Open Access

Genetic variations in genes of metabolic enzymes predict postoperational prognosis of patients with colorectal cancer

  • Guanglong Dong1,
  • Xianli He2,
  • Yibing Chen3,
  • Haiyan Cao3,
  • Jiaojiao Wang3,
  • Xiaonan Liu4,
  • Shukui Wang5,
  • Shaogui Wan6Email author and
  • Jinliang Xing3Email author
Contributed equally
Molecular Cancer201514:171

https://doi.org/10.1186/s12943-015-0442-x

Received: 13 April 2015

Accepted: 3 September 2015

Published: 17 September 2015

Abstract

Background

Genetic alterations in tricarboxylic acid (TCA) cycle metabolic enzymes were recently linked to various cancers. However, the associations of single nucleotide polymorphisms (SNPs) in genes of these enzymes have not been well studied.

Methods

We genotyped 16 SNPs from 7 genes encoding TCA cycle metabolic enzymes in 697 colorectal carcinoma (CRC) patients receiving surgical resection and analyzed their associations with clinical outcomes by multivariate Cox proportional hazard model. Then, the significant results were validated in another cohort of 256 CRC patients.

Results

We identified 4 SNPs in 2 genes had significant associations with CRC death risk and 5 SNPs in 3 genes had significant associations with CRC recurrence risk. Similar significant results were confirmed for rs4131826 in SDHC gene, rs544184 in SDHD gene and rs12071124 in FH gene in a validation cohort. Further analysis indicated that unfavorable genotypes exhibited a significant cumulative effect on overall and recurrence-free survival in a dose-dependent manner. Moreover, survival tree analysis indicated that SNP rs4131826 in SDHC gene and SNP rs12071124 in FH gene were the primary factors contributing to the different overall survival time and recurrence-free survival time of CRC patients, respectively. Immunohistochemical analysis further validated the effect of rs4131826 and rs544184 on expression of SDHC and SDHD in tissue samples.

Conclusions

Our study suggests that SNPs in TCA cycle metabolic enzymes might be significantly associated with clinical outcomes in Chinese population diagnosed with CRC. Further functional and validated studies are warranted to expend our results to clinical utility.

Keywords

Colorectal cancerPrognosisSingle nucleotide polymorphismSuccinate dehydrogenaseTricarboxylic acid cycle

Introduction

Colorectal cancer (CRC) is the third most common malignancy leading to a major cause of cancer mortality and morbidity worldwide [1]. Recently, increased incidence rates of CRC have been observed in regions that previously had relative lower CRC risk, especially in China [2]. Specific and sensitive prognostic biomarkers which are capable of identifying CRC patients with benefit from treatment will improve patients’ outcomes. To date, TNM staging system is the only widely accepted clinical prognostic factor [3, 4], although many biomarkers, such as microsatellite instability (MSI), chromosomal instability (CIN) and mutations of some tumor suppressor genes, have been reported to be associated with CRC patients’ outcomes. Novel biomarkers that can be adapted to CRC clinical utility are still urgently needed.

Altered metabolism is a hallmark of cancer, contributing to the initiation, growth, and maintenance of tumors [5]. Tricarboxylic acid (TCA) cycle which occurs in mitochondria is a well-known central metabolic pathway in the metabolism of sugars, lipids and amino acids. Main regulators of TCA cycle consist of three key metabolic enzymes, i.e. succinate dehydrogenase (SDH), fumarate hydratase (FH), and isocitrate dehydrogenase (IDH). Recently, more and more evidence has suggested that mutations of metabolic enzyme genes in TCA cycle are involved in the development of cancer [68]. It has been found that loss-of-function mutations in genes of SDH complex and FH lead to the accumulation of their substrates, i.e. succinate and fumarate, respectively, while gain-of-function mutations of IDH with neomorphic enzyme activity produces a novel metabolite, D-2-hydroxyglutarate (2-HG). All of these metabolites have recently been found to be associated with cancer development [914]. Although many studies are focused on the genetic mutations of TCA cycle core enzymes in cancers, there is no study so far to investigate the roles of SNPs in genes encoding these enzymes in CRC prognosis.

