Dataset Information

Integrated tumor stromal features of hepatocellular carcinoma reveals two distinct subtypes with prognostic/predictive significance.

ABSTRACT: Current clinical classification of hepatocellular carcinoma (HCC) is unable to predict prognosis efficiently. Our aim is to classify HCC into clinically/biologically relevant subtypes according to stromal factors. We detected seven types of stromal features in tumors from 161 HCC patients by immunohistochemical staining and Hematoxylin-eosin staining. Five stromal features were selected out of seven types of stromal features to construct stromal type based on LASSO COX regression model. Then, integrating multiple clinicopathologic characteristics and stromal type, we built two nomograms for overall survival (OS) and disease-free survival (DFS). Further validation of the stromal type and nomograms were performed in the testing cohort (n = 160) and validation cohort (n = 120). Using the LASSO model, we classified HCC patients into stromal type A subgroup (CD34^lowTIL-stromal-ratio^highStromal-tumor-ratio^low?-SMA^weakStroma^mature) and stromal type B subgroup (CD34^highTIL-stromal-ratio^lowStromal-tumor-ratio^high?-SMA^strongStroma^immature). The stromal type was an independent prognostic factor for OS and DFS in the training, testing and validation cohorts. Two nomograms (for OS and DFS) that integrated the stromal type and clinicopathologic risk factors also showed good predictive accuracy and discriminatory power. In addition, immune cell recruitment in the tumor microenvironment (TME) was conditioned by the tumor stromal type. In conclusion, the newly developed tumor stromal type was an effective predictor of OS and DFS. Furthermore, the stromal type is associated with the immune phenotype in the TME.

SUBMITTER: Li W

PROVIDER: S-EPMC6660041 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated tumor stromal features of hepatocellular carcinoma reveals two distinct subtypes with prognostic/predictive significance.

Li Wei W Han Jun J Yuan Kefei K Wu Hong H

Aging 20190701 13

Current clinical classification of hepatocellular carcinoma (HCC) is unable to predict prognosis efficiently. Our aim is to classify HCC into clinically/biologically relevant subtypes according to stromal factors. We detected seven types of stromal features in tumors from 161 HCC patients by immunohistochemical staining and Hematoxylin-eosin staining. Five stromal features were selected out of seven types of stromal features to construct stromal type based on LASSO COX regression model. Then, in ...[more]

PMID: 31299011

Similar Datasets

Project description:BackgroundHepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. This study aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC.MethodsUnivariate Cox regression analysis was performed to determine the prognosis-related AS events and gene set enrichment analysis (GSEA) was employed for functional annotation, followed by the development of prognostic signatures using univariate Cox, LASSO and multivariate Cox regression. K-M survival analysis, proportional hazards model, and ROC curves were conducted to validate prognostic value. ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed to uncover the context of TIME in HCC. Quantitative real-time polymerase chain reaction was implemented to detect ZDHHC16 mRNA expression. Cytoscape software 3.8.0 were employed to visualize AS-splicing factors (SFs) regulatory networks.ResultsA total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, eight AS prognostic signature with robust prognostic predictive accuracy were constructed. Furthermore, quantitative prognostic nomogram was developed and exhibited robust validity in prognostic prediction. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. ZDHHC16 presented promising prospect as prognostic factor in HCC. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs).ConclusionHerein, exploration of AS patterns may provide novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.,) for prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And pivotal players of AS events in context of TIME and immunotherapy efficiency were revealed, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Project description:Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer. Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan-Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the "maftools" package to delineate the tumor mutation burden (TMB). Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5'-cytosine-phosphate-guanine-3' (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)-related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status. Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Project description:Background Previous study has shown that chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family member 4 (CMTM4) can bind and maintain programmed cell death ligand 1 (PD-L1) expression to promote tumor progression by alleviating the suppression of tumor-specific T cell activity, suggesting its potential role in tumor immunotherapy. However, the role of CMTM4 in tumor immunity has not been well clarified, especially in hepatocellular carcinoma (HCC). Methods The protein expression of CMTM4/PD-L1/CD4/CD8 was detected by immunohistochemistry (IHC) detection in 90 cases of HCC tissues. The mRNA expression profiles and related prognosis data were obtained from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC). Two immune therapy cohorts were from Imvigor210 and GSE176307. Results Though the single protein expression of CMTM4, PD-L1, CD4 or CD8 in HCC tissues by IHC detection didn’t show a significant relationship with the prognosis of HCC patients, we found that high co-expression of CMTM4/PD-L1/CD4 showed a good prognosis of HCC patients. Further Timer 2.0 analysis identified that HCC patients with high expression of CMTM4/PD-L1 and high infiltration of CD4+ T cells had a better overall survival than those with low infiltration of CD4+ T cells. Moreover, a series of bioinformatics analyses revealed that CMTM4-related genes posed important effects on prognosis and immunity in HCC patients, and CMTM4 had a positive correlation with infiltration of CD4+ and CD8+ T cells in HCC. At last, we used two immunotherapy cohorts to verify that the combination of CMTM4 with PD-L1 could improve the prognosis of tumor patients underwent immunotherapy. Conclusions CMTM4 and PD-L1 co-expression with T cell infiltration shows prognostic significance in HCC, suggesting combined effect from multiple proteins should be considered in HCC treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09999-y.

Dataset Information

Integrated tumor stromal features of hepatocellular carcinoma reveals two distinct subtypes with prognostic/predictive significance.

Publications

Integrated tumor stromal features of hepatocellular carcinoma reveals two distinct subtypes with prognostic/predictive significance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets