Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma

Abstract

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone's regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-β signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

Keywords: BAG1; BAG2; Gene signature; Immune infiltration; Liposarcoma; Prognostic biomarker.

Figure 1

BAG1 and BAG2 displayed distinct gene expression patterns. (A–F) BAG1 expression was down-regulated while BAG2 was up-regulated in DDLPS. Scatter plots of BAG1 (A–C) or BAG2 (D–F) expression in normal adipose tissue, DDLPS and WDLPS. ROC curve of BAG1 or BAG2 expression compared between DDLPS and WDLPS (G); between normal adipose tissue and DDLPS (H); between normal adipose tissue and WDLPS (I). (J–L) Comparison of BAG1 expression in DDLPS with different clinicopathological features. BAG1 expression difference: (J) in distinct responses to treatment of patients; (K) compared with the survival status of patients; (L) in different tumor volume sizes. Patients were subdivided into two groups according to the tumor volume (≥ 4500 vs.  < 4500).

Figure 2

BAG1 and BAG2 have distinct predictive performances for LPS prognosis. (A–D) Kaplan–Meier plots of OS and DSS for DDLPS patients classified by BAG1 (A,B) or BAG2 (C,D) expression. Survival curves based on BAG1 (E) or BAG2 (F,G) expression of patients suffered from LPS and DDLPS, respectively. (H,I) Survival curves were plotted to compare OS or DRFS of patients with differential expression of BAG1 and BAG2: patients were subdivided into four groups. BAG1 + BAG2–: the group with high BAG1 but low BAG2 expression; BAG1 + BAG2 + : the group with high BAG1 and BAG2 expression; BAG1- BAG2–: the group with low BAG1 and BAG2 expression; BAG1– BAG2 + : the group with low BAG1 but high BAG2 expression; cut-off: 50%.

Figure 3

Correlation analysis between BAG1/BAG2 and previously reported prognostic biomarkers or DEGs of LPS. (A) Association of BAG1/BAG2 with the known biomarkers or DEGs of LPS was estimated by Pearson's rank correlation test with correlation coefficient marked on the matrix plot. Positive and negative correlations were indicated by red and blue boxes, respectively. Color intensity and absolute value are proportional to the correlation strength. (B) Survival curves were plotted to verify the prognostic abilities of the known biomarkers or DEGs in LPS.

Figure 4

Co-expressed gene and protein–protein interaction (PPI) analysis of BAG1 or BAG2. (A,C) Co-expressed genes of BAG1 and BAG2, respectively. (B,D) The comprehensive PPI network of BAG1 and BAG2, respectively.

Figure 5

GO and KEGG analysis of the co-expressed genes of BAG1 or BAG2. (A,E) Biological process, BP. (B,F) Cell component, CC. (C,G) Molecular function, MF. (D,H) KEGG analysis. KEGG pathways were obtained from KEGG software (www.kegg.jp/kegg/kegg1.html). Each point on the graph represents a group of genes. The point size represents the number of genes. The redder the point color, the smaller the p value, the more statistically significant.

Figure 6

Gene set enrichment analysis of BAG1 or BAG2. (A,D) Heat map of the top 50 DEGs enriched in BAG1 or BAG2 differential expression group generated by GSEA software (version 4.2.2; https://www.gsea-msigdb.org/gsea/index.jsp). (B,C) The gene sets enriched in BAG1 high (B) and low (C) expression group, respectively. (E) Ranked gene list correlation profile, a summary of GSEA results.

Figure 7

Analysis of immune infiltration in TME of LPS tissues. (A) Comparison of immune cell compositions between BAG1 high-expression (Upper 50%) and low-expression group (Lower 50%). (B) Volcano map of (A). (C) The association between BAG1 expression and immune score. (D) The correlation between BAG1 expression and enrichments of immune cells.

Figure 8

Establishment of a BAG1-BAG2-based signature and evaluation of its prognostic ability for LPS using data from GES30929 dataset. (A) Distribution of risk scores based on a 2-gene signature, and scatter plots of survival status (distant recurrence risk) of LPS patients with high or low risk scores. (B) Comparison of risk scores between high and low risk group. (C) Distribution of Lasso regression coefficients for BAG1 and BAG2. Red represents the risk factor and blue denotes the protective factor. (D) A comprehensive heatmap plotted by R (version 4.2.3; https://www.r-project.org/) including risk level, pathological subtype, DRFS as well as expression of BAG1 and BAG2. DDLPS, dedifferentiated liposarcoma; WDLPS, well differentiated liposarcoma; PLPS pleomorphic liposarcoma; MLPS myxoid liposarcoma. (E) Kaplan–Meier survival curve of DRFS based on the risk scores (cut-off: 50%). (F) Time-conditioned ROC curves for evaluating the prognostic ability of the 2-gene risk assessment model at 1, 3, 5 years. (G–I) Analysis of risk score-related immune infiltration in TME of LPS tissues. (G) Comparison of immune cell proportion between high and low risk group. (H) Survival curves based on immune cell infiltration level. Kaplan–Meier plots of DRFS for LPS patients classified by the proportion of activated mast cells. (I) Survival curves were plotted to compare DRFS of patients with high or low risk and high or low proportion of activated mast cells: patients were subdivided into four groups. Risk + Cell fraction + : the group with high risk and high proportion of activated mast cells; Risk + Cell fraction–: the group with high risk but low proportion of activated mast cells; Risk- Cell fraction + : the group with low risk but high proportion of activated mast cells; Risk- Cell fraction–: the group with low risk and low proportion of activated mast cells; cut-off: 50%. DRFS distant recurrence-free survival.