Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Comprehensive molecular characteristics of hepatocellular carcinoma based on multi-omics analysis

BACKGROUND

The high heterogeneity of hepatocellular carcinoma (HCC) poses challenges for precision treatment strategies. This study aims to use multi-omics methodologies to better understand its pathogenesis and discover biomarkers.

METHODS

Quantitative proteomics was used to investigate hepatocellular carcinoma tissues (HCT) and their corresponding adjacent non-tumor tissues (DNT), obtained from six HCC patients. Untargeted metabolomics was applied to analyze the metabolic profiles of HCT and DNT of ten HCC patients. Statistical analyses, such as the Student's t-test, were performed to identify differentially expressed proteins (DEPs) and metabolites (DEMs) between the two groups. The functions and metabolic pathways involving DEPs and DEMs were annotated and enriched using the gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) databases. Bioinformatics methods were then utilized to analyze consistency between proteomics and metabolomics results, leading to identification of potential biomarkers along with key altered pathways associated with HCC.

RESULTS

This study identified 1556 DEPs between HCT and DNT samples. These DEPs were primarily enriched in crucial biological pathways such as amino acid degradation, fatty acid metabolism, and DNA replication. Subsequently, the analysis of metabolomics identified 500 DEMs that mainly participated in glycerophospholipid metabolism, the phospholipase D signaling pathway, and choline metabolism related to cancer. Integrated analysis of proteomics and metabolomics data unveiled significant dysfunctions in bile secretion, multiple amino acid and fatty acid metabolic pathways among HCC patients. Further investigation revealed that five proteins (PTP4A3, B4GALT5, GAB1, ME2, and PKM) along with seven metabolites (PI(6 keto-PGF1alpha/16:0), 13, 16, 19-docosatrienoic acid, PA(18:2(9Z, 12Z)/20:1(11Z)), Citric Acid, PG(20:3(6, 8, 11)-OH(5)/18:2(9Z, 12Z)), Spermidine, and N2-Acetylornithine) exhibited excellent diagnostic efficiency for HCC and could serve as its potential biomarkers.

CONCLUSION

Our integrated proteome and metabolome analysis revealed 10 key HCC-related pathways and proposed 12 potential biomarkers, which may enhance our understanding of HCC pathophysiology and be helpful in facilitating early diagnosis and treatment strategies.

Whole transcriptome and proteome analyses identify ncRNAs and mRNAs to predict competing endogenous RNA networks in hepatitis B virus-induced hepatocellular carcinoma

The presence of the Hepatitis B virus (HBV) is considered as a valuable risk factor of hepatocellular carcinoma (HCC). To more deeply comprehend the molecular mechanism and transcriptome of HBV-induced HCC, we utilized tandem mass tagging (TMT)-based quantitative proteomics analysis and whole-transcriptome sequencing to analyze three sets of matched HepG2 hepatoma cells and HBV-positive HepAD38 cells. The differentially expressed (DE) proteins (1596), mRNAs (5263), miRNAs (581), lncRNAs (2672) and circRNAs (222) were subjected to differential expression and enrichment analyses in order to thoroughly assess the gene-regulatory circuits of HBV-induced HCC. Subsequently, the amounts of 321 DEproteins-DEmRNAs with common alterations were confirmed. According to functional pathway analysis, the DEproteins-DEmRNAs were primarily linked to signaling pathways, amino acid metabolism, and cellular function. Furthermore, the viability and significance of the ceRNA regulatory networks, LOC105377730/miR-4726-5p/FHL2 and hsa_circ_0001098/miR-2110/IGF2BP1, were randomly chosen and confirmed. Our work provides a valuable asset in terms of understanding regulatory activities at the RNA level, and might reveal fresh information about the fundamental mechanism and potential therapeutic targets of HBV-induced HCC.

