Review Open Access
Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 7, 2018; 24(21): 2236-2246
Published online Jun 7, 2018. doi: 10.3748/wjg.v24.i21.2236
Prognostic and predictive blood biomarkers in gastric cancer and the potential application of circulating tumor cells
Ting-Ting Li, Hao Liu, Jiang Yu, Guang-Yao Shi, Li-Ying Zhao, Guo-Xin Li
Ting-Ting Li, Hao Liu, Jiang Yu, Li-Ying Zhao, Guo-Xin Li, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Guang-Yao Shi, Division of Cardiology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
ORCID number: Ting-Ting Li (0000-0001-9209-0602); Hao Liu (0000-0001-6131-9398); Jiang Yu (0000-0003-0086-1604); Guang-Yao Shi (0000-0002-2571-9252); Li-Ying Zhao (0000-0002-3723-5626); Guo-Xin Li (0000-0003-2773-7048).
Author contributions: Li TT, Liu H and Yu J contributed equally to the design and preparation of this study and should be considered co-first authors; Yu J and Li GX designed the research; Li TT and Liu H contributed to data acquisition and writing of article; Zhao LY and Shi GY contributed to data analysis; Liu H, Yu J and Li GX contributed to editing, reviewing and final approval of article.
Supported by the Guangdong Provincial Natural Science Foundation, No. 2016A030313843.
Conflict-of-interest statement: Authors declare no conflict of interests for this article.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Correspondence to: Guo-Xin Li, MD, PhD, Professor, Department of General Surgery, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, Guangdong Province, China.
Telephone: +86-20-61641681 Fax: +86-20-62787626
Received: April 3, 2018
Peer-review started: April 4, 2018
First decision: April 19, 2018
Revised: April 27, 2018
Accepted: May 18, 2018
Article in press: May 18, 2018
Published online: June 7, 2018


Gastric cancer (GC), with its high incidence and mortality rates, is a highly fatal cancer that is common in East Asia particularly in China. Its recurrence and metastasis are the main causes of its poor prognosis. Circulating tumor cells (CTCs) or other blood biomarkers that are released into the circulating blood stream by tumors are thought to play a crucial role in the recurrence and metastasis of gastric cancer. Therefore, the detection of CTCs and other blood biomarkers has an important clinical significance; in fact, they can help predict the prognosis, assess the staging, monitor the therapeutic effects and determine the drug susceptibility. Recent research has identified many blood biomarkers in GC, such as various serum proteins, autoantibodies against tumor associated antigens, and cell-free DNAs. The analysis of CTCs and circulating cell-free tumor DNA (ctDNA) in the peripheral blood of patients with gastric cancer is called as liquid biopsy. These blood biomarkers provide the disease status for individuals and have clinical meaning. In this review, we focus on the recent scientific advances regarding CTCs and other blood biomarkers, and discuss their origins and clinical meaning.

Key Words: Gastric cancer, Biomarker, Circulating tumor cells, Autoantibodies, Cell-free DNA

Core tip: As liquid biopsy, the detection of circulating tumor cells (CTCs) and other blood biomarkers have their certain clinical significance. In this review, we focus on the recent scientific advances of CTCs and some other blood biomarkers, and discuss their origin and clinical usefulness.


Gastric cancer (GC) ranks as the fifth most common malignant tumor and the third leading cause of cancer deaths, with more than 951000 new cases and 723000 deaths estimated per year (GLOBOCAN 2012)[1]. Despite the development of diagnostic techniques, surgical techniques and perioperative management in recent years, the prognostic outcomes for GC remain poor. Because early stage GC tends to be asymptomatic and because mass screening is not popular, most patients in China are diagnosed at an advanced stage[2]. The prognosis of peritoneal metastasis from gastric cancer is very poor. In addition, the median survival is 4-12 mo, and the 5-year actuarial survival rate of patients with peritoneal metastasis is less than 5%[3,4]. Therefore, finding useful diagnostic and monitoring tools for gastric cancer patients should be considered as the most important clinical objectives.

A “liquid biopsy” for gastric cancer patients is used to detect physiological indicators or parameters in the serum; the procedure is less invasive than an endoscopic or surgical biopsy, and it allows practitioners to detect the disease earlier and visualize the dynamics and development of gastric tumors, as well as treatment efficiency and chemotherapy resistance. Carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9) and cancer antigen 72-4 (CA72-4) are regarded as clinically popular gastrointestinal tumor biomarkers. However, their positivity rates are less than 40% in GC patients, and the sensitivity and specificity of these blood biomarkers are not sufficient[5,6]. Indeed, if a blood biomarker is to be used in a population-based screening program, it should be reliable in repeated applications and easily measurable in blood serum or plasma by common laboratory equipment. Moreover, it should be present in the bloodstream before the onset of manifestations and clinical symptoms, be able to distinguish between cancer and inflammation and have high positive predictive value for malignant tumors. Therefore, there is an urgent need to identify more precise and effective blood biomarkers to provide optimal management for GC patients; these blood biomarkers should be able to provide early detection, clinical staging, therapy response monitoring, and prognosis for GC.

Cells can be released into the blood stream from the original tumor and/or corresponding distant metastatic sites. These circulating tumor cells (CTCs) could be collected and detected through respective technologies according to their physical and biologic features. CTCs from cancer patients may be considered as a type of real-time “liquid biopsy” that could provide real-time information about the cancer status. CTCs have already been accepted by the FDA as a prognostic biomarker for monitoring patients with breast, prostate and colorectal cancer[7]. Currently, the concept of “liquid biopsy” has also been accepted for the clinical application of evaluating ctDNAs that apoptotic and necrotic cancer cells discharge into the blood circulation[8]. As we know, there are numerous genetic and epigenetic aberrations that could activate oncogenes and promote tumor progression. Therefore, we have developed sensitive molecular assays for the detection of ctDNAs in the blood plasma to find tumor-specific aberrations. Moreover, several autoantibodies against specific tumor associated antigens (TAAs) that are expressed by cancer cells and can be detected in the blood plasma more than five years prior to diagnosis have already been identified[9]. Therefore, CTC, ctDNAs and autoantibodies could become potential blood biomarkers for gastric cancer[10].

In this article, we focus on the clinical applications of CTC, ctDNAs and autoantibodies after a brief introduction of the biology and detection technologies, and we explore the future prospects of blood biomarkers in gastric cancer patients.


The cancer cells that are released from the original tumor or corresponding distant metastatic sites into the circulating blood are called CTCs. However, these epithelial tumor cells cannot stay in the harsh conditions of the bloodstream, and it is possible that CTCs are selected through these harsh conditions[11]. This proposal is consistent with the phenomenon that there are many apoptotic or fragmented CTCs in the peripheral blood stream of cancer patients[12]. The treacherous journey through the vasculature is necessary for the spread of cancer cells to additional sites. CTCs are closely associated with activated platelets and macrophages[13]. Moreover, the transference of metastatic cancer cells into the circulating blood often relies on various chemokines, such as CCR4, CCR7, CCR9, and CXCR4, which guide the cancer cells across the blood vessels[14]. Even a few months or years after primary tumor removal, CTCs can be detected in the peripheral bloodstream of cancer patients, which indicates that cancer cells can be released into the circulation from other metastatic sites[15,16]. However, how these CTCs give rise to tumor metastasis and progression remains unclear. Future comparative genomic analyses of primary carcinoma and metastatic specimens along with CTCs from the same patient might provide more insight (Figure 1).

Figure 1
Figure 1 Flow chart of current and potential applications of circulating tumor cell. Circulating tumor cells (CTCs): The blood samples from cancer patients are processed through various isolation/enrichment and detection techniques. CTCs are usually captured along with contaminating leukocytes. Various detection methods are utilized to detect the rare cell population in the bloodstream.

Currently, CTCs are often detected by epithelial markers such as epithelial cell adhesion molecule (EpCAM) and cytokeratins (CKs), which are not expressed on the surface of blood cells and distinguish CTCs from the masses of blood cells[17]. Epithelial cancer cells can make an epithelial-to-mesenchymal transition (EMT) that leads to decreased epithelial marker expression and enhanced plasticity and migration and invasion capacity. The CTCs that undergo EMT could be resistant to anoikis, which are necessary for the survival and dissemination of CTCs[17]. It has been previously indicated that EMT might particularly affect the stemness of tumor cells[18]. CTCs that undergo EMT might escape detection by EpCAM-based collection methods, such as the CellSearch system. Our previous study explored mesenchymal markers (Vimentin and Twist) to identify the mesenchymal phenotypes of CTCs in the bloodstream and their relevance to therapy responses[19].


The evolution of various technologies to enrich and detect CTCs has been considerable, even resulting in the detection and verification of new CTC markers[17]. It is vital that we pay close attention to the biological characteristics of tumor cells dissemination and potential stem cell like properties that are affected by EMT, particularly in the field of CTCs[18]. Therefore, many companies have optimized their devices to select and detect CTCs that have undergone EMT[17].

After an enrichment step, we could greatly increase the concentration of CTCs and enable the easy detection of even a single tumor cell. Then, CTCs can be detected by different techniques. In theory, CTCs could be positively or negatively chosen based on physical features (e.g., size, density, deforming character, and electric charges) and biologic features (e.g., the expression of protein markers). The enrichment of positively or negatively chosen CTCs could also be achieved based on particular combinations of physical and biologic features in a device. Then, the CTCs could be detected through immunologic, molecular, and/or functional assays. Recently, increasing numbers of research teams have attempted functional tests using cultures and xenografts of CTCs[20,21]. In vitro and in vivo CTCs models can be applied to detect individualized drug susceptibility. However, the ability to establish CTCs cultures and xenografts of CTCs should be improved to design personalized medicine. Currently, hundreds or thousands of CTCs are required to construct cancer cell cultures or xenografts, which limits this approach to individual therapy (Figure 1).

The new technical developments that we focus on are based on new discoveries in CTC biology. A lack of knowledge has hindered the development of the application of CTCs for clinical diagnosis. However, new significant perspectives regarding the biological meaning of CTCs and various revolutionary techniques have been reported[22]. We believe that equipment for the combined collection, detection, and characterization of CTCs will soon be applied clinically.


Recurrence and metastasis not only predict clinical outcomes but also affect the quality of life of GC patients. They are the most critical factors in the treatment of GC. It was originally thought that incomplete surgical resection resulted in recurrence and metastasis after the operative treatment of GC; therefore, extensive radical resection was applied. However, this procedure was not successful, indicating that there are other possible reasons for recurrence and metastasis. Some researchers found that tumor cells could be released into the bloodstream at the early stage of solid tumors (e.g., breast, colon, lung, and gastric cancer)[7]. Therefore, CTCs may also play a vital role in monitoring the dissemination of gastric cancer and guiding the treatment of GC patients with recurrence and metastasis.

As summarized in Table 1, many studies have reported the clinical value of CTCs as prognostic indicators by different detection methods, including the CellSearch system, RT-PCR/qRT-PCR, and FISH. Uenosono et al[23] detected CTCs using the CellSearch system in 251 gastric cancer patients and found that the overall survival (OS) was obviously lower in patients with CTCs than in patients without CTCs (P < 0.0001). Subgroup analysis revealed that the relapse-free survival and OS were significantly lower in patients with CTCs than in patients without CTCs in the resection group (P < 0.0001). In a prospective study, Matsusaka et al[24] also assessed the correlation between CTCs detected by the CellSearch system and chemotherapy and clinical outcomes. They found that GC patients with at least 4 CTCs at 2 and 4 wk after the onset of chemotherapy had an obviously shorter overall survival and progression-free survival than the patients with less than 4 CTCs. However, the CTCs levels at baseline (i.e., before chemotherapy) had no positive correlation with the clinical outcomes. These findings may indicate that the treatment response of CTCs is correlated with clinical outcomes. The number of studies using RT-PCR/qRT-PCR methods is relatively small. However, Mimori et al[25] detected a candidate marker, the membrane type 1 matrix metalloproteinase (MT1-MMP) mRNA level, in more than 800 GC patients. This marker was chosen based on the results of a cDNA microarray analysis, and its correlation with prognosis was subsequently validated using qRT-PCR. As a consequence, the MT1-MMP mRNA level in the peripheral blood may be an independent prognostic indicator of recurrence and metastasis in GC patients (P = 0.0018).

