Tissue and serum metabolomic phenotyping for diagnosis and prognosis of hepatocellular carcinoma

Clinical potential of serum prostaglandin A2 as a novel diagnostic biomarker for hepatocellular cancer

BACKGROUND

Hepatocellular cancer (HCC) is one of the most harmful tumors to human health. Currently, there is still a lack of highly sensitive and specific HCC biomarkers in clinical practice. In this study, we aimed to explore the diagnostic performance of prostaglandin A2 (PGA2) for the early detection of HCC.

METHODS

Untargeted metabolomic analyses on normal control (NC) and HCC participants in the discovery cohort were performed, and PGA2 was identified to be dysregulated in HCC. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for detecting serum PGA2 was established and applied to validate the dysregulation of PGA2 in another independent validation cohort. Receiver operating characteristic (ROC), decision curve analysis (DCA) and some other statistical analyses were performed to evaluate the diagnostic performance of PGA2 for HCC.

RESULTS

At first, PGA2 was found to be dysregulated in HCC in untargeted metabolomic analyses. Then a precise quantitative LC-MS/MS method for PGA2 has been established and has passed rigorous method validation. Targeted PGA2 analyses confirmed that serum PGA2 was decreased in HCC compared to normal-risk NC and high-risk cirrhosis group. Subsequently, PGA2 was identified as a novel biomarker for the diagnosis of HCC, with an area under the ROC curve (AUC) of 0.911 for differentiating HCC from the combined NC + cirrhosis groups. In addition, PGA2 exhibited high performance for differentiating small-size (AUC = 0.924), early-stage (AUC = 0.917) and AFP (-) HCC (AUC = 0.909) from the control groups. The combination of PGA2 and AFP might be useful in the surveillance of risk population for HCC and early diagnosis of HCC.

CONCLUSION

This study establishes that PGA2 might be a novel diagnostic biomarker for HCC.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

DNA damage repair-related gene signature for identifying the immune status and predicting the prognosis of hepatocellular carcinoma

The heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.

Metformin reduces hepatocarcinogenesis by inducing downregulation of Cyp26a1 and CD8+ T cells

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with major challenges in both prevention and therapy. Metformin, adenosine monophosphate-activated protein kinase (AMPK) activator, has been suggested to reduce the incidence of HCC when used for patients with diabetes in preclinical and clinical studies. However, the possible effects of metformin and their mechanisms of action in non-diabetic HCC have not been adequately investigated.

METHODS

Fah-/-  mice were used to construct a liver-injury-induced non-diabetic HCC model for exploring hepatocarcinogenesis and therapeutic potential of metformin. Changes in relevant tumour and biochemical indicators were measured. Bulk and single-cell RNA-sequencing analyses were performed to validate the crucial role of proinflammatory/pro-tumour CD8+ T cells. In vitro and in vivo experiments were performed to confirm Cyp26a1-related antitumour mechanisms of metformin.

RESULTS

RNA-sequencing analysis showed that chronic liver injury led to significant changes in AMPK-, glucose- and retinol metabolism-related pathways in Fah-/- mice. Metformin prevented the formation of non-diabetic HCC in Fah-/- mice with chronic liver injury. Cyp26a1 ddexpression in hepatocytes was significantly suppressed after metformin treatment. Moreover, downregulation of Cyp26a1 occurred in conjunction with increased levels of all-trans-retinoic acid (atRA), which is involved in the activation of metformin-suppressed hepatocarcinogenesis in Fah-/- mice. In contrast, both CD8+  T-cell infiltration and proinflammatory/pro-tumour cytokines in the liver were significantly upregulated in Fah-/- mice during chronic liver injury, which was notably reversed by either metformin or atRA treatment. Regarding mechanisms, metformin regulated the decrease in Cyp26a1 enzyme expression and increased atRA expression via the AMPK/STAT3/Gadd45β/JNK/c-Jun pathway.

CONCLUSIONS

Metformin inhibits non-diabetic HCC by upregulating atRA levels and downregulating CD8+ T cells. This is the first reporting that the traditional drug metformin regulates the metabolite atRA via the Cyp26a1-involved pathway. The present study provides a potential application of metformin and atRA in non-diabetic HCC.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

An inflammation-related gene landscape predicts prognosis and response to immunotherapy in virus-associated hepatocellular carcinoma

Background

Due to the viral infection, chronic inflammation significantly increases the likelihood of hepatocellular carcinoma (HCC) development. Nevertheless, an inflammation-based signature aimed to predict the prognosis and therapeutic effect in virus-related HCC has rarely been established.

Method

Based on the integrated analysis, inflammation-associated genes (IRGs) were systematically assessed. We comprehensively investigated the correlation between inflammation and transcriptional profiles, prognosis, and immune cell infiltration. Then, an inflammation-related risk model (IRM) to predict the overall survival (OS) and response to treatment for virus-related HCC patients was constructed and verified. Also, the potential association between IRGs and tumor microenvironment (TME) was investigated. Ultimately, hub genes were validated in plasma samples and cell lines via qRT-PCR. After transfection with shCCL20 combined with overSLC7A2, morphological change of SMMC7721 and huh7 cells was observed. Tumorigenicity model in nude mouse was established.

