Lysophosphatidylcholine acyltransferase 1 altered phospholipid composition and regulated hepatoma progression

Elucidating the role of S100A10 in CD8+ T cell exhaustion and HCC immune escape via the cPLA2 and 5-LOX axis

Hepatocellular carcinoma (HCC) is a common malignant tumor with a complex immune evasion mechanism posing a challenge to treatment. The role of the S100A10 gene in various cancers has garnered significant attention. This study aims to elucidate the impact of S100A10 on CD8+ T cell exhaustion via the cPLA2 and 5-LOX axis, thereby elucidating its role in immune evasion in HCC. By analyzing the HCC-related data from the GEO and TCGA databases, we identified differentially expressed genes associated with lipid metabolism and developed a prognostic risk model. Subsequently, through RNA-seq and PPI analyses, we determined vital lipid metabolism genes and downstream factors S100A10, ACOT7, and SMS, which were significantly correlated with CD8+ T cell infiltration. Given the most significant expression differences, we selected S100A10 for further investigation. Both in vitro and in vivo experiments were conducted, including co-culture experiments of CD8+ T cells with MHCC97-L cells, Co-IP experiments, and validation in an HCC mouse model. S100A10 was significantly overexpressed in HCC tissues and potentially regulates CD8+ T cell exhaustion and lipid metabolism reprogramming through the cPLA2 and 5-LOX axis. Silencing S100A10 could inhibit CD8+ T cell exhaustion, further suppressing immune evasion in HCC. S100A10 may activate the cPLA2 and 5-LOX axis, initiating lipid metabolism reprogramming and upregulating LTB4 levels, thus promoting CD8+ T cell exhaustion in HCC tissues, facilitating immune evasion by HCC cells, ultimately impacting the growth and migration of HCC cells. This research highlights the critical role of S100A10 via the cPLA2 and 5-LOX axis in immune evasion in HCC, providing new theoretical foundations and potential targets for diagnosing and treating HCC.

Phospholipid isotope tracing suggests β-catenin-driven suppression of phosphatidylcholine metabolism in hepatocellular carcinoma

Activating mutations in the CTNNB1 gene encoding β-catenin are among the most frequently observed oncogenic alterations in hepatocellular carcinoma (HCC). Profound alterations in lipid metabolism, including increases in fatty acid oxidation and transformation of the phospholipidome, occur in HCC with CTNNB1 mutations, but it is unclear what mechanisms give rise to these changes. We employed untargeted lipidomics and targeted isotope tracing to measure phospholipid synthesis activity in an inducible human liver cell line expressing mutant β-catenin, as well as in transgenic zebrafish with activated β-catenin-driven HCC. In both models, activated β-catenin expression was associated with large changes in the lipidome including conserved increases in acylcarnitines and ceramides and decreases in triglycerides. Lipid isotope tracing analysis in human cells revealed a reduction in phosphatidylcholine (PC) production rates as assayed by choline incorporation. We developed lipid isotope tracing analysis for zebrafish tumors and observed reductions in phosphatidylcholine synthesis by both the CDP-choline and PEMT pathways. The observed changes in the β-catenin-driven HCC phospholipidome suggest that zebrafish can recapitulate conserved features of HCC lipid metabolism and may serve as a model for identifying future HCC-specific lipid metabolic targets.

Role of blood metabolites in mediating the effect of gut microbiome on the mutated-RAS/BRAF metastatic colorectal cancer-specific survival

BACKGROUND

Recent studies have linked alterations in the gut microbiome and metabolic disruptions to the invasive behavior and metastasis of colorectal cancer (CRC), thus affecting patient prognosis. However, the specific relationship among gut microbiome, metabolite profiles, and mutated-RAS/BRAF metastatic colorectal cancer (M-mCRC) remains unclear. Furthermore, the potential mechanisms and prognostic implications of metabolic changes induced by gut microbiome alterations in patients with M-mCRC still need to be better understood.

METHODS

We conducted Mendelian randomization (MR) to evaluate the causal relationship of genetically predicted 196 gut microbiome features and 1400 plasma metabolites/metabolite ratios on M-mCRC-specific survival. Additionally, we identified significant gut microbiome-metabolites/metabolite ratio associations based on M-mCRC. Metabolite information was annotated, and functional annotation and pathway enrichment analyses were performed on shared proteins corresponding to significant metabolite ratios, aiming to reveal potential mechanisms by which gut microbiome influences M-mCRC prognosis via modulation of human metabolism.

RESULTS

We identified 11 gut microbiome features and 49 known metabolites/metabolite ratios correlated with M-mCRC-specific survival. Furthermore, we identified 17 gut microbiome-metabolite/metabolite ratio associations specific to M-mCRC, involving eight lipid metabolites and three bilirubin degradation products. The shared proteins corresponding to significant metabolite ratios were predominantly localized within the integral component of the membrane and exhibited enzymatic activities such as glucuronosyltransferase and UDP-glucuronosyltransferase, crucial in processes such as glucuronidation, bile secretion, and lipid metabolism. Moreover, these proteins were significantly enriched in pathways related to ascorbate and aldarate metabolism, pentose and glucuronate interconversions, steroid hormone biosynthesis, and bile secretion.

CONCLUSION

Our study offers novel insights into the potential mechanisms underlying the impact of the gut microbiome on the prognosis of M-mCRC. These findings serve as a meaningful reference for exploring potential therapeutic targets and strategies in the future.

LPCAT1 Facilitates Keratinocyte Hyperproliferation and Skin Inflammation in Psoriasis by Regulating GLUT3

Psoriasis is a chronic and relapsing inflammatory skin disorder characterized by keratinocyte hyperproliferation and immune cell infiltration. LPCAT1 has been identified as a cancer promoter in cutaneous squamous cell carcinoma by us, yet its role in psoriasis remains elusive. In this study, we report that LPCAT1 is highly expressed in psoriatic skin lesions. LPCAT1 promotes keratinocyte hyperproliferation and enhances the secretion of IL-1β, IL-6, CXCL10, CCL20, S100A9, and platelet-activating factor. In psoriasiform keratinocytes, LPCAT1 promotes proliferation and inflammatory mediator production by activating protein kinase B/NF-κB and signal transducer and activator of transcription 3 signaling pathways. Furthermore, LPCAT1 inhibition attenuated epidermal hyperplasia and relieved skin inflammation in imiquimod-treated mice. Importantly, we identify the glucose transporter GLUT3, a recently reported promising target to mitigate T helper 17 cell-mediated inflammatory diseases, as a critical downstream effector of LPCAT1. GLUT3 deficiency impaired the proliferation and inflammation of psoriatic keratinocytes. LPCAT1 regulates GLUT3 in keratinocytes through NF-κB/signal transducer and activator of transcription 3 signaling, enhancing keratinocyte glycolysis and promoting proproliferative and proinflammatory effects. In addition, suppressing GLUT3 in mice alleviated imiquimod-induced dermatitis. Taken together, our study indicates the critical role of the LPCAT1-GLUT3 axis in psoriasis pathogenesis and proposes LPCAT1 or GLUT3 as a potential therapeutic target for psoriasis.

Comprehensive profiling of lipid metabolic reprogramming expands precision medicine for HCC

BACKGROUND AND AIMS

Liver HCC is the second leading cause of cancer-related deaths worldwide. The heterogeneity of this malignancy is driven by a wide range of genetic alterations, leading to a lack of effective therapeutic options. In this study, we conducted a systematic multi-omics characterization of HCC to uncover its metabolic reprogramming signature.

APPROACH AND RESULTS

Through a comprehensive analysis incorporating transcriptomic, metabolomic, and lipidomic investigations, we identified significant changes in metabolic pathways related to glucose flux, lipid oxidation and degradation, and de novo lipogenesis in HCC. The lipidomic analysis revealed abnormal alterations in glycerol-lipids, phosphatidylcholine, and sphingolipid derivatives. Machine-learning techniques identified a panel of genes associated with lipid metabolism as common biomarkers for HCC across different etiologies. Our findings suggest that targeting phosphatidylcholine with saturated fatty acids and long-chain sphingolipid biosynthesis pathways, particularly by inhibiting lysophosphatidylcholine acyltransferase 1 ( LPCAT1 ) and ceramide synthase 5 ( CERS5 ) as potential therapeutic strategies for HCC in vivo and in vitro. Notably, our data revealed an oncogenic role of CERS5 in promoting tumor progression through lipophagy.

CONCLUSIONS

In conclusion, our study elucidates the metabolic reprogramming nature of lipid metabolism in HCC, identifies prognostic markers and therapeutic targets, and highlights potential metabolism-related targets for therapeutic intervention in HCC.

