SQSTM1 is overexpressed in patients with breast cancer at RNA and protein levels
We first explored SQSTM1 mRNA expression distribution in various tumors using TIMER dataset. It was shown that SQSTM1 mRNA was significantly higher in most common tumor tissues compared with normal tissues. It is noteworthy that SQSTM1 acted as an oncogene in different subtypes (Luminal, Her2, basal) of breast cancer (Fig. 2A). Furthermore, same results were validated in 2 independent GEO cohorts (GSE54002, GSE42568) when compared SQSTM1 mRNA expression between breast cancer tissues and normal tissues (Fig. 2B).
SQSTM1 expression in protein level was examined by IHC staining. As shown in Fig. 3, p62 was mainly expressed in the cytoplasm of breast cancer cells and significantly overexpressed in breast cancer tissues compared with adjacent non-tumorous tissues (P < 0.001). The diagnostic performance of p62 for distinguishing breast cancer from non-breast cancer was assessed by ROC analysis with AUC of 0.846 (95% CI = 0.760–0.933, P < 0.001). Our results indicated that p62 can be used as a diagnostic biomarker (Fig. 3). All the above data demonstrated the oncogene role of SQSTM1 in breast cancer and can be utilized as a predictive tool for achieving precision medicine.
Correlation of SQSTM1 mRNA expression with clinicopathologic characteristics
In order to explore the clinical significance of SQSTM1 in breast cancer, we explored RNA-seq data from METABRIC database with SQSTM1 mRNA expression (n = 1336) and detailed clinical information. Using Chi-square test, we assessed the correlation between SQSTM1 mRNA expression and clinical-pathologic characteristics. As were shown in Table 1, the expression level of SQSTM1 mRNA was significantly associated with ER status (P = 0.018), hormone therapy (P = 0.006).Multivariate logistic regression indicated that ER status (OR = 1.762, P < 0.05) and PR status (OR = 0.743, P < 0.05) were independent influence factors of SQSTM1 mRNA expression in breast cancer patients (Table 2).
Table 1
Relationship between SQSTM1 mRNA expression and clinicopathological characteristics
Variables
|
Number
|
SQSTM1
|
P value
|
|
|
Low
|
High
|
|
Age
|
|
|
|
0.064
|
< 50
|
306
|
239
|
67
|
|
≥ 50
|
1030
|
750
|
280
|
|
ER
|
|
|
|
0.018
|
Positive
|
1024
|
742
|
282
|
|
Negative
|
312
|
247
|
65
|
|
PR
|
|
|
|
0.348
|
Positive
|
695
|
522
|
173
|
|
Negative
|
641
|
467
|
174
|
|
HER2
|
|
|
|
0.329
|
Positive
|
165
|
117
|
48
|
|
Negative
|
1171
|
872
|
299
|
|
Chemotherapy
|
|
|
0.252
|
YES
|
299
|
229
|
70
|
|
NO
|
1037
|
760
|
277
|
|
Hormone therapy
|
|
|
0.006
|
YES
|
818
|
584
|
234
|
|
NO
|
518
|
405
|
113
|
|
Radio therapy
|
|
|
0.869
|
YES
|
900
|
665
|
235
|
|
NO
|
436
|
324
|
112
|
|
Neoplasm Histologic Grade
|
|
0.304
|
1 + 2
|
640
|
482
|
158
|
|
3
|
696
|
507
|
189
|
|
Nottingham prognostic index
|
|
0.629
|
≤ 5.4
|
1192
|
880
|
312
|
|
> 5.4
|
144
|
109
|
35
|
|
Primary Tumor Laterality
|
|
0.858
|
Left
|
687
|
510
|
177
|
|
Right
|
649
|
479
|
170
|
|
Tumor stage
|
|
|
|
0.766
|
0,1,2
|
1218
|
903
|
315
|
|
3, 4
|
118
|
86
|
32
|
|
Abbreviation: ER: estrogen receptor; PR: progesterone receptor; HER-2: Human epidermal growth factor receptor-2 |
Table 2
Correlation of SQSTM1 mRNA expression with clinicopathological characteristics in by multivariate logistic regression analysis.