Results

An inflammatory response-related gene signature model, containing MEP1A, CCL20, ADORA2B, TNFSF9, ICAM4, and SLC7A2, was constructed by conjoint analysis of least absolute shrinkage and selection operator (LASSO) Cox regression and gaussian finite mixture model (GMM). Besides, survival analysis attested that higher IRG scores were positively relevant to worse survival outcomes in virus-related HCC patients, which was testified by external validation cohorts (the ICGC cohort and GSE84337 dataset). Univariate and multivariate Cox regression analyses commonly proved that the IRG was an independent prognostic factor for virus-related HCC patients. Thus, a nomogram with clinical factors and IRG was also constructed to superiorly predict the prognosis of patients. Featured with microsatellite instability-high, mutation burden, and immune activation, lower IRG score verified a superior OS for sufferers. Additionally, IRG score was remarkedly correlated with the cancer stem cell index and drug susceptibility. The measurement of plasma samples further validated that CCL20 upexpression and SLC7A2 downexpression were positively related with virus-related HCC patients, which was in accord with the results in cell lines. Furthermore, CCL20 knockdown combined with SLC7A2 overexpression availably weakened the tumor growth in vivo.

Conclusions

Collectively, IRG score, serving as a potential candidate, accurately and stably predicted the prognosis and response to immunotherapy in virus-related HCC patients, which could guide individualized treatment decision-making for the sufferers.

Biomarker discovery and application-An opportunity to resolve the challenge of liver cancer diagnosis and treatment

Liver cancer is one of the most common malignancies, with severe morbidity and mortality. While considerable progress has been made in liver cancer treatment, the 5-year overall survival (OS) of patients has not improved significantly. Reasons include the inadequate capability of early screening and diagnosis, a high incidence of recurrence and metastasis, a high degree of tumor heterogeneity, and an immunosuppressive tumor microenvironment. Therefore, the identification and validation of specific and robust liver cancer biomarkers are of major importance for early screening, timely diagnosis, accurate prognosis, and the prevention of tumor progression. In this review, we highlight some of the latest research progress and potential applications of liver cancer biomarkers, describing hotspots and prospective directions in biomarker discovery.

Simultaneous Quantification of Serum Lipids and Their Association with Type 2 Diabetes Mellitus-Positive Hepatocellular Cancer

Type 2 diabetes mellitus (T2DM) has been recognized as one of the most important and independent risk factors for hepatocellular cancer (HCC). However, there is still a lack of ideal tumor markers for HCC detection in the T2DM population. Serum lipids have been revealed as potential tumor markers for HCC. In this study, our objective was to develop a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to detect several lipids including 8,15-dihydroxy-5,9,11,13-eicosatetraenoic acid (8,15-DiHETE), hexadecanedioic acid (HDA), 15-keto-13,14-dihydroprostaglandin A2 (DHK-PGA2), ricinoleic acid (RCL), octadecanedioic acid (OA) and 16-hydroxy hexadecanoic acid (16OHHA) in serum and explore their diagnostic potential for T2DM-positive [T2DM(+)] HCC. A robust LC-MS/MS method was established for the measurement of 8,15-DiHETE, HDA, DHK-PGA2, RCL, OA, and 16OHHA. The methodology validation was conducted, and the results suggested the reliability of this LC-MS/MS method for targeted lipids. Several serum lipids, including 8,15-DiHETE, HDA, DHK-PGA2, and OA were increased in T2DM(+) HCC patients. A biomarker signature that incorporated HDA, DHK-PGA2, and AFP was established and showed good diagnostic potential for T2DM(+) HCC, and the area under the ROC curve (AUC) was 0.87 for diagnosing T2DM(+) HCC from T2DM individuals. Additionally, the biomarker signature diagnosed small-size (AUC = 0.88) and early-stage (AUC = 0.79) tumors with high efficacy. Moreover, the biomarker signature could differentiate T2DM(+) HCC from other T2DM(+) tumors, including pancreatic, gastric and colorectal cancer (AUC = 0.88) as well. In conclusion, our study develops a novel tool for early diagnosis of T2DM(+) HCC in T2DM patients.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Circulating Metabolic Markers Related to the Diagnosis of Hepatocellular Carcinoma

Primary liver carcinoma is the sixth most common cancer worldwide, while hepatocellular carcinoma (HCC) is the most dominant cancer type. Chronic hepatitis B and C virus infections and aflatoxin exposure are the main risk factors, while nonalcoholic fatty liver disease caused by obesity, diabetes, and metabolic syndrome are the more common risk factors for HCC. Metabolic disorders caused by these high-risk factors are closely related to the tumor microenvironment of HCC, revealing a possible cause-and-effect relationship between the two. These metabolic disorders involve many complex metabolic pathways, such as carbohydrate, lipid, lipid derivative, amino acid, and amino acid derivative metabolic processes. The resulting metabolites with significant abnormal changes in the concentration level in circulating blood may be used as biomarkers to guide the diagnosis, treatment, or prognosis of HCC. At present, there are high-throughput technologies that can quickly detect small molecular metabolites in many samples. Compared to tissue biopsy, blood samples are easier to obtain, and patients’ willingness to participate is higher, which makes it possible to study blood HCC biomarkers. Over the past few years, a substantial body of research has been performed worldwide, and other potential biomarkers have been identified. Unfortunately, due to the limitations of each study, only a few markers have been widely verified and are suitable for clinical use. This review briefly summarizes the potential blood metabolic markers related to the diagnosis of HCC, mainly focusing on amino acids and their derivative metabolism, lipids and their derivative metabolism, and other possible related metabolisms.

