Abstract

   Disruption of the cell cycle pathway has previously been related to
   development of human cancers. However, associations between genetic
   variants of cell cycle pathway genes and prognosis of hepatocellular
   carcinoma (HCC) remain largely unknown. In this study, we evaluated the
   associations between 24 potential functional single nucleotide
   polymorphisms (SNPs) of 16 main cell cycle pathway genes and
   disease‐free survival (DFS) of 271 HCC patients who had undergone
   radical surgery resection. We identified two SNPs, i.e., SMAD3
   rs11556090 A>G and RBL2 rs3929G>C, that were independently predictive
   of DFS in an additive genetic model with false‐positive report
   probability (FPRP) <0.2. The SMAD3 rs11556090G allele was associated
   with a poorer DFS, compared with the A allele [hazard ratio
   (HR) = 1.46, 95% confidential interval (95% CI) = 1.13–1.89,
   P = 0.004]; while the RBL2 rs3929 C allele was associated with a
   superior DFS, compared with the G allele (HR = 0.74, 95%
   CI = 0.57–0.96, P = 0.023). Additionally, patients with an increasing
   number of unfavorable genotypes (NUGs) of these loci had a significant
   shorter DFS (P [trend] = 0.0001). Further analysis using receiver
   operating characteristic (ROC) curves showed that the model including
   the NUGs and known prognostic clinical variables demonstrated a
   significant improvement in predicting the 1‐year DFS (P = 0.011).
   Moreover, the RBL2 rs3929 C allele was significantly associated with
   increased mRNA expression levels of RBL2 in liver tissue
   (P = 1.8 × 10^−7) and the whole blood (P = 3.9 × 10^−14). Our data
   demonstrated an independent or a joint effect of SMAD3 rs11556090 and
   RBL2 rs3929 in the cell cycle pathway on DFS of HCC, which need to be
   validated by large cohort and biological studies.

   Keywords: Cell cycle pathway, hepatocellular carcinoma,
   single‐nucleotide polymorphism, survival

Introduction

   Hepatocellular carcinoma (HCC) is one of the most frequently diagnosed
   cancers and the leading causes of cancer death worldwide. As a high
   incidence area of HCC, China accounts for about 50% of the total number
   of cases and deaths worldwide [42]1, [43]2. In 2015, it had an
   estimated of 466,100 and 422,100 patients is diagnosed and die of HCC
   in China, respectively[44]1, which makes HCC a major public health
   concern in China. For the past decade, cancer staging systems, such as
   the Barcelona Clinic Liver Cancer (BCLC) staging system and Cancer of
   the Liver Italian Program (CLIP) score, have been established for the
   prognosis assessment by using clinical variables mainly including tumor
   size, number of primary tumors, portal vein tumor thrombosis, and
   inflammatory degree [45]3, [46]4. But none of these systems has been
   universally adopted by clinicians. Many novel molecular biomarkers,
   such as Alpha‐fetoprotein, circulating tumor cells and circulating
   microRNAs, have been also applied to predict HCC progression [47]5.
   However, limitations of low sensitivity and specificity are still
   associated with these biomarkers when used in disease staging [48]5.
   Long‐term outcome prediction for HCC patients remains unfavorable
   [49]6, [50]7, which hampers the development of personalized clinical
   assessment for HCC patients and calls for identifying additional, more
   discriminant prognostic indicators.

   In recent years, the development of human genome research has been
   witnessed much success in mining cancer‐related germline genetic
   variants, especially single nucleotide polymorphisms (SNPs). The
   current advance in genome‐wide association studies (GWASs) has led to
   the identification of several SNPs that are associated with HCC risk
   [51]8, [52]9, [53]10, [54]11. Additionally, a series of studies also
   reported that the HCC patients survival was associated with SNPs in
   genes functioning in pivotal biological pathways or processes, such as
   cytokine genes [55]12, [56]13, telomere maintenance genes [57]14, DNA
   repair genes [58]15, Wnt/β‐catenin pathway genes [59]16. These pieces
   of evidence demonstrate that genetic variants act as indicators of
   different processes of HCC and may provide further information beyond
   current clinical staging and pathologic prognostic assessments.

   The cell cycle is a set of organized and monitored events responsible
   for proper cell division into two daughter cells, which includes four
   traditional subdivision phases (i.e., G1, S, G2 and M) and is
   controlled by the signaling pathway comprising genes encoding cyclins,
   cyclin‐dependent kinases, and cyclin kinase inhibitors as well as the
   related regulators (e.g., MYC) [60]17, [61]18, [62]19. Disruption of
   the cell cycle pathway can result in cell cycle arrest and has
   previously been related to prognosis of human cancers [63]20, [64]21.
   For example, CDKN2A and RB1 expression levels have been associated with
   survival of patients with advanced‐stage ovarian cancer [65]22. In
   addition, several studies have also demonstrated that aberrant
   expression of cell cycle pathway genes, including cyclin (A, D1 and E),
   CDC2, p27 and p21, is associated with prognosis for patients with HCC
   [66]23, [67]24, [68]25, [69]26, [70]27.

   We have previously investigated the association of genetic variants in
   cell cycle pathway genes with susceptibility to HCC and found that SNPs
   in MCM4, CHEK1 and KAT2B were associated with HCC risk [71]28. There
   are additional studies demonstrating the associations between genetic
   variants of cell cycle pathway genes and survival of cancers of the
   ovaries, oral cavity and lung [72]29, [73]30, [74]31. However, the
   associations between genetic variants of cell cycle pathway genes and
   prognosis of HCC remain largely unknown. In this study, we hypothesized
   that genetic variants in cell cycle pathway genes are associated with
   survival of HCC patients. To test this hypothesis, we evaluated the
   associations of potential functional SNPs in 16 main cell cycle pathway
   genes (CDC25C, CDC7, CDKN1A, CDKN2A, CHEK1, MCM4, MCM7, MYC, ORC6L,
   KAT2B, PLK1, RAD21, RBL2, SMAD3, TGFB3, and YWHAB) with clinical
   outcomes of 271 HCC cases who had undergone radical surgery resection.

Materials and Methods

Study population

   The patients were derived from our previously published case–control
   study [75]28. A total of 1127 patients with newly diagnosed and
   pathologically confirmed HCC were recruited consecutively between June
   2007 and December 2013 from the First Affiliated Hospital and
   Affiliated Tumor Hospital of Guangxi Medical University. Among these
   patients, 271 undergone radical surgery resection with completed
   clinical information and long‐term follow‐up data for survival
   analysis. Clinical information (including age at diagnosis, sex,
   ethnicity, hepatitis B virus infection, smoking status, drinking
   status, capsule, portal vein tumor thrombosis, cirrhosis, and BCLC
   staging) was collected from medical records. Peripheral venous blood
   was collected in a vacuum EDTA anticoagulant tube from each
   participant. Genomic DNA was extracted using a standard
   phenol/chloroform extraction method and stored at −80°C. The study
   protocol was approved by the ethical committee of Guangxi Medical
   University, and a written informed consent was obtained from each of
   the participants.

Follow‐up

   All of the patients were followed up to collect data on postoperative
   recurrence, distant metastasis and death condition through phone calls
   or the medical records. The endpoint was disease‐free survival (DFS).
   DFS was defined as the interval between the date of tumor resection and
   the date of the presence of relapse/metastasis/death or until the last
   follow‐up. Patients who were lost to follow‐up, who were alive at the
   last follow‐up and who did not have developed recurrence/metastasis
   were censored.

SNP selection and genotyping

   The methods of SNP selection and genotyping have been described
   previously [76]28. In brief, we found 3826 differential expressed genes
   by analysis of three HCC‐related genome expression microarrays
   ([77]GSE14520, [78]GSE25097, and [79]GSE12941) from GEO database. After
   performing the gene ontology classification and pathway enrichment
   analysis by using blast2GO and DAVID ([80]https://david.ncifcrf.gov/),
   we identified 40 cell cycle pathway genes involved in the cellular
   process. Considering the literature on these genes with cancer
   survival, we selected 16 genes (CDC25C, CDC7, CDKN1A, CDKN2A, CHEK1,
   MCM4, MCM7, MYC, ORC6L, KAT2B, PLK1, RAD21, RBL2, SMAD3, TGFB3, and
   YWHAB) for further analysis. The genotype information of the 16 genes
   were downloaded from Hapmap website
   ([81]http://hapmap.ncbi.nlm.nih.gov/), and functional SNPs were
   selected using SNPinfo ([82]https://snpinfo.niehs.nih.gov/) [83]32.
   Considering the minor allele frequency (MAF ≥ 0.05), linkage
   disequilibrium (LD, r ^2 > 0.8) and potential functions in
   transcription factor‐binding, microRNA‐binding and splicing sites, we
   finally included 24 SNPs with putative functions in this study
   (Table [84]1). Then, we performed the genotyping by using a MassARRAY
   system (Sequenom, San Diego, CA, USA) and a matrix‐assisted laser
   desorption ionization‐time of flight mass spectrometry method according
   to the manufacturer's instructions. All the primers for PCR were
   designed using the Assay Designer software package of the Sequenom
   system (San Diego, CA, USA).

