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Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

  • Yufeng Sun
  • Wenchao Li
  • Shiqi Shen
  • Xuejing Yang
  • Bing Lu
  • Xiaojing Zhang
  • Peng Lu
  • Yi ShenEmail author
  • Juling JiEmail author
Open Access
Research
  • 157 Downloads
Part of the following topical collections:
  1. Immunovirology

Abstract

Background

Accumulated studies reported abnormal gene expression profiles of hepatocellular carcinoma (HCC) by cDNA microarray. We tried to merge cDNA microarray data from different studies to search for stably changed genes, and to find out better diagnostic and prognostic markers for HCC.

Methods

A systematic review was performed by searching publications indexed in Pubmed from March 1, 2001 to July 1, 2016. Studies that reporting cDNA microarray profiles in HCC, containing both tumor and nontumor data and published in English-language were retrieved. The differentially expressed genes from eligible studies were summarized and ranked according to the frequency. High frequency genes were subjected to survival analyses. The expression and prognostic value of alanine-glyoxylate and serine-pyruvate aminotransferase (AGXT) was further evaluated in HCC datasets in Oncomine and an independent HCC tissue array cohort. The role of AGXT in HCC progression was evaluated by proliferation and migration assays in a human HCC cell line.

Results

A total of 43 eligible studies that containing 1917 HCC patients were included, a list of 2022 non redundant abnormally expressed genes in HCC were extracted. The frequencies of reported genes were ranked. We finally obtained a list of only five genes (AGXT; ALDOB; CYP2E1; IGFBP3; TOP2A) that were differentially expressed in tumor and nontumor tissues across studies and were significantly correlated to HCC prognosis. Only AGXT had not been reported in HCC. Reduced expression of AGXT reflected poor differentiation of HCC and predicts poor survival. Knocking down of AGXT enhanced cell proliferation and migration of HCC cell line.

Conclusions

The present study supported the feasibility and necessity of systematic review on discovering new and reliable biomarkers for HCC. We also identified a list of high frequency prognostic genes and emphasized a critical role of AGXT deletion during HCC progression.

Keywords

Hepatocellular carcinoma Systematic review Alanine-glyoxylate and serine-pyruvate aminotransferase Diagnostic marker Prognostic marker 

Abbreviations

AGXT

alanine-glyoxylate and serine-pyruvate aminotransferase

ALDOB

aldolase B

CYP2E1

cytochrome P450 family 2 subfamily E member 1

HBV

hepatitis B virus

HCC

hepatocellular carcinoma

HCV

hepatitis C virus

IHC

immunohistochemistry

IGFBP3

insulin-like growth factor binding protein-3

OS

overall survival

TMAs

tissue microarrays

TOP2A

topoisomerase 2α

Background

Liver cancer is the third leading cause of cancer death and the sixth most common cancer worldwide [1]. Hepatocellular carcinoma (HCC) accounts for 70–85% of the total liver cancer burden. The accurate diagnosis and proper treatment of HCC is particularly challenging. Options for HCC treatment remain limited, surgical resection is considered the only “curative treatment”. But HCC patients do not have overt symptoms in the early stages, 80% of patients have widespread HCC at the time of diagnosis and are not candidates for surgical treatment. Even with surgical resection, the 5-year survival rate is poor (about 38%) [2].

Accumulated data from high-throughput analyses by cDNA microarray provide an accurate landscape of gene expression in HCC, and revealed a lot of pathogenic and prognostic genes for HCCs, thus enabled us to delineate some of the key events that might dominate tumor development and progression. Hopefully, translation of this knowledge into new targets and biomarkers might impact HCC decision making, and ultimately improve patient’s outcomes [3]. However, these studies were performed with different platforms and in different populations, the suggested diagnostic markers and potential therapeutic targets for HCC varied across studies, hence prevented the application of these findings. It’s the high time to merge these cDNA microarray data from different studies using different platforms, to search those widely and stably changed genes, and to find out better diagnostic markers and potential therapeutic targets for HCC.

In the present study, we performed a systematic review of studies that reported cDNA microarray data for both tumor and nontumor liver tissues of HCC patients and came up with a list of five genes that were differentially expressed in tumor and nontumor tissues across different studies and were significantly correlated to HCC prognosis. Among the five genes, except for alanine-glyoxylate and serine-pyruvate aminotransferase (AGXT) which is an essential gene involved in glyoxylate detoxification, the other four genes were well documented in HCC. Here, we reported that AGXT was involved in the progression of HCC and loss of AGXT expression predicted poor prognosis of HCC. AGXT might be a novel diagnostic and prognostic marker and a potential therapeutic target for HCC.

Methods

Data sources and search strategy

Literature retrieval was originally performed with PubMed, a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The relevant Mesh Terms were chose as the key words, including hepatocellular carcinoma, liver neoplasms, gene expression profiling, human, gene expression regulation, oligonucleotide array sequence analysis. Date of publications was restricted to July 2016. Besides, the literature must be published in English language. In addition to use (((((((carcinoma, hepatocellular[MeSH Terms]) AND liver neoplasms[MeSH Terms]) AND gene expression profiling[MeSH Terms]) AND human[MeSH Terms]) AND gene expression regulation[MeSH Terms]) AND oligonucleotide array sequence analysis[MeSH Terms])) AND (“1781/01/01”[Date-MeSH]: “2016/06/30”[Date-MeSH]) as the search strategy to obtain literatures, we also reviewed the bibliographies of eligible studies as well as those of relevant review articles to identify additional studies not captured by our database searches. To identify all potentially eligible studies, two investigators independently conducted structured searches in selected databases.

