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24. Dominant neoantigen verification in hepatocellular carcinoma by a single-plasmid system coexpressing patient HLA and antigen

Author

Chen, Pu, primary, Chen, Dongbo, additional, Bu, Dechao, additional, Gao, Jie, additional, Qin, Wanying, additional, Deng, Kangjian, additional, Ren, Liying, additional, She, Shaoping, additional, Xu, Wentao, additional, Yang, Yao, additional, Xie, Xingwang, additional, Liao, Weijia, additional, and Chen, Hongsong, additional

Published
2023
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39. A signature based on NKG2D ligands to predict the recurrence of hepatocellular carcinoma after radical resection.

Author

Chen, Dongbo, Gao, Jie, Ren, Liying, Chen, Pu, Yang, Yao, She, Shaoping, Xie, Yong, Liao, Weijia, and Chen, Hongsong

Subjects
*HEPATOCELLULAR carcinoma, *LIGANDS (Biochemistry), *RADICALS (Chemistry), *GENE expression, *PROTEIN expression
Abstract

Introduction: Due to the high recurrence, the HCC prognosis remains poor. Yet, the biomarkers for predicting the recurrence of high‐risk patients are currently lacking. We aimed to develop a signature to predict the recurrence of HCC based on NKG2D ligands. Methods: The multivariate Cox proportional hazards regression was used to select recurrence‐related variables of NKG2D ligands in HCC patients from The Cancer Genome Atlas (TCGA). HCC patients from the OEP000321 dataset and Guilin cohort were used to validate the predictive signature. The mRNA expression of NKG2D ligands was measured by QRT‐PCR. Immunohistochemistry analysis of HCC tissue microarray samples was used to identify the expression of NKG2D ligands. Results: In this study, NKG2D ligands expression in the mRNA and protein level was both abnormally expressed in HCC and associated with recurrence‐free survival (RFS). Then, the recurrence‐related variables of NKG2D ligands in HCC were selected by the multivariate Cox proportional hazards regression. Among the eight NKG2D ligands, MICA (HR = 1.347; 95% CI = 1.012–1.793; p = 0.041), ULBP3 (HR = 0.453; 95% CI = 0.231–0.889; p = 0.021) and ULBP5 (HR = 3.617; 95% CI = 1.819–7.194; p < 0.001) were significantly correlated with RFS in the TCGA‐LIHC cohort. Then, the signature was constructed by the three NKG2D ligands. The predictive effectiveness of this signature was also validated in the OEP000321 dataset and Guilin cohort. Further, HCC patients were classified into low‐risk and high‐risk subgroups by the predictive score. Compared with the low‐risk group, the high‐risk group had poor RFS in both training and validation cohorts. Importantly, compared with the low‐risk patients with the G1‐G2 stage, the levels of infiltrated NK‐activated cells and NKG2D expression were both lower in the high‐risk patients. Conclusions: The signature based on MICA, ULBP3, and ULBP5 could predict HCC recurrence. [ABSTRACT FROM AUTHOR]

Published
2023
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42. IGFBP1 hi WNT3A lo Subtype in Esophageal Cancer Predicts Response and Prolonged Survival with PD-(L)1 Inhibitor.

Author

Liu, Meichen, Yan, Wanpu, Chen, Dongbo, Luo, Jiancheng, Dai, Liang, Chen, Hongsong, and Chen, Ke-Neng

Subjects
ESOPHAGEAL cancer, WNT signal transduction, WNT genes, CELL analysis, GENE clusters
Abstract

Simple Summary: The lack of precision biomarkers hinders the development of individualized immunotherapy for esophageal cancer (ESCA) patients. The activation of WNT signaling has proved to be associated with the primary resistance to immunotherapy. Therefore, our study aims to develop efficient biomarkers based on WNT signaling to guide ESCA immunotherapy. In the TCGA cohort with 196 cases and our BJCH cohort with 95 cases, we successfully constructed an IGFBP1hiWNT3Alo signature and validated its correlation with the poor prognosis of ESCA patients. Moreover, in 2 GEO cohorts with a total of 68 cases and our BJIM cohort with 21 cases, we verified that IGFBP1hiWNT3Alo ESCA patients had a good response and prognosis with immunotherapy. Our findings indicate that the IGFBP1hiWNT3Alo signature was a potential immunotherapeutic biomarker for ESCA. PD-(L)1 inhibitor could improve the survival of locally advanced esophageal cancer (ESCA) patients, but we cannot tailor the treatment to common biomarkers. WNT signaling activation was associated with primary resistance to immunotherapy. In this study, we used our two clinical cohorts (BJCH n = 95, BJIM n = 21) and three public cohorts to evaluate and verify a new immunotherapeutic biomarker based on WNT signaling in ESCA patients. Our findings showed that WNT signaling-related genes stratified TCGA patients into Cluster 1, 2, and 3, among which, Cluster 3 had the worst prognosis. The most up- and down-regulated genes in Cluster 3 were IGFBP1 and WNT3A. Further analysis validated that IGFBP1hiWNT3Alo ESCA patients had significantly poor RFS and OS in the TCGA and BJCH cohorts. Interestingly, IGFBP1hiWNT3Alo patients had a good response and prognosis with immunotherapy in three independent cohorts, exhibiting better predictive value than PD-L1 expression (signature AUC = 0.750; PD-L1 AUC = 0.571). Moreover, IGFBP1hiWNT3Alo patients may benefit more from immunotherapy than standard treatment (p = 0.026). Immune cell infiltration analysis revealed a significant increase in DC infiltration in IGFBP1hiWNT3Alo patients post-immunotherapy (p = 0.022), which may enhance immune response. The IGFBP1hiWNT3Alo signature could predict patients who benefited from PD-(L)1 inhibitor treatment and may serve as a biomarker in ESCA. [ABSTRACT FROM AUTHOR]

Published
2022
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43. Additional file 1 of Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection

Author

Ren, Liying, Chen, Dongbo, Xu, Wentao, Xu, Tingfeng, Wei, Rongyu, Suo, Liya, Huang, Yingze, Chen, Hongsong, and Liao, Weijia

Abstract

Additional file 1. Table S1. The other clinicopathologic characteristics of patients in the training and validation cohorts. Fig. S1. ROC curves showed nomogram had more effectiveness compared with a single factor. ROC curves and AUCs of NLR, tumor size, GMWG and nomogram to predict overall survival in the training cohort (A) and validation cohort (B). Fig. S2. Overall survival and disease-free survival curves based on the best cut-off values. The optimal cut-off value (A and C) and Kaplan–Meier curve (B and D) for overall survival in the training cohort (A and B) and validation cohort (C and D). The optimal cut-off value (E and G) and Kaplan–Meier curve (F and H) for disease-free survival in the training cohort (E and F) and validation cohort (G and H).

Published
2021
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