Prognostic biomarker in advanced gastric cancer

Introduction

Gastric cancer (GC) still remains one of the leading challenges in the oncologic research area due to its frequent occurrence as well as its poor prognosis. The incidence of GC has decreased in most parts of the world, with estimated 952,000 new cases in 2012 (1). However, the drop in the number of cases of GC is not coupled with a decrease in the mortality rate. GC ranks third in terms of cancer-related death worldwide (2,3). Substantial geographic variation exists in incidence and mortality, as more than 50% of GCs arise in Japan and other East Asian countries, including Korea and China (4,5). Japan owned superior 5-year survival rates of approximately 60% and the percentage of early gastric cancer (EGC) cases reported in Japan is higher (more than 50%) compared with other areas, where screening programs have not been implemented because of cost ineffectiveness (6-8). In Western countries and China, the majority of patients is always diagnosed at an advanced, unresectable stage and tends to have disease relapse within 5 years after initial curative-intent surgery. As a result, the 5-year survival rate of locally advanced disease in these areas is only about 25% (1,9). The average time between EGC diagnosis and progression is about 37 months (10), and it commonly takes 8 months for EGC to progress into advanced gastric cancer (AGC). Once a patient presented classic symptoms of weight loss, consistent and dull pain in the epigastrium, loss of appetite, nausea, vomiting and chronic bleeding, he or she might already be at a later stage rather than the first stage of disease progression (11). The median survival of patients with late stage GC who do not receive chemotherapy is less than 6 months (12).

To achieve better survival results, currently recommended management of AGC is a standardized multidisciplinary approach, which involves surgery, neoadjuvant and adjuvant chemotherapies, chemoradiation as well as targeted therapy. Depending on the site and extent of cancer, surgery is still the unique potentially curative treatment for AGC. Tumor staging has been conceived and validated as the best predictor of patient survival. The TNM classification is the most important tool for making therapeutic plan in oncology and for predicting the patient’s prognosis. Nevertheless, even the latest edition of the TNM classification has limited power to fully reflect the complicated progression events because of the heterogeneous clinical behavior of GC (13). Prognosis varies from individual to individual even though the two patient stay in a similar tumor stage, therefore disease staging alone is not able to accurately predict the outcome for individual patients. Another well-recognized classification system is Lauren’s classification, subdividing GC as two histomorphologic subtypes—“intestinal-differentiated” and “diffuse-undifferentiated” (14,15). Case comes the same; Lauren’s classification also fails to accurately guide patient therapy, especially when dealing with the molecular heterogeneity of GC (16,17).

Figuring out the molecular biology of the individual tumor might be the key point to better understand the nature of GC and to improve the prognosis of GC patients. The introduction of targeted therapies in molecularly selected populations offers a real opportunity for better outcomes. The identification of various tumor biomarkers has added to our basic knowledge of molecular and cellular mechanisms of GC tumorigenesis and progression. The National Cancer Institute defines a biomarker as “a biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease” (18).

Throughout the whole process of carcinogenesis, there are several opportunities to identify cancer biomarkers. These include assessment of genetic mutations and epigenetic modifications, altered gene expression, protein production, and metabolites; and changes in molecular pathways that control the hallmarks of cancer (19,20). With the advent of complete human genome sequence era, and advancement in key technologies such as high-throughput DNA sequencing, microarrays, and mass spectrometry, the potential tumor biomarkers has expanded dramatically to include the sequence and expression levels of DNA, RNA, and protein as well as metabolites (21,22). The majority of tumor markers are effective prognostic tools that might be used to identify groups of patients at risk of relapse or metastasis or to monitor cancer survivors following treatment. In this review, we will outline the currently available and developing tumor markers associating with the advancement of GC, focusing on markers with potential prognostic significance.

Tissue-based prognostic biomarkers

Currently well-known tissue-based biomarkers

Vascular endothelial growth factor (VEGF)

The recruitment of new blood vessels for the supply of the growing tumor with nutrients and oxygen is essential for tumor growth, development, and distal metastasis (23). Angiogenesis is the process by which blood vessels sprout in an uncontrolled manner from pre-existing vasculature and the VEGF family participated in continually encouraging angiogenesis in neoplasms included five members VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E, and placental growth factor (PGF) (24). In addition, VEGF and its downstream effectors are ubiquitously over-expressed in various types of tumors including GC (25,26). It has been reported that VEGF is more frequently dysregulated in intestinal type than in diffuse type GC (36% and 16%, respectively) (25). A prospective biomarker analysis showed that higher levels of VEGF-A are associated with worse survival in GC (27,28). Furthermore, low expression of a co-receptor for VEGF, neuropilin-1 is found to be associated with poor prognosis (28,29). Meanwhile, VEGF and its receptors (VEGFRs) acted as predictive biomarkers in GC as well. Bevacizumab, a humanized monoclonal antibody against VEGF-A, has been shown promising results on GC progression in some phase II studies (30,31). However, the results of phase III studies were disappointing. The phase III randomized controlled Avastin in Gastric Cancer (AVAGAST) trial studied the effect on overall survival (OS) when bevacizumab was added to capecitabine and cisplatin in the first-line setting for patients with AGC. Although the overall median survival was slightly longer in patients given bevacizumab plus standard chemotherapy, these results did not reach a statistically significant level (12.1 and 10.1 mo, P=0.1002) (27,28). Therefore, bevacizumab currently is not recommended in the treatment of AGC at present.

Epidermal growth factor receptor (EGFR)

EGFR is over-expressed in 60% to 70% of GC cases, and there is evidence that EGFR overexpression in GC is associated with poor survival and poor response to chemotherapy (32,33). Contrarily, there are also studies demonstrating that EGFR expression may promise good prognosis or have no prognostic significance at all (34). Considering its central role in signaling pathways and its biological functions, EGFR has also been investigated as a predictive biomarker in GC. cetuximab, an EGFR­-targeted monoclonal antibody, functioned well in stage 4 colorectal cancer (with k-ras wild type) and in several head and neck malignancies (35,36). Nevertheless, the outcome of the phase III Erbitux (cetuximab) in Combination With Xeloda (capecitabine) and Cisplatin in Advanced Esophagogastric Cancer (EXPAND) trial was disappointing, because the addition of cetuximab to chemotherapy did not significantly improve the primary endpoint of progression-free survival (PFS). The explorers also made retrospective assessment of EGFR immunohistochemistry (IHC) score, coming to a conclusion that there was no relationship between EGFR IHC score and PFS or OS in either treatment group (37). According to these results, the prognostic role of EGFR is considered unclear and that EGFR seems not to be a predictive biomarker for GC.

Human epidermal growth factor receptor 2 protein (HER2)

The HER2 protein is a member of the EGFR family and its receptors localize in the nucleus, where they function as transcription factors for cyclin D1 and p53 (38). Therefore, HER2 is involved in the regulation of cell proliferation, differentiation, motility and apoptosis (39). Depending on tumor location and subtype, HER2 overexpression or gene amplification ranges from approximately 10% to 30% in GC (36,40,41). HER2 amplification is more often found in intestinal-type tumors and in tumors located in gastroesophageal junction (GEJ) (40,41). In the Trastuzumab for Gastric Cancer (ToGA) trial, 22.1% of the patients enrolled in were HER2 positive. Moreover, tumors in GEJ presented higher rates of HER2 positivity (33.2%) than tumors located in gastric (20.9%). The rates of HER2 positivity in the intestinal subtype GC are 32.4% while those in the diffuse or mixed type GC are 6.1% (42). Various evidences show that HER2 overexpression or amplification is associated with worse prognosis (40,43,44). Although other studies have suggested that HER2 expression may be of no prognostic significance at all (45,46). A recent study attempted to clarify whether HER2 is a prognostic marker in Western patients with metastatic GC. Using currently recommended guidelines, this study determined that HER2 status alone is not an independent prognostic marker (47). Although HER2 is not associated with an adverse prognosis of GC the same as it is in breast cancer, inhibition of the HER2 pathway in patients with tumors overexpressing HER2 has definite clinical benefits. The phase III multicenter, international ToGA trial is well-known as a milestone, as it established trastuzumab as the first biological therapy to confer survival benefits upon patients with GC (48).

Mesenchymal-epithelial transition factor receptor (MET)

MET is the receptor of hepatocellular growth factor (HGF) and play a central role in the process of embryonic development, wound healing and organ regeneration (49). Activated MET undergoes dimerization and phosphorylation, resulting in the activation of multiple pathways, including the phosphatidylinositol-3 kinase (PI3K)-protein kinase B (AKT) pathway and the RAS-mitogen activated kinase pathway. MET may also has cross talk with the EGFR and VEGFR pathways (50,51). It is known that MET kinase mutation or aberrant activation of MET is associated with renal cell carcinomas, and more recently, abnormalities in MET have been implicated in the pathogenesis of other solid tumors, including GC (52). The MET protein is over-expressed in up to 50% of AGC, and the MET gene is amplified in up to 20% of GC (53). MET amplification or over-expression is a poor prognostic marker and is associated with more aggressive disease (53,54). MET overexpression has been demonstrated to be a predictive marker. A phase III trial of rilotumumab, a fully human monoclonal antibody targeting HGF, in addition to chemotherapy is pending.

Other protein markers in the preclinical setting

Except for above-mentioned markers, which have been already in clinical practice, various forthcoming protein biomarkers discovered from microarray and proteomics research have already been evaluated for a relatively long time. Mammalian target of rapamycin (mTOR) is a protein kinase that participated in regulating cell growth and survival (55). Phosphorylated mTOR expression is considered as a poor prognostic marker because it has been associated with worse disease-free survival (56). Protein p27 inhibits progression from gap 1 to the synthesis phase of the cell cycle. A retrospective study shows that GC tumors that are negative for p27 have been associated with higher rates of lymph node metastasis, a higher proliferative index, and worse OS, indicating that p27 negativity is a potential poor prognostic marker (57). There is a suggestion that metallothioneins are intracellular proteins involved in cell proliferation and apoptosis (58). Low levels of metallothionein 2A have been associated with worse clinical outcomes (59). CD44 and CD133 are both markers of cancer stem cells and are potential poor prognostic markers in GC (60-62). Conversely, patients with higher levels of the proapoptotic protein p53 and low levels of the antiapoptotic protein Bcl-2 may have a better prognosis (63).

Emerging tissue—based biomarkers in gastric cancer (GC)

Genetic biomarkers

Over the last few decades, cancer genomics have been extensively used in biomedical research. Microarray and next generation sequencing has become a very powerful tool for discovering novel tumor biomarkers and treatment target. The use of high throughput whole genome sequencing has brought an improved appreciation of common genetic alterations in GC. With the respect of prognosis, GC gene expression profiling is able to predict which GC patients have good or poor clinical outcomes, thus classifying tumors into intrinsic subtypes and predict the survival of GC patients (64). Several studies show that gene expression techniques can help to predict the risk of recurrence and thus can potentially improve clinical outcome of GC (65,66). Yamada et al. identify a 98-gene signature by analyzing 40 GC samples obtained from endoscopic biopsy, showing that the 98-gene signature are significantly correlated with the OS. In addition, PDCD6 was proved to be a prognostic biomarker of GC through a multivariate analysis (67). Lo Nigro et al. compared gene expression profiling of three long-term survival cases with that of four normal cases, identifying an 8-gene signature to distinguish long survivors from the control cases (68). Wang et al. collected 158 GC patients, among which 33 cases were used as a training set and 125 cases for RT-PCR as a testing set. As a result, 5-gene signature was established for clinical and prognostic (69). Based on the whole genome expression profiling, we also found and validated a 10-­gene prognostic marker for OS prognosis of GC patients, which may be used with the TNM staging system as a parallel and complementary approach (70).

Loss of heterozygosity as well as mutations within several proto-oncogenes can lead to microsatellite instability (MSI) (71). Although detection of MSI in tumor tissue samples seems a little complex because it requires a comparison with normal tissue, but it presents a valuable tool for early detection and can also be used for evaluation for prognosis and chemotherapeutic response (71,72). Accumulating evidence suggests that MSI is associated with increased patient survival and a favorable prognosis; accordingly MSI status may be a useful prognostic and predictive marker in GC (73,74).

Epigenetic biomarkers

Cancer is now being recognized mostly an “epigenetic” disease rather than a “genetic” disease, which was once believed to be. The primary processes responsible for epigenetic regulation include DNA methylation, histone modifications and posttranscriptional gene regulation through non-coding RNAs including microRNAs and long ncRNAs (lncRNAs) etc. (75).

DNA methylation

Aberrant DNA methylation in the promoter regions of gene is the most well defined epigenetic hallmark in GC. In general, cancer cells exhibit hypermethylation of the CpG islands of some genes, such as BRCA1, VHL, MLH1 and CDKN2A (76,77). In contrast, cancer cells exhibit global hypomethylation at many genomic sequences, which can result in chromosomal instability as well as activation of proto-oncogenes (78).

Hypermethylation of CpG islands can lead to silence of neighboring genes and inactivation of tumor suppressor genes in the absence of changes to the genetic sequence of these genes. For instance, promoter methylation of hMLH1, a gene encoding a mismatch repair enzyme, has been involved in the development of GC and is closely associated with poor prognosis of GC patients (79). Tumor suppressor gene p16 is an inhibitor of cyclin-dependent kinase 4 (CDK4) and 6 (CDK6), which bind cyclin D1 and phosphorylate the retinoblastoma protein (Rb) tumor suppressor genes. It is reported that p16 methylation affected the overall prognosis in GC regardless whether the patients stay at early-stage or late-stage of the disease (79-81). E-cadherin protein encoded by CDH1 gene localized mainly to the adherents’ junctions of epithelial cells (82). Loss of E-cadherin is well known for promoting tumor progression through increased proliferation, invasion, and metastasis. CDH1 is frequently methylated in primary GC, particularly in the poorly differentiated GC and diffuse histotype (83). CDH1 methylation is believed to be associated with poor prognosis of GC patients (84). The suppressor of cytokine signaling (SOCS)-1 is a protein involved in a negative feedback loop for cytokine signaling, especially the JAK/STAT pathway (85). It has been suggested that SOCS-1 methylation is significantly associated with lymph node metastasis and advanced tumor stage in GC (86). Other well-defined gene methylation that are associated with poor prognosis in GC include: MGMT (79,81), HoxD10 (87), HAI-2/SPINT2 (88), DAPK (89,90), BNIP3 (89), RASSF1A (91), RAR-β (91), Dkk-3 (92) etc.

Several genes involved in tumorigenesis and progression of GC have been found to be hypomethylated, which leads to silence of target genes. For instance, Kwon et al. demonstrated that the promoter of ASCL2, which encodes a basic helix-loop-helix transcription factor, shows hypomethylation in GC tissues and high expression levels of this gene are correlated with poor survival of GC patients (93).

miRNAs

The miRNAs are noncoding single-stranded RNA molecules that post-transcriptional regulate the expression of targeted mRNAs and therefore act as major regulators of gene expression (94). miRNAs play a central role in cellular differentiation, development, proliferation and apoptosis. In cancer, all of the above-mentioned processes are deregulated due to altered expression of miRNAs, indicating that miRNAs are involved in carcinogenesis and progression of cancer (95).

