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He is a member of various international professional societies and a founding member of the Pakistan Society for Computational Biology.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"185476",title:"Dr.",name:"Mahmood-Ur-",middleName:null,surname:"Rahman",slug:"mahmood-ur-rahman",fullName:"Mahmood-Ur- Rahman",profilePictureURL:"https://mts.intechopen.com/storage/users/185476/images/system/185476.jpg",biography:"Dr. Mahmood-ur-Rahman Ansari is an Assistant Professor of Molecular Biology at Department of Bioinformatics and Biotechnology, GC University – Faisalabad, Pakistan. He obtained his BSc (Hons) in Plant Breeding and Genetics from University of Agriculture, Faisalabad, Pakistan in 2003. He got MPhil and PhD in Molecular Biology from National Centre of Excellence in Molecular Biology, Lahore, Pakistan in 2006 and 2011 respectively. He has published over 50 papers in international peer-reviewed journals in the field of Molecular Biology, Biotechnology, and Bioinformatics. Moreover, he has published more than 10 book chapters and edited 3 books so far. His research group aims to understand the molecular mechanisms of stress tolerance in plants. He is member of various national and international professional societies. 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From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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\n
1. Introduction
\n
Aflatoxin B1 (AFB1) is a type of secondary metabolite of Aspergillus parasiticus and Aspergillus flavus, and frequently contaminates a series of staple foods, such as ground nuts, and maize [1, 2, 3]. Once this type foods contaminated by AFB1 entered into human bodies, it is metabolized into its epoxides consisting of AFB1–8,9-exo-epoxide (AFBEX) and AFB1–8,9-endo-epoxide (AFBEN) by cytochrome P450 (CYP) metabolic system [3]. These products of AFB1, especially AFBEX, are characterized by high reaction, genic toxicity, and carcinogenicity [3]. Evidence from molecular epidemiology and animal models has shown that AFB1 is an important carcinogen inducing hepatocellular carcinoma (HCC) [4, 5, 6, 7, 8, 9, 10]. Mechanically, the carcinogenesis of AFB1-related HCC mainly involves in the formation of DNA damage (including AFB1-DNA adducts, DNA single-strand breaks, DNA double-strands breaks, and gene mutations), the inactivation of such tumor suppressor gene as TP53, and the activation of cancer genes such as Ras [3, 11, 12, 13, 14, 15]. Although some advance in the pathogenesis of AFB1-related HCC has obtained in the past decades [16, 17, 18], it is still far for us to elucidate more detailed mechanisms.
\n
The chromobox 4 (Cbx4) (GenBank accession NO. 8535) consists of six exons and spans about 6.26 kb on chromosome 17q25.3. This gene encodes a 560-amino acid protein which is the important component of polycomb repressive complex 1 (PRC1) [19, 20, 21, 22]. Functionally, CBX4 involves in PRC1-regulated transcription repression and post-translation modification [19, 20, 21, 22]. Recently, increasing evidence has exhibited that the dysregulation of this gene may affect the carcinogenic process of some tumors such as HCC, colorectal cancer, breast cancer, and so on, and may be a significant prognostic biomarker [19, 21, 23, 24, 25, 26, 27, 28, 29]. However, it is not clear whether CBX4 modify the prognosis of AFB1-related HCC. Here, we conducted a hospital-based retrospective study to investigate whether the CBX4 expression in the cancerous tissues is associated with the outcome of HCC related to AFB1 expression in the Guangxi Region, a high AFB1 exposure area.
\n
\n
\n
2. Materials and methods
\n
\n
2.1. Study population
\n
Between January 2009 and December 2012, 428 consecutive patients with histopathologically confirmed hepatocarcinoma were recruited at the Divisions of Oncology and Pathology, the affiliated Hospitals of Guangxi Medical University and Youjiang Medical University for Nationalities. During the recruitment phase, only 5 cases refused to participate in the study (response rate 98.8%). All cases were from high AFB1 exposure areas, including Nanning, Bose, Tiandong, and Tianyang. After informed consent was obtained, surgically removed tumor samples were collected to analyze the amounts of AFB1-DNA adducts and CBX4 protein in the cancerous tissues. Additionally, all corresponding clinicopathological and survival following-up data were also collected in the hospitals as previously described methods [30-32]. In this study, the status of hepatitis B virus (HBV) and hepatitis C virus (HCV) infection was evaluated using serum hepatitis B surface antigen (HBsAg) and anti-HCV, respectively; whereas the grade and stage of tumor was elucidated using the Edmondson and Steiner (ES) grading system and the Barcelona Clinic Liver Cancer (BCLC) staging system, respectively. For survival analyses, the last follow-up day was set on December 31, 2017. The study protocol was carried out according to the approved guidelines by the Institutional Ethics Committee from the Affiliated Hospitals of Youjiang Medical University for Nationalities and Guangxi Medical University.
\n
\n
\n
2.2. Microvessel density (MVD) assay
\n
MVD in the cancerous tissues was assessed using the immunohistochemistry staining of CD31 as our previously descripted [30]. In this study, positive status of MVD was defined as microvessel counts more than 50 per ×200 magnifications.
\n
\n
\n
2.3. AFB1 exposure data
\n
AFB1 exposure levels were evaluated using the amounts of AFB1-DNA adducts in the cancerous tissues as our previously descripted [31, 32]. The amounts of AFB1-DNA adduct were tested using the competitive enzyme-linked immunosorbent assay. In this study, a value than less 1.00 μmol/mol DNA was considered as negative status for AFB1 exposure.
\n
\n
\n
2.4. CBX4 expression assays
\n
The level of CBX4 protein expression in cancerous tissues was elucidated using our previously published immunohistochemistry method [33, 34]. Briefly, the amounts of CBX4 protein were tested using anti-CBX4 antibody and calculated using immunoreactive score system (IRS). In the present study, positive CBX4 protein in cancerous tissues was define as IRS > 4.
\n
\n
\n
2.5. Statistical analysis
\n
Logistic regression model with enter method for variables (including all known clinicopathological features) was used for statistical comparison between groups. The odd ratios (ODs) and corresponding 95% confidence intervals (CIs) were calculated in this model for evaluating the association between clinicopathological features of HCCs and either AFB1 exposure or CBX4 expression. Kaplan–Meier survival method with log-rank test was used for statistical comparisons between different levels of AFB1 expression and CBX4 expression. Multivariate Cox regression model (with retread method based on likelihood ratio test) analyses were performed to calculate the risk strength of independent variates and prognostic values. In this study, all analyses were finished using the SPSS soft version 18.0 (SPSS Inc. Chicago, IL), and a P-value less than 0.05 was defined as statistical significance.
\n
\n
\n
\n
3. Results
\n
\n
3.1. The clinicopathological and survival features of HCC cases
\n
Table 1 gave the clinicopathological characteristics of all cases, and a total of 428 patients with HCC were included in the final analyses. All cases were followed-up more than 5 years to obtain median survival time. During the follow-up period, 261 patients with HCC featured cancer recurrences with 30.00 (22.20–37.80) months of median recurrence-free survival time (MRT), and 270 died with 45.00 (38.98–51.02) months of median overall survival time (MST).
\n
\n
\n
\n
\n\n
\n
Variables
\n
n
\n
%
\n
\n\n\n
\n
Total
\n
428
\n
100.0
\n
\n
\n
Age, years
\n
\n
\n
Mean ± SE
\n
47.9 ± 10.1
\n
—
\n
\n
\n
Range
\n
30–75
\n
—
\n
\n
\n
Sex
\n
\n
\n
Man
\n
290
\n
68.9
\n
\n
\n
Female
\n
138
\n
32.8
\n
\n
\n
Ethnicity
\n
\n
\n
Han
\n
229
\n
54.4
\n
\n
\n
Zhuang
\n
199
\n
47.3
\n
\n
\n
HBV status
\n
\n
\n
HBsAg (−)
\n
113
\n
26.8
\n
\n
\n
HBsAg (+)
\n
315
\n
74.8
\n
\n
\n
HCV status
\n
\n
\n
anti-HCV (−)
\n
378
\n
89.8
\n
\n
\n
anti-HCV (+)
\n
50
\n
11.9
\n
\n
\n
Smoking status
\n
\n
\n
No
\n
315
\n
74.8
\n
\n
\n
Yes
\n
113
\n
26.8
\n
\n
\n
Drinking status
\n
\n
\n
No
\n
304
\n
72.2
\n
\n
\n
Yes
\n
124
\n
29.5
\n
\n
\n
AFP (ng/mL)
\n
\n
\n
≤ 20
\n
154
\n
36.6
\n
\n
\n
> 20
\n
274
\n
65.1
\n
\n
\n
Liver cirrhosis
\n
\n
\n
No
\n
104
\n
24.7
\n
\n
\n
Yes
\n
324
\n
77.0
\n
\n
\n
BCLC stage
\n
\n
\n
A
\n
167
\n
39.7
\n
\n
\n
B
\n
121
\n
28.7
\n
\n
\n
C
\n
140
\n
33.3
\n
\n
\n
Tumor size
\n
\n
\n
≤ 3 cm
\n
211
\n
50.1
\n
\n
\n
> 3 cm
\n
217
\n
51.5
\n
\n
\n
MVD
\n
\n
\n
Negative
\n
192
\n
45.6
\n
\n
\n
Positive
\n
236
\n
56.1
\n
\n
\n
ES grade
\n
\n
\n
Low
\n
226
\n
53.7
\n
\n
\n
High
\n
202
\n
48.0
\n
\n\n
Table 1.
