Dataset Information

Spatial maps of hepatocellular carcinoma transcriptomes highlight an unexplored landscape of heterogeneity and a novel gene signature for survival.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) often presents with satellite nodules, rendering current curative treatments ineffective in many patients. The heterogeneity of HCC is a major challenge in personalized medicine. The emergence of spatial transcriptomics (ST) provides a powerful strategy for delineating the complex molecular landscapes of tumours.

Methods

In this study, the heterogeneity of tissue-wide gene expression in tumour and adjacent nonneoplastic tissues using ST technology were investigated. The transcriptomes of nearly 10,820 tissue regions and identified the main gene expression clusters and their specific marker genes (differentially expressed genes, DEGs) in patients were analysed. The DEGs were analysed from two perspectives. First, two distinct gene profiles were identified to be associated with satellite nodules and conducted a more comprehensive analysis of both gene profiles. Their clinical relevance in human HCC was validated with Kaplan-Meier (KM) Plotter. Second, DEGs were screened with The Cancer Genome Atlas (TCGA) database to divide the HCC cohort into high- and low-risk groups according to Cox analysis. HCC patients from the International Cancer Genome Consortium (ICGC) cohort were used for validation. KM analysis was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS.

Results

Novel markers for the prediction of satellite nodules were identified and a tumour clusters-specific marker gene signature model (6 genes) for HCC prognosis was constructed.

Conclusion

The establishment of marker gene profiles may be an important step towards an unbiased view of HCC, and the 6-gene signature can be used for prognostic prediction in HCC. This analysis will help us to clarify one of the possible sources of HCC heterogeneity and uncover pathogenic mechanisms and novel antitumour drug targets.

SUBMITTER: Zhao N

PROVIDER: S-EPMC8812006 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

Spatial maps of hepatocellular carcinoma transcriptomes highlight an unexplored landscape of heterogeneity and a novel gene signature for survival.

Zhao Nan N Zhang Yanhui Y Cheng Runfen R Zhang Danfang D Li Fan F Guo Yuhong Y Qiu Zhiqiang Z Dong Xueyi X Ban Xinchao X Sun Baocun B Zhao Xiulan X

Cancer cell international 20220202 1

<h4>Background</h4>Hepatocellular carcinoma (HCC) often presents with satellite nodules, rendering current curative treatments ineffective in many patients. The heterogeneity of HCC is a major challenge in personalized medicine. The emergence of spatial transcriptomics (ST) provides a powerful strategy for delineating the complex molecular landscapes of tumours.<h4>Methods</h4>In this study, the heterogeneity of tissue-wide gene expression in tumour and adjacent nonneoplastic tissues using ST te ...[more]

PMID: 35109839

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Project description:ObjectiveTelomeres, made of repetitive DNA sequences and shelterin complexes, which were found at the ends of chromosomes and had been extensively studied in cancer research. However, in hepatocellular carcinoma (HCC) was still relatively scarce. In this study, we investigated the correlation between telomerase-related genes (TRGs) and the prognosis and immunotherapy of HCC patients to enhance clinical outcomes.MethodsIn this work, TRGs were gathered using TelNet, while clinical information and gene expression data for HCC patients were retrieved from the Cancer Genome Atlas (TCGA) database. A risk prediction model based on TRGs was created using COX and Lasso regression analyses, with ROC curves used to assess prognostic efficacy. Univariate and multifactorial COX regression analyses were used to determine if the risk model had an independent impact on prognosis. Nomograms were created to enhance clinical usability, and calibration curves were used to assess predictive ability at various time points. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to analyze differences in immune infiltrating cells between risk groups. The study analyzed the relationship between risk ratings and drug treatment effectiveness using data from the CellMiner database. The hub gene was identified and its relationship to prognostic markers of HCC patients was examined. The expression of hub genes in immune cell subpopulations was also investigated by single-cell data.Results2093 TRGs were identified, with 949 showing significant differences in expression between HCC and paracancerous tissues. Seven risk genes were overexpressed in tumor tissues, leading to lower survival rates in high-risk patients. Risk model could independently predict the prognosis of HCC patients. Analysis of tumor immune infiltrating cells revealed significant differences in cell abundance between risk groups, with notable variations in immune subset enrichment between subgroups. Higher risk scores correlated with increased sensitivity to sorafenib, mitoxantrone, oxaliplatin, gemcitabine, and entinostat, while sensitivity decreased for vincristine, etc. CDCA8 was identified as a key gene in the Protein Interaction Network, while high expression associated with poorer overall survival, tumor proliferation and metastasis. The results of single-cell data analysis suggest that CDCA8 may promote the development of HCC by affecting T lymphocytes.ConclusionThe TRG-based risk model could predict HCC patient prognosis and closely linked to tumor immune environment, which could offer new possibilities for clinical treatment.

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Project description:BackgroundFifty years of residual insecticide spraying to control Triatoma infestans in the Gran Chaco region of northern Argentina, Paraguay and Bolivia shows that vertically coordinated interventions aiming at full coverage have limited effects and are unsustainable. We quantified the spatial distribution of T. infestans domestic infestation at the district level, identified environmental factors associated with high infestation and then explored the usefulness of risk maps for the spatial stratification of interventions.Methods and findingsWe performed spatial analyses of house infestation data collected by the National Chagas Service in Moreno Department, northern Argentina (1999-2002). Clusters of high domestic infestation occurred in the southwestern extreme of the district. A multi-model selection approach showed that domestic infestation clustered in areas of low elevation, with few farmlands, high density of rural houses, high mean maximum land surface temperature, large NDVI, and high percentage of degraded and deforested lands. The best model classified 98.4% of the communities in the training dataset (sensitivity, 93.3%; specificity, 95.4%). The risk map evidenced that the high-risk area only encompassed 16% of the district. By building a network-based transportation model we assessed the operational costs of spatially contiguous and spatially targeted interventions. Targeting clusters of high infestation would have reached -80% of all communities slated for full-coverage insecticide spraying, reducing in half the total time and economic cost incurred by a spatially contiguous strategy.Conclusions and significanceIn disperse rural areas where control programs can accomplish limited coverage, consideration of infestation hot spots can contribute to the design and execution of cost-effective interventions against Chagas disease vectors. If field validated, targeted vertical control in high risk areas and horizontal control in medium to low risk areas may provide both a logistically and economically feasible alternative to blanket vertical insecticide spraying when resources are limited.

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Dataset Information

Spatial maps of hepatocellular carcinoma transcriptomes highlight an unexplored landscape of heterogeneity and a novel gene signature for survival.

Background

Methods

Results

Conclusion

Publications

Spatial maps of hepatocellular carcinoma transcriptomes highlight an unexplored landscape of heterogeneity and a novel gene signature for survival.

Similar Datasets

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