# OGDHL Expression as a Prognostic Biomarker for Liver Cancer Patients

Authors: Yan Jiao; Yanqing Li; Zhuo Fu; Lin Hou; Qingmin Chen; Yujie Cai; Peiqiang Jiang; Miao He; Zhaoying Yang
Journal: Disease Markers
 (2019)

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## Abstract

Background and Objective. Liver cancer is a highly malignant tumor, and patients typically have poor prognoses. Metabolic reprogramming is a hallmark of cancer, and downregulation of oxoglutarate dehydrogenase-like (OGDHL) contributes to the onset and progression of several cancers. We examined the role of altered OGDHL expression in liver cancer and determined its value as a diagnostic and prognostic indicator for patients. Material and Methods. R (version 3.5.1) and several R extensions were used for data mining of The Cancer Genome Atlas (TCGA) dataset (including RNAseq and clinical information) and statistical analysis. Receiver operating characteristic analysis was used to determine the diagnostic value of OGDHL. The chi-squared test was used to identify the clinical correlates of OGDHL downregulation. Survival analysis (with the log-rank test) and univariate and multivariate Cox analysis were used to evaluate the effect of OGDHL expression on overall survival (OS) and relapse-free survival. TCGA was used for analysis of gene set enrichment. Results. OGDHL had lower expression in cancerous liver tissues than noncancerous adjacent tissues, and low expression correlated with more advanced patient age, histologic grade, stage, T classification, and poor survival. Patients with lower OGDHL expression had shorter OS and relapse-free survival. Multivariate Cox regression indicated that low OGDHL expression was an independent risk factor for poor prognosis. Gene set enrichment analysis indicated enrichment of the mitotic spindle, G2M checkpoint, and E2F targets in the OGDHL low expression phenotype. Conclusion. OGDHL has potential as a diagnostic and prognostic biomarker for liver cancer.


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## Body

## 1. Introduction

Liver cancer is one of the most common digestive cancers in the world [1]. Although there have been improvements in clinical treatments in recent years, there have not been significant improvements in the prognosis of affected patients. There is an urgent need to identify novel prognostic biomarkers for liver cancer so that treatment selection can be improved.Metabolic reprogramming is one of the hallmarks of cancer. Oxoglutarate dehydrogenase-like (OGDHL) is an essential regulatory gene and a putative tumor suppressor gene. The OGDHL protein is an isoform of 2-oxoglutarate dehydrogenase and functions as the first and rate-limiting step of the multienzyme OGDH complex (OGDHC), which degrades glucose and glutamate [2, 3]. Previous studies have reported enrichment of OGDHL in the brain and undetectable levels in the heart [2]. Subsequent studies examined the downregulation and methylation of OGDHL in breast cancer [4], cervical cancer [5], and colorectal cancer [6].However, the diagnostic value, prognostic value, and role of OGDHL in liver cancer remain unknown. In this study, we comparedOGDHL expression in cancerous and healthy liver tissues and evaluated its diagnostic value by receiver operating characteristic (ROC) analysis. We also examined the correlation of OGDHL expression with clinical features and performed survival analysis using the Cox model to assess its function as an independent prognostic indicator in liver cancer.


## 2. Materials and Methods

### 2.1. Data Mining of The Cancer Genome Atlas Database

The RNAseq data ofOGDHL and clinical information were downloaded from The Cancer Genome Atlas (TCGA) dataset. No ethical approval was necessary because these are anonymized public datasets.


### 2.2. Statistical Analysis

All data analyses were performed using R (version 3.5.1) [7] and several R extensions. Boxplots were used to display expression of OGDHL mRNA. The chi-squared test was used to evaluate the correlation between OGDHL expression and the clinical features of patients. The pROC package was used to perform ROC analysis, to determine the optimal OGDHL cut-off point and to assess the diagnostic value of OGDHL expression by calculation of the area under the curve (AUC) [8]. Survival curves were plotted for different groups of patients, and curves were compared using the log-rank test. A survival package executed univariate and multivariate Cox analyses [9].Ggplot2 was used for data visualization [10].