The clinic-pathological features of cases with hepatocellular carcinoma.
Abbreviations: AFP, α-fetoprotein; BCLC, the Barcelona Clinic Liver Cancer staging system; ES, Edmondson and Steiner grading system; HBsAg, hepatitis B surface antigen; HBV, hepatitis B virus; HCV, hepatitis C virus; MVD, microvessel density.
\n
\n
\n
3.2. The effects of AFB1 exposure on the clinicopathological features and the prognosis of HCC cases
\n
In this study, the status of AFB1 exposure was elucidated using the amount of AFB1-DNA adducts in the cancerous tissues. Results from competitive ELISA exhibited the patients with HCC featured a 2.82 ± 1.60 μmol/mol DNA of AFB1 exposure level. To investigate the effects of AFB1 exposure on the clinicopathological features of HCC cases, we defined the amount of AFB1-DNA adducts ≤1.00 μmol/mol DNA as negative AFB1 exposure according to our previous published results [31, 32]. Our results showed that these patients with positive AFB1 status (AFB1-DNA adducts: > 1.00 μmol/mol DNA) had higher BCLC stage (adjusted OR = 2.09 and adjusted 95% CI = 1.04–4.24), bigger tumor size (adjusted OR = 69.06 and adjusted 95% CI = 33.62–141.86), and higher MVD (adjusted OR = 2.56 and adjusted 95% CI = 1.36–4.81) compared with those without positive AFB1 status (OR = 1) (Table 2). Additionally, we also found that the levels of AFB1 exposure were significantly associated with the age of patients with hepatocarcinoma (adjusted OR = 1.80, adjusted 95% CI = 1.22–2.66, and P = 3.07×10−3). However, AFB1 exposure was not correlated with other clinicopathological features of HCCs (Table 2).
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
AFB1 (−)
\n
AFB1 (+)
\n
\n
\n
\n
\n
Variables
\n
n
\n
%
\n
n
\n
%
\n
OR (95% CI)
\n
Ptrend
\n
\n\n\n
\n
Total
\n
244
\n
100.0
\n
184
\n
100.0
\n
—
\n
—
\n
\n
\n
Age (years)
\n
\n
\n
≤ 48
\n
148
\n
60.7
\n
86
\n
46.7
\n
Reference
\n
\n
\n
\n
> 48
\n
96
\n
39.3
\n
98
\n
53.3
\n
1.80 (1.22–2.66)
\n
3.07 × 10−3
\n
\n
\n
Sex
\n
\n
\n
Man
\n
160
\n
65.6
\n
130
\n
70.7
\n
Reference
\n
\n
\n
\n
Female
\n
84
\n
34.4
\n
54
\n
29.3
\n
1.13 (0.59–2.13)
\n
0.72
\n
\n
\n
Ethnicity
\n
\n
\n
Han
\n
124
\n
50.8
\n
105
\n
57.1
\n
Reference
\n
\n
\n
\n
Zhuang
\n
120
\n
49.2
\n
79
\n
42.9
\n
0.99 (0.55–1.78)
\n
0.98
\n
\n
\n
HBsAg
\n
\n
\n
Negative
\n
65
\n
26.6
\n
48
\n
26.1
\n
Reference
\n
\n
\n
\n
Positive
\n
179
\n
73.4
\n
136
\n
73.9
\n
1.19 (0.60–2.34)
\n
0.61
\n
\n
\n
anti-HCV
\n
\n
\n
Negative
\n
217
\n
88.9
\n
161
\n
87.5
\n
Reference
\n
\n
\n
\n
Positive
\n
27
\n
11.1
\n
23
\n
12.5
\n
1.25 (0.51–3.09)
\n
0.62
\n
\n
\n
Smoking status
\n
\n
\n
No
\n
181
\n
74.2
\n
134
\n
72.8
\n
Reference
\n
\n
\n
\n
Yes
\n
63
\n
25.8
\n
50
\n
27.2
\n
0.48 (0.12–1.85)
\n
0.28
\n
\n
\n
Drinking status
\n
\n
\n
No
\n
174
\n
71.3
\n
130
\n
70.7
\n
Reference
\n
\n
\n
\n
Yes
\n
70
\n
28.7
\n
54
\n
29.3
\n
2.61 (0.69–9.89)
\n
0.27
\n
\n
\n
AFP (ng/mL)
\n
\n
\n
≤ 20
\n
82
\n
33.6
\n
72
\n
39.1
\n
Reference
\n
\n
\n
\n
> 20
\n
162
\n
66.4
\n
112
\n
60.9
\n
1.00 (0.55–1.82)
\n
0.99
\n
\n
\n
Liver cirrhosis
\n
\n
\n
No
\n
58
\n
23.8
\n
46
\n
25.0
\n
Reference
\n
\n
\n
\n
Yes
\n
186
\n
76.2
\n
138
\n
75.0
\n
0.84 (0.42–1.69)
\n
0.63
\n
\n
\n
BCLC stage
\n
\n
\n
A
\n
113
\n
46.3
\n
54
\n
29.3
\n
Reference
\n
\n
\n
\n
B
\n
69
\n
28.3
\n
52
\n
28.3
\n
1.27 (0.61–2.61)
\n
0.52
\n
\n
\n
C
\n
62
\n
25.4
\n
78
\n
42.4
\n
2.09 (1.04–4.24)
\n
0.04
\n
\n
\n
Tumor size
\n
\n
\n
≤ 3 cm
\n
197
\n
80.7
\n
14
\n
7.6
\n
Reference
\n
\n
\n
\n
> 3 cm
\n
47
\n
19.3
\n
170
\n
92.4
\n
69.06 (33.62–141.86)
\n
9.36 × 10−31
\n
\n
\n
MVD
\n
\n
\n
Negative
\n
118
\n
48.4
\n
74
\n
40.2
\n
Reference
\n
\n
\n
\n
Positive
\n
126
\n
51.6
\n
110
\n
59.8
\n
2.56 (1.36–4.81)
\n
3.46 × 10−3
\n
\n
\n
ES grade
\n
\n
\n
Low
\n
129
\n
52.9
\n
97
\n
52.7
\n
Reference
\n
\n
\n
\n
High
\n
115
\n
47.1
\n
87
\n
47.3
\n
1.52 (0.64–2.07)
\n
0.64
\n
\n\n
Table 2.
The association between AFB1 exposure and clinic-pathological features of hepatocellular carcinoma cases.
Abbreviations: AFP, α-fetoprotein; BCLC, the Barcelona Clinic Liver Cancer staging system; ES, Edmondson and Steiner grading system; HBsAg, hepatitis B surface antigen; HBV, hepatitis B virus; HCV, hepatitis C virus; MVD, microvessel density.
\n
Next, we investigated the effects of AFB1 exposure on the HCC prognosis using Kaplan–Meier survival model (Figure 1A). Results exhibited that HCC cases with negative AFB1 status (AFB1-DNA adducts: ≤ 1.00 μmol/mol DNA) featured longer median overall survival time (MST) [69.00 (55.41–82.59) months] and median tumor reoccurrence-free survival time (MRT) [70.00 (44.93–95.07) months] compared with those with positive AFB1 status [20.00 (13.04–26.96) months for MST and 13.00 (9.54–16.46) months for MRT, respectively].
\n
Figure 1.
Both AFB1 exposure and CBX4 expression significantly correlating with hepatocellular carcinoma. AFB1 exposure levels were elucidated using the amount of AFB1-DNA adducts in the cancerous tissues. The CBX4 expression in cancerous tissues from 428 patients with hepatocellular carcinoma was tested using immunohistochemistry technique based on immunoreactive score system (IRS). To analyze, the levels of CBX4 expression were divided into two groups: Negative group (IRS ≤ 4) and positive group (IRS > 4). AFB1 exposure (A) and CBX4 expression (B) are associated with overall survival (left) and tumor recurrence-free survival (right) of hepatocellular carcinoma. Cumulative hazard function was plotted by Kaplan–Meier’s methodology, and P value was calculated with two-sided log-rank tests. Abbreviations: CBX4, chromobox 4; MST, median overall survival time; MRT, median tumor recurrence-free survival time; CI, confidence interval.