Development of a Novel four-gene Model for Monitoring the Progression from Metabolic Dysfunction-associated Steatotic Liver Disease to Hepatocellular Carcinoma in Males

The pathogenesis of metabolic dysfunction-associated steatotic liver disease-associated hepatocellular carcinoma (MASLD-HCC) is complex and exhibits sex-specific differences. Effective methods for monitoring MASLD progression to HCC are lacking. Transcriptomic data from liver tissue samples sourced from multiple public databases were integrated. Utilizing both differential expression analysis and robust rank aggregation analysis, differentially expressed genes (DEGs) in patients with MASLD-HCC were identified. Based on these DEGs, diagnostic prediction models for MASLD (DP.MASLD) and HCC (DP.HCC) were constructed using elastic net analysis for various comparisons, including steatosis versus normal, steatohepatitis versus steatosis, and cancer versus non-cancer. Weighted gene correlation network analysis and gene set enrichment analysis were conducted to unveil the underlying pathogenesis of MASLD-HCC in males. Five overlapping DEGs with diagnostic significance in the progression from MASLD to HCC were identified, namely, AKR1B10, CYR61, FABP4, GNMT, and THBS1. DP.HCC demonstrated excellent predictive accuracy, with an area under the curve of 0.910 in the training group and 0.981 in the validation group. Similarly, DP.MASLD showed robust predictive accuracy. The pathogenesis of MASLD-HCC in males primarily involves extracellular matrix-receptor interaction, DNA replication, cell cycle, and T-cell receptor signaling. Overall, our study provides a quantitative assessment tool for the early detection and monitoring of MASLD-HCC, highlighting the male-specific molecular characteristics involved in its progression.

The significance of MDK growth factor in the antler development of sika deer (Cervus nippon): An in-depth analysis

Deer antlers exhibit rapid growth during the velvet phase. As a critical endogenous growth factor in animals, midkine (MDK) is likely closely associated with the growth of antlers. However, the spatio-temporal expression pattern of MDK during the velvet phase was unclear. This study explored the physiological role of MDK by analyzing its molecular characterization and spatio-temporal expression dynamics during the growth of sika deer antlers. The study cloned the coding sequences (CDS) of MDK, which spanned 429 bp and encoded 142 amino acids. The results of bioinformatics prediction analysis showed that MDK was an extracellular hydrophilic secreted protein, which was mainly composed of random coil. MDK protein was relatively conserved in evolution and MDK protein of sika deer had the closest relatives to ruminants and the furthest relatives to Aves. The tip tissues (dermis, mesenchyme, precartilage, cartilage) of antlers were collected from three important growth and development nodes (early period, EP. middle period, MP. late period, LP), and quantitative real-time polymerase chain reaction (qRT-PCR) was chosen to detect the spatio-temporal expression of the MDK. The results showed that MDK was expressed in all tissue sites of antler tip in EP, MP, LP. MDK had a consistent expression pattern under all growth periods and was strongly expressed in dermis and cartilage. The expression of MDK was consistently up-regulated in precartilage, whereas it was first up-regulated and then down-regulated in other tissues, and it was highly significant in MP compared to EP and LP (P < 0.01). This study suggested that MDK may regulate the growth of dermis and cartilage tissues mainly by participating in the process of angiogenesis and bone formation, thus promoting the rapid growth of antlers.

Mitoepigenetics pathways and natural compounds: a dual approach to combatting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading liver cancer that significantly impacts global life expectancy and remains challenging to treat due to often late diagnoses. Despite advances in treatment, the prognosis is still poor, especially in advanced stages. Studies have pointed out that investigations into the molecular mechanisms underlying HCC, including mitochondrial dysfunction and epigenetic regulators, are potentially important targets for diagnosis and therapy. Mitoepigenetics, or the epigenetic modifications of mitochondrial DNA, have drawn wide attention for their role in HCC progression. Besides, molecular biomarkers such as mitochondrial DNA alterations and non-coding RNAs showed early diagnosis and prognosis potential. Additionally, natural compounds like alkaloids, resveratrol, curcumin, and flavonoids show promise in HCC show promise in modulating mitochondrial and epigenetic pathways involved in cancer-related processes. This review discusses how mitochondrial dysfunction and epigenetic modifications, especially mitoepigenetics, influence HCC and delves into the potential of natural products as new adjuvant treatments against HCC.