### 2.3. Gene Set Enrichment Analysis

Gene set enrichment analysis (GSEA) was used to assess the distributions of predefined gene sets in gene lists sorted by phenotype correlation and to determine the contribution of different genes to phenotype [11, 12]. This analysis was performed using the GSEA 3.0 software and the gene set of “h.all.v6.2.symbols.gmt” from the Molecular Signatures Database. The normalized enrichment score (NES) was obtained from 1000 permutations.


## 2.1. Data Mining of The Cancer Genome Atlas Database

The RNAseq data ofOGDHL and clinical information were downloaded from The Cancer Genome Atlas (TCGA) dataset. No ethical approval was necessary because these are anonymized public datasets.


## 2.2. Statistical Analysis

All data analyses were performed using R (version 3.5.1) [7] and several R extensions. Boxplots were used to display expression of OGDHL mRNA. The chi-squared test was used to evaluate the correlation between OGDHL expression and the clinical features of patients. The pROC package was used to perform ROC analysis, to determine the optimal OGDHL cut-off point and to assess the diagnostic value of OGDHL expression by calculation of the area under the curve (AUC) [8]. Survival curves were plotted for different groups of patients, and curves were compared using the log-rank test. A survival package executed univariate and multivariate Cox analyses [9].Ggplot2 was used for data visualization [10].


## 2.3. Gene Set Enrichment Analysis

Gene set enrichment analysis (GSEA) was used to assess the distributions of predefined gene sets in gene lists sorted by phenotype correlation and to determine the contribution of different genes to phenotype [11, 12]. This analysis was performed using the GSEA 3.0 software and the gene set of “h.all.v6.2.symbols.gmt” from the Molecular Signatures Database. The normalized enrichment score (NES) was obtained from 1000 permutations.


## 3. Results

### 3.1. Patient Characteristics and OGDHL Expression

Table1 shows the clinical characteristics of the 373 liver cancer patients from TCGA dataset, including age, sex, histological type, histologic grade, stage, TNM classification, receipt of radiation therapy, presence of residual tumor, vital status, and relapse. Analysis of OGDHL expression (Figure 1) indicated significantly lower expression in cancerous liver tissues than adjacent normal tissues (P<2.2×10−16). In addition, OGDHL expression was inversely correlated with more advanced histologic grade (P=2.6×10−8), stage (P=0.0014), T classification (P=0.002), M classification (P=0.043), and age (P=0.0016) but positively correlated with longer survival (P=0.035).Table 1
Clinical characteristics of the liver cancer patients.
Characteristics
Number of pts (%)
Age
<55
117 (31.45)
≥55
255 (68.55)
Gender
Female
121 (32.44)
Male
252 (67.56)
Histological type
Fibrolamellar carcinoma
3 (0.8)
Hepatocellular carcinoma
363 (97.32)
Hepatocholangiocarcinoma (mixed)
7 (1.88)
Histologic grade
NA
5 (1.34)
G1
55 (14.75)
G2
178 (47.72)
G3
123 (32.98)
G4
12 (3.22)
Stage
NA
24 (6.43)
I
172 (46.11)
II
87 (23.32)
III
85 (22.79)
IV
5 (1.34)
T classification
NA
2 (0.54)
T1
182 (48.79)
T2
95 (25.47)
T3
80 (21.45)
T4
13 (3.49)
TX
1 (0.27)
N classification
NA
1 (0.27)
N0
253 (67.83)
N1
4 (1.07)
NX
115 (30.83)
M classification
M0
267 (71.58)
M1
4 (1.07)
MX
102 (27.35)
Radiation therapy
NA
25 (6.7)
No
340 (91.15)
Yes
8 (2.14)
Residual tumor
NA
7 (1.88)
R0
326 (87.4)
R1
17 (4.56)
R2
1 (0.27)
RX
22 (5.9)
Vital status
Deceased
130 (34.85)
Living
243 (65.15)
Relapse
No
179 (55.94)
Yes
141 (44.06)
OGDHL
High
270 (72.39)
Low
103 (27.61)Figure 1
Expression ofOGDHL in cancerous vs. normal liver tissues and in groups with different histologic grade, stage, TNM classification, sex, age, and vital status.


