Identification and Functional Characterization of Upregulated DEGs in Docetaxel- and Doxorubicin-treated TNBC Cells
To identify the potential genes related to sensitivity to docetaxel and doxorubicin in TNBC, we first performed differential expression analysis using the RNA-seq data of HS578T cells treated with docetaxel or doxorubicin. The results showed the presence of 3902 DEGs in docetaxel-treated HS578T cells (Figure 1A), of which 2280 DEGs were found to be significantly upregulated and 1622 DEGs were downregulated (Figure 1B).
To explore the underlying biological function and signaling pathways, functional enrichment analysis for these DEGs was performed as previously described. Specifically, the BP group genes were enriched in anatomical structure development, movement of cell or subcellular component, cell motility, and localization of cell. In addition, the CC group genes were primarily related to extracellular region, plasma membrane, membrane, and cell periphery. The MF group genes were primarily enriched in ion binding, nucleic acid binding transcription factor activity, translation factor activity, RNA binding, and protein transporter activity (Figure 1C). The KEGG pathway analysis of the DEGs showed that the mTOR signaling pathway, microRNAs in cancer, and PI3K/AKT signaling pathway were most significantly enriched (Figure 1D).
Concurrently, 8727 DEGs were identified in doxorubicin-treated HS578T cells (Figure 2A), of which 5136 DEGs were found to be significantly upregulated and 3591 DEGs were downregulated (Figure 2B). The findings indicated that the expression of these DEGs was strongly associated with cellular metabolic process, protein binding, DNA binding, and membrane−bounded organelle (Figure 2C). As shown in Figure 2D, the DEGs were enriched in oxidative phosphorylation, p53 signaling pathway, and Wnt signaling pathway.
Validation of DDIT4 Expression Correlates with Chemotherapy in TNBC
To investigate more sensitive targets and verify the reliability of the results, we retrieved the GSE62931 datasets, which included TNBC and ER+/PR+ samples. As shown in Figure 3A, 2944 DEGs were identified, among which 1501 DEGs were upregulated and 1443 DEGs were significantly downregulated in TNBC cells (Figure 3B). The GO terms showed that the DEGs were primarily related to collagen-containing extracellular matrix (Figure 3C) and were enriched in cell cycle pathway, EMC-receptor interaction, and P53 and PI3K/AKT signaling pathway (Figure 3D). Upon comparing the DEGs significantly upregulated in the three abovementioned gene sets (Figure 4), five genes were identified, namely DDIT4, S100P[28], TTYH1[29], NANOS1[30], and SLC7A5[31]. The expression of these genes was previously reported to be associated with the development of breast cancer. Limited data are available on DDIT4 expression in the context of chemotherapy and immunotherapy resistance in TNBC. Therefore, we further chose DDIT4 as the potential target gene of interest in this study.
DDIT4 As a Key Indicator of the Chemotherapeutic Response in TNBC
To determine the role of DDIT4 in TNBC, we first evaluated its expression levels and diagnostic and prognostic value in patients with TNBC. TIMER data revealed that the mRNA expression of DDIT4 was significantly higher in breast cancer tissues than in normal tissues (Figures 5). Following this, we analyzed the transcription levels of DDIT4 based on the stages of breast cancer, patient gender, age, primary subtypes, major subclasses with TNBC, menopausal status, nodal metastasis status, and TP53 mutation status. The DDIT4 transcription levels in breast cancer samples were significantly higher than those in normal samples. In particular, TCGA data indicated a higher expression of DDIT4 in TNBC than in other subtypes of breast cancer (Figure 6). Furthermore, we investigated the correlation between DDIT4 overexpression at the mRNA level and patient prognosis by plotting and comparing the OS, PPS, DMFS, and RFS of patients with BC and TNBC using the Kaplan–Meier plotter (Figure 7). TNBC patients with high levels of DDIT4 expression had a shorter RFS (HR=1.65 (1.32-2.07), p< 0.001). Further, DDIT4 overexpression was associated with worse OS (HR=1.34 (1.11-1.62), p < 0.01), PPS (HR=1.43 (1.13-1.8), p < 0.01), DMFS (HR=1.3 (1.12-1.52), p < 0.001), and RFS (HR=1.5 (1.35-1.66), p < 0.001) in breast cancer. Overall, the findings imply that the mRNA expression of DDIT4 was significantly correlated with the poor prognosis of patients with breast cancer and TNBC.
Relationship Between the Transcriptional Level of DDIT4 and Immune Infiltration in TNBC
Immunotherapy has evolved into one of the most promising therapeutic regimens for TNBC[32]. However, the role of DDIT4 in immune infiltration in TNBC is unknown. Using the TIMER database, we further investigated the relationship between the transcriptional level of DDIT4 and immune infiltration. It was found that DDIT4 expression correlated negatively with the infiltration of B cells (Cor=-0.198, p = 2.73e−02), CD8+ T cells (Cor=-0.194, p = 3.17e−02), and CD4+ T cells (Cor=-0.187, p = 3.95e−02). No significant association was observed between tumor purity and the infiltration of macrophages, neutrophils, and dendritic cells. We also analyzed the correlation between the DDIT4 transcription level and immune cell infiltration in breast cancer. The level of DDIT4 expression correlated positively with the infiltration of CD4+ T cells (Cor=0.081, p = 1.15e−02), neutrophils (Cor=0.097, p = 2.86e−03), and dendritic cells (Cor=0.102, p = 1.60e−03) and negatively with tumor purity (Cor=-0.179, p =1.27e−08) (Figures 8).