Single nucleotide polymorphism (SNP) is the most common genetic variation, and numerous previous studies have shown that SNPs may be promising surrogate biomarkers to predict therapeutic responses or prognosis of cancer patients [15]. However, the association between TCA cycle enzyme genes and the prognosis of CRC has never been investigated. In the present study, we sought to systemically evaluate the associations between functional SNPs in genes encoding TCA cycle core enzymes, including all subunits of SDH, FH, and IDH genes, and postoperational clinical outcomes in a hospital-based Chinese patient cohort diagnosed with CRC.

Materials and methods

Study population

The subjects in this study were enrolled between May 2006 and June 2012 from Xijing Hospital and Tangdu Hospital affiliated to the Fourth Military Medical University in Xi’an, China. The enrolled patients have to match the following criteria: 1) histologically confirmed with primary colorectal adenocarcinoma and no history of other cancers; 2) received curative surgical resection treatment, but without any preoperative anticancer treatment; 3) with complete clinical and follow-up data, as well as common epidemiological data. After excluding 16 patients who died within 1 month after surgical resection, a total of 697 CRC patients were included in this analysis. An additional validation cohort of 256 CRC patients was recruited from Nanjing First Hospital in Nanjing, China based on same enrollment criteria. Prior to surgical resection, 5 ml of peripheral blood sample was collected from each patient for DNA preparation. This study was approved by the Ethic Committees of FMMU and Nanjing First Hospital. Written informed consents were obtained from all patients.

Demographic and clinical data

Demographic variables were collected by in-person interviewing using a standardized epidemiological questionnaire including gender, age, tumor position, differentiation, stage, and chemotherapy. Major clinical data were collected from medical records and consulting with the treating doctors, including variables of tumor position, TNM stage, tumor differentiation, and the adjuvant chemotherapy. The standard follow-up was updated at 6-month intervals through onsite interview, direct calling, or medical chart review by trained clinical specialists. The latest follow-up data in our analysis was obtained in January 2014 for both patient cohorts.

SNP selection and genotyping

The candidate functional SNPs in TCA cycle metabolic enzymes were selected by web-based SNP selection tools (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm). The potential functional SNPs were selected based on the following criteria: 1) the SNPs had minor allele frequency ≥5 % in Han Chinese population (CHB) in the HapMap database; 2) the position of candidate SNP should be located in miRNA binding sites of 3’-UTR region, in transcription factor binding site of 5’-flanking region (2000 bp upstream from the transcription start site), or in mRNA splice site or exons. If there were multiple functional SNPs within the same haplotype block and the linkage coefficient r2 > 0.8, only one SNP was included. Finally, 18 potential functional SNPs in SDH genes (including subunits of SDHA, SDHB, SDHC, and SDHD), FH gene, and IDH genes (including subunits of IDH1 and IDH2) were identified for genotyping with Sequenom iPLEX platform (Sequenom Inc., San Diego, CA, USA). Significant SNPs were also genotyped in validation cohort with Sequenom iPLEX platform. Strictly quality controls were implemented in each assay when genotyping and only those SNPs with call rate > 95 % were included for further analysis.

Immunohistochemical staining of SDHC and SDHD

Formalin-fixed, paraffin-embedded CRC tissues from patients with different genotypes of SNPs rs4131826 and rs544184 were collected from Department of Pathology in Xijing Hopital. HE slides from these patients were viewed under a light microscope by a pathologist and 4-μm-thick tissue sections were cut from corresponding blocks containing representative tumor regions. Immunohistochemical staining of SDHC and SDHD was performed as previously described [16]. A rabbit anti-human SDHC antibody (1:200, Abcam) or rabbit anti-human SDHD antibody (1:200, Abcam) was used. The intensity and extent of immunostaining were evaluated for all samples under double-blinded conditions as previously described [16].