### 3.2. Diagnostic Capability of OGDHL Expression and Correlation with Clinical Features

We performed receiver operating characteristic (ROC) analysis to determine the diagnostic value ofOGDHL expression (Figure 2). OGDHL expression had excellent diagnostic value overall (AUC=0.909) and was also able to distinguish noncancerous tissue from stage I cancer (AUC=0.885), stage II cancer (AUC=0.920), stage III cancer (AUC=0.949), and stage IV cancer (AUC=0.998). We also found that low OGDHL expression correlated with more advanced patient age (P=0.009), histologic grade (P=0.000), stage (P=0.015), T classification (P=0.020), and poor survival (P=0.037) (Table 2).Figure 2
ROC analysis of the performance ofOGDHL expression in identification of cancerous vs. normal tissues in all patients and subgroup analysis of patients with different stages of liver cancer.Table 2
Relationship between the clinical features and OGDHL expression in liver cancer patients.
Clinical characteristics
Variable
No. of patients
OGDHL expression
χ2
P value
High
%
Low
%
Age
<55
117
74
27.41
43
42.16
6.802
0.009
≥55
255
196
72.59
59
57.84
Gender
Female
121
86
31.85
35
33.98
0.072
0.788
Male
252
184
68.15
68
66.02
Histological type
Fibrolamellar carcinoma
3
3
1.11
0
0
1.809
0.617
Hepatocellular carcinoma
363
261
96.67
102
99.03
Hepatocholangiocarcinoma (mixed)
7
6
2.22
1
0.97
Histologic grade
G1
55
41
15.47
14
13.59
25.673
0.000
G2
178
147
55.47
31
30.1
G3
123
69
26.04
54
52.43
G4
12
8
3.02
4
3.88
Stage
I
172
135
54
37
37.37
10.116
0.015
II
87
60
24
27
27.27
III
85
51
20.4
34
34.34
IV
5
4
1.6
1
1.01
T classification
T1
182
144
53.73
38
36.89
10.765
0.020
T2
95
64
23.88
31
30.1
T3
80
49
18.28
31
30.1
T4
13
10
3.73
3
2.91
TX
1
1
0.37
0
0
N classification
N0
253
178
65.93
75
73.53
3.519
0.149
N1
4
2
0.74
2
1.96
NX
115
90
33.33
25
24.51
M classification
M0
267
186
68.89
81
78.64
3.523
0.156
M1
4
3
1.11
1
0.97
MX
102
81
30
21
20.39
Radiation therapy
No
340
245
97.22
95
98.96
0.320
0.572
Yes
8
7
2.78
1
1.04
Residual tumor
R0
326
239
90.53
87
85.29
4.018
0.245
R1
17
12
4.55
5
4.9
R2
1
1
0.38
0
0
RX
22
12
4.55
10
9.8
Vital status
Deceased
130
85
31.48
45
43.69
4.371
0.037
Living
243
185
68.52
58
56.31


### 3.3. Correlation of OGDHL Expression with Survival

Survival analysis showed that patients with lowerOGDHL levels had shorter overall survival (OS), and subgroup analysis indicated this relationship also held for patients with grade G1/G2, stage I/II, T3, N0, and M0 cancers (Figure 3). In addition, patients with lower OGDHL levels had shorter relapse-free survival, and subgroup analysis indicated this relationship also held for patients with grade G1/G2, stage III/IV, T1, T3, N0, and M1 cancers (Figure 4).Figure 3
Relationship ofOGDHL expression with OS in all patients and subgroup analysis of patients with different classifications of liver cancer (G1/G2, G3/G4, I/II, III/IV, T1-T4, N0, N1, and M0).Figure 4
Relationship ofOGDHL expression with relapse-free survival in all patients and subgroup analysis of patients with different classifications of liver cancer (G1/G2, G3/G4, I/II, III/IV, T1-T4, N0, and M0).


### 3.4. Low OGDHL as an Independent Risk Factor for Survival

We initially used univariate Cox analysis to select the potential variables for multivariable analysis (Tables3 and 4). The subsequent multivariate Cox regression analysis indicated that low OGDHL expression was an independent risk factor for poor OS (hazard ratio HR=1.75; 95% confidence interval CI=1.2 to 2.54; P=0.003) and poor relapse-free survival (HR=1.58; 95%CI=1.09 to 2.3; P=0.016).Table 3
Univariate analysis and multivariate analysis of liver cancer patients’ overall survival.
Parameters
Univariate analysis
Multivariate analysis
Hazard ratio
95% CI (lower~upper)
P value
Hazard ratio
95% CI (lower-upper)
P value
Age
1
0.69-1.45
0.997
Gender
0.8
0.56-1.14
0.220
Histological type
0.99
0.27-3.66
0.986
Histologic grade
1.04
0.84-1.3
0.698
Stage
1.38
1.15-1.66
0.001
0.83
0.67-1.04
0.105
T classification
1.66
1.39-1.99
0.000
1.84
1.46-2.32
0.000
N classification
0.73
0.51-1.05
0.086
M classification
0.72
0.49-1.04
0.077
Radiation therapy
0.51
0.26-1.03
0.060
Residual tumor
1.42
1.13-1.8
0.003
1.38
1.08-1.77
0.011
OGDHL
1.93
1.34-2.79
0.000
1.75
1.2-2.54
0.003Table 4
Univariate analysis and multivariate analysis of liver cancer patients’ relapse-free survival.
Parameters
Univariate analysis
Multivariate analysis
Hazard ratio
95% CI (lower~upper)
P value
Hazard ratio
95% CI (lower-upper)
P value
Age
0.9
0.63-1.28
0.550
Gender
0.99
0.7-1.41
0.966
Histological type
2.02
0.66-6.24
0.220
Histologic grade
0.98
0.8-1.21
0.883
Stage
1.66
1.38-1.99
0.000
1.09
0.85-1.41
0.497
T classification
1.78
1.49-2.12
0.000
1.69
1.3-2.19
0.000
N classification
0.97
0.67-1.4
0.874
M classification
1.17
0.79-1.74
0.432
Radiation therapy
0.74
0.26-2.16
0.584
Residual tumor
1.28
1.01-1.61
0.042
1.3
1.03-1.66
0.030
OGDHL
1.66
1.15-2.39
0.007
1.58
1.09-2.3
0.016


### 3.5. OGDHL-Related Signaling Pathways

We used GSEA to identify the signaling pathway(s) activated in HCC by comparing data sets that had low and high expression ofOGDHL (Table 5, Figure 5). The results indicate significant differences in the enrichment of the MSigDB Collection (false discovery rate<0.25, nominal P value < 0.05; h.all.v6.2.symbols.gmt). We then identified the most significant signaling pathways based on NES. These results show that E2F targets, the mitotic spindle, and the G2M checkpoint were enriched in the OGDHL low-expression phenotype.Table 5
Gene sets enriched in phenotype high.
NAME
ES
NES
NOMP value
FDRq value
HALLMARK_MITOTIC_SPINDLE
0.608
1.963
0.000
0.027
HALLMARK_G2M_CHECKPOINT
0.763
1.930
0.000
0.019
HALLMARK_E2F_TARGETS
0.748
1.881
0.002
0.020
Notes: gene sets with NOMP value < 0.05 and FDR q value < 0.25 are considered as significant. Abbreviations: FDR: false discovery rate; NES: normalized enrichment score; NOM: nominal.Figure 5
GSEA and identification of the potential targets of OGDHL as E2F, G2M checkpoint and mitotic spindle pathway.