ACSL4-Mediated Membrane Phospholipid Remodeling Induces Integrin β1 Activation to Facilitate Triple-Negative Breast Cancer Metastasis

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and has a poor prognosis and a high propensity to metastasize. Lipid metabolism has emerged as a critical regulator of tumor progression and metastasis in other cancer types. Characterization of the lipid metabolic features of TNBC could provide important insights into the drivers of TNBC metastasis. Here, we showed that metastatic TNBC tumors harbor more unsaturated phospholipids, especially long-chain polyunsaturated fatty acids, at the sn-2 position of phosphatidylcholine and phosphatidylethanolamine compared with primary tumors. Metastatic TNBC tumors upregulated ACSL4, a long-chain polyunsaturated acyl-CoA synthetase that drives the preferential incorporation of polyunsaturated fatty acids into phospholipids, resulting in the alteration of membrane phospholipid composition and properties. Moreover, ACSL4-mediated phospholipid remodeling of the cell membrane induced lipid-raft localization and activation of integrin β1 in a CD47-dependent manner, which led to downstream focal adhesion kinase phosphorylation that promoted metastasis. Importantly, pharmacologic inhibition of ACSL4 suppressed tumor growth and metastasis and increased chemosensitivity in TNBC models in vivo. These findings indicate that ACSL4-mediated phospholipid remodeling enables TNBC metastasis and can be inhibited as a potential strategy to improve the efficacy of chemotherapy in TNBC.

SIGNIFICANCE

ACSL4 upregulation in triple-negative breast cancer alters cell membrane phospholipid composition to increase integrin β1 activation and drive metastasis, indicating that targeting ACSL4 could potentially block metastasis and improve patient outcomes.

Tailoring therapeutics via a systematic beneficial elements comparison between photosynthetic bacteria-derived OMVs and extruded nanovesicles

Photosynthetic bacteria (PSB) has shown significant potential as a drug or drug delivery system owing to their photothermal capabilities and antioxidant properties. Nevertheless, the actualization of their potential is impeded by inherent constraints, including their considerable size, heightened immunogenicity and compromised biosafety. Conquering these obstacles and pursuing more effective solutions remains a top priority. Similar to extracellular vesicles, bacterial outer membrane vesicles (OMVs) have demonstrated a great potential in biomedical applications. OMVs from PSB encapsulate a rich array of bioactive constituents, including proteins, nucleic acids, and lipids inherited from their parent cells. Consequently, they emerge as a promising and practical alternative. Unfortunately, OMVs have suffered from low yield and inconsistent particle sizes. In response, bacteria-derived nanovesicles (BNVs), created through controlled extrusion, adeptly overcome the challenges associated with OMVs. However, the differences, both in composition and subsequent biological effects, between OMVs and BNVs remain enigmatic. In a groundbreaking endeavor, our study meticulously cultivates PSB-derived OMVs and BNVs, dissecting their nuances. Despite minimal differences in morphology and size between PSB-derived OMVs and BNVs, the latter contains a higher concentration of active ingredients and metabolites. Particularly noteworthy is the elevated levels of lysophosphatidylcholine (LPC) found in BNVs, known for its ability to enhance cell proliferation and initiate downstream signaling pathways that promote angiogenesis and epithelialization. Importantly, our results indicate that BNVs can accelerate wound closure more effectively by orchestrating a harmonious balance of cell proliferation and migration within NIH-3T3 cells, while also activating the EGFR/AKT/PI3K pathway. In contrast, OMVs have a pronounced aptitude in anti-cancer efforts, driving macrophages toward the M1 phenotype and promoting the release of inflammatory cytokines. Thus, our findings not only provide a promising methodological framework but also establish a definitive criterion for discerning the optimal application of OMVs and BNVs in addressing a wide range of medical conditions.

LPCAT1-mediated membrane phospholipid remodelling promotes ferroptosis evasion and tumour growth

The mechanisms underlying the dynamic remodelling of cellular membrane phospholipids to prevent phospholipid peroxidation-induced membrane damage and evade ferroptosis, a non-apoptotic form of cell death driven by iron-dependent lipid peroxidation, remain poorly understood. Here we show that lysophosphatidylcholine acyltransferase 1 (LPCAT1) plays a critical role in ferroptosis resistance by increasing membrane phospholipid saturation via the Lands cycle, thereby reducing membrane levels of polyunsaturated fatty acids, protecting cells from phospholipid peroxidation-induced membrane damage and inhibiting ferroptosis. Furthermore, the enhanced in vivo tumour-forming capability of tumour cells is closely associated with the upregulation of LPCAT1 and emergence of a ferroptosis-resistant state. Combining LPCAT1 inhibition with a ferroptosis inducer synergistically triggers ferroptosis and suppresses tumour growth. Therefore, our results unveil a plausible role for LPCAT1 in evading ferroptosis and suggest it as a promising target for clinical intervention in human cancer.

Distinctly altered lipid components in hepatocellular carcinoma relate to impaired T cell-dependent antitumor immunity

BACKGROUND AND AIMS

T cells are master effectors of anti-tumor immunity in cancer. Recent studies suggest that altered lipid metabolism imposed by the tumor microenvironment constrains anti-tumor immunity. However, the tumor-associated lipid species changes that dampen T cell ability to control tumor progression are not fully understood. Here, we plan to clarify the influences of distinctly altered lipid components in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) on T-cell function, aiming to seek lipid metabolic targets for improving T cell anti-tumor effects.

METHODS

Tumor tissues and non-tumor liver from HCC patients were collected for RNA-sequencing, lipid profiling and T cell characterizing, followed by correlation analysis. Additionally, the effects of significantly changed lipid components on anti-tumor potential of T cells were tested by in vitro cell experiments and/or in vivo tumor inoculated model.

RESULTS

Altered lipid metabolism coincides with impaired T cell response in HBV-related HCC. Characteristic lipid composition, significantly marked by accumulation of long-chain acylcarnitines (LCACs) and reduction of lysophosphatidylcholines (LPCs), are found in the tumor tissue. Notably, LCACs accumulated are associated with T cells exhaustion and deficient functionality, while LPCs correlate to anti-tumor effects of T cells. In particular, supplement of LPCs, including LPC (20:0) and LPC (22:0), directly promote the activation and IFN-γ secretion of T cells in vitro, and suppress tumor growth in vivo.

CONCLUSIONS

Our study highlights the distinctly changed lipid components closely related to T cell dysregulation in HCC, and suggests a promising strategy by decreasing LCACs and increasing LPCs for anti-tumor immunotherapy.

PIM1 drives lipid droplet accumulation to promote proliferation and survival in prostate cancer

Lipid droplets (LDs) are dynamic organelles with a neutral lipid core surrounded by a phospholipid monolayer. Solid tumors exhibit LD accumulation, and it is believed that LDs promote cell survival by providing an energy source during energy deprivation. However, the precise mechanisms controlling LD accumulation and utilization in prostate cancer are not well known. Here, we show peroxisome proliferator-activated receptor α (PPARα) acts downstream of PIM1 kinase to accelerate LD accumulation and promote cell proliferation in prostate cancer. Mechanistically, PIM1 inactivates glycogen synthase kinase 3 beta (GSK3β) via serine 9 phosphorylation. GSK3β inhibition stabilizes PPARα and enhances the transcription of genes linked to peroxisomal biogenesis (PEX3 and PEX5) and LD growth (Tip47). The effects of PIM1 on LD accumulation are abrogated with GW6471, a specific inhibitor for PPARα. Notably, LD accumulation downstream of PIM1 provides a significant survival advantage for prostate cancer cells during nutrient stress, such as glucose depletion. Inhibiting PIM reduces LD accumulation in vivo alongside slow tumor growth and proliferation. Furthermore, TKO mice, lacking PIM isoforms, exhibit suppression in circulating triglycerides. Overall, our findings establish PIM1 as an important regulator of LD accumulation through GSK3β-PPARα signaling axis to promote cell proliferation and survival during nutrient stress.

Integrated plasma metabolomics and lipidomics profiling highlights distinctive signature of hepatocellular carcinoma in HCV patients

BACKGROUND

Early diagnosis of hepatocellular carcinoma (HCC) is essential towards the improvement of prognosis and patient survival. Circulating markers such as α-fetoprotein (AFP) and micro-RNAs represent useful tools but still have limitations. Identifying new markers can be fundamental to improve both diagnosis and prognosis. In this approach, we harness the potential of metabolomics and lipidomics to uncover potential signatures of HCC.

