Metabonomics analysis of ovarian cancer tissue and ascites
The metabonomic data were visualized by unsupervised principal component analysis(PCA). In 37 cases of ascites supernatant, 23 cases of precipitated cells and 22 cases of cancer tissue samples, QC samples in PCA plots were clustered tightly (Fig. S1), which indicated that the data quality was very good. In a supervised model of PLS-DA, ascites and tissue samples were clearly separated between N and C, CS and CR, indicating that all the three groups had different metabolic profiles and there was no over-fitting (Fig. 1).
Select differential metabolites in ovarian cancer tissues and ascites
Metabolic characteristics of supernatant, precipitated cells and cancer tissues in ovarian cancer
A total of 7579, 10053 and 11053 metabolic peaks were detected in 37 cases of ascites supernatant, 23 cases of precipitated cells and 22 cases of ovarian cancer, respectively (Fig. 2a). Heatmap and correlation analysis showed that metabolites were well distinguished in no-chemotherapy, chemosensitivity and Chemotherapy resistance groups, and there was a certain correlation between them (Fig. 2b). It is suggested that there may be different metabolic patterns in N vs C and CS vs CR and the metabolites promote or inhibit each other.
Analysis of differential metabolites and common differential metabolites in each sample set
Differential metabolites were screened in N vs C and CS vs CR for each sample set(Table S2-3). The corresponding coexisting metabolites of differential metabolites in other samples of each group were 49, 31 and 9, respectively (Table 1). All the differential metabolites were combined and the differential metabolites with higher significance were screened by random forest analysis (n = 15, respectively (Fig. 2c). The coexistence metabolites were intersected with random forest results in N vs C and CS vs CR, respectively. Screening shared metabolites in co-existing metabolites and random forests, and further combining them (Fig. 2d). 10 and 9 differential metabolites were obtained in N vs C and CS vs CR, respectively.
Table 1
Results of different metabolites in ovarian cancer tissues, ascites supernatant and precipitated cells
|
37 unpaired cases(n = 82)-I
|
12 paired cases (n = 44)-II
|
12 paired cases (n = 36)-III
|
|
different
|
coexisting
|
different
|
coexisting
|
different
|
coexisting
|
|
N vs C
|
CS vs CR
|
N vs C
|
CS vs CR
|
N vs C
|
CS vs CR
|
N vs C
|
CS vs CR
|
N vs C
|
N vs C
|
supernatant
|
26
|
20
|
13
|
14
|
18
|
4
|
10
|
2
|
7
|
5
|
cells
|
6
|
10
|
2
|
2
|
|
|
|
|
3
|
1
|
tissues
|
14
|
16
|
10
|
8
|
14
|
16
|
10
|
9
|
7
|
3
|
Total
|
46
|
46
|
25
|
24
|
32
|
20
|
20
|
11
|
17
|
9
|
sum
|
92
|
49
|
52
|
31
|
17
|
9
|
Note: 37 cases of unmatched-I group, 22 cases of paired-II group, 12 cases of paired-III group; bold text means co-existing metabolites.
Correlation between differential metabolites and clinical pathological data
ROC analysis of differential metabolites combined with serum CA125
ROC analysis showed that the distinguishing ability of metabolites was less than that of CA125, but when combined with CA125, nine metabolites increased. They are carnosine, 20-COOH-leukotriene E4 (20-COOH-LTE4), sebacic acid in N vs C; 1,25-dihydroxyvitamin D3-26,23-lactone (1,25-Lactone), 20a,22b-dihydroxycholesterol, 3a,6a,7b-trihydroxy-5b-cholicacid (Muricholic acid), 3a,7a,12a,19-tetrahydroxy-5b-cholic aci (3,7,12,19-THCA), dihydrothymine, hexadecanoic acid in CS vs CR (Fig. 3a). The combination of metabolites and CA125 may improve the predictive value of ovarian cancer diagnosis.
Correlation between differential metabolites and clinical serum biochemical indexes in patients
The serum biochemical indexes of 82 patients with ovarian cancer showed that serum CA125, uric acid and α-hydroxybutyrate dehydrogenase (HBDH) were significantly decreased, γ-glutamyl transpeptidase (γ-GGT) was significantly increased in chemotherapy group, serum CA125, creatinine, lactate dehydrogenase (LDH), alkaline phosphatase (ALP) were increased, and total cholesterol was significantly decreased in Chemotherapy resistance group (p < 0.05, Fig. 3b). Correlation analysis showed that 20-COOH-LTE4 and hexadecanoic acid were positively correlated with CA125 (p < 0.05), 20-COOH-LTE4 was negatively correlated with creatinine (p < 0.05), carnosine was positively correlated with uric acid (p < 0.05), 20a-22b-dihydroxycholesterol was positively correlated with γ-GGT and ALP, hexadecanoic acid was positively correlated with γ-GGT and HBDH, and other metabolites were not significantly correlated with serum biochemical indexes (Fig. 3c).