\n
\n
\n
3.3. The effects of CBX4 expression on the clinicopathological features and the prognosis of HCC cases
\n
In the present, the levels of CBX4 protein in the cancerous tissues were amounted using immunohistochemistry technique with IRS counting system and the median IRS value was 5.58 for all cases with hepatocarcinoma. According to the results from the CBX4 expression in cancerous tissues based on a large sample, IRS > 4 was regarded as positive CBX4 status. Table 3 summarized the association between CBX4 expression in the cancerous tissues and the clinicopathological features, and results from multivariable logistic regression models proved that the levels of CBX4 expression were significantly related to increasing risk of liver cirrhosis (OR = 1.75 and 95% CI = 1.07–2.88), higher tumor stage (OR = 2.02 and 95% CI = 1.23–3.33), and increasing MVD (OR = 2.66 and 95% CI = 1.74–4.07). However, CBX4 expression levels did not affect other clinicopathological features such as tumor size, grade, AFP, and so on.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
CBX4 (−)
\n
CBX4 (+)
\n
\n
\n
\n\n\n
\n
Variables
\n
n
\n
%
\n
n
\n
%
\n
OR (95% CI)
\n
Ptrend
\n
\n
\n
Total
\n
233
\n
100.0
\n
195
\n
100.0
\n
—
\n
—
\n
\n
\n
Age (years)
\n
\n
\n
≤ 48
\n
136
\n
58.4
\n
98
\n
50.3
\n
Reference
\n
\n
\n
\n
> 48
\n
97
\n
41.6
\n
97
\n
49.7
\n
1.35 (0.89–2.05)
\n
0.67
\n
\n
\n
Sex
\n
\n
\n
Man
\n
160
\n
68.7
\n
130
\n
66.7
\n
Reference
\n
\n
\n
\n
Female
\n
73
\n
31.3
\n
65
\n
33.3
\n
1.14 (0.73–1.78)
\n
0.57
\n
\n
\n
Ethnicity
\n
\n
\n
Han
\n
121
\n
51.9
\n
108
\n
55.4
\n
Reference
\n
\n
\n
\n
Zhuang
\n
112
\n
48.1
\n
87
\n
44.6
\n
0.96 (0.63–1.46)
\n
0.85
\n
\n
\n
HBsAg
\n
\n
\n
Negative
\n
65
\n
27.9
\n
48
\n
24.6
\n
Reference
\n
\n
\n
\n
Positive
\n
168
\n
72.1
\n
148
\n
75.9
\n
1.17 (0.73–1.89)
\n
0.51
\n
\n
\n
anti-HCV
\n
\n
\n
Negative
\n
209
\n
89.7
\n
169
\n
86.7
\n
Reference
\n
\n
\n
\n
Positive
\n
24
\n
10.3
\n
26
\n
13.3
\n
1.43 (0.74–2.75)
\n
0.29
\n
\n
\n
Smoking status
\n
\n
\n
No
\n
176
\n
75.5
\n
139
\n
71.3
\n
Reference
\n
\n
\n
\n
Yes
\n
57
\n
24.5
\n
56
\n
28.7
\n
1.04 (0.37–2.93)
\n
0.94
\n
\n
\n
Drinking status
\n
\n
\n
No
\n
172
\n
73.8
\n
132
\n
67.7
\n
Reference
\n
\n
\n
\n
Yes
\n
61
\n
26.2
\n
63
\n
32.3
\n
1.40 (0.51–3.83)
\n
0.51
\n
\n
\n
AFP (ng/mL)
\n
\n
\n
≤ 20
\n
84
\n
36.1
\n
70
\n
35.9
\n
Reference
\n
\n
\n
\n
> 20
\n
149
\n
63.9
\n
125
\n
64.1
\n
1.11 (0.72–1.70)
\n
0.64
\n
\n
\n
Liver cirrhosis
\n
\n
\n
No
\n
69
\n
29.6
\n
35
\n
17.9
\n
Reference
\n
\n
\n
\n
Yes
\n
164
\n
70.4
\n
160
\n
82.1
\n
1.75 (1.07–2.88)
\n
0.03
\n
\n
\n
BCLC stage
\n
\n
\n
A
\n
112
\n
48.1
\n
55
\n
28.2
\n
Reference
\n
\n
\n
\n
B
\n
58
\n
24.9
\n
63
\n
32.3
\n
1.94 (1.16–3.24)
\n
0.01
\n
\n
\n
C
\n
63
\n
27.0
\n
77
\n
39.5
\n
2.02 (1.23–3.33)
\n
5.79×10−3
\n
\n
\n
Tumor size
\n
\n
\n
≤ 3 cm
\n
121
\n
51.9
\n
90
\n
46.2
\n
Reference
\n
\n
\n
\n
> 3 cm
\n
112
\n
48.1
\n
105
\n
53.8
\n
1.24 (0.81–1.88)
\n
0.33
\n
\n
\n
MVD
\n
\n
\n
Negative
\n
131
\n
56.2
\n
61
\n
31.3
\n
Reference
\n
\n
\n
\n
Positive
\n
102
\n
43.8
\n
134
\n
68.7
\n
2.66 (1.74–4.07)
\n
6.65×10−6
\n
\n
\n
ES grade
\n
\n
\n
Low
\n
132
\n
56.7
\n
94
\n
48.2
\n
Reference
\n
\n
\n
\n
High
\n
101
\n
43.3
\n
101
\n
51.8
\n
1.39 (0.92–2.11)
\n
0.12
\n
\n\n
Table 3.
The correlation between CBX4 expression and clinical pathological features of hepatocellular carcinoma.
Abbreviations: AFP, α-fetoprotein; BCLC, the Barcelona Clinic Liver Cancer staging system; CBX4, chromobox 4; ES, Edmondson and Steiner grading system; HBsAg, hepatitis B surface antigen; HBV, hepatitis B virus; HCV, hepatitis C virus; MVD, microvessel density.
\n
Results from Kaplan–Meier survival analyses further displayed that HCC patients with positive status of CBX4 protein expression had short MST [22.00 (18.00–26.00) months] and MRT [16.00 (10.88–21.12) months] compared with those with negative-status CBX4 protein [69.00 (52.75–85.25) months for MST and 48.00 (23.69–72.31) months for MRT, respectively] (Figure 1B). Taken together, CBX4 expression in the cancerous might be an important biomarker for HCC prognosis.
\n
\n
\n
3.4. The joint effects of AFB1 exposure and CBX4 expression on HCC prognosis
\n
Given that both AFB1 exposure and CBX4 expression modified HCC outcome, we questioned whether CBX4 expression interacted with AFB1 expression, and whether this interaction affected the prognosis of hepatocarcinoma. First, we analyzed the joint effects of AFB1 exposure and CBX4 expression on the prognosis of patients with HCC using Kaplan–Meier survival model (Figure 2). In this model, the combination of AFB1 exposure and CBX4 expression was divided into four groups: cases with negative-AFB1 and negative-CBX4 status (AC-1), cases with negative-AFB1 and positive-CBX4 status (AC-2), cases with positive-AFB1 and negative-CBX4 status (AC-3), and cases with positive-AFB1 and positive-CBX4 status (AC-4). We found MST and MRT gradually decreased from AC-1 to AC-4 (89.00–11.00 months for MST and more than 125.00–7.00 months for MRT, respectively) (Figure 2A and B).
\n
Figure 2.
Survival analysis of CBX4 expression binding AFB1 exposure levels. The combination of CBX4 expression and AFB1 exposure was divided into 4 strata: Cases with negative-AFB1 and negative-CBX4 status (AC-1), cases with negative-AFB1 and positive-CBX4 status (AC-2), cases with positive-AFB1 and negative-CBX4 status (AC-3), and cases with positive-AFB1 and positive-CBX4 status (AC-4). This kind of joint analyses showed that interactive effects on the overall survival (A) and tumor recurrence-free survival (B) of patients with hepatocarcinoma. Cumulative hazard function was plotted by Kaplan–Meier’s methodology, and P value was calculated with two-sided log-rank tests. Abbreviations: CBX4, chromobox 4; MST, median overall survival time; MRT, median tumor recurrence-free survival time; CI, confidence interval.
\n
We next finished multivariable Cox regression analyses based on the retread method with likelihood ratio test (including significant variables and all kinds of possible interactive variables) (Table 4), and found both AFB1 exposure and CBX4 expression in the cancerous tissues were independent prognostic factors. Furthermore, we also observed that AFB1 exposure significantly and multiplicatively interacted with CBX4 protein expression (interactive values, 1.98 for overall survival and 1.94 for tumor reoccurrence-free survival, respectively).
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
OS
\n
RFS
\n
\n\n\n
\n
Variables
\n
HR (95%CI)
\n
Ptrend
\n
HR (95%CI)
\n
Ptrend
\n
\n
\n
AFB1
\n
2.09 (1.64–2.65)
\n
2.34 × 10−9
\n
2.29 (1.79–2.93)
\n
3.82 × 10−11
\n
\n
\n
CBX4
\n
1.76 (1.38–2.24)
\n
4.66 × 10−6
\n
1.80 (1.41–2.30)
\n
3.12 × 10−6
\n
\n
\n
AFB1 × CBX4
\n
1.98 (1.61–2.59)
\n
9.43 × 10−7
\n
1.94 (1.58–2.54)
\n
8.17 × 10−5
\n
\n\n
Table 4.
The effects of AFB1 and CBX4 expression on the prognosis of cases with hepatocellular carcinoma.
HR and corresponding 95% CI was calculated using multivariable Cox regression model (with retread method based on likelihood ratio test). Abbreviations: AFB1, aflatoxin B1; CBX4, chromobox 4; OS, overall survival; RFS, tumor reoccurrence-free survival; HR, hazard ratio; CI, confidence interval.
\n
\n
\n
\n
4. Discussion
\n
In Guangxi Zhuang Autonomous Region, HCC is the most malignant disease. In the past decades, the annual incidence and death rate (AIR and ADR) of hepatocarcinoma in this area has been reported to remarkably increase (up to about 100–200 per 10,000 for AIR about 50 per 10,000 for ADR) [1]. Lots of epidemiological studies have shown that AFB1 exposure is the most important cause for this high AIR and ADR [1]. AFB1 is a known I-type chemical carcinogen produced by Aspergillus parasiticus and Aspergillus flavus, and has been proved to involve in the carcinogenesis and progression of HCC [4, 5, 6, 7, 8, 9, 10]. This carcinogenicity of AFB1 mainly results from its metabolic product binding to DNA and inducing DNA damage. Among DNA damage types induced by AFB1, AFB1-DNA adducts are very important, because of its non-enzymatic, time-dependent, and apparent persistent characteristics in the genomic DNA strands [3, 35]. Our previous studies have exhibited that AFB1-DNA adducts, especially from liver tissues, are highly associated not only with increasing HCC risk, but with the poor prognosis of HCC [2, 31, 36, 37, 38, 39]. Here, our data displayed that increasing levels of AFB1 exposure significantly correlated with higher tumor stage, increasing tumor size, and higher MVD; furthermore, AFB1 was also poor prognostic marker for HCC. Taken together, these data suggest that AFB1 may involve in the startup and progression of HCC.