METHODS

A combined untargeted metabolomics and lipidomics plasma profiling of 102 HCV-positive patients was performed by HILIC and RP-UHPLC coupled to Mass Spectrometry. Biochemical parameters of liver function (AST, ALT, GGT) and liver cancer biomarkers (AFP, CA19.9 e CEA) were evaluated by standard assays.

RESULTS

HCC was characterized by an elevation of short and long-chain acylcarnitines, asymmetric dimethylarginine, methylguanine, isoleucylproline and a global reduction of lysophosphatidylcholines. A supervised PLS-DA model showed that the predictive accuracy for HCC class of metabolomics and lipidomics was superior to AFP for the test set (100.00% and 94.40% vs 55.00%). Additionally, the model was applied to HCC patients with AFP values < 20 ng/mL, and, by using only the top 20 variables selected by VIP scores achieved an Area Under Curve (AUC) performance of 0.94.

CONCLUSION

These exploratory findings highlight how metabo-lipidomics enables the distinction of HCC from chronic HCV conditions. The identified biomarkers have high diagnostic potential and could represent a viable tool to support and assist in HCC diagnosis, including AFP-negative patients.

Nuclear respiratory factor 1 drives hepatocellular carcinoma progression by activating LPCAT1-ERK1/2-CREB axis

BACKGROUND

Nuclear respiratory factor 1 (NRF1) is a transcription factor that participates in several kinds of tumor, but its role in hepatocellular carcinoma (HCC) remains elusive. This study aims to explore the role of NRF1 in HCC progression and investigate the underlying mechanisms.

RESULTS

NRF1 was overexpressed and hyperactive in HCC tissue and cell lines and high expression of NRF1 indicated unfavorable prognosis of HCC patients. NRF1 promoted proliferation, migration and invasion of HCC cells both in vitro and in vivo. Mechanistically, NRF1 activated ERK1/2-CREB signaling pathway by transactivating lysophosphatidylcholine acyltransferase 1 (LPCAT1), thus promoting cell cycle progression and epithelial mesenchymal transition (EMT) of HCC cells. Meanwhile, LPCAT1 upregulated the expression of NRF1 by activating ERK1/2-CREB signaling pathway, forming a positive feedback loop.

CONCLUSIONS

NRF1 is overexpressed in HCC and promotes HCC progression by activating LPCAT1-ERK1/2-CREB axis. NRF1 is a promising therapeutic target for HCC patients.

Phospholipid isotope tracing reveals β-catenin-driven suppression of phosphatidylcholine metabolism in hepatocellular carcinoma

Background and Aims

Activating mutations in the CTNNB1 gene encoding β-catenin are among the most frequently observed oncogenic alterations in hepatocellular carcinoma (HCC). HCC with CTNNB1 mutations show profound alterations in lipid metabolism including increases in fatty acid oxidation and transformation of the phospholipidome, but it is unclear how these changes arise and whether they contribute to the oncogenic program in HCC.

Methods

We employed untargeted lipidomics and targeted isotope tracing to quantify phospholipid production fluxes in an inducible human liver cell line expressing mutant β-catenin, as well as in transgenic zebrafish with activated β-catenin-driven HCC.

Results

In both models, activated β-catenin expression was associated with large changes in the lipidome including conserved increases in acylcarnitines and ceramides and decreases in triglycerides. Lipid flux analysis in human cells revealed a large reduction in phosphatidylcholine (PC) production rates as assayed by choline tracer incorporation. We developed isotope tracing lipid flux analysis for zebrafish and observed similar reductions in phosphatidylcholine synthesis flux accomplished by sex-specific mechanisms.

Conclusions

The integration of isotope tracing with lipid abundances highlights specific lipid class transformations downstream of β-catenin signaling in HCC and suggests future HCC-specific lipid metabolic targets.

CrMP-Sol database: classification, bioinformatic analyses and comparison of cancer-related membrane proteins and their water-soluble variant designs

Membrane proteins are critical mediators for tumor progression and present enormous therapeutic potentials. Although gene profiling can identify their cancer-specific signatures, systematic correlations between protein functions and tumor-related mechanisms are still unclear. We present here the CrMP-Sol database ( https://bio-gateway.aigene.org.cn/g/CrMP ), which aims to breach the gap between the two. Machine learning was used to extract key functional descriptions for protein visualization in the 3D-space, where spatial distributions provide function-based predictive connections between proteins and cancer types. CrMP-Sol also presents QTY-enabled water-soluble designs to facilitate native membrane protein studies despite natural hydrophobicity. Five examples with varying transmembrane helices in different categories were used to demonstrate the feasibility. Native and redesigned proteins exhibited highly similar characteristics, predicted structures and binding pockets, and slightly different docking poses against known ligands, although task-specific designs are still required for proteins more susceptible to internal hydrogen bond formations. The database can accelerate therapeutic developments and biotechnological applications of cancer-related membrane proteins.

Deep dissection of stemness-related hierarchies in hepatocellular carcinoma

BACKGROUND

Increasing evidence suggests that hepatocellular carcinoma (HCC) stem cells (LCSCs) play an essential part in HCC recurrence, metastasis, and chemotherapy and radiotherapy resistance. Multiple studies have demonstrated that stemness-related genes facilitate the progression of tumors. However, the mechanism by which stemness-related genes contribute to HCC is not well understood. Here, we aim to construct a stemness-related score (SRscores) model for deeper analysis of stemness-related genes, assisting with the prognosis and individualized treatment of HCC patients.Further, we found that the gene LPCAT1 was highly expressed in tumor tissues by immunohistochemistry, and sphere-forming assay revealed that knockdown of LPCAT1 inhibited the sphere-forming ability of hepatocellular carcinoma cells.

METHODS

We used the TCGA-LIHC dataset to screen stemness-related genes of HCC from the MSigDB database. Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. We also examined risk scores for different cell types using single-cell RNA sequencing data and correlated transcription factor activity in cancer stem cells with SRscores genes. Finally, we tested core marker expression and biological functions.

RESULTS

Patients can be divided into two subtypes (Cluster1 and Cluster2) based on the TCGA-LIHC dataset's identification of 11 stemness-related genes. Additionally, a SRscores was developed based on subtypes. Cluster2 and the group with the lowest SRscores had superior survival and immunotherapy response than Cluster1 and the group with the highest SRscores. The group with a high SRscores was significantly more enriched in classical tumor pathways than the group with a low SRscores. Multiple transcription factors and SRscores genes are correlated. The core gene LPCAT1 is highly expressed in rat liver cancer tissues and promotes tumor cell sphere formation.

CONCLUSION

A SRscores model can be utilized to predict the prognosis of HCC patients as well as their response to immunotherapy.

Prognosis and therapeutic benefits prediction based on NK cell marker genes through single-cell RNA-seq with integrated bulk RNA-seq analysis for hepatocellular carcinoma

Tumor-infiltrating immune cells greatly participate in regulating tumorigenesis and metastasis of hepatocellular carcinoma (HCC). Natural killer cell, as an important role of innate immunity, plays an indispensable role in antitumor immunity and regulate tumor development. In this study, we firstly identified 251 NK cell marker genes of HCC based on single-cell RNA sequencing data. Subsequently, an NK cell marker genes-related prognostic signature (NKPS) was developed in the cancer genome atlas (TCGA) cohort for risk stratification and prognosis prediction. The predictive value of the NKPS in prognosis was well validated in different clinical subgroups and three external datasets (ICGC-LIHC cohort, GSE14520 cohort and Guilin cohort). Moreover, multivariate analysis revealed the independent prognostic value of NKPS for OS in HCC. Further functional analysis indicated the NKPS was associated with basic cellular processes, that may contribute to the development and progression of HCC. Thereafter, immune characteristics as well as the therapeutic benefits in NKPS risk score-defined subgroups were analyzed. Patients with low-risk score exhibited immune-active status, manifested as higher immune scores, more infiltration of CD8+ T cells and macrophage M1, and higher T-cell receptor (TCR) richness and diversity. Remarkably, the NKPS was negatively correlated with immunotherapy response-related signatures. In addition, the low-risk group exhibited significantly improved therapeutic benefits, either from immunotherapy or traditional chemotherapy and target therapy. Overall, the NKPS showed an excellent predictive value for prognosis and therapeutic responses for HCC, which might also provide novel insights into better HCC management strategies.

Hypoxia-based classification and prognostic signature for clinical management of hepatocellular carcinoma

OBJECTIVE

Intratumoral hypoxia is an essential feature of hepatocellular carcinoma (HCC). Herein, we investigated the hypoxia-based heterogeneity and relevant clinical implication in HCC.