Mass spectrometry-based multi-omics analysis reveals distinct molecular features in early and advanced stages of hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is a serious primary solid tumor that is prevalent worldwide. Due to its high mortality rate, it is crucial to explore both early diagnosis and advanced treatment for HCC. In recent years, multi-omics approaches have emerged as promising tools to identify biomarkers and investigate molecular mechanisms of biological processes and diseases. In this study, we performed proteomics, phosphoproteomics, metabolomics, and lipidomics to reveal the molecular features of early- and advanced-stage HCC. The data obtained from these omics were analyzed separately and then integrated to provide a comprehensive understanding of the disease. The multi-omics results unveiled intricate biological pathways and interaction networks underlying the initiation and progression of HCC. Moreover, we proposed specific potential biomarker panels for both early- and advanced-stage HCC by overlapping our data with CPTAC database for HCC diagnosis, and deduced novel insights and mechanisms related to HCC origination and development, such as glucose depletion during tumor progression, ROCK1 deactivation and GSK3A activation.

Using long columns to quantify over 9200 unique protein groups from brain tissue in a single injection on an Orbitrap Exploris 480 mass spectrometer

The most exciting advancement in LC-MS/MS-based bottom-up proteomics has centered around enhancing mass spectrometers. Among these, the latest and most advanced mass spectrometer for bottom-up proteomics is the Orbitrap Astral that has the highest scan rate to accelerate throughput and the highest sensitivity to handle a very small amount of peptide samples and to achieve deeper proteomics. However, its affordability remains a challenge for most laboratories. While significant strides have been made in improving mass spectrometry, advancing liquid chromatography (LC) to achieve deeper proteomics has not achieved significant successes since the innovation of Multidimensional Protein Identification Technology (MudPIT) in 2001. To achieve deeper proteomics in a less labor-intensive and more reproducible approach while using a more cost-effective mass spectrometer, such as the Orbitrap Exploris 480, we evaluated trap columns as long as 40 cm and analytical column as long as 600 cm besides sample loading amount, gradient time, and analytical column particle size to enable a fractionation-free method for a single injection to obtain deeper proteomics. The length of trap and analytic columns is the key factor. Using a 30 cm trap column and 250 cm analytical column with other optimized LC conditions, we quantified over 9200 unique protein groups from brain tissue in a single injection using a 24-h gradient on an Orbitrap Exploris 480 mass spectrometer.

Unveiling EFNB2 as a Key Player in Sorafenib Resistance: Insights from Bioinformatics Analysis and Functional Validation in Hepatocellular Carcinoma

Sorafenib resistance has become a big hurdle for treating advanced HCC; thus, identifying novel targets to overcome sorafenib resistance is of great importance. Thanks to the massive progress in the sequencing and data analysis, high-throughput screening of novel targets in HCC development has been extensively used in recent years. In present study, we harnessed the public dataset and aimed to identify novel targets related to sorafenib resistance in HCC via bioinformatics analysis and in vitro validation. This study examined three GEO datasets (GSE140202, GSE143233, GSE182593) and identified 20 common DEGs. Functional enrichment analysis suggested these DEGs might play a role in regulating drug resistance pathways. PPI network analysis pinpointed 14 hub genes, with EFNB2 showing high connectivity to other genes. Subsequent in vitro experiments demonstrated that EFNB2 was up-regulated in sorafenib-resistant HCC cells. EFNB2 suppression sensitized HepG2 and Huh7 sorafenib-resistant cells. Furthermore, EFNB2 knockdown increased caspase-3/-7 activities and hindered EMT in sorafenib-resistant HCC cells. Conversely, EFNB2 overexpression promoted sorafenib resistance, decreased caspase-3/-7 activity, and enhanced EMT in HCC cells. Overall, this study identified 14 promising genes potentially linked to sorafenib resistance in HCC, with EFNB2 emerging as a potential contributor to this resistance mechanism.