Study inclusion criteria

Eligible studies were included in the systematic review if they met the following criteria: (1) they were gene expression studies in hepatocellular carcinoma; (2) they used tissue samples obtained from surgically resected tumor and corresponding non-tumor or normal tissues in human for comparison; (3) validation of method and sample set were reported; (4) clinical and experimental study. Articles were excluded based on the following criteria: (1) review articles or letters; (2) non-liver cancer; (3) non-gene expression profile; (4) non-human tissue samples.

Data extraction

From the full text and corresponding additional information, the following items were eligible to collect and record for each study: authors, year of publication, region, selection number and characteristics of recruited liver cancer patients, the members of abnormally expressed genes in liver cancer. Two investigators (K. D. and YL. J.) independently evaluated and extracted the data with the inclusion criteria. Conflicts in study selection at this stage were resolved by consensus, referring back to the original article in consultation with the principal.

Gene statistics and screening

For genes that have multiple names/synonyms, we standardized the gene name for later work by using widely accepted and used HGNC database (https://www.genenames.org/). Frequency and composition analysis of the abnormally expressed genes in hepatocellular carcinomas were conducted by IBM SPSS Statistics 20.0 software (Endicott, New York, NY). The genes presented in more than four studies were regarded as high frequent genes. The expression of the high frequent genes in other malignant tumors were obtained from Oncomine (https://www.oncomine.org/). To identify a prognostic gene list across different studies, the prognostic value of these high frequent genes were evaluated by survival risk prediction in a previously described cohort of 247 Chinese HCC patients with publicly available Affymetrix U133A array data (National Center for Biotechnology Information [NCBI] Gene Expression Omnibus [GEO] Accession number GSE14520) [4]. BRB-Array Tools (version 4.3.1) was used for survival risk prediction. By reviewing related publications, the prognostic genes that have been intensively investigated were excluded, and the only one gene AGXT (alanine-glyoxylate and serine-pyruvate aminotransferase) that had not been reported in HCC were chosen for further validation.

Tissue microarray and immunohistochemistry staining

Tissue microarrays of 192 HCC patients were used for validation. All of the patients underwent curative hepatectomy for primary HCC at the Affiliated Hospital of Nantong University between March 2004 and August 2009. No patients received either radiotherapy or chemotherapy before the surgery. The study was performed on the basis of the protocol approved by the Declaration of Helsinki, and written informed consent was obtained from all patients. The histological Grading of HCC was defined according to the Edmondson–Steiner grading system. In the present study, grade I and II were termed as well differentiated, grade III was termed as moderate differentiated, and grade IV was termed as poor differentiated. Tumor stage was assigned according to the American Joint Committee on Cancer TNM staging. Patients were followed up every 2–3 months during the 1st year after surgery and every 3–6 months thereafter until September 2016. Totally 101 out of the 192 HCC patients had integrated clinical and follow up data (Additional file 1: Table S1).

Immunohistochemistry was performed with Envision + kits (DAKO, Carpinteria, CA) according to the manufacturer’s instructions [4], of which positive staining appeared in brown. The primary antibodies used were rabbit anti-AGXT monoclonal antibody (1:100, Abcam). Immunostained slides were analyzed with a semi quantitative scoring approach which combined staining intensity and percentage of positive cells: grade 0 for no reaction or focal weak reaction; grade 1 for intense focal or diffuse weak reaction; grade 2 for moderate diffuse reaction; and grade 3 for intense diffuse reaction, and the corresponding slides were scored from 0 to 3 respectively. The staining scores were evaluated independently by two pathologists who were blinded to the clinical outcomes.

Cell culture

Human HCC cell lines were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco, CA, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, Carlsbad, USA) in a 5% CO2 atmosphere at 37 °C.

siRNA transfection

Huh-7 cells were seeded into 6-well culture plates and transfected with small interfering RNAs (siRNAs) against human AGXT (siAGXT) (SASI_Hs01_00153951, SASI_Hs01_00153952 and SASI_Hs01_00153953, Sigma) using Lipofectamine™ 2000 (Invitrogen, Carlsbad, CA), at a final concentration of 75 nm. Non-targeting control siRNA (siNC, Sigma) was used as negative control. Cy3 labeled siRNA transfection control (Cy3-siTC, RiboBio, Guangzhou, China) was used to optimize siRNA concentration for transfection. Knockdown efficiency was determined by real-time quantitative reverse transcription-PCR (RT-PCR) 48 h after the transfection.

Real-time quantitative RT-PCR

The RNAs from Huh-7 or HepG2 cells were reverse-transcribed with Thermoscript RT-PCR system (Invitrogen). Real-time quantitative PCR was performed on RotorGene 3000 instrument (Corbett Research, New South Wales, Australia) with SYBR qPCR master lit (TaKaRa, Dalian, China). Gene specific primers were: AGXT forward primer 5′-CTGGGGACTCCTTCCTGGTT-3′, AGXT reverses primer 5′ -CACCTCCTGCAGTGTGTAGT-3′; β-actin forward primer 5′-TTGTTACAGGAAGTCCCTTGCC-3′, β-actin reverses primer 5′ -ATGCTATCACCTCCCCTGTGTG-3′. Relative gene expression was normalized to housekeeping gene β-actin and calculated as 2 − ΔCτ.

Western blot

For western blot analysis, whole cell protein was extracted by RIPA lysis buffer (Beyotime, Shanghai) according to the manufacturer’s instructions. Equal amounts of protein (20 µg) were separated on 10% SDS PAGE gel and transferred onto polyvinyldifluoride (PVDF) membranes (Millipore). PVDF membranes were blocked with 5% non-fat milk for 1 h, then incubated with specific primary antibodies for anti-AGXT antibody (Abcam, Cambridge, UK), and β-actin (CST, Boston, America) at 4 °C overnight, then incubated with horseradish peroxidase-conjugated secondary antibody for an additional 1 h at room temperature. The protein expression was visualized with the ECL chemiluminescence detection system (Biorad).

Proliferation and migration assays

Proliferation ability of HCC cells was measured by cell counting using a Counting Chamber or Cell Counting Kit-8 reagent (CCK8, Dojindo Laboratories, Kumamoto, Japan) according to the manufacturer’s recommendation. Cells were seeded at 1 × 105 cells per 25 cm2 Corning cell culture flask or 2 × 103 cells per well in 96-well plates and cultured for 0 h, 24 h, 48 h, 72 h, 96 h, 120 h and 144 h. For cells cultured in 96-well plates, CCK8 solution was added (10 μl each well) and incubated at 37 °C for 2 h. The optical density readings at 450 nm were determined by a micro plate reader (Bio-Rad, Tokyo, Japan).

For transwell migration assays, a single cell suspension of 50,000 Huh-7 and HepG2 cells, or siAGXT/siNC treated Huh-7 cells resuspended in 200 μl serum-free DMEM were plated in the upper chambers (Millicell, 8.0 μm; Corning, USA), 750 μl DMEM medium with 10% FBS was used as a chemoattractant in the lower chambers. After 24 h, nonmigrating cells were removed from the upper surface softly by a cotton swab. The cells that migrated through the membrane to the lower surface were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet, then counted under a microscope (Olympus) at 200-fold magnification, five fields were counted for each well.

Cell cycle assay and apoptosis assay

HCC cells were seeded at 2 × 105 per well in 6 well plates. After overnight incubation, cells were treated with siAGXT or siNC for 72 h. After treatment, HCC cells were digested using 0.25% pancreatic enzyme, washed twice with PBS, fixed in precooled 70% cold ethanol at 4 °C for 24 h. Cells were centrifuged again, washed with cold PBS twice, and stained with Propidium iodide (0.1 mg/ml) (Propidium iodide, PI; Beyotime) at 37 °C in the dark for 30 min. DNA contents were measured with a BD FACS Calibur system (BD Biosciences, Franklin Lake, NJ). Data were analyzed using ModFit LTTM software (Verity Software House, Topsham, ME). For apoptosis assay, cells were harvested, washed, and resuspended with PBS and stained with BD Annexin V-FITC Apoptosis Detection Kit. Data was acquired using a BD FACS Calibur system and BD FACSuite software (BD Biosciences).

Statistical analysis

Statistical analyses were performed with Graphpad Prism 6.0 (Graphpad Software, Inc., La Jolla, CA) or IBM SPSS Statistics 20.0 software (Endicott, New York, NY). Quantitative data were expressed as mean ± SD. Comparisons between groups were made by Student’s t-test or two-way ANOVA. Categorical data were evaluated by the χ2 test. All P-values were two-sided and the statistical significance was defined as P < 0.05. All experiments were performed in triplicates.

Results

Study selection and characteristics of included studies

According to the literature retrieval strategy, 392 studies were identified in the initial search (Fig. 1). Based on the independent screening of titles and abstracts by two investigators (K. D. and YL. J.), 293 studies were excluded, of which 47 were animal studies, 34 were not performed with liver cancer tissues, 105 were not gene expression profiling studies, 107 articles were not correlated with the present study, and 99 papers remained. The full text of these remaining 99 studies was retrieved and further assessed for eligibility. Of these 99 publications, 56 were excluded (one was animal study, 3 were small RNA studies, 2 were review articles, 4 were non-HCC, 15 were excluded because they did not have gene expression profiling data, 4 studies had difficult in data extraction, 27 were excluded because they were irrelevant to our study). In total, 43 articles were included. All of these 43 articles were based on HCC cohort study containing a total of 1917 HCC patients. After a preliminary check of these 43 articles, we included “Author”, “Year of publication”, “Country”, “Number of patients” (NP), “Number of significant genes” (NG),”Clinical tissue” (Tissue), “Contain adjacent tissue” (C&A), “Hepatitis B surface antigen” (HBs-Ag), “Hepatitis C antibody” (HCV-Ab), “Alcoholism “, “Tumor size” (TS),”Tumor differentiations” (TD), and “AFP” as the baseline variables to characterize the enrolled studies. Thirteen studies contained the information of both hepatitis B surface antigen (HBs-Ag) and hepatitis C antibody (HCV-Ab), seven studies only enrolled person carrying HCV-Ab, four studies only included patients carrying HBs-Ag, and two studies had the information of alcoholism. So we combined these three variables into the variable “Etiology”. Only six articles provided the record of tumor size, of which four articles [5, 6, 7, 8] used 5 cm and two articles [9, 10] used 3 cm to distinguish large or small tumor. The differentiation of tumors was described as highly, moderately and poorly differentiated in four articles [5, 11, 12, 13]. Few literatures discussed AFP. Due to the excessive missing information for tumor size, tumor differentiation and AFP, only “Author”, “Year of publication”, “Country”, “Number of patients” (NP), “Number of significant genes” (NG),”Clinical tissue” (Tissue), “Contain adjacent tissue” (C&A) and “Etiology” were included. These main characteristics of the 43 studies were summarized in Table 1.
Fig. 1

Flowchart of study selection and gene screening

Table 1

Characteristics of the 43 included studies

Article

Year of publication

Country

NP

NG

Tissue

C&A

Etiology

Engelmann [14]

2015

Germany

15

30

F

T

 

Itzel [15]

2015

USA

163

61

F

F

 

Yu [16]

2015

China

154

67

T

F

HBV

Shi [17]

2014

China and Singapore

26

6

T

F

 

Mayinuer [18]

2013

Japan

152

154

T

F

 

Gehrau [19]

2012

USA

107

17

F

F

HCV

Nagai [11]

2012

Japan

21

25

T

F

HCV

Roessler [20]

2012

China

76

6

F

F

HBV

Sato [21]

2012

Japan

48

20

T

F

HCV

Yu [22]

2012

China

100

59

T

F

HBV

Zekri [23]

2012

Egypt

31

25

T

T

HCV

Satow [24]

2010

Japan

84

124

T

T

 

De Giorgi [25]

2009

Italy

21

39

F

F

HCV

Sugawara [26]

2010

Japan

1CL

36

F

F

 

Derambure [12]

2008

France

40

63

T

F

HBV, HCV, alcoholism

Gao [9]

2008

Japan

38

24

T

T

HCV, HBV

Guo [27]

2008

China

3CL

17

F

F

 

Hamaguchi [10]

2008

Japan

60

22

T

F

 

Kittaka [5]

2008

Japan

100

25

T

F

HCV, HBV

Sakai [13]

2008

Japan

12

93

F

T

HCV

Skawran [28]

2008

Germany

32

185

F

F

HBV, HCV

Yamashita [29]

2008

China

40

71

T

F

HBV

Lin [30]

2007

Taiwan

19

F

F

 

Liu [31]

2007

China

1

45

T

F

 

Wang [32]

2007

Singapore

80

57

T

F

HCV, HBV

Wang [33]

2007

USA

14

134

F

F

 

Danenberg [34]

2006

USA

1CL

50

F

F

 

Chen [35]

2005

USA

63

54

F

F

 

Ge [36]

2005

Japan

36

58

F

F

 

Iizuka [37]

2005

Japan

76

110

T

F

HCV, HBV

Matoba [38]

2005

Japan

33

46

T

F

HCV, HBV

Nam [6]

2005

Korea

42

92

T

F

 

Wong [39]

2005

China

10

55

F

F

HBV, HCV

Breuhahn [40]

2004

Germany

39

118

T

T

HBV, HCV, alcoholism

Chen [41]

2004

USA

29

T

F

 

Lee [7]

2004

Korea

10

20

T

F

HCV, HBV

Kurokawa [42]

2004

Japan

20

63

T

F

HBV, HCV

Smith [8]

2003

USA

20

50

T

F

HCV

Kim [43]

2003

USA

74

30

T

F

 

Chung [44]

2002

Korea

8

62

T

T

 

Iizuka [45]

2002

Japan

51

83

T

F

HBV, HCV

Lee [46]

2002

USA

19CL

119

F

F

 

Okabe [47]

2001

Japan

20

T

T

HBV, HCV

NP number of patients, NG number of significant genes, Tissue clinical tissue, C&A contain adjacent tissue, CL cell line, T true, F false

Abnormally expressed genes in HCC across different studies

Totally 2739 abnormally expressed genes in HCC were extracted from the 43 studies. After standardization of gene names according to HGNC database (https://www.genenames.org/), 2576 genes remained (including repetitive). When repetitive genes were excluded by SPSS 20.0, 2022 non-redundant genes remained (Fig. 1). The frequency of each gene that appeared in the 43 studies included was counted. The frequency ranged from 1 to 6, with 6 the highest frequency, while 1 the lowest frequency (Fig. 2, Additional file 2: Table S2). Proportion of frequency 6 was the minimum, the frequency 1 constituted the largest proportion of all. Genes that appeared ≥ 4 times were regarded as genes of high frequency genes (37 genes), and genes that appeared ≤ 3 times were regarded as genes of low frequency (1985 genes). There are four genes that appeared in six studies (0.2%), including C8A, MT1E, MT1H and NNMT; 13 genes appeared in five studies (0.6%), including: AFP, ALDOB, C9, CXCL12, CYP2E1, HPX, IGF2, IGFBP3, MT1F, MT2A, RAF1, SULT1E1 and TOP2A; 20 genes appeared in four studies (1.0%), including: AGXT, AKR1B10, BCL2, C4A, C6, CES1, CLU, CYP2E1, EPCAM, FCN3, FDPS, FOS, GPC3, MFSD2A, PDCD4, PPIB, RGS5, SAA1, SAA4 and TAT; 66 genes appeared in three studies (3.3%); 290 genes appeared in two studies (14.3%); 1629 genes only appeared once (80.6%).
Fig. 2

Frequency analysis of the abnormally expressed genes in hepatocellular carcinoma. Data presented in the frequency table and pie chart were from the 43 studies included

The publication history of these 37 high frequent genes in HCC was searched in Pubmed (Additional file 3: Table S3). About half of these genes have been extensively studied in HCC and appeared in more than 5 directly related studies, including: AFP on the top with 824 publications, followed by EPCAM, GPC3, CYP2E1, FOS, BCL2, IGF2, CXCL12, AKR1B10, PDCD4, TAT, RAF1, IGFBP3 and TOP2A, while seven genes has not been reported in HCC by the time of literature searching, including: AGXT, FCN3, FDPS, MFSD2A, RGS5, SAA1 and SAA4. The relative expression of these 37 high frequent genes in liver cancer and other malignant tumors were obtained from Oncomine (https://www.oncomine.org/) and provided in Additional file 3: Table S3.

Genes significantly associated with the survival of HCC patients

The prognostic value of the 37 high frequent genes were evaluated by survival risk prediction in a previously described cohort of 247 Chinese HCC patients with publicly available Affymetrix U133A array data (Gene Expression Omnibus accession number GSE14520) [4]. BRB-Array Tools (version 4.3.1) was used for survival risk prediction. Among the 37 genes, under-expression of three genes (alanine-glyoxylate and serine-pyruvate aminotransferase, AGXT; aldolase B, ALDOB; cytochrome P450 family 2 subfamily E member 1, CYP2E1) and over-expression of two genes (insulin-like growth factor binding protein-3, IGFBP3; topoisomerase 2α, TOP2A) were significantly correlated to poor prognosis (Fig. 3). By literature reviewing, among the five genes, four have been well documented in HCC, including: ALDOB [48, 49, 50], IGFBP3 [51, 52, 53], CYP2E1 [54, 55, 56] and TOP2A [57, 58], while AGXT was mostly studies in primary hyperoxaluria type 1 [59, 60], there are little reports of AGXT in HCC. Thus, AGXT was chosen for further validation.
Fig. 3

The expression of AGXT, ALDOB, CYP2E1, IGFBP3 and TOP2A predicted clinical outcome in HCC. The prognostic value of the 37 high frequent genes was evaluated by survival risk prediction in a cohort of 247 Chinese HCC patients (Gene Expression Omnibus Accession number GSE14520). BRB-Array Tools (version 4.3.1) was used for survival risk prediction. Among the 37 genes, under-expression of three genes (alanine-glyoxylate and serine-pyruvate aminotransferase, AGXT; aldolase B, ALDOB; cytochrome P450 family 2 subfamily E member 1, CYP2E1) and over-expression of two genes (insulin-like growth factor binding protein-3, IGFBP3; topoisomerase 2α, TOP2A) were significantly correlated to poor prognosis. Red line represented samples with over-expressed gene, and blue line represented samples with under-expressed gene

AGXT was highly expressed in liver but lost in HCC

According to the mRNA expression in normal human tissues from GTEx, Illumina, BioGPS, and CGAP SAGE for AGXT gene; as well as protein expression in normal tissues and cell lines from ProteomicsDB, MaxQB, and MOPED for AGXT gene, we found that AGXT was highly expressed in liver, fetal liver and liver secretome (Additional file 4: Figure S1, https://www.genecards.org/). AGXT protein is mostly localized in the peroxisomes where it is involved in glyoxylate detoxification. The expression of AGXT in HCC vs. normal liver were searched and analyzed by Oncomine (https://www.oncomine.org/). The differential AGXT mRNA expression analyses between HCC vs. normal were feasible in four datasets, including: Roessler Liver 2 [61], Chen Liver [62], Wurmbach Liver [63] and Roessler Liver [61], totally containing data from 327 livers and 385 HCC tissues. Compared to normal liver, the expression of AGXT mRNA significantly decreased in HCC in all of the four datasets with the fold-changes ranged from − 3.694 to − 6.176, P-values ranged from 1.92E−35 to 4.20E−5 (Fig. 4). The cohort used for survival risk prediction of the 37 high frequent genes was partially overlapped with Roessler Liver 2 [20, 61].
Fig. 4