METHODS

Three HCC cohorts: TCGA-LIHC, LICA-FR, and LIRI-JP were retrospectively gathered. Consensus clustering analysis was utilized for hypoxia-based classification based upon transcriptome of hypoxia genes. Through LASSO algorithm, a hypoxia-relevant prognostic signature was built. Immunotherapeutic response was inferred through analyzing immune checkpoints, T cell inflamed score, TIDE score, and TMB score. RNF145 expression was measured in normoxic or hypoxic HCC cells. In RNF145-knockout cells, CCK-8, TUNEL, and scratch tests were implemented.

RESULTS

HCC patients were classified into two hypoxia subtypes, with more advanced stages and poorer prognosis in cluster2 than cluster1. The heterogeneity in tumor infiltrating immune cells and genetic mutation was found between subtypes. The hypoxia-relevant prognostic model was proposed, composed of ANLN, CBX2, DLGAP5, FBLN2, FTCD, HMOX1, IGLV1-44, IL33, LCAT, LPCAT1, MKI67, PFN2, RNF145, S100A9, and SPP1). It was predicted that high-risk patients presented worse prognosis with an independent and reliable manner. Based upon high expression of immune checkpoints (CD209, CTLA4, HAVCR2, SIRPA, TNFRSF18, TNFRSF4, and TNFRSF9), high T cell inflamed score, low TIDE score and high TMB score, high-risk patients might respond to immunotherapy. Experimental validation showed that RNF145 was upregulated in hypoxic HCC cells, RNF145 knockdown attenuated proliferation and migration, but aggravated apoptosis in HCC cells.

CONCLUSION

Altogether, the hypoxia-based classification and prognostic signature might be useful for prognostication and guiding treatment of HCC.

The validation and clinical significance of LPCAT1 down-regulation in acute myeloid leukemia

BACKGROUND

Overexpression of lysophosphatidylcholine acyltransferase 1 (LPCAT1) has been found in various solid cancers and is associated with disease progression, metastasis, and recurrence. However, the expression pattern of LPCAT1 in acute myeloid leukemia (AML) bone marrow remains unknown. The present study aimed to compare LPCAT1 expression differences in bone marrow samples from AML patients and healthy controls and assess the clinical relevance of LPCAT1 in AML.

METHODS AND RESULTS

LPCAT1 expression in bone marrow was significantly lower in AML than in healthy controls predicted by public databases. Furthermore, real-time quantitative PCR (RQ-PCR) validated that LPCAT1 expression in bone marrow was significantly down-regulated in AML compared to healthy controls [0.056 (0.000-0.846) vs 0.253 (0.031-1.000)]. The DiseaseMeth version 2.0 and The Cancer Genome Atlas analysis revealed that the LPCAT1 promoter was hypermethylated in AML, and there was a strong negative correlation between LPCAT1 expression and methylation (R = - 0.610, P < 0.001). RQ-PCR revealed that the frequency of LPCAT1 low expression was lower in the FAB-M4/M5 subtype than in the other subtypes (P = 0.018). The ROC curve revealed that LPCAT1 expression could serve as a potential diagnostic marker for differentiating AML from controls with an area under the ROC curve of 0.819 (95% CI 0.743-0.894, P < 0.001). In cytogenetically normal AML, patients with LPCAT1 low expression had significantly longer overall survival than those without LPCAT1 low expression (median 19 versus 5.5 months, P = 0.036).

CONCLUSIONS

LPCAT1 is down-regulated in AML bone marrow, and LPCAT1 down-regulation could be used as a potential biomarker for AML diagnosis and prognosis.

The prognostic value of the GPAT/AGPAT gene family in hepatocellular carcinoma and its role in the tumor immune microenvironment

Background

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related death worldwide. Hepatocellular carcinoma accounts for an estimated 90% of all liver cancers. Many enzymes of the GPAT/AGPAT family are required for the synthesis of triacylglycerol. Expression of AGPAT isoenzymes has been reported to be associated with an increased risk of tumorigenesis or development of aggressive phenotypes in a variety of cancers. However, whether members of the GPAT/AGPAT gene family also influence the pathophysiology of HCC is unknown.

Methods

Hepatocellular carcinoma datasets were obtained from the TCGA and ICGC databases. Predictive models related to the GPAT/AGPAT gene family were constructed based on LASSO-Cox regression using the ICGC-LIRI dataset as an external validation cohort. Seven immune cell infiltration algorithms were used to analyze immune cell infiltration patterns in different risk groups. IHC, CCK-8, Transwell assay, and Western blotting were used for in vitro validation.

Results

Compared with low-risk patients, high-risk patients had shorter survival and higher risk scores. Multivariate Cox regression analysis showed that risk score was a significant independent predictor of overall survival (OS) after adjustment for confounding clinical factors (p < 0.001). The established nomogram combined risk score and TNM staging to accurately predict survival at 1, 3, and 5 years in patients with HCC with AUC values of 0.807, 0.806, and 0.795, respectively. This risk score improved the reliability of the nomogram and guided clinical decision-making. In addition, we comprehensively analyzed immune cell infiltration (using seven algorithms), response to immune checkpoint blockade, clinical relevance, survival, mutations, mRNA expression-based stemness index, signaling pathways, and interacting proteins related to the three core genes of the prognostic model (AGPAT5, LCLAT1, and LPCAT1). We also performed preliminary validation of the differential expression, oncological phenotype, and potential downstream pathways of the three core genes by IHC, CCK-8, Transwell assay, and Western blotting.

Conclusion

These results improve our understanding of the function of GPAT/AGPAT gene family members and provide a reference for prognostic biomarker research and individualized treatment of HCC.

sn-1 Specificity of Lysophosphatidylcholine Acyltransferase-1 Revealed by a Mass Spectrometry-Based Assay

Lysophosphatidylcholine acyltransferase-1 (LPCAT1) plays a critical role in the remodeling of phosphatidylcholines (PCs) in cellular lipidome. However, evidence is scarce regarding its sn-selectivity, viz. the preference of assembling acyl-Coenzyme A (CoA) at the C1 or C2-hydroxyl on a glycerol backbone because of difficulty to quantify the thus-formed PC sn-isomers. We have established a multiplexed assay to measure both sn- and acyl-chain selectivity of LPCAT1 toward a mixture of acyl-CoAs by integrating isomer-resolving tandem mass spectrometry. Our findings reveal that LPCAT1 shows exclusive sn-1 specificity regardless of the identity of acyl-CoAs. We further confirm that elevated PC 18 : 1/16:0 relative to its sn-isomer results from an increased expression of LPCAT1 in human hepatocellular carcinoma (HCC) tissue as compared to normal liver tissue. MS imaging via desorption electrospray ionization of PC 18 : 1/16:0 thus enables visualization of HCC margins in human liver tissue at a molecular level.

iPLA2β-Null Mice Show HCC Protection by an Induction of Cell-Cycle Arrest after Diethylnitrosamine Treatment

Group VIA phospholipase A2 (iPLA2β) play diverse biological functions in epithelial cells and macrophages. Global deletion in iPLA2β-null (KO) mice leads to protection against hepatic steatosis in non-alcoholic fatty liver disease, in part, due to the replenishment of the loss of hepatocellular phospholipids. As the loss of phospholipids also occurs in hepatocellular carcinoma (HCC), we hypothesized that global deletion in KO mice may lead to protection against HCC. Here, HCC induced by diethylnitrosamine (DEN) was chosen because DEN causes direct injury to the hepatocytes. Male wild-type (WT) and KO mice at 3-5 weeks of age (12-13 mice/group) were subjected to a single intraperitoneal treatment with 10 mg/kg DEN, and mice were killed 12 months later. Analyses of histology, plasma cytokines, and gene expression were performed. Due to the low-dose DEN used, we observed a liver nodule in 3 of 13 WT and 2 of 12 KO mice. Only one DEN-treated WT mouse was confirmed to have HCC. DEN-treated KO mice did not show any HCC but showed suppressed hepatic expression of cell-cycle cyclinD2 and BCL2 as well as inflammatory markers IL-1β, IL-10, and VCAM-1. Notably, DEN-treated KO mice showed increased hepatic necrosis and elevated levels of plasma lactate dehydrogenase suggesting an exacerbation of liver injury. Thus, global iPLA2β deficiency in DEN-treated mice rendered HCC protection by an induction of cell-cycle arrest. Our results suggest the role of iPLA2β inhibition in HCC treatment.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t-tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene co-expression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% CI: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

Identifying a Novel Endoplasmic Reticulum-Related Prognostic Model for Hepatocellular Carcinomas