## 3.1. Patient Characteristics and OGDHL Expression

Table1 shows the clinical characteristics of the 373 liver cancer patients from TCGA dataset, including age, sex, histological type, histologic grade, stage, TNM classification, receipt of radiation therapy, presence of residual tumor, vital status, and relapse. Analysis of OGDHL expression (Figure 1) indicated significantly lower expression in cancerous liver tissues than adjacent normal tissues (P<2.2×10−16). In addition, OGDHL expression was inversely correlated with more advanced histologic grade (P=2.6×10−8), stage (P=0.0014), T classification (P=0.002), M classification (P=0.043), and age (P=0.0016) but positively correlated with longer survival (P=0.035).Table 1
Clinical characteristics of the liver cancer patients.
Characteristics
Number of pts (%)
Age
<55
117 (31.45)
≥55
255 (68.55)
Gender
Female
121 (32.44)
Male
252 (67.56)
Histological type
Fibrolamellar carcinoma
3 (0.8)
Hepatocellular carcinoma
363 (97.32)
Hepatocholangiocarcinoma (mixed)
7 (1.88)
Histologic grade
NA
5 (1.34)
G1
55 (14.75)
G2
178 (47.72)
G3
123 (32.98)
G4
12 (3.22)
Stage
NA
24 (6.43)
I
172 (46.11)
II
87 (23.32)
III
85 (22.79)
IV
5 (1.34)
T classification
NA
2 (0.54)
T1
182 (48.79)
T2
95 (25.47)
T3
80 (21.45)
T4
13 (3.49)
TX
1 (0.27)
N classification
NA
1 (0.27)
N0
253 (67.83)
N1
4 (1.07)
NX
115 (30.83)
M classification
M0
267 (71.58)
M1
4 (1.07)
MX
102 (27.35)
Radiation therapy
NA
25 (6.7)
No
340 (91.15)
Yes
8 (2.14)
Residual tumor
NA
7 (1.88)
R0
326 (87.4)
R1
17 (4.56)
R2
1 (0.27)
RX
22 (5.9)
Vital status
Deceased
130 (34.85)
Living
243 (65.15)
Relapse
No
179 (55.94)
Yes
141 (44.06)
OGDHL
High
270 (72.39)
Low
103 (27.61)Figure 1
Expression ofOGDHL in cancerous vs. normal liver tissues and in groups with different histologic grade, stage, TNM classification, sex, age, and vital status.


## 3.2. Diagnostic Capability of OGDHL Expression and Correlation with Clinical Features

We performed receiver operating characteristic (ROC) analysis to determine the diagnostic value ofOGDHL expression (Figure 2). OGDHL expression had excellent diagnostic value overall (AUC=0.909) and was also able to distinguish noncancerous tissue from stage I cancer (AUC=0.885), stage II cancer (AUC=0.920), stage III cancer (AUC=0.949), and stage IV cancer (AUC=0.998). We also found that low OGDHL expression correlated with more advanced patient age (P=0.009), histologic grade (P=0.000), stage (P=0.015), T classification (P=0.020), and poor survival (P=0.037) (Table 2).Figure 2
ROC analysis of the performance ofOGDHL expression in identification of cancerous vs. normal tissues in all patients and subgroup analysis of patients with different stages of liver cancer.Table 2
Relationship between the clinical features and OGDHL expression in liver cancer patients.
Clinical characteristics
Variable
No. of patients
OGDHL expression
χ2
P value
High
%
Low
%
Age
<55
117
74
27.41
43
42.16
6.802
0.009
≥55
255
196
72.59
59
57.84
Gender
Female
121
86
31.85
35
33.98
0.072
0.788
Male
252
184
68.15
68
66.02
Histological type
Fibrolamellar carcinoma
3
3
1.11
0
0
1.809
0.617
Hepatocellular carcinoma
363
261
96.67
102
99.03
Hepatocholangiocarcinoma (mixed)
7
6
2.22
1
0.97
Histologic grade
G1
55
41
15.47
14
13.59
25.673
0.000
G2
178
147
55.47
31
30.1
G3
123
69
26.04
54
52.43
G4
12
8
3.02
4
3.88
Stage
I
172
135
54
37
37.37
10.116
0.015
II
87
60
24
27
27.27
III
85
51
20.4
34
34.34
IV
5
4
1.6
1
1.01
T classification
T1
182
144
53.73
38
36.89
10.765
0.020
T2
95
64
23.88
31
30.1
T3
80
49
18.28
31
30.1
T4
13
10
3.73
3
2.91
TX
1
1
0.37
0
0
N classification
N0
253
178
65.93
75
73.53
3.519
0.149
N1
4
2
0.74
2
1.96
NX
115
90
33.33
25
24.51
M classification
M0
267
186
68.89
81
78.64
3.523
0.156
M1
4
3
1.11
1
0.97
MX
102
81
30
21
20.39
Radiation therapy
No
340
245
97.22
95
98.96
0.320
0.572
Yes
8
7
2.78
1
1.04
Residual tumor
R0
326
239
90.53
87
85.29
4.018
0.245
R1
17
12
4.55
5
4.9
R2
1
1
0.38
0
0
RX
22
12
4.55
10
9.8
Vital status
Deceased
130
85
31.48
45
43.69
4.371
0.037
Living
243
185
68.52
58
56.31