Using miRNA microarray technology, it has been discovered that thousands of miRNAs are dysregulated in GC. Many of these miRNAs function as tumor promoters or suppressors in GC cells. Besides their potential as diagnostic markers, the expression levels of specific miRNAs can also be used as prognostic markers. Several miRNA expression changes seem to promise poor prognosis. For example, the down-regulation of miR-451 is associated with poor prognosis and the over-expression of miR-451 increased tumor sensitivity to radiotherapy (96). In a very nearly study, miR-630 expression was examined in 236 GC and adjacent normal tissues. Statistic data suggested that miR-630 was elevated in GC tissues and that increased expression of miR-630 was significantly associated with depth of the tumor, lymph node metastasis, distant metastasis and poor OS. These results indicated that miR-630 might serve as a potential marker for the initiation and progression of GC (97). MiRNAs can be combined with other proteins and thus constitute a comprehensive complex marker. An example of this is miR-200c, which combines with GDF15, and is indicative of poor outcomes in GC patients (98).

Long ncRNAs (lncRNAs)

Besides the relatively well-described miRNAs, accumulating knowledge of the non-coding transcriptome has revealed that the genome is also replete with lncRNAs. LncRNAs, once thought to be junk in cells, have attracted increasing attention in various biomedicine fields. However, lncRNA research is still a young field and up to now only a small number of lncRNAs have been partially characterized, some of which have association with GC prognosis. lncRNA HOTAIR functions as a competing endogenous RNA to regulate HER2 expression by sponging miR-331-3p. Expression of HOTAIR plays a role in invasion and the epithelial-mesenchymal transition of GC cells and overexpression of HOTAIR is characteristic of poor prognosis in GC (99,100). Hypoxia inducible factor 1 alpha antisense RNA-2 (HIF1A-AS2) is an antisense long noncoding RNA, which is a natural antisense transcript of HIF-1a. Chen et al. reported that upregulation of HIF1A-AS2 was found in GC tissues and significantly correlated with tumor depth, lymph node metastasis, advanced stage and poor prognosis (101). Gastric adenocarcinoma predictive long intergenic noncoding RNA (GAPLINC) enhanced the cell migration and proliferation abilities of GC cells, and overexpression of GAPLINC in GC tissues had a significantly worse prognosis (102). Unlike miRNAs regulate protein-coding RNAs via direct binding, lncRNAs work through guiding chromatin modifiers to the target genes. Studies on the role of lncRNAs in GC have only recently begun, and we are just starting to understand their functions. Nonetheless, this limited evidence still touts the promise that lncRNAs may also have the potential to become biomarkers for diagnosis and prognosis in cancer patients. It is undoubtedly that further studies on noncoding RNAs will reveal a new paradigm in the field of GC research.

Novel protein biomarkers

Paired tissues of gastric tumors and adjacent normal tissues obtained after surgical resection are usually analyzed by 2-DE combined with MALDI or LC-MS/MS analysis to discriminate tumor from normal tissues or early stages from advanced stages of GC (103). Potential prognostic biomarkers for GC have also been identified using proteomic technology. S100P and S100A6 are calcium-binding proteins. Researches demonstrated that the down regulation of S100P in GC patients is associated with poor outcomes. Moreover, S100A6 was found to interact with annexin A2 and p53 to regulate tumor progression and metastasis (104,105). Our previous study showed that the high expression of S100A6 was associated with tumor local invasion, lymph node metastasis, cancer embolus, distant metastasis and TNM stages (P<0.05) (106). Other studies showed that CLIC1 (107), DDX39 (108), 14-3-3β (109), CRIP1, HNP-1, were biomarkers for poor prognosis as well.

Blood biomarkers

Classic serum-based tumor markers

In clinical, the most used tumor markers for GC are carcinoembryonic antigen (CEA), CA19-9, CA72-4. The main limitations of their use are limited organ specificity as well as low sensitivity, which prevent early cancer diagnosis. Several researches have explored the association between these markers and prognosis.

CEA is a glycoprotein attached to the surface of enterocytes and plays a role in programmed cell death and cell adhesion (110). High pre-therapeutic levels of CEA are correlated with the stage of the disease, especially in patients with peritoneal serous carcinoma (111). Normal pre-therapeutic levels promise better survival, in particular in patients receiving perioperative chemotherapy (112). In the case of liver metastasis relapse, the CEA level may increase about 3 months before the radiological diagnosis of the disease (113). Normal postoperative levels for a period within 2 months also correlate with a better OS (114). An increase in its level generally indicates relapse, at least at peritoneal level. Yet, it is less sensitive for other sites of cancer metastasis (115,116).

CA 19-9 protein plays a role in cell adhesion and statistically correlated with lymph node involvement (117), but did not contribute as much as CEA in the identification of operable patients (118). CA 19-9 may be an independent predictive factor for metastatic or recurrent patients and possibly for those undergoing curative surgery as well (119).

CA 72-4 is a glycoprotein found on the surface of tumor cells. In recent years, CA72-4 has attracted more and more attention. Several literatures reported that CA72-4 is the most sensitive and the most specific marker in GC. Furthermore, CA72-4 expression is associated with disease progression, lymph node metastasis and distant metastasis (120,121). It appears that the surge in serum levels best correlates with the stage of lymph node involvement (N category), whereas peritoneal fluid levels correlate with both the N stage and T stage (120,122). Our previous study confirmed that, CA72-4 positive expression has correlation with vascular invasion and III, IV stage. Multivariate Analysis showed that CA72-4 was an independent prognostic factor (P=0.012), which is consistent with others’ results (122,123).

Innovative blood-based tumor markers

Prognosis for patients with AGC is poor. Early detection plays a key role in reducing the morbidity and mortality. Increasing patient adherence to GC screening will increase the rate of tumors detected at earlier, more treatable disease stages. This urgent need and technological advances resulted in the advent of noninvasive, blood-based GC biomarkers.

Cell-free nucleic acids (cfNAs)

Tumor cell necrosis, apoptosis, and possibly secretion will lead to the release of DNA, RNA, and noncoding RNAs into circulation in cancer patients. Alterations in the concentration and detection of tumor-specific changes in DNA and/or dysregulated RNA expression profiles have been proposed as biomarkers. The development of diagnostic methods based on the detection of cfNAs in circulating is really attractive because they can provide valuable molecular information about the tumor that may be used for diagnostic, predictive, and prognostic purposes (124).

Genetic and epigenetic alterations in circulation

Higher levels of cell-free DNA (cfDNA) in plasma or serum are generally found in cancer patients. To date, there are several reports regarding circulating cfDNA in GC patients. Among those reports, studies with respect to the concentration of circulating cfDNA are rare. In contrast, the detection of genetic and epigenetic alterations in plasma/serum appears to be the most widely used approach. Several studies have used qPCR to quantify the copy number of genes known to be amplified in GC tissues, such as MYC and HER2, in cell-free plasma from GC patients (125,126). An increased MYC/GAPDH ratio in plasma significantly correlated with that in the GC tissues and could distinguish between GC patients and healthy controls (125). However, they might prove to be highly relevant for detecting the presence or loss of therapeutic targets, and for monitoring treatment efficacy and the course of the disease.

Several other studies have explored the possibility of detecting cancer-associated hypermethylated DNA fragments in the cfDNA of cancer patients. Methylation markers in the bloodstream were first discovered in breast and lung cancer patients in 1999 (127,128). In 2002, the potent application of detecting methylated DNA of death-associated protein-kinase, GSTP1, E-cadherin, p15 and p16 in the serum of GC patients was firstly reported (129). Thereafter, accumulated evidence in this field sprang out. Researchers demonstrated that methylation of several genes could be easily detected in blood circulation; and that this methylation in circulating nucleic acids significantly correlated with methylation levels of these genes in GC tissues and showed diagnostic and prognostic value (129-135). Most recently, Ling et al. clearly demonstrated the potent usefulness of detecting methylated XAF1 DNA as a diagnostic as well as prognostic biomarker (136). RUNX3, which plays important roles in both normal developmental processes and carcinogenesis, is frequently inactivated by methylation-induced silencing (137). Hypermethylation of RUNX3 was detected in 45.2% GC patients (138). The quantification of serum RUNX3 methylation can be used as a biomarker for postoperative monitoring of tumor recurrence in these patients (139).