\n
Because several previous studies have exhibited that CBX4 can progress tumorigenesis via several signal pathways, including CBX4/HIF-1α/VEGF pathway [20, 25, 26, 34], CBX4/HDAC3/Runx2 pathway [21], CBX4/P63 pathway [22], CBX4/miR-195 pathway [40], CBX4/CtIP pathway [41], and CBX4/P53 pathway [42, 43], here we investigated the effects of CXB4 expression on HCC outcome. We not only found that increasing CBX4 expression in the cancerous tissues is a poor prognostic biomarker for HCC, but this increasing expression is associated with clinicopathological features such as tumor size, tumor stage, and angiogenesis. Supporting our findings, several recent reports further prove that CBX4 can govern the several biofunctions of HCC, including proliferation, invasion and metastasis, angiogenesis, and metastasis [20, 25, 26, 34, 40, 44].
\n
Noticeably, some evidence of the joint effects of CBX4 and AFB1 on HCC outcome was observed in the prognostic analyses based on the gene-environmental joint effects. Our results showed that CBX4 expression significantly and multiplicatively interacted with AFB1 exposure levels, and that this multiplicative interaction remarkably increased the death risk and tumor reoccurrence risk of patients with HCC. Recently, two studies from high AFB1 exposure areas have also reported that the dysregulation of CBX4 in the cancerous tissues from patients with hepatocarcinoma increases MVD, promotes angiogenesis, and increases sensitivity of HCC cells on anti-cancer drugs [33, 34]. Altogether, these results are indicative of the angiogenesis induced by CBX4 involving in the progression of AFB1-related HCC.
\n
In summary, our present study proposes that CBX4 expression in the cancerous tissues can act as a valuable biomarker for AFB1-related HCC. However, several limitations confine the value of this study. First, because of the hospital-based retrospective design, selective bias may take place. Second, because liver damage itself affects AFB1 metabolite and may increase the amount of AFB1-DNA adducts, the prognostic and interactive values of AFB1 and CBX4 may be underestimated. Finally, we did not do functional and mechanical analyses. Therefore, detailed functional analyses deserve further evaluation on the basis of the foresighted design and the combination of AFB1 and CBX4.
\n
\n
Conflicts of interest and source of funding
\n
The authors declare no competing financial interests. This study was supported in part by the National Natural Science Foundation of China (Nos. 81,760,502, 81,572,353, 81,372,639, 81,472,243, 81,660,495, and 81,460,423), the Innovation Program of Guangxi Municipal Education Department (Nos. 201204LX674 and 201204LX324), Innovation Program of Guangxi Health Department (No. Z2013781), the Natural Science Foundation of Guangxi (Nos. 2017GXNSFAA198002, 2017GXNSFGA198002, 2016GXNSFDA380003, 2015GXNSFAA139223, 2013GXNSFAA019251, 2014GXNSFDA118021, and 2014GXNSFAA118144), Research Program of Guangxi “Zhouyue Scholar” (No. 2017–2038), Research Program of Guangxi Specially invited Expert (No. 2017-6th), Research Program of Guangxi Clinic Research Center of Hepatobiliary Diseases (No.AD17129025), and Open Research Program from Molecular Immunity Study Room Involving in Acute & Severe Diseases in Guangxi Colleges and Universities (Nos. kfkt20160062 and kfkt20160063).\n
Abbreviations
\n\nAFB1\n\n
aflatoxin B1
\n\n\n\nAFBEX\n\n
AFB1–8,9-exo-epoxide
\n\n\n\nAFBEN\n\n
AFB1–8,9-endo-epoxide
\n\n\n\nBCLC\n\n
The Barcelona Clinic Liver Cancer
\n\n\n\nCBX4\n\n
chromobox 4
\n\n\n\nCI\n\n
confidence interval
\n\n\n\nCYP\n\n
cytochrome P450
\n\n\n\nES\n\n
The Edmondson and Steiner
\n\n\n\nHBV\n\n
hepatitis B virus
\n\n\n\nHCC\n\n
hepatocellular carcinoma
\n\n\n\nHCV\n\n
hepatitis C virus
\n\n\n\nHBsAg\n\n
hepatitis B surface antigen
\n\n\n\nIRS\n\n
immunoreactive score system
\n\n\n\nMRT\n\n
median tumor reoccurrence-free survival time
\n\n\n\nMST\n\n
median overall survival time
\n\n\n\nMVD\n\n
microvessel density
\n\n\n\nOD\n\n
odd ratio.
\n\n\n
\n
\n
\n\n',keywords:"CBX4, AFB1, HCC, prognosis",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/61893.pdf",chapterXML:"https://mts.intechopen.com/source/xml/61893.xml",downloadPdfUrl:"/chapter/pdf-download/61893",previewPdfUrl:"/chapter/pdf-preview/61893",totalDownloads:426,totalViews:135,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"January 30th 2018",dateReviewed:"May 10th 2018",datePrePublished:"November 5th 2018",datePublished:"December 5th 2018",dateFinished:null,readingETA:"0",abstract:"Background: Previous studies have shown that chromobox 4 (CBX4) expression may involve in the progression of liver cancer, however, it is unclear whether it affects the prognosis of hepatocellular carcinoma (HCC) related to aflatoxin B1 (AFB1).",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/61893",risUrl:"/chapter/ris/61893",book:{slug:"cancer-prognosis"},signatures:"Qun-Ying Su, Jun Lu, Xiao-Ying Huang, Jin-Guang Yao, Xue-Min\nWu, Bing-Chen Huang, Chao Wang, Qiang Xia and Xi-Dai Long",authors:[{id:"202142",title:"Prof.",name:"Xi-Dai",middleName:null,surname:"Long",fullName:"Xi-Dai Long",slug:"xi-dai-long",email:"sjtulongxd@263.net",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1. Study population",level:"2"},{id:"sec_3_2",title:"2.2. Microvessel density (MVD) assay",level:"2"},{id:"sec_4_2",title:"2.3. AFB1 exposure data",level:"2"},{id:"sec_5_2",title:"2.4. CBX4 expression assays",level:"2"},{id:"sec_6_2",title:"2.5. Statistical analysis",level:"2"},{id:"sec_8",title:"3. Results",level:"1"},{id:"sec_8_2",title:"3.1. The clinicopathological and survival features of HCC cases",level:"2"},{id:"sec_9_2",title:"3.2. The effects of AFB1 exposure on the clinicopathological features and the prognosis of HCC cases",level:"2"},{id:"sec_10_2",title:"3.3. The effects of CBX4 expression on the clinicopathological features and the prognosis of HCC cases",level:"2"},{id:"sec_11_2",title:"3.4. The joint effects of AFB1 exposure and CBX4 expression on HCC prognosis",level:"2"},{id:"sec_13",title:"4. Discussion",level:"1"},{id:"sec_13_2",title:"Conflicts of interest and source of funding",level:"2"}],chapterReferences:[{id:"B1",body:'Wu XM, Xi ZF, Lu J, Wang XZ, Zhang TQ, Huang XY, Yao JG, Wang C, Wei ZH, Luo CY, Huang BC, Xu QQ, Yang WP, Xia Q, Long XD. Genetic single nucleotide polymorphisms (GSNPs) in the DNA repair genes and hepatocellular carcinoma related to aflatoxin B1 among Guangxiese population. In: Parine NR, editor. Genetic Polymorphisms. Vol. 1. Rijeka, Croatia: InTech; 2017. pp. 97-119. DOI: 10.5772/intechopen.69530\n'},{id:"B2",body:'Long XD, Yao JD, Yang Q, Huang CH, Liao P, Nong LG, Tang YJ, Huang XY, Wang C, Wu XM, Huang BC, Ban FZ, Zeng LX, Ma Y, Zhai B, Zhang JQ, Xue F, Lu CX, Xia Q. Polymorphisms of DNA repair genes and toxicological effects of aflatoxin B1 exposure. In: Faulkner AG, editor. Aflatoxins: Food Sources, Occurrence and Toxicological Effects. 1st ed. New York: Nova Science Publishers; 2014. pp. 125-156. DOI: 978-1-63117-298-4\n'},{id:"B3",body:'Kew MC. Aflatoxins as a cause of hepatocellular carcinoma. Journal of Gastrointestinal and Liver Diseases. 2013;22:305-310\n'},{id:"B4",body:'Rushing BR, Selim MI. Adduction to arginine detoxifies aflatoxin B1 by eliminating genotoxicity and altering in vitro toxicokinetic profiles. Oncotarget. 2018;9:4559-4570. DOI: 10.18632/oncotarget.23382\n'},{id:"B5",body:'Xiang X, Qin HG, You XM, Wang YY, Qi LN, Ma L, Xiang BD, Zhong JH, Li LQ. Expression of P62 in hepatocellular carcinoma involving hepatitis B virus infection and aflatoxin B1 exposure. Cancer Medicine. 2017;6:2357-2369. DOI: 10.1002/cam4.1176\n'},{id:"B6",body:'Weng MW, Lee HW, Choi B, Wang HT, Hu Y, Mehta M, Desai D, Amin S, Zheng Y, Tang MS. AFB1 hepatocarcinogenesis is via lipid peroxidation that inhibits DNA repair, sensitizes mutation susceptibility and induces aldehyde-DNA adducts at p53 mutational hotspot codon 249. Oncotarget. 2017;8:18213-18226. DOI: 10.18632/oncotarget.15313\n'},{id:"B7",body:'Narkwa PW, Blackbourn DJ, Mutocheluh M. Aflatoxin B1 inhibits the type 1 interferon response pathway via STAT1 suggesting another mechanism of hepatocellular carcinoma. Infectious Agents and Cancer. 2017;12:17. DOI: 10.1186/s13027-017-0127-8\n'},{id:"B8",body:'Maurya BK, Trigun SK. Fisetin attenuates AKT associated growth promoting events in AflatoxinB1 induced hepatocellular carcinoma. Anti-Cancer Agents in Medicinal Chemistry. 2017. DOI: 10.2174/1871520618666171229223335. E-pub Ahead of Print\n'},{id:"B9",body:'Huang MN, Yu W, Teoh WW, Ardin M, Jusakul A, Ng AWT, Boot A, Abedi-Ardekani B, Villar S, Myint SS, Othman R, Poon SL, Heguy A, Olivier M, Hollstein M, Tan P, Teh BT, Sabapathy K, Zavadil J, Rozen SG. Genome-scale mutational signatures of aflatoxin in cells, mice, and human tumors. Genome Research. 