Autoantibody against 14-3-3 zeta: a serological marker in detection of gastric cancer

Simultaneous Assay of CA 72-4, CA 19-9, CEA and CA 125 in Biological Samples Using Needle Three-Dimensional Stochastic Microsensors

Two-needle 3D stochastic microsensors based on boron- and nitrogen-decorated gra-phenes, modified with N-(2-mercapto-1H-benzo[d]imidazole-5-yl), were designed and used for the molecular recognition and quantification of CA 72-4, CA 19-9, CEA and CA 125 biomarkers in biological samples such as whole blood, urine, saliva and tumoral tissue. The NBGr-2 sensor yielded lower limits of determination. For CEA, the LOD was 4.10 × 10-15 s-1 g-1 mL, while for CA72-4, the LOD was 4.00 × 10-11 s-1 U-1 mL. When the NBGr-1 sensor was employed, the best results were obtained for CA12-5 and CA19-9, with values of LODs of 8.37 × 10-14 s-1 U-1 mL and 2.09 × 10-13 s-1 U-1 mL, respectively. High sensitivities were obtained when both sensors were employed. Broad linear concentration ranges favored their determination from very low to higher concentrations in biological samples, ranging from 8.37 × 10-14 to 8.37 × 103 s-1 U-1 mL for CA12-5 when using the NBGr-1 sensor, and from 4.10 × 10-15 to 2.00 × 10-7 s-1 g-1 mL for CEA when using the NBGr-2 sensor. Student's t-test showed that there was no significant difference between the results obtained utilizing the two microsensors for the screening tests, at a 99% confidence level, with the results obtained being lower than the tabulated values.

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system

Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen‐antibody system) were taken as candidate tumor‐associated antigens (TAAs). Enzyme‐linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha‐fetoprotein (AFP)‐negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early‐HCC cases.

Serum Anti-14-3-3 Zeta Autoantibody as a Biomarker for Predicting Hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is a common malignancy worldwide. Alpha-fetoprotein (AFP) is still the only serum biomarker widely used in clinical settings. However, approximately 40% of HCC patients exhibit normal AFP levels, including very early HCC and AFP-negative HCC; for these patients, serum AFP is not applicable as a biomarker of early detection. Thus, there is an urgent need to identify novel biomarkers for patients for whom disease cannot be diagnosed early. In this study, we screened and identified novel proteins in AFP-negative HCC and evaluated the feasibility of using autoantibodies to those protein to predict hepatocarcinogenesis. First, we screened and identified differentially expressed proteins between AFP-negative HCC tissue and adjacent non-tumor liver tissue using SWATH-MS proteome technology. In total, 2,506 proteins were identified with a global false discovery rate of 1%, of which 592 proteins were expressed differentially with 175 upregulated and 417 downregulated (adjusted p-value <0.05, fold-change FC ≥1.5 or ≤0.67) between the tumor and matched benign samples, including 14-3-3 zeta protein. For further serological verification, autoantibodies against 14-3-3 zeta in serum were evaluated using enzyme-linked immunosorbent, Western blotting, and indirect immunofluorescence assays. Five serial serum samples from one patient with AFP-negative HCC showed anti-14-3-3 zeta autoantibody in sera 9 months before the diagnosis of HCC, which gradually increased with an increase in the size of the nodule. Based on these findings, we detected the prevalence of serum anti-14-3-3 zeta autoantibody in liver cirrhosis (LC) patients, which is commonly considered a premalignant liver disease of HCC. We found that the prevalence of autoantibodies against 14-3-3 zeta protein was 16.1% (15/93) in LC patient sera, which was significantly higher than that in patients with chronic hepatitis (0/75, p = 0.000) and normal human sera (1/60, 1.7%, p = 0.01). Therefore, we suggest that anti-14-3-3 zeta autoantibody might be a biomarker for predicting hepatocarcinogenesis. Further follow-up and research of patients with positive autoantibodies will be continued to confirm the relationship between anti-14-3-3 zeta autoantibody and hepatocarcinogenesis.

The application of CA72-4 in the diagnosis, prognosis, and treatment of gastric cancer

The role of conventional serum tumor marker, carbohydrate antigen 72-4 (CA72-4), in assisting diagnosis, monitoring dynamic progression, and evaluating the prognosis of gastric cancer (GC) should not be ignored, especially in the Chinese population. Even though CA72-4 has been used in clinical practice for decades, its modest positivity rate, sensitivity, and specificity did not meet the high demand of the clinical application. However, over the years, some progress in the functions of CA72-4 has been achieved, suggesting that CA72-4 can still be considered a promising marker in oncology. As a biomarker, CA72-4 can achieve improved sensitivity (SEN) and specificity (SPE) when combined with other biomarkers, selecting suitable reference values, improving detection techniques, and identifying the risk threshold. As a predictor, elevated serum CA72-4 levels were found to be significantly associated with prognostic risk factors, further assessing therapeutic validity and resectability. Recently, an effective method to reduce the toxicity of CA72-4 targeted therapy has been developed. Moreover, CA72-4 could induce novel aptamers to react with tumor cells and enhance the efficacy of trastuzumab in HER2-positive GC. Therefore, in this review, we discuss the most recent application of CA72-4 in the diagnosis, prognosis, and treatment of GC.