Exploring Selenoprotein P in Liver Cancer: Advanced Statistical Analysis and Machine Learning Approaches

Selenoprotein P (SELENOP) acts as a crucial mediator, distributing selenium from the liver to other tissues within the body. Despite its established role in selenium metabolism, the specific functions of SELENOP in the development of liver cancer remain enigmatic. This study aims to unravel SELENOP's associations in hepatocellular carcinoma (HCC) by scrutinizing its expression in correlation with disease characteristics and investigating links to hormonal and lipid/triglyceride metabolism biomarkers as well as its potential as a prognosticator for overall survival and predictor of hypoxia. SELENOP mRNA expression was analyzed in 372 HCC patients sourced from The Cancer Genome Atlas (TCGA), utilizing statistical methodologies in R programming and machine learning techniques in Python. SELENOP expression significantly varied across HCC grades (p < 0.000001) and among racial groups (p = 0.0246), with lower levels in higher grades and Asian individuals, respectively. Gender significantly influenced SELENOP expression (p < 0.000001), with females showing lower altered expression compared to males. Notably, the Spearman correlation revealed strong positive connections of SELENOP with hormonal markers (AR, ESR1, THRB) and key lipid/triglyceride metabolism markers (PPARA, APOC3, APOA5). Regarding prognosis, SELENOP showed a significant association with overall survival (p = 0.0142) but explained only a limited proportion of variability (~10%). Machine learning suggested its potential as a predictive biomarker for hypoxia, explaining approximately 18.89% of the variance in hypoxia scores. Future directions include validating SELENOP's prognostic and diagnostic value in serum for personalized HCC treatment. Large-scale prospective studies correlating serum SELENOP levels with patient outcomes are essential, along with integrating them with clinical parameters for enhanced prognostic accuracy and tailored therapeutic strategies.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Research progress of protein induced by vitamin K absence or antagonist II in liver transplantation for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common pathologic type of primary liver cancer. Liver transplantation (LT) is a radical strategy for treating patients with early-stage HCC, which may lead to a better prognosis compared to hepatectomy and ablation. However, survival of patients who develop HCC recurrence after LT is short, and early recurrence is the most common cause of death. Thus, efficient biomarkers are also needed in LT to guide precision therapy to improve patient prognosis and 5-year survival. Protein induced by vitamin K absence or antagonist II (PIVKA-II) is an abnormal prothrombin that cannot activate coagulation, and it is significantly increased in patients with HCC, obstructive jaundice, and those taking vitamin K antagonists. Over the past decades, substantial progress has been made in the study of PIVKA-II in diagnosing, surveilling, and treating HCC, but its role in LT still needs to be elaborated. In this review, we focused on the role of PIVKA-II as a biomarker in LT for HCC, especially its relationship with clinicopathologic features, early recurrence, long-term survival, and donor-recipient selection.

Angiopoietin-Related Protein 4-Transcript 3 Increases the Proliferation, Invasion, and Migration of Hepatocellular Carcinoma Cells and Inhibits Apoptosis

To investigate the functional differences of angiopoietin-related protein 4 (ANGPTL4) transcripts in hepatocellular carcinoma (HCC) cells. By transfecting ANGPTL4-Transcript 1 and ANGPTL4-Transcript 3 overexpression vectors into HepG2 and Huh7 cell lines with ANGPTL4 knockdown, the effects of overexpression of two transcripts on cell viability, invasion, migration, and apoptosis were analyzed. The expression of two transcripts was compared in human liver cancer tissue, and their effects on tumor development were validated in vivo experiments in mice. Compared with control, the overexpression of ANGPTL4-Transcript 1 had no significant effect on viability, invasion, healing, and apoptosis of HepG2 and Huh7 cells. However, these two cell lines overexpressing ANGPTL4-Transcript 3 showed remarkably enhanced cell viability, invasive and healing ability, and decreased apoptosis ability. Furthermore, the mRNA level of ANGPTL4-Transcript 3 was significantly increased in human HCC tissues and promoted tumor growth compared with Transcript 1. Different transcripts of gene ANGPTL4 have distinct effects on HCC. The abnormally elevated Transcript 3 with the specific ability of promoting HCC proliferation, infiltration, and migration is expected to become a new biological marker and more precise intervention target for HCC.