Table 1.

   SNPs selection and prediction function in SNPinfo
   Genes SNP CHR Position Allele[85]^a MAF Prediction function in
   SNPinfo[86]^b
   CDC25C rs3734166 5 137693222 A/G 0.42 Nonsynonymous variant
   CDC7 rs13447539 1 91758416 A/G 0.05 Splicing site and nonsynonymous
   variant
   CDKN1A rs3176320 6 36754766 A/G 0.24 Transcription factor binding site
   CDKN1A rs3176329 6 36755441 G/T 0.09 Transcription factor binding site
   CDKN2A rs3088440 9 21958159 G/A 0.12 Transcription factor and
   microRNA‐binding site
   CHEK1 rs3731399 11 125002457 T/C 0.17 Transcription factor binding site
   CHEK1 rs515255 11 125002355 C/T 0.40 Transcription factor binding site
   MCM4 rs2305952 8 49037162 T/C 0.17 Transcription factor binding site
   MCM7 rs2070215 7 99534733 A/G 0.17 Splicing site and nonsynonymous
   variant
   MCM7 rs2261360 7 99530929 C/A 0.40 Transcription factor and
   microRNA‐binding site
   MCM7 rs4928 7 99528626 C/G 0.06 Splicing site
   MYC rs4645948 8 128817680 C/T 0.13 Transcription factor binding site
   ORC6L rs12596237 16 45284886 C/T 0.05 Transcription factor binding site
   ORC6L rs33994299 16 45281099 T/C 0.06 Transcription factor binding site
   and splicing site
   PCAF rs17006625 3 20136100 A/G 0.14 Splicing site and nonsynonymous
   variant
   PCAF rs4858770 3 20169427 C/T 0.47 MicroRNA‐binding site
   PLK1 rs2230915 16 23608959 G/A 0.05 Splicing site and microRNA‐binding
   variant
   RAD21 rs16889105 8 117952045 A/G 0.40 Transcription factor and
   microRNA‐binding site
   RAD21 rs6987652 8 117951462 G/A 0.16 Transcription factor binding site
   RBL2 rs3929 16 52081809 G/C 0.21 MicroRNA‐binding site
   SMAD3 rs11556090 15 65273437 A/G 0.11 MicroRNA‐binding site
   SMAD3 rs8025774 15 65270330 C/T 0.50 MicroRNA‐binding site
   TGFB3 rs3917148 14 75516274 A/C 0.14 Transcription factor binding site
   YWHAB rs2425675 20 42968348 G/A 0.18 Transcription factor binding site
   [87]Open in a new tab

   SNP, single‐nucleotide polymorphism; CHR, chromosome; MAF, minor allele
   frequency.
   ^a

   Reference allele/minor allele.
   ^b

   SNPinfo: [88]https://snpinfo.niehs.nih.gov/.

Statistical methods

   Cox proportional hazards regression analysis was conducted to assess
   the associations between SNPs and DRS in an additive model with
   adjustment for age at diagnosis, sex, ethnic, hepatitis B virus
   infection, smoking status, drinking status, capsule, portal vein tumor
   thrombosis, cirrhosis and BCLC staging. The false‐positive report
   probability (FPRP) approach was applied to correct for multiple
   comparisons [89]33. We assigned a prior probability of 0.1 to detect an
   HR of 2.0 for the adverse genotypes and alleles of SNPs with an
   elevated risk. Only the significant results with FPRP <0.2 were
   considered noteworthy. The stepwise Cox model including noteworthy SNPs
   and clinical variables was conducted to choose independent predictive
   SNPs. Kaplan–Meier estimation of survival functions and Log‐rank tests
   were used to evaluate the single and combined effects of risk genotypes
   on DFS. The ROC curve was used to evaluate the performance of different
   prediction models, and Delong's test was conducted to compare the area
   under the curves (AUC) across different models [90]34. Finally, to
   assess the correlation between genetic variants and mRNA expression of
   the corresponding genes, the expression quantitative trait loci (eQTL)
   analysis was also performed using data from the GTEx project
   ([91]http://www.gtexportal.org/home/) and the HapMap3 Project (Release
   version 2) by general linear regression model in additive genetic model
   [92]35, [93]36. All analyses were performed using SAS (version 9.1.3;
   SAS Institute, Cary, NC), unless otherwise specified.

Results

Basic characteristics of study populations

   The details of clinical information about age at diagnosis, sex,
   ethnicity, hepatitis B virus infection, smoking status, drinking
   status, capsule, portal vein tumor thrombosis, cirrhosis status, BCLC
   staging and survival outcomes are showed in Table [94]2. There were 271
   cases of staging 0‐C (BCLC staging) who had undergone radical surgery
   resection with a median follow‐up time of 19.0 months. At the time of
   analysis, 185 (68.3%) patients reached the DFS endpoint. The median DFS
   time was 20.3 months and 1, 3 and 5‐year survival rate were 55.4%,
   40.2%, and 28.9%, respectively. Univariate analysis indicated that only
   portal vein tumor thrombosis, cirrhosis, and BCLC staging were
   significantly associated with HCC DFS (P = 0.007, 0.030 and 0.004,
   respectively).

Table 2.

   Association between characteristic factors and HCC DFS by univariate
   Cox proportional hazards regression model
   Factors       Patients Event (%)[95]^a HR (95% CI)      P
   Sex
   Female        24       19 (79.2)       1.00
   Male          247      166 (67.2)      0.99 (0.62–1.59) 0.964
   Age
   ≤50           164      112 (68.3)      1.00
   >50           107      73 (68.2)       0.88 (0.66–1.18) 0.403
   Ethnicity
   Han           175      125 (71.4)      1.00
   Zhuang/others 96       60 (62.5)       0.85 (0.62–1.15) 0.285
   HBsAg
   Negative      32       22 (68.8)       1.00
   Positive      239      163 (68.2)      1.00 (0.64–1.56) 0.987
   Smoking
   Never         175      120 (68.6)      1.00
   Ever          96       65 (67.7)       1.11 (0.82–1.50) 0.503
   Drinking
   Never         173      118 (68.2)      1.00
   Ever          98       67 (68.4)       1.09 (0.81–1.47) 0.587
   Intact capsule
   No            226      152 (67.3)      1.00
   Yes           45       33 (73.3)       1.11 (0.77–1.62) 0.573
   Portal vein tumor thrombosis
   No            214      139 (65)        1.00
   Yes           57       46 (80.7)       1.59 (1.13–2.22) 0.007
   Cirrhosis
   No            98       75 (76.5)       1.00
   Yes           173      110 (63.6)      0.72 (0.54–0.97) 0.030
   BCLC staging
   0/A           86       50 (58.1)       1.00
   B/C           176      128 (72.7)      1.62 (1.17–2.25) 0.004
   Missing       9
   [96]Open in a new tab

   HCC, hepatocellular carcinoma; DFS, disease‐free survival; HRs, hazards
   ratio; CI, confidence interval; HBsAg, hepatitis B virus surface
   antigens; BCLC, Barcelona Clinic Liver Cancer.
   ^a

   Including relapse, metastasis and death.

Survival analysis of SNPs and DFS

   As shown in Table [97]3, 16 SNPs were genotyped successfully in all HCC
   patients, while the other eight SNPs had a few missing data. Because
   ORC6L rs12596237 had only one single genotype, we included 23 SNPs for
   further analysis. We first performed Cox proportional hazards
   regression analysis to assess associations of individual SNPs in genes
   of the cell cycle pathway with DFS. We found that four SNPs were
   significantly associated with DFS at P < 0.05 under an additive genetic
   model, of which three SNPs, i.e. rs11556090 (P = 0.004, FPRP = 0.03),
   rs8025774 (P = 0.020, FPRP = 0.15) and rs3929 (P = 0.023, FPRP = 0.17),
   had FPRP <0.2. To assess the independent prognostic factor of DFS, we
   further conducted stepwise Cox regression analysis by including the
   clinical variables and the significant SNPs. The results demonstrated
   that BCLC staging, SMAD3 rs11556090, and RBL2 rs3929 were still
   significantly associated with DFS of HCC patients (Table [98]4). Taken
   all together, we selected SMAD3 rs11556090 A>G and RBL2 rs3929G>C as
   the final independent SNPs for further analyses. The identified SNPs
   were both predicted to be in the miRNA‐binding sites (Table [99]1),
   which may affect the binding capacity of miRNA and the corresponding
   gene. As shown in Table [100]5, our results demonstrated that the SMAD3
   rs11556090G allele was significantly associated with a shorter DFS
   (trend test: P = 0.004), while the RBL2 rs3929 C allele was associated
   with a superior DFS (trend test: P = 0.023). Similarly, the SMAD3
   rs11556090 GG+GC genotypes were associated with a shorter DFS, compared
   with the CC genotype (HR = 1.54, 95% CI = 1.13–2.09, P = 0.006), and
   similar results were found for the RBL2 rs3929 CC+GC genotypes,
   compared with the GG genotype (HR = 4.98, 95% CI = 1.57–15.83,
   P = 0.006). For the visual effect, we used Kaplan–Meier curves to
   depict associations between the significant SNPs and DFS
   (Fig. [101]1A–D).

Table 3.

   Association between SNPs of cell cycle pathway genes and HCC DFS by
   multivariate Cox proportional hazards regression model
   SNP Gene Allele[102]^a Frequency of genotypes (%) Missing DFS
   MhoG HetG RhoG HR (95% CI)[103]^b P [104]^b FPRP
   rs3734166 CDC25C A/G 108 (39.9) 136 (50.2) 27 (10) – 0.92 (0.73–1.16)
   0.460 0.81
   rs13447539 CDC7 A/G 260 (95.9) 11 (4.1) – – 0.69 (0.28–1.73) 0.433 0.84
   rs3176320 CDKN1A A/G 145 (53.5) 112 (41.3) 14 (5.2) – 0.91 (0.70–1.17)
   0.445 0.80
   rs3176329 CDKN1A G/T 177 (65.3) 73 (26.9) 7 (2.6) 4 (1.5) 1.12
   (0.83–1.50) 0.466 0.81
   rs3088440 CDKN2A G/A 193 (71.2) 73 (26.9) 5 (1.8) – 0.86 (0.63–1.19)
   0.370 0.77
   rs3731399 CHEK1 T/C 206 (76.0) 59 (21.8) 5 (1.8) 1 (0.4) 1.22
   (0.89–1.68) 0.224 0.67
   rs515255 CHEK1 C/T 106 (39.1) 129 (47.6) 31 (11.4) 5 (1.8) 1.01
   (0.81–1.26) 0.938 0.89
   rs2305952 MCM4 T/C 195 (72.0) 74 (27.3) 2 (0.7) – 1.02 (0.74–1.41)
   0.917 0.89
   rs2070215 MCM7 A/G 123 (45.4) 110 (40.6) 38 (14) – 1.08 (0.88–1.33)
   0.464 0.81
   rs2261360 MCM7 C/A 117 (43.2) 111 (41.0) 42 (15.5) 1 (0.4) 0.99
   (0.80–1.22) 0.910 0.89
   rs4928 MCM7 C/G 243 (89.7) 27 (10.0) 1 (0.4) – 0.93 (0.57–1.54) 0.782
   0.88
   rs4645948 MYC C/T 192 (70.8) 74 (27.3) 5 (1.8) – 0.95 (0.70–1.30) 0.744
   0.87
   rs12596237 ORC6L C/T 271 (100) – – – – – –
   rs33994299 ORC6L T/C 254 (93.7) 16 (5.9) – 1 (0.4) 0.44 (0.21–0.96)
   0.039 0.48
   rs17006625 PCAF A/G 153 (56.5) 100 (36.9) 18 (6.6) – 1.03 (0.81–1.30)
   0.823 0.88
   rs4858770 PCAF C/T 99 (36.5) 130 (48.0) 38 (14.0) 4 (1.5) 0.98
   (0.77–1.24) 0.861 0.89
   rs2230915 PLK1 G/A 251 (92.6) 19 (7.0) 1 (0.4) – 1.09 (0.65–1.83) 0.742
   0.87
   rs16889105 RAD21 A/G 114 (42.1) 112 (41.3) 45 (16.6) – 1.17 (0.94–1.45)
   0.157 0.59
   rs6987652 RAD21 G/A 199 (73.4) 66 (24.4) 6 (2.2) – 0.88 (0.65–1.21)
   0.438 0.80
   rs3929 RBL2 G/C 160 (59.0) 95 (35.1) 14 (5.2) 2 (0.7) 0.74 (0.57–0.96)
   0.023 0.17
   rs11556090 SMAD3 A/G 162 (59.8) 98 (36.2) 11 (4.1) – 1.46 (1.13–1.89)
   0.004 0.03
   rs8025774 SMAD3 C/T 79 (29.2) 141 (52.0) 50 (18.5) 1 (0.4) 0.76
   (0.61–0.96) 0.020 0.15
   rs3917148 TGFB3 A/C 212 (78.2) 56 (20.7) 2 (0.7) 1 (0.4) 0.97
   (0.69–1.38) 0.872 0.89
   rs2425675 YWHAB G/A 173 (63.8) 88 (32.5) 10 (3.7) – 1.01 (0.77–1.33)
   0.919 0.89
   [105]Open in a new tab

