Integrated analysis reveals critical glycolytic regulators in hepatocellular carcinoma

Glucosamine Inhibits the Proliferation of Hepatocellular Carcinoma Cells by Eliciting Apoptosis, Autophagy, and the Anti-Warburg Effect

Although glucosamine (GlcN) exhibits antitumor effects, its mechanism of action remains controversial. Additionally, its impact on hepatocellular carcinoma (HCC) is not well understood. This study aimed to investigate the antitumor effects of GlcN and its underlying mechanism in a mouse HCC cell line, Hepa1-6. GlcN treatment significantly inhibited Hepa1-6 cell proliferation. Gene expression analysis revealed that GlcN upregulated Chop and Bax while downregulating Bcl2, indicating the involvement of endoplasmic reticulum (ER) stress-induced apoptosis in the antiproliferative effects of GlcN. GlcN also increased the expression of FoxO1 and FoxO3, known tumor suppressors in various cancers. Furthermore, GlcN treatment elevated the levels of LC3II (an autophagy marker) and AMP-activated protein kinase activity, suggesting intracellular energy shortage. Indeed, GlcN treatment significantly suppressed glycolytic flux, lactate, and ATP production. Supplementing GlcN treatment with a high glucose concentration (20 mM) significantly attenuated its effect. We postulate that GlcN inhibits Hepa1-6 cell growth by inducing ER stress-induced apoptosis and autophagy and by inhibiting aerobic glycolysis (the Warburg effect), a key hallmark of cancer metabolism. Given that glucose transporter 2 (GLUT2), which is abundantly expressed in hepatocytes, has a high affinity for GlcN, these effects may result from GlcN competing with glucose for hepatocyte uptake by GLUT2. Our novel findings have potential implications for HCC treatment.

Decoding 17-Beta-hydroxysteroid Dehydrogenase 13: A Multifaceted Perspective on Its Role in Hepatic Steatosis and Associated Disorders

Chronic liver disease (CLD) represents a significant global health burden, with hepatic steatosis-associated disorders-such as metabolic dysfunction-associated steatohepatitis (MASH), alcoholic liver disease, and hepatitis C virus infection-being major contributors. Recent genome-wide association studies have identified the rs72613567:TA variant in the 17-beta-hydroxysteroid dehydrogenase 13 (HSD17B13) gene as a protective factor against the development and progression of these conditions. In this review, we summarized the current evidence surrounding the HSD17B13 rs72613567 variant, aiming to elucidate its impact on CLD risk and outcomes, and to explore the potential mechanisms behind its hepatoprotective effects. The rs72613567:TA variant induces a splice donor site mutation, resulting in a truncated, non-functional HSD17B13 protein. Numerous studies have demonstrated that this loss-of-function mutation confers protection against the development of cirrhosis and hepatocellular carcinoma (HCC) in patients with MASH, alcoholic liver disease, and hepatitis C virus infection. Moreover, the rs72613567:TA variant has been associated with reduced liver enzyme levels and improved survival in HCC patients. Integrating this variant into genetic risk scores has shown promise in predicting the progression of fatty liver disease to cirrhosis and HCC. Furthermore, inhibiting HSD17B13 expression through RNA interference and small molecule inhibitors has emerged as a potential therapeutic strategy for MASH. However, the precise molecular mechanisms underlying the hepatoprotective effects of the HSD17B13 rs72613567 variant remain to be fully elucidated. Future research should focus on clarifying the structure-function relationship of HSD17B13 and its role in liver pathophysiology to facilitate the development of targeted therapies for CLD associated with hepatic steatosis.

ETV5 promotes lupus pathogenesis and follicular helper T cell differentiation by inducing osteopontin expression

Follicular helper T (TFH) cells mediate germinal center reactions to generate high affinity antibodies against specific pathogens, and their excessive production is associated with the pathogenesis of systemic autoimmune diseases such as systemic lupus erythematosus (SLE). ETV5, a member of the ETS transcription factor family, promotes TFH cell differentiation in mice. In this study, we examined the role of ETV5 in the pathogenesis of lupus in mice and humans. T cell-specific deletion of Etv5 alleles ameliorated TFH cell differentiation and autoimmune phenotypes in lupus mouse models. Further, we identified SPP1 as an ETV5 target that promotes TFH cell differentiation in both mice and humans. Notably, extracellular osteopontin (OPN) encoded by SPP1 enhances TFH cell differentiation by activating the CD44-AKT signaling pathway. Furthermore, ETV5 and SPP1 levels were increased in CD4+ T cells from patients with SLE and were positively correlated with disease activity. Taken together, our findings demonstrate that ETV5 is a lupus-promoting transcription factor, and secreted OPN promotes TFH cell differentiation.

Involvement of lncRNAs in the regulation of aerobic glycolysis in hepatocellular carcinoma: Main functions, regulatory mechanisms and potential therapeutic implications (Review)

Even under aerobic conditions, tumor cells can reprogram their metabolism to preferentially metabolize glucose into lactic acid. This abnormal metabolic pattern, known as the 'Warburg' effect or aerobic glycolysis, promotes cancer progression. Long non‑coding RNAs (lncRNAs) are RNAs that are >200 nucleotides in length and do not have protein‑coding capabilities. However, these RNAs play a key role in tumor development. There is increasing evidence to indicate that lncRNAs regulate glucose metabolism in tumor cells by affecting metabolic enzymes and some signaling pathways, thereby regulating the occurrence and progression of hepatocellular carcinoma (HCC). Therefore, it is crucial to understand which lncRNAs play a regulatory role in HCC glycolysis and to determine the related molecular mechanisms. The present review summarized and discussed the functions of lncRNAs, focusing on the regulatory mechanisms of lncRNAs in the process of glycolysis in HCC. In addition, the present review suggests the importance of lncRNAs as future therapeutic targets for antitumor cell metabolism.