The relationship between DDIT4 expression and immune marker expression was also analyzed. As shown in Table 1, the expression of DDIT4 correlated significantly with the expression of different genes with respect to the different immune subset cells in TNBC. The immune biomarkers identified were as follows: T cells markers, CD8A; B cells markers, CD19 and CD79A; neutrophil markers, CCR7; dendritic cell markers, HLA-DPB1, HLA-DPA1, and BDCA-1 (CD1C); Th1 markers, TNF-a (TNF); Treg markers, FOXP3 and CCR8. DDIT4 expression was negatively correlated with various immune cells. Further analysis of the relationship between the expression of ten immune checkpoint-related genes and DDIT4 showed that DDIT4 expression was correlated with BTLA, CD274, CTLA4, HAVCR2, ICOS, LAG3, PDCD1, PDCD1LG2, TIGIT, and VSIR expression in breast cancer (Figures 9).
TABLE 1
Correlation analysis between DDIT4 and gene biomarkers of immune cells in TNBC (TIMER)
Immune cell
|
Biomarker
|
None
|
Purity
|
|
|
Cor
|
P-value
|
Cor
|
P-value
|
CD8+ T cells
|
CD8A
|
−0.179
|
3.40e−02
|
−0.177
|
4.53e−02
|
|
CD8B
|
−0.158
|
6.25e−02
|
−0.155
|
7.99e−02
|
T cells (general)
|
CD3D
|
−0.151
|
7.58e−02
|
−0.145
|
1.02e−01
|
|
CD3E
|
−0.16
|
5.91e−02
|
−0.156
|
7.80e−02
|
B cells
|
CD2
|
−0.167
|
4.81e−02
|
−0.172
|
5.25e−02
|
|
CD19
|
−0.241
|
4.10e−03
|
−0.256
|
3.60e−03
|
|
CD79A
|
−0.227
|
6.98e−03
|
−0.239
|
6.67e−03
|
Monocytes
|
CD86
|
−0.139
|
1.02e−01
|
−0.12
|
1.78e−01
|
|
CD115 (CSF1R)
|
−0.125
|
1.43e−01
|
−0.094
|
2.91e−01
|
TAMs
|
CCL2
|
−0.046
|
5.87e−01
|
−0.026
|
7.72e−01
|
|
CD68
|
−0.094
|
2.70e−01
|
−0.046
|
6.05e−01
|
|
IL10
|
−0.114
|
1.80e−01
|
−0.078
|
3.80e−01
|
M1 Macrophages
|
INOS (NOS2)
|
−0.037
|
6.64e−01
|
−0.079
|
3.72e−01
|
|
IRF5
|
−0.082
|
3.34e−01
|
−0.075
|
4.00e−01
|
|
COX2 (PTGS2)
|
0.058
|
4.95e−01
|
0.089
|
3.16e−01
|
M2 Macrophages
|
CD163
|
−0.049
|
5.61e−01
|
0.002
|
9.82e−01
|
|
VSIG4
|
−0.03
|
7.23e−01
|
0.024
|
7.91e−01
|
|
MS4A4A
|
−0.142
|
9.41e−02
|
−0.11
|
2.18e−01
|
Neutrophils
|
CD66b (CEACAM8)
|
−0.048
|
5.74e−01
|
−0.095
|
2.88e−01
|
|
CD11b (ITGAM)
|
−0.058
|
4.93e−01
|
−0.023
|
7.95e−01
|
|
CCR7
|
−0.215
|
1.09e−02
|
−0.236
|
7.43e−03
|
Natural killer cells
|
KIR2DL1
|
−0.052
|
5.45e−01
|
−0.017
|
8.48e−01
|
|
KIR2DL3
|
0.027
|
7.51e−01
|
0.095
|
2.87e−01
|
|
KIR2DL4
|
0.063
|
4.60e−01
|
0.127
|
1.53e−01
|
|
KIR3DL1
|
−0.076
|
3.72e−01
|
−0.055
|
5.34e−01
|
|
KIR3DL2
|
−0.087
|
3.06e−01
|
−0.021
|
8.13e−01
|
|
KIR3DL3
|
−0.072
|
4.01e−01
|
−0.055
|
5.37e−01
|
|
KIR2DS4
|
−0.126
|
1.38e−01
|
−0.096
|
2.81e−01
|
Dendritic cells
|
HLA-DPB1
|
−0.211
|
1.26e−02
|
−0.206
|
1.94e−02
|
|
HLA-DQB1
|
−0.16
|
5.84e−02
|
−0.144
|
1.04e−01
|
|
HLA-DRA
|
−0.174
|
3.95e−02
|
−0.152
|
8.66e−02
|
|
HLA-DPA1
|
−0.207
|
1.43e−02
|
−0.191
|
3.10e−02
|
|
BDCA-1 (CD1C)
|
−0.233
|
5.52e−03
|
−0.216
|
1.42e−02
|
|
BDCA-4 (NPR1)
|
−0.08
|
3.49e−01
|
−0.071
|
4.24e−01
|
|
CD11C (ITGAX)
|
−0.162
|
5.55e−02
|
−0.151
|
8.92e−02
|
Th1
|
T-bet (TBX21)
|
−0.147
|
8.23e−02
|
−0.135
|
1.29e−01
|
|
STAT4
|
−0.15
|
7.61e−02
|
−0.139
|
1.18e−01
|
|
STAT1
|
−0.063
|
4.61e−01
|
−0.058
|
5.13e−01
|
|
IFN-g (IFNG)
|
−0.094
|
2.70e−01
|
−0.07