Statistical analysis

In this study, the clinical outcomes of CRC patients include two major endpoints, overall survival (OS) and recurrence-free survival (RFS). Overall survival time was defined as the time from initial surgery to death from any cause. Recurrence-free survival time was defined as the time from initial surgery to local recurrence, distant recurrent metastasis. The associations between SNPs and CRC outcomes were estimated as hazard ratios (HRs) by Cox proportional hazards regression model with adjustment for gender, age, hospital site, tumor position, clinical stage, tumor differentiation and adjuvant chemotherapy. Three genetic models (dominant, recessive, and additive) were tested and the best fitting model (with smallest P value) was selected for further analysis. Kaplan-Meier survival curves and log-rank test were used to assess the differences in OS and RFS. We also calculated the false-positive report probability (FPRP) at prior probability levels of 0.001, 0.01, 0.1, and 0.25 to validate all statistically significant findings as previously described [17]. A FPRP value <0.1 was considered to be noteworthy. Cumulative effect was evaluated by the combination of unfavorable genotypes identified from the main effect analysis of individual SNP. The SPSS Statistics19.0 software (IBM) was used for all statistical analyses. Survival tree analyses were used to determine the higher-order gene-gene interactions, which were performed by the STREE program (http://masal.med.yale.edu/stree/) using recursive-partitioning to identify subgroups of individuals at higher risk.

Results

Characteristics of the study population

The distributions of 697 patients’ demographic and clinicopathologic characteristics by death or recurrence were summarized in Additional file 1: Table S1. During the median follow-up time of 35.7 months (range, 3.1-83.4 months), there were 205 patients (29.4 %) died and 240 patients (34.4 %) developed recurrence among CRC patients. Significant higher death and recurrence risks were observed in patients at late stage (stages III + IV) with HRs of 3.82(95 % CI 2.75-5.33, P < 0.001) and 3.13 (95 % CI 2.32-4.23, P < 0.001), respectively, when comparing to those at early stage (stages I + II). Expectably, patients with adjuvant chemotherapy exhibited significant protective effect on OS and RFS with HRs of 0.36 (95 % CI 0.25-0.52, P < 0.001) and 0.45 (95 % CI 0.32-0.63, P < 0.001), respectively, when comparing to those without adjuvant chemotherapy. There was no significantly different death or recurrence risk observed in patients with regard to gender, age, hospital site, tumor position, or tumor differentiation. Very similar demographic and clinicopathologic characteristics were observed in validation cohort of 256 CRC patients (Additional file 2: Table S2).

The associations between individual SNPs and CRC clinical outcomes and gene expression

After excluding 2 SNPs with unqualified call rate (84.9 % for rs9708193 and 66.6 % for rs4932158), we eventually analyzed 16 SNPs in 7 genes encoding the TCA cycle core metabolic enzymes. We assessed the association of each individual SNP with CRC death or recurrence risk by multivariate Cox regression with adjustment for gender, age, hospital site, tumor position, clinical stage, tumor differentiation and adjuvant chemotherapy. As shown in Table 1, there were 2 SNP in SDHC gene and 2 SNPs in SDHD gene exhibited significant association with increased CRC death risk under additive and recessive models, respectively. And the most significant result was found in the association of rs4131826 of SDHC gene with OS (HR 0.61, 95 % CI 0.47-0.79, P < 0.001) under additive model. In addition, 2 SNPs in SDHD and FH genes exhibited borderline significant association with CRC death risk under additive model. In the RFS analysis, we found that 1 SNP (rs4131826) in SDHC gene, 3 SNPs (rs10789859, rs544184 and rs7121782) in SDHD gene, and 1 SNP (rs12071124) in FH gene had significant associations with CRC recurrence risk (Table 1). We calculated the false-positive report probability (FPRP) to validate all statistically significant findings. As shown in Additional file 3: Table S3, rs4131826 in SDHC gene and 3 SNPs in SDHD gene were considered to be noteworthy. Further analysis showed that 3 SNPs (rs10789859, rs544184 and rs7121782) in SDHD gene were located within the same haplotype block with strong linkage disequilibrium (LD = 0.947, 0.896 and 0.923 respectively). Therefore, we only select one (SNP rs544184) for further validation. As shown in Table 2 with a total of 4 validated SNPs, similar significant results were confirmed for rs4131826 in SDHC gene, rs544184 in SDHD gene and rs12071124 in FH gene, but not for rs12064957 in SDHC gene. We have further examined the expression of SDHC and SDHD in tissue samples from CRC patients with different genotypes of SNPs rs4131826 and rs544184 using immunohistochemical staining (IHC). As showed in Fig. 1, patients carrying VV or WV genotype of rs4131826 exhibited the significantly higher expression of SDHC when compared with those carrying WW genotype, and patients carrying WW or WV genotypes of rs544184 exhibited significantly higher expression of SDHD when compared with those carrying VV genotype.
Table 1

Association between SNPs in TCA cycle related genes and CRC outcomes in primary cohort

Gene

SNP

Call rate (%)

Function

OS

RFS

Best fitting model

HR (95 % CI)a

P value

Best fitting model

HR (95 % CI)a

P value

SDHA

rs13173911

99.0

TFBS

Additive

0.86(0.52-1.44)

0.573

Additive

0.88(0.55-1.40)

0.589

 

rs2864963

98.4

TFBS

Dominant

0.92(0.69-1.21)

0.533

Dominant

0.92(0.71-1.19)

0.522

SDHB

rs3754509

98.0

TFBS

Recessive

0.96(0.66-1.40)

0.823

Recessive

0.88(0.62-1.27)

0.504

SDHC

rs12064957

98.9

TFBS

Additive

1.36(1.06-1.74)

0.015

Additive

1.21(0.95-1.54)

0.117

 

rs3935401

98.7

miRNA

Recessive

1.25(0.68-2.31)

0.476

Dominant

0.91(0.70-1.19)

0.484

 

rs4131826

96.1

TFBS

Additive

0.61(0.47-0.79)

1.8x10 -4

Additive

0.73(0.58-0.91)

0.005

SDHD

rs10789859

98.3

TFBS

Additive

1.19(0.97-1.45)

0.088

Additive

1.29(1.08-1.55)

0.006

 

rs544184

96.8

TFBS

Recessive

1.52(1.05-2.19)

0.026

Additive

1.31(1.08-1.58)

0.005

 

rs7121782

98.3

TFBS

Recessive

1.49(1.04-2.14)

0.031

Additive

1.29(1.07-1.55)

0.008

IDH1

rs12478635

97.4

TFBS

Additive

1.04(0.85-1.28)

0.682

Dominant

1.02(0.76-1.38)

0.875

IDH2

rs11540478

99.0

Splicing

Additive

0.91(0.69-1.19)

0.490

Additive

0.96(0.75-1.23)

0.731

 

rs11632348

98.3

TFBS

Recessive

0.67(0.36-1.24)

0.203

Recessive

0.69(0.39-1.24)

0.218

 

rs4283211

98.6

TFBS

Dominant

0.88(0.66-1.17)

0.387

Additive

0.94(0.75-1.18)

0.593

FH

rs12071124

97.4

TFBS

Additive

0.83(0.67-1.03)

0.086

Recessive

0.62(0.43-0.90)

0.013

 

rs1414493

97.8

TFBS

Recessive

1.08(0.73-1.58)

0.709

Dominant

1.14(0.86-1.53)

0.359

 

rs7530270

98.7

TFBS

Recessive

0.85(0.60-1.21)

0.361

Recessive

0.76(0.54-1.05)

0.100

Abbreviations: CI confidence interval, HR hazard ratio, OS overall survival, RFS recurrence free survival. Significant p values were in bold

aAdjusted for gender, age, hospital site, tumor position, clinical stage, differentiation and treatment after surgery

Table 2

Association between significant SNPs and 256 CRC patients’ outcome in validation cohort

Gene

SNP

Call rate (%)

Function

OS

RFS

Best Fitting Model

HRa (95 % CI)

P-value

Best Fitting Model

HRa (95 % CI)

P-value

SDHC

rs12064957

98.8

TFBS

Additive

1.16(0.79-1.70)

0.450

Additive

1.14(0.84-1.54)

0.406

 

rs4131826

96.1

TFBS

Additive

0.66(0.46-0.94)

0.022

Additive

0.73(0.55-0.97)

0.027

SDHD

rs544184

97.3

TFBS

Recessive

2.30(1.40-3.78)

0.001

Recessive

2.02(1.33-3.07)

0.001

FH

rs12071124

96.5

TFBS

Additive

0.90(0.67-1.22)

0.511

Additive

0.77(0.60-0.98)

0.033

Abbreviations: CI confidence interval, HR hazard ratio, OS overall survival, RFS recurrence free survival. Significant P values were in bold

aAdjusted by gender, age,tumor position, clinical stage, differentiation and treatment after surgery where appropriate

Fig. 1

Immunohistochemical staining of SDHC (a) and SDHD (b) in tumor tissue samples from CRC patients with different genotypes of SNP rs4131826 or SNP rs544184

Cumulative effects of unfavorable genotypes on CRC clinical outcomes

To assess the cumulative effect of combined SNPs on CRC clinical outcomes, we grouped the patients by the number of unfavorable genotypes of SNPs with significant or borderline significant association under dominant model, and evaluated their associations with CRC death or recurrence risk by multivariate Cox proportional hazard model. As shown in Table 3, significant increased death risk were observed in patient groups with 2, 3, and 4 unfavorable genotypes, with HRs of 1.93 (95 % CI 1.18-3.18), 2.14 (95 % CI 1.28-3.58), and 3.17 (95 % CI 1.79-5.61), respectively, when comparing to group with no more than 1 unfavorable genotype. The cumulative death risk of CRC patients conferred by combined unfavorable genotypes exhibited a significant dose-dependent manner with P for trend < 0.001. Similarly, the significant increased recurrence risks were observed in patient groups with 2, 3, and 4 unfavorable genotypes, when comparing to those with zero or 1 unfavorable genotype. And also the increased combined recurrence risk exhibited a dose-dependent manner with P for trend = 0.001. As shown in Additional file 4: Table S4, very similar cumulative effect of unfavorable genotypes was also observed in validation cohort.
Table 3

Cumulative effect of unfavorable genotypes on CRC outcomes

Group(number of unfavorable genotypesa)

Death/Total

HR (95 % CI)b

P value

Recurrence/Total

HRb (95 % CI)

P value

Group 1 (0-1)

20/138

Ref.

 

29/138

Ref.

 

Group 2 (2)

78/246

1.93(1.18-3.18)

0.009

94/246

1.81(1.19-2.76)

0.006

Group 3 (3)

59/190

2.14(1.28-3.58)

0.004

68/190

1.89(1.21-2.93)

0.005

Group 3 (4)

31/69

3.17(1.79-5.61)

7.30 × 10 -5

31/69

2.45(1.47-4.09)

0.001

P for trend

  

1.12 × 10 -4

  

0.001

Abbreviations: CI confidence interval, HR hazard ratio, Ref. reference

aUnfavorable genotypes: SDHC rs12064957 (WV + VV) and rs4131826 WW, SDHD rs544184 (WV + VV), and FH rs12071124 (WW + WV)

bAdjusted for gender, age, hospital site, tumor position, clinical stage, differentiation and treatment after surgery

Significant P values (<0.05) were bolded

Modulated effects of chemotherapy on CRC outcomes stratified by the number of unfavorable genotypes

