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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.

Background: This study aims to evaluate the clinicopathological characteristics of metastatic hepatocellular carcinoma (HCC) patients and develop nomograms to predict their long-term overall survival (OS) and cancer-specific survival (CSS). Methods: Information on metastatic HCC from 2010 to 2015 was retrieved from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute. The metastatic HCC patients were divided into a long-term survival (LTS) group and a short-term survival (STS) group with 1 year selected as the cut-off value. Then, we compared the demographic and clinicopathological features between the two groups. Next, all patients were randomly divided into a training group and validation group at a 7:3 ratio. Univariate and multivariate Cox regression analyses were used to identify potential predictors for OS and CSS in the training group, and nomograms of OS and CSS were established. These predictive models were further validated in the validation group. Results: A total of 2163 patients were included in the current study according to the inclusion and exclusion criteria. Patients with characteristics including lower T stage and N stage; treatment with surgery, radiation or chemotherapy; no lung metastasis; and AFP negative status showed better survival. The concordance index (C-index) of the OS nomogram was 0.72 based on 9 variables. The C-index of the CSS nomogram was 0.71 based on 8 variables.Conclusions: These nomograms may help clinicians make better treatment recommendations for metastatic HCC patients.

Background This study aims to evaluate the clinicopathological characteristics of metastatic hepatocellular carcinoma (HCC) patients and develop nomograms to predict their long-term overall survival (OS) and cancer-specific survival (CSS). Methods Information on metastatic HCC from 2010 to 2015 was retrieved from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute. The metastatic HCC patients were divided into a long-term survival (LTS) group and a short-term survival (STS) group with 1 year selected as the cut-off value. Then, we compared the demographic and clinicopathological features between the two groups. Next, all patients were randomly divided into a training group and validation group at a 7:3 ratio. Univariate and multivariate Cox regression analyses were used to identify potential predictors for OS and CSS in the training group, and nomograms of OS and CSS were established. These predictive models were further validated in the validation group. Results A total of 2163 patients were included in the current study according to the inclusion and exclusion criteria. Patients with characteristics including lower T stage and N stage; treatment with surgery, radiation or chemotherapy; no lung metastasis; and AFP negative status showed better survival. The concordance index (C-index) of the OS nomogram was 0.72 based on 9 variables. The C-index of the CSS nomogram was 0.71 based on 8 variables. Conclusions These nomograms may help clinicians make better treatment recommendations for metastatic HCC patients.

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide with a high recurrence rate. Although immune-checkpoint treatment provides survival benefits for patients with HCC, the mortality of HCC is still high. Therefore, it is necessary to understand the molecular mechanism, immune microenvironment and related prognostic factors of HCC. Methods: HCC patients with both RNAseq and miRNA expression data and their clinical information were downloaded from the NCI's Genomic Data Commons. Gene expression data were analyzed to identify differentially expressed genes (DEGs). The results were further used to construct the competing endogenous RNA (ceRNA) networks related to HCC. Meanwhile, the proportions of 22 immune cell types in HCC were estimated by using CIBERSORT (https://cibersort.stanford.edu). Subsequently, two nomograms based on the expression value of ceRNAs and the percentage of immune cells were constructed to predict the prognosis of HCC patients, respectively. The calibration and time series ROC curves were used to evaluate these models. Finally, the correlation between ceRNAs and immune cells were explored to reveal the underlying molecular mechanism. Results: Gene expression data and clinical information of 367 primary HCC and 50 adjacent normal samples were downloaded from the TCGA database. These data were used to identify DEGs. A total of 12 lncRNAs, 71 mRNAs and 39 miRNAs were identified to be in ceRNA networks according to starBase v2.0 database. The results showed that stages of cancer, the expression values of CBX2, MAFG, DNM3, BUB1, ARL5B and other 23 genes were important predictors. The AUCs of the constructed nomogram in predicting the 3- and 5-year OS were 0.79 and 0.81 respectively. Stages of cancer, proportions of resting memory CD4+ T cell, M0 macrophages and resting mast cells constructed another nomogram with AUC of 0.79 and 0.81 in predicting the 3- and 5-year OS. CIBERSORT results showed that the proportions of regulatory T cells, CD8+ T cells, follicular helper T cells and M0 macrophages were slightly higher while the proportions of M2 macrophages, monocytes and activated mast cells were slightly lower in HCC than in adjacent normal samples. Integration analysis of CIBERSORT results, ceRNAs and gene set enrichment analysis of immunologic signatures identified BUB1, E2F8, H2AFZ, JPT1, LPCAT1, MAFG and PSMC3IP to be positively related with regulatory T cells, follicular helper T cells, activated memory CD4+ T cells and M0 macrophages. This is consistent with current knowledge that immune activity is suppressed in certain types of cancers. However, the underlying mechanism warrant further exploration. Conclusions: In this study, two nomograms were constructed to predict the prognosis of HCC patients, which might be useful in predicting the prognosis of HCC patients. The results suggest that the immune suppress effect of HCC may be related to BUB1, E2F8, H2AFZ, JPT1, LPCAT1, MAFG and PSMC3IP genes. These genes may serve as potential therapeutic targets. Acknowledgments: This work was supported by the National Science and Technology Major Project of China (2018ZX10302205). Citation Format: Jianxiang Shi, Chi Cui, Yaru Duan, Hua Ye, Peng Wang, Jitian Li, Shuiling Jin, Liping Dai, Jianying Zhang. Construction of ce-RNA networks and comprehensive analysis with tumor infiltrating immune cells in hepatocellular carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 35.