   SNP, single‐nucleotide polymorphism; HCC, hepatocellular carcinoma;
   DFS, disease‐free survival; MhoG, major homozygous genotype; HetG,
   heterozygous genotype; RhoG, rare homozygous genotype; HR, hazards
   ratio; CI, confidence interval; FPRP, false positive report
   probability.
   ^a

   Reference allele/minor allele.
   ^b

   Adjusted by age, sex, ethnicity, HBsAg status, smoking status, drinking
   status, intact capsule, portal vein tumor thrombosis, cirrhosis and
   BCLC staging.

Table 4.

   Predictors of DFS obtained from stepwise Cox proportional hazards
   regression analysis
   Parameter Parameter estimate Standard error Chi‐square HR (95%
   CI)[106]^a P [107]^a
   Age −0.11 0.16 0.45 0.90 (0.66–1.22) 0.501
   Sex 0.02 0.25 0.01 1.02 (0.63–1.67) 0.930
   Ethnicity −0.13 0.15 0.77 0.88 (0.65–1.18) 0.380
   BCLC staging 0.45 0.14 9.73 1.57 (1.18–2.08) 0.002
   rs3929 −0.27 0.13 4.39 0.76 (0.59–0.98) 0.036
   rs11556090 0.30 0.13 5.49 1.36 (1.05–1.75) 0.019
   [108]Open in a new tab

   DFS, disease‐free survival; HR, hazards ratio; CI, confidence interval;
   BCLC, Barcelona Clinic Liver Cancer.
   ^a

   Stepwise analysis included sex, age, ethnicity, HBsAg status, smoking
   status, drinking status, intact capsule, portal vein tumor thrombosis,
   cirrhosis, BCLC staging, RBL2 rs3929, SMAD3 rs8025774, and SMAD3
   rs11556090.

Table 5.

   Association of SMAD3 rs11556090 and RBL2 rs3929 with DFS of HCC
   patients
   Genotypes Patients Events (%)[109]^a Univariate analysis Multivariate
   analysis[110]^b
   HR (95%CI) P HR (95%CI) P
   SMAD3 rs11556090
   AA 162 103 (63.6) 1 1
   AG 98 73 (74.5) 1.42 (1.05–1.92) 0.021 1.50 (1.09–2.06) 0.013
   GG 11 9 (81.8) 1.45 (0.73–2.86) 0.289 2.00 (0.98–4.05) 0.056
   Trend test 0.017 0.004
   AA 162 103 (63.6) 1 1
   AG+GG 109 82 (75.2) 1.32 (1.04–1.67) 0.023 1.54 (1.13–2.09) 0.006
   RBL2 rs3929
   GG 160 114 (71.3) 1 1
   GC 95 67 (70.5) 0.91 (0.68–1.24) 0.554 0.94 (0.68–1.29) 0.696
   CC 14 3 (21.4) 0.18 (0.06–0.57) 0.004 0.20 (0.06–0.63) 0.006
   Trend test 0.009 0.023
   CC 14 3 (21.4) 1 1
   GG+GC 255 181 (71.0) 5.31 (1.7–16.6) 0.004 4.98 (1.57−15.83) 0.006
   Combined analysis[111]^c
   0 NUGs 12 3 (25.0) 1 1
   1 NUG 151 99 (65.6) 3.79 (1.20−11.96) 0.023 3.53 (1.10−11.36) 0.034
   2 NUGs 106 82 (77.4) 5.42 (1.71−17.17) 0.004 5.46 (1.70−17.58) 0.004
   Trend test 0.0003 0.0001
   [112]Open in a new tab

   DFS, disease‐free survival; HCC, hepatocellular carcinoma; HR, hazards
   ratio; CI, confidence interval; NUG, number of unfavorable genotypes.
   ^a

   Including relapse, metastasis and death.
   ^b

   Adjusted by age, sex, ethnicity, HBsAg status, smoking status, drinking
   status, intact capsule, hepatic vein tumor thrombosis, cirrhosis and
   BCLC staging.
   ^c

   Unfavorable genotypes were SMAD3 rs11556090 AG+GG and RBL2 rs3929
   GG+GC.

Figure 1.

