Influence of hypoxia‑related genetic polymorphisms on the prognosis of patients with metastatic gastric cancer treated with EOF
- Authors:
- Published online on: November 15, 2017 https://doi.org/10.3892/ol.2017.7414
- Pages: 1334-1342
Abstract
Introduction
Notable progress has been achieved in previous years in the treatment of metastatic gastric cancer (MGC). For instance, trastuzumab combined with chemotherapy markedly prolongs overall survival (OS) in comparison with chemotherapy alone for patients with human epidermal growth factor receptor 2 (HER2)-positive MGC (1). However, HER2-positive disease accounts for only 7–34% of all gastric cancer (GC) (1); for patients with the HER2-negative disease, chemotherapy alone remains the standard treatment. EOF [oxaliplatin and 5-fluorouracil (5-FU) combined with epirubicin] is one of the classic first-line chemotherapeutic treatments for MGC. Nevertheless, <50% of all patients respond to this treatment (2), driving the demand for predictive biomarkers in order to improve the selection of patients likely to respond to EOF therapy.
Tumor hypoxia develops in the majority of solid tumors due to the imbalance between the tumor growth and blood supply (3). In GC, previous studies have revealed that hypoxia may lead to drug resistance and stimulate the epithelial-mesenchymal transition (EMT) of GC cells (4,5), and the presence of low levels of oxygenation in the tumor tissues of patients with GC has been associated with poor survival (6). Prior studies have additionally revealed that hypoxia-inducible factor (HIF)-1, the major transcription factor significantly activated by hypoxia, may confer hypoxia-induced drug resistance via the inhibition of drug-induced apoptosis, a decrease in intracellular drug accumulation and by prompting multidrug resistance (MDR) (7). Thus, the study of genes associated with tumor hypoxia may be of value in predicting the treatment response and prognosis of patients with MGC.
Myoglobin (MB) is an oxygen-binding respiratory protein that has been identified in various non-muscle tissues (8). Previous studies have revealed that mRNA and/or protein levels of MB may be induced by hypoxia and were correlated with the expression of numerous hypoxia biomarkers, including HIF-1α, HIF-2α, and carbonic anhydrase IX (CAIX) in various types of cancer (9–13).
The ATP-binding cassette sub-family G member 2 (ABCG2) promoter has been revealed to involve three hypoxia response elements (14). Under conditions of hypoxia, ABCG2 may be induced, thus the tumor cells would be provided with a survival advantage by reducing the accumulation of heme or porphyrin (15).
MutL homolog 1 (MLH1), a key DNA mismatch repair (MMR) gene, has been revealed to be specifically reduced in tumor cells and stem cells under hypoxia (16,17), which eventually leads to genetic instability, tumor progression and resistance to chemotherapeutic agents including oxaliplatin, 5-FU, and irinotecan (18–21).
Poly(ADP-ribose) polymerase-1 (PARP-1) has been demonstrated to serve important functions in carcinogenesis (22). Previous studies have revealed that PARP-1 interacts with HIF-1α and HIF-2α (23,24), and that pre-exposure to hypoxia followed by oxidative stress may lead to the over activation of PARP-1 in human lung cancer cells (25).
In the present study, a retrospective analysis was conducted to evaluate the effect of 6 hypoxia-associated genetic polymorphisms (rs7292, rs7293, rs2231142, rs1800734, rs9852810, and rs1136410) on the treatment response and survival of Chinese patients with MGC receiving EOF chemotherapy.
Materials and methods
Patients
The present study retrospectively enrolled 108 Chinese patients with untreated MGC from May 2008 to June 2012 at the Fudan University Shanghai Cancer Center (Shanghai, China). All patients had pathologically diagnosed gastric adenocarcinoma with metastasis confirmed by magnetic resonance imaging or computed tomography, and were receiving the EOF treatment (intravenous epirubicin 50 mg/m2 combined with a 2 h intravenous infusion of oxaliplatin 130 mg/m2 on day 1, followed by a 24 h continuous infusion of 5-FU 375–425 mg/m2/day for 5 days) as first-line chemotherapy. The chemotherapy was repeated every 3 weeks until disease progression, intolerable toxicity, or withdrawal of consent. The response evaluation criteria in solid tumors (1.0) guidelines were used to evaluate tumor responses (26). Patients who achieved complete remission, partial remission, or stable disease were defined as ‘controlled,’ and patients with progressive disease were defined as ‘uncontrolled.’ Follow-up was conducted every 3–6 months, and OS was defined as the interval between the dates of the beginning of treatment and the first documentation of mortality from any cause, while progression-free survival (PFS) was defined as the interval between the dates of the beginning of treatment and the first documentation of disease progression or mortality from any cause.
The present study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (Shanghai, China) and was conducted in accordance with the Declaration of Helsinki. The blood samples obtained prior to treatment were acquired from the tissue bank of Fudan University Shanghai Cancer Center with written informed consent from the patients.
Genotyping
A total of six single nucleotide polymorphisms (SNP) in four hypoxia-related genes from the National Center for Biotechnology Information dbSNP database: MB (rs7292, rs7293), ABCG2 (rs2231142), MLH1 (rs1800734, rs9852810), and PARP-1 (rs1136410) were genotyped (Table I). Genomic DNA extraction and SNP genotyping were performed as described previously (27). Specifically, genomic DNA was extracted from the peripheral blood using the standard phenol-chloroform method. All probes and primers were designed by the Assay-on-Design service. The primer sequences were as follows: 1) rs7292, forward primer (5′-3′): ACG TTG GAT GCT CAT GAT GCC CCT TCT TCT, reverse primer (5′-3′): ACG TTG GAT GGA GGA CTT AAA GAA GCA TGG, extension primer (5′-3′): tct gAA AGA AGC ATG GTG CCA C. 2) rs7293, forward primer (5′-3′): ACG TTG GAT GGT TTG ACA AGT TCA AGC ACC, reverse primer (5′-3′): ACG TTG GAT GTG GCA CCA TGC TTC TTT AAG, extension primer (5′-3′): GCT TCT TTA AGT CCT CAG A. 3) rs2231142, forward primer (5′-3′): ACG TTG GAT GTG ATG TTG TGA TGG GCA CTC, reverse primer (5′-3′): ACG TTG GAT GGT CAT AGT TGT TGC AAG CCG, extension primer (5′-3′): ccc tCA AGC CGA AGA GCT GCT GAG AAC T. 4) rs1800734, forward primer (5′-3′): ACG TTG GAT GAT CAA TAG CTG CCG CTG AAG, reverse primer (5′-3′): ACG TTG GAT GAG TGC CTC GTG CTC ACG TTC, extension primer (5′-3′): gGC TCA CGT TCT TCC TT. 5) rs9852810, forward primer (5′-3′): ACG TTG GAT GTT TAT GGA GCA TCT ACG GTG, reverse primer (5′-3′): ACG TTG GAT GAC TTT CCC TGC AGG GAT AAG, extension primer (5′-3′): atG GAT AAG AGC ATT AAA TGA GAT AA. 6) rs1136410, forward primer (5′-3′): ACG TTG GAT GTG AGC AGA CTG TAG GCC AC, reverse primer (5′-3′): ACG TTG GAT GGC TTT CTT TTG CTC CTC CAG, extension primer (5′-3′): cct taCT TTT GCT CCT CCA GGC CAA GG.
All SNPs were genotyped with the TaqMan assay method using the ABI 7900 DNA Detection System (Applied Biosystems, Thermo Fisher Scientific, Inc., Waltham, MA, USA). The polymerase chain reaction (PCR) was performed using the TaqMan Universal PCR Master Mix reagent (Applied Biosystems; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. PCR was performed at 95°C for 2 min, followed by 45 cycles at 95°C for 30 sec, 56°C for 30 sec and 72°C for 1 min, with a final incubation at 72°C for 5 min; the extension reactions were conducted at 94°C for 30 sec and then 94°C for 5 sec, followed by 5 cycles at 52°C for 5 sec and at 80°C for 5 sec for a total of 40 cycles and finally at 72°C for 3 min.