A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy.

Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs.

Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability.

Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.

Fuzheng Xiaozheng prescription exerts anti-hepatocellular carcinoma effects by improving lipid and glucose metabolisms via regulating circRNA-miRNA-mRNA networks

BACKGROUND

Hepatocellular carcinoma (HCC) is a major threat to human health due to its high lethality. Our previous studies suggested that Fuzheng Xiaozheng prescription (FZXZP), an effective Chinese medicine, demonstrated significant suppressive effects on HCC. However, its underlying mechanism remains largely unclear.

PURPOSE

This study aimed to investigate the anti-HCC mechanisms of FZXZP from transcriptomic sequencing based on a holistic perspective.

METHODS

Rat HCC model was induced by diethylnitrosamine, and then the model was administered with two doses of FZXZP, high and low. Sodium demethylcantharidate was used as a positive control. Subsequently, microarrays of circRNA, miRNA and mRNA were performed on the blank, model, high and low dose groups, respectively, and the competitive binding mechanisms among them were further analyzed by bioinformatics. Then, the circRNA-miRNA-mRNA networks were constructed to mine the targeted-RNAs of FZXZP in HCC, as well as to explore their potential regulatory mechanisms. Finally, functions and pathways of the FZXZP targeted genes in rat HCC were annotated with GO and KEGG, and qRT-PCR was performed to validate the accuracy of the above analyses in this study.

RESULTS

The results showed that FZXZP significantly inhibited the development and progression of HCC in rats, improved the pathological conditions and suppressed the proliferation of HCC cells. Subsequently, after a series of screening, the competing endogenous RNA networks (circRNA-miRNA-mRNA), consisting of 2 circRNAs, 7 miRNAs and 104 mRNAs, were finally established. KEGG and GO analyses of the networks revealed that lipid metabolism related pathways, such as fatty acid metabolism, bile secretion and PPAR pathway, were significantly enriched. In the further hubgene network analysis, in addition to lipid metabolism, aberrant glucose metabolism was found to be ameliorated by G6pc and Pklr in hubgenes. Finally, the qRT-PCR analyses confirmed that the expression tendencies of the above targeted genes were correct and believable in transcriptomic sequencings, and qRT-PCR results of the genes closely related to proliferation, invasion and apoptosis of HCC also indicated the inhibitory effects of FZXZP on HCC obviously.

CONCLUSION

FZXZP demonstrated significant anti-HCC effects through improving lipid and glucose metabolism, restoring the metabolic homeostasis of the liver via circRNA-miRNA-mRNA networks.

A Serum Metabolite Classifier for the Early Detection of Type 2 Diabetes Mellitus-Positive Hepatocellular Cancer

Type 2 diabetes mellitus (T2DM) has been identified as an independent risk factor for hepatocellular cancer (HCC). However, there are no ideal biomarkers for the surveillance and early detection of HCC in the T2DM population at present. In this study, we aimed to explore novel metabolite biomarkers for T2DM-positive [T2DM(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in T2DM(+) HCC patients in untargeted metabolomic analyses. Targeted metabolite analyses confirmed that serum benzoic acid and citrulline were increased, and creatine was decreased in T2DM(+) HCC compared to the T2DM group. A metabolite classifier including benzoic acid, creatine, and citrulline was identified as a novel biomarker for the diagnosis of T2DM(+) HCC, with an area under the ROC curve (AUC) of 0.93 for discriminating T2DM(+) HCC patients from T2DM patients. In addition, the metabolite classifier detected small-size (AUC = 0.94), early-stage (AUC = 0.94), and AFP-negative (AUC = 0.96) tumors with high sensitivity and specificity. The combination of this metabolite classifier and AFP might be useful in the surveillance and early detection of HCC in the T2DM population. In conclusion, this study establishes a novel diagnostic tool for T2DM(+) HCC.

Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes

Hepatocellular carcinoma is a disastrous cancer with an aberrant metabolism. In this study, we aimed to assess the role of metabolism in the prognosis of hepatocellular carcinoma. Ten metabolism-related pathways were identified to classify the hepatocellular carcinoma into two clusters: Metabolism_H and Metabolism_L. Compared with Metabolism_L, patients in Metabolism_H had lower survival rates with more mutated TP53 genes and more immune infiltration. Moreover, risk scores for predicting overall survival based on eleven differentially expressed metabolic genes were developed by the least absolute shrinkage and selection operator (LASSO)-Cox regression model in The Cancer Genome Atlas (TCGA) dataset, which was validated in the International Cancer Genome Consortium (ICGC) dataset. The immunohistochemistry staining of liver cancer patient specimens also identified that the 11 genes were associated with the prognosis of liver cancer patients. Multivariate Cox regression analyses indicated that the differentially expressed metabolic gene-based risk score was also an independent prognostic factor for overall survival. Furthermore, the risk score (AUC = 0.767) outperformed other clinical variables in predicting overall survival. Therefore, the metabolism-related survival-predictor model may predict overall survival excellently for HCC patients.

Navigating through the Lipid Metabolism Maze: Diagnosis and Prognosis Metabolites of Hepatocellular Carcinoma versus Compensated Cirrhosis

(1) Background: The pursuit of finding biomarkers for the diagnosis and prognosis of hepatocellular carcinoma (HCC) has never been so paramount in the days of personalized medicine. The main objective of our study is to identify new biomarkers for diagnosing HCC, and to identify which patients are at risk of developing tumor recurrence, decompensation, or even possesses the risk of cancer-related death. (2) Methods: We have conducted an untargeted metabolomics study from the serum of 69 European patients—32 compensated cirrhotic patients without HCC (controls), and 37 cirrhotic patients with HCC with compensated underlying liver disease (cases), that underwent curative treatment (surgery or ablation), performing ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-QTOF- (ESI+)-MS) with an emphasis on lipid metabolites. (3) Results: 1,25-dihydroxy cholesterol (m/z = 419.281), myristyl palmitate (m/z = 453.165), 25-hydroxy vitamin D2 (m/z = 413.265), 12-ketodeoxycholic acid (m/z = 391.283), lysoPC (21:4) (m/z = 558.291), and lysoPE (22:2) (m/z = 534.286) represent notable biomarkers that differentiate compensated cirrhosis from early HCC, and ceramide species are depleted in the serum of HCC patients. Regarding prognosis, no metabolite identified in our study could determine tumor relapse. To distinguish between the HCC patients that survived curative treatment and those at risk that developed tumor burden, we have identified two notable phosphocholines (PC (30:2); PC (30:1)) with AUROCs of 0.820 and 0.807, respectively, that seem to increase when patients are at risk. In a univariate analysis, arachidonic acid was the only metabolite to predict decompensation (OR = 0.1, 95% CI: 0−0.16, p < 0.005), while in the multivariate analysis, dismally, no variable was associated with decompensation. Furthermore, in the multivariate analysis, we have found out for the first time that the increased expression of 1,25-dihydroxy cholesterol, myristyl palmitate, 12-keto deoxycholic acid, lysoPC (21:4), and lysoPE (22:2) are independent markers of survival. (4) Conclusions: Our study reveals that lipids play a crucial role in discriminating compensated cirrhosis and early hepatocellular carcinoma, and might represent markers of survival and prognosis in personalized and minimally invasive medicine.

SMYD5 acts as a potential biomarker for hepatocellular carcinoma

Determining the prognosis of patients remains a challenge due to the phenotypic and molecular diversities of hepatocellular carcinomas (HCC). We aimed to evaluate the role of SMYD5 in HCC. Wilcoxon signed-rank test and logistic regression analyzed the relationship between clinical pathologic features and SMYD5. We found that increased expression of SMYD5 in HCC was closely associated with high histologic grade, stage, T stage and nodal stage. Kaplan-Meier method, Cox regression, univariate analysis and multivariate analysis detected overall survival of TCGA-HCC patients. It turned out that high expression of SMYD5 predicted a worse prognosis in HCC. Gene Set Enrichment Analysis (GSEA) was applied via TCGA data set, which indicated that complement and coagulation cascades, fatty acid metabolism, primary bile acid biosynthesis, drug metabolism cytochrome P450, PPAR signaling pathway and retinol metabolism were differentially enriched in SMYD5 high expression phenotype. Interestingly, we proved that SMYD5 upregulation in HCC cells was induced by promoter hypo-methylation. Moreover, functional experiments demonstrated that SMYD5 silencing abrogated cell proliferation, migration and invasion and enhanced paclitaxel sensitivity in HCC. All findings implied that SMYD5 might be an underlying biomarker for prognosis and treatment of HCC.

Liver-specific LINC01146, a promising prognostic indicator, inhibits the malignant phenotype of hepatocellular carcinoma cells both in vitro and in vivo

Background

Long non-coding RNAs (lncRNAs) are involved in the development of hepatocellular carcinoma (HCC). We aimed to investigate the function of LINC01146 in HCC.

Methods

The expression of LINC01146 in HCC tissues was explored via The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and was verified using quantitative real-time polymerase chain reaction (qRT–PCR) in our HCC cohort. Kaplan–Meier analysis was used to assess the relationship between LINC01146 and the prognosis of HCC patients. Cell Counting Kit 8, colony formation assays, Transwell assays, flow cytometric assays, and tumour formation models in nude mice were conducted to reveal the effects of LINC01146 on HCC cells both in vitro and in vivo. Bioinformatic methods were used to explore the possible potential pathways of LINC01146 in HCC.

Results

LINC01146 was significantly decreased in HCC tissues compared with adjacent normal tissues and was found to be related to the clinical presentations of malignancy and the poor prognosis of HCC patients. Overexpression of LINC01146 inhibited the proliferation, migration, and invasion of HCC cells in vitro, while promoting their apoptosis. In contrast, downregulation of LINC01146 exerted the opposite effects on HCC cells in vitro. In addition, overexpression of LINC01146 significantly inhibited tumour growth, while downregulation of LINC01146 promoted tumour growth in vivo. Furthermore, the coexpressed genes of LINC01146 were mainly involved in the “metabolic pathway” and “complement and coagulation cascade pathway”.

Conclusion

LINC01146 expression was found to be decreased in HCC tissues and associated with the prognosis of HCC patients. It may serve as a cancer suppressor and prognostic biomarker in HCC.

Metabolomic biomarkers for hepatocellular carcinoma: A systematic review

Hepatocellular carcinoma (HCC) is a highly malignant cancer which lack of effective diagnosis and prognosis biomarkers, therefore surging studies focused on the metabolite candidates for HCC. The current study was designed to systematically review the metabolic studies for HCC, summarize the current available evidence and provide implication for further studies within this area. By systematically screening Pubmed and Embase, and eligibility assessment, we eventually included 55 pieces of studies. After summarized their characteristics, we reviewed them by 3 parts, regarding to the different biofluid they carried out the experiments. By collecting the candidates from all the included studies, we carried out pathway enrichment to see the representative of the reported candidates, as expected the pathway consistent with the current knowledge of HCC. Next, we conduct quality assessment on the included studies. Only 36% of the current evidence grouped as high quality, indicating the quality of metabolic studies needs further improvement.

MicroRNA target-based network predicts androgen receptor-linked mycotoxin stress

Stress-responsive microRNAs (miRNAs) contribute to the regulation of cellular homeostasis or pathological processes, including carcinogenesis, by reprogramming target gene expression following human exposure to environmental or dietary xenobiotics. Herein, we predicted the targets of carcinogenic mycotoxin-responsive miRNAs and analyzed their association with disease and functionality. miRNA target-derived prediction indicated potent associations of oncogenic mycotoxin exposure with metabolism- or hormone-related diseases, including sex hormone-linked cancers. Mechanistically, the signaling network evaluation suggested androgen receptor (AR)-linked signaling as a common pivotal cluster associated with metabolism- or hormone-related tumorigenesis in response to aflatoxin B1 and ochratoxin A co-exposure. Particularly, high levels of AR and AR-linked genes for the retinol and xenobiotic metabolic enzymes were positively associated with attenuated disease biomarkers and good prognosis in patients with liver or kidney cancers. Moreover, AR-linked signaling was protective against OTA-induced genetic insults in human hepatocytes whereas it was positively involved in AFB1-induced genotoxic actions. Collectively, miRNA target network-based predictions provide novel clinical insights into the progression or intervention against malignant adverse outcomes of human exposure to environmental oncogenic insults.

Metabolic Reprogramming and Risk Stratification of Hepatocellular Carcinoma Studied by Using Gas Chromatography-Mass Spectrometry-Based Metabolomics

Hepatocellular carcinoma (HCC) displays a high degree of metabolic and phenotypic heterogeneity and has dismal prognosis in most patients. Here, a gas chromatography-mass spectrometry (GC-MS)-based nontargeted metabolomics method was applied to analyze the metabolic profiling of 130 pairs of hepatocellular tumor tissues and matched adjacent noncancerous tissues from HCC patients. A total of 81 differential metabolites were identified by paired nonparametric test with false discovery rate correction to compare tumor tissues with adjacent noncancerous tissues. Results demonstrated that the metabolic reprogramming of HCC was mainly characterized by highly active glycolysis, enhanced fatty acid metabolism and inhibited tricarboxylic acid cycle, which satisfied the energy and biomass demands for tumor initiation and progression, meanwhile reducing apoptosis by counteracting oxidative stress. Risk stratification was performed based on the differential metabolites between tumor and adjacent noncancerous tissues by using nonnegative matrix factorization clustering. Three metabolic clusters displaying different characteristics were identified, and the cluster with higher levels of free fatty acids (FFAs) in tumors showed a worse prognosis. Finally, a metabolite classifier composed of six FFAs was further verified in a dependent sample set to have potential to define the patients with poor prognosis. Together, our results offered insights into the molecular pathological characteristics of HCC.

Overweight/obesity-related transcriptomic signature as a correlate of clinical outcome, immune microenvironment, and treatment response in hepatocellular carcinoma

BACKGROUNDS

The pandemic of overweight and obesity (quantified by body mass index (BMI) ≥ 25) has rapidly raised the patient number of non-alcoholic fatty hepatocellular carcinoma (HCC), and several clinical trials have shown that BMI is associated with the prognosis of HCC. However, whether overweight/obesity is an independent prognostic factor is arguable, and the role of overweight/obesity-related metabolisms in the progression of HCC is scarcely known.

MATERIALS AND METHODS

In the present study, clinical information, mRNA expression profile, and genomic data were downloaded from The Cancer Genome Atlas (TCGA) as a training cohort (TCGA-HCC) for the identification of overweight/obesity-related transcriptome. Machine learning and the Cox regression analysis were conducted for the construction of the overweight/obesity-associated gene (OAG) signature. The Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and the Cox regression analysis were performed to assess the prognostic value of the OAG signature, which was further validated in two independent retrospective cohorts from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Subsequently, functional enrichment, genomic profiling, and tumor microenvironment (TME) evaluation were utilized to characterize biological activities associated with the OAG signature. GSE109211 and GSE104580 were retrieved to evaluate the underlying response of sorafenib and transcatheter arterial chemoembolization (TACE) treatment, respectively. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic response.