Triptolide Reduces Neoplastic Progression in Hepatocellular Carcinoma by Downregulating the Lipid Lipase Signaling Pathway

Hepatocellular carcinoma (HCC), which is the third leading cause of cancer-related mortality in the world, presents a significant medical challenge. Triptolide (TP) has been identified as an effective therapeutic drug for HCC. However, its precise therapeutic mechanism is still unknown. Understanding the mechanism of action of TP against HCC is crucial for its implementation in the field of HCC treatment. We hypothesize that the anti-HCC actions of TP might be related to its modulation of HCC lipid metabolism given the crucial role that lipid metabolism plays in promoting the progression of HCC. In this work, we first demonstrate that, both in vitro and in vivo, TP significantly reduces lipid accumulation in HCC cells. Additionally, we notice that lipoprotein lipase (LPL) expression is markedly upregulated in HCC, and that its levels are positively connected with the disease's progression. It is interesting to note that TP dramatically reduces LPL activity, which in turn prevents HCC growth and reduces lipid accumulation. Additionally, the effect of TP on LPL is a direct correlation. These results definitely demonstrate that TP protects hepatocytes against abnormal accumulation of lipids by transcriptionally suppressing LPL, which reduces the development of HCC. This newly identified pathway provides insight into the process through which TP exerts its anti-HCC actions.

Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma

Background

This study aimed to examine glycolysis/gluconeogenesis-related genes in hepatocellular carcinoma (HCC) and evaluate their potential roles in HCC progression and immunotherapy response.

Methods

Data analyzed in this study were collected from GSE14520, GSE76427, GSE174570, The Cancer Genome Atlas (TCGA), PXD006512, and GSE149614 datasets, metabolic pathways were collected from MSigDB database. Differentially expressed genes (DEGs) were identified between HCC and controls. Differentially expressed glycolysis/gluconeogenesis-related genes (candidate genes) were obtained and consensus clustering was performed based on the expression of candidate genes. Bioinformatics analysis was used to evaluate candidate genes and screen prognostic genes. Finally, the key results were tested in HCC patients.

Results

Thirteen differentially expressed glycolysis/gluconeogenesis-related genes were validated in additional datasets. Consensus clustering analysis identified two distinct patient clusters (C1 and C2) with different prognoses and immune microenvironments. Immune score and tumor purity were significantly higher in C1 than in C2, and CD4+ memory activated T cell, Tfh, Tregs, and macrophage M0 were higher infiltrated in HCC and C1 group. The study also identified five intersecting DEGs from candidate genes in TCGA, GSE14520, and GSE141198 as prognostic genes, which had a protective role in HCC patient prognosis. Compared with the control group, the prognostic genes all showed decreased expression in HCC patients in RT-qPCR and Western blot analyses. Flow cytometry verified the abnormal infiltration level of immune cells in HCC patients.

Conclusion

Results showed that glycolysis/gluconeogenesis-related genes were associated with patient prognosis, immune microenvironment, and response to immunotherapy in HCC. It suggests that the model based on five prognostic genes may valuable for predicting the prognosis and immunotherapy response of HCC patients.

