Combined proteomic/transcriptomic signature of recurrence post-liver transplantation for hepatocellular carcinoma beyond Milan

GraphPI: Efficient Protein Inference with Graph Neural Networks

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

Hedgehog components are overexpressed in a series of liver cancer cases

Liver cancers, including hepatocellular carcinoma (HCC), are the sixth most common cancer and the third leading cause of cancer-related death worldwide, representing a global public health problem. This study evaluated nine patients with HCC. Six of the cases involved hepatic explants, and three involved hepatic segmentectomy for tumor resection. Eight out of nine tumors were HCC, with one being a combined hepatocellular-cholangiocarcinoma tumor. Conventional markers of hepatocellular differentiation (Hep Par-1, arginase, pCEA, and glutamine synthetase) were positive in all patients, while markers of hepatic precursor cells (CK19, CK7, EpCAM, and CD56) were negative in most patients, and when positive, they were detected in small, isolated foci. Based on in silico analysis of HCC tumors from The Cancer Genome Atlas database, we found that Hedgehog (HH) pathway components (GLI1, GLI2, GLI3 and GAS1) have high connectivity values (module membership > 0.7) and are strongly correlated with each other and with other genes in biologically relevant modules for HCC. We further validated this finding by analyzing the gene expression of HH components (PTCH1, GLI1, GLI2 and GLI3) in our samples through qPCR, as well as by immunohistochemical analysis. Additionally, we conducted a chemosensitivity analysis using primary HCC cultures treated with a panel of 18 drugs that affect the HH pathway and/or HCC. Most HCC samples were sensitive to sunitinib. Our results offer a comprehensive view of the molecular landscape of HCC, highlighting the significance of the HH pathway and providing insight into focused treatments for HCC.

Pushing the limits of treatment for hepatocellular carcinoma

PURPOSE OF REVIEW

We review existing and newer strategies for treatment and surveillance of hepatocellular carcinoma (HCC) both pre and postliver transplantation.

SUMMARY

HCC is rising in incidence and patients are often diagnosed at later stages. Consequently, there is a need for treatment strategies which include collaboration of multiple specialties. Combinations of locoregional, systemic, and surgical therapies are yielding better postliver transplantation (post-LT) outcomes for patients with HCC than previously seen. Tumor biology (tumor size, number, location, serum markers, response to therapy) can help identify patients who are at high risk for HCC recurrence posttransplantation and may expand transplant eligibility for some patients.

Molecular profiling in the management of hepatocellular carcinoma

PURPOSE OF REVIEW

The purpose of this review is to both summarize the current knowledge of hepatocellular carcinoma molecular biology and to suggest a framework in which to prospectively translate this knowledge into patient care. This is timely as recent guidelines recommend increased use of these technologies to advance personalized liver cancer care.

RECENT FINDINGS

The main themes covered here address germline and somatic genetic alterations recently discovered in hepatocellular carcinoma, largely owing to next generation sequencing technologies, and nascent efforts to translate these into contemporary practice.

SUMMARY

Early efforts of translating molecular profiling to hepatocellular carcinoma care demonstrate a growing number of potentially actionable alterations. Still lacking are a consensus on what biomarkers and technologies to adopt, at what scale and cost, and how to integrate them most effectively into care.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma

Background

M2-like tumor-associated macrophages (M2-like TAMs) have important roles in the progression and therapeutics of cancers. We aimed to detect novel M2-like TAM-related biomarkers in hepatocellular carcinoma (HCC) via integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to construct a novel prognostic signature, reveal the “immune landscape”, and screen drugs in HCC.

Methods

M2-like TAM-related genes were obtained by overlapping the marker genes of TAM identified from scRNA-seq data and M2 macrophage modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were carried out to screen prognostic genes from M2-like TAM-related genes, followed by a construction of a prognostic signature, delineation of risk groups, and external validation of the prognostic signature. Analyses of immune cells, immune function, immune evasion scores, and immune-checkpoint genes between high- and low-risk groups were done to further reveal the immune landscape of HCC patients. To screen potential HCC therapeutic agents, analyses of gene–drug correlation and sensitivity to anti-cancer drugs were conducted.

Results

A total of 127 M2-like TAM-related genes were identified by integrative analysis of scRNA-seq and bulk-seq data. PDLIM3, PAM, PDLIM7, FSCN1, DPYSL2, ARID5B, LGALS3, and KLF2 were screened as prognostic genes in HCC by univariate Cox regression and LASSO regression analyses. Then, a prognostic signature was constructed and validated based on those genes for predicting the survival of HCC patients. In terms of drug screening, expression of PAM and LGALS3 was correlated positively with sensitivity to simvastatin and ARRY-162, respectively. Based on risk grouping, we predicted 10 anticancer drugs with high sensitivity in the high-risk group, with epothilone B having the lowest half-maximal inhibitory concentration among all drugs tested.

Conclusions

Our findings enhance understanding of the M2-like TAM-related molecular mechanisms involved in HCC, reveal the immune landscape of HCC, and provide potential targets for HCC treatment.

The Role of Chronic Liver Diseases in the Emergence and Recurrence of Hepatocellular Carcinoma: An Omics Perspective

Hepatocellular carcinoma (HCC) typically develops from a background of cirrhosis resulting from chronic inflammation. This inflammation is frequently associated with chronic liver diseases (CLD). The advent of next generation sequencing has enabled extensive analyses of molecular aberrations in HCC. However, less attention has been directed to the chronically inflamed background of the liver, prior to HCC emergence and during recurrence following surgery. Hepatocytes within chronically inflamed liver tissues present highly activated inflammatory signaling pathways and accumulation of a complex mutational landscape. In this altered environment, cells may transform in a stepwise manner toward tumorigenesis. Similarly, the chronically inflamed environment which persists after resection may impact the timing of HCC recurrence. Advances in research are allowing an extensive epigenomic, transcriptomic and proteomic characterization of CLD which define the emergence of HCC or its recurrence. The amount of data generated will enable the understanding of oncogenic mechanisms in HCC from the CLD perspective and provide the possibility to identify robust biomarkers or novel therapeutic targets for the treatment of primary and recurrent HCC. Importantly, biomarkers defined by the analysis of CLD tissue may permit the early detection or prevention of HCC emergence and recurrence. In this review, we compile the current omics based evidence of the contribution of CLD tissues to the emergence and recurrence of HCC.

Retinoids in the Pathogenesis and Treatment of Liver Diseases

Vitamin A (VA), all-trans-retinol (ROL), and its analogs are collectively called retinoids. Acting through the retinoic acid receptors RARα, RARβ, and RARγ, all-trans-retinoic acid, an active metabolite of VA, is a potent regulator of numerous biological pathways, including embryonic and somatic cellular differentiation, immune functions, and energy metabolism. The liver is the primary organ for retinoid storage and metabolism in humans. For reasons that remain incompletely understood, a body of evidence shows that reductions in liver retinoids, aberrant retinoid metabolism, and reductions in RAR signaling are implicated in numerous diseases of the liver, including hepatocellular carcinoma, non-alcohol-associated fatty liver diseases, and alcohol-associated liver diseases. Conversely, restoration of retinoid signaling, pharmacological treatments with natural and synthetic retinoids, and newer agonists for specific RARs show promising benefits for treatment of a number of these liver diseases. Here we provide a comprehensive review of the literature demonstrating a role for retinoids in limiting the pathogenesis of these diseases and in the treatment of liver diseases.

Glycosylation-related molecular subtypes and risk score of hepatocellular carcinoma: Novel insights to clinical decision-making

BACKGROUND

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer deaths worldwide, seriously affecting human community health and care. Emerging evidence has shown that aberrant glycosylation is associated with tumor progression and metastasis. However, the role of glycosylation-related genes in HCC has notbeen reported.

METHODS

Weighted gene coexpression network analysis and non-negative matrix factorization analysis were applied to identify functional modules and molecularm subtypes in HCC. The least absolute shrinkage and selection operator Cox regression was used to construct the glycosylation-related signature. The independent prognostic value of the risk model was confirmed and validated by systematic techniques, including principal component analysis, T-distributed random neighbor embedding analysis, Kaplan-Meier survival analysis, the ROC curve, multivariate Cox regression, the nomogram, and the calibration curve. The single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were evaluated by the immune microenvironment and potential biological processes. The quantitative real-time polymerase chain reaction and immunohistochemistry analysis were used to verify the expression of five genes.

RESULTS

We identified the glycosylation-related genes with bioinformatics analysis to construct and validate a five-gene signature for the prognosis of HCC patients. Patients with HCC in the high-risk group had a worse prognosis. The risk score could be an independent factor and was associated with clinical features, such as the grade and stage. The nomogram exhibited an accurate score that included the risk score and clinical parameters. The infiltration levels of antitumor cells were upregulated in the low-risk group, including B_cells, Mast_cells, neutrophils, NK_cells, and T_helper_cells. Moreover, glycosylation was more sensitive to immunotherapy, and may play a critical role in the metabolic processes of HCC, such as bile acid metabolism and fatty acid metabolism. In addition, the five-gene messenger RNA (mRNA) and protein expression were overexpressed in HCC cells and tissues.

CONCLUSIONS

The glycosylation-related signature is effective for prognostic recognition, immune efficacy evaluation, and substance metabolism in HCC, providing a novel insight for therapeutic target prediction and clinical decision-making.