Association of elevated autoantibody to high expression of GNAS in hepatocellular carcinoma.

Purpose: This study was based on hepatocellular carcinoma (HCC) patients of early-stage to explore the diagnostic capability and possible production causes of anti-GNAS autoantibody. Methods: We evaluated the frequency of anti-GNAS autoantibody in sera from patients with early-stage HCC by enzyme-linked immunosorbent assay (ELISA) and the expression of GNAS protein in early-stage HCC tissues by immunohistochemistry. Western blotting (WB) and real-time polymerase chain reaction (RT-PCR) were utilized to examine the expressions of GNAS protein and mRNA in cell lines. GEO and International Cancer Genome Consortium (ICGC) databases were inquired to explore mRNA expression and mutation of GNAS in HCC tissues. Results: The positive rates of anti-GNAS autoantibody in HCC patients at clinical stage I (78.1 %) and clinical stage II (57.1 %) were all significantly higher than that in healthy control (20 %). There was also a significant difference in GNAS protein expression between HCC and its adjacent normal liver tissues. The results from WB and RT-PCR showed a significant difference at the mRNA level but no statistical difference at the protein level between HCC and normal liver cell lines. The difference in mRNA level between HCC and adjacent normal liver tissues was verified to be significant. Furthermore, the ICGC database demonstrated a 10.6 % mutation frequency for GNAS in HCC patients. Conclusion: The coordination of elevated anti-GNAS autoantibody, high expression of GNAS in the mRNA and protein levels in HCC, and high frequency of GNAS mutation indicates that anti-GNAS autoantibody may be used as an early indicator of HCC.

A Luminex Approach to Develop an Anti-Tumor-Associated Antigen Autoantibody Panel for the Detection of Prostate Cancer in Racially/Ethnically Diverse Populations.

(1) Background: Autoantibodies to tumor-associated antigens (TAAs) have emerged as promising cancer biomarkers. Luminex technology offers a powerful approach for the simultaneous detection of multiple anti-TAA autoantibodies. (2) Methods: We aimed to utilize Luminex technology to evaluate and optimize a panel of anti-TAAs autoantibodies for detecting prostate cancer (PCa), which included autoantibodies to fourteen TAAs. A total of 163 serum samples (91 PCa, 72 normal controls) were screened to determine the levels of the autoantibodies using the Luminex assay. (3) Results: Twelve autoantibodies exhibited significantly high frequencies ranging from 19.8% to 51.6% in the PCa group. Receiver operating characteristic (ROC) curve analysis revealed area under the curve (AUC) values ranging from 0.609 to 0.868 for the twelve autoantibodies individually. We further confirmed the performance of the HSP60 autoantibody by using an enzyme-linked immunosorbent assay (ELISA) in a larger sample comprising 200 PCa sera, 20 benign prostatic hyperplasia (BPH) sera, and 137 normal control sera. The results obtained from the Luminex assay were consistent with the ELISA findings. We developed a panel consisting of three autoantibodies (p16, IMP2, and HSP60) which achieved an impressive AUC of 0.910 with a sensitivity of 71.4% and a specificity of 95.8%. The panel was also evaluated in PCa patients from different races/ethnicities with the best performance observed in distinguishing the Hispanic American patients with PCa from normal controls. (4) Conclusions: We developed an anti-TAA autoantibody panel for the detection of PCa that exhibits promising performance. This panel holds significant potential as a high-throughput tool to facilitate PCa detection.

Autoantibodies to PAX5, PTCH1, and GNA11 as Serological Biomarkers in the Detection of Hepatocellular Carcinoma in Hispanic Americans.