The differential expression of AGXT mRNA between HCC vs. normal liver was analyzed by Oncomine. The expression of AGXT in HCC vs. normal liver were searched and analyzed by Oncomine (https://www.oncomine.org/). The differential AGXT mRNA expression analyses between HCC vs. normal were feasible in four datasets, including: Roessler Liver 2, Chen Liver, Wurmbach Liver and Roessler Liver, totally containing data from 327 livers and 385 HCC tissues. Compared to normal liver, the expression of AGXT mRNA significantly decreased in HCC through all of the four datasets with the fold-changes ranged from − 3.694 to − 6.176, P-values ranged from 1.92E−35 to 4.20E−5

The differential expression of AGXT in liver and HCC was validated by immunohistochemistry (IHC) study with tissue microarray from an independent cohort of 192 HCC patients. Comparison of AGXT by IHC between exactly paired HCC tumor and nontumor tissues was applicable in 87 cases. The IHC staining of AGXT in nontumor liver tissues was strongly positive and was characterized by a granular, diffuse, cytoplasmic staining pattern; while in tumor tissues, the staining of AGXT on malignant hepatocytes were focal and/or pale in a majority of cases (Fig. 5a). Immunostained slides were analyzed with a semi quantitative scoring approach as described in “Methods”, and were scored from 0 to 3 according to the staining intensity (Fig. 5b). The IHC observation reaffirmed that AGXT was highly expressed in nontumor liver tissues and was down-regulated in HCC tumor tissues (P < 0.0001) (Fig. 5c).
Fig. 5

Protein expression of AGXT in HCC and the correlation to prognosis and tumor differentiation. a The protein expression of AGXT in paired nontumor (N) and tumor (T) tissues from HCC patients was detected by immunohistochemistry (IHC). b Immunostained slides were scored from 0 to 3 according to staining intensity and percentage of positive cells. c AGXT IHC scoring of 87 paired HCC samples which contained both nontumor (N) and matched tumor (T) tissues. d Kaplan–Meier survival analysis for 101 HCC patients based on AGXT expression, P = 0.0227. e The AGXT expression in well, moderate and poor differentiated HCC, ***P < 0.0001

Loss of AGXT expression was correlated with a poor prognosis and differentiation of HCC

As described earlier, under-expression of AGXT correlated to poor prognosis in HCC cohort GSE14520 (Fig. 3), we tried to verify the prognostic value of AGXT in this tissue array HCC cohort. Totally 101 out of the 192 HCC patients that had integrated clinical and follow up data were included, and were divided into two groups based on IHC scores: AGXT high (IHC score 2 and 3, n = 60) and AGXT low (IHC score 0 and 1, n = 41) (Fig. 5b). Overall survival was compared between AGXT high and low cases. It confirmed that low expression of AGXT was associated with poor prognosis (P = 0.0348) (Fig. 5d).

When examining the immunostained HCC slides, we noticed that the staining of AGXT tended to be stronger in well differentiated HCC, but weaker or totally lost in poorly differentiated HCC. Combined with the above finding that AGXT was highly expressed in nontumor liver but down-regulated in HCC, we wondered that if AGXT expression represented the degree of hepatocyte differentiation and maturation. We then compared the IHC score of AGXT among well, moderate and poorly differentiated HCCs. All of the 192 HCC patients were involved. The well differentiated HCCs showed the highest score of AGXT, while the poorly differentiated HCCs scored the lowest. The expression of AGXT in HCC was significantly associated to tumor differentiation (P < 0.0001) (Fig. 5e).

To further explore the clinical parameters associated with AGXT in HCC, we investigated the correlation between AGXT expression and clinicopathological characteristics by performing univariate Cox proportional hazards regression analyses. The 101 HCC patients that had integrated clinical data were included. We found that AGXT expression levels only related to tumor differentiation (P < 0.0001), and there was no significant correlation with patient’s age, gender, serum AFP, tumor size, TNM stage, tumor number, vascular invasion, portal vein tumor thrombus and liver cirrhosis (Table 2). The above observations suggested that reduced expression of AGXT related to tumor differentiation and predicted poor prognosis in HCC.
Table 2

Correlation between AGXT expression and clinicopathological features in 101 HCC patients

Variable

AGXT

P-value

Low (n = 41)

High and moderate (n = 60)

Age

0.277

 ≤ 50

16

30

 

 > 50

25

30

Gender

0.257

 Male

28

47

 

 Female

13

13

HBsAg

0.092

 Positive

34

56

 

 Negative

6

3

 

Portal vein tumor thrombus

0.930

 No

17

38

 

 Yes

1

2

Cirrhosis

0.499

 Yes

28

40

 

 No

8

16

Tumor number

0.882

 Single

13

31

 

 Multiple

3

8

Serum AFP (ng/ml)

0.078

 ≤ 300

15

32

 

 > 300

17

16

Vascular invasion

0.984

 Yes

17

25

 

 No

24

35

Tumour size (cm)

0.955

 ≤ 3

19

27

 

 > 3

22

32

 

Tumor differentiation

< 0.0001*

 Well

0

20

 

 Moderate

7

32

 Poor

34

8

TNM

0.720

 I

18

25

 

 II

17

24

 III–IV

3

6

*Significant P values are highlighted in italic

Loss of AGXT expression promoted the malignant phenotypes of HCC cell lines

To further understand the role of AGXT in HCC progression, the biofunctions of AGXT was investigated in human HCC cell lines. First, we compared the expression of AGXT in a panel of HCC cell lines by quantitative real-time PCR. It turned out that Huh-7 exhibited very high level of AGXT, whereas others, such as HepG2, SMMC-7721, MHCC97-H and SK-Hep1 expressed little AGXT (Fig. 6a). Compared to AGXT high-expression Huh-7, AGXT low-expression HepG2 showed higher proliferation ability (P < 0.0001) and greater migration activity (P < 0.0001) (Fig. 6b, c). To further verify the effects of reduced AGXT expression on HCC cells, AGXT high-expression Huh-7 cells were transfected with siAGXT. The transfection efficiency exceeded 95% as it was evaluated by Cy3-siTC. The expression of AGXT was effectively down-regulated by siAGXT at both mRNA and protein levels as validated by RT-PCR and western blot analyses (Fig. 6d). By CCK8 proliferation assay and transwell migration assay, we verified that reduced AGXT expression in Huh-7 accelerated cell proliferation (P < 0.0001) and promoted cell migration (P < 0.0001) (Fig. 6e, f).
Fig. 6

Loss of AGXT expression promoted the malignant phenotypes of HCC cell in vitro. a The mRNA expressions of AGXT in a panel of HCC cell lines were detected by quantitative real-time PCR. b Proliferation ability of HCC cells was measured by cell counting. c Transwell migration assay for HCC cells. d The transfection efficiency of siRNA was measured 48 h after Cy3-siTC transfection (original magnification, ×400); and the knockdown efficiency of siAGXT in Huh7 cells was determined by quantitative real-time PCR and western blot. e Proliferation of siAGXT transfected Huh7 cells was measured by CCK8 assay. f Transwell migration assay for siAGXT transfected Huh7 cells, representative images and quantitative results were provided (original magnification, ×200). g Cell cycle assay for siAGXT transfected Huh7 cells, representative histogram of the gated cells in G0/G1, S, and G2/M phases and quantitative analyses, at least 10,000 cells per sample. h Apoptosis assay, representative histogram and quantitative analyses of the cells in each phase, at least 10,000 cells per sample. Each bar represents mean ± SD of the data obtained from three independent experiments. ***P < 0.0001, **P < 0.001, *P < 0.05

The potential effects of AGXT on cell cycle and apoptosis in HCC cells were further examined by flow cytometry. We found that siAGXT treated HCC cells showed a cell cycle shift from G0/G1 to S and G2/M phases (Fig. 6g) indicating enhanced proliferating activity, which was consistent with the CCK8 proliferation assays mentioned earlier. In apoptosis assays, early apoptotic cells were Annexin V-FITC (+) PI (−) (low right quadrant) and late apoptotic/necrotic cells were Annexin V-FITC (+) PI (+) (upper right quadrant). The proportions of both early apoptotic and late apoptotic/necrotic cells increased as the expression of AGXT decreased (Fig. 6h). Increased apoptosis/necrosis along with increased proliferation suggested the controls that integrate cell proliferation and death in normal tissues persist during malignancy. Proapoptotic factors can induce proliferation of neighboring surviving cells to replace dying cells [64, 65]. Increased apoptosis/necrosis along with increased proliferation are always associated with malignant tumors, and are more noticeable in high grade tumors [64, 66].

Discussion

To date, a large proportion of existed systematic reviews or meta studies on HCC markers were about serum markers [67, 68], and others were focused on one or a set of genes [69, 70], inflammatory-based markers [71, 72]. Due to the differences across experimental methods, sample size and quality, inconsistent annotation and the methods used for data processing and analysis, it is difficult to do integrated bioinformatics analysis with microarray data from different platforms, and sometimes even the raw data are not available in public databases, so there are very few studies that combined the data form different HCC gene expression profiles [73, 74]. Zhang et al. reanalyzed three publicly available datasets of gene expression profiles in the Oncomine database; they identified 17 hub genes (10 unregulated and 7 down-regulated in HCC tissues). Among the 17 genes, 13 genes (SMAD2, PTK2, MAPK1, HDAC1, CDC25A, IGFI, FOS, ESR1, EGFR, SOCS3, SP1, YY1 and JUNB) have been identified as an HCC-related gene [73]. Shi et al. analyzed the integrated microarray data from four independent studies (GSE14520, GSE25097, GSE36376 and GSE57957) from public databases Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo); they found that KLHL21 was a potential target for therapeutic intervention [74].

In the present study, we conducted a systematic review of studies that reported cDNA microarray data and differentially expressed genes between HCC tumor and nontumor tissues. We searched the PubMed databases for eligible studies published in English-language before July 2016 and retrieved 392 articles. The list of differentially expressed genes from 43 carefully designed studies that satisfied further inclusion criteria were summarized. A total of 1917 HCC patients were involved and 2022 non redundant abnormally expressed genes in HCC were extracted. The frequencies of reported genes were ranked. We finally obtained a list of only five genes (AGXT; ALDOB; CYP2E1; IGFBP3; TOP2A) that were differentially expressed in tumor and nontumor tissues across studies, and were significantly correlated to HCC prognosis. Among the five genes, four (ALDOB; CYP2E1; IGFBP3; TOP2A) were well documented HCC related genes, while AGXT was mostly studies in primary hyperoxaluria type 1 [57, 58], but had not been reported in HCC.