variables
|
P value
|
OR (95%CI)
|
Age (≥ 50 vs < 50)
|
0.346
|
1.177 (0.839, 1.651)
|
ER status (Positive vs Negative)
|
0.007
|
1.762 (1.164, 2.667)
|
PR status (Positive vs Negative)
|
0.043
|
0.743 (0.557, 0.990)
|
HER-2 status (Positive vs Negative)
|
0.173
|
1.308 (0.889, 1.926)
|
Chemotherapy (YES vs NO)
|
0.879
|
0.972 (0.672, 1.405)
|
Hormone therapy (YES vs NO)
|
0.161
|
1.234 (0.919, 1.656)
|
Radio therapy (YES vs NO)
|
0.839
|
1.029 (0.783, 1.352)
|
Neoplasm Histologic Grade (3 vs 1 + 2)
|
0.089
|
1.280 (0.693, 1.702)
|
Nottingham prognostic index (> 5.4 vs ≤ 5.4)
|
0.247
|
0.758 (0.474, 1.211)
|
Primary Tumor Laterality (Right vs Left)
|
0.701
|
1.050 (0.819, 1.345)
|
Tumor stage (3 + 4 vs 0 + 1 + 2)
|
0.604
|
1.133 (0.708, 1.812)
|
Abbreviation: ER: estrogen receptor; PR: progesterone receptor; HER-2: human epidermal growth factor receptor-2; OR: Odds ratio |
Table 3
Significant neighboring genes associated with SQSTM1 in breast cancer
Rank
|
Gene
|
ENSEMBLE ID
|
Description
|
Score
|
1
|
LAMTOR2
|
ENSG00000116586
|
Late endosomal/lysosomal adaptor,
MAPK and MTOR activator 2
|
3.668
|
2
|
PIR
|
ENSG00000087842
|
pirin
|
2.132
|
3
|
GULP1
|
ENSG00000144366
|
GULP, engulfment adaptor PTB domain containing 1
|
2.073
|
4
|
TXNRD1
|
ENSG00000198431
|
thioredoxin reductase 1
|
1.747
|
5
|
FAM129A
|
ENSMUSG00000026483
|
family with sequence similarity 129 member A
|
1.714
|
6
|
ARL4C
|
ENSG00000188042
|
ADP ribosylation factor like GTPase 4C
|
1.633
|
7
|
SEL1L3
|
ENSG00000091490
|
sel-1 suppressor of lin-12-like 3 (C. elegans)
|
1.606
|
8
|
SLPI
|
ENSG00000124107
|
secretory leukocyte peptidase inhibitor
|
1.460
|
9
|
KIZ
|
ENSG00000088970
|
kizuna centrosomal protein
|
1.429
|
10
|
SUPT4H1
|
ENSG00000213246
|
SPT4 homolog, DSIF elongation factor subunit
|
1.394
|
11
|
RTCA
|
ENSG00000137996
|
RNA 3'-terminal phosphate cyclase
|
1.376
|
12
|
PPP1R15A
|
ENSG00000087074
|
protein phosphatase 1 regulatory subunit 15A
|
1.364
|
13
|
DNAJB9
|
ENSG00000128590
|
DnaJ heat shock protein family (Hsp40) member B9
|
1.329
|
14
|
CASP4
|
ENSG00000196954
|
caspase 4
|
1.263
|
15
|
HHLA3
|
ENSG00000197568
|
HERV-H LTR-associating 3
|
1.254
|
16
|
IDH1
|
ENSG00000138413
|
isocitrate dehydrogenase 1 (NADP+)
|
1.253
|
17
|
PTEN
|
ENSG00000171862
|
phosphatase and tensin homolog
|
1.224
|
18
|
HMOX1
|
ENSG00000100292
|
heme oxygenase 1
|
1.213
|
19
|
GFPT1
|
ENSG00000198380
|
glutamine–fructose-6-phosphate transaminase 1
|
1.062
|
Screening for prognostic factors for breast cancer patients
Using univariate and multivariate cox regression analysis, we identified age at diagnosis, ER status, PR status, HER2 status, chemotherapy, hormone therapy, neoplasm histologic grade, Nottingham prognostic index, tumor stage, SQSTM1 mRNA expression influenced the OS of breast cancer patients. Moreover, age at diagnosis, HER2 status, radio therapy, Nottingham prognostic index, tumor stage, SQSTM1 mRNA were independent prognostic factors for breast cancer patients (Fig. 4).
Prognostic value of SQSTM1 in breast cancer
In our METABRIC cohort, by plotting Kaplan-Meier curve, we found that breast cancer patients with higher SQSTM1 mRNA expression (median survival time = 130.7 months) tended to have a worse overall survival (OS) than patients with lower SQSTM1 mRNA expression (median survival time = 172.9 months, P < 0.001) (Fig. 5A). By using GEO database, we validated the prognostic role of SQSTM1 in breast cancer patients. Lower SQSTM1 expression indicated favorable prognosis (OS, DFS and RFS) in breast cancer from 2 independent cohorts (GSE1456, GSE9195) (Fig. 5B and C). All these results indicated that high SQSTM1 mRNA expression may be a poor prognostic biomarker of breast cancer.
Association between SQSTM1 expression and immune infiltration level in breast cancer
Tumor microenvironment has been demonstrated to serve as a “complex network” of different tumor cells, extracellular matrix components, chemotactic factor and other types of cells which forms the basis for tumor cancer cell proliferation and metastasis (19). Here, we analyzed the correlation between SQSTM1 expression and immune infiltration levels in breast cancer. As were shown in Fig. 6A, SQSTM1 expression was inversely associated with infiltrating levels in breast cancer. It is noteworthy that SQSTM1 expression has positive correlation with tumor purity in breast cancer.
Next, we compared SQSTM1 expression in breast cancer patients with different GSVA score (lowest 25% versus highest 25%) of multiple immune cells through TCGA dataset. The results indicated that higher SQSTM1 expression was significantly correlated with higher infiltration of memory B cell, activated CD4 + T cell and neutrophil. However, it was shown different result in activated CD8 + T cells (Fig. 6B). These results further demonstrated that SQSTM1 may serve as an immune modulatory role in breast cancer and large-scale projects are still urgently needed in the near future.
The mechanism of SQSTM1 expression dysregulation in patients with breast cancer
Numerous studies have indicated that CNV unbalanced gene expression by disrupting the structure of gene coding regions. Next, we evaluated the copy number alterations of SQSTM1 using a cohort of 1904 breast cancer patients from METABRIC database (Shallow Deletion, n = 192; Diploid, n = 1460; Amplification, n = 31; Gain, n = 221). We found that 56.8% (252/444) patients in the altered group harboring SQSTM1 amplification/gain. This result indicated that the amplification/gain of gene copy numbers was likely to be one of the main mechanisms of over-expression of SQSTM1 in breast cancer patients. Consistently, breast cancer patients with SQSTM1 amplification/gain exhibited higher SQSTM1 mRNA expression compared with shallow deletion and diploid (no alteration) group. By drawing the Kaplan–Meier survival curve, the results revealed that patients with SQSTM1 amplification/gain significantly associated with worse overall survival compared with other groups (Fig. 7).
In order to explore the potential clinical significance of SQSTM1 mutation, we first evaluated its mutation profile in the METABRIC database. The results showed that there was no SQSTM1 mutation in the selected patients. Next, patients obtained from TCGA database with mutation profiles were validated. Compared with the high-frequency altered genes such as PIK3CA, AKT1 and PTEN, SQSTM1 mutation frequency is rare and has no predictive value on the prognosis of breast cancer patients (P = 0.338) (Fig. 8).
In addition to point mutations and CNV, epigenetic changes (especially DNA methylation) also play an important role in regulating specific genes expression and the development of breast cancer. We then investigated characteristics of the SQSTM1 promoter methylation in breast cancer. First, the heat map of the SQSTM1 methylation value used different probes were drawn from TCGA dataset. The Kaplan-Meier survival analysis showed that patients with lower methylation of SQSTM1 experienced longer overall survival and disease specific survival significantly, which further suggested that the high expression of SQSTM1 plays a critical prognostic role in breast cancer (Fig. 9). All of the above data showed that upregulation of SQSTM1 expression involved in the development and progression of breast cancer.
Regulation of SQSTM1 in other transcription and post-transcription level
Next, we investigated what transcription factors might regulate SQSTM1 in the upstream in breast cancer by GCBI platform. It can predict the transcription factors through the Transfac database from 2000 bp upstream and 500 bp downstream of the start site based on the transcript of each gene (Ensembl database). First, we identified the transcription factors which have the highest grade among the predicted genes (Supplementary Fig. 1). Based on this analysis, we then used Chip-seq data of Cistrome and confirmed that CTCF, ERG, EP300, E2F1, FOXA1 can directly bind to SQSTM1 DNA in breast cancer (Supplementary Table 1).
In addition, using miRDB, DIANA tools, Targetscan databases, we explored what miRNAs were involved in the post-transcription regulation of SQSTM1. Notably, we set strict screening criteria for the databases (miRDB: Score > 70, Targetscan: context + + score <-0.4. context + + score percentile > 98, DIANA tools: miTG score > 0.8). Finally, 6 common miRNAs (miR-106b-5p, miR-20a-5p, miR-106a-5p, miR-93-5p, miR-17-5p, miR-20b-5p) were identified in three datasets (Supplementary Fig. 2).
SQSTM1 is related to cell signal transduction, oxidative stress and autophagy
To clarify the biological molecular mechanism of SQSTM1 in breast cancer, we first performed differential gene expression analysis based on LIMMA package in samples with high expression of SQSTM1 (N = 552) and low expression of SQSTM1 (N = 552) from TCGA database. Our analysis found that a total of 387 genes were significantly up-regulated and 561 genes were significantly down-regulated (Fig. 10A). In addition, SQSTM1 was observed to be associated with various signal transduction pathways according to KEGG analysis, such as JAK/STAT and PI3K/Akt, which was consistent with previous reports (Fig. 10B). Next, we used the MSigDB Hallmark gene set (Fig. 10C) for GSEA. The results showed that compared with high expression levels of SQSTM1, low levels of SQSTM1 were significantly related to oxidative phosphorylation, peroxisome, DNA repair and reactive oxygen species pathway.
Coexpedia is a distinct co-expression database which offers biomedical hypotheses through medical subject headings. In our study, the co-expression genes in breast cancer associated with SQSTM1 were explored from Coexpedia database in order to clarify the underlying regulation network and mechanism of breast cancer. Through exploring GSE12237, GSE7848 and GSE14018, a total of 19 genes, such as LAMTOR2, PIR, and GULP1 were identified (Supplementary Fig. 3) (Table 4).
The GO analysis based on SQSTM1 and its related genes were then constructed. The top 20 Go terms enrichment of the gene lists was showed in Supplementary Fig. 4. The most significantly enriched GO terms of BP, CC and MF for SQSTM1 and co-expressed genes were negative regulation of endoplasmic reticulum unfolded protein response (GO: 1900102; P = 7.12E-05), ionotropic glutamate receptor binding (GO:0035255; P = 0.0005), DSIF complex (GO:0032044; P = 0.0017) and amphisome (GO:0044753, P = 0.001693), respectively. Altogether, these data indicated that SQSTM1 is related to cell signal transduction, oxidative stress and autophagy thus plays a key role in the progression of breast cancer.