Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis

Machine learning-based pathomics model predicts ANGPT2 expression and prognosis in hepatocellular carcinoma

Angiopoietin 2 (ANGPT2) is a promising prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 expression and prognosis from histopathological images with naked eye is challenging. In this study, machine learning was employed to develop a pathomics model that analyzed histopathological images to predict ANGPT2 status. 267 cases, obtained from TCGA-HCC were divided into training and testing set. 91 cases from a single center were employed as a validation set. ANGPT2 was demonstrated up-regulated in HCC and patients with high ANGPT2 expression had a significant overall survival (OS) decline in TCGA-HCC cohort. Histopathological features in the training set were extracted, screened, and incorporated into a gradient boosting machine (GBM) model that generated pathomics score (PS), which successfully identified ANGPT2 expression level in three sets and showed remarkable risk stratification for OS in TCGA-HCC cohort (P < 0.0001) and the single center cohort (P = 0.001). Multivariate analysis suggested that PS could serve as a predictor for prognosis (P < 0.001). Bioinformatics analysis illustrated distinction of tumor growth and development related gene enriched pathways, VEGF-related genes expression and immune cell infiltration in different PS value. Our research indicates that histopathological image features can enhance prediction of molecular status and prognosis in HCC. The integration of image features with machine learning has potential for improving prognosis prediction in HCC.

Investigation on the mechanism of hepatotoxicity of dictamnine on juvenile zebrafish by integrating metabolomics and transcriptomics

Dictamnine(DIC), as the key pharmacological component of the classical Chinese herbal medicine cortex dictamni, possesses multiple pharmacological activities such as anti-microbial, anti-allergic, anti-cancer, and anti-inflammatory activities, however it is also the main toxicant of cortex dictamni induced hepatic damage, yet the underlying molecular mechanisms causing hepatic damage are still largely unknown. With the purpose of explore possibilities hepatotoxicity of dictamnine in zebrafish and to identify the key regulators and metabolites involved in the biological process, we administered zebrafish to dictamnine at a sub-lethal dose ( Collapse

Key Words

• Dictamnine
• Hepatotoxicity
• Metabolomics
• Transcriptome sequencing
• Zebrafish

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MESH Headings

• Animals
• Zebrafish/genetics
• Chemical and Drug Induced Liver Injury/genetics
• Chemical and Drug Induced Liver Injury/metabolism
• Metabolomics/methods
• Liver/drug effects
• Liver/metabolism
• Liver/pathology
• Transcriptome/drug effects
• Gene Expression Profiling/methods
• Drugs, Chinese Herbal/toxicity
• Apoptosis/drug effects

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Affiliation(s)

Xiaomin Xu Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
Fang Lu Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
Yu Wang Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
Shumin Liu Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.

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Relationship between EZH2 expression and prognosis of patients with hepatocellular carcinoma using a pathomics predictive model

Background

Enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) is overexpressed in hepatocellular carcinoma, promoting tumorigenesis and correlating with poor prognosis. Traditional histopathological examinations are insufficient to accurately predict hepatocellular carcinoma (HCC) survival; however, pathomics models can predict EZH2 expression and HCC prognosis. This study aimed to investigate the relationship between pathomics features and EZH2 expression for predicting overall survival of patients with HCC.

Methods

We analyzed 267 patients with HCC from the Cancer Genome Atlas database, with available pathological images and gene expression data. RNA sequencing data were divided into high and low EZH2 expression groups for prognosis and survival analysis. Pathological image features were screened using mRMR_RFE. A pathological model was constructed using a gradient boosting machine (GBM) algorithm, and efficiency evaluation and survival analysis of the model were performed. The R package "survminer" took the pathomics score (PS) cutoff value of 0.4628 to divide the patients into two groups: high and low PS expression. Survival analyses included Kaplan-Meier curve analysis, univariate and multivariate Cox regression analyses, and interaction tests. Potential pathomechanisms were explored through enrichment, differential, immune cell infiltration abundance, and gene mutation analyses.

Result

EZH2 was highly expressed in tumor samples but poorly expressed in normal tissue samples. Univariate and multivariate Cox regression analyses revealed that EZH2 was an independent risk factor for HCC (hazard ratio [HR], 2.792 and 3.042, respectively). Seven imaging features were selected to construct a pathomics model to predict EZH2. Decision curve analysis showed that the model had high clinical utility. Multivariate Cox regression analysis showed that high PS expression was an independent risk factor for HCC prognosis (HR, 2.446). The Kaplan-Meier curve showed that high PS expression was a risk factor for overall survival.

Conclusion

EZH2 expression can affect the prognosis of patients with liver cancer. Our pathological model could predict EZH2 expression and prognosis of patients with HCC with high accuracy and robustness, making it a new and potentially valuable tool.

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy and evasion of immune cell destruction. Using single-cell RNA sequencing (scRNA-seq) data from LUAD patients, we categorized the cells into two subclusters: dormant and active cells. Utilizing high-density Weighted Gene Co-expression Network Analysis (hdWGCNA) and pseudo-time cell trajectory, aberrant expression of genes involved in protein O-glycosylation was detected in dormant cells, suggesting a crucial role for O-glycosylation in maintaining the dormant state. Intercellular communication analysis highlighted the interaction between fibroblasts and dormant cells, where the Insulin-like Growth Factor (IGF) signaling pathway regulated by O-glycosylation was crucial. By employing Gene Set Variation Analysis (GSVA) and machine learning, a risk score model was developed using hub genes, which showed high accuracy in determining LUAD prognosis. The model also demonstrated robust performance on the training dataset and excellent predictive capability, providing a reliable basis for predicting patient clinical outcomes. The group with a higher risk score exhibited a propensity for adverse outcomes in the tumor microenvironment (TME) and tumor mutational burden (TMB). Additionally, the 50% inhibitory concentration (IC50) values for chemotherapy exhibited significant variations among the different risk groups. In vitro experiments demonstrated that EFNB2, PTTG1IP, and TNFRSF11A were upregulated in dormant tumor cells, which also contributed greatly to the diagnosis of LUAD. In conclusion, this study highlighted the crucial role of O-glycosylation in the dormancy state of LUAD tumors and developed a predictive model for the prognosis of LUAD patients.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients

OBJECTIVE

The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear.

METHODS

The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored.

RESULTS

A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes.

CONCLUSION

Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

Single-cell transcriptome analysis revealed heterogeneity in glycolysis and identified IGF2 as a therapeutic target for ovarian cancer subtypes

BACKGROUND

As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive.

METHODS

We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay.

RESULTS

Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells.

CONCLUSIONS

We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC's molecular mechanisms and personalized treatment approaches.

A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes.

RESULTS

Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group.

CONCLUSIONS

The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.

The switch triggering the invasion process: Lipid metabolism in the metastasis of hepatocellular carcinoma

ABSTRACT

In humans, the liver is a central metabolic organ with a complex and unique histological microenvironment. Hepatocellular carcinoma (HCC), which is a highly aggressive disease with a poor prognosis, accounts for most cases of primary liver cancer. As an emerging hallmark of cancers, metabolic reprogramming acts as a runaway mechanism that disrupts homeostasis of the affected organs, including the liver. Specifically, rewiring of the liver metabolic microenvironment, including lipid metabolism, is driven by HCC cells, propelling the phenotypes of HCC cells, including dissemination, invasion, and even metastasis in return. The resulting formation of this vicious loop facilitates various malignant behaviors of HCC further. However, few articles have comprehensively summarized lipid reprogramming in HCC metastasis. Here, we have reviewed the general situation of the liver microenvironment and the physiological lipid metabolism in the liver, and highlighted the effects of different aspects of lipid metabolism on HCC metastasis to explore the underlying mechanisms. In addition, we have recapitulated promising therapeutic strategies targeting lipid metabolism and the effects of lipid metabolic reprogramming on the efficacy of HCC systematical therapy, aiming to offer new perspectives for targeted therapy.

Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity

As single-cell RNA sequencing enables the detailed clustering of T-cell subpopulations and facilitates the analysis of T-cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T-cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour-specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T-cell subsets is key to obtaining field-level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T-cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.