Noncoding RNAs in circulation

Since the discovery of miRNAs and the association of particular miRNAs with GC, intense research efforts have focused on the identification of tumor specific miRNA transcripts as potential blood biomarkers. Evaluation of new miRNA candidate markers, both individually and in panels, in large independent studies is necessary to determine if miRNA markers can be implemented as screening, diagnostic, and/or prognostic tools in the future.

Several groups reported the successful detection of circulating miRNAs and their significance in malignant diseases. It was reported that the miR-200c blood expression levels in GC patients were significantly higher than in normal controls. There was a correlation (P=0.016) with the number of lymph node metastases and the increased expression levels of miR-200c in blood were significantly associated with a poor OS (median OS, 9 vs. 24 months; P=0.016) and PFS (median PFS, 4 vs. 11 months; P=0.044). Multivariate analyses confirmed that the upregulation of miR-200c in the blood was associated with OS (HR =2.24; P=0.028) and PFS (HR =2.27; P=0.028), independent of clinical covariates (140). MiR-199a-3p is expressed at very high levels in the plasma of GC patients. Of these, miR-199a-3p was significantly associated with lymph node metastasis and progression of primary tumor (T), regional nodes (N), and metastasis (M) (TNM) staging (141). Recently, Li et al. investigated the expression level of miR-25 in plasma and GC tissues and found that overexpression of miR-25 in patients was associated with lymph node metastasis. Furthermore, patients with high plasma expression of miR-25 had poor prognoses (142). MiR-25 facilitates GC progression through repression of transducer of ERBB2, 1 and may represent a noninvasive biomarker for GC.

Circulating cfRNA, in particular miRNA, has been found to be remarkably resistant to endogenous and exogenous RNase activity, extreme pH conditions and freeze-thaw cycles. This could be explained by some protective mechanisms, which involve packaging in secretory particles (apoptotic bodies, exosomes etc.) (143). Cancer-derived extracellular vesicles (EV) are considered to be a liquid tumor biopsy because they are found in elevated levels in the circulation and they have been shown to carry cancer cell-derived lipids, proteins, mRNAs, non-coding and structural RNAs and even genomic DNA, which at least partially reflect parental cells and represents attractive shuttles for cancer biomarkers (144). However, there is little data on circulating GC EVs. Considering the current advances in this field, further studies on EVs released in patients with GC are warranted.

Several recent studies reported that lncRNAs whose expression is deregulated in GC tissues can also be reflected in patients’ blood (145-148), and thus, they may represent a novel source for circulating biomarker discovery. However, a deeper understanding in their biology, mode of action and mechanism of release into the circulation is required to evaluate their clinical significance. Typically, these non-coding RNAs promote high tumor aggressiveness, and lead to a poor prognosis. In a very newly study, Dong et al. identified a GC-associated 3-lncRNA signature (CUDR, LSINCT-5 and PTENP1) in serum. After examining the correlation between the expression levels of the 3-lncRNA in circulation and clinical parameters, they came to a conclusion that GC patients with low value of expression level of the panel of 3 lncRNAs showed higher survival rate than those with high value (149).

Circulating tumor cells (CTCs)

The presence of CTCs in blood is associated with tumor progression or metastasis. With the development of detection techniques, not only was it shown that presence of CTC in blood correlates with disease state, but also that patients with low CTCs have more favorable median PFS and OS rates when compared to patients with elevated CTCs levels (150,151). In 1869, Ashworth reported the discovery of CTCs for the first time, demonstrating that there existed tumor cells in the blood similar to those in the primary tumors (152). Thereafter, a series of studies have demonstrated the identification and characterization of CTCs in peripheral blood of patients with various malignancies, validating Ashworth’s previous remarks. In 2004, prognostic significance of CTCs in breast cancer was first reported by Cristofanilli et al. in a multicenter prospective analysis (153). A recent large multi-center analysis in Europe confirmed an independent prognostic value of CTCs in breast cancer patients with regard to PFS and OS with level-one evidence (154).

To date, many researchers have tried to detect CTCs in patients with GC and demonstrated its biological and clinical significance. Since its introduction, RT-PCR technology has become the most widely used approach detect CTCs whose concentration are extremely low in the bloodstream. Accumulating reports have suggested the significance of CTC detection as a prognostic indicator. Yie et al. examined survivin-expressing CTCs in peripheral blood, showing that the presence of survivin-expressing CTCs was significantly associated with the degree of tumor penetration, nodal status and disease stages. They concluded that the detection of CTCs expressing survivin mRNA could be used to accurately identify GC patients with high risks of relapse (155). Mimori et al. checked one candidate marker, membrane type 1 matrix metalloproteinase (MT1-MMP) mRNA levels, in a qRT-PCR based study involving more than 800 GC patients. As a result, MT1-MMP-expressing CTCs in peripheral blood were indicated to be an independent factor for determining recurrence and distant metastasis of GC (P=0.0018) (156).

One of the advantages that detection of CTCs may have is that it may be used to monitor advanced stage disease in patients who do not have measurable levels of other surveillance markers in blood, such as CEA. However, the research on this type of “liquid biopsy” for gastric detection remains in its infancy, and future research will undoubtedly shed light on clinical applications for CTCs in gastric.

Conclusions

On 21^st January 2015, US president Barack Obama called for a Precision Medicine Initiative that select cancer as one of its immediate targets. Subsequently, in March 2015, China announced their Precision Medicine Initiative in search for prediction of hereditary diseases in newborns, investigations on antibiotics resistance, preventive measures development and personalized cancer therapy. Nowadays, precision medicine already took center stage in the world’s healthcare goals. The cancer precision medicine will leverage advances in biotechnologies, such as next generation sequencing, proteomics, transcriptome, and epigenetics, to identify precise causes for cancers and develop tailor-fit personalized therapies. As for personalized management of GC, it is now widely appreciated that genomic complexity and heterogeneity determined clinical outcomes. Because many of the current strategies are generally only applicable to a limited number of patients, further improvement in the outcome of AGC patients will depend on the identification of biomarkers in different patient populations. Advances in genomics, transcriptomics and proteomics have already identified large amounts of candidates’ biomarkers for GC. In addition, high throughput sequencing technology will undoubtedly accelerate the research in this respect. Also, intense efforts aiming at identifying molecular markers (DNA, RNA or protein) to develop novel, noninvasive biomarker for GC in circulation are underway. Many of these molecular markers have potential to predict prognosis and promise to shift the field to a more individualized approach to GC treatment. Future studies are warranted to settle the controversy surrounding the prognostic values of some of the currently used and newly proposed molecular biomarkers.

Acknowledgements

None.

Footnote

Conflicts of Interest: The authors have no conflicts of interest to declare.