Statistical analysis
Hardy-Weinberg equilibrium and pairwise linkage disequilibrium (LD), as well as allele and genotype distributions, were analyzed using SHEsis (http://analysis.bio-x.cn/myAnalysis.php) (28). The discrepancies in allelic and genotype frequency between the controlled and uncontrolled groups were analyzed using χ2 or Fisher's exact tests. OS and PFS were analyzed using the Kaplan-Meier method with the log-rank test using SPSS software (version 19.0; IBM Corp., Armonk, NY, USA). Risk factors with P<0.1 were further analyzed using multivariate Cox regression models to assess the influence of genotypes on treatment-association survival. The software Testing Haplotype Effects in Association Studies (version 3.1) was used to perform haplotype analysis (29). A two-tailed P<0.05 was considered to indicate a statistically significant difference.
Results
Study population and SNPs
The study population included 108 Chinese patients with MGC receiving EOF chemotherapy. Baseline demographic and clinical data were described in a previous study (30). In brief, 41.0% (44/108) of the patients were female, and the age of the patients ranged from 23–74 years. Within the study population, 95.4% (103/108) of patients had an Eastern Cooperative Oncology Group (ECOG) score (31) of 0–1. All the patients had at least one unresectable lesion including lung metastasis, liver metastasis, ascites, pleural effusion or retroperitoneal lymph node invasion. According to the best treatment response, 89 (82.4%) patients were regarded as controlled and 19 (17.6%) patients were uncontrolled. There was no difference between the controlled and uncontrolled patients in terms of sex, age, ECOG score, tumor differentiation, synchronous metastasis status and number of lesions. The median follow-up duration was 28.4 (range, 7.7–69.0) months.
Table I presents the 6 SNPs analyzed in the present study. Each of the SNPs was in Hardy-Weinberg equilibrium. Patients for whom data was lacking for certain SNPs (for instance, 1/108 patients for rs2231142) were excluded from the corresponding analysis.
LD analysis
Pairwise LD analysis revealed strong LD in MB SNPs rs7292 and rs7293 (D'=1.000, r2=1.000; Fig. 1A), as well as MLH1 SNPs rs1800734 and rs9852810 (D'=1.000, r2=0.084; Fig. 1B). Since rs7292 and rs7293 were in complete LD (D'=1.000, r2=1.000), only rs7292 was included in the subsequent analysis to avoid unnecessary duplication.
Association of alleles and genotype frequency with disease control
Differences between the four SNPs analyzed in the present study and disease control are presented in Table II. No allele or genotype exhibited a significant association with treatment response.
Survival analysis
In addition to pathological grade and number of lesions, as reported in a previous study (27), the genotypes of MB rs7292 and MLH1 rs9852810 were revealed to be significantly associated with PFS (Table III). T carriers of MB rs7292 had significantly more favorable PFS compared with CC carriers (median PFS: 179.0 and 71.0 days, respectively P=0.001; Fig. 2A). Similarly, GG carriers of MLH1 rs9852810 had significantly longer PFS in comparison to A carriers (median PFS: 178.0 and 147.0 days, respectively; P=0.040; Fig. 2B). In the Cox regression models, the T allele of MB rs7292 and the GG genotype of MLH1 rs9852810 were associated with decreased risks of disease progression [rs7292: hazard ratio (HR)=0.135, 95% confidence interval (CI)=0.057–0.321; P<0.001; rs9852810: HR=0.494, 95% CI=0.267–0.913; P=0.024, respectively], indicating that, along with pathological grade (P=0.005) and number of lesions (P=0.020), MB rs7292 and MLH1 rs9852810 were independent predictive factors of PFS for patients with MGC receiving EOF chemotherapy (Table IV). However, none of the SNPs were revealed to have an association with OS using Kaplan-Meier analysis (Table III).