   Figure 1
   [113]Open in a new tab

   SMAD3 rs11556090 and RBL2 rs3929 associated with disease‐free survival
   of hepatocellular carcinoma patients. (A–D) Kaplan–Meier survival
   curves of single nucleotide polymorphisms in different genetic models:
   rs11556090 in an additive genetic model (A) and dominant model (B);
   rs3929 in an additive genetic model (C) and recessive model (D). (E)
   Kaplan–Meier survival curves of combined effects of the unfavorable
   genotypes. (F) Receiver operating characteristic (ROC) curve and area
   under the curve (AUC) estimation for prediction of 1‐year DFS.

Combined effects and ROC curves for DFS prediction

   To evaluate the joint effect of SMAD3 rs11556090 and RBL2 rs3929, we
   combined risk genotypes of rs11556090 CC and rs3929GG+GC into a single
   variable as the number of unfavorable genotypes (NUGs) (Table [114]5).
   The trend test indicated that increased NUG was associated with a
   shorter DFS regardless of univariate (trend test: P = 0.0003) or
   multivariate analysis (trend test: P = 0.0001). The Kaplan–Meier curves
   for depicting associations between NUG and DFS is also shown in
   Figure [115]1E. Using the ROC curves, we further evaluated predictive
   value of the unfavorable genotypes. The results demonstrated that, as
   classification of 1‐year DFS, the AUC was significantly increased from
   68.4% to 74.1% (P = 0.011), when adding NUG to the model including
   clinical variables (age, sex, ethnic, hepatitis B virus infection,
   smoking status, drinking status, capsule, portal vein tumor thrombosis,
   cirrhosis and BCLC staging) as classifiers (Fig. [116]1F).

In silico functional validation

   As mapped on the UCSC website ([117]https://genome.ucsc.edu/),
   rs11556090 is located in exon 9, 3ʹUTR of SMAD3, while rs3929 is
   located in exon 22, 3ʹUTR of RBL2 (Fig. [118]2A and B). Both of them
   were predicted to be miRNA binding sites of the corresponding genes by
   the SNPinfo. We further conducted the expression quantitative trait
   loci (eQTL) analysis by searching on the GTEx Portal
   ([119]http://www.gtexportal.org/home/) and using data from the 1000
   Genomes Project to examine the effects of SNPs on the mRNA expression
   levels of corresponding genes. We only found information about RBL2 on
   GTEx Portal that the rs3929 C allele was significantly associated with
   increased expression levels of RBL2 in liver tissues (P = 1.8 × 10^−7,
   Fig. [120]2C) and the whole blood (P = 3.9 × 10^−14, Fig. [121]2D) by
   general linear regression analysis. Data of Asian population (the
   Chinese and Japanese together) from the HapMap 3 Project showed no
   significant association for neither rs11556090 nor rs3929 with their
   corresponding gene expression levels (P = 0.486 and 0.686,
   respectively, Fig. [122]2E and F).

Figure 2.

   Figure 2
   [123]Open in a new tab

   In silico functional validation of SMAD3 rs11556090 and RBL2 rs3929.
   Location‐map of rs11556090 (A) and rs3929 (B) in UCSC website
   ([124]https://genome.ucsc.edu/); The expression quantitative trait loci
   analysis (eQTL) from the GTEx Portal
   ([125]http://www.gtexportal.org/home/) for RBL2 rs3929 in liver tissue
   (C) and the whole blood (D); eQTL analysis by using Asian population of
   HapMap3 data for rs11556090 (E) and rs3929 (F).

Discussion

   In this hypothesis‐driven study, we identified SMAD3 rs11556090 A>G and
   RBL2 rs3929 G>C in the cell cycle pathway may independently or jointly
   modulate DFS of HCC patients. We also observed that combining with
   unfavorable genotypes of these loci significantly improved prediction
   performance of HCC survival in the model that included the known
   clinical prognostic variables. Furthermore, the results from the GTEx
   Portal demonstrated that the C allele was significantly associated with
   increasing expression levels of RBL2, suggesting that RBL2 rs3929 may
   modulate DFS of HCC patients possibly through a mechanism of modulating
   gene expression.

   Cell cycle control is an important process for DNA damage repair. When
   DNA damage occurs, cell cycle checkpoints are activated and cell cycle
   progression is paused, allowing cells sufficient time to repair the
   damage and determining whether cells will survive or die, which is
   controlled by the cell cycle pathway players [126]21. Previous studies
   have shown that the identified genes, SMAD3 and RBL2, are important
   players in the cell cycle control. For example, SMAD3 plays a critical
   part in the regulation of transforming growth factor‐β (TGF‐β), which
   is a potent inhibitor of cell cycle progression at the G1 phase
   [127]37. Likewise, RBL2, encoding one of the retinoblastoma family
   proteins (RBL2, also known as p130 and pRb2), is a key switch at the
   restriction point (R) and inhibits the S phase entry by physical
   combination with E2F transcription factor and actively represses gene
   transcription that regulates DNA synthesis in the S phase [128]38.
   Other studies reported that SMAD3 functioned as a tumor suppressor in
   cancers of the stomach, [129]39, breast [130]40 and prostate [131]41,
   while RBL2 functions as a tumor suppressor in cancers of the breasts,
   ovaries and endometrium [132]42. A similar function in HCC has also
   been illustrated for both of SMAD3 and RBL2 in the previous studies
   [133]43, [134]44, suggesting the significant function of SMAD3 and RBL2
   as a tumor suppressor in HCC.

   In this study, both of the two loci we identified were computationally
   predicted to be located in the microRNA‐binding sites by the SNPinfo.
   SMAD3 rs11556090 is located in the binding site of hsa‐miR‐132,
   hsa‐miR‐188, hsa‐miR‐212, hsa‐miR‐30b, hsa‐miR‐337, hsa‐miR‐431,
   hsa‐miR‐532 and hsa‐miR‐9, while RBL2 rs3929 was located only in
   has‐miR‐134. Surprisingly, the above‐mentioned miRNAs, except for
   has‐miR‐337 and has‐miR‐532, have been associated with development or
   survival of HCC [135]45, [136]46, [137]47, [138]48, [139]49, [140]50,
   [141]51. Therefore, it is likely that the underlying mechanisms of the
   two SNPs in modulating HCC survival are to regulate the transcription
   of proteins by affecting activities of miRNA‐binding. SNPs in the
   potential miRNA‐binding sites are suggested to be functional and always
   good candidates for causal variants of human disease occurrence or
   progress [142]52. Specifically, we found that the RBL2 rs3929 C allele
   associated with a longer DFS was correlated with increased mRNA
   expression levels of RBL2 in normal liver tissues and peripheral blood
   lymphocytes by using data from the GTEx Portal. These results are
   consistent with the characteristics of a tumor suppressor, although the
   majority of the donors for the GTEx project are of the white
   population. However, somatic mutations in RBL2 were observed previously
   only in primary nasopharyngeal carcinomas, lung tumors, and Burkitt's
   lymphomas but not in HCC. Taken together, we can infer that RBL2 rs3929
   may be a functional and important variant for HCC progression.

   The results also demonstrated a significant locus‐dosage effect in
   patients with a larger number of unfavorable genotypes, who had a
   shorter DFS, suggesting an interaction of SMAD3 rs11556090 and RBL2
   rs3929 in the cell cycle pathway on HCC progress. The potential
   mechanism under the observed association is probably that both of SMAD3
   and RBL2 are the key substrates of CDK4, which can control cell cycle
   progression from the G1 to S phase by phosphorylation of these proteins
   [143]37, [144]53. Importantly, The combination of the two loci
   significantly improved prediction performance of the model that
   included the known prognosis factors of HCC patients.

   It should be noted that there were limitations of this study. The first
   limitation included restriction of the recruit population. We conducted
   the study only in the population from Guangxi Zhuang Autonomous Region,
   where the main cause of HCC is HBV infection [145]28. The results may
   not apply to other ethnic groups or disease etiologies, such as
   hepatitis C virus infection, nonalcoholic fatty liver disease and
   alcoholic liver disease. Secondly, the significance of RBL2 rs3929 and
   SMAD3 rs11556090 in the prognosis of HCC has been demonstrated in this
   study, however, it still lack epidemiology validation and biological
   mechanism confirmation, besides the significant eQTL results for RBL3
   rs3929 from the GTEx project. Finally, because of lack of more detailed
   clinical information, we did not efficiently evaluated the potential
   effects of different therapies after radical surgery resection on the
   outcomes of HCC patients, or their potential associations with the
   identified SNPs. Therefore, larger scale, more comprehensive and
   multi‐institutional epidemiology investigations and biological studies
   are warranted to further validate these results.

   In conclusion, this study identified a prognostic role of SMAD3
   rs11556090 and RBL2 rs3929 of the cell cycle pathway in HCC patients.
   Although both of SMAD3 and RBL2 are important in the control of cell
   cycle progression from the G1 to S phase, the interpretation of our
   findings should be cautious, until validated by mort patient cohorts
   and HCC cell lines.

Conflict of Interest

   The authors declare that they have no conflict of interest.

Acknowledgements