References

1. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011;61:69-90. [PubMed]
2. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer 2013;49:1374-403. [PubMed]
3. Nagini S. Carcinoma of the stomach: A review of epidemiology, pathogenesis, molecular genetics and chemoprevention. World J Gastrointest Oncol 2012;4:156-69. [PubMed]
4. Bertuccio P, Chatenoud L, Levi F, et al. Recent patterns in gastric cancer: a global overview. Int J Cancer 2009;125:666-73. [PubMed]
5. Jung KW, Won YJ, Kong HJ, et al. Prediction of cancer incidence and mortality in Korea, 2013. Cancer Res Treat 2013;45:15-21. [PubMed]
6. Uedo N, Takeuchi Y, Ishihara R. Endoscopic management of early gastric cancer: endoscopic mucosal resection or endoscopic submucosal dissection: data from a Japanese high-volume center and literature review. Ann Gastroenterol 2012;25:281-290. [PubMed]
7. Karimi P, Islami F, Anandasabapathy S, et al. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev 2014;23:700-13. [PubMed]
8. Fenoglio-Preiser CM, Noffsinger AE, Belli J, et al. Pathologic and phenotypic features of gastric cancer. Semin Oncol 1996;23:292-306. [PubMed]
9. Kunz PL, Gubens M, Fisher GA, et al. Long-term survivors of gastric cancer: a California population-based study. J Clin Oncol 2012;30:3507-15. [PubMed]
10. Tsukuma H, Oshima A, Narahara H, et al. Natural history of early gastric cancer: a non-concurrent, long term, follow up study. Gut 2000;47:618-21. [PubMed]
11. Kram A, Peychewa M, Bachurska S, et al. Morphometric distinction of signet-ring cell adenocarcinoma cells from foamy macrophages in gastric endoscopic biopsies. Pol J Pathol 2011;62:145-7. [PubMed]
12. American Cancer Society. Cancer Facts & Figures 2014. Atlanta, Ga. Available online: http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2014/
13. Warneke VS, Behrens HM, Hartmann JT, et al. Cohort study based on the seventh edition of the TNM classification for gastric cancer: proposal of a new staging system. J Clin Oncol 2011;29:2364-71.
14. Laurén P. Histogenesis of intestinal and diffuse types of gastric carcinoma. Scand J Gastroenterol Suppl 1991;180:160-4. [PubMed]
15. Tahara E. Molecular mechanism of stomach carcinogenesis. J Cancer Res Clin Oncol 1993;119:265-72. [PubMed]
16. Cervantes A, Rodríguez Braun E, Pérez Fidalgo A, et al. Molecular biology of gastric cancer. Clin Transl Oncol 2007;9:208-15. [PubMed]
17. Barreto-Zuñiga R, Maruyama M, Kato Y, et al. Significance of Helicobacter pylori infection as a risk factor in gastric cancer: serological and histological studies. J Gastroenterol 1997;32:289-94. [PubMed]
18. National Cancer Institute. National Cancer Institute dictionary of cancer terms. Available online: .www.cancer.gov/dictionary
19. Bhatt AN, Mathur R, Farooque A, et al. Cancer biomarkers - current perspectives. Indian J Med Res 2010;132:129-49. [PubMed]
20. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646-74. [PubMed]
21. Brettingham-Moore KH, Duong CP, Heriot AG, et al. Using gene expression profiling to predict response and prognosis in gastrointestinal cancers-the promise and the perils. Ann Surg Oncol 2011;18:1484-91. [PubMed]
22. Chibon F. Cancer gene expression signatures - the rise and fall? Eur J Cancer 2013;49:2000-9. [PubMed]
23. Waddell T, Chau I, Cunningham D, et al. Epirubicin, oxaliplatin, and capecitabine with or without panitumumab for patients with previously untreated advanced oesophagogastric cancer (REAL3): a randomised, open-label phase 3 trial. Lancet Oncol 2013;14:481-9. [PubMed]
24. Fontanella C, Ongaro E, Bolzonello S, et al. Clinical advances in the development of novel VEGFR2 inhibitors. Ann Transl Med 2014;2:123. [PubMed]
25. Takahashi Y, Cleary KR, Mai M, et al. Significance of vessel count and vascular endothelial growth factor and its receptor (KDR) in intestinal-type gastric cancer. Clin Cancer Res 1996;2:1679-84. [PubMed]
26. Miao HQ, Hu K, Jimenez X, et al. Potent neutralization of VEGF biological activities with a fully human antibody Fab fragment directed against VEGF receptor 2. Biochem Biophys Res Commun 2006;345:438-45. [PubMed]
27. Ohtsu A, Shah MA, Van Cutsem E, et al. Bevacizumab in combination with chemotherapy as first-line therapy in advanced gastric cancer: a randomized, double-blind, placebo-controlled phase III study. J Clin Oncol 2011;29:3968-76. [PubMed]
28. Van Cutsem E, de Haas S, Kang YK, et al. Bevacizumab in combination with chemotherapy as first-line therapy in advanced gastric cancer: a biomarker evaluation from the AVAGAST randomized phase III trial. J Clin Oncol 2012;30:2119-27. [PubMed]
29. Gaur P, Bielenberg DR, Samuel S, et al. Role of class 3 semaphorins and their receptors in tumor growth and angiogenesis. Clin Cancer Res 2009;15:6763-70. [PubMed]
30. Shah MA, Ramanathan RK, Ilson DH, et al. Multicenter phase II study of irinotecan, cisplatin, and bevacizumab in patients with metastatic gastric or gastroesophageal junction adenocarcinoma. J Clin Oncol 2006;24:5201-6. [PubMed]
31. El-Rayes BF, Zalupski M, Bekai-Saab T, et al. A phase II study of bevacizumab, oxaliplatin, and docetaxel in locally advanced and metastatic gastric and gastroesophageal junction cancers. Ann Oncol 2010;21:1999-2004. [PubMed]
32. Galizia G, Lieto E, Orditura M, et al. Epidermal growth factor receptor (EGFR) expression is associated with a worse prognosis in gastric cancer patients undergoing curative surgery. World J Surg 2007;31:1458-68. [PubMed]
33. Lieto E, Ferraraccio F, Orditura M, et al. Expression of vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) is an independent prognostic indicator of worse outcome in gastric cancer patients. Ann Surg Oncol 2008;15:69-79. [PubMed]
34. Sanz-Ortega J, Steinberg SM, Moro E, et al. Comparative study of tumor angiogenesis and immunohistochemistry for p53, c-ErbB2, c-myc and EGFr as prognostic factors in gastric cancer. Histol Histopathol 2000;15:455-62. [PubMed]
35. Karapetis CS, Khambata-Ford S, Jonker DJ, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008;359:1757-65. [PubMed]
36. Bonner JA, Harari PM, Giralt J, et al. Radiotherapy plus cetuximab for locoregionally advanced head and neck cancer: 5-year survival data from a phase 3 randomised trial, and relation between cetuximab-induced rash and survival. Lancet Oncol 2010;11:21-8. [PubMed]
37. Lordick F, Kang YK, Chung HC, et al. Capecitabine and cisplatin with or without cetuximab for patients with previously untreated advanced gastric cancer (EXPAND): a randomised, open-label phase 3 trial. Lancet Oncol 2013;14:490-9. [PubMed]
38. Lin SY, Makino K, Xia W, et al. Nuclear localization of EGF receptor and its potential new role as a transcription factor. Nat Cell Biol 2001;3:802-8. [PubMed]
39. Tai W, Mahato R, Cheng K. The role of HER2 in cancer therapy and targeted drug delivery. J Control Release 2010;146:264-75. [PubMed]
40. Tanner M, Hollmén M, Junttila TT, et al. Amplification of HER-2 in gastric carcinoma: association with Topoisomerase IIalpha gene amplification, intestinal type, poor prognosis and sensitivity to trastuzumab. Ann Oncol 2005;16:273-8. [PubMed]
41. Gravalos C, Jimeno A. HER2 in gastric cancer: a new prognostic factor and a novel therapeutic target. Ann Oncol 2008;19:1523-9. [PubMed]
42. Bang Y, Chung H, Xu J, et al. Pathological features of advanced gastric cancer (GC): Relationship to human epidermal growth factor receptor 2 (HER2) positivity in the global screening programme of the ToGA trial. J Clin Oncol 2009;27:abstr 4556.
43. Allgayer H, Babic R, Gruetzner KU, et al. c-erbB-2 is of independent prognostic relevance in gastric cancer and is associated with the expression of tumor-associated protease systems. J Clin Oncol 2000;18:2201-9. [PubMed]
44. Begnami MD, Fukuda E, Fregnani JH, et al. Prognostic implications of altered human epidermal growth factor receptors (HERs) in gastric carcinomas: HER2 and HER3 are predictors of poor outcome. J Clin Oncol 2011;29:3030-6. [PubMed]
45. Tateishi M, Toda T, Minamisono Y, et al. Clinicopathological significance of c-erbB-2 protein expression in human gastric carcinoma. J Surg Oncol 1992;49:209-12. [PubMed]
46. Grabsch H, Sivakumar S, Gray S, et al. HER2 expression in gastric cancer: Rare, heterogeneous and of no prognostic value - conclusions from 924 cases of two independent series. Cell Oncol 2010;32:57-65. [PubMed]
47. Janjigian YY, Werner D, Pauligk C, et al. Prognosis of metastatic gastric and gastroesophageal junction cancer by HER2 status: a European and USA International collaborative analysis. Ann Oncol 2012;23:2656-62. [PubMed]
48. Bang YJ, Van Cutsem E, Feyereislova A, 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 2010;376:687-97. [PubMed]
49. Gherardi E, Birchmeier W, Birchmeier C, et al. Targeting MET in cancer: rationale and progress. Nat Rev Cancer 2012;12:89-103. [PubMed]
50. Lai AZ, Abella JV, Park M. Crosstalk in Met receptor oncogenesis. Trends Cell Biol 2009;19:542-51. [PubMed]
51. Ishibe S, Karihaloo A, Ma H, et al. Met and the epidermal growth factor receptor act cooperatively to regulate final nephron number and maintain collecting duct morphology. Development 2009;136:337-45. [PubMed]
52. Ma PC, Tretiakova MS, MacKinnon AC, et al. Expression and mutational analysis of MET in human solid cancers. Genes Chromosomes Cancer 2008;47:1025-37. [PubMed]
53. Lennerz JK, Kwak EL, Ackerman A, et al. MET amplification identifies a small and aggressive subgroup of esophagogastric adenocarcinoma with evidence of responsiveness to crizotinib. J Clin Oncol 2011;29:4803-10. [PubMed]
54. Erichsen R, Oliner KS, Kelsh MA, et al. Prognostic impact of tumor MET expression among patients with stage IV gastric cancer: A Danish cohort study. J Clin Oncol 2014;32:abstr 43.
55. Ciuffreda L, Di Sanza C, Incani UC, et al. The mTOR pathway: a new target in cancer therapy. Curr Cancer Drug Targets 2010;10:484-95. [PubMed]
56. An JY, Kim KM, Choi MG, et al. Prognostic role of p-mTOR expression in cancer tissues and metastatic lymph nodes in pT2b gastric cancer. Int J Cancer 2010;126:2904-13. [PubMed]
57. Aoyagi K, Kouhuji K, Miyagi M, et al. Expression of p27Kip1 protein in gastric carcinoma. Hepatogastroenterology 2013;60:390-4. [PubMed]
58. Pedersen MØ, Larsen A, Stoltenberg M, et al. The role of metallothionein in oncogenesis and cancer prognosis. Prog Histochem Cytochem 2009;44:29-64. [PubMed]
59. Pan YM, Xing R, Cui JT, et al. Clinicopathological significance of altered metallothionein 2A expression in gastric cancer according to Lauren's classification. Chin Med J (Engl) 2013;126:2681-6. [PubMed]
60. Chen S, Hou JH, Feng XY, et al. Clinicopathologic significance of putative stem cell marker, CD44 and CD133, in human gastric carcinoma. J Surg Oncol 2013;107:799-806. [PubMed]
61. Wen L, Chen XZ, Yang K, et al. Prognostic value of cancer stem cell marker CD133 expression in gastric cancer: a systematic review. PLoS One 2013;8:e59154. [PubMed]
62. Doventas A, Bilici A, Demirell F, et al. Prognostic significance of CD44 and c-erb-B2 protein overexpression in patients with gastric cancer. Hepatogastroenterology 2012;59:2196-201. [PubMed]
63. Sezer C, Yildirim M, Yildiz M, et al. Prognostic significance of biological apoptosis factors in gastric cancer. J BUON 2013;18:138-46. [PubMed]
64. Tan IB, Ivanova T, Lim KH, et al. Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. Gastroenterology 2011;141:476-85, 485.e1-11.
65. Salem A, Hashem S, Mula-Hussain LY, et al. Management strategies for locoregional recurrence in early-stage gastric cancer: retrospective analysis and comprehensive literature review. J Gastrointest Cancer 2012;43:77-82. [PubMed]
66. Fujiwara M, Kodera Y, Misawa K, et al. Longterm outcomes of early-stage gastric carcinoma patients treated with laparoscopy-assisted surgery. J Am Coll Surg 2008;206:138-43. [PubMed]
67. Yamada Y, Arao T, Gotoda T, et al. Identification of prognostic biomarkers in gastric cancer using endoscopic biopsy samples. Cancer Sci 2008;99:2193-9. [PubMed]
68. Lo Nigro C, Monteverde M, Riba M, et al. Expression profiling and long lasting responses to chemotherapy in metastatic gastric cancer. Int J Oncol 2010;37:1219-28. [PubMed]
69. Wang Z, Yan Z, Zhang B, et al. dentification of a 5-gene signature for clinical and prognostic prediction in gastric cancer patients upon microarray data. Med Oncol 2013;30:678. [PubMed]
70. Zhang YZ, Zhang LH, Gao Y, et al. Discovery and validation of prognostic markers in gastric cancer by genome-wide expression profiling. World J Gastroenterol 2011;17:1710-7. [PubMed]
71. Arzimanoglou II, Gilbert F, Barber HR. Microsatellite instability in human solid tumors. Cancer 1998;82:1808-20. [PubMed]
72. Weber JL, May PE. Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. Am J Hum Genet 1989;44:388-96. [PubMed]
73. Wirtz HC, Müller W, Noguchi T, et al. Prognostic value and clinicopathological profile of microsatellite instability in gastric cancer. Clin Cancer Res 1998;4:1749-54. [PubMed]
74. Choi SW, Choi JR, Chung YJ, et al. Prognostic implications of microsatellite genotypes in gastric carcinoma. Int J Cancer 2000;89:378-83. [PubMed]
75. Ducasse M, Brown MA. Epigenetic aberrations and cancer. Mol Cancer 2006;5:60. [PubMed]
76. Esteller M. Epigenetics in cancer. N Engl J Med 2008;358:1148-59. [PubMed]
77. Jones PA, Baylin SB. The epigenomics of cancer. Cell 2007;128:683-92. [PubMed]
78. Eden A, Gaudet F, Waghmare A, et al. Chromosomal instability and tumors promoted by DNA hypomethylation. Science 2003;300:455. [PubMed]
79. Shi J, Zhang G, Yao D, et al. Prognostic significance of aberrant gene methylation in gastric cancer. Am J Cancer Res 2012;2:116-29. [PubMed]
80. Lukas J, Parry D, Aagaard L, et al. Retinoblastoma-protein-dependent cell-cycle inhibition by the tumour suppressor p16. Nature 1995;375:503-6. [PubMed]