2017;27:1475-1486. 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Aflatoxins and food pathogens: Impact of biologically active aflatoxins and their control strategies. Journal of the Science of Food and Agriculture. 2017;97:1698-1707. DOI: 10.1002/jsfa.8144\n'},{id:"B13",body:'Sarma UP, Bhetaria PJ, Devi P, Varma A. Aflatoxins: Implications on health. Indian Journal of Clinical Biochemistry. 2017;32:124-133. DOI: 10.1007/s12291-017-0649-2\n'},{id:"B14",body:'Kowalska A, Walkiewicz K, Koziel P, Muc-Wierzgon M. Aflatoxins: Characteristics and impact on human health. Postȩpy Higieny i Medycyny Doświadczalnej (Online). 2017;71:315-327. DOI: 10.5604/01.3001.0010.3816\n'},{id:"B15",body:'Woloshuk CP, Shim WB. Aflatoxins, fumonisins, and trichothecenes: A convergence of knowledge. FEMS Microbiology Reviews. 2013;37:94-109. DOI: 10.1111/1574-6976.12009\n'},{id:"B16",body:'Khlangwiset P, Shephard GS, Wu F. Aflatoxins and growth impairment: A review. Critical Reviews in Toxicology. 2011;41:740-755. DOI: 10.3109/10408444.2011.575766\n'},{id:"B17",body:'Wu Q, Jezkova A, Yuan Z, Pavlikova L, Dohnal V, Kuca K. Biological degradation of aflatoxins. Drug Metabolism Reviews. 2009;41:1-7. DOI: 10.1080/03602530802563850\n'},{id:"B18",body:'Villar S, Ortiz-Cuaran S, Abedi-Ardekani B, Gouas D, Nogueira da Costa A, Plymoth A, Khuhaprema T, Kalalak A, Sangrajrang S, Friesen MD, Groopman JD, Hainaut P. Aflatoxin-induced TP53 R249S mutation in hepatocellular carcinoma in Thailand: Association with tumors developing in the absence of liver cirrhosis. PLoS One. 2012;7:e37707. DOI: 10.1371/journal.pone.0037707\n'},{id:"B19",body:'Zeng JS, Zhang ZD, Pei L, Bai ZZ, Yang Y, Yang H, Tian QH. CBX4 exhibits oncogenic activities in breast cancer via Notch1 signaling. The International Journal of Biochemistry & Cell Biology. 2018;95:1-8. DOI: 10.1016/j.biocel.2017.12.006\n'},{id:"B20",body:'Yang J, Cheng D, Zhu B, Zhou S, Ying T, Yang Q. Chromobox homolog 4 is positively correlated to tumor growth, survival and activation of HIF-1alpha signaling in human osteosarcoma under normoxic condition. Journal of Cancer. 2016;7:427-435. DOI: 10.7150/jca.13749\n'},{id:"B21",body:'Wang X, Li L, Wu Y, Zhang R, Zhang M, Liao D, Wang G, Qin G, Xu RH, Kang T. CBX4 suppresses metastasis via recruitment of HDAC3 to the Runx2 promoter in colorectal carcinoma. Cancer Research. 2016;76:7277-7289. DOI: 10.1158/0008-5472.CAN-16-2100\n'},{id:"B22",body:'Cohen I, Ezhkova E. Cbx4: A new guardian of p63\'s domain of epidermal control. The Journal of Cell Biology. 2016;212:9-11. DOI: 10.1083/jcb.201512032\n'},{id:"B23",body:'Liang YK, Lin HY, Chen CF, Zeng. Prognostic values of distinct CBX family members in breast cancer. Oncotarget. 2017;8:92375-92387. DOI: 10.18632/oncotarget.21325\n'},{id:"B24",body:'Lin FM, Kumar S, Ren J, Karami S, Bahnassy S, Li Y, Zheng X, Wang J, Bawa-Khalfe T. SUMOylation of HP1alpha supports association with ncRNA to define responsiveness of breast cancer cells to chemotherapy. Oncotarget. 2016;7:30336-30349. DOI: 10.18632/oncotarget.8733\n'},{id:"B25",body:'Mei Z, Jiao H, Wang W, Li J, Chen G, Xu Y. Polycomb chromobox 4 enhances migration and pulmonary metastasis of hepatocellular carcinoma cell line MHCC97L. Science China. Life Sciences. 2014;57:610-617. DOI: 10.1007/s11427-014-4663-9\n'},{id:"B26",body:'Li J, Xu Y, Jiao H, Wang W, Mei Z, Chen G. Sumoylation of hypoxia inducible factor-1alpha and its significance in cancer. Science China. Life Sciences. 2014;57:657-664. DOI: 10.1007/s11427-014-4685-3\n'},{id:"B27",body:'Oh Y, Chung KC. Small ubiquitin-like modifier (SUMO) modification of zinc finger protein 131 potentiates its negative effect on estrogen signaling. The Journal of Biological Chemistry. 2012;287:17517-17529. DOI: 10.1074/jbc.M111.336354\n'},{id:"B28",body:'Ismail IH, Gagne JP, Caron MC, McDonald D, Xu Z, Masson JY, Poirier GG, Hendzel MJ. CBX4-mediated SUMO modification regulates BMI1 recruitment at sites of DNA damage. Nucleic Acids Research. 2012;40:5497-5510. DOI: 10.1093/nar/gks222\n'},{id:"B29",body:'Vandamme J, Volkel P, Rosnoblet C, Le Faou P, Angrand PO. Interaction proteomics analysis of polycomb proteins defines distinct PRC1 complexes in mammalian cells. Molecular & Cellular Proteomics. 2011;10:M110-M002642. DOI: 10.1074/mcp.M110.002642\n'},{id:"B30",body:'Liu YX, Long XD, Xi ZF, Ma Y, Huang XY, Yao JG, Wang C, Xing TY, Xia Q. MicroRNA-24 modulates aflatoxin B1-related hepatocellular carcinoma prognosis and tumorigenesis. BioMed Research International. 2014;2014:482926. DOI: 10.1155/2014/482926\n'},{id:"B31",body:'Long XD, Yao JG, Zeng Z, Ma Y, Huang XY, Wei ZH, Liu M, Zhang JJ, Xue F, Zhai B, Xia Q. Polymorphisms in the coding region of X-ray repair complementing group 4 and aflatoxin B1-related hepatocellular carcinoma. Hepatology. 2013;58:171-181. DOI: 10.1002/hep.26311\n'},{id:"B32",body:'Long XD, Ma Y, Huang HD, Yao JG, Qu de Y, Lu YL. Polymorphism of XRCC1 and the frequency of mutation in codon 249 of the p53 gene in hepatocellular carcinoma among Guangxi population, china. Molecular Carcinogenesis. 2008;47:295-300. DOI: 10.1002/mc.20384\n'},{id:"B33",body:'Jiao HK, Xu Y, Li J, Wang W, Mei Z, Long XD, Chen GQ. Prognostic significance of Cbx4 expression and its beneficial effect for transarterial chemoembolization in hepatocellular carcinoma. Cell Death & Disease. 2015;6:e1689. DOI: 10.1038/cddis.2015.57\n'},{id:"B34",body:'Li J, Xu Y, Long XD, Wang W, Jiao HK, Mei Z, Yin QQ, Ma LN, Zhou AW, Wang LS, Yao M, Xia Q, Chen GQ. Cbx4 governs HIF-1alpha to potentiate angiogenesis of hepatocellular carcinoma by its SUMO E3 ligase activity. Cancer Cell. 2014;25:118-131. DOI: 10.1016/j.ccr.2013.12.008\n'},{id:"B35",body:'Kensler TW, Roebuck BD, Wogan GN, Groopman JD. Aflatoxin: A 50-year odyssey of mechanistic and translational toxicology. Toxicological Sciences. 2011;120(Suppl 1):S28-S48. DOI: 10.1093/toxsci/kfq283\n'},{id:"B36",body:'Yao JG, Huang XY, Long XD. Interaction of DNA repair gene polymorphisms and aflatoxin B1 in the risk of hepatocellular carcinoma. International Journal of Clinical and Experimental Pathology. 2014;7:6231-6244. DOI: 10.2016/1936-2625.25337275\n'},{id:"B37",body:'Long XD, Zhao D, Wang C, Huang XY, Yao JG, Ma Y, Wei ZH, Liu M, Zeng LX, Mo XQ, Zhang JJ, Xue F, Zhai B, Xia Q. Genetic polymorphisms in DNA repair genes XRCC4 and XRCC5 and aflatoxin B1-related hepatocellular carcinoma. Epidemiology. 2013;24:671-681. DOI: 10.1097/EDE.0b013e31829d2744\n'},{id:"B38",body:'Long XD, Yao JG, Zeng Z, Huang CH, Huang ZS, Huang YZ, Ban FZ, Huang XY, Yao LM, Fan LD, Fu GH. DNA repair capacity-related to genetic polymorphisms of DNA repair genes and aflatoxin B1-related hepatocellular carcinoma among Chinese population. In: Kruman I, editor. DNA Repair. Rijeka, Croatia: InTech; 2011. pp. 505-524. DOI: 10.5772/20792\n'},{id:"B39",body:'Long XD, Ma Y, Zhou YF, Ma AM, Fu GH. Polymorphism in xeroderma pigmentosum complementation group C codon 939 and aflatoxin B1-related hepatocellular carcinoma in the Guangxi population. Hepatology. 2010;52:1301-1309. DOI: 10.1002/hep.23807\n'},{id:"B40",body:'Zheng C, Li J, Wang Q, Liu W, Zhou J, Liu R, Zeng Q, Peng X, Huang C, Cao P, Cao K. microRNA-195 functions as a tumor suppressor by inhibiting CBX4 in hepatocellular carcinoma. Oncology Reports. 2015;33:1115-1122. DOI: 10.3892/or.2015.3734\n'},{id:"B41",body:'Soria-Bretones I, Cepeda-Garcia C, Checa-Rodriguez C, Heyer V, Reina-San-Martin B, Soutoglou E, Huertas P. DNA end resection requires constitutive sumoylation of CtIP by CBX4. Nature Communications. 2017;8:113. DOI: 10.1038/s41467-017-00183-6\n'},{id:"B42",body:'Peuget S, Bonacci T, Soubeyran P, Iovanna J, Dusetti NJ. Oxidative stress-induced p53 activity is enhanced by a redox-sensitive TP53INP1 SUMOylation. Cell Death and Differentiation. 2014;21:1107-1118. DOI: 10.1038/cdd.2014.28\n'},{id:"B43",body:'Pelisch F, Pozzi B, Risso G, Munoz MJ, Srebrow A. DNA damage-induced heterogeneous nuclear ribonucleoprotein K sumoylation regulates p53 transcriptional activation. The Journal of Biological Chemistry. 2012;287:30789-30799. DOI: 10.1074/jbc.M112.390120\n'},{id:"B44",body:'Wang B, Tang J, Liao D, Wang G, Zhang M, Sang Y, Cao J, Wu Y, Zhang R, Li S, Ding W, Zhang G, Kang T. Chromobox homolog 4 is correlated with prognosis and tumor cell growth in hepatocellular carcinoma. Annals of Surgical Oncology. 2013;20(Suppl 3):S684-S692. DOI: 10.1245/s10434-013-3171-7\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Qun-Ying Su",address:null,affiliation:'
Department of Pathology, The Affiliated Hospital of Youjiang Medical University for Nationalities, China
Department of Pathology, The Affiliated Hospital of Youjiang Medical University for Nationalities, China
Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China
Guangxi Clinic Research Center of Hepatobiliary Diseases, China
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1. Introduction
The human gastrointestinal tract hosts a complex population of microorganisms. The function and composition of the gut microbiota vary from an individual to another, factors contributing to its differences being various. The mode of birth, the type of diet, exercise, body mass index, different diseases and therapies are factors that influence the gut microbiota composition and function. Type 2 diabetes (T2D), a highly prevalent metabolic disease, is lately characterized as a disease with significant alteration of the composition and function of the gut microbiota. New therapeutic targets are revealed, and researchers are thoroughly exploring these possible pathways and hypotheses to understand the pathogeny of the disease better and also to better manage the treatment options. Metformin, one of the most widely used first-line medication for the management of type 2 diabetes, looks to present other modes of action than the classic ones involving liver metabolism. Studies proved that metformin could modulate the gut microbiota disturbances encountered in type 2 diabetes, in this way improving the outcome of the disease.
1.1 The gut microbiota: definition, development and structure
Among other things, the cohabitation of the man with the environment is at the root of the human evolution, an extraordinary example in this sense being the relationship between humans and microorganisms.
The digestive tract hosts a complex, vast and dynamic community of microorganisms, called the microbiota. Together they form a mutualist relationship, with profound implications for the host both during homeostasis and disease [1].
It is worth mentioning that the gut is not the only place where there is a population of microorganisms with which the human organism is in such a connection (e.g., the skin also harbouring a plethora of bacteria) [2].
The composition of the microbiota varies from individual to another but also from segment to segment of the digestive tract and includes species from all three domains of life: bacteria, Archaea and Eukarya. All of the species are classified into 12 different phyla, of which more than 90% belong to Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria [3].
The process of colonizing the digestive tube with microorganisms is classically believed to begin at birth by “seeding” the newborn with microorganisms originating from the mother’s genital area (vaginal passage, mother’s areola), the skin, and the microbiota of the contacts in the surrounding environment, and from then the development continues throughout life. In recent years, however, this theory is challenged by a series of studies that have shown the presence of microorganisms in uterine tissues (e.g., the placenta, suggesting that colonization could be initiated before birth, by haematogenous sowing) [4]. At the age of 3–4 years, the core of the microbiota is relatively defined, and its structure is similar to that of the adult but is continuously subject to change depending on various external and internal factors.
Even if the core of the microbiota is established from an early age, several factors contribute to carve its form, explaining its variations from an individual to another [5]:
Type of birth
Gestational age
Diet (starting with breast milk that plays an essential role in the development of the flora)
Ageing
Geographic region and cultural habits
Physical activity
Diseases
Drugs (especially antibiotic therapy)
As stated before, the composition also depends on the digestive tract region (biogeography), this being explained by the physiological properties of the digestive segment. For instance, in the small intestine, the pH is lower and the transit time is shorter, which is why only rapidly growing bacteria, with the ability to adhere to the surface, are thought to survive. On the other hand, the colon shows a favourable environment for the development of microorganisms. It is worth mentioning that there are differences in the composition between faecal/luminal and mucosal bacteria [6].
In its final form, this whole microsystem consists of over 2000 species, which make up altogether more than 100 trillion cells, about 10 times more than the cells of the human body, hence the name of “superorganism”.
1.2 Functions
The microbiota exerts a significant influence on the host during homeostasis and disease, with profound implications for the proper body’s physiological functions, considering the microbiota as a “forgotten organ”.
The leading roles of the gut flora are the following:
Mechanic barrier—strengthening the gut integrity, shaping and regenerating the intestinal epithelium, protecting against pathogens [7]
Biologic active barrier—consuming the feeding substrates for pathogens [7]
Key regulators of digestion—involvement in the metabolism of biliary salts, short-chain fatty acids (SCFAs), lipids and glucides [8]
Synthesis of vitamins—principal reservoir for B complex vitamins [11]
Synthesis of dopamine, serotonin and other neurotransmitters [12]
1.3 Dysbiosis: definition, causes and consequences
Any perturbation of the healthy gut microbiota that disrupts the mutualist relationship between the organism and the associated microbes is called dysbiosis. The antagonist term of dysbiosis is eubiosis.
The underlying cause of a gut dysbiosis may be the following [13]:
Unbalanced diet
Drug therapy: antibiotics, chemotherapy, antiviral drugs and hormone therapy
Diseases: cancers, hepatopancreatic diseases and diabetes
Chronic and acute infections
Local inflammation
Presence of intestinal parasites
Frequent enemas
The effects of dysbiosis are reflected in the processes of the internal environment, contributing to the emergence of numerous pathological conditions such as [14]:
The relationship between gut microbiota and diabetes is not fully understood, but changes in its composition and function can contribute to the onset and maintenance of insulin resistance, thus influencing the prognosis of this illness. Both T2D patients and those that are at high risk of developing this disease seem to have an imbalance in the composition and function of the microbiota, just like a “metabolic dysbiosis”.
Analysing the literature, the main changes observed in the microbiota composition of diabetic patients are [20, 21, 22, 23]:
Reduced Gram-positive bacteria such as bacteria from phyla Firmicutes
Reduced butyrate-producing bacteria, such as Roseburia and Butyrivibrio
Decrease in bacteria that regulate intestinal permeability, such as Akkermansia muciniphila
Increased Gram-negative bacteria, such as Bacteroides, E. coli and Proteobacteria
Increase in various opportunistic pathogens such as Clostridium symbiosum and Eggerthella lenta
The Gram-negative bacteria (E. coli, Bacteroidetes and Proteobacteria) present lipopolysaccharides (LPS) at the surface of the membrane. Lipopolysaccharides are also known as endotoxins. They are large molecules consisting of a lipid and a polysaccharide composed of O-antigen with an outer core and an inner core joined by a covalent bond [24]. LPS are found to be elevated in the plasma of diabetic and obese patients by crossing an altered intestinal barrier (leaky gut). Accumulating, they trigger an inflammatory reaction called endotoxinemia. This systemic inflammatory response is associated with dyslipidaemia, increased blood pressure, but also, with insulin resistance and earlier onset of diabetes through a variety of mechanisms such as [25]:
Activation of pro-inflammatory kinases: mitogen-activated protein kinases and I kappa B kinase complex
Increased expression of inflammatory proteins: tumour necrosis factor-α (TNFα), monocyte chemotactic protein and interleukin 6
Impaired insulin signalling at the level of insulin receptor substrate 1
Inhibition of glucose transport
The passage of LPS through the intestinal mucosa is due to increased intestinal permeability (so-called leaky gut) that can be explained by the diminishing of butyrate and mucin-degrading bacteria such as Roseburia, Butyrivibrio and Akkermansia muciniphila. Furthermore, this epithelial dysfunction can determine an important translocation of intestinal bacteria into the adipose tissue, which maintains a low-grade inflammation and insulin resistance, process called “metabolic infection” [26, 27].
2.1 Dysbiosis: microbiota in type 1 diabetes (T1D)
At the moment, the amount of information regarding an alleged link between gut microbiota and T1D is modest. Several studies showed similarities between the disturbances of the microbiota found in T2D and T1D patients: reduced population of Firmicutes and increased the population of Bacteroidetes and increased in intestinal permeability. Increased gut permeability might contribute to pancreatic β-cell damage due to the increased absorption of exogenous antigens such as Streptomyces toxin—streptozotocin—that has tropism for pancreatic tissue and can cause lesions at its level [28].
2.1.1 Dysbiosis: protective anti-inflammatory- and anti-insulin-resistant mechanisms
There are also mechanisms mediated by the gut microbiota such as the production of short-chain fatty acids and secondary bile acids (SBA) that counteract those pro-inflammatory- and insulin-resistant effects. These mechanisms can be affected in the case of dysbiosis.
SCFAs are produced from dietary fibres that are fermented by the intestinal bacteria. Acetate, butyrate and propionate are the three most common SCFAs. They exert an essential role in the metabolism of carbohydrates, lipids, in maintaining the integrity of the intestinal barrier and in modulating inflammatory reactions through a variety of functions [29]:
Maintaining the integrity of the colon epithelium: Butyric acid is the primary energy source of the colon’s epithelial cells. It stimulates the proliferation but also the differentiation and apoptosis of the colonocyte, thus participating in the coordination of its life cycle. It also participates in the regulation of tight junction proteins (claudin 1 and zonula occludens).
Improves carbohydrate metabolism: Propionate lowers the accumulation of lipids in the adipose tissue and reduces hepatic lipogenesis thus decreases the insulin resistance. Propionate and acetate also stimulate the production of glucagon-like peptide-1.
Anti-inflammatory role: Butyric acid plays an essential role in maintaining the integrity of the intestinal mucosa, preventing endotoxemia and metabolic infection. Butyric acid also inhibits the nuclear factor kappa-beta from the macrophages that cause a suppression of TNF-alpha, IL-6 and myeloperoxidase activity.
At the intestinal level, bacteria metabolize primary bile acids (cholic and chenodeoxycholic acids) to secondary bile acids (deoxycholic and lithocholic acids). Bile acids are involved in multiple metabolic pathways, research over the last decades, demonstrating an essential role against inflammation and insulin resistance. Secondary bile acids contribute to a decrease in insulin resistance through:
Stimulating the production of glucagon-like peptide-1 by binding to G-protein-coupled receptor 1 [30]
Modulating glucose absorption through interaction with farnesoid X receptor (FXR)
Modulating energy expenditure: increase energy expenditure in brown adipose tissue by activating enzyme type 2 iodothyronine deiodinase and oxygen consumption, thus contributing to the prevention of obesity [31]
Increasing triglyceride clearance
Bile acids are the major pathway for catabolism of cholesterol, thus regulating the metabolism of lipids
In terms of their anti-inflammatory role, lithocholic acid inhibits the release of pro-inflammatory cytokines TNF-alpha, IL1 and IL6 from colon epithelium [32].
3. Metformin and the gut
Metformin presents as a sophisticated drug having multiple sites of action and various molecular mechanisms. Lately, attention has been directed to other modes of action, different than the classic ones. Its action at the intestinal level was suggested by the results of several studies that showed the following:
A delayed-release formula is retained almost entirely in the gut, with minimal systemic absorption. It is effective at lowering blood glucose as the standard immediate-release formulation in individuals with type 2 diabetes [33].
In diabetic rats, intravenous administration of metformin is less effective than intra-duodenal administration for lowering blood glucose levels [34].
Human genetic studies proved that variants in SLC22A1 gene (the gene encoding OCT1), which reduce hepatic uptake of metformin, do not impact upon the efficacy of metformin to lower HbA1c in individuals with type 2 diabetes [35].
Regarding the effects of metformin on the gut microbiota, studies have shown that administration of metformin produced several changes in the composition of the intestinal flora such as the following [36]:
Increase microbes from Verrucomicrobiaceae, Porphyromonadaceae, Rikenellaceae, Akkermansia muciniphila, and Prevotellaceae spp. moreover, species from Escherichia-Shigella sp.
Decrease of the Lachnospiraceae, Rhodobacteraceae spp., Peptostreptococcaceae and Clostridiaceae
Furthermore by comparing the modified microbiome profile by metformin treatment, with the microbiome profiles under various disease situations, these changes have been negatively correlated with multiple diseases that have an inflammatory pathogenic substrate such as colitis, chronic diarrhoea and irritable bowel syndrome, suggesting that its anti-inflammatory proprieties can be determined through regulation of the microbiota homeostasis.
The main side effects of metformin are gastrointestinal: nausea, vomiting, diarrhoea and abdominal pain. These side effects occur most frequently at the beginning of treatment, and in most cases, they disappear spontaneously. The cause of these side effects is not fully understood and may be due to the growth of opportunistic pathogenic bacteria from Escherichia to Shigella spp. which are shown to increase at the beginning of treatment. If we relate to the increase of these opportunistic pathogens, the further reduction of side effects can be caused by a reduction of the substrate to which these microorganisms are dependent (substrates provided by polysaccharide-degrading anaerobes) through diet and an increase of anaerobic mucus-associated bacteria such as Akkermansia muciniphila [37].
4. Metformin and the microbiota of type 2 diabetes
As stated before, the gut microbiota profile is profoundly modified in T2D patients in terms of its structure and composition. Administration of metformin results in improved glucose metabolism, but the way this is achieved is not fully understood, and its implications upon the intestinal flora are incompletely discovered. Analysing data from the literature, administration of metformin causes the composition to change and, therefore, the physiology of the microbiota as well.
5. Metformin and Bacteroides fragilis
Administration of metformin is associated with an essential decrease in Bacteroides fragilis [38].
Bacteroides fragilis is an obligately anaerobic, Gram-negative, rod-shaped bacteria, whose essential feature in metabolic pathology is the presence of capsular lipopolysaccharides. LPS are found to be elevated in the plasma of diabetic and obese patients and are associated with dyslipidaemia and increased blood pressure but also with insulin resistance and earlier onset of diabetes through a plenty of mechanisms that have been described previously. Colonizing mice with Bacteroides fragilis by transferring stool samples enriched with these bacteria determines an increase in body weight, impaired glucose tolerance and a decrease in insulin sensitivity.
Mechanisms by which metformin has determined the decrease of this species have not been elucidated but have been assumed since Bacteroides fragilis were reduced in mice that received stool samples from patients who had been given metformin.
Besides reducing Bacteroides fragilis, the bile acid glycoursodeoxycholic (GUDCA) is increased through decreasing the bacteria’s bile salt hydrolase activity. GUDCA is a glycine-conjugated form of the secondary bile acid deoxycholic acid, which has been known to have anti-inflammatory proprieties by reducing the levels of pro-inflammatory cytokines. Another biological function of GUDCA is to antagonize the farnesoid X receptor.
The FXR is predominantly found at the intestinal and hepatic tissue. Bile acids are the major ligands (activators) of this receptor. It is mainly involved in the metabolism of bile acids but also of carbohydrates and lipids.
The primary functions of FXR activation is the suppression of cholesterol 7 alpha-hydroxylase (CYP7A1), which reduces the synthesis of bile acids (via the feedback mechanism, FXR is activated by bile acids and further determines the suppression of this enzyme, thus reducing the synthesis of bile acids). FXR inhibition produces an increase in bile acids improving metabolic endpoints due to their anti-inflammatory and insulin sensitivity effects [39].
6. Metformin and Akkermansia muciniphila
As stated before, the epithelial barrier of T2D patients is affected by an increase in its permeability (so-called leaky gut) followed by a migration of different toxins such as LPS in the systemic circulation causing inflammatory responses, insulin resistance and impaired glucose tolerance. In addition to these changes, a decrease in the Akkermansia muciniphila population was observed.
Akkermansia muciniphila is a mucin-degrading bacterium of the phylum Verrucomicrobia that resides predominantly in the mucus layer of the colon, where it is involved in maintaining intestinal integrity by promoting mucus secretion and making the barrier mechanism more stable and therefore decreasing its epithelial permeability. Oral supplementation with this bacterial population was shown to reduce intestinal permeability and improve glucose metabolism [40, 41].
A significant change in the composition of the microbiota under metformin treatment regarding intestinal permeability is represented by an increase in the population of Akkermansia muciniphila. The mechanism by which this process is accomplished is not fully understood, but it seems that these bacteria metabolise unabsorbable carbohydrates and mucin in short-chain fatty acids, which in turn will be used as fuel for goblet cells. Stimulated goblet cells will further produce mucin, in this way leading to the thickening of the mucus layer and thus to a decrease in the epithelial permeability. Besides increasing the population of A. muciniphila, administration of metformin is associated with an increase in the density of mucin-producing goblet cells probably through the indirect mechanism stated above [42, 43] Figure 1.