Table 1 Prognostic value of circulating tumor cells in gastric cancer.
Characteristic and number of patientsDetection methodStatistic valueRef.
17RT-PCRCA19 mRNAOSP = 0.014CK19 (+) vs (-)Yeh et al[44], 1998
I-IV57RT-PCRCEA mRNALiver metastasis recurrenceP = 0.03CEA (+) vs (-)Miyazono et al[45], 2001
I-IV106RT-PCRCEA mRNARecurrence/metastasisP = 0.02CEA (+) vs (-)Sumikura et al[46], 2003
I-IV46qRT-PCRCK20 mRNA2-yr-survivalP < 0.05CK20 (+) vs (-)Friederichs et al[47], 2005
I-IV41RT-PCRCK20 mRNAOSP = 0.0363CK20 (+) vs (-)Illert et al[48], 2005
I-III46RT-PCRCEA mRNARecurrenceP ≤ 0.00022CEA after sugery (+) vs (-)Seo et al[49], 2005
I-IV52RT-PCRC-Met mRNAOSP = 0.0178C-Met (+) vs (-)Uen et al[50], 2006
MUC1 mRNAOSP = 0.0352MUC1 (+) vs (-)
I-IV42qRT-PCRCEA mRNARecurrence/metastasisP = 0.032CEA (+) vs (-)Wu et al[51], 2006
I-IV64MAHhTERT/CK19/CEA/MUC1Recurrence/metastasisP = 0.009All marker (+) vs the othersWu et al[52], 2006
I-IV57RT-PCRCK20 mRNA5-yr survivalP > 0.05CK20 (+) vs (-)Pituch-Noworolska et al[53], 2007
Metastatic27CellSearch SystemEpCAM CK8/18/19OSP = 0.039CTC ≥ 2 vs < 2Hiraiwa et al[54], 2008
I-IV69RT-PCRCK19 mRNAOSP = 0.0347CK19 (+) vs (-)Koga et al[55], 2008
CK20 mRNAOSP = 0.049CK20 (+) vs (-)
I-IV810RT-PCRMT1-MMPRecurrence/metastasisP = 0.0018MT1-MMP (+) vs (-)Mimori et al[25], 2008
I-IV55RT-PCR, ELISASurvivin mRNARFSP = 0.026Survivin (+) vs (-)Yie et al[56], 2008
I-IV70qRT-PCRSurvivin mRNAOSP = 0.036Survivin high vs lowBertazza et al[57], 2009
Advanced51 (2 wk after chemotherapy) 48 (4 wk after chemotherapy)CellSearch systemEpCAM CK8/18/19PFS ,OS (2 wk after chemotherapy) PFS ,OS (4 wk after chemotherapy)P < 0.001CTC ≥ 4 vs < 4Matsusaka et al[24], 2010
I-IV123qRT-PCRCEA mRNARecurrenceP = 0.001CEA (+) vs (-)Qiu et al[58], 2010
DFSP = 0.001
I-IV30qRT-PCRCK18 mRNARFSP < 0.001CK18 (+) vs (-)Saad et al[59], 2010
OSP = 0.001
I-IV95qRT-PCRB7-H3 mRNAOSP = 0.046B7-H3 high vs lowArigami et al[60], 2011
I-IV98RT-PCR, ELISASurvivin mRNADFSP < 0.001Survivin (+) vs (-)Cao et al[61], 2011
I-IV52qRT-PCRmiR-200cOSP = 0.016miR-200c high vs lowValladares-Ayerbes et al[62], 2012
RFSP = 0.044
I-IV75ImmunofluorescenceGFPOSP =0.0021CTC ≥ 5 vs < 5Ito et al[63], 2012
I-IV251CellSearch systemEpCAM CK8/18/19OSP < 0.001CTC (+) vs (-)Uenosono et al[23], 2013
RFSP < 0.001
I-IV22CellSearch systemEpCAM CK8/18/19OSP = 0.23CTC ≥ 2 vs < 2Sclafani et al[64], 2014
PFSP = 0.91
I-IV62qRT-PCRKRT19/MUC1/EPCAM/CEACAM5/BIRC5 mRNAOSP = 0.003All marker (+) vs the othersKubisch et al[65], 2015
PFSP < 0.001
I-IV36Flow cytometryCD133 ABCG2OSP = 0.034CD133 (+) vs (-)Xia et al[66], 2015
I-IV136CellSearch systemEpCAM CK8/18/19PFSP = 0.016CTC (+) vs (-)Okabe et al[67], 2015
I-IV100Cell Search systemEpCAM CK8/18/19OSP = 0.004CTC ≥ 5 vs < 5Lee et al[68], 2015
PFSP = 0.004
I-IV24FACS-ICCEpCAMOSP = 0.014CTC ≥ 2 vs < 2Meulendijks et al[69], 2016
PFSP = 0.007
I-IV136CellSearch systemEpCAM CK8/18/19OSP < 0.001CTC ≥ 3 vs < 3Li et al[70], 2016
PFSP = 0.001
I-IV65ImmunofluorescenceOBP-401OSP = 0.183OBP-401 (+) vs (-)Ito et al[71], 2016
RFSP = 0.034
I-IV106CellSearch systemEpCAM CK8/18/19OSP = 0.003CTC ≥ 2 vs < 2Peront et al[72], 2017
RFSP = 0.0002
I-IV43IsoFlux platformEpCAMOSP = 0.0013CTC ≥ 17 vs < 17Brungs et al[73], 2018

Taken together, these studies indicate that CTCs result in GC recurrence and metastasis and may act as vital therapeutic targets for the treatment of GC recurrence and metastasis after radical resection.

Cell-free nucleic acids

Tumor DNA can be released into the blood stream from the primary tumors, circulating tumor cells, or metastases of cancer patients. The majority of circulating cell-free tumor DNAs (ctDNAs) come from apoptotic or necrotic cancer cells that release fragmented DNA into the circulating blood. Dying nonmalignant host cells can also release cell-free DNAs (cfDNAs) into the circulating blood. These normal cfDNAs can dilute the ctDNAs concentrations in cancer patients, particularly in circumstances when tissue-damaging procedures, including surgery, chemotherapy, or radiotherapy, were carried out. Even though the length of DNA fragments might provide some information about the derivation of cfDNAs[26,27], we should further explore the biological features of the ctDNAs in the circulating blood. Several studies have indicated that ctDNAs can even be absorbed by host cells, and this uptake can affect the biology of these host cells[28,29]. Thus, ctDNAs may be indicated as a new target for anti-tumor treatment in order to dilute this type of oncogenic DNA, an idea proposed decades ago[30]. Several clinical applications of ctDNAs have been used for gastric cancer. ctDNAs is not only a tool for the early detection of cancer but also a prognostic or predictive factor (Table 2).

Table 2 Detection of cell-free tumor DNA in gastric cancer.
Candidate biomarkersSample sizeSample typeMethod/technologyDiagnostic value/outcomeRef.
Total cell-free DNA level b-actinGC = 53, HC = 21PlasmaqPCRAUC = 0.75, P < 0.0001Sai et al[31], 2007
DNA methylation markers RPRM (Reprimo)GC = 43, HC = 31GC tissues and plasmaMSP95.3% GC, 9.7% HC, P < 0.00001; Strong correlation between methyl status in tissues and plasmaBernal et al[74], 2008
Gene amplification MYC gene copy number (MYC/GAPDH ratio)GC = 57, HC = 39Tissues and plasmaqPCRAUC = 0.816; Strong positive correlation between MYC levels in GC tissues and plasma (r = 0.342; P = 0.009)Park et al[75], 2009
RUNX3GC (preoperative) = 65, GC (postoperative) = 43, HC = 50Tissues and serumqMSPAUC = 0.8651, Sn = 95.5%, Sp = 62.5%; Decrease after surgical resectionSakakura et al[76], 2009
KCNA4 + CYP26B1GC = 46, GPL = 46, HC = 30SerumDiscovery: Methylation microarray in tissues; Testing: MSPAUC = 0.917, Sn = 91.3%, Sp = 92.1%Zheng et al[77], 2011
SLC19A3Discovery: GC = 45, HC = 60; Validation: GC = 20, HC = 20PlasmaMSRED-qPCRIncreased in GC, P < 0.0001Ng et al[78], 2011
Alu DNA sequencesGC = 54, HC = 59PlasmaAlu81-qPCRAUC = 0.784, Sn = 75%, Sp = 63%Park et al[32], 2012
FAM5C + MYLKGC = 58, GPL = 46, HC = 30SerumDiscovery: MeDIP in cell lines; Testing: MSPAUC = 0.838, Sn = 77.6%, Sp = 90% for GC vs HC; Sn = 30.4% for GPL vs HC; Decrease after surgical resectionChen et al[79], 2012
XAF1GC = 202, HC = 88Tumor tissues and serumqMSPAUC = 0.909, P < 0.0001; 83.9% concordance between tissues and serumLing et al[33], 2013
Total cfDNA levelEarly GC = 16; advanced GC = 14; HC = 34PlasmaMeasurement of cfDNA concentrationAUC = 0.991, Sn = 96.67%, Sp = 94.11% for GC vs HCKim et al[80], 2014
HER2 + MYCGC = 81; gastritis = 63; HC = 32Plasma and tissuesFISH and qPCRAUC = 0.850, Sn = 69%, Sp = 92%Park et al[34], 2014
HER2 gene copy number (HER2/RPPH1 ratio)Discovery: GC = 52 (pre and post-operative treatment), HC = 40;Validation: GC = 25 plasmaPlasma and tissuesqPCRAUC = 0.746, Sn = 53.9%, Sp = 96.7%; Positive correlation between GC tissues and plasma (r = 0.424; P = 0.00721); Decrease in post-treatment plasma in HER2 + GC cases; Sn = 66.7%, Sp = 100%Shoda et al[35], 2015
TP53GC = 6PlasmaParallel sequencingctDNATP53 mutation in three out of six patients (50%)Hamakawa et al[81], 2015
AKT1, AKT3, PIK3CA, PTEN, ARID1A, TP53 and BRAFGC = 277Plasma and tissuesMassARRAY system32 out of 94 patients (34%) with a tissue mutation had a corresponding mutation in plasmaFang et al[82], 2016
HER2GC = 70Plasma and tissuesdual-color ISH assayctDNA had a high concordance of HER2 amplification with tumor tissues(91.4%, Kappa index = 0.784, P < 0.001)Gao et al[83], 2017
HER2GC = 60; HC = 30Plasma and tissuesdigital droplet PCRThe preoperative plasma HER2 ratio correlated with the tumor HER2 status (P < 0.001); Sn = 73.3%, Sp = 93.3%Shoda et al[36], 2017

Among previous studies of ctDNAs in GC patients, we found that some studies focused on the concentration of ctDNAs. In these studies, the housekeeping gene, beta-actin[31], and a non-coding DNA sequence, ALU[32], were assessed. By comparison, the most widely used method for detecting ctDNAs is the measurement of methylated DNA in the plasma or serum; this measurement is often performed with methylation specific-PCR (MSP) or quantitative methylation specific-PCR (qMSP) assays. With the advances in technology and verification of more sensitive and specific genes, evidence has accumulated in this field. Comprehensive analyses using methylation CpG island microarray have indicated the possibility of more meaningful genes for measuring methylated DNA. Furthermore, Ling et al[33] have shown the effective application of methylated XAF1 DNA. This DNA could be used as a diagnostic or prognostic biomarker with high specificity and sensitivity. In addition to mutation analyses, gene amplification appears to provide relevant blood biomarkers. Park et al[34] found that the combination of plasma HER2 and MYC concentrations to diagnose GC had a sensitivity and specificity of 69% and 92%, respectively. To determine the effect of sequencing methods upon the overall diagnostic accuracy, Shoda et al[35] compared qPCR and digital droplet PCR (ddPCR)[36] for detecting the HER2 amplification ratio in 60 patients with GC. A correlation between the plasma and tissue HER2 amplification ratios was observed by ddPCR (ρ = 0.424, 95%CI: 0.125-0.652, P = 0.00721).

Cancer-associated autoantibodies

IgG Autoantibodies against specific tumor associated antigens (TAAs) can be detected in the blood more than five years prior to a clinical diagnosis of cancer, thus indicating their important role in the prognosis of early-stage cancer[37,38]. Additionally, autoantibodies have other promising biomarker qualities: they can be detected in every type of tumor that has ever been tested[39,40] and they are very stable and have antigen specificity. Assessing the autoantibody response against TAAs with multiplex immunoassays is supposed to be viable, and this method might make them clinically applicable.

To the best of our knowledge, ten studies have reported the clinical diagnosis values of diverse GC associated autoantibodies or their combinations (Table 3). In these studies, the recognized biomarkers can distinguish GC patients from healthy controls with comparatively excellent specificity (87%-100%), but discrepant sensitivity (19.3%-98.9%). There are three studies that described the AUC: Zhou et al[41] reported that autoantibodies against seven TAAs could distinguish GC patients from healthy subjects with an AUC of 0.73. Zayakin et al[42] showed that 45 autoantibodies could distinguish GC patients from healthy subjects with an AUC of 0.79, while Meistere et al[43] reported an AUC of 0.60. These ten studies of autoantibodies in GC vary greatly regarding the number of autoantibodies measured (ranging from 2 to 102), the techniques used to detect the autoantibodies, the definition of suitable control groups, and the methods used to normalize the data and define cut-off values. Taken together, these factors may greatly hinder the clinical application of the reported biomarkers.