81. Ben Ayed-Guerfali D, Benhaj K, Khabir A, et al. Hypermethylation of tumor-related genes in Tunisian patients with gastric carcinoma: clinical and biological significance. J Surg Oncol 2011;103:687-94. [PubMed]
82. Christofori G, Semb H. The role of the cell-adhesion molecule E-cadherin as a tumour-suppressor gene. Trends Biochem Sci 1999;24:73-6. [PubMed]
83. Tamura G, Yin J, Wang S, et al. E-Cadherin gene promoter hypermethylation in primary human gastric carcinomas. J Natl Cancer Inst 2000;92:569-73. [PubMed]
84. Yu QM, Wang XB, Luo J, et al. CDH1 methylation in preoperative peritoneal washes is an independent prognostic factor for gastric cancer. J Surg Oncol 2012;106:765-71. [PubMed]
85. Starr R, Hilton DJ. SOCS: suppressors of cytokine signalling. Int J Biochem Cell Biol 1998;30:1081-5. [PubMed]
86. Oshimo Y, Kuraoka K, Nakayama H, et al. Epigenetic inactivation of SOCS-1 by CpG island hypermethylation in human gastric carcinoma. Int J Cancer 2004;112:1003-9. [PubMed]
87. Wang L, Chen S, Xue M, et al. Homeobox D10 gene, a candidate tumor suppressor, is downregulated through promoter hypermethylation and associated with gastric carcinogenesis. Mol Med 2012;18:389-400. [PubMed]
88. Kongkham PN, Northcott PA, Ra YS, et al. An epigenetic genome-wide screen identifies SPINT2 as a novel tumor suppressor gene in pediatric medulloblastoma. Cancer Res 2008;68:9945-53. [PubMed]
89. Sugita H, Iida S, Inokuchi M, et al. Methylation of BNIP3 and DAPK indicates lower response to chemotherapy and poor prognosis in gastric cancer. Oncol Rep 2011;25:513-8. [PubMed]
90. Ji M, Guan H, Gao C, et al. Highly frequent promoter methylation and PIK3CA amplification in non-small cell lung cancer (NSCLC). BMC Cancer 2011;11:147. [PubMed]
91. Yao D, Shi J, Shi B, et al. Quantitative assessment of gene methylation and their impact on clinical outcome in gastric cancer. Clin Chim Acta 2012;413:787-94. [PubMed]
92. Yu J, Tao Q, Cheng YY, et al. Promoter methylation of the Wnt/beta-catenin signaling antagonist Dkk-3 is associated with poor survival in gastric cancer. Cancer 2009;115:49-60. [PubMed]
93. Kwon OH, Park JL, Baek SJ, et al. Aberrant upregulation of ASCL2 by promoter demethylation promotes the growth and resistance to 5-fluorouracil of gastric cancer cells. Cancer Sci 2013;104:391-7. [PubMed]
94. Lagos-Quintana M, Rauhut R, Lendeckel W, et al. Identification of novel genes coding for small expressed RNAs. Science 2001;294:853-8. [PubMed]
95. Taft RJ, Pang KC, Mercer TR, et al. Non-coding RNAs: regulators of disease. J Pathol 2010;220:126-39. [PubMed]
96. Bandres E, Bitarte N, Arias F, et al. microRNA-451 regulates macrophage migration inhibitory factor production and proliferation of gastrointestinal cancer cells. Clin Cancer Res 2009;15:2281-90. [PubMed]
97. Chu D, Zhao Z, Li Y, et al. Increased microRNA-630 expression in gastric cancer is associated with poor overall survival. PLoS One 2014;9:e90526. [PubMed]
98. Blanco-Calvo M, Tarrío N, Reboredo M, et al. Circulating levels of GDF15, MMP7 and miR-200c as a poor prognostic signature in gastric cancer. Future Oncol 2014;10:1187-202. [PubMed]
99. Liu XH, Sun M, Nie FQ, et al. Lnc RNA HOTAIR functions as a competing endogenous RNA to regulate HER2 expression by sponging miR-331-3p in gastric cancer. Mol Cancer 2014;13:92. [PubMed]
100. Zhang ZZ, Shen ZY, Shen YY, et al. HOTAIR Long Noncoding RNA Promotes Gastric Cancer Metastasis through Suppression of Poly r(C)-Binding Protein (PCBP) 1. Mol Cancer Ther 2015;14:1162-70. [PubMed]
101. Chen WM, Huang MD, Kong R, et al. Antisense Long Noncoding RNA HIF1A-AS2 Is Upregulated in Gastric Cancer and Associated with Poor Prognosis. Dig Dis Sci 2015;60:1655-62. [PubMed]
102. Hu Y, Wang J, Qian J, et al. Long noncoding RNA GAPLINC regulates CD44-dependent cell invasiveness and associates with poor prognosis of gastric cancer. Cancer Res 2014;74:6890-902. [PubMed]
103. Jia SQ, Niu ZJ, Zhang LH, et al. Identification of prognosis-related proteins in advanced gastric cancer by mass spectrometry-based comparative proteomics. J Cancer Res Clin Oncol 2009;135:403-11. [PubMed]
104. Nedjadi T, Kitteringham N, Campbell F, et al. S100A6 binds to annexin 2 in pancreatic cancer cells and promotes pancreatic cancer cell motility. Br J Cancer 2009;101:1145-54. [PubMed]
105. Słomnicki ŁP, Nawrot B, Leśniak W. S100A6 binds p53 and affects its activity. Int J Biochem Cell Biol 2009;41:784-90. [PubMed]
106. Li J, Wang XH, Li ZY, et al. Regulation mechanism study of S100A6 on invasion and metastasis in gastric cancer. Zhonghua Wei Chang Wai Ke Za Zhi 2013;16:1096-101. [PubMed]
107. Liantonio A, Accardi A, Carbonara G, et al. Molecular requisites for drug binding to muscle CLC-1 and renal CLC-K channel revealed by the use of phenoxy-alkyl derivatives of 2-(p-chlorophenoxy)propionic acid. Mol Pharmacol 2002;62:265-71. [PubMed]
108. Kikuta K, Kubota D, Saito T, et al. Clinical proteomics identified ATP-dependent RNA helicase DDX39 as a novel biomarker to predict poor prognosis of patients with gastrointestinal stromal tumor. J Proteomics 2012;75:1089-98. [PubMed]
109. Tseng CW, Yang JC, Chen CN, et al. Identification of 14-3-3β in human gastric cancer cells and its potency as a diagnostic and prognostic biomarker. Proteomics 2011;11:2423-39. [PubMed]
110. Téllez-Avila FI, García-Osogobio SM. The carcinoembryonic antigen: apropos of an old friend. Rev Invest Clin 2005;57:814-9. [PubMed]
111. Zhang YH, Li Y, Chen C, et al. Carcinoembryonic antigen level is related to tumor invasion into the serosa of the stomach: study on 166 cases and suggestion for new therapy. Hepatogastroenterology 2009;56:1750-4. [PubMed]
112. Chen S, Chen YB, Li YF, et al. Normal carcinoembryonic antigen indicates benefit from perioperative chemotherapy to gastric carcinoma patients. World J Gastroenterol 2012;18:3910-6. [PubMed]
113. Xiao Y, Zhang J, He X, et al. Diagnostic values of carcinoembryonic antigen in predicting peritoneal recurrence after curative resection of gastric cancer: a meta-analysis. Ir J Med Sci 2014;183:557-64. [PubMed]
114. Nam DH, Lee YK, Park JC, et al. Prognostic value of early postoperative tumor marker response in gastric cancer. Ann Surg Oncol 2013;20:3905-11. [PubMed]
115. Han ES, Lee HH, Lee JS, et al. At which stage of gastric cancer progression do levels of carcinoembryonic antigen and carbohydrate antigen 19-9 increase? Application in advanced gastric cancer treatment. J Gastric Cancer 2014;14:123-8. [PubMed]
116. Takata A, Kurokawa Y, Fujiwara Y, et al. Prognostic value of CEA and CK20 mRNA in the peritoneal lavage fluid of patients undergoing curative surgery for gastric cancer. World J Surg 2014;38:1107-11. [PubMed]
117. Dilege E, Mihmanli M, Demir U, et al. Prognostic value of preoperative CEA and CA 19-9 levels in resectable gastric cancer. Hepatogastroenterology 2010;57:674-7. [PubMed]