Studies have demonstrated that autoantibodies to tumor-associated antigens (TAAs) may be used as efficient biomarkers with low-cost and highly sensitive characteristics. In this study, an enzyme-linked immunosorbent assay (ELISA) was conducted to analyze autoantibodies to paired box protein Pax-5 (PAX5), protein patched homolog 1 (PTCH1), and guanine nucleotide-binding protein subunit alpha-11 (GNA11) in sera from Hispanic Americans including hepatocellular carcinoma (HCC) patients, patients with liver cirrhosis (LC), patients with chronic hepatitis (CH), as well as normal controls. Meanwhile, 33 serial sera from eight HCC patients before and after diagnosis were used to explore the potential of these three autoantibodies as early biomarkers. In addition, an independent non-Hispanic cohort was used to evaluate the specificity of these three autoantibodies. In the Hispanic cohort, at the 95.0% specificity for healthy controls, 52.0%, 44.0%, and 44.0% of HCC patients showed significantly elevated levels of autoantibodies to PAX5, PTCH1, and GNA11, respectively. Among patients with LC, the frequencies for autoantibodies to PAX5, PTCH1, and GNA11 were 32.1%, 35.7%, and 25.0%, respectively. The area under the ROC curves (AUCs) of autoantibodies to PAX5, PTCH1, and GNA11 for identifying HCC from healthy controls were 0.908, 0.924, and 0.913, respectively. When these three autoantibodies were combined as a panel, the sensitivity could be improved to 68%. The prevalence of PAX5, PTCH1, and GNA11 autoantibodies has already occurred in 62.5%, 62.5%, or 75.0% of patients before clinical diagnosis, respectively. In the non-Hispanic cohort, autoantibodies to PTCH1 showed no significant difference; however, autoantibodies to PAX5, PTCH1, and GNA11 showed potential value as biomarkers for early detection of HCC in the Hispanic population and they may monitor the transition of patients with high-risk (LC, CH) to HCC. Using a panel of the three anti-TAA autoantibodies may enhance the detection of HCC.

Oridonin Induces Apoptosis in Esophageal Squamous Cell Carcinoma by Inhibiting Cytoskeletal Protein LASP1 and PDLIM1.

Esophageal squamous cell carcinoma is a severe malignancy for its high mortality and poor prognosis. Mainstay chemotherapies cause serious side effects for their ways of inducing cell death. Oridonin is the main bioactive constituent from natural plants that has anticancer ability and weak side effects. The proteomics method is efficient to understand the anticancer mechanism. However, proteins identified by proteomics aimed at understanding oridonin's anticancer mechanism is seldom overlapped by different groups. This study used proteomics based on two-dimensional electrophoresis sodium dodecyl sulfate-polyacrylamide gel electrophoresis (2-DE SDS-PAGE) integrated with mass spectrometry and Gene Set Enrichment Analysis (GSEA) to understand the anticancer mechanism of oridonin on esophageal squamous cell carcinoma (ESCC). The results showed that oridonin induced ESCC cell death via apoptosis by decreasing the protein expression of LASP1 and PDLIM1.

Human Proteome Microarray identifies autoantibodies to tumor-associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma.

The identification of the high-efficiency and non-invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor-associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which eight passed multiple-stage validations by ELISA with a total of 1625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer, and colorectal cancer. Finally, an immunodiagnostic model including six TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of six TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers.

Autoantibody against Tumor-Associated Antigens as Diagnostic Biomarkers in Hispanic Patients with Hepatocellular Carcinoma.

BACKGROUND: Tumor-associated antigens (TAAs) have been investigated for many years as potential early diagnosis tools, especially for hepatocellular carcinoma (HCC). Nonetheless, very few studies have focused on the Hispanic HCC group that may be associated with distinct etiological risk factors. In the present study, we investigated novel anti-TAA autoantibodies as diagnostic biomarkers for Hispanic HCC patients. METHODS: Novel TAA targets were identified by the serological proteome analysis (SERPA) and from differentially expressed HCC driver genes via bioinformatics. The autoantibody levels were validated by enzyme-linked immunosorbent assay (ELISA). RESULTS: Among 19 potential TAA targets, 4 anti-TAA autoantibodies were investigated as potential diagnostic biomarkers with significantly high levels in Hispanic HCC sera, including DNA methyltransferase 3A (DNMT3A), p16, Hear shock protein 60 (Hsp60), and Heat shock protein A5 (HSPA5). The area under the ROC curve (AUC) value of the single autoantibodies varies from 0.7505 to 0.8885. After combining all 4 autoantibodies, the sensitivity of the autoantibody panel increased to 75% compared to the single one with the highest value of 45.8%. In a separate analysis of the Asian cohort, autoantibodies against HSPA5 and p16 showed significantly elevated levels in HCC compared to normal healthy controls, but not for DNMT3A or HSP60. CONCLUSION: Anti-DNMT3A, p16, HSPA5, and HSP60 autoantibodies have the potential to be diagnostic biomarkers for Hispanic HCC patients, of which DNMT3A and HSP60 might be exclusive for Hispanic HCC diagnosis.