RESULTS

Overweight/obesity-associated transcriptome was mainly involved in metabolic processes and noticeably and markedly correlated with prognosis and TME of HCC. Afterward, a novel established OAG signature (including 17 genes, namely, GAGE2D, PDE6A, GABRR1, DCAF8L1, DPYSL4, SLC6A3, MMP3, RIBC2, KCNH2, HTRA3, PDX1, ATHL1, PRTG, SHC4, C21orf29, SMIM32, and C1orf133) divided patients into high and low OAG score groups with distinct prognosis (median overall survival (OS): 24.87 vs. 83.51 months, p < 0.0001), and the values of area under ROC curve (AUC) in predicting 1-, 2-, 3-, and 4-year OS were 0.81, 0.80, 0.83, and 0.85, respectively. Moreover, the OAG score was independent of clinical features and also exhibited a good ability for prognosis prediction in the ICGC-LIHC-JP cohort and GSE54236 dataset. Expectedly, the OAG score was also highly correlated with metabolic processes, especially oxidative-related signaling pathways. Furthermore, abundant enrichment of chemokines, receptors, MHC molecules, and other immunomodulators as well as PD-L1/PD-1 expression among patients with high OAG scores indicated that they might have better responses to immunotherapy. However, probably exclusion of T cells from infiltrating tumors resulting in lower infiltration of effective T cells would restrict immunotherapeutic effects. In addition, the OAG score was significantly associated with the response of sorafenib and TACE treatment.

CONCLUSIONS

Overall, this study comprehensively disclosed the relationship between BMI-guided transcriptome and HCC. Moreover, the OAG signature had the potential clinical applications in the future to promote clinical management and precision medicine of HCC.

Decreased DHRS1 expression is a novel predictor of poor survival in patients with hepatocellular carcinoma

Aim: To investigate the effect of aberrant expression of DHRS1 on hepatocellular carcinoma (HCC). Materials & methods: Kaplan-Meier and Cox regression analyses were performed to evaluate the correlation between DHRS1 and overall survival. Gene set enrichment analysis was performed to explore the potential function of DHRS1 in HCC. Results: Multiple data analysis revealed that DHRS1 mRNA and protein expression level were remarkably lower in HCC than that in normal tissues. In survival analysis, patients with low DHRS1 expression presented a poorer prognosis, and was an independent risk factor for HCC. Conclusion: Decreased DHRS1 expression may be a potential predictor of poor prognosis in HCC.

Malnutrition and liver disease in a developing country

Malnutrition is a highly prevalent and under recognized condition in developing countries of South Asia. The presence of malnutrition causes a severe impact on patients with liver cirrhosis. The etiology of cirrhosis differs in the South Asian region compared to the West, with hepatitis B and C still being the leading causes and the prevalence of nonalcoholic fatty liver disease increasing over time. Comorbid malnutrition worsens outcomes for cirrhosis patients. Urgent attention to address malnutrition is needed to improve patient outcomes. The etiology and pathophysiology of malnutrition in liver diseases is multifactorial, as reduction in liver function affects both macronutrients and micronutrients. A need for nutritional status assessment for liver disease patients exists in all parts of the world. There are many widely studied tools in use to perform a thorough nutritional assessment, of which some tools are low cost and do not require extensive training. These tools can be studied and evaluated for use in the resource limited setting of a country like Pakistan. Treatment guidelines for proper nutrition maintenance in chronic liver disease exist for all parts of the world, but the knowledge and practice of nutritional counseling in Pakistan is poor, both amongst patients and physicians. Emphasis on assessment for nutritional status at the initial visit with recording of vital signs is needed. Simultaneously, treating physicians need to be made aware of the misconceptions surrounding nutritional restrictions in cirrhosis so that patient education is done correctly based on proper scientific evidence.

Identification of the molecular targets and mechanisms of compound mylabris capsules for hepatocellular carcinoma treatment through network pharmacology and bioinformatics analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese herbal formulas have been proven to exert an inhibitory effect on tumor. Compound mylabris capsules (CMC) has been used for treating cancer, especially hepatocellular carcinoma (HCC), for years in China. However, its therapeutic mechanisms on HCC remain unclear.

AIM OF THE STUDY

This research aimed to elucidate the molecular targets and mechanisms of CMC for treating HCC.

MATERIALS AND METHODS

First, the bioactive ingredients and potential targets of CMC, as well as HCC-related targets were retrieved from publicly available databases. Next, the overlapped genes between potential targets of CMC and HCC-related targets were determined using bioinformatics analysis. Then, networks of ingredient-target and gene-pathway were constructed. Finally, cell experiments were carried out to examine the effects of CMC-medicated serum on HCC and validate the core molecular targets.

RESULTS

In total, 151 bioactive ingredients and 255 potential targets of CMC were selected, 982 differentially expressed genes of HCC were identified. Among them, 34 overlapped genes were finally selected. In addition, 20 pathways and 429 GO terms were significantly enriched. Protein-protein interaction and gene-pathway networks indicated that Cyclin B1(CCNB1) and Cyclin Dependent Kinase 1(CDK1) were the core gene targets for the treatment of CMC on HCC. Moreover, in vitro studies showed that CMC-medicated serum significantly inhibited the viability of HepG2 cells. Furthermore, CMC downregulated CCNB1 and CDK1 expressions and induced G2/M phase cell cycle arrest.

CONCLUSIONS

CMC plays a therapeutic role in HCC via multi-component, -target and -pathway mechanisms, in which CCNB1 and CDK1 may be the core molecular targets. This study indicates that the integration of network pharmacology and bioinformatics analysis, followed by experimental validation, can serves as an effective tool for studying the therapeutic mechanisms of traditional Chinese medicine.