Meeting at the Crossroad between Obesity and Hepatic Carcinogenesis: Unique Pathophysiological Pathways Raise Expectations for Innovative Therapeutic Approaches

The escalating global prevalence of obesity and its intricate association with the development of hepatocellular carcinoma (HCC) pose a substantial challenge to public health. Obesity, acknowledged as a pervasive epidemic, is linked to an array of chronic diseases, including HCC, catalyzing the need for a comprehensive understanding of its molecular underpinnings. Notably, HCC has emerged as a leading malignancy with rising incidence and mortality. The transition from viral etiologies to the prominence of metabolic dysfunction-associated fatty liver disease (MAFLD)-related HCC underscores the urgent need to explore the intricate molecular pathways linking obesity and hepatic carcinogenesis. This review delves into the interwoven landscape of molecular carcinogenesis in the context of obesity-driven HCC while also navigating using the current therapeutic strategies and future prospects for combating obesity-related HCC. We underscore the pivotal role of obesity as a risk factor and propose an integrated approach encompassing lifestyle interventions, pharmacotherapy, and the exploration of emerging targeted therapies. As the obesity-HCC nexus continues to challenge healthcare systems globally, a comprehensive understanding of the intricate molecular mechanisms and innovative therapeutic strategies is imperative to alleviate the rising burden of this dual menace.

Differentially expressed non-coding RNAs and their regulatory networks in liver cancer

The vast majority of human transcriptome is represented by various types of small RNAs with little or no protein-coding capability referred to as non-coding RNAs (ncRNAs). Functional ncRNAs include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), which are expressed at very low, but stable and reproducible levels in a variety of cell types. ncRNAs regulate gene expression due to miRNA capability of complementary base pairing with mRNAs, whereas lncRNAs and circRNAs can sponge miRNAs off their target mRNAs to act as competitive endogenous RNAs (ceRNAs). Each miRNA can target multiple mRNAs and a single mRNA can interact with several miRNAs, thereby creating miRNA-mRNA, lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks. Over the past few years, a variety of differentially expressed miRNAs, lncRNAs, and circRNAs (DEMs, DELs, and DECs, respectively) have been linked to cancer pathogenesis. They can exert both oncogenic and tumor suppressor roles. In this review, we discuss the recent advancements in uncovering the roles of DEMs, DELs, and DECs and their networks in aberrant cell signaling, cell cycle, transcription, angiogenesis, and apoptosis, as well as tumor microenvironment remodeling and metabolic reprogramming during hepatocarcinogenesis. We highlight the potential and challenges in the use of differentially expressed ncRNAs as biomarkers for liver cancer diagnosis and prognosis.

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.

Expression profile of adrenomedullin and its specific receptors in liver tissues from patients with hepatocellular carcinoma and in tumorigenic cell line-secreted extracellular vesicles

The transcriptional profile of adrenomedullin (AM), a new metastasis-related factor involved in hepatocellular carcinoma (HCC), and its specific receptors (CLR, RAMP1, RAMP3) were evaluated in liver tissues of HCV-positive HCC subjects undergoing liver transplantation (LR) and in donors (LD). AM and its specific receptor expression were also assessed in extracellular vesicles (EVs) secreted by tumorigenic (HepG2) and non-tumorigenic (WRL68) cells by Real-Time PCR. AM expression resulted significantly elevated in LR concerning LD (p = 0.0038) and, for the first time, significantly higher levels in HCC patients as a function of clinical severity (MELD score), were observed. RAMP3 and CLR expression increased in LR as a function of clinical severity while RAMP1 decreased. Positive correlations were found among AM, its receptors, and apoptotic markers. No AM mRNA expression difference was observed between HepG2 and WRL68 EVs. RAMP1 and RAMP3 resulted lower in HepG2 concerning WRL68 while significantly higher levels were observed for CLR. While results at tissue level characterize AM as a regulator of carcinogenesis-tumor progression, those obtained in EVs do not indicate AM as a target candidate, neither as a pathological biomarker nor as a marker involved in cancer therapy.