METTL3 as a novel diagnosis and treatment biomarker and its association with glycolysis, cuproptosis and ceRNA in oesophageal carcinoma

METTL3 has been shown to be involved in regulating a variety of biological processes. However, the relationship between METTL3 expression and glycolysis, cuproptosis-related genes and the ceRNA network in oesophageal carcinoma (ESCA) remains unclear. ESCA expression profiles from databases were obtained, and target genes were identified using differential analysis and visualization. Immunohistochemistry (IHC) staining assessed METTL3 expression differences. Functional enrichment analysis using GO, KEGG and GSEA was conducted on the co-expression profile of METTL3. Cell experiments were performed to assess the effect of METTL3 interference on tumour cells. Correlation and differential analyses were carried out to assess the relationship between METTL3 with glycolysis and cuproptosis. qRT-PCR was used to validate the effects of METTL3 interference on glycolysis-related genes. Online tools were utilized to screen and construct ceRNA networks based on the ceRNA theory. METTL3 expression was significantly higher in ESCA compared to the controls. The IHC results were consistent with the above results. Enrichment analysis revealed that METTL3 is involved in multiple pathways associated with tumour development. Significant correlations were observed between METTL3 and glycolysis-related genes and cuproptosis-related gene. Experiments confirmed that interfered with METTL3 significantly inhibited glucose uptake and lactate production in tumour cells, and affected the expression of glycolytic-related genes. Finally, two potential ceRNA networks were successfully predicted and constructed. Our study establishes the association between METTL3 overexpression and ESCA progression. Additionally, we propose potential links between METTL3 and glycolysis, cuproptosis and ceRNA, presenting a novel targeted therapy strategy for ESCA.

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

SFXN1 as a potential diagnostic and prognostic biomarker of LUAD is associated with 18F-FDG metabolic parameters

BACKGROUND

Sideroflexin 1 (SFXN1) has been discovered as a novel tumor marker for lung adenocarcinoma, but data on its importance in the development of lung adenocarcinoma is still limited. This study evaluated the correlation between SFXN1 and parameters related to 18F-flurodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), and further explored the role of SFXN1 in the value-added and glycolytic processes of LUAD.

METHOD

The expression and prognostic value of SFXN1 mRNA in LUAD were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data base. Retrospective analysis of 18F-FDG PET imaging and metabolic parameters in 42 patients to explore the relationship between the expression of SFXN1 and glucose metabolism levels in lung adenocarcinoma and its clinical significance. H1975 cells were selected as the in vitro research object, and the biological effects of SFXN1 on LUAD were further elucidated through Edu proliferation assay, CCK8 activity assay, wound healing experiment, and cell flow cytometry.

RESULT

SFXN1 is highly expressed in various tumors, including LUAD, and its high expression can serve as an independent predictor of overall survival in lung adenocarcinoma. In addition, the expression of SFXN1 in LUAD was significantly correlated with 18F-FDG PET/CT parameters: maximum and average standardized uptake values (SUVmax and SUVmean), as well as total lesion glycolysis (TLG) (rho = 0.574, 0.589, and 0.338, p < 0.05), which can predict the expression of SFXN1 with an accuracy of 0.934. In vitro functional experiments have shown that knocking down SFXN1 inhibits the proliferation and migration of LUAD cells, promotes cell apoptosis, and may inhibit tumor activity by regulating the expression of glycolytic related genes SLC2A1, HK2, GPI, ALDOA, GAPDH, ENO1, PKM, and LDHA.

CONCLUSION

The overexpression of SFXN1 is closely related to FDG uptake, and SFXN1, as a promising prognostic biomarker, may mediate the development of LUAD through the glycolytic pathway.

SPC25 as a novel therapeutic and prognostic biomarker and its association with glycolysis, ferroptosis and ceRNA in lung adenocarcinoma

OBJECTIVE

Spindle pole body component 25 (SPC25) is an important cyclin involved in chromosome segregation and spindle dynamics regulation during mitosis. However, the role of SPC25 in lung adenocarcinoma (LAUD) is unclear.

MATERIALS AND METHODS

The differential expression of SPC25 in tumor samples and normal samples was analyzed using TIMER, TCGA, GEO databases, and the correlation between its expression and clinicopathological features and prognosis in LUAD patients. Biological pathways that may be enriched by SPC25 were analyzed using GSEA. In vitro cell experiments were used to evaluate the effect of knocking down SPC25 expression on LUAD cells. Correlation analysis and differential analysis were used to assess the association of SPC25 expression with genes related to cell cycle, glycolysis, and ferroptosis. A ceRNA network involving SPC25 was constructed using multiple database analyses.

RESULTS

SPC25 was highly expressed in LUAD, and its expression level could guide staging and predict prognosis. GSEA found that high expression of SPC25 involved multiple cell cycles and glycolytic pathways. Knocking down SPC25 expression significantly affected the proliferation, migration and apoptosis of LUAD cells. Abnormal SPC25 expression levels can affect cell cycle progression, glycolytic ability and ferroptosis regulation. A ceRNA network containing SPC25, SNHG15/hsa-miR-451a/SPC25, was successfully predicted and constructed.

CONCLUSIONS

Our findings reveal the association of up-regulation of SPC25 in LUAD and its expression with clinical features, prognosis prediction, proliferation migration, cell cycle, glycolysis, ferroptosis, and ceRNA networks. Our results indicate that SPC25 can be used as a biomarker in LUAD therapy and a target for therapeutic intervention.

Role of osteopontin in cancer development and treatment

Osteopontin (OPN) is a multifunctional protein secreted intracellularly and extracellularly by various cell types, including NK cells, macrophages, osteoblasts, T cells, and cancer cells. Owing to its diverse distribution, OPN plays a role in cell proliferation, stem-cell-like properties, epithelial-mesenchymal transformation, glycolysis, angiogenesis, fibrosis, invasion, and metastasis. In this review, we discuss recent findings, interpret representative studies on OPN expression in cancer, clarify that elevated OPN levels are observed in multiple cancer types (including colorectal, breast, lung, and liver cancer), and explore how OPN-macrophage interactions shape the tumor microenvironment. We also summarize progress in OPN research with regard to tumor therapy, which can facilitate the development of novel anti-tumor treatment strategies.