|
4.30e−01
|
|
TNF-a (TNF)
|
0.168
|
4.75e−02
|
0.2
|
2.39e−02
|
Th2
|
GATA3
|
0.109
|
2.00e−01
|
0.123
|
1.65e−01
|
|
STAT6
|
−0.149
|
7.93e−02
|
−0.168
|
5.86e−02
|
|
STAT5A
|
0.024
|
7.81e−01
|
0.035
|
6.93e−01
|
|
IL13
|
0
|
9.99e−01
|
0.043
|
6.33e−01
|
Tfh
|
BCL6
|
−0.077
|
3.64e−01
|
−0.041
|
6.49e−01
|
Th17
|
STAT3
|
0.135
|
1.11e−01
|
0.154
|
8.26e−02
|
|
IL17A
|
0.012
|
8.91e−01
|
0.029
|
7.45e−01
|
Tregs
|
FOXP3
|
−0.224
|
7.91e−03
|
−0.245
|
5.28e−03
|
|
CCR8
|
−0.197
|
1.99e−02
|
−0.207
|
1.89e−02
|
|
STAT5B
|
−0.08
|
3.48e−01
|
−0.084
|
3.48e−01
|
|
TGFb (TGFB1)
|
−0.174
|
3.95e−02
|
−0.173
|
5.08e−02
|
T cell exhaustion
|
PD-1 (PDCD1)
|
−0.032
|
7.07e−01
|
0.014
|
8.78e−01
|
|
CTLA4
|
−0.118
|
1.64e−01
|
−0.092
|
3.01e−01
|
|
LAG3
|
−0.031
|
7.13e−01
|
−0.004
|
9.68e−01
|
|
TIM-3 (HAVCR2)
|
−0.141
|
9.65e−02
|
−0.119
|
1.83e−01
|
|
GZMB
|
0.03
|
7.24e−01
|
0.083
|
3.53e−01
|
Analysis of Genes exhibiting Co‑expression with DDIT4 in Breast Cancer
To gain additional insight into the biological significance of DDIT4, we investigated the potential role of DDIT4 in breast cancer by analyzing the mRNA sequencing data of 1093 patients with breast cancer, obtained from the TCGA database, using the LinkFinder module in LinkedOmics. As shown in Figure 10, 7047 genes (red dots) showed positive correlation with DDIT4, whereas 5472 genes (green dots) showed negative correlation (Figure 10A). In addition, the heatmaps showed the top 50 important genes exhibiting positive and negative co-expression with DDIT4 in breast cancer (Figures 10B, C). Moreover, the top four significant genes, namely ADM (Person correlation=5.255e-01, p =1.242e-78), ENO1 (Person correlation=4.786e-01, p =1.149e-63), PLOD1 (Person correlation=4.585e-01, p =6.321e-58), and CEBPB (Person correlation=4.578e-01, p =9.956e-58) were considered as hub genes; the expression of these genes was strongly associated with DDIT4 expression in breast cancer.
Analysis of the Hub Genes of DDIT4 in Breast Cancer
To further explore the function of DDIT4 and its hub genes in greater detail, we constructed PPI networks using the GeneMANIA tools. DDIT4 and its hub genes showed interactions with 20 genes (Figure 11A). GO analysis revealed that the genes associated with DDIT4 are primarily related to chemokine activity, tubulin binding, and histone kinase activity and involved in physiological processes such as condensed chromosome, centromeric region, and spindle microtubule. Their molecular functions include mitotic sister chromatid segregation, organelle fission, and nuclear division (Figure 11B). KEGG analysis showed that DDIT4 may play a crucial role in the development and progression of BC by participating in cellular senescence, oocyte meiosis, cell cycle, and PPAR signaling pathways (Figure 11C).
Furthermore, we demonstrated that DDIT4 and its hub genes participate in the activation of the apoptosis, cell cycle, and EMT pathways (Figure 12). Finally, KM Plotter analysis of the hub genes showed that the high expression of ADM, ENO1, PLOD1, and CEBPB was significantly correlated with a shorter OS and poor prognosis in patients with breast cancer (Figure 13).