Table 3 Detection of autoantibodies against tumor associated antigens in gastric cancer.
Biomarker signature descriptionTechnologyStudy designSample size (GC/controls)Diagnostic valueRef.
2 TAAs-p62 and KocELISAGC vs HC135/82Sn = 19.3%, Sp = 97.6%, P < 0.01Zhang et al[84], 2001
3TAAs-IQGAP3, KRT23 and REG3APARSE assayGC vs HC (age and sex matched)48/46Sn = 22.9%, Sp = 100%, P < 0.001Xu et al[85], 2012
3 TAAs-p16, p53 and c-mycELISAGC vs HC74/82Sn = 21.6%, Sp = 97.6%; P < 0.001Looi et al[86], 2006
6 TAAs-p53, Hsp70, HCC-22-5, PrxVI, KM-HN-1 and p90ELISAGC vs HC, training set100/79Sn = 49.0%, Sp = 92.4%, P < 0.01Hoshion et al[87], 2017
GC vs HC, validation set248/74Sn = 52.0%, Sp = 90.5%, P < 0.01
7 TAAs - p53, C-myc, p16, IMP1, Koc, p62 and SurvivinELISACardia GC vs HC88/140AUC = 0.73, Sn = 64%, Sp = 87%, P < 0.001Zhou et al[41], 2015
7 TAAs - C-myc, Cyclin B1, IMP1, Koc, P53, p62 and SurvivinELISA, fixed cut-offGC vs HC91/346Sn = 52.7%, Sp = 89.9%, P < 0.01Zhang et al[88], 2003
ELISA, individual cut-offGC vs HC91/346Sn = 98.9%, Sp = 93.1%, P < 0.001Koziol et al[89], 2003
45 T7 phage-displayed TAA clones (including NY-ESO-1, DDX53, MAGE antigens etc.)T7 phage displayed TAA microarrayGC vs HC (age and sex matched)T:100/100AUC = 0.79, Sn = 59%, Sp = 90%, P < 0.001Zayakin et al[42], 2013
V:235/213AUC = 0.64, Sn = 58.7%, Sp = 55%, P < 0.001
GC vs gastritis235/100AUC = 0.76, Sn = 58.7%, Sp = 81.5%, P < 0.001
GC vs gastric ulcer235/54
64 TAAs (including MAGEA4, CTAG1, TP53, ERBB2_C and SDCCAG8 antigens etc.)Bead-based multiplex serologyGC vs HCT:155/224Sn = 0-12%, Sp = 98%; P > 0.05Werner et al[90], 2016
GC vs HCV:146/97Sn = 32%, Sp = 87%; P < 0.001
102 TAAs (including CTAG1B/CTAG2, DDX53, IGF2BP2, TP53 and MAGEA3 antigens etc.)A recombinant antigen microarrayGC vs HC829/929AUC = 0.60, Sn = 21%, Sp = 91%, P < 0.001Meistere et al[43], 2017

In general, measuring autoantibodies against TAAs has been reported to have excellent specificity but general sensitivity, which would hamper its use in clinical medicine. The biological mechanisms underlying the limitations of autoantibody sensitivity are currently unknown. Additionally, the heterogeneity of TAAs among cancer patients is very high, and one cancer-specific autoantibody usually has a low probability of detection and is thus unlikely to have statistical significance. Therefore, recently published studies are likely to be statistically inefficient. However, diagnostic biomarker panels result in the low repeatability of initial results and reduce the diagnostic value of autoantibodies, but this issue could be remedied by analyzing combinations with good statistical significance.


In general, the field of CTCs, ctDNAs and autoantibodies is stimulating discovery regarding the tumor recurrence and metastasis, but it is still in the early stages. The transformation of these blood biomarkers into conventional clinical indicators is hampered by the absence of consistency among different technical methods. The CellSearch system is the first standardized semi-automatic technique approved by the FDA to enrich and detect CTCs in patients with breast, prostate or colorectal cancer. Many studies have shown that the results of CTCs detection with the CellSearch system could serve as a clinical prognostic and therapeutic effectiveness indicator for these cancers. Recently, a few studies have shown that detection of CTCs in GC patients using the CellSearch system could be used for staging, predicting patients’ overall survival and evaluating the treatment effectiveness. However, large-scale clinical studies are needed to further validate the important role of CTCs and to explore an applicable cut-off value for the CTCs score in GC patients.

Although various methods and techniques have been recommended for ultimately establishing an applicable, sensitive and real-time monitoring system using circulating blood, few methods can currently be applied in clinical practice. Large-scale clinical trials and further exploration of the biology and significance of blood biomarkers might solve the associated problems and improve their application as blood biomarkers. Therefore, the exploration of revolutionary blood biomarkers, such as CTCs, ctDNAs and autoantibodies, could provide many advantages for gastric cancer patients and improve their clinical outcomes in the future.


Manuscript source: Invited manuscript

Specialty type: Gastroenterology and hepatology

Country of origin: China

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P- Reviewer: Aoyagi K, Surlin VM S- Editor: Gong ZM L- Editor: A E- Editor: Huang Y

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