Figure 1.
Akkermansia muciniphila mode of action [42, 43].
6.1 Metformin and SCAF-producing bacteria
One of the main features of the dysbiosis found in T2D patients is the decrease in butyrate-producing bacteria such as Roseburia and Butyrivibrio.
Butyrivibrio is a Gram-negative, anaerobic bacteria belonging to the Clostridia class, which was first described in the mid-twentieth century [44].
Roseburia is a Gram-positive anaerobic bacteria member of the Firmicutes phyla named in honour of distinguished microbiologist Theodor Rosebury [45].
As stated above, short-chain fatty acids such as butyrate, propionate, and acetate are the product of gut microbiota activity, resulting from the fermentation of the carbohydrates that escapes the absorption process, playing an essential role in the process of enhancing intestinal integrity, reducing inflammation and improving the metabolism of glucose and lipids.
Significant increase of butyrate-producing bacteria, especially Butyrivibrio and Roseburia, is observed in T2D patients treated with metformin [43].
6.2 Metformin and probiotics
The genus Bifidobacterium is a Gram-positive microorganism, member of the Bifidobacteriaceae family, belonging to the great Actinobacteria phylum, one of the most abundant species of the gut microbiota.
Lactobacillus is a Gram-positive, facultative anaerobic or microaerophilic, rod-shaped, non-spore-forming bacteria that produces lactic acid from converting carbohydrates.
Oral supplementation of L. casei and B. bifidum, which are frequently used as a probiotic treatment option, alone and in combination, has been shown to improve insulin resistance (decreased fasting blood glucose, decrease HbA1C) and lower the serum lipid levels by enhancing short-chain fatty acids production, and thus improving the outcome of T2D patients [46].
Administration of metformin has been shown to increase the population of Bifidobacterium adolescentis, Bifidobacterium bifidum, and also Lactobacillus [47].
6.3 Metformin and Adlercreutzia
The soybean, a legume species native from East Asia, is widely grown for its edible bean, which has numerous uses. It has been assumed that soy foods contribute to reducing the risk of T2D and the progression of this disease in diabetic patients although opinions are divided by the results of studies which inform this theory or rather confirm it [48].
At the gut level, the main species that metabolizes soybean isoflavonoids to equol are the ones from Adlercreutzia. It is worth mentioning that not in all people isoflavonoids are metabolized to equol (so-called equol producers). It was speculated that the health benefits of soy-based diets might be higher in equol producers than in equol nonproducers [49].
It seems that metformin treatment increases the population of Adlercreutzia in diabetic patients and therefore stimulating the production of equol, thus enhancing soy-based diet health benefits [50].
6.4 Summary of changes found after and before metformin treatment
These tables help summarise the changes found in the gut microbiota both before and after metformin treatment in T2D patients Tables 1 and 2.
Structure before metformin treatment
Structure after metformin treatment
Reduced Gram-positive bacteria, such as bacteria from phyla Firmicutes
Increased Firmicutes
Reduced butyrate-producing bacteria, such as Roseburia and Butyrivibrio
Increased Roseburia and Butyrivibrio
Decrease in bacteria that regulate intestinal permeability, such as Akkermansia muciniphila
Significant increase of Akkermansia muciniphila
Increased Gram-negative bacteria, such as Bacteroides, E. coli and Proteobacteria
Significant decrease of Bacteroides fragilis
Increase in various opportunistic pathogens, such as Clostridium symbiosum and Eggerthella lenta
Increased probiotic bacteria, such as Bifidobacterium and
Increase Adlercreutzia
Table 1.
Summary of changes in microbiota composition before and after metformin treatment of T2D.
Mechanisms before metformin
Mechanisms after metformin
Decrease production of SCFAs
Increased production of SCFAs
Decrease production of bile acids
Increased bile acid production, especially GUDCA Inhibition of farnesoid X receptor
Epithelial dysfunction and increased intestinal permeability
Enhancing the intestinal barrier, decreasing its permeability
Increased systemic LPS
Decreased in LPS migration, reduced systemic LPS
Endotoxemia and metabolic infection
Reduced endotoxemia
Inflammation
Decreased inflammation
Insulin resistance
Increased insulin sensitivity
Increased production of equol
Table 2.
Summary of changes in the functions of microbiota before and after metformin treatment.
7. Conclusions
Alterations of the intestinal microbiota are a key element in understanding the pathophysiology of diabetes and maybe to explain the variability in terms of its therapeutic response and complications occurrence in different patients.
Metformin exerts a significant influence on the bacterial constellation found in the gut, bringing a significant contribution to restoring its balance.
With changes in both composition and function, modulation of the intestinal flora of patients with type 2 diabetes mellitus, obtained by various methods, can bring a better outcome of diabetes patients and can improve the morbidity and mortality rates of this widely present metabolic disease.
\n',keywords:"metformin, diabetes mellitus, gut dysbiosis, improvement, microbiota",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68972.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68972.xml",downloadPdfUrl:"/chapter/pdf-download/68972",previewPdfUrl:"/chapter/pdf-preview/68972",totalDownloads:281,totalViews:0,totalCrossrefCites:1,dateSubmitted:"April 29th 2019",dateReviewed:"July 22nd 2019",datePrePublished:"October 23rd 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"The human gastrointestinal tract presents a vastly population of microorganisms, called the microbiota. The presence of these microorganisms offers many benefits to the host, through a range of physiological functions. However, there is a potential for these mechanisms to be disrupted condition, known as dysbiosis. Recent results are showing important associations between diabetes and the gut microbiota and how the intestinal flora can influence the prognosis of this illness. Microbial intestinal imbalance has been linked to alterations in insulin sensitivity and in glucose metabolism and may play an important role in the development of diabetes. Metformin is one of the most important and widely used first-line medications for the management of type 2 diabetes (T2D). It is a complex drug with multiple sites of action and multiple molecular mechanisms. In recent years, attention has been directed to other modes of action, other than the classic ones, with increasing evidence of a major key role of the intestine. By analysing the effects of metformin on the homeostasis of the microbiota of diabetes patients, our present topic becomes one of the major importance in understanding how metformin therapy can improve gut microbiota dysbiosis and thus provide a better outcome for this illness.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68972",risUrl:"/chapter/ris/68972",signatures:"Andra Iulia-Suceveanu, Sergiu Ioan Micu, Claudia Voinea, Madalina Elena Manea, Doina Catrinoiu, Laura Mazilu, Anca Pantea Stoian, Irinel Parepa, Roxana Adriana Stoica and Adrian-Paul Suceveanu",book:{id:"7994",title:"Metformin",subtitle:null,fullTitle:"Metformin",slug:"metformin",publishedDate:"May 27th 2020",bookSignature:"Anca Mihaela Pantea Stoian and Manfredi Rizzo",coverURL:"https://cdn.intechopen.com/books/images_new/7994.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-pantea-stoian",fullName:"Anca Pantea Stoian"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 The gut microbiota: definition, development and structure",level:"2"},{id:"sec_2_2",title:"1.2 Functions",level:"2"},{id:"sec_3_2",title:"1.3 Dysbiosis: definition, causes and consequences",level:"2"},{id:"sec_5",title:"2. Dysbiosis: microbiota in type 2 diabetes",level:"1"},{id:"sec_5_2",title:"2.1 Dysbiosis: microbiota in type 1 diabetes (T1D)",level:"2"},{id:"sec_5_3",title:"2.1.1 Dysbiosis: protective anti-inflammatory- and anti-insulin-resistant mechanisms",level:"3"},{id:"sec_8",title:"3. Metformin and the gut",level:"1"},{id:"sec_9",title:"4. Metformin and the microbiota of type 2 diabetes",level:"1"},{id:"sec_10",title:"5. Metformin and Bacteroides fragilis",level:"1"},{id:"sec_11",title:"6. Metformin and Akkermansia muciniphila",level:"1"},{id:"sec_11_2",title:"6.1 Metformin and SCAF-producing bacteria",level:"2"},{id:"sec_12_2",title:"6.2 Metformin and probiotics",level:"2"},{id:"sec_13_2",title:"6.3 Metformin and Adlercreutzia",level:"2"},{id:"sec_14_2",title:"6.4 Summary of changes found after and before metformin treatment",level:"2"},{id:"sec_16",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Boucher DH, James S, Keeler KH. The ecology of mutualism. Annual Review of Ecology, Evolution, and Systematics 1982;13:315-347'},{id:"B2",body:'Chen YE, Fischbach MA, Belkaid Y. Skin microbiota-host interactions. Nature. 2018;555(7697):543. DOI: 10.1038/nature25177'},{id:"B3",body:'Thursby E, Juge N. Introduction to the human gut microbiota. Biochemical Journal. 2017;474(11):1823-1836. DOI: 10.1042/BCJ20160510'},{id:"B4",body:'Aagaard K, Ma J, Antony KM, Ganu R, Petrosino J, Versalovic J. The placenta harbors a unique microbiome. 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DOI: 10.1155/2018/1890978'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Andra Iulia-Suceveanu",address:"andrasuceveanu@yahoo.com",affiliation:'
Internal Medicine—Gastroenterology Department, Faculty of Medicine, “Ovidius” University, Romania
'},{corresp:null,contributorFullName:"Sergiu Ioan Micu",address:null,affiliation:'
Internal Medicine—Gastroenterology Department, Faculty of Medicine, “Ovidius” University, Romania
Internal Medicine—Gastroenterology Department, Faculty of Medicine, “Ovidius” University, Romania
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Currently strongest OA platform with over 130 million downloads
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