells.(A) Validation of BCL2L14 overexpression in MDA-MB-231 and MDA-MB-468 cells verified by Western blot. (B) CCK8 was used to detect the cell growth rate at 24 h, 48 h and 72 h, respectively. (C) EDU was used to detect the growth rate of cells which overexpressed of BCL2L14 or empty vector, respectively. (D and E) Scratch assay and invasion assay were used to examine the effect of BCL2L14 overexpression on cell invasion ability, respectively. (F) GSEA bioinformatics enrichment analysis. (G)Western blot was used to detect the expression of NF-κb signaling pathway-related proteins under different treatment. The significance levels are indicated as follows: *P <0.05, **P <0.01, ***P <0.001.