## 3.3. Correlation of OGDHL Expression with Survival

Survival analysis showed that patients with lowerOGDHL levels had shorter overall survival (OS), and subgroup analysis indicated this relationship also held for patients with grade G1/G2, stage I/II, T3, N0, and M0 cancers (Figure 3). In addition, patients with lower OGDHL levels had shorter relapse-free survival, and subgroup analysis indicated this relationship also held for patients with grade G1/G2, stage III/IV, T1, T3, N0, and M1 cancers (Figure 4).Figure 3
Relationship ofOGDHL expression with OS in all patients and subgroup analysis of patients with different classifications of liver cancer (G1/G2, G3/G4, I/II, III/IV, T1-T4, N0, N1, and M0).Figure 4
Relationship ofOGDHL expression with relapse-free survival in all patients and subgroup analysis of patients with different classifications of liver cancer (G1/G2, G3/G4, I/II, III/IV, T1-T4, N0, and M0).


## 3.4. Low OGDHL as an Independent Risk Factor for Survival

We initially used univariate Cox analysis to select the potential variables for multivariable analysis (Tables3 and 4). The subsequent multivariate Cox regression analysis indicated that low OGDHL expression was an independent risk factor for poor OS (hazard ratio HR=1.75; 95% confidence interval CI=1.2 to 2.54; P=0.003) and poor relapse-free survival (HR=1.58; 95%CI=1.09 to 2.3; P=0.016).Table 3
Univariate analysis and multivariate analysis of liver cancer patients’ overall survival.
Parameters
Univariate analysis
Multivariate analysis
Hazard ratio
95% CI (lower~upper)
P value
Hazard ratio
95% CI (lower-upper)
P value
Age
1
0.69-1.45
0.997
Gender
0.8
0.56-1.14
0.220
Histological type
0.99
0.27-3.66
0.986
Histologic grade
1.04
0.84-1.3
0.698
Stage
1.38
1.15-1.66
0.001
0.83
0.67-1.04
0.105
T classification
1.66
1.39-1.99
0.000
1.84
1.46-2.32
0.000
N classification
0.73
0.51-1.05
0.086
M classification
0.72
0.49-1.04
0.077
Radiation therapy
0.51
0.26-1.03
0.060
Residual tumor
1.42
1.13-1.8
0.003
1.38
1.08-1.77
0.011
OGDHL
1.93
1.34-2.79
0.000
1.75
1.2-2.54
0.003Table 4
Univariate analysis and multivariate analysis of liver cancer patients’ relapse-free survival.
Parameters
Univariate analysis
Multivariate analysis
Hazard ratio
95% CI (lower~upper)
P value
Hazard ratio
95% CI (lower-upper)
P value
Age
0.9
0.63-1.28
0.550
Gender
0.99
0.7-1.41
0.966
Histological type
2.02
0.66-6.24
0.220
Histologic grade
0.98
0.8-1.21
0.883
Stage
1.66
1.38-1.99
0.000
1.09
0.85-1.41
0.497
T classification
1.78
1.49-2.12
0.000
1.69
1.3-2.19
0.000
N classification
0.97
0.67-1.4
0.874
M classification
1.17
0.79-1.74
0.432
Radiation therapy
0.74
0.26-2.16
0.584
Residual tumor
1.28
1.01-1.61
0.042
1.3
1.03-1.66
0.030
OGDHL
1.66
1.15-2.39
0.007
1.58
1.09-2.3
0.016


## 3.5. OGDHL-Related Signaling Pathways

We used GSEA to identify the signaling pathway(s) activated in HCC by comparing data sets that had low and high expression ofOGDHL (Table 5, Figure 5). The results indicate significant differences in the enrichment of the MSigDB Collection (false discovery rate<0.25, nominal P value < 0.05; h.all.v6.2.symbols.gmt). We then identified the most significant signaling pathways based on NES. These results show that E2F targets, the mitotic spindle, and the G2M checkpoint were enriched in the OGDHL low-expression phenotype.Table 5
Gene sets enriched in phenotype high.
NAME
ES
NES
NOMP value
FDRq value
HALLMARK_MITOTIC_SPINDLE
0.608
1.963
0.000
0.027
HALLMARK_G2M_CHECKPOINT
0.763
1.930
0.000
0.019
HALLMARK_E2F_TARGETS
0.748
1.881
0.002
0.020
Notes: gene sets with NOMP value < 0.05 and FDR q value < 0.25 are considered as significant. Abbreviations: FDR: false discovery rate; NES: normalized enrichment score; NOM: nominal.Figure 5
GSEA and identification of the potential targets of OGDHL as E2F, G2M checkpoint and mitotic spindle pathway.