From the standpoint of the ER (endoplasmic reticulum), we were interested in identifying hub genes that impact clinical prognosis for HCC (hepatocellular carcinoma) patients and developing an ER-related prognostic model. Using TCGA-LIHC (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) and GSE14520 datasets, we conducted a series of analyses, which included differential gene screening, clinical prognostic analysis, Lasso regression, nomogram prediction, tumour clustering, gene functional enrichment, and tumour infiltration of immune cells. Following our screening for ER-related genes ( $n=1975$ ), we conducted a Lasso regression model to obtain five hub genes, KPNA2, FMO3, SPP1, KIF2C, and LPCAT1, using TCGA-LIHC as a training set. According to risk scores, HCC samples within either the TCGG-LIHC or GSE14520 cohort were categorized into high- and low-risk groups. Compared to the high-risk group of HCC patients, patients in the low-risk group had a better prognosis of OS (overall survival) or RFS (relapse-free survival). For TCGA-LIHC training set, with the factors of risk score, stage, age, and sex, we plotted a nomogram for 1-, 3-, and 5-year survival predictions. Our model demonstrated better clinical validity in both TCGA-LIHC and GSE14520 cohorts. Additionally, events related to biological enzyme activity, biological metabolic processes, or the cell cycle were associated with the prognostic risk of ER. Furthermore, two HCC prognosis-associated tumour clusters were identified by ER hub gene-based consensus clustering. Our findings indicated a link between ER prognostic signature-related high/low risk and tumour infiltration levels of several immune cells, such as “macrophages M2/M0” and “regulatory T cells (Tregs).” Overall, we developed a novel ER-related clinical prognostic model for HCC patients.

Overexpression of LPCAT1 enhances endometrial cancer stemness and metastasis by changing lipid components and activating the TGF/β-Smad2/3 signaling pathway

The incidence of endometrial cancer (EC) increases annually and tends to occur in younger women. A particularly important relationship exists between EC and metabolic disorders. As one of the most important components of lipid metabolism, phospholipids play an indispensable role in metabolic balance. LPCAT1 is a key enzyme regulating phospholipid metabolism. In this study, we perform further investigations to seek mechanistic insight of LPCAT1 in EC. Our results demonstrate that silencing of LPCAT1 inhibits the growth of endometrial cancer, while overexpression of LPCAT1 results in enhanced stemness and metastasis in endometrial cancer cell lines. Meanwhile, the contents of various phospholipids including phosphatidylethanolamine (PE), phosphatidylcholine (PC), and triglyceride (TG) change significantly after overexpression of LPCAT1. In addition, through RNA-sequencing and western blot analysis, we observe that the TGF-β/Smad2/3 signaling pathway is of great importance in the tumor-promoting function of LPCAT1. LPCAT1 promotes the expressions of stem cell-related transcription factors and epithelial-mesenchymal transition (EMT) related proteins through the TGF-β/Smad2/3 signaling pathway. Moreover, we find that TSI-01, which can inhibit the activity of LPCAT1, is able to restrain the proliferation of EC cell lines and promote cell apoptosis. Collectively, we demonstrate that LPCAT1 enhances the stemness and metastasis of EC by activating the TGF-β/Smad2/3 signaling pathway and that TSI-01 may have potential use for the treatment of EC.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

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.

Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation

A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix.

Lysophosphatidylcholine acyltransferase 1 promotes epithelial-mesenchymal transition of hepatocellular carcinoma via the Wnt/β-catenin signaling pathway

INTRODUCTION AND OBJECTIVES

Hepatocellular carcinoma (HCC) is one of the most malignant digestive tumors, and its insidious onset and rapid progression are the main reasons for the difficulty in effective treatment. Lysophosphatidylcholine acyltransferase 1 (LPCAT1) is a key enzyme that regulates phospholipid metabolism of the cell membrane. However, the mechanism by which LPCAT1 regulates HCC metastasis remains unknown. This study aimed to explore its biological function and potential mechanisms concerning migration and invasion in HCC.

MATERIALS AND METHODS

LPCAT1 expression in HCC tissues and its association with clinical outcomes were investigated by western blotting and bioinformatic methods, respectively. The role of LPCAT1 in migration and invasion was assessed via Transwell assays. The expression pattern of epithelial-mesenchymal transition (EMT) markers was quantified by western blotting. The biological behaviors of LPCAT1 in vivo were evaluated using xenograft tumor models and caudal vein metastatic models. Signaling pathways related to LPCAT1 were predicted using gene set enrichment analysis (GSEA) and further confirmed by western blotting.

RESULTS

LPCAT1 expression was significantly upregulated in HCC tissues and indicated a poor prognosis of HCC patients. Several EMT-related markers were found to be regulated by LPCAT1. HCC cells overexpressing LPCAT1 exhibited remarkably high migration and invasion capacities, upregulated expression of mesenchymal markers and reduced E-cadherin expression. In vivo, LPCAT1 promoted HCC pulmonary metastasis. Furthermore, the Wnt/β-catenin signaling pathway was confirmed to be activated by LPCAT1.

CONCLUSIONS

LPCAT1 could serve as a promising biomarker of HCC and as a novel therapeutic target for the treatment of metastatic HCC.

Mechanism of cancer stemness maintenance in human liver cancer

Primary liver cancer mainly includes the following four types: hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), hepatoblastoma (HB), and combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA). Recent studies have indicated that there are differences in cancer stem cell (CSC) properties among different types of liver cancer. Liver cancer stem cells (LCSCs), also called liver tumor-initiating cells, have been viewed as drivers of tumor initiation and metastasis. Many mechanisms and factors, such as mitophagy, mitochondrial dynamics, epigenetic modifications, the tumor microenvironment, and tumor plasticity, are involved in the regulation of cancer stemness in liver cancer. In this review, we analyze cancer stemness in different liver cancer types. Moreover, we further evaluate the mechanism of cancer stemness maintenance of LCSCs and discuss promising treatments for eradicating LCSCs.

LPCAT1 functions as a novel prognostic molecular marker in hepatocellular carcinoma

This study aimed to investigate the relationships between LPCAT1 expression and clinicopathologic parameters of hepatocellular carcinoma (HCC), further, to explore the effect of LPCAT1 on overall survival (OS) in patients with HCC, and its possible mechanism. Bioinformatics analysis using high throughput RNA-sequencing data from TCGA was utilized to explore the differential expression of LPCAT1 between normal and tumor tissues, and the associations between LPCAT1 expression and clinicopathological parameters. Survival analyses and subgroup survival analyses were utilized to elucidate the effect of LPCAT1 on OS in patients with HCC. Univariate analysis and multivariate analysis were used to investigate the prognostic factors. Potential LPCAT1 related tumor genes were identified by the methodology of differentially expressed genes (DEGs) screening. GO term enrichment analysis, KEGG pathway analysis and the PPI network were used to explore the potential mechanism. LPCAT1 was significantly overexpressed in HCC tumor tissues compared with normal tissues. The LPCAT1 expression was related to tumor grade, ECOG score, AFP and TNM stage, with P values of 0.000, 0.000, 0.007 and 0.000, respectively. Multivariate analysis demonstrated that LPCAT1 expression was independently associated with OS, with an HR of 1.04 (CI: 1.01-1.06, P = 0.003). The KEGG pathway enrichment analyses showed that overlapped DEGs mainly participate in the cell cycle. Finally, we identified a hub gene, CDK1, which has been reported to act on the cell cycle, consistent with the result of KEGG enrichment analysis. Collectively, these data confirmed LPCAT1 was upregulated in HCC, and was an independent predictor of the prognosis.

Development of an Eight-gene Prognostic Model for Overall Survival Prediction in Patients with Hepatocellular Carcinoma

BACKGROUND AND AIMS

The overall survival (OS) of hepatocellular carcinoma (HCC) remains dismal. Bioinformatic analysis of transcriptome data could identify patients with poor OS and may facilitate clinical decision. This study aimed to develop a prognostic gene model for HCC.

METHODS

GSE14520 was retrieved as a training set to identify differential expressed genes (DEGs) between tumor and adjacent liver tissues in HCC patients with different OS. A DEG-based prognostic model was then constructed and the TCGA-LIHC and ICGC-LIRI datasets were used to validate the model. The area under the receiver operating characteristic curve (AUC) and hazard ratio (HR) of the model for OS were calculated. A model-based nomogram was established and verified.

RESULTS

In the training set, differential expression analysis identified 80 genes dysregulated in oxidation-reduction and metabolism regulation. After univariate Cox and LASSO regression, eight genes (LPCAT1, DHRS1, SORBS2, ALDH5A1, SULT1C2, SPP1, HEY1 and GOLM1) were selected to build the prognostic model. The AUC for 1-, 3- and 5-year OS were 0.779, 0.736, 0.754 in training set and 0.693, 0.689, 0.693 in the TCGA-LIHC validation set, respectively. The AUC for 1- and 3-year OS were 0.767 and 0.705 in the ICGC-LIRI validation set. Multivariate analysis confirmed the model was an independent prognostic factor (training set: HR=4.422, p<0.001; TCGA-LIHC validation set: HR=2.561, p<0.001; ICGC-LIRI validation set: HR=3.931, p<0.001). Furthermore, a nomogram combining the model and AJCC stage was established and validated, showing increased OS predictive efficacy compared with the prognostic model (p=0.035) or AJCC stage (p<0.001).

CONCLUSIONS

Our eight-gene prognostic model and the related nomogram represent as reliable prognostic tools for OS prediction in HCC patients.

Comprehensive Analysis of LPCATs Highlights the Prognostic and Immunological Values of LPCAT1/4 in Hepatocellular Carcinoma

Background

The prognosis of patients with advanced hepatocellular carcinoma (HCC) remains poor. Lipid remodeling modulators are considered promising therapeutic targets of cancers, owing to their functions of facilitating cancer cells’ adaption to the limited environment. Lysophosphatidylcholine acyltransferases (LPCATs) are enzymes regulating bio-membrane remodeling, whose roles in HCC have not been fully illuminated.

Methods

Multiple bioinformatic tools were applied to comprehensively evaluate the expression, genetic alterations, clinical relevance, prognostic values, DNA methylation, biological functions, and correlations with immune infiltration of LPCATs in HCC.

Results

We found LPCAT1 was significantly overexpressed and the most frequently altered in HCC. The high-expression of LPCAT1/4 indicated clinicopathological advancements and poor prognoses of HCC patients. Even though the global DNA methylation of LPCATs in HCC showed no significant difference with that in normal liver, the hypermethylation of numerous CpG sites of them implied worse survivals of HCC patients. Thirty LPCATs’ interactive genes were identified, which were generally membrane components and partook in phospholipid metabolism pathways. Finally, we found the expression of LPCATs was extensively positively correlated with the infiltration of various stimulatory and suppressive tumor-infiltrating immune cells (TIICs) in the tumor microenvironment.

Conclusion

This study addressed LPCAT1/4 were potential prognostic and immunotherapeutic biomarkers of HCC targeting bio-membrane lipid remodeling.

A rapid increase in lysophospholipids after geranylgeranoic acid treatment in human hepatoma-derived HuH-7 cells revealed by metabolomics analysis

Geranylgeranoic acid (GGA) was developed as a preventative agent against second primary hepatoma, and was reported to induce cell death in human hepatoma cells via Toll-like receptor 4 (TLR4)-mediated pyroptosis. We recently reported that GGA is enzymatically biosynthesized from mevalonic acid in human hepatoma-derived HuH-7 cells and that endogenous GGA is found in most rat organs including the liver. An unbiased metabolomics analysis of ice-cold 50% acetonitrile extracts from control and GGA-treated cells was performed in this study to characterize the intracellular metabolic changes in GGA-induced pyroptosis and to analyze their relationship with the mechanism of GGA-induced cell death. The total positive ion chromatograms of the cellular extracts in ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry were apparently unchanged after GGA treatment, but an orthogonal partial least squares-discriminant analysis score plot clearly discriminated the intracellular metabolite profiles of GGA-treated cells from that of control cells. S-plot analysis revealed 15 potential biomarkers up-regulated by 24-h GGA treatment according to their variable importance in the projection value of more than 1, and the subsequent metabolomics analysis identified nine of these metabolites as a group of lysophospholipids containing lysophosphatidylcholine with C16:0, C20:4, or C20:3 fatty acids. The possible roles of these lysophospholipids in GGA-induced pyroptosis are discussed.

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Metabolomics analysis was performed on geranylgeranoic acid (GGA)-treated cells.

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Total positive ion chromatograms were apparently similar after GGA treatment.

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The OPLS-DA score plot distinguished the GGA-treated cells from control cells.

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The S-plot analysis revealed GGA-induced upregulation of lysophospholipids.

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The possible roles of lysophospholipids in GGA-induced pyroptosis are discussed.

Accumulation of cholesterol, triglycerides and ceramides in hepatocellular carcinomas of diethylnitrosamine injected mice

BACKGROUND

Dysregulated lipid metabolism is critically involved in the development of hepatocellular carcinoma (HCC). The respective metabolic pathways affected in HCC can be identified using suitable experimental models. Mice injected with diethylnitrosamine (DEN) and fed a normal chow develop HCC. For the analysis of the pathophysiology of HCC in this model a comprehensive lipidomic analysis was performed.

METHODS

Lipids were measured in tumor and non-tumorous tissues by direct flow injection analysis. Proteins with a role in lipid metabolism were analysed by immunoblot. Mann-Whitney U-test or paired Student´s t-test were used for data analysis.

RESULTS

Intra-tumor lipid deposition is a characteristic of HCCs, and di- and triglycerides accumulated in the tumor tissues of the mice. Peroxisome proliferator-activated receptor gamma coactivator 1 alpha, lipoprotein lipase and hepatic lipase protein were low in the tumors whereas proteins involved in de novo lipogenesis were not changed. Higher rates of de novo lipogenesis cause a shift towards saturated acyl chains, which did not occur in the murine HCC model. Besides, LDL-receptor protein and cholesteryl ester levels were higher in the murine HCC tissues. Ceramides are cytotoxic lipids and are low in human HCCs. Notably, ceramide levels increased in the murine tumors, and the simultaneous decline of sphingomyelins suggests that sphingomyelinases were involved herein. DEN is well described to induce the tumor suppressor protein p53 in the liver, and p53 was additionally upregulated in the tumors.

CONCLUSIONS

Ceramides mediate the anti-cancer effects of different chemotherapeutic drugs and restoration of ceramide levels was effective against HCC. High ceramide levels in the tumors makes the DEN injected mice an unsuitable model to study therapies targeting ceramide metabolism. This model is useful for investigating how tumors evade the cytotoxic effects of ceramides.

Transcriptional and epigenetic landscape of Ca2+-signaling genes in hepatocellular carcinoma

Calcium (Ca2+) signaling has a major role in regulating a wide range of cellular mechanisms, including gene expression, proliferation, metabolism, cell death, muscle contraction, among others. Recent evidence suggests that ~ 1600 genes are related to the Ca2+ signaling. Some of these genes' expression is altered in several pathological conditions, including different cancer types, and epigenetic mechanisms are involved. However, their expression and regulation in hepatocellular carcinoma (HCC) and the liver are barely known. Here, we aimed to explore the expression of genes involved in the Ca2+-signaling in HCC, liver regeneration, and hepatocyte differentiation, and whether their expression is regulated by epigenetic mechanisms such as DNA methylation and histone posttranslational modifications (HPM). Results show that several Ca2+-signaling genes' expression is altered in HCC samples; among these, a subset of twenty-two correlate with patients' survival. DNA methylation correlates with eight of these genes' expression, and Guadecitabine, a hypomethylating agent, regulates the expression of seven down-regulated and three up-regulated genes in HepG2 cells. The down-regulated genes displayed a marked decrease of euchromatin histone marks, whereas up-regulated genes displayed gain in these marks. Additionally, the expression of these genes is modulated during liver regeneration and showed similar profiles between in vitro differentiated hepatocytes and liver-derived hepatocytes. In conclusion, some components of the Ca2+-signaling are altered in HCC and displayed a correlation with patients' survival. DNA methylation and HMP are an attractive target for future investigations to regulate their expression. Ca2+-signaling could be an important regulator of cell proliferation and differentiation in the liver.

LPCAT1 overexpression promotes the progression of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains one of the most common malignant neoplasms. Lysophosphatidylcholine acyltransferase 1 (LPCAT1) plays a key role in the lipid remodelling and is correlated with various neoplasms. Nonetheless, the biological functions and molecular mechanisms of LPCAT1 underlying HCC remain obscure.

METHODS

In the present study, we investigated the role of LPCAT1 in the progression of HCC. In-house RT-qPCR, tissue microarrays, and immunohistochemistry were performed to detect the expression levels and the clinical value of LPCAT1 in HCC. External datasets were downloaded to confirm the results. Proliferation, migration, invasiveness, cell cycle, and apoptosis assays were conducted to reveal the biological effects LPCAT1 has on SMMC-7721 and Huh7 cells. HCC differentially expressed genes and LPCAT1 co-expressed genes were identified to explore the molecular mechanisms underlying HCC progression.