To evaluate whether the number of unfavorable genotypes could modify the protective effects of postsurgical adjuvant chemotherapy on CRC patients’ clinical outcomes, Kaplan-Meier curves analysis and log-rank test were performed to assess the difference of death and recurrence risks between patient groups with lower and higher number of unfavorable genotype. As shown in Fig. 2, more prominent protective effects conferred by chemotherapy on CRC patients were observed in patient group with higher number of unfavorable genotypes in both overall survival and recurrence-free survival analyses with HRs of 0.28 (95 % CI 0.15-0.52, P = 6.02 x 10-5) and 0.35 (95 % CI 0.19-0.62, P = 3.76 x 10-4), respectively (Fig. 2b and d). While less prominent protective effects were observed in patient group with lower number unfavorable genotype in both the overall survival analysis (Fig. 2a) and recurrence-free survival analysis (Fig. 2c).
Fig. 2

The protective effects of chemotherapy on CRC patients stratified by the number of unfavorable genotypes in the overall survival analyses (a and b) and in the recurrence-free survival analysis (c and d)

Higher order gene-gene interactions and CRC patient clinical outcomes

We further explored the higher order gene-gene interactions to reveal whether complex interactions among these significant SNPs could potentially predict the CRC outcomes by survival tree analysis. In the OS analysis, 4 SNPs, including SDHC: rs4131826, SDHC: rs12064957, SDHD: rs544184, and FH: rs12071124, exhibited gene-gene interactions, resulting in 5 terminal nodes with different OS times (Fig. 3a). SNP SDHC: rs4131826, which initially split the survival trees under dominant model, was the primary factor contributing to the survival difference. Patient groups with lower risk were composed of Node 1 and Node 2 with longest median survival time (MST) of 42.9 months, while patient groups with higher risk were composed of Node 3 with shorter MST of 35.6 months, and Node 4 or Node 5 with shortest MST of 31.5 months (Fig. 3b). In the RFS analysis, another set of 3 SNPs, including FH: rs12071124, SDHC: rs4131826 and SDHD: rs544184, exhibited gene-gene interactions, resulting in 4 terminal nodes with different RFS times (Fig. 3c). The initial split site on the recurrence-free survival tree was due to FH: rs12071124, suggesting that this SNP was the primary factor contributing to the recurrence risk difference in our cohort. Longest MST for recurrence was 41.0 months observed in Node1 CRC patients, and significant shorter recurrent MST were observed in Node 2, 3, and 4 patient groups with a P value of 0.001 (Fig. 3d).
Fig. 3

Potential higher order gene-gene interactions among TCA pathway polymorphisms in the overall survival and recurrence-free survival analysis. a, c Tree structure identifying subgroups of patients with different genetic backgrounds; (b, d) Kaplan-Meier survival curves for patients based on survival tree analysis

Discussion

In the present study, we evaluated the effects of SNPs in genes encoding TCA cycle key metabolic enzymes on the clinical outcomes of 697 CRC patients. We identified 4 SNPs to be significantly associated with CRC death risk and 5 SNPs to be significantly associated with CRC recurrence risk. Cumulative effect analysis showed that these SNPs exhibited significant dose-dependent effects on CRC clinical outcomes. Similar significant results were confirmed in a validation cohort. Further survival tree analysis revealed that SNP rs4131826 in SDHC gene and SNP rs12071124 in FH gene were the primary factors contributing to OS and RFS, respectively. As the best of our knowledge, our study for the first time comprehensively assessed the association of SNPs in TCA cycle metabolic key enzyme genes and CRC prognosis.

Accumulating evidences have suggested that genetic backgrounds may affect the risk and prognosis of CRC [1820]. Previous studies have reported that SNPs in genes of most metabolic enzymes, such as glutathione S-transferases, N-acetyltransferase, and cytochrome P450 superfamily of enzymes, can affect enzyme activities and be linked to CRC susceptibility [2123]. TCA cycle in mitochondria is the core metabolic pathway, which consists of three key metabolic enzymes complexities (SDH, FH and IDH). Recent metabonomic studies have reported that altered metabolite profiles of TCA cycle are observed in serum and urinary samples from CRC patients [24, 25]. Despite the extensive investigations of metabolic enzyme and TCA cycle metabolite markers in CRC, there is no study focusing on the association between SNPs in TCA cycle metabolic enzyme genes and CRC prognosis so far.