Identification of hub genes associated with prognosis, diagnosis, immune infiltration and therapeutic drug in liver cancer by integrated analysis

Background

Liver cancer is one of the most common cancers and causes of cancer death worldwide. The objective was to elucidate novel hub genes which were benefit for diagnosis, prognosis, and targeted therapy in liver cancer via integrated analysis.

Methods

GSE84402, GSE101685, and GSE112791 were filtered from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified by using the GEO2R. The GO and KEGG pathway of DEGs were analyzed in the DAVID. PPI and TF network of the DEGs were constructed by using the STRING, TRANSFAC, and Harmonizome. The relationship between hub genes and prognoses in liver cancer was analyzed in UALCAN based on The Cancer Genome Atlas (TCGA). The diagnostic value of hub genes was evaluated by ROC. The relationship between hub genes and tumor-infiltrate lymphocytes was analyzed in TIMER. The protein levels of hub genes were verified in HPA. The interaction between the hub genes and the drug were identified in DGIdb.

Results

In total, 108 upregulated and 60 downregulated DEGs were enriched in 148 GO terms and 20 KEGG pathways. The mRNA levels and protein levels of CDK1, HMMR, PTTG1, and TTK were higher in liver cancer tissues compared to normal tissues, which showed excellent diagnostic and prognostic value. CDK1, HMMR, PTTG1, and TTK were positively correlated with tumor-infiltrate lymphocytes, which might involve tumor immune response. The CDK1, HMMR, and TTK had close interaction with anticancer agents.

Conclusions

The CDK1, HMMR, PTTG1, and TTK were hub genes in liver cancer; hence, they might be potential biomarkers for diagnosis, prognosis, and targeted therapy of liver cancer.

Identification of RNA binding protein interacting with circular RNA and hub candidate network for hepatocellular carcinoma

The interaction between RNA binding protein (RBP) and circular RNA (circRNA) is important for the regulation of tumor progression. This study aimed to identify the RBP-circRNA network in hepatocellular carcinoma (HCC). 22 differentially expressed (DE) circRNAs in HCC were screened out from Gene Expression Omnibus (GEO) database and their binding RBPs were predicted by Circular RNA Interactome. Among them, 17 DERBPs, which were commonly dysregulated in HCC from The Clinical Proteomic Tumor Analysis Consortium (CPTAC), The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects, were utilized to construct the RBP-circRNA network. Through survival analysis, we found TARDBP was the only prognostic RBP for HCC in CPTAC, TCGA and ICGC projects. High expression of TARDBP was correlated with high grade, advanced stage and low macrophage infiltration of HCC. Additionally, gene set enrichment analysis showed that dysregulated TARDBP might be involved in some pathways related to the HCC pathogenesis. Therefore, a hub RBP-circRNA network was generated based on TARDBP. RNA immunoprecipitation and RNA pull-down confirmed that hsa_circ_0004913 binds to TARDBP. These findings indicated certain RBP-circRNA regulatory network potentially involved in the pathogenesis of HCC, which provides novel insights into the mechanism study and biomarker identification for HCC.

Prognosis prediction of hepatocellular carcinoma after surgical resection based on serum metabolic profiling from gas chromatography-mass spectrometry

Comprehensive prognostic risk prediction of hepatocellular carcinoma (HCC) after surgical treatment is particularly important for guiding clinical decision-making and improving postoperative survival. Hence, we aimed to build prognostic models based on serum metabolomics data, and assess the prognostic risk of HCC within 5 years after surgical resection. A pseudotargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomics method was applied to analyze serum profiling of 78 HCC patients. Important metabolic features with discriminant ability were identified by a novel network-based metabolic feature selection method based on combinational significance index (N-CSI). Subsequently, phenylalanine and galactose were further identified to be relevant with mortality by the Cox regression analysis, while galactose and tyrosine were associated with recurrence and metastasis. Two models to predict risk of mortality (risk score of overall survival, RS_OS) and risk of recurrence and metastasis (risk score of disease-free survival, RS_DFS) were generated based on two panels of metabolites, respectively, which present favorable ability to predict prognosis of HCC, especially when combined with clinical staging system. The performance of models was further validated in an external independent cohort from 91 HCC patients. This study demonstrated that metabolomics is a powerful tool for risk screening of HCC prognosis.

Urea as a By-Product of Ammonia Metabolism Can Be a Potential Serum Biomarker of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is highly malignant; nearly half of the new cases and deaths are in China. The poor prognosis of HCC is mainly due to late diagnosis; many new biomarkers have been developed for HCC diagnosis. However, few markers are quickly translated into clinical practice; early and differential diagnosis of HCC from cirrhosis and/or hepatitis is still a clinical challenge. Metabolomics and biochemical methods were used to reveal specific serum biomarkers of HCC. Most of the elevated metabolites in HCC and HBV patients were overlapped compared with controls. Urea was the specifically elevated serum biomarker of HCC patients. Moreover, urea combined with AFP and CEA can improve the sensitivity of HCC diagnosis. The plasma ammonia of HCC patients was significantly higher than healthy controls. Co-culture cell model revealed normal liver cells cooperated with cancer cells to metabolize ammonia into urea. The urea metabolism in cancer cells marginally depended on the expression of CPS1. However, the expression of CPS1 did not change with ammonium chloride, which might regulate the urea cycle through enzyme activity. The urea cycle could detoxify high concentrations of ammonia to promote cancer cell proliferation. Therefore, urea was a by-product of ammonia metabolism and could be a potential serum biomarker for HCC. The combined application of metabolomics and biochemical methods can discover new biomarkers for the early diagnosis of HCC and be quickly applied to clinical diagnosis.

An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.

Methods

In this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.

Results

An inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.

Conclusion

A novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.

Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays

Background

The incidence and mortality rates of hepatocellular carcinoma are among the highest of all cancers all over the world. However the survival rates are relatively low due to lack of effective treatments. Efforts to elucidate the mechanisms of HCC and to find novel prognostic markers and therapeutic targets are ongoing. Here we tried to identify prognostic genes of HCC through co-expression network analysis.

Methods

We conducted weighted gene co-expression network analysis with a microarray dataset GSE14520 of HCC from Gene Expression Omnibus database and identified a hub module associated with HCC prognosis. Function enrichment analysis of the hub module was performed. Clinical information was analyzed to select candidate hub genes. The expression profiles and survival analysis of the selected genes were performed using additional datasets (GSE45267 and TCGA-LIHC) and the hub gene was identified. GSEA and in vitro experiments were conducted to further verify the function of the hub gene.

Results

Genes in the hub module were mostly involved in the metabolism pathway. Four genes (SLC27A5, SLC10A1, PCK2 and FMO4) from the module were identified as candidate hub genes according to correlation analysis with prognostic indicators. All these genes were significantly down-regulated in tumor tissues compared with non-tumor tissues in additional datasets. After survival analysis and network construction, SLC27A5 was selected as a prognostic marker. GSEA analysis and in vitro assays suggested that SLC27A5 downregulation promoted tumor cell migration via enhancing epithelial-mesenchymal transition.

Conclusion

SLC27A5 is a potential biomarker of HCC and SLC27A5 downregulation promoted HCC progression by enhancing EMT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01871-6.

Is the Rationale of Anatomical Liver Resection for Hepatocellular Carcinoma Universally Adoptable? A Hypothesis-Driven Review

Surgical resection is the first-line curative treatment modality for resectable hepatocellular carcinoma (HCC). Anatomical resection (AR), described as systematic removal of a liver segment confined by tumor-bearing portal tributaries, may improve survival by reducing the risk of tumor recurrence compared with non-AR. In this article, we propose the rationale for AR and its universal adoption by providing supporting evidence from the advanced understanding of a tumor microenvironment and accumulating clinical experiences of locoregional tumor ablation therapeutics. AR may be advantageous because it completely removes the en-bloc by interrupting tumor vascular supply and thus extirpates the spreading of tumor microthrombi, if they ever exist, within the supplying portal vein. However, HCC is a hypervascular tumor that can promote neoangiogenesis in the local tumor microenvironment, which in itself can break through the anatomical boundary within the liver and even retrieve nourishment from extrahepatic vessels, such as inferior phrenic or omental arteries. Additionally, increasing clinical evidence for locoregional tumor ablation therapies, such as radiofrequency ablation, predominantly performed as a non-anatomical approach, suggests comparable outcomes for surgical resection, particularly in small HCC and colorectal, hepatic metastases. Moreover, liver transplantation for HCC, which can be considered as AR of the whole liver followed by implantation of a new graft, is not universally free from post-transplant tumor recurrence. Overall, AR should not be considered the gold standard among all surgical resection methods. Surgical resection is fundamentally reliant on choosing the optimal margin width to achieve en-bloc tumor niche removal while balancing between oncological radicality and the preservation of postoperative liver function. The importance of this is to liberate surgical resilience in hepatocellular carcinoma. The overall success of HCC treatment is determined by the clearance of the theoretical niche. Developing biomolecular-guided navigation device/technologies may provide surgical guidance toward the total removal of microscopic tumor niche to achieve superior oncological outcomes.

Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)>1, Fold change (FC) > 1.5, and p < 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of >0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

Metabolic Profiling Identified a Novel Biomarker Panel for Metabolic Syndrome-Positive Hepatocellular Cancer

Metabolic syndrome (MetS) is an independent risk factor for hepatocellular cancer (HCC). Currently, there is no highly sensitive and specific biomarkers for HCC surveillance in MetS population. Metabolomics has been reported as a powerful technology for biomarker discovery. In the present study, we aimed to explore novel biomarkers with high sensitivity and specificity for MetS-positive [MetS(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in MetS(+) HCC individuals. Validation of the dysregulated metabolites by targeted metabolite analyses revealed that serum L-glutamic acid (L-glu), pipecolic acid (PA) and 7-methylguanine (7-mG) were increased in MetS(+) HCC compared to MetS group. Then a biomarker panel including L-glu, PA and alpha-fetoprotein (AFP) was identified as a novel biomarker for the diagnosis of MetS(+) HCC. Receiver operating characteristic (ROC) curve was drawn and the area under the ROC curve (AUC) was 0.87 for discriminating MetS(+) HCC from MetS group. The biomarker panel was capable of detecting small (AUC = 0.82) and early-stage (AUC = 0.78) tumors as well. Moreover, it exhibited great diagnostic performance (AUC = 0.93) for discriminating MetS(+) HCC from other MetS-associated cancers, including colorectal cancer and gastric cancer. Collectively, our study establishes a novel diagnostic tool for MetS(+) HCC.

Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics

Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.