RESULTS

LPCAT1 showed upregulated expression in 3715 HCC specimens as opposed to 3105 non-tumour specimens. Additionally, LPCAT1 might be an independent prognostic factor for HCC. LPCAT1-knockout hampered cellular proliferation, migration, and metastasis in SMMC-7721 and Huh7 cells. More importantly, the cell cycle and chemical carcinogenesis were the two most enriched signalling pathways.

CONCLUSIONS

The present study demonstrated that increased LPCAT1 correlated with poor prognosis in HCC patients and fuelled HCC progression by promoting cellular growth, migration, and metastasis.

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Background

Alpha-fetoprotein (AFP) is the most important diagnostic and prognostic index of hepatocellular carcinoma (HCC). AFP-positive HCC can be easily diagnosed based on the serum AFP level and typical imaging features, but a number of HCC patients are negative (AFP < 20 ng/mL) for AFP. Therefore, it is necessary to develop novel diagnostic and prognostic biomarkers for AFP-negative HCC.

Methods

RNA data from TCGA and differential expression of lncRNAs, miRNAs, and mRNAs were downloaded to analyze the differential RNA expression patterns between AFP-negative HCC tissues and normal tissues. A lncRNA-miRNA-mRNA ceRNA regulatory network was constructed to elucidate the interaction mechanism of RNAs. Functional enrichment analysis of these DEmRNAs was performed to indirectly reveal the mechanism of action of lncRNAs. A PPI network was built using STRING, and the hub genes were identified with Cytoscape. The diagnostic value of hub genes was assessed with receiver operating characteristic (ROC) analysis. And the prognostic value of RNAs in the ceRNA was estimated with Kaplan-Meier curve analysis.

Results

A total of 131 lncRNAs, 185 miRNA, and 1309 mRNAs were found to be differentially expressed in AFP-negative HCC. A ceRNA network consisting of 12 lncRNA, 23 miRNA, and 74 mRNA was constructed. The top ten hub genes including EZH2, CCNB1, E2F1, PBK, CHAF1A, ESR1, RRM2, CCNE1, MCM4, and ATAD2 showed good diagnostic power under the ROC curve; and 2 lncRNAs (LINC00261, LINC00482), 3 miRNAs (hsa-miR-93, hsa-miR-221, hsa-miR-222), and 2 mRNAs (EGR2, LPCAT1) were found to be associated with the overall survival of AFP-negative patients.

Conclusion

This study could provide a novel insight into the molecular pathogenesis of AFP-negative HCC and reveal some candidate diagnostic and prognostic biomarkers for AFP-negative HCC.

LPCAT1 Promotes Cutaneous Squamous Cell Carcinoma via EGFR-Mediated Protein Kinase B/p38MAPK Signaling Pathways

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of skin cancer. LPCAT1, a lysophosphatidylcholine acyltransferase, takes a center stage in membrane lipid remodeling. LPCAT1 is elevated in several cancers and contributes to cancer development. However, its role and molecular mechanisms in cSCC remain to be elucidated. In this study, we found that LPCAT1 was upregulated in cSCC tissues and in cell lines. In vitro, loss-of-function and gain-of-function experiments demonstrated that LPCAT1 facilitated cSCC cell proliferation, protected cells against apoptosis, accelerated epithelial‒mesenchymal transition, and enhanced cell metastasis. Mechanistically, LPCAT1 regulated EGFR signaling. The oncogenic effect of LPCAT1 was mediated by EGFR/protein kinase B and EGFR/p38MAPK pathways in cSCC. Using the xenograft mouse model, we consolidated the results mentioned earlier. In conclusion, LPCAT1 contributed to cSCC progression through EGFR-mediated protein kinase B and p38MAPK signaling pathways. LPCAT1 may serve as a target for therapeutic intervention in cSCC.

High level of phosphatidylcholines/lysophosphatidylcholine ratio in urine is associated with prostate cancer

The altered levels of phospholipids (PLs) and lysophospholipids (LPLs) in prostate cancer (CaP) and benign tissues in our previous findings prompted us to explore PLs and LPLs as potential biomarkers for CaP. Urinary lipidomics has attracted increasing attention in clinical diagnostics and prognostics for CaP. In this study, 31 prostate tissues obtained from radical prostatectomy were assessed using high‐resolution matrix‐assisted laser desorption/ionization imaging mass spectrometry (HR‐MALDI‐IMS). Urine samples were collected after digital rectal examination (DRE), and urinary lipids were extracted using the acidified Bligh‐Dyer method. The discovery set comprised 75 patients with CaP and 44 with benign prostatic hyperplasia (BPH) at Kyoto University Hospital; the validation set comprised 74 patients with CaP and 59 with BPH at Osaka University Hospital. Urinary lipidomic screening was performed using MALDI time‐of‐flight MS (MALDI‐TOF/MS). The levels of urinary lysophosphatidylcholine (LPC) and phosphatidylcholines (PCs) were compared between the CaP and BPH groups. The (PC [34:2] + PC [34:1])/LPC (16:0) ratio was significantly higher (P < .001) in CaP tissues than in benign epithelial tissues. The urinary PCs/LPC ratio was significantly higher (P < .001) in the CaP group than in the BPH group in the discovery and validation sets.

Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC)

Background

Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC.

Methods

The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining.

Results

A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients.

Conclusion

The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.

LpCat1 Promotes Malignant Transformation of Hepatocellular Carcinoma Cells by Directly Suppressing STAT1

Hepatocellular carcinoma (HCC) is a malignant cancer with rapid proliferation and high metastasis ability. To explore the crucial genes that maintain the aggressive behaviors of cancer cells is very important for clinical gene therapy of HCC. LpCat1 was reported to be highly expressed and exert pro-tumorigenic effect in a variety of cancers, including HCC. However, its detailed molecular mechanism remained unclear. In this study, we confirmed that LpCat1 was up-regulated in HCC tissues and cancer cell lines. The overexpressed LpCat1 promoted the proliferation, migration and invasion of HCC cells, and accelerated cell cycle progression, while knocking down LpCat1 significantly inhibited cell proliferation, migration and invasion in vitro and in vivo, and arrested HCC cells at G0/G1 phase. Moreover, we proved for the first time that LpCat1 directly interacted with STAT1 which was generally recognized as a tumor suppressor in HCC. High levels of LpCat1 in HCC could inhibit STAT1 expression, up-regulate CyclinD1, CyclinE, CDK4 and MMP-9, and decrease p27kip1 to promote cancer progression. Conversely, down-regulation of LpCat1 would cause the opposite changes to repress the viability and motility of HCC cells. Consequently, we concluded that LpCat1 was a contributor to progression and metastasis of HCC by interacting with STAT1.

Alterations in lipid profile upon uterine fibroids and its recurrence

Uterine fibroids (UF) is the most common (about 70% cases) type of gynecological disease, with the recurrence rate varying from 11 to 40%. Because UF has no distinct symptomatology and is often asymptomatic, the specific and sensitive diagnosis of UF as well as the assessment for the probability of UF recurrence pose considerable challenge. The aim of this study was to characterize alterations in the lipid profile of tissues associated with the first-time diagnosed UF and recurrent uterine fibroids (RUF) and to explore the potential of mass spectrometry (MS) lipidomics analysis of blood plasma samples for the sensitive and specific determination of UF and RUF with low invasiveness of analysis. MS analysis of lipid levels in the myometrium tissues, fibroids tissues and blood plasma samples was carried out on 66 patients, including 35 patients with first-time diagnosed UF and 31 patients with RUF. The control group consisted of 15 patients who underwent surgical treatment for the intrauterine septum. Fibroids and myometrium tissue samples were analyzed using direct MS approach. Blood plasma samples were analyzed using high performance liquid chromatography hyphened with mass spectrometry (HPLC/MS). MS data were processed by discriminant analysis with projection into latent structures (OPLS-DA). Significant differences were found between the first-time UF, RUF and control group in the levels of lipids involved in the metabolism of glycerophospholipids, sphingolipids, lipids with an ether bond, triglycerides and fatty acids. Significant differences between the control group and the groups with UF and RUF were found in the blood plasma levels of cholesterol esters, triacylglycerols, (lyso) phosphatidylcholines and sphingomyelins. Significant differences between the UF and RUF groups were found in the blood plasma levels of cholesterol esters, phosphotidylcholines, sphingomyelins and triacylglycerols. Diagnostic models based on the selected differential lipids using logistic regression showed sensitivity and specificity of 88% and 86% for the diagnosis of first-time UF and 95% and 79% for RUF, accordingly. This study confirms the involvement of lipids in the pathogenesis of uterine fibroids. A diagnostically significant panel of differential lipid species has been identified for the diagnosis of UF and RUF by low-invasive blood plasma analysis. The developed diagnostic models demonstrated high potential for clinical use and further research in this direction.