Serum Autoantibodies against LRDD, STC1, and FOXA1 as Biomarkers in the Detection of Ovarian Cancer.

Purpose: This study is aimed at evaluating serum autoantibodies against four tumor-associated antigens, including LRDD, STC1, FOXA1, and EDNRB, as biomarkers in the immunodiagnosis of ovarian cancer (OC). Methods: The autoantibodies against LRDD, STC1, FOXA1, and EDNRB were measured using an enzyme-linked immunosorbent assay (ELISA) in 94 OC patients and 94 normal healthy controls (NHC) in the research group. In addition, the diagnostic values of different autoantibodies were validated in another independent validation group, which comprised 136 OC patients, 136 NHC, and 181 patients with benign ovarian diseases (BOD). Results: In the research group, autoantibodies against LRDD, STC1, and FOXA1 had higher serum titer in OC patients than NHC (P < 0.001). The area under receiver operating characteristic curves (AUCs) of these three autoantibodies were 0.910, 0.879, and 0.817, respectively. In the validation group, they showed AUCs of 0.759, 0.762, and 0.817 and sensitivities of 49.3%, 42.7%, and 48.5%, respectively, at specificity over 90% for discriminating OC patients from NHC. For discriminating OC patients from BOD, they showed AUCs of 0.718, 0.729, and 0.814 and sensitivities of 47.1%, 39.0%, and 51.5%, respectively, at specificity over 90%. The parallel analyses demonstrated that the combination of anti-LRDD and anti-FOXA1 autoantibodies achieved the optimal diagnostic performance with the sensitivity of 58.1% at 87.5% specificity and accuracy of 72.8%. The positive rate of the optimal autoantibody panel improved from 62.4% to 87.1% when combined with CA125 in detecting OC patients. Conclusion: Serum autoantibodies against LRDD, STC1, and FOXA1 have potential diagnostic values in detecting OC.

Autoantibody to GNAS in Early Detection of Hepatocellular Carcinoma: A Large-Scale Sample Study Combined with Verification in Serial Sera from HCC Patients.

The aim of this study was to explore the value of autoantibody to GNAS in the early detection of hepatocellular carcinoma (HCC). In a large-scale sample set of 912 participants (228 cases in each of HCC, liver cirrhosis (LC), chronic hepatitis B (CHB), and normal controls (NCs) groups), autoantibody to GNAS was detected with a positive result in 47.8% of HCC patients, which was significantly higher than that in patients with LC (35.1%), CHB (19.7%), and NCs (19.7%). Further analysis showed that the frequency of autoantibody to GNAS started increasing in compensated cirrhosis patients (37.0%) with a jump in decompensated cirrhosis patients (53.2%) and reached a peak in early HCC patients (62.4%). The increasing autoantibody response to GNAS in patients at different stages was closely associated with the progression of chronic liver lesions. The result from 44 human serial sera demonstrated that 5 of 11 (45.5%) HCC patients had elevated autoantibody to GNAS before and/or at diagnosis of HCC. Moreover, 46.1% and 62.4% of high positive rates in alpha-fetoprotein (AFP) negative and early-stage HCC patients can supplement AFP in early detection of HCC. These findings suggest that autoantibody to GNAS could be used as a potential biomarker for the early detection of HCC.

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.

Diagnostic value of RNA for hepatocellular carcinoma: a network meta-analysis.

Aim: The aim of this study was to evaluate the capacity of RNA in the diagnosis of hepatocellular carcinoma (HCC). Methods: A systematic review was conducted from PubMed, Cochrane Library, EMBASE and Web of Science databases via well-designed retrieval strategy. Subsequently, the network meta-analysis was performed by the STATA software. Results: Through statistical analysis, the three hypotheses of the network meta-analysis were established. In view of these hypotheses, the diagnostic efficacy of the three markers in HCC (HCC vs healthy people) may be consistent, and the cumulative ranking results showed such a trend: circular RNA >long noncoding RNA >microRNA. Conclusion: Circular RNA may be most effective for diagnosing HCC across the three types of RNA.

Serum Anti-PDLIM1 Autoantibody as Diagnostic Marker in Ovarian Cancer.

Background: Serum autoantibodies (AAbs) against tumor-associated antigens (TAAs) could be useful biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody against PDLIM1 for improving the detection of ovarian cancer (OC). Methods: Immunohistochemistry (IHC) test in tissue array containing 280 OC tissues, 20 adjacent tissues, and 8 normal ovarian tissues was performed to analyze the expression of PDLIM1 in tissues. Enzyme-linked immunosorbent assay (ELISA) was employed to measure the autoantibody to PDLIM1 in 545 sera samples from 182 patients with OC, 181 patients with ovarian benign diseases, and 182 healthy controls. Results: The results of IHC indicated that 84.3% (236/280) OC tissues were positively stained with PDLIM1, while no positive staining was found in adjacent or normal ovarian tissues. The frequency of anti-PDLIM1 autoantibody was significantly higher in OC patients than that in healthy and ovarian benign controls in both training (n=122) and validation (n=423) sets. The area under the curves (AUCs) of anti-PDLIM1 autoantibody for discriminating OC from healthy controls were 0.765 in training set and 0.740 in validation set, and the AUC of anti-PDLIM1 autoantibody for discriminating OC from ovarian benign controls was 0.757 in validation set. Overall, it was able to distinguish 35.7% of OC, 40.6% of patients with early-stage, and 39.5% of patients with late-stage. When combined with CA125, the AUC increased to 0.846, and 79.2% of OC were detected, which is statistically higher than CA125 (61.7%) or anti-PDLIM1(35.7%) alone (p<0.001). Also, anti-PDLIM1 autoantibody could identify 15% (18/120) of patients that were negative with CA125 (CA125 <35 U/ml). Conclusions: The anti-PDLIM1 autoantibody response in OC patients was positively correlated with PDLIM1 high expression in OC tissues, suggesting that the autoantibody against PDLIM1 might have the potential to be a novel serological biomarker of OC, serving as a complementary measure of CA125, which could improve the power of OC detection.

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer.

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

Identification of Novel Autoantibodies Based on the Human Proteomic Chips and Evaluation of Their Performance in the Detection of Gastric Cancer.

Autoantibodies against tumor-associated antigens (TAAbs) can be used as potential biomarkers in the detection of cancer. Our study aims to identify novel TAAbs for gastric cancer (GC) based on human proteomic chips and construct a diagnostic model to distinguish GC from healthy controls (HCs) based on serum TAAbs. The human proteomic chips were used to screen the candidate TAAbs. Enzyme-linked immunosorbent assay (ELISA) was used to verify and validate the titer of the candidate TAAbs in the verification cohort (80 GC cases and 80 HCs) and validation cohort (192 GC cases, 128 benign gastric disease cases, and 192 HCs), respectively. Then, the diagnostic model was established by Logistic regression analysis based on OD values of candidate autoantibodies with diagnostic value. Eleven candidate TAAbs were identified, including autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, RRAS2, RGS4, RHOG, SRARP, RAC1, and TMEM243 by proteomic chips. The titer of autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, and RRAS2 were significantly higher in GC cases while the titer of autoantibodies against RGS4, RHOG, SRARP, RAC1, and TMEM243 showed no difference in the verification group. Next, six potential TAAbs were validated in the validation cohort. The titer of autoantibodies against F8, NRAS, MFGE8, RRAS2, and PTP4A1 was significantly higher in GC cases. Finally, an optimal prediction model with four TAAbs (anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2) showed an optimal diagnostic performance of GC with AUC of 0.87 in the training group and 0.83 in the testing group. The proteomic chip approach is a feasible method to identify TAAbs for the detection of cancer. Moreover, the panel consisting of anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2 may be useful to distinguish GC cases from HCs.

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.

Discovery and Validation of Serum Autoantibodies Against Tumor-Associated Antigens as Biomarkers in Gastric Adenocarcinoma Based on the Focused Protein Arrays.