Identification of a novel glycolysis-related prognosis risk signature in triple-negative breast cancer

Introduction

Triple-negative breast cancer (TNBC) is a particularly aggressive cluster of breast cancer characterized by significant molecular heterogeneity. Glycolysis is a metabolic pathway that is significantly associated with cancer progression, metastasis, recurrence and chemoresistance. However, the potential roles of glycolysis-related genes in TNBC remain unclear.

Methods

In the present study, we identified 108 glycolysis-related differentially expressed genes (DEGs) between breast cancer (BRCA) tumor tissues and normal tissues, and we divided patients into two different clusters with significantly distinct molecular characteristics, clinicopathological features, prognosis, immune cell infiltration and mutation burden. We then constructed a 10-gene signature that classified all TNBCs into low- and high-risk groups.

Results

The high-risk group had significantly lower survival than the low-risk group, which implied that the risk score was an independent prognostic indicator for TNBC patients. Consequently, we constructed and validated a prognostic nomogram, which accurately predicted individual overall survival (OS) of TNBC. Moreover, the risk score predicted the drug sensitivity of chemotherapeutic agents and immunotherapy for TNBC patients.

Discussion

The present comprehensive analysis of glycolysis-related DEGs in TNBC provides new methods for prognosis prediction and more effective treatment strategies.

LINC00659 Inhibits Hepatocellular Carcinoma Malignant Progression by Blocking Aerobic Glycolysis through FUS Recruitment and SLC10A1 Modulation

Hepatocellular carcinoma (HCC) is a malignant type of liver cancer that poses severe threat to human health worldwide. Aerobic glycolysis is a hallmark of HCC and facilitates its progression. Solute carrier family 10 member 1 (SLC10A1) and long intergenic non-protein coding RNA 659 (LINC00659) were detected to be downregulated in HCC cells, yet their potential functions underlying HCC progression remained unidentified. In the current work, colony formation and transwell assays were used to detect HCC cells (HepG2 and HuH-7) proliferation and migration in vitro study. The quantitative real-time polymerase chain reaction (qRT-PCR) and western blot assays were used for gene/protein expression determination. Seahorse assay was performed for aerobic glycolysis assessment. RNA immunoprecipitation (RIP) and RNA pull-down assays were conducted for detection of the molecular interaction between LINC00659 and SLC10A1. The results showed that overexpressed SLC10A1 significantly suppressed the proliferation, migration, and aerobic glycolysis in HCC cells. Mechanical experiments further demonstrated that LINC00659 positively regulated SLC10A1 expression in HCC cells by recruiting fused protein in sarcoma (FUS). Our work elucidated that LINC00659 inhibited HCC progression and aerobic glycolysis via the FUS/SLC10A1 axis, revealing a novel lncRNA-RNA-binding protein-mRNA network in HCC, which might provide potential therapeutic targets for HCC.

ACE2 negatively regulates the Warburg effect and suppresses hepatocellular carcinoma progression via reducing ROS-HIF1α activity

Aerobic glycolysis has pleiotropic roles in the pathogenesis of hepatocellular carcinoma (HCC). Emerging studies revealed key promoters of aerobic glycolysis, however, little is known about its negative regulators in HCC. In this study, an integrative analysis identifies a repertoire of differentially expressed genes (DNASE1L3, SLC22A1, ACE2, CES3, CCL14, GYS2, ADH4, and CFHR3) that are inversely associated with the glycolytic phenotype in HCC. ACE2, a member of the rennin-angiotensin system, is revealed to be downregulated in HCC and predicts a poor prognosis. ACE2 overexpression significantly inhibits the glycolytic flux as evidenced by reduced glucose uptake, lactate release, extracellular acidification rate, and the expression of glycolytic genes. Opposite results are noticed in loss-of-function studies. Mechanistically, ACE2 metabolizes Ang II to Ang-(1-7), which activates Mas receptor and leads to the phosphorylation of Src homology 2-containing inositol phosphatase 2 (SHP-2). SHP2 activation further blocks reactive oxygen species (ROS)-HIF1α signaling. Addition of Ang-(1-7) or the antioxidant N-acetylcysteine compromises in vivo additive tumor growth and aerobic glycolysis induced by ACE2 knockdown. Moreover, growth advantages afforded by ACE2 knockdown are largely glycolysis-dependent. In clinical settings, a close link between ACE2 expression and HIF1α or the phosphorated level of SHP2 is found. Overexpression of ACE2 significantly retards tumor growth in patient-derived xenograft model. Collectively, our findings suggest that ACE2 is a negative glycolytic regulator, and targeting the ACE2/Ang-(1-7)/Mas receptor/ROS/HIF1α axis may be a promising therapeutic strategy for HCC treatment.

Sodium selenite inhibits proliferation of lung cancer cells by inhibiting NF-κB nuclear translocation and down-regulating PDK1 expression which is a key enzyme in energy metabolism expression

As a trace element that maintains homeostasis in human body, selenium has significant anti-tumor activity. However, its exact molecular mechanism remains to be elucidated. Sodium selenite (SSe) is the most widely-distributed inorganic selenium in nature. In this study, we selected SSe as the research object to explore its anti-tumor mechanism in lung cancer. In vitro experiment showed that SSe could inhibit the activation of NF-κB signaling pathway, knowing that NF-κB is an important intracellular nuclear transcription factor that regulates the expression of pyruvate dehydrogenase kinase 1 (PDK1), a key energy metabolism switch affecting the survival status of the whole cell.At the same time, Bay11-7082(NF-κB signaling pathway inhibitors) and SSe resulted in phosphorylation of p65 and IκBα, decreased expression of PDK1 and Bcl-2,and increased expression of Bax in lung cancer cells. Our further study demonstrated that the reduction of PDK1 activity inhibited lactate secretion, reduced mitochondrial membrane potential, caused the release of Cytochrome C (Cyto C), activated mitochondrial respiration, and promoted the apoptosis of lung cancer cells. The in vivo experiment revealed that SSe inhibited the activation of NF-κB signaling pathway, decreased the expression of PDK1, and induced lung cancer cell proliferation and apoptosis. All these findings indicated that SSe promoted lung cancer cell apoptosis by inhibiting the activation of NF-κB signaling pathway, down-regulating PDK1 and activating mitochondrial apoptosis pathway.

Osteopontin and Cancer: Insights into Its Role in Drug Resistance

Cancer is one of the leading causes of mortality worldwide. Currently, drug resistance is the main obstacle in cancer treatments with the underlying mechanisms of drug resistance yet to be fully understood. Osteopontin (OPN) is a member of the integrin binding glycophosphoprotein family that is overexpressed in several tumour types. It is involved in drug transport, apoptosis, stemness, energy metabolism, and autophagy, which may contribute to drug resistance. Thus, understanding the role of OPN in cancer drug resistance could be important. This review describes the OPN-based mechanisms that might contribute to cancer drug resistance, demonstrating that OPN may be a viable target for cancer therapy to reduce drug resistance in sensitive tumours.

Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells' evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells.

Signature construction and molecular subtype identification based on cuproptosis-related genes to predict the prognosis and immune activity of patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, with high incidence, high malignancy, and low survival rate. Cuproptosis is a novel form of cell death mediated by lipoylated TCA cycle proteins-mediated novel cell death pathway and is highly associated with mitochondrial metabolism. However, the relationship between the expression level of cuproptosis-related genes (CRGs) and the prognosis of HCC is still unclear.

Methods

Combining the HCC transcriptomic data from The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus (GEO) databases, we identified the differentially expressed cuproptosis-related genes (DECRGs) and obtained the prognosis-related DECRGs through univariate regression analysis.LASSO and multivariate COX regression analyses of these DECRGs yielded four genes that were used to construct the signature. Next, we use ROC curves to evaluate the performance of signatures. The tumor microenvironment, immune infiltration, tumor mutation load, half-maximum suppression concentration, and immunotherapy effects were also compared between the low-risk and high-risk groups. Finally, we analyzed the expression level, prognosis, and immune infiltration correlation on the four genes that constructed the model.

Results

Four DECRGs s were used to construct the signature. The ROC curves indicated that signature can better assess the prognosis of HCC patients. Patients were grouped according to the signature risk score. Patients in the low-risk group had a significantly longer survival time than those in the high-risk group. Furthermore, the tumor mutation burden (TMB) values were associated with the risk score and the higher-risk group had a higher proportion of TP53 mutations than the low-risk group.ESTIMATE analysis showed significant differences in stromal scores between the two groups.N6-methyladenosine (m6A) and multiple immune checkpoints were expressed at higher levels in the high-risk group. Then, we found that signature score correlated with chemotherapeutic drug sensitivity and immunotherapy efficacy in HCC patients. Finally, we further confirmed that the four DECRGs genes were associated with the prognosis of HCC through external validation.

Conclusions

We studied from the cuproptosis perspective and developed a new prognostic feature to predict the prognosis of HCC patients. This signature with good performance will help physicians to evaluate the overall prognosis of patients and may provide new ideas for clinical decision-making and treatment strategies.

Machine learning-based transcriptome analysis of lipid metabolism biomarkers for the survival prediction in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a very high fatality rate. Our goal in this study is to find a reliable lipid metabolism-related signature associated with prognostic significance for HCC. In this study, HCC lipid metabolism-related molecular subtype analysis was conducted based on the 243 lipid metabolism genes collected from the Molecular Signatures Database. Several significant disparities in prognosis, clinicopathological characteristics, and immune and ferroptosis-related status were found across the three subtypes, especially between C1 and C3 subgroups. Differential expression analysis yielded 57 differentially expressed genes (DEGs) between C1 and C3 subtypes. GO and KEGG analysis was employed for functional annotation. Three of 21 prognostic DEGs (CXCL8, SLC10A1, and ADH4) were finally selected through machine-learning-based discovery and validation strategy. The risk score = (0.103) × expression value of CXCL8 + (−0.0333) × expression value of SLC10A1 + (−0.0812) × expression value of ADH4. We used these three to construct a HCC prognostic risk model, which stratified the patients of the validation cohort into two risk subtypes with significantly different overall survival. Our work provides possible significance of the lipid metabolism-associated model in stratifying patient prognosis and its feasibility to guide therapeutic selection.

Prognostic value of LECT2 and relevance to immune infiltration in hepatocellular carcinoma

Background: Previous studies have shown that Leukocyte cell-derived chemotaxin2 (LECT2) is associated with the development of HCC. However, there are still no studies with a comprehensive analysis of the role of LECT2 in hepatocellular carcinoma (HCC).

Methods: TCGA data sets were used to analyze the expression of LECT2 in HCC. In addition, the prognostic value of LECT2 in HCC was also investigated. DriverDBv3 was used to analyze the Mutation, CNV, and methylation profiles of LECT2. And, validated by immunohistochemistry in 72 HCC samples. The prognostic value of LECT2 and the correlation with clinicopathological features were analyzed. The GO/KEGG enrichment analysis of LECT2 co-expression and gene set enrichment analysis (GSEA) was performed using the R software package. The PPI interaction network was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) database. Immune infiltration was estimated by the XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT abs and CIBERSORT algorithms, and Spearman was used to analyzing their correlation with LECT2. Moreover, we analyzed the correlation of LECT2 expression with immune checkpoint molecules and HLA genes. Finally, we analyzed the IC50 values of six chemotherapeutic drugs by the pRRophetic package.

Results: Reduced LECT2 expression levels found in HCC patients. Moreover, decreased levels of LECT2 were associated with poor overall survival, disease-free survival, disease-specific survival, and progression-free survival. Besides, methylation was significantly associated with LECT2 expression. The functional enrichment analysis revealed that LECT2 may affect HCC progression through various pathways such as JAK/STAT signaling pathway, cell cycle, and pathways in cancer. Additionally, the results showed that LECT2 expression was negatively correlated with immune infiltration of B cells, Neutrophil, Monocyte, Cancer-associated fibroblast, and Myeloid dendritic cell, and positively correlated with T cell CD8⁺ naive, Endothelial cell, and Hematopoietic stem cell. LECT2 expression was negatively correlated with multiple immune checkpoint molecules and HLA genes. Chemosensitivity analysis showed that chemosensitivity was lower in the LECT2 high expression group. We validated the prognostic value of LECT2 and analysis of clinicopathological features showed a lower TNM stage in the group with high expression of LECT2.

Conclusion: Low expression of LECT2 in HCC is closely associated with poor prognosis, LECT2 may have potential clinical applications due to its unique immunological effects.

Identification of an amino acid metabolism-associated gene signature predicting the prognosis and immune therapy response of clear cell renal cell carcinoma

Background

The upregulation of amino acid metabolism is an essential form of metabolic reprogramming in cancer. Here, we developed an amino acid metabolism signature to predict prognosis and anti-PD-1 therapy response in clear cell renal cell carcinoma (ccRCC).

Methods

According to the amino acid metabolism-associated gene sets contained in the Molecular Signature Database, consensus clustering was performed to divide patients into two clusters. An amino acid metabolism-associated signature was identified and verified. Immune cell infiltrates and their corresponding signature risk scores were investigated. Two independent cohorts of clinical trials were analyzed to explore the correspondence between the signature risk score and the immune therapy response.

Results

Two clusters with different amino acid metabolic levels were identified by consensus clustering. The patients in the two clusters differed in overall survival, progression-free survival, amino acid metabolic status, and tumor microenvironment. We identified a signature containing eight amino acid metabolism-associated genes that could accurately predict the prognosis of patients with ccRCC. The signature risk score was positively correlated with infiltration of M1 macrophages, CD8+ T cells, and regulatory T cells, whereas it was negatively correlated with infiltration of neutrophils, NK cells, and CD4+ T cells. Patients with lower risk scores had better overall survival but worse responses to nivolumab.

Conclusion

Amino acid metabolic status is closely correlated with tumor microenvironment, response to checkpoint blockade therapy, and prognosis in patients with ccRCC. The established amino acid metabolism-associated gene signature can predict both survival and anti-PD-1 therapy response in patients with ccRCC.

Biological and Clinicopathological Characteristics of OPN in Cervical Cancers

Background: Cervical cancer (CC) is the most common gynecological malignancy. Recently, an increasing number of studies have indicated that osteopontin (OPN) is a promising diagnostic and prognostic biomarker for CC. However, the biological role and detailed mechanism of OPN in CC remain unclear.

Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets and a clinical sample microarray were used in our study. To identify the clinicopathological characteristics of OPN in CC, we compared the expression of OPN between normal and CC tissue samples and analyzed the correlations between OPN expression and multiple clinicopathological features. To identify biological processes involving OPN, OPN-associated genes were screened with Pearson correlation analysis and applied in hallmark gene set enrichment analysis (GSEA). Additionally, leukocyte infiltration was evaluated based on OPN expression. Finally, OPN-related signaling pathways were identified by GSEA.

Results: OPN expression was higher in CC samples than in normal tissue samples and positively correlated with age, FIGO stage, tumor size, lymphovascular invasion and an unfavorable prognosis. OPN-associated genes were mainly enriched in the immune response, and increased OPN expression was accompanied by increased M2 macrophage infiltration. Additionally, OPN was correlated with hypoxia, high glycolytic metabolism, apoptosis, angiogenesis, epithelial-mesenchymal transition and multiple signaling pathways (the p53 pathway, the PI3K/Akt pathway, IL6/STAT3 signaling, mTORC1 signaling and KRAS signaling).

Conclusion: Our study showed that OPN is involved in immunological activities and multiple tumor processes, identifying it as a potential therapeutic target and useful prognostic factor in CC patients.

Impact of Exogenous Treatment with Histidine on Hepatocellular Carcinoma Cells

Simple Summary

Sorafenib (Nexavar@) is the only currently approved anti-cancer drug for patients with advanced hepatocellular carcinoma (HCC). However, despite the development of strategies combining sorafenib with other cytotoxic chemotherapeutic agents to overcome sorafenib resistance, clinical trial results are still disappointing. In this study, we examined the enhancement of tumor responses to sorafenib by exogenous histidine treatment.

Abstract

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Sorafenib, a multi-kinase inhibitor, is the first-line therapy for advanced HCC. However, long-term exposure to sorafenib often results in reduced sensitivity and the development of resistance. Although various amino acids have been shown to contribute to cancer initiation and progression, little is known about the effects of histidine, a dietary essential amino acid that is partially taken up via histidine/large neutral amino acid transporter (LAT1), on cancer cells. In this study, we evaluated the effects of histidine on HCC cells and sensitivity to sorafenib. Remarkably, we found that exogenous histidine treatment induced a reduction in the expression of tumor markers related to glycolysis (GLUT1 and HK2), inflammation (STAT3), angiogenesis (VEGFB and VEGFC), and stem cells (CD133). In addition, LAT1 expression was downregulated in HCC tumor regions with high expression of GLUT1, CD133, and pSTAT3, which are known to induce sorafenib resistance. Finally, we demonstrated that combined treatment with sorafenib and histidine could be a novel therapeutic strategy to enhance the sensitivity to sorafenib, thereby improving long-term survival in HCC.

HNF1A POU Domain Mutations Found in Japanese Liver Cancer Patients Cause Downregulation of HNF4A Promoter Activity with Possible Disruption in Transcription Networks

Hepatocyte nuclear factor 1A (HNF1A) is the master regulator of liver homeostasis and organogenesis and regulates many aspects of hepatocyte functions. It acts as a tumor suppressor in the liver, evidenced by the increased proliferation in HNF1A knockout (KO) hepatocytes. Hence, we postulated that any loss-of-function variation in the gene structure or composition (mutation) could trigger dysfunction, including disrupted transcriptional networks in liver cells. From the International Cancer Genome Consortium (ICGC) database of cancer genomes, we identified several HNF1A mutations located in the functional Pit-Oct-Unc (POU) domain. In our biochemical analysis, we found that the HNF1A POU-domain mutations Y122C, R229Q and V259F suppressed HNF4A promoter activity and disrupted the binding of HNF1A to its target HNF4A promoter without any effect on the nuclear localization. Our results suggest that the decreased transcriptional activity of HNF1A mutants is due to impaired DNA binding. Through structural simulation analysis, we found that a V259F mutation was likely to affect DNA interaction by inducing large conformational changes in the N-terminal region of HNF1A. The results suggest that POU-domain mutations of HNF1A downregulate HNF4A gene expression. Therefore, to mimic the HNF1A mutation phenotype in transcription networks, we performed siRNA-mediated knockdown (KD) of HNF4A. Through RNA-Seq data analysis for the HNF4A KD, we found 748 differentially expressed genes (DEGs), of which 311 genes were downregulated (e.g., HNF1A, ApoB and SOAT2) and 437 genes were upregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping revealed that the DEGs were involved in several signaling pathways (e.g., lipid and cholesterol metabolic pathways). Protein–protein network analysis suggested that the downregulated genes were related to lipid and cholesterol metabolism pathways, which are implicated in hepatocellular carcinoma (HCC) development. Our study demonstrates that mutations of HNF1A in the POU domain result in the downregulation of HNF1A target genes, including HNF4A, and this may trigger HCC development through the disruption of HNF4A–HNF1A transcriptional networks.

Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies have revealed that the Warburg effect is related to the development, progression and treatment of various cancers; however, there have been a few explorations of the relationship between glycolysis and HCC prognosis.

Methods

mRNA expression profiling was downloaded from public databases. Gene set enrichment analysis (GSEA) was used to explore glycolysis-related genes (GRGs), and the LASSO method and Cox regression analysis were used to identify GRGs related to HCC prognosis and to construct predictive models associated with overall survival (OS) and disease-free survival (DFS). The relationship between the predictive model and the tumor mutation burden (TMB) and tumor immune microenvironment (TIME) was explored. Finally, real-time PCR was used to validate the expression levels of the GRGs in clinical samples and different cell lines.

Results

Five GRGs (ABCB6, ANKZF1, B3GAT3, KIF20A and STC2) were identified and used to construct gene signatures to predict HCC OS and DFS. Using the median value, HCC patients were divided into low- and high-risk groups. Patients in the high-risk group had worse OS/DFS than those in the low-risk group, were related to higher TMB and were associated with a higher rate of CD4+ memory T cells resting and CD4+ memory T cells activated. Finally, real-time PCR suggested that the five GRGs were all dysregulated in HCC samples compared to adjacent normal samples.

Conclusions

We identified five GRGs associated with HCC prognosis and constructed two GRGs-related gene signatures to predict HCC OS and DFS. The findings in this study may contribute to the prediction of prognosis and promote HCC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09209-9.

Screening of a novel autophagy-related prognostic signature and therapeutic targets in hepatocellular carcinoma

BACKGROUND

Many studies have indicated that autophagy plays an important role in multiple cancers, including hepatocellular carcinoma (HCC). This study aimed to establish a prognostic signature for HCC based on autophagy-related genes (ARGs) to predict the prognosis of patients.

METHODS

The list of ARGs was derived from screening National Center for Biotechnology Information (NCBI)-Gene and Molecular Signatures Database (MSigDB) datasets. Differential analysis was conducted via the R limma package in HCC patients based on The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to identify key prognostic ARGs via the survival package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by clusterProfiler package. The Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was used to conduct immune analysis. Finally, the correlation between the prognostic model and clinical characteristics was also assessed, including age, tumor-node-metastasis (TNM) stages, and tumor grades.

RESULTS

Firstly, 106 differential ARGs were identified and 10 candidates were further confirmed via Cox regression analysis, including BAMBI, HIF1A, SERPINE1, EZH2, SLC9A3R1, IGFBP3, HSPB8, DAB2, CXCL1 and PRNP. The receiver operating characteristic (ROC) curve analysis revealed that the ARGs risk model had a well diagnostic positive rate with 1-year area under the curve (AUC) =0.688 and 3-year AUC =0.674. Correlation analysis indicated that only advanced tumor stages were positively associated with high ARGs scores with P=0.0227. There were also significant differences in tumor purity (P=6.71e-05), infiltrating cell analysis (P=7.77e-05), immune analysis (P=7.9e-05), and stromal cells analysis (P=0.0015) in high- and low-risk ARGs samples. The genes HIF1A, IGFBP3, and DAB2 were found to have high frequent missense mutations in samples with high-risk ARGs scores. Lastly, we also established a nomogram to predict overall survival (OS) of HCC by integrating ARGs scores and other clinical parameters.

CONCLUSIONS

Our study established an autophagy-related signature for predicting the prognosis of HCC patients, providing a thorough understanding of the underlying mechanisms of autophagy in HCC.

Quantitative Expression of Key Cancer Markers in the AS-30D Hepatocarcinoma Model

Hepatocellular carcinoma is one of the cancers with the highest mortality rate worldwide. HCC is often diagnosed when the disease is already in an advanced stage, making the discovery and implementation of biomarkers for the disease a critical aim in cancer research. In this study, we aim to quantify the transcript levels of key signaling molecules relevant to different pathways known to participate in tumorigenesis, with special emphasis on those related to cancer hallmarks and epithelial-mesenchymal transition, using as a model the murine transplantable hepatocarcinoma AS-30D. Using qPCR to quantify the mRNA levels of genes involved in tumorigenesis, we found elevated levels for Tgfb1 and Spp1, two master regulators of EMT. A mesenchymal signature profile for AS-30D cells is also supported by the overexpression of genes encoding for molecules known to be associated to aggressiveness and metastatic phenotypes such as Foxm1, C-met, and Inppl1. This study supports the use of the AS-30D cells as an efficient and cost-effective model to study gene expression changes in HCC, especially those associated with the EMT process.