PTRF/cavin-1 remodels phospholipid metabolism to promote tumor proliferation and suppress immune responses in glioblastoma by stabilizing cPLA2

BACKGROUND

Metabolism remodeling is a hallmark of glioblastoma (GBM) that regulates tumor proliferation and the immune microenvironment. Previous studies have reported that increased polymerase 1 and transcript release factor (PTRF) levels are associated with a worse prognosis in glioma patients. However, the biological role and the molecular mechanism of PTRF in GBM metabolism remain unclear.

METHODS

The relationship between PTRF and lipid metabolism in GBM was detected by nontargeted metabolomics profiling and subsequent lipidomics analysis. Western blotting, quantitative real-time PCR, and immunoprecipitation were conducted to explore the molecular mechanism of PTRF in lipid metabolism. A sequence of in vitro and in vivo experiments (both xenograft tumor and intracranial tumor mouse models) were used to detect the tumor-specific impacts of PTRF.

RESULTS

Here, we show that PTRF triggers a cytoplasmic phospholipase A2 (cPLA2)-mediated phospholipid remodeling pathway that promotes GBM tumor proliferation and suppresses tumor immune responses. Research in primary cell lines from GBM patients revealed that cells overexpressing PTRF show increased cPLA2 activity-resulting from increased protein stability-and exhibit remodeled phospholipid composition. Subsequent experiments revealed that PTRF overexpression alters the endocytosis capacity and energy metabolism of GBM cells. Finally, in GBM xenograft and intracranial tumor mouse models, we showed that inhibiting cPLA2 activity blocks tumor proliferation and prevents PTRF-induced reduction in CD8+ tumor-infiltrating lymphocytes.

CONCLUSIONS

The PTRF-cPLA2 lipid remodeling pathway promotes tumor proliferation and suppresses immune responses in GBM. In addition, our findings highlight multiple new therapeutic targets for GBM.

Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multiomics Data

Untargeted mass spectrometry (MS)-based proteomics provides a powerful platform for protein biomarker discovery, but clinical translation depends on the selection of a small number of proteins for downstream verification and validation. Due to the small sample size of typical discovery studies, protein markers identified from discovery data may not be generalizable to independent datasets. In addition, a good protein marker identified using a discovery platform may be difficult to implement in verification and validation platforms. Moreover, although multiomics characterization is being increasingly used in discovery cohort studies, there is no existing method for multiomics-facilitated protein biomarker selection. Here, we present ProMS, a computational algorithm for protein marker selection. The algorithm is based on the hypothesis that a phenotype is characterized by a few underlying biological functions, each manifested by a group of coexpressed proteins. A weighted k-medoids clustering algorithm is applied to all univariately informative proteins to identify both coexpressed protein clusters and a representative protein for each cluster as markers. In two clinically important classification problems, ProMS shows superior performance compared with existing feature selection methods. ProMS can be extended to the multiomics setting (ProMS_mo) through a constrained weighted k-medoids clustering algorithm, and the protein panels selected by ProMS_mo show improved performance on independent test data compared with ProMS. In addition to superior performance, ProMS and ProMS_mo also have two unique strengths. First, the feature clusters enable functional interpretation of the selected protein markers. Second, the feature clusters provide an opportunity to select replacement protein markers, facilitating a robust transition to the verification and validation platforms. In summary, this study provides a unified and effective computational framework for selecting protein biomarkers using proteomics or multiomics data. The software implementation is publicly available at https://github.com/bzhanglab/proms.

Blood-Based Targeted Metabolomics Discriminate Patients with Alcoholic Liver Cirrhosis from Those with Non-Cirrhotic Liver Damage: An Explorative Study

BACKGROUND

Early detection of liver cirrhosis is crucial for secondary prevention of complications. However, noninvasive blood-based patient monitoring tools are lacking. In this explorative study, we conducted a targeted metabolomic analysis in order to identify possible serum markers indicating alcoholic liver cirrhosis (aLiC) with or without hepatocellular carcinoma (HCC).

METHODS

Venous blood of 30 individuals was collected: healthy controls ("Con", n = 12), patients with aLiC without and with HCC ("aLiC": n = 6 and "aLiC + HCC": n = 6), and patients with other liver diseases ("oLiD": n = 6). A targeted metabolomic analysis was conducted using the AbsoluteIDQ® p180 Kit (Biocrates Life Sciences®, Innsbruck, Austria). Statistical analysis was performed by applying a one-way ANOVA on all subgroups followed by a t test for pairwise comparison of subgroups and logistic regression analysis.

RESULTS

ANOVA revealed 29 metabolites that significantly discriminate between the different cohorts. Among these analytes, 25 were significantly altered in Con versus aLiC, as indicated by t test, most importantly SM C18:1 (p < 0.001), SM C20:2 (p = 0.001), SM (OH) C22:2 (p < 0.001), lysoPC a C20:4 (p < 0.001), and PC aa C36:5 (p < 0.001). To a similar extent, the metabolites discriminated also between the oLiD and aLiC but less between the Con or oLiD and aLiC + HCC cohorts. Most of these analytes were either lyso- and phosphatidylcholines or sphingomyelins. Results were not significant for comparison of Con versus oLiD and aLiC versus aLiC + HCC.

CONCLUSION

Decreased lyso- and phosphatidylcholine as well as sphingomyelin species in venous blood could help to detect liver cirrhosis in patients with non-cirrhotic liver disease.

Evolution of the liver biopsy and its future

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Development and Validation of a Metabolic Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a malignant tumor with great variation in prognosis among individuals. Changes in metabolism influence disease progression and clinical outcomes. The objective of this study was to determine the overall survival (OS) risk of HCC patients from a metabolic perspective.

Patients and Methods

The model was constructed using the least absolute shrinkage and selection operator (LASSO) COX regression based on The Cancer Genome Atlas (TCGA, n=342) dataset. The International Cancer Genome Consortium (ICGC, n=232), GSE14520 (n=242) datasets, and a clinical cohort (n=64) were then used to assess the prognostic value of the signature.

Results

A 10 metabolic gene-based signature was constructed and verified as a robust and independent prognostic classifier in public and real-world validation cohorts. Meanwhile, the signature enabled the identification of HCC molecular subtypes, yielding an AUC value of 0.678 [95% CI: 0.592–0.763]. Besides, the signature was associated with metabolic processes like glycolysis, supported by a clear correlation between the risk score and expression of rate-limiting enzymes. Furthermore, high-risk tumor was likely to have a high tumor infiltration status of immunosuppressive cells, as well as elevated expression of some immune checkpoint molecules. For final clinical translation, a nomogram integrating the signature and tumor stage was established, and showed improved predictive accuracy of 3- and 5-year OS and brought more net benefit to patients.

Conclusion

We developed a prognostic signature based on 10 metabolic genes, which has proven to be an independent and reliable prognostic predictor for HCC and reflects the metabolic and immune characteristics of tumors.

Construction and validation of a metabolic risk model predicting prognosis of colon cancer

Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan-Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan-Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein-protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.

Nondestructive molecular imaging by Raman spectroscopy vs. marker detection by MALDI IMS for an early diagnosis of HCC

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide with a steadily increasing mortality rate. Fast diagnosis at early stages of HCC is of key importance for the improvement of patient survival rates. In this regard, we combined two imaging techniques with high potential for HCC diagnosis in order to improve the prediction of liver cancer. In detail, Raman spectroscopic imaging and matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) were applied for the diagnosis of 36 HCC tissue samples. The data were analyzed using multivariate methods, and the results revealed that Raman spectroscopy alone showed a good capability for HCC tumor identification (sensitivity of 88% and specificity of 80%), which could not be improved by combining the Raman data with MALDI IMS. In addition, it could be shown that the two methods in combination can differentiate between well-, moderately- and poorly-differentiated HCC using a linear classification model. MALDI IMS not only classified the HCC grades with a sensitivity of 100% and a specificity of 80%, but also showed significant differences in the expression of glycerophospholipids and fatty acyls during HCC differentiation. Furthermore, important differences in the protein, lipid and collagen compositions of differentiated HCC were detected using the model coefficients of a Raman based classification model. Both Raman and MALDI IMS, as well as their combination showed high potential for resolving concrete questions in liver cancer diagnosis.