## 4. Discussion

Our team previously used TCGA to identify diagnostic and prognostic biomarkers for several cancers [13–19]. In the present study, we found that OGDHL had low expression in liver cancer and that low expression correlated with more advanced patient age, histologic grade, stage, T classification, and shorter survival. In addition, our multivariable analysis indicated that low OGDHL expression was a significant diagnostic and prognostic biomarker for liver cancer.Previous research identified OGDHL as an isoform of 2-oxoglutarate dehydrogenase, which regulated the degradation of glucose and glutamate [3]. An initial study of OGDHL function found enrichment of this protein in the brain but undetectable levels in the heart [2]. Subsequent studies focused on the relationship of OGDHL expression in several cancers and reported low expression in breast cancer [4], cervical cancer [5], and colorectal cancer [6]. Consistent with these results, we found low expression of OGDHL in liver cancer. Moreover, our ROC analysis showed that OGDHL expression had good diagnostic performance for patients with different stages of liver cancer, supporting its clinical use as a diagnostic biomarker. OGDHL expression also gradually decreased as histologic grade increased from G1 to G4, as stage increased from I to III, and as T classification increased from T1 to T3. The reason for the slightly higher expression in patients with the stage IV and T4 liver cancer is unknown, but it may be because we only analyzed a small number of patients with advanced cancer. We also found lower OGDHL expression in deceased than living patients, suggesting that OGDHL expression may be useful as a prognostic indicator.Several previous studies have examined the functions of OGDHL. For example, Bunik and Degtyarev reported that OGDHL was located in the mitochondria (as predicted based on its sequence) and was an isoform of 2-oxoglutarate dehydrogenase [3]. Fujisawa et al. found that OGDHL functioned in adenylate kinase 4- (AK4-) regulated mitochondrial activity [20]. Yoon et al. identified nardilysin (NRD1) as a mitochondrial cochaperone for OGDH [21]. Sherrill et al. reported that certain variants of OGDHL lead to mitochondrial dysfunction and eosinophilic esophagitis [22]. Sen et al. found that OGDHL functioned as an antiproliferative gene and inhibited tumorigenesis via the AKT signaling pathway [5]. In conjunction with our results, this suggests that the downregulation of OGDHL, which alters mitochondrial function and increases cell proliferation, might explain our observation of a correlation of low OGDHL expression with more advanced cancer.Many studies ofOGDHL that examined its regulation of cancer have focused on methylation of its promoter region [4, 6, 23, 24]. However, no previous studies have examined its clinical significance or prognostic value. We found that patients with liver cancer who had lower OGDHL expression had shorter OS and shorter relapse-free survival. Our subgroup analysis indicated that OGDHL had prognostic value for specific groups of patients in predicting OS (G1/G2, I/II, T3, N0, and M0) and in specific group of patients for predicting relapse-free survival (G1/G2, III/IV, T1, T3, N0, and M1). These results suggest that OGDHL may be useful as a prognostic biomarker for liver cancer.This study is the first to identifyOGDHL as a novel diagnostic and prognostic biomarker for liver cancer. The targets of this protein appear to be the mitotic spindle, G2M checkpoint, and E2F. However, a limitation of this study is that we only examined a small number of patients with advanced-stage liver cancer; the cause of higher OGDHL expression in late stage liver cancer patients needs to be explored in the future study.


## 5. Conclusion

In conclusion, we found low expression ofOGDHL in liver cancer and that low expression correlated with advanced patient age, histologic grade, stage, T classification, and poor survival. We also found that OGDHL expression had value as a diagnostic and prognostic indicator of liver cancer and that low OGDHL expression was an independent prognostic risk factor. Our GSEA analysis indicated that the potential targets of OGDHL were the mitotic spindle, G2M checkpoint, and E2F. This study is the first to identify the diagnostic and prognostic value of OGDHL in liver cancer, and our results indicate that OGDHL might be useful as a novel biomarker for liver cancer.These results require verification by studies of larger populations.



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*Source: 9037131-2019-10-17.xml*