Transcriptional level evaluation of osteopontin/miRNA-181a axis in hepatocellular carcinoma cell line-secreted extracellular vesicles

Recent evidence suggested the role of secreted extracellular vesicles (EVs) in the intracellular signalling within the liver becoming a promising candidate as biomarker in hepatocellular carcinoma (HCC). Osteopontin (OPN) seems to play a relevant role both for early diagnosis of HCC than on the mechanisms that drive oncogenesis but, to date, information on the expression levels of OPN in EVs secreted by HCC tumor cell line are missing. The study aimed to verify, by transcriptional and proteomic study, the presence of OPN in EVs secreted by tumorigenic (HepG2) and non-tumorigenic hepatocyte cell line (WRL68), and to analyse the expression variations of OPN, its isoforms and miRNA-181a in both these EVs. "In silico analysis" was also performed via the Gene expression Profiling Interactive analysis (GEPIA) and Hepatocellular Carcinoma Database (HCCDB). An up-regulation of OPN in EVs secreted by HepG2 with respect to WRL68 was found in line with the results obtained by the "in silico analysis". The study demonstrates, for the first time, the OPN isoforms and its modulator miRNA-181a expression in EVs secreted by both cell lines, highlighting high levels of OPN isoforms in EVs secreted by HepG2 and identifying OPN as a promising biomarker for HCC diagnosis.

Macrotrabecular-Massive Hepatocellular Carcinoma: Light and Shadow in Current Knowledge

The subject of this narrative review is macrotrabecular-massive hepatocellular carcinoma (MTM‐HCC). Despite their rarity, these tumours are of general interest because of their epidemiological and clinical features and for representing a distinct model of the interaction between the angiogenetic system and neoplastic cells. The MTM‐HCC subtype is associated with various adverse biological and pathological parameters (the Alfa-foetoprotein (AFP) serum level, tumour size, vascular invasion, and satellite nodules) and is a key determinant of patient prognosis, with a strong and independent predictive value for early and overall tumour recurrence. Gene expression profiling has demonstrated that angiogenesis activation is a hallmark feature of MTM-HCC, with overexpression of both angiopoietin 2 (ANGPT2) and vascular endothelial growth factor A (VEGFA).

Higher Expression of SPP1 Predicts Poorer Survival Outcomes in Head and Neck Cancer

Secreted phosphoprotein 1 (SPP1) participated in various biological processes in many cancers, including immune response, tumor progression, and prognosis. However, SPP1 in head and neck squamous cell carcinoma (HNSCC) remains unknown. Clinical-genetic data of HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. The differential expression of SPP1 in HNSCC tissues and adjacent normal tissues was quantified by bioinformatics methods and verified by western blot and other differential biological methods. We concluded that SPP1 is significantly upregulated in tumor tissues and can become a prognostic biomarker for HNSCC.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Genomic Landscape of Liquid Biopsy for Hepatocellular Carcinoma Personalized Medicine

Hepatocellular carcinoma (HCC) is the sixth most frequently diagnosed cancer and the third leading cause of cancer-related deaths worldwide. Advanced-stage HCC patients have poor survival rates and this requires the discovery of novel clear biomarkers for HCC early diagnosis and prognosis, identifying risk factors, distinguishing HCC from non-HCC liver diseases, and assessment of treatment response. Liquid biopsy has emerged as a novel minimally invasive approach to enable monitoring tumor progression, metastasis, and recurrence. Since the liquid biopsy analysis has relatively high specificity and low sensitivity in cancer early detection, there is a risk of bias. Next-generation sequencing (NGS) technologies provide accurate and comprehensive gene expression and mutational profiling of liquid biopsies including cell-free circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and genomic components of extracellular vesicles (EVs) including micro-RNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). Since HCC is a highly heterogeneous cancer, HCC patients can display various genomic, epigenomic, and transcriptomic patterns and exhibit varying sensitivity to treatment options. Identification of individual variabilities in genomic signatures in liquid biopsy has the potential to greatly enhance precision oncology capabilities. In this review, we highlight and critically discuss the latest progress in characterizing the genomic landscape of liquid biopsy, which can advance HCC personalized medicine.