Survival analysis of genes related to differential metabolites
Progression free survival analysis (PFS) showed that the high expression of enzyme gene had a poor prognosis, which were CNDP1 (carnosine), LTC4S (20-COOH-LTE4), CYP11A1 (20a,22b-dihydroxy cholesterol), CYP7A1 (W-Muricholic acid), CYP27A1 (3,7,12,19-THCA) (p < 0.05, Fig. 4a). Low expression has a poor prognosis, which are ACADL (sebacic acid), DHCR24 (1,25-lactone), AGXT2 (dihydrothymine), ACSL1 (hexadecanoic acid) (p < 0.05, Fig. 4b). Elevated carnosine in no-chemotherapy group is also higher in patients with poor progression free survival. Carnosine that is increased in no chemotherapy group is also higher in patients with poor disease-free survival. Similarly, among drug-resistant patients, increased 20a, 22b-dihydroxycholesterol is also higher in patients with poor progression-free survival, and decreased 1,25-lactone, dihydrothymine and hexadecanoic acid are also lower in patients with poor progression-free survival. In addition, LTC4S, CYP11A1, CYP27A1, ACSL1, and AGXT2 all showed significance in overall survival and progression-free survival analysis. It suggests that 20a, 22b-dihydroxycholesterol, 1,25-lactone, dihydrothymine, and hexadecanoic acid may become the most promising signs for judging whether ovarian cancer develops drug resistance Things.
Pathway analysis of differential metabolites in ovarian cancer tissue and ascites
Metabolite pathway analysis showed that 25 and 12 pathways were enriched in N vs C, CS vs CR (Table S4-5), and there were 6 common pathways (Fig. 5a-b). Differential metabolites and clinical serological indicators also participate in the same pathway, including arachidonic acid, purine, arginine, folate metabolism and steroid synthesis, etc. The change trend of metabolic pathways is consistent. This may explain that changes in metabolites in cancer tissue not only lead to changes in ascites metabolites, but also cause serological changes.
The potential mechanism of differential metabolites in the progression of ovarian cancer
For 51 genes of 9 metabolite-related enzymes, STRING protein interaction network analysis (Fig. 5c) was performed, which has significant correlation(p < 0.001). KEGG enrichment analysis shows that it is mainly involved in the PPAR signaling pathway, steroid biosynthesis, fatty acid metabolism, secondary bile acid biosynthesis, et al. Molecular pathways that may be involved in ovarian cancer are constructed (Fig. 5d). Metabolites and related enzymes may play a role in the progression of ovarian cancer and the development of drug resistance, including malignant proliferation, invasion, apoptosis, et al. (Table 2).
Table 2
Biological function of potential biomarkers and related enzyme genes in ovarian cancer
Metabolites
|
Log FC
|
CA125 + AUC
|
Gene
|
KM p-value
|
poor survial
|
pathway
|
faction
|
Carnosine
|
0.65
|
0.884
|
CNDP1
|
< 0.001
|
high
|
Histidine metabolism
|
Apoptosis
|
20-COOH-LTE4
|
-0.28
|
0.899
|
LTC4S
|
0.012
|
high
|
Arachidonic acid metabolism
|
Angiogenesis
|
Sebacic acid
|
0.2
|
0.879
|
ACADL
|
< 0.001
|
low
|
Fatty acid metabolism
|
Inhibit metastasis
|
Calcitriol lactone
|
-0.66
|
0.963
|
DHCR24
|
0.011
|
low
|
Steroid biosynthesis
|
Inhibit proliferation
|
20a,22b-
Dihydroxycholesterol
|
0.3
|
0.88
|
CYP11A1
|
0.046
|
high
|
Steroid hormone biosynthesis
|
Proliferation
|
Muricholic acid
|
-0.28
|
0.944
|
CYP7A1
|
0.032
|
high
|
bile acid biosynthesis
|
Proliferation drug resistance
|
3,7,12,19-THCA
|
-0.47
|
0.935
|
CYP27A1
|
0.01
|
high
|
bile acid biosynthesis
|
Proliferation drug resistance
|
Dihydrothymine
|
-0.48
|
0.88
|
AGXT2
|
0.034
|
low
|
Pyrimidine metabolism
|
Anti-apoptosis drug resistance
|
Hexacosanoic acid
|
-0.14
|
0.926
|
ACSL1
|
0.018
|
low
|
Fatty acid metabolism
|
Proliferation drug
|