Genetics Is of the Essence to Face NAFLD

Nonalcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease worldwide. It is closely related to obesity, insulin resistance (IR) and dyslipidemia so much so it is considered the hepatic manifestation of the Metabolic Syndrome. The NAFLD spectrum extends from simple steatosis to nonalcoholic steatohepatitis (NASH), a clinical condition which may progress up to fibrosis, cirrhosis and hepatocellular carcinoma (HCC). NAFLD is a complex disease whose pathogenesis is shaped by both environmental and genetic factors. In the last two decades, several heritable modifications in genes influencing hepatic lipid remodeling, and mitochondrial oxidative status have been emerged as predictors of progressive hepatic damage. Among them, the patatin-like phospholipase domain-containing 3 (PNPLA3) p.I148M, the Transmembrane 6 superfamily member 2 (TM6SF2) p.E167K and the rs641738 membrane bound-o-acyltransferase domain-containing 7 (MBOAT7) polymorphisms are considered the most robust modifiers of NAFLD. However, a forefront frontier in the study of NAFLD heritability is to postulate score-based strategy, building polygenic risk scores (PRS), which aggregate the most relevant genetic determinants of NAFLD and biochemical parameters, with the purpose to foresee patients with greater risk of severe NAFLD, guaranteeing the most highly predictive value, the best diagnostic accuracy and the more precise individualized therapy.

The molecular structure and role of LECT2 or CHM-II in arthritis, cancer, and other diseases

Leukocyte cell-derived chemotaxin-2 (LECT2 or LECT-2), also called chondromodulin II (ChM-II or CHM2) plays a versatile role in various tissues. It was first identified as a chemotactic factor to promote the migration of neutrophils. It was also reported as a hepatokine to regulate glucose metabolism, obesity, and nonalcoholic fatty liver disease. As a secreted factor, LECT2 binds to several cell surface receptors CD209a, Tie1, and Met to regulate inflammatory reaction, fibrogenesis, vascular invasion, and tumor metastasis in various cell types. As an intracellular molecule, it is associated with LECT2-mediated amyloidosis, in which LECT2 misfolding results in insoluble fibrils in multiple tissues such as the kidney, liver, and lung. Recently, LECT2 was found to be associated with the development of rheumatoid arthritis and osteoarthritis, involving the dysregulation of osteoclasts, mesenchymal stem cells, osteoblasts, chondrocytes, and endothelial cells in the bone microenvironment. LECT2 is implicated in the development of cancers, such as hepatocellular carcinoma via MET-mediated PTP1B/Raf1/ERK signaling pathways and is proposed as a biomarker. The mechanisms by which LECT2 regulates diverse pathogenic conditions in various tissues remain to be fully elucidated. Further research to understand the role of LECT2 in a tissue tropism-dependent manner would facilitate the development of LECT2 as a biomarker for diagnosis and therapeutic target.

A novel prognostic signature based on four glycolysis-related genes predicts survival and clinical risk of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common cancer with limited cure and poor survival. In our study, a bioinformatic analysis was conducted to investigate the role of glycolysis in the pathogenesis and progression of HCC.

METHODS

Single-sample gene set enrichment analysis (ssGESA) was used to calculate enrichment scores for each sample in TCGA-LIHC and GEO14520 according to the glycolysis gene set. Weighted gene co-expression network analysis identified a gene module closely related to glycolysis, and their function was investigated. Prognostic biomarkers were screened from these genes. Cox proportional hazard model and least absolute shrinkage and selection operator regression were used to construct the prognostic signature. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curve analyses evaluated the prediction performance of the prognostic signature in TCGA-LIHC and ICGC-LIRI-JP. Combination analysis data of clinical features and prognostic signature constructed a nomogram. Area under ROC curves and decision curve analysis were used to compare the nomogram and its components.

RESULTS

The glycolysis pathway was upregulated in HCC and was unfavorable for survival. The determined gene module was mainly enriched in cell proliferation. A prognostic signature (CDCA8, RAB5IF, SAP30, and UCK2) was developed and validated. KM and ROC curves showed a considerable predictive effect. The risk score derived from the signature was an independent prognostic factor. The nomogram increased prediction efficiency by combining risk signature and TNM stage and performed better than component factors in net benefit.

CONCLUSION

The gene signature may contribute to individual risk estimation, survival prognosis, and clinical management.

A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.

METHODS

Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the "edgeR" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.

RESULTS

Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.

CONCLUSION

The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.

Antioxidant Gene Signature Impacts the Immune Infiltration and Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC.

Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated.

Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC.

Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.

Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer

BACKGROUND

Liver cancer is the sixth most commonly diagnosed cancer and the fourth most common cause of cancer death. The purpose of this work is to find new diagnostic biomarkers or prognostic biomarkers and explore the biological functions related to the prognosis of liver cancer.

METHODS

GSE25097 datasets were firstly obtained and compared with TCGA LICA datasets and an analysis of the overlapping differentially expressed genes (DEGs) was conducted. Cytoscape was used to screen out the Hub Genes among the DEGs. ROC curve analysis was used to screen the Hub Genes to determine the genes that could be used as diagnostic biomarkers. Kaplan-Meier analysis and Cox proportional hazards model screened genes associated with prognosis biomarkers, and further Gene Set Enrichment Analysis was performed on the prognosis genes to explore the mechanism affecting the survival and prognosis of liver cancer patients.

RESULTS

790 DEGs and 2162 DEGs were obtained respectively from the GSE25097 and TCGA LIHC data sets, and 102 Common DEGs were identified by overlapping the two DEGs. Further screening identified 22 Hub Genes from 102 Common DEGs. ROC and survival curves were used to analyze these 22 Hub Genes and it was found that there were 16 genes with a value of AUC > 90%. Among these, the expression levels of ESR1,SPP1 and FOSB genes were closely related to the survival time of liver cancer patients. Three common pathways of ESR1, FOBS and SPP1 genes were identified along with seven common pathways of ESR1 and SPP1 genes and four common pathways of ESR1 and FOSB genes.

CONCLUSIONS

SPP1, AURKA, NUSAP1, TOP2A, UBE2C, AFP, GMNN, PTTG1, RRM2, SPARCL1, CXCL12, FOS, DCN, SOCS3, FOSB and PCK1 can be used as diagnostic biomarkers for liver cancer, among which FOBS and SPP1 genes can also be used as prognostic biomarkers. Activation of the cell cycle-related pathway, pancreas beta cells pathway, and the estrogen signaling pathway, while on the other hand inhibition of the hallmark heme metabolism pathway, hallmark coagulation pathway, and the fat metabolism pathway may promote prognosis in liver cancer patients.

Osteopontin Takes Center Stage in Chronic Liver Disease

Osteopontin (OPN) was first identified in 1986. The prefix osteo- means bone; however, OPN is expressed in other tissues, including liver. The suffix -pontin means bridge and denotes the role of OPN as a link protein within the extracellular matrix. While OPN has well-established physiological roles, multiple "omics" analyses suggest that it is also involved in chronic liver disease. In this review, we provide a summary of the OPN gene and protein structure and regulation. We outline the current knowledge on how OPN is involved in hepatic steatosis in the context of alcoholic liver disease and non-alcoholic fatty liver disease. We describe the mechanisms whereby OPN participates in inflammation and liver fibrosis and discuss current research on its role in hepatocellular carcinoma and cholangiopathies. To conclude, we highlight important points to consider when doing research on OPN and provide direction for making progress on how OPN contributes to chronic liver disease.

Pan-cancer analysis identifies ITIH1 as a novel prognostic indicator for hepatocellular carcinoma

Although a previous pan-cancer study has reported the expression patterns of ITIHs in various tumors, their analyses have been restricted to limited cancer types. We thus comprehensively analyzed the expression profiles and clinical significances of ITIHs in a broader spectrum of cancers from TCGA. Our results showed that ITIHs were primarily down-regulated in tested cancers. The ITIH members were associated with either survival advantage or disadvantage, depending on the cancer type tested and the genes queried. Importantly, we for the first time demonstrated that ITIH1 had substantially decreased expression in liver hepatocellular carcinoma (LIHC) compared with corresponding normal tissue, and its down-regulation adversely impacted patient outcome. Moreover, ITIH1 expression was consistently declining during the progression of LIHC. Further analysis revealed that ITIH1 may be involved in cellular metabolic processes. Our findings established ITIH1 as a potential diagnostic and prognostic biomarker for LIHC, which awaits future experimental validation.

Integrated Proteomics and Bioinformatics to Identify Potential Prognostic Biomarkers in Hepatocellular Carcinoma

BACKGROUND

Liver hepatocellular carcinoma (HCC) is the third most common cause of death by cancer and has a high mortality world-widely. Approximately 75-85% of primary liver cancers are caused by HCC. Uncovering novel genes with prognostic significance would shed light on improving the HCC patient's outcome.

OBJECTIVE

In this research, we aim to identify novel prognostic biomarkers in hepatocellular carcinoma.

METHODS

Integrated proteomics and bioinformatics analysis were performed to investigate the expression landscape of prognostic biomarkers in 24 paired HCC patients.

RESULTS

As a result, eight key genes related to prognosis, including ACADS, HSD17B13, PON3, AMDHD1, CYP2C8, CYP4A11, SLC27A5, CYP2E1, were identified by comparing the weighted gene co-expression network analysis (WGCNA), proteomic differentially expressed genes (DEGs), proteomic turquoise module, The Cancer Genome Atlas (TCGA) cohort DEGs of HCC. Furthermore, we trained and validated eight pivotal genes integrating these independent clinical variables into a nomogram with superior accuracy in predicting progression events, and their lower expression was associated with a higher stage/risk score. The Gene Set Enrichment Analysis (GSEA) further revealed that these key genes showed enrichment in the HCC regulatory pathway.

CONCLUSION

All in all, we found that these eight genes might be the novel potential prognostic biomarkers for HCC and also provide promising insights into the pathogenesis of HCC at the molecular level.

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

The effect of a novel glycolysis-related gene signature on progression, prognosis and immune microenvironment of renal cell carcinoma

Background

Glycolysis is a central metabolic pathway for tumor cells. However, the potential roles of glycolysis-related genes in renal cell carcinoma (RCC) have not been investigated.

Methods

Seven glycolysis-related gene sets were selected from MSigDB and were analyzed through GSEA. Using TCGA database, the glycolysis-related gene signature was constructed. Prognostic analyses were based on the Kaplan–Meier method. The cBioPortal database was employed to perform the mutation analyses. The CIBERSORT algorithm and TIMER database were used to determine the immunological effect of glycolytic gene signature. The expressions in protein level of eight glycolytic risk genes were determined by HPA database. Finally, qPCR, MTT and Transwell invasion assays were conducted to validate the roles of core glycolytic risk genes (CD44, PLOD1 and PLOD2) in RCC.

Results

Four glycolysis-related gene sets were significantly enriched in RCC samples. The glycolytic risk signature was constructed (including CD44, PLOD2, KIF20A, IDUA, PLOD1, HMMR, DEPDC1 and ANKZF1) and identified as an independent RCC prognostic factor (HR = 1.204). Moreover, genetic alterations of glycolytic risk genes were uncommon in RCC (10.5%) and glycolytic risk signature can partially affect immune microenvironment of RCC. Six glycolytic risk genes (except for IDUA and HMMR) were over-expression in A498 and 786-O renal cancer cells through qPCR test. MTT and Transwell assays revealed that silencing of CD44, PLOD1 and PLOD2 suppressed the proliferation and invasion of renal cancer cells.

Conclusions

The glycolysis-related risk signature is closely associated with RCC prognosis, progression and immune microenvironment. CD44, PLOD1 and PLOD2 may serve as RCC oncogenes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07702-7.

Roles of Farnesyl-Diphosphate Farnesyltransferase 1 in Tumour and Tumour Microenvironments

Farnesyl-diphosphate farnesyltransferase 1 (FDFT1, squalene synthase), a membrane-associated enzyme, synthesizes squalene via condensation of two molecules of farnesyl pyrophosphate. Accumulating evidence has noted that FDFT1 plays a critical role in cancer, particularly in metabolic reprogramming, cell proliferation, and invasion. Based on these advances in our knowledge, FDFT1 could be a potential target for cancer treatment. This review focuses on the contribution of FDFT1 to the hallmarks of cancer, and further, we discuss the applicability of FDFT1 as a cancer prognostic marker and target for anticancer therapy.