INTRODUCTION: Previous studies have demonstrated that autoantibodies against tumor-associated antigens (TAAs) in patients with cancer can be used as sensitive immunodiagnostic biomarkers for the detection of cancer. Most of these TAAs are involved in the tumorigenesis pathway. Cancer driver genes with intragenic mutations can promote tumorigenesis. This study aims to identify autoantibodies against TAAs encoded by cancer driver genes in sera as potential immunodiagnostic biomarkers for gastric adenocarcinoma (GAC). METHODS: Protein arrays based on cancer driver genes were customized for screening candidate TAAs in 100 GAC sera and 50 normal control (NC) sera. Autoantibodies against candidate TAAs were assessed by enzyme-linked immunosorbent assay in both training group (205 GAC sera and 205 NC sera) and independent validation group (126 GAC sera and 126 NC sera). Moreover, the immunodiagnostic models were respectively established and validated in the training group and validation group. RESULTS: A panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti-serine/arginine-rich splicing factor 2, and anti-SMARCB1 was selected by the Fisher linear discriminant analysis model with an areas under receiver operating characteristic curve (AUC) of 0.928 (95% confidence interval [CI]: 0.888-0.967) in the training cohort and an AUC of 0.885 (95% CI: 0.852-0.918) in the validation cohort. Besides, the panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti-PBRM1, and anti-ACVR1B which were selected by the binary logistic regression model showed an AUC of 0.885 (95% CI: 0.852-0.919) in the training cohort and 0.884 (95% CI: 0.842-0.925) in the validation cohort. DISCUSSION: Two panels which were selected in this study could boost the detection of anti-TAA autoantibodies in sera as biomarkers for the detection of GAC.

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.

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.

Autoantibodies against tumor-associated antigens combined with microRNAs in detecting esophageal squamous cell carcinoma.

Esophageal carcinoma (EC) is a common malignant disease worldwide, especially in China. There is currently no specific blood test for detecting EC. Autoantibodies against tumor-associated antigens (TAAbs) and microRNAs (miRNAs) are promising markers for cancer diagnosis and this study focuses on combining TAAbs and miRNAs to evaluate the diagnostic value in esophageal squamous cell carcinoma (ESCC). The expression levels of seven TAAbs and five microRNAs in plasmas from 125 patients diagnosed with ESCC and 125 healthy individuals were detected by enzyme-linked immunosorbent assay (ELISA) and reverse transcription quantitative-polymerase chain reaction (RT-qPCR), respectively. The receiver operating characteristic (ROC) analysis was applied to estimate the diagnostic value of these markers for distinguishing ESCC patients from normal individuals. Logistic regression analysis was performed to generate prediction model and calculate the probability of individuals being diagnosed with ESCC. Three panels were established including four TAAbs, three miRNAs, and three TAAbs combined with three miRNAs. The panel consisting of three TAAbs (HCCR, C-myc, and MDM2) and three miRNAs (miR-21, miR-223, and miR-375) attained great diagnostic value for ESCC, with an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.85-0.93) with the sensitivity of 69%, the specificity of 90%, the PPV of 83%, the NPV of 79%, and the coincidence rate of 81%. The optimal panel of six-member markers was able to effectively discriminate the patients with ESCC from normal individuals, especially for early esophageal squamous cell carcinoma.

Selective admission policy of medical undergraduates in western China: applicants' real attitudes to the choice of a rural medical career.

INTRODUCTION: Since 2010, the Chinese government has been introducing selective admission policy to recruit rural students for 5-year western medicine and traditional Chinese medicine undergraduate education in order to improve rural townships' medical services system in western China. This study aimed to analyse the selective admission policy in western China from the perspective of medical students' attitudes towards rural career choice. METHODS: A cross-sectional survey was conducted and an anonymous questionnaire was used to investigate a sample of medical undergraduates chosen under the selective admission policy. RESULTS: The results indicate that medical undergraduates' enthusiasm to work in rural areas was very limited in Gansu province, western China. Extrinsic motivation played a more important role in rural career choice than intrinsic motivation. The students' attitudes were affected by socioeconomic and cultural conditions, which determined their personal and professional environment. Course major and family economic conditions were associated with their self-decisions. CONCLUSION: Further educational intervention should emphasise the students' humanistic inner qualities and recognition of professional value. Further policy adjustment should considered, for example improving social policy-based regional character and national development strategies.