Dataset Information

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma.

ABSTRACT:

Background/aims

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.

Methods

We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses.

Results

Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types.

Conclusion

Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.

SUBMITTER: Hong J

PROVIDER: S-EPMC11261225 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma.

Hong Jiwon J Eun Jung Woo JW Baek Geum Ok GO Cheong Jae Youn JY Park Seryoung S Kim Soon Sun SS Cho Hyo Jung HJ Lim Su Bin SB

Clinical and molecular hepatology 20240315 3

<h4>Background/aims</h4>Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.<h4>Methods</h4>We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological ...[more]

PMID: 38486508

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Project description:ObjectivesSepsis, caused by the body's response to infection, poses a life-threatening condition and represents a significant global health challenge. Characterized by dysregulated immune response to infection, sepsis may lead to organ dysfunction and failure, ultimately resulting in high mortality rates. The liver plays a crucial role in sepsis, yet the role of differentially expressed genes in septic patients remains unclear in hepatocellular carcinoma (HCC). In this study, we aim to investigate the significance of differentially expressed genes related to sepsis in the occurrence and prognosis of tumors in HCC.MethodsWe conducted analyses by obtaining gene transcriptome data and clinical data of HCC cases from The Cancer Genome Atlas (TCGA). Furthermore, we obtained transcriptomic sequencing results of septic patients from the Gene Expression Omnibus (GEO) database, identified intersecting differentially expressed genes between the two, and performed survival analysis on the samples using LASSO and Cox regression analysis. Combining analyses of tumor mutation burden (TMB) and immune function, we further elucidated the mechanisms of sepsis-related genes in the prognosis and treatment of HCC.ResultsWe established a prognostic model consisting of four sepsis-related genes: KRT20, PAEP, CCR3, and ANLN. Both the training and validation sets showed excellent outcomes in the prognosis of tumor patients, with significantly longer survival times observed in the low-risk group based on this model compared to the high-risk group. Furthermore, analyses, such as differential analysis of tumor mutation burden, immune function analysis, GO/KEGG pathway enrichment analysis, and drug sensitivity analysis, also demonstrated the potential mechanisms of action of sepsis-related genes.ConclusionsModels constructed based on sepsis-related genes have shown excellent predictive ability in prognosis and differential analysis of drug sensitivity among tumor patients. These predictive models can enhance patient prognosis and inform the creation of early treatment protocols for sepsis, consequently aiding in the prevention of sepsis-induced HCC development through the modulation of the overall immune status. This may play a crucial role in patient management and immunotherapy, providing valuable reference for subsequent research.

Project description:Liver hepatocellular carcinoma (LIHC) is a highly lethal form of cancer that is among the deadliest cancer types globally. In terms of cancer-related mortality rates, liver cancer ranks among the top three, underscoring the severity of this disease. Insufficient analysis has been conducted to fully understand the potential value of the extracellular matrix (ECM) in immune infiltration and the prognostic stratification of LIHC, despite its recognised importance in the development of this disease. The scRNA-seq data of GSE149614 was used to conduct single-cell analysis on 10 LIHC samples. CellChat scores were calculated for seven cell populations in the descending cohort to investigate cellular communication, while PROGENy scores were calculated to determine tumour-associated pathway scores in different cell populations. The pathway analysis using GO and KEGG revealed the enrichment of ECM-associated genes in the pathway, highlighting the potential role of the ECM in LIHC development. By utilizing the TCGA-LIHC cohort, an ECM-based prognostic model for LIHC was developed using Lasso regression. Immune infiltration scores were calculated using two methods, and the performance of the ECM-related risk score was evaluated using an independent cohort from the CheckMate study. To determine the precise expression of ECM-associated risk genes in LIHC, we evaluated hepatocellular carcinoma cell lines using a range of assays, including Western blotting, invasion assays and Transwell assays. Using single-cell transcriptome analysis, we annotated the spatially-specific distribution of major immune cell types in single-cell samples of LIHC. The main cell types identified and annotated included hepatocytes, T cells, myeloid cells, epithelial cells, fibroblasts, endothelial cells and B cells. The utilisation of cellchat and PROGENy analyses enabled the investigation and unveiling of signalling interactions, protein functionalities and the prominent influential pathways facilitated by the primary immune cell types within the LIHC. Numerous tumour pathways, including PI2K, EGFR and TGFb, demonstrated a close correlation with the involvement of ECM in LIHC. Moreover, an evaluation was conducted to assess the primary ECM-related functional changes and biological pathway enrichment in LIHC. Differential genes associated with ECM were identified and utilised to create prognostic models. The prognostic stratification value of these models for LIHC patients was confirmed through validation in multiple databases. Furthermore, through immune infiltration analysis, it was discovered that ECM might be linked to the irregular expression and regulation of numerous immune cells. Additionally, histone acetylation was mapped against gene mutation frequencies and differential expression profiles. The prognostic stratification efficacy of the ECM prediction model constructed in the context of PD-1 inhibitor therapy was also examined, and it exhibited strong stratification performance. Cellular experiments, including Western blotting, invasion and Transwell assays, revealed that ECM-associated risk genes have a promoting effect on the development of LIHC. The creation of biomarkers for LIHC using ECM-related genes unveiled substantial correlations with immune microenvironmental infiltration and functional mutations in various tumour pathways. This enlightens us to the possibility that the influence of ECM on tumours may extend beyond simply promoting the fibrotic process and the stromal composition of tumours.

Dataset Information

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma.

Background/aims

Methods

Results

Conclusion

Publications

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma.

Similar Datasets

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