PFS analysis based on the prognostic index
According to the favorable SNPs for PFS (MB rs7292 CT/TT genotype and MLH1 rs9852810 GG genotype), patients were classified into a low-risk group (involving one or two of the two SNPs) and a high-risk group (involving neither of the two SNPs). Patients from the low-risk group revealed a significantly longer PFS than patients from the high-risk group (median PFS: 180.0 and 117.0 days, respectively; P=0.002; Fig. 2C). In comparison with the low-risk group, the high-risk group demonstrated a 2.2-fold increased risk of disease progression (HR=2.223, 95% CI=1.335–3.704; P=0.002).
Haplotype analysis
Haplotype analysis was performed in SNPs that were in strong LD. The analysis demonstrated that the GA haplotype of MLH1 rs1800734/rs9852810 was associated with unfavorable PFS (HR=1.84, 95% CI=1.07–3.16; P=0.027), while no haplotype of MLH1 rs1800734/rs9852810 was associated with OS (Table V). The haplotypes of MB had no significant influence on survival (Table VI).
Discussion
Hypoxia is common in numerous types of solid tumor, including GC, and is considered to be an important factor associated with aggressive phenotypes and treatment resistance of tumors (6,32). Several studies have investigated the influence of hypoxia on the chemosensitivity, metastasis, and prognosis of GC. Liu et al (4) demonstrated that GC cells developed resistance to 5-FU, vincristine, cisplatin, etoposide, and adriamycin under conditions of hypoxia. Matsuoka et al (5) demonstrated that hypoxia may stimulate the EMT process of GC cells. In addition, Osinsky et al (6) revealed that low levels of oxygenation in tumor tissues were associated with poor survival in patients with GC.
The present study explored the relationship between polymorphisms of genes associated with tumor hypoxia and the clinical outcome of patients with MGC treated with EOF, and to the best of our knowledge, revealed for the first time that the CT/TT genotype of MB rs7292 and the GG genotype of MLH1 rs9852810 were independent favorable predictive factors of PFS for patients with MGC treated with the EOF regimen. Since PFS more directly reflects the therapeutic effect of first-line chemotherapy, MB rs7292 and MLH1 rs9852810 may serve as biomarkers that predict the short-term efficacy of the first-line EOF regimen in patients with MGC.
The MB rs7292 CT/TT genotype was demonstrated to be an independent favorable predictor of PFS for patients with MGC treated with EOF chemotherapy, while MB rs7292 CT/TT carriers appeared to possess a tendency toward an improved OS compared with CC carriers (for CT/TT and CC genotypes, median OS: 465.0 and 244.0 days, respectively; P=0.245). Although the difference was not statistically significant, considering the markedly separative tendency of OS curves and the limited sample size of the present study, there may still be a link between the CT/TT genotype and survival. With respect to the prognostic function of MB rs7293, since MB rs7292 and rs7293 were in complete LD (D'=1.000, r2=1.000), the results of the analysis of the rs7293 GG genotype were identical to those of the rs7292 CC genotype (data not shown).
The function of MB in GC has not previously been reported, and the function of MB in tumors varies and remains controversial. For example, one previous study demonstrated that 71% of 1,027 breast cancer cases demonstrated MB expression, with a significant correlation with hypoxia markers (HIF-2α, CAIX) and an improved prognosis (9). Furthermore, another previous study revealed that MB was induced in breast cancer cells by prolonged hypoxia, partially by HIF-1/2-dependent transactivation, and additionally revealed that MB may damage mitochondria in hypoxic cancer cells, indicating the tumor-suppressive potential of MB in breast cancer (10). However, the function of MB in non-small-cell lung cancer is controversial. For instance, Galluzzo et al (33) demonstrated that overexpression of MB in non-small-cell lung cancer cells suppressed the hypoxia response, inhibited tumor growth, reduced vessel density, promoted tumor differentiation and suppressed tumor metastasis. On the other hand, Oleksiewicz et al (13) reported that patients with non-small-cell lung cancer with low MB levels had a longer survival duration than patients with high MB expression. Hypoxia may also induce the expression of MB in prostate cancer (11) and renal cell carcinoma (12). However, to date, no studies have reported the polymorphisms of MB in malignant tumors, and the function of MB in gastric cancer has not been previously demonstrated. Further research is required to elucidate whether the change of rs7292 may affect the expression of MB, and whether and how MB may influence the efficacy of the EOF treatment in patients with GC.
The link between MLH1 rs1800734 and rs9852810 and GC was also reported in the present study. In addition to the prolonged PFS of patients with MGC carrying the GG genotype of MLH1 rs9852810, MLH1 rs9852810 GG carriers were revealed to possess an improved OS compared with A carriers (for GG and AA/AG genotypes, median OS: 465.0 and 241.0 days, respectively; P=0.287). The results demonstrating that OS difference did not reach statistical significance may be attributed to the relatively small sample size and the therapy following first-line chemotherapy. Additionally, haplotype analysis revealed that the GA haplotype of MLH1 rs1800734/rs9852810 was associated with poorer PFS compared with the common haplotype AG; the reason may be that the GG genotype of rs9852810 had a significantly favorable PFS.
As a key DNA MMR gene, the expression of MLH1 was demonstrated to be reduced under hypoxia as a result of transcriptional, translational, or methylation modification (18,34,35). In addition, several studies have revealed that methylation of the MLH1 gene promoter was associated with the stages of carcinogenesis and progression as well as oxaliplatin resistance in GC (36–38).
With respect to the influence of the polymorphisms of MLH1 on cancer, Langeberg et al (39) reported that the rs9852810 A allele was associated with a higher risk of prostate cancer incidence and recurrence, as well as being associated with more aggressive types of prostate cancer. Furthermore, rs1800734 was located in the CpG island of MLH1, and the A allele of rs1800734 was significantly associated with MLH1 methylation and high-degree microsatellite instability in colorectal cancer (40–42). In GC, Zhu et al (43) demonstrated that the G allele of MLH1 rs1800734 was associated with a decreased risk of GC. However, the present study demonstrated no significant association between rs1800734 and the clinical outcome of patients with MGC who received EOF chemotherapy, a result that may be explained by the controversy surrounding the influence of microsatellite instability on GC (44,45). Since the influence of the two polymorphisms on the survival and treatment response of patients with MGC has, to the best of our knowledge, never previously been reported, and additionally whether the two SNPs may alter the function of MLH1 and subsequently influence GC progression is unknown, additional work is required to validate the predictive value of those polymorphisms for GC.
When the patients were classified into a low-risk group and a high-risk group according to the number of favorable SNPs for PFS (MB rs7292 CC genotype and MLH1 rs9852810 A allele) they possessed, the high-risk group was revealed to possess a significantly shorter PFS; thus, this type of classification may assist in a more comprehensive identification of patients with poor prognosis.
To conclude, to the best of our knowledge, the present study revealed for the first time, that in Chinese patients with MGC treated with the EOF regimen as first-line chemotherapy, the CT/TT genotype of MB rs7292 and the GG genotype of MLH1 rs9852810 were independent favorable predictive factors of PFS, and that these may serve as biomarkers to predict the short-term efficacy of the first-line EOF regimen in patients with MGC. Since all the SNPs of the four hypoxia-associated genes investigated in the present study have seldom been reported in GC, the results of the present study will require confirmation with larger cohort studies and further functional investigations.
Acknowledgements
The present study was supported by the Natural Science Foundation of Shanghai, Shanghai, China (grant no. 13ZR1408200) and the Foundation of the Shanghai Municipal Science and Technology Commission, Shanghai, China (grant no. 134119a8600).
References
|
Bang YJ, Van Cutsem E, Feyereislova A, Chung HC, Shen L, Sawaki A, Lordick F, Ohtsu A, Omuro Y, Satoh T, et al: Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): A phase 3, open-label, randomised controlled trial. Lancet. 376:687–697. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Van Cutsem E, Moiseyenko VM, Tjulandin S, Majlis A, Constenla M, Boni C, Rodrigues A, Fodor M, Chao Y, Voznyi E, et al: Phase III study of docetaxel and cisplatin plus fluorouracil compared with cisplatin and fluorouracil as first-line therapy for advanced gastric cancer: A report of the V325 study group. J Clin Oncol. 24:4991–4997. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Balkwill FR, Capasso M and Hagemann T: The tumor microenvironment at a glance. J Cell Sci. 125:5591–5596. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Yang SY, Song BQ, Dai SL, Yang KX, Jin Z and Shi KW: Effects of hypoxia-inducible factor-1α silencing on drug resistance of human pancreatic cancer cell line Patu8988/5-Fu. Hepatogastroenterology. 61:2395–2401. 2014.PubMed/NCBI | |
|
Zhao M, Zhang Y, Zhang H, Wang S, Zhang M, Chen X, Wang H, Zeng G, Chen X, Liu G and Zhou C: Hypoxia-induced cell stemness leads to drug resistance and poor prognosis in lung adenocarcinoma. Lung Cancer. 87:98–106. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Hockel M and Vaupel P: Tumor hypoxia: Definitions and current clinical, biologic, and molecular aspects. J Natl Cancer Inst. 93:266–276. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Röhwer N, Dame C, Haugstetter A, Wiedenmann B, Detjen K, Schmitt CA and Cramer T: Hypoxia-inducible factor 1alpha determines gastric cancer chemosensitivity via modulation of p53 and NF-kappaB. PLoS One. 5:e120382010. View Article : Google Scholar : PubMed/NCBI | |
|
Fraser J, de Mello LV, Ward D, Rees HH, Williams DR, Fang Y, Brass A, Gracey AY and Cossins AR: Hypoxia-inducible myoglobin expression in nonmuscle tissues. Proc Natl Acad Sci USA. 103:pp. 2977–2981. 2006; View Article : Google Scholar : PubMed/NCBI | |
|
Kristiansen G, Rose M, Geisler C, Fritzsche FR, Gerhardt J, Lüke C, Ladhoff AM, Knüchel R, Dietel M, Moch H, et al: Endogenous myoglobin in human breast cancer is a hallmark of luminal cancer phenotype. Br J Cancer. 102:1736–1745. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Kristiansen G, Hu J, Wichmann D, Stiehl DP, Rose M, Gerhardt J, Bohnert A, Ten Haaf A, Moch H, Raleigh J, et al: Endogenous myoglobin in breast cancer is hypoxia-inducible by alternative transcription and functions to impair mitochondrial activity: A role in tumor suppression? J Biol Chem. 286:43417–43428. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Meller S, Bicker A, Montani M, Ikenberg K, Rostamzadeh B, Sailer V, Wild P, Dietrich D, Uhl B, Sulser T, et al: Myoglobin expression in prostate cancer is correlated to androgen receptor expression and markers of tumor hypoxia. Virchows Arch. 465:419–427. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Behnes CL, Bedke J, Schneider S, Küffer S, Strauss A, Bremmer F, Ströbel P and Radzun HJ: Myoglobin expression in renal cell carcinoma is regulated by hypoxia. Exp Mol Pathol. 95:307–312. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Oleksiewicz U, Daskoulidou N, Liloglou T, Tasopoulou K, Bryan J, Gosney JR, Field JK and Xinarianos G: Neuroglobin and myoglobin in non-small cell lung cancer: Expression, regulation and prognosis. Lung Cancer. 74:411–418. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Krishnamurthy P and Schuetz JD: The ABC transporter Abcg2/Bcrp: Role in hypoxia mediated survival. Biometals. 18:349–358. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Krishnamurthy P, Ross DD, Nakanishi T, Bailey-Dell K, Zhou S, Mercer KE, Sarkadi B, Sorrentino BP and Schuetz JD: The stem cell marker Bcrp/ABCG2 enhances hypoxic cell survival through interactions with heme. J Biol Chem. 279:24218–24225. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Mihaylova VT, Bindra RS, Yuan J, Campisi D, Narayanan L, Jensen R, Giordano F, Johnson RS, Rockwell S and Glazer PM: Decreased expression of the DNA mismatch repair gene Mlh1 under hypoxic stress in mammalian cells. Mol Cell Biol. 23:3265–3273. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Rodríguez-Jiménez FJ, Moreno-Manzano V, Lucas-Dominguez R and Sánchez-Puelles JM: Hypoxia causes downregulation of mismatch repair system and genomic instability in stem cells. Stem Cells. 26:2052–2062. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Nakamura H, Tanimoto K, Hiyama K, Yunokawa M, Kawamoto T, Kato Y, Yoshiga K, Poellinger L, Hiyama E and Nishiyama M: Human mismatch repair gene, MLH1, is transcriptionally repressed by the hypoxia-inducible transcription factors, DEC1 and DEC2. Oncogene. 27:4200–4209. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Li Y, Yang Y, Lu Y, Herman JG, Brock MV, Zhao P and Guo M: Predictive value of CHFR and MLH1 methylation in human gastric cancer. Gastric Cancer. 18:280–287. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Meyers M, Wagner MW, Hwang HS, Kinsella TJ and Boothman DA: Role of the hMLH1 DNA mismatch repair protein in fluoropyrimidine-mediated cell death and cell cycle responses. Cancer Res. 61:5193–5201. 2001.PubMed/NCBI | |
|
Tentori L, Leonetti C, Muzi A, Dorio AS, Porru M, Dolci S, Campolo F, Vernole P, Lacal PM, Praz F and Graziani G: Influence of MLH1 on colon cancer sensitivity to poly(ADP-ribose) polymerase inhibitor combined with irinotecan. Int J Oncol. 43:210–218. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Vyas S and Chang P: New PARP targets for cancer therapy. Nat Rev Cancer. 14:502–509. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Klymenko T, Brandenburg M, Morrow C, Dive C and Makin G: The novel Bcl-2 inhibitor ABT-737 is more effective in hypoxia and is able to reverse hypoxia-induced drug resistance in neuroblastoma cells. Mol Cancer Ther. 10:2373–2383. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Kim JW, Ho WJ and Wu BM: The role of the 3D environment in hypoxia-induced drug and apoptosis resistance. Anticancer Res. 31:3237–3245. 2011.PubMed/NCBI | |
|
Erdélyi K, Pacher P, Virág L and Szabó C: Role of poly(ADP-ribosyl) ation in a ‘two-hit’ model of hypoxia and oxidative stress in human A549 epithelial cells in vitro. Int J Mol Med. 32:339–346. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, Verweij J, Van Glabbeke M, van Oosterom AT, Christian MC and Gwyther SG: New guidelines to evaluate the response to treatment in solid tumors. European Organization for research and treatment of cancer, national cancer institute of the United States, national cancer institute of Canada. J Natl Cancer Inst. 92:205–216. 2000. View Article : Google Scholar : PubMed/NCBI | |
|
Geng R, Chen Z, Zhao X, Qiu L, Liu X, Liu R, Guo W, He G, Li J and Zhu X: Oxidative stress-related genetic polymorphisms are associated with the prognosis of metastatic gastric cancer patients treated with epirubicin, oxaliplatin and 5-fluorouracil combination chemotherapy. PLoS One. 9:e1160272014. View Article : Google Scholar : PubMed/NCBI | |
|
Shi YY and He L: SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 15:97–98. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Tregouet DA and Garelle V: A new JAVA interface implementation of THESIAS: Testing haplotype effects in association studies. Bioinformatics. 23:1038–1039. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Liu X, Chen Z, Zhao X, Huang M, Wang C, Peng W, Yin J, Li J, He G, Li X and Zhu X: Effects of IGF2BP2, KCNQ1 and GCKR polymorphisms on clinical outcome in metastatic gastric cancer treated with EOF regimen. Pharmacogenomics. 16:959–970. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET and Carbone PP: Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 5:649–656. 1982. View Article : Google Scholar : PubMed/NCBI | |
|
Teicher BA: Hypoxia and drug resistance. Cancer Metastasis Rev. 13:139–168. 1994. View Article : Google Scholar : PubMed/NCBI | |
|
Galluzzo M, Pennacchietti S, Rosano S, Comoglio PM and Michieli P: Prevention of hypoxia by myoglobin expression in human tumor cells promotes differentiation and inhibits metastasis. J Clin Invest. 119:865–875. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Lu Y, Wajapeyee N, Turker MS and Glazer PM: Silencing of the DNA mismatch repair gene MLH1 induced by hypoxic stress in a pathway dependent on the histone demethylase LSD1. Cell Rep. 8:501–513. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Min L, Chen Q, He S, Liu S and Ma Y: Hypoxia-induced increases in A549/CDDP cell drug resistance are reversed by RNA interference of HIF-1α expression. Mol Med Rep. 5:228–232. 2012.PubMed/NCBI | |
|
Teng Y, Dai D, Shen W and Liu H: Effect of methylation of hMLH1 gene promotor on stage tumorigenesis and progression of human gastric cancer. Zhonghua Wei Chang Wai Ke Za Zhi. 18:166–170. 2015.(In Chinese). PubMed/NCBI | |
|
Guo H, Yan W, Yang Y and Guo M: Promoter region methylation of DNA damage repair genes in human gastric cancer. Zhonghua Yi Xue Za Zhi. 94:2193–2196. 2014.(In Chinese). PubMed/NCBI | |
|
Milane L, Ganesh S, Shah S, Duan ZF and Amiji M: Multi-modal strategies for overcoming tumor drug resistance: Hypoxia, the Warburg effect, stem cells, and multifunctional nanotechnology. J Control Release. 155:237–247. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Langeberg WJ, Kwon EM, Koopmeiners JS, Ostrander EA and Stanford JL: Population-based study of the association of variants in mismatch repair genes with prostate cancer risk and outcomes. Cancer Epidemiol Biomarkers Prev. 19:258–264. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Chen H, Taylor NP, Sotamaa KM, Mutch DG, Powell MA, Schmidt AP, Feng S, Hampel HL, de la Chapelle A and Goodfellow PJ: Evidence for heritable predisposition to epigenetic silencing of MLH1. Int J Cancer. 120:1684–1688. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Samowitz WS, Curtin K, Wolff RK, Albertsen H, Sweeney C, Caan BJ, Ulrich CM, Potter JD and Slattery ML: The MLH1-93 G>A promoter polymorphism and genetic and epigenetic alterations in colon cancer. Genes Chromosomes Cancer. 47:835–844. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Raptis S, Mrkonjic M, Green RC, Pethe VV, Monga N, Chan YM, Daftary D, Dicks E, Younghusband BH, Parfrey PS, et al: MLH1-93G>A promoter polymorphism and the risk of microsatellite-unstable colorectal cancer. J Natl Cancer Inst. 99:463–474. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Rho JK, Choi YJ, Lee JK, Ryoo BY, Na II, Yang SH, Kim CH, Yoo YD and Lee JC: Gefitinib circumvents hypoxia-induced drug resistance by the modulation of HIF-1alpha. Oncol Rep. 21:801–807. 2009.PubMed/NCBI | |
|
Liu L, Sun L, Zhang H, Li Z, Ning X, Shi Y, Guo C, Han S, Wu K and Fan D: Hypoxia-mediated up-regulation of MGr1-Ag/37LRP in gastric cancers occurs via hypoxia-inducible-factor 1-dependent mechanism and contributes to drug resistance. Int J Cancer. 124:1707–1715. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Willger SD, Puttikamonkul S, Kim KH, Burritt JB, Grahl N, Metzler LJ, Barbuch R, Bard M, Lawrence CB and Cramer RA Jr: A sterol-regulatory element binding protein is required for cell polarity, hypoxia adaptation, azole drug resistance, and virulence in Aspergillus fumigatus. PLoS Pathog. 4:e10002002008. View Article : Google Scholar : PubMed/NCBI |