In our study, SNP rs4131826 in SDHC gene, SNP rs544184 in SDHD gene and SNP rs12071124 in FH gene were found to be significantly associated with the OS or RFS of CRC patients. IHC analysis further indicated the potential effect of SNPs rs4131826 and rs544184 on the expression of SDHC and SDHD in tissue samples, respectively. Our findings was supported by previous studies showing that the expression of SDHC and SDHD was reduced in tumor tissues and their roles as tumor suppressors have been identified [2628]. The mutations in genes encoding enzymes of SDH complex and FH in TCA cycle pathway have previously been linked with mitochondrial dysfunction and cancers [11]. Mutations in SDH complex have been initially observed in paraganglioma [29, 30], and subsequent studies have reported those mutations to be involved in T-cell leukemia and gastrointestinal stromal tumor [31, 32]. The potential mechanism of genetic alterations in SDH genes associated with cancers has been proposed that these mutations cause the reduction of SDH activity, increase the mitochondrial succinate levels and thus increase mitochondrial reactive oxygen species and oxidative stress [28]. Mutations in FH metabolic enzyme have been observed in multiple uterine leiomyomata and aggressive renal cell carcinoma [33, 34]. FH converts fumarate to malate in the TCA cycle, and FH mutations lead to the accumulation of oncometabolite furmarate [35]. Although the specific mechanisms by which SDH/FH is connected with malignancies are probably multifactorial, strong evidences have indicated that dysfunctional cell signaling stimulated by oncometabolites such as succinate and fumurate may be the primary mechanism [6, 8, 13, 36]. In our study, the significant SNPs identified in SDH and FH genes were all located in the predicted transcriptional factor binding sites (TFBS), suggesting that these SNPs may affect the expression of SDH and FH enzymes. Different enzyme levels may lead to different intracellular concentrations of oncogenic metabolites, resulting in different characteristics of cancer cells which may further affect the prognosis of patients. However further functional studies are needed to reveal the underlying mechanism of our observational results.

Additionally, further stratified analysis indicated that more prominent protective effects conferred by adjuvant chemotherapy after surgical resection on CRC patients were observed in subgroup with higher number of unfavorable genotypes (Fig. 2), in both the overall survival and recurrence-free survival analyses. These results suggest that the SNPs in TCA cycle metabolic enzymes may serve as potential surrogate biomarker for individualized CRC therapy. Although some histological factors such as tumor stage, differentiation and grade, have been identified to be associated with CRC prognosis, there is still a large lack of specific and sensitive biomarker for predicting CRC outcomes [37]. Our findings, together with other molecular biomarker such as KRAS, BRAF and p53 mutations, may be incorporated into the histological prognostic factors and improve the predictive value in outcomes and treatment response.

Conclusively, our study indicates that genetic variations in TCA cycle metabolic enzyme genes are significantly associated with OS and RFS of CRC patients. Further functional studies are needed to reveal the underlying mechanisms implied in our observational findings.

Notes

Declarations

Acknowledgments

This study is supported by International S&T Cooperation Program of China (2013DFA32110).

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’ Affiliations

(1)
Department of General Surgery, The General Hospital of PLA
(2)
Department of General Surgery, Tangdu Hospital, Fourth Military Medical University
(3)
State Key Laboratory of Cancer Biology, Experimental Teaching Center of Basic Medicine, Fourth Military Medical University
(4)
Xijing Hospital of Digestive Disease, Fourth Military Medical University
(5)
Central Laboratory, Nanjing First Hospital, Nanjing Medical University
(6)
Institute of Pharmacy, Pharmaceutical College of Henan University

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