118. Duraker N, Celik AN. The prognostic significance of preoperative serum CA 19-9 in patients with resectable gastric carcinoma: comparison with CEA. J Surg Oncol 2001;76:266-71. [PubMed]
119. Jo JH, Chung MJ, Park JY, et al. High serum CA19-9 levels are associated with an increased risk of cholangiocarcinoma in patients with intrahepatic duct stones: a case-control study. Surg Endosc 2013;27:4210-6. [PubMed]
120. Li F, Li S, Wei L, et al. The correlation between pre-operative serum tumor markers and lymph node metastasis in gastric cancer patients undergoing curative treatment. Biomarkers 2013;18:632-7. [PubMed]
121. Emoto S, Ishigami H, Yamashita H, et al. Clinical significance of CA125 and CA72-4 in gastric cancer with peritoneal dissemination. Gastric Cancer 2012;15:154-61. [PubMed]
122. Yamamoto M, Yoshinaga K, Matsuyama A, et al. CEA/CA72-4 levels in peritoneal lavage fluid are predictive factors in patients with gastric carcinoma. J Cancer Res Clin Oncol 2014;140:607-12. [PubMed]
123. Zhu YB, Ge SH, Zhang LH, et al. Clinical value of serum CEA, CA19-9, CA72-4 and CA242 in the diagnosis and prognosis of gastric cancer. Zhonghua Wei Chang Wai Ke Za Zhi 2012;15:161-4. [PubMed]
124. Jung K, Fleischhacker M, Rabien A. Cell-free DNA in the blood as a solid tumor biomarker--a critical appraisal of the literature. Clin Chim Acta 2010;411:1611-24. [PubMed]
125. Park KU, Lee HE. MYC quantitation in cell-free plasma DNA by real-time PCR for gastric cancer diagnosis. Clin Chem Lab Med 2009;47:530-6. [PubMed]
126. Shoda K, Masuda K, Ichikawa D, et al. HER2 amplification detected in the circulating DNA of patients with gastric cancer: a retrospective pilot study. Gastric Cancer 2015;18:698-710. [PubMed]
127. Silva JM, Dominguez G, Garcia JM, et al. Presence of tumor DNA in plasma of breast cancer patients: clinicopathological correlations. Cancer Res 1999;59:3251-6. [PubMed]
128. Esteller M, Sanchez-Cespedes M, Rosell R, et al. Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients. Cancer Res 1999;59:67-70. [PubMed]
129. Lee TL, Leung WK, Chan MW, et al. Detection of gene promoter hypermethylation in the tumor and serum of patients with gastric carcinoma. Clin Cancer Res 2002;8:1761-6. [PubMed]
130. Cheng YY, Yu J, Wong YP, et al. Frequent epigenetic inactivation of secreted frizzled-related protein 2 (SFRP2) by promoter methylation in human gastric cancer. Br J Cancer 2007;97:895-901. [PubMed]
131. Chen Z, Fan JQ, Li J, et al. Promoter hypermethylation correlates with the Hsulf-1 silencing in human breast and gastric cancer. Int J Cancer 2009;124:739-44. [PubMed]
132. Zhang Y, Ye X, Geng J, et al. Epigenetic inactivation of deleted in lung and esophageal cancer 1 gene by promoter methylation in gastric and colorectal adenocarcinoma. Hepatogastroenterology 2010;57:1614-9. [PubMed]
133. Bernal C, Aguayo F, Villarroel C, et al. Reprimo as a potential biomarker for early detection in gastric cancer. Clin Cancer Res 2008;14:6264-9. [PubMed]
134. Guo W, Dong Z, Guo Y, et al. Aberrant methylation of the CpG island of HLTF gene in gastric cardia adenocarcinoma and dysplasia. Clin Biochem 2011;44:784-8. [PubMed]
135. Ng EK, Leung CP, Shin VY, et al. Quantitative analysis and diagnostic significance of methylated SLC19A3 DNA in the plasma of breast and gastric cancer patients. PLoS One 2011;6:e22233. [PubMed]
136. Ling ZQ, Lv P, Lu XX, et al. Circulating Methylated XAF1 DNA Indicates Poor Prognosis for Gastric Cancer. PLoS One 2013;8:e67195. [PubMed]
137. Bae SC, Choi JK. Tumor suppressor activity of RUNX3. Oncogene 2004;23:4336-40. [PubMed]
138. Zheng Y, Zhang Y, Huang X, et al. Analysis of the RUNX3 gene methylation in serum DNA from esophagus squamous cell carcinoma, gastric and colorectal adenocarcinoma patients. Hepatogastroenterology 2011;58:2007-11. [PubMed]
139. Sakakura C, Hamada T, Miyagawa K, et al. Quantitative analysis of tumor-derived methylated RUNX3 sequences in the serum of gastric cancer patients. Anticancer Res 2009;29:2619-25. [PubMed]
140. Valladares-Ayerbes M, Reboredo M, Medina-Villaamil V, et al. Circulating miR-200c as a diagnostic and prognostic biomarker for gastric cancer. J Transl Med 2012;10:186. [PubMed]
141. Li C, Li JF, Cai Q, et al. miRNA-199a-3p in plasma as a potential diagnostic biomarker for gastric cancer. Ann Surg Oncol 2013;20 Suppl 3:S397-405. [PubMed]
142. Li BS, Zuo QF, Zhao YL, et al. MicroRNA-25 promotes gastric cancer migration, invasion and proliferation by directly targeting transducer of ERBB2, 1 and correlates with poor survival. Oncogene 2015;34:2556-65. [PubMed]
143. Kosaka N, Iguchi H, Yoshioka Y, et al. Secretory mechanisms and intercellular transfer of microRNAs in living cells. J Biol Chem 2010;285:17442-52. [PubMed]
144. Thakur BK, Zhang H, Becker A, et al. Double-stranded DNA in exosomes: a novel biomarker in cancer detection. Cell Res 2014;24:766-9. [PubMed]
145. Arita T, Ichikawa D, Konishi H, et al. Circulating long non-coding RNAs in plasma of patients with gastric cancer. Anticancer Res 2013;33:3185-93. [PubMed]
146. Zeng X, Shi H, Wang J, et al. Long noncoding RNA aberrant expression profiles after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy of AGC ascertained by microarray analysis. Tumour Biol 2015;36:5021-9. [PubMed]
147. Li Q, Shao Y, Zhang X, et al. Plasma long noncoding RNA protected by exosomes as a potential stable biomarker for gastric cancer. Tumour Biol 2015;36:2007-12. [PubMed]
148. Li P, Chen S, Chen H, et al. Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin Chim Acta 2015;444:132-6. [PubMed]
149. Dong L, Qi P, Xu MD, et al. Circulating CUDR, LSINCT-5 and PTENP1 long noncoding RNAs in sera distinguish patients with gastric cancer from healthy controls. Int J Cancer 2015;137:1128-35. [PubMed]
150. Cohen SJ, Punt CJ, Iannotti N, et al. Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J Clin Oncol 2008;26:3213-21. [PubMed]
151. Cohen SJ, Punt CJ, Iannotti N, et al. Prognostic significance of circulating tumor cells in patients with metastatic colorectal cancer. Ann Oncol 2009;20:1223-9. [PubMed]
152. Ashworth TR. A case of cancer in which cells similar to those in the tumours were seen in the blood after death. Aus Med J 1869;14:146-9.
153. Cristofanilli M, Budd GT, Ellis MJ, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004;351:781-91. [PubMed]
154. Bidard FC, Peeters DJ, Fehm T, et al. Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data. Lancet Oncol 2014;15:406-14. [PubMed]
155. Yie SM, Lou B, Ye SR, et al. Detection of survivin-expressing circulating cancer cells (CCCs) in peripheral blood of patients with gastric and colorectal cancer reveals high risks of relapse. Ann Surg Oncol 2008;15:3073-82. [PubMed]
156. Mimori K, Fukagawa T, Kosaka Y, et al. A large-scale study of MT1-MMP as a marker for isolated tumor cells in peripheral blood and bone marrow in gastric cancer cases. Ann Surg Oncol 2008;15:2934-42. [PubMed]