Unlike the unstable changes of the other four genes, the expression of AGXT was constantly reduced in tumors compared to nontumor tissues in a variety of malignant, including liver cancer, gastric cancer, kidney cancer, lung cancer and pancreatic cancer (Additional file 3: Table S3). In the present study, we found that the expression of AGXT reflected the differentiation of HCC and reduced AGXT expression was correlated to poor overall survival of HCC patients. Knocking down of AGXT in HCC cell line could induce a cell cycle shift from G0/G1 to S and G2/M together with enhanced cell proliferation, increased cell death and migration, suggesting a role of AGXT in promoting tumor progression. We provided a high level of evidence on AGXT to serve as a new biomarker and prognostic factor that related to tumor differentiation and progression for HCC.

AGXT gene encoded an enzyme called alanine-glyoxylate and serine-pyruvate aminotransferase which is found in liver cells, specifically within peroxisomes. Serine-pyruvate aminotransferase converts glyoxylate to glycine and is involved in glyoxylate detoxification. The role of AGXT in tumor biology has not been reported yet. One relevant observation is that the metabolic changes to pyruvate and glyoxylate might contribute to the improved therapeutic effects of sorafenib and everolimus combination therapy for HCC [75]. The underlying mechanisms for the contribution of decreased AGXT expression to HCC progression deserved further investigation.

As we were preparing the manuscript, another study group reported AGXT as a novel immunohistochemical marker for the diagnosis of HCC, and demonstrated comparable specificity and higher sensitivity of AGXT compared to arginase-1 [76]. The reason why they were interested in AGXT was uninterpreted; they only mentioned that AGXT was expressed in the liver exclusively. Their finding reaffirmed that AGXT as a new HCC biomarker, and backed up the feasibility and necessity of systematic review on discovering new and reliable biomarkers for HCC as well as for other cancer.

Conclusion

In conclusion, the present study was an effort to merge abnormally expressed genes in hepatocellular carcinoma form multiple independent studies, which increased sample size and enhanced reliability. The data of present study identified a list of only five high frequency prognostic genes and emphasized a critical role of AGXT deletion during HCC progression, indicated a potential relevance of AGXT restoration for HCC therapy. Further well-designed and larger sample studies are surely warranted to identify the role of the AGXT in the development and progression of HCC and other malignant tumors.

Notes

Acknowledgements

The authors acknowledge K. D. and YL. J. for their assistant in screening of relevant publications.

Authors’ contributions

JLJ and YS: conceived and designed the experiments. YFS and SQS performed the experiments and analyzed the experiments data. WCL and XJY: data acquisition and data analysis. BL and XJZ: follow up and quality control of data. JLJ, YS and YFS wrote the manuscript. PL reviewed the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by Grants from the National Natural Science Foundation of China, No. 81761128018, 81572871; Natural Science Foundation of Jiangsu Province, No. BK20151277; Undergraduate Training Programs for Innovation and Entrepreneurship of Jiangsu Province, No. 201610304034Z, No. 201710304028Z. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Affiliated Hospital of Nantong University. The study was performed on the basis of the protocol approved by the 1964 Declaration of Helsinki, and written informed consent was obtained from all patients.

Consent for publication

Informed consent was obtained from all participants for publication.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12967_2019_2138_MOESM1_ESM.xls (24 kb)
Additional file 1: Table S1. Clinical characteristics of patients in HCC tissue array cohort at the time of surgery (n = 101).
12967_2019_2138_MOESM2_ESM.xls (248 kb)
Additional file 2: Table S2. Frequency of the abnormally expressed genes in HCC.
12967_2019_2138_MOESM3_ESM.xls (30 kb)
Additional file 3: Table S3. The publication history and the expression of the 37 high frequency genes in liver cancer and other malignant tumors.
12967_2019_2138_MOESM4_ESM.doc (4.3 mb)
Additional file 4: Figure S1. Expression for AGXT Gene. (A) mRNA expression in normal human tissues from GTEx, Illumina, BioGPS, and CGAP SAGE for AGXT Gene. (B) Integrated Proteomics: protein expression in normal tissues and cells from ProteomicsDB, MaxQB, and MOPED for AGXT Gene. (https://www.genecards.org/).

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of PathologyMedical School of Nantong UniversityNantongChina
  2. 2.Basic Medical Research Centre in Medical College of Nantong UniversityNantongChina
  3. 3.Department of Epidemiology and Medical StatisticsPublic Health School of Nantong UniversityNantongChina
  4. 4.Medical School of Nantong UniversityNantongChina
  5. 5.Department of Clinical BiobankAffiliated Hospital of Nantong UniversityNantongChina
  6. 6.Department of BiostatisticsPublic Health School of Shandong UniversityJinanChina

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