Immune regulations by 14-3-3: A misty terrain

The 14-3-3 proteins are known for their functions related to the cell cycle and play a prominent role in cancer-related diseases. Recent studies show that 14-3-3 proteins are also regulators of immune responses and are involved in the pathogenesis of autoimmune and infectious diseases. This focused review highlights the significant and recent studies on how 14-3-3 proteins influence innate and adaptive immune responses; specifically, their roles as immunogens and cytokine signaling regulators are discussed. These revelations have added numerous questions to the pre-existing list of challenges, including understanding the 14-3-3 proteins' mechanism of immunogenicity to dissecting the isoform-specific immune regulations.

YY1-modulated long non-coding RNA SNHG12 promotes gastric cancer metastasis by activating the miR-218-5p/YWHAZ axis

Long non-coding RNA (lncRNA) small nucleolar RNA host gene 12 (SNHG12) plays important roles in the pathogenesis and progression of cancers. However, the role of SNHG12 in the metastasis of gastric cancer (GC) has not yet been thoroughly investigated. In the present study, we demonstrated that SNHG12 was upregulated in GC tissues and cell lines. In addition, the expression level of SNHG12 in GC samples was significantly related to tumor invasion depth, TNM stage and lymph node metastasis and was associated with disease-free survival (DFS) and overall survival (OS) in GC patients. In vivo and in vitro assays indicated that SNHG12 promotes GC metastasis and epithelial-mesenchymal transition (EMT). Bioinformatics and mechanistic analyses revealed that SNHG12 can directly target miR-218-5p to regulate YWHAZ mRNA, forming an SNHG12/miR-218-5p/YWHAZ axis and decreasing the ubiquitination of β-catenin. In addition, SNHG12 stabilizes CTNNB1 mRNA by binding with HuR, thus activating the β-catenin signaling pathway. Further analysis also revealed that the transcription factor YY1 negatively modulates SNHG12 transcription. In conclusion, SNHG12 is a potential prognostic marker and therapeutic target for GC. Negatively modulated by YY1, SNHG12 promotes GC metastasis and EMT by regulating the miR-218-5p/YWHAZ axis and stabilizing CTNNB1 via activation of the β-catenin signaling pathway.

Anti-Cancer Auto-Antibodies: Roles, Applications and Open Issues

Auto-antibodies are classically associated with autoimmune diseases, where they are an integral part of diagnostic panels. However, recent evidence is accumulating on the presence of auto-antibodies against single or selected panels of auto-antigens in many types of cancer. Auto-antibodies might initially represent an epiphenomenon derived from the inflammatory environment induced by the tumor. However, their effect on tumor evolution can be crucial, as is discussed in this paper. It has been demonstrated that some of these auto-antibodies can be used for early detection and cancer staging, as well as for monitoring of cancer regression during treatment and follow up. Interestingly, certain auto-antibodies were found to promote cancer progression and metastasis, while others contribute to the body's defense against it. Moreover, auto-antibodies are of a polyclonal nature, which means that often several antibodies are involved in the response to a single tumor antigen. Dissection of these antibody specificities is now possible, allowing their identification at the genetic, structural, and epitope levels. In this review, we report the evidence available on the presence of auto-antibodies in the main cancer types and discuss some of the open issues that still need to be addressed by the research community.

Using Serological Proteome Analysis to Identify and Evaluate Anti-GRP78 Autoantibody as Biomarker in the Detection of Gastric Cancer

The serological biomarkers as noninvasive tests are the most promising way for diagnosing gastric cancer (GC). Serological proteome analysis (SERPA) has been used to identify tumor-associated antigens (TAAs) and the corresponding autoantibodies in many studies. To explore the relationship between gastric cancer development and serum autoantibody anti-GRP78 response found by the method of SERPA with the GC cell line AGS, we included two cohorts (133 GC and 133 normal individuals in test group; 300 GC and 300 normal individuals in validation group) of patients with newly diagnosed GC for verification. All GC and normal controls were matched by age and gender. The autoantibody levels of the sera in two cohorts were measured by immunoassay. Finally, the results showed that 78-kDa glucose-regulated protein (GRP78) was identified in GC by SERPA and the level of anti-GRP78 antibody in GC was higher than that in normal individuals in the two cohorts. Receiver operating characteristic (ROC) curve analysis showed similar diagnostic value of anti-GRP78 antibody in test group (AUC: 0.718) and validation group (AUC: 0.666) to identify GC patients from normal individuals. The AUCs of anti-GRP78 autoantibody in the diagnosis of GC patients with different clinical characteristic ranged from 0.676 to 0.773 in test group and ranged from 0.645 to 0.707 in validation group. In conclusion, autoantibody against GRP78 might be a potential diagnostic biomarker. Further large-scale studies will be needed to validate and improve its performance of the sensitivity, specificity, and AUC value in distinguishing GC from other diseases.

Using protein microarray to identify and evaluate autoantibodies to tumor-associated antigens in ovarian cancer

The aim of this study was to develop a noninvasive serological diagnostic approach in identifying and evaluating a panel of candidate autoantibodies to tumor‐associated antigens (TAAs) based on protein microarray technology for early detection of ovarian cancer (OC). Protein microarray based on 154 proteins encoded by 138 cancer driver genes was used to screen candidate anti‐TAA autoantibodies in a discovery cohort containing 17 OC and 27 normal controls (NC). Indirect enzyme‐linked immunosorbent assay (ELISA) was used to detect the content of candidate anti‐TAA autoantibodies in sera from 140 subjects in the training cohort. Differential anti‐TAA autoantibodies were further validated in the validation cohort with 328 subjects. Subsequently, 112 sera from the patients with ovarian benign diseases with 104 OC sera and 104 NC sera together were recruited to identify the specificity of representative autoantibodies to OC among ovarian diseases. Five TAAs (GNAS, NPM1, FUBP1, p53, and KRAS) were screened out in the discovery phase, in which four of them presented higher levels in OC than controls (P < .05) in the training cohort, which was consistent with the result in the subsequent validation cohort. An optimized panel of three anti‐TAA (GNAS, p53, and NPM1) autoantibodies was identified to have relatively high sensitivity (51.2%), specificity (86.0%), and accuracy (68.6%), respectively. This panel can identify 51% of OC patients with CA125 negative. This study supports our assumption that anti‐TAA autoantibodies can be considered as potential diagnostic biomarkers for detection of OC; especially a panel of three anti‐TAA autoantibodies could be a good tool in immunodiagnosis of OC.

Serological Biomarkers for Early Detection of Hepatocellular Carcinoma: A Focus on Autoantibodies against Tumor-Associated Antigens Encoded by Cancer Driver Genes

Substantial evidence manifests the occurrence of autoantibodies to tumor-associated antigens (TAAs) in the early stage of hepatocellular carcinoma (HCC), and previous studies have mainly focused on known TAAs. In the present study, protein microarrays based on cancer driver genes were customized to screen TAAs. Subsequently, autoantibodies against selected TAAs in sera were tested by enzyme-linked immunosorbent assays (ELISA) in 1175 subjects of three independent datasets (verification dataset, training dataset, and validation dataset). The verification dataset was used to verify the results from the microarrays. A logistic regression model was constructed within the training dataset; seven TAAs were included in the model and yielded an area under the receiver operating characteristic curve (AUC) of 0.831. The validation dataset further evaluated the model, exhibiting an AUC of 0.789. Remarkably, as the aggravation of HCC increased, the prediction probability (PP) of the model tended to decrease, the trend of which was contrary to alpha-fetoprotein (AFP). For AFP-negative HCC patients, the positive rate of this model reached 67.3% in the training dataset and 50.9% in the validation dataset. Screening TAAs with protein microarrays based on cancer driver genes is the latest, fast, and effective method for finding indicators of HCC. The identified anti-TAA autoantibodies can be potential biomarkers in the early detection of HCC.

The role of YWHAZ in cancer: A maze of opportunities and challenges

YWHAZ (also named 14-3-3ζ) is a central hub protein for many signal transduction pathways and plays a significant role in tumor progression. Accumulating evidences have demonstrated that YWHAZ is frequently up-regulated in multiple types of cancers and acts as an oncogene in a wide range of cell activities including cell growth, cell cycle, apoptosis, migration, and invasion. Moreover, YWHAZ was reported to be regulated by microRNAs (miRNAs) or long non-coding RNAs and exerted its malignant functions by targeting downstream molecules like protein kinase, apoptosis proteins, and metastasis-related molecules. Additionally, YWHAZ may be a potential biomarker of diagnosis, prognosis and chemoresistance in several cancers. Targeting YWHAZ by siRNA, shRNA or miRNA was reported to have great help in suppressing malignant properties of cancer cells. In this review, we perform literature and bioinformatics analysis to reveal the oncogenic role and molecular mechanism of YWHAZ in cancer, and discuss the potential clinical applications of YWHAZ concerning diagnosis, prognosis, and therapy in malignant tumors.

Establishment and validation of an immunodiagnostic model for prediction of breast cancer

Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919–0.967) in training cohort and 0.916 (95% CI, 0.886–0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848–0.914) and 0.849 (95% CI, 0.803–0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer.