Mining featured biomarkers associated with vascular invasion in HCC by bioinformatics analysis with TCGA RNA sequencing data

The role of anoctamin 1 in liver disease

Liver diseases include all types of viral hepatitis, alcoholic liver disease (ALD), nonalcoholic fatty liver disease (NAFLD), cirrhosis, liver failure (LF) and hepatocellular carcinoma (HCC). Liver disease is now one of the leading causes of disease and death worldwide, which compels us to better understand the mechanisms involved in the development of liver diseases. Anoctamin 1 (ANO1), a calcium-activated chloride channel (CaCC), plays an important role in epithelial cell secretion, proliferation and migration. ANO1 plays a key role in transcriptional regulation as well as in many signalling pathways. It is involved in the genesis, development, progression and/or metastasis of several tumours and other diseases including liver diseases. This paper reviews the role and molecular mechanisms of ANO1 in the development of various liver diseases, aiming to provide a reference for further research on the role of ANO1 in liver diseases and to contribute to the improvement of therapeutic strategies for liver diseases by regulating ANO1.

OLFM2 promotes epithelial-mesenchymal transition, migration, and invasion in colorectal cancer through the TGF-β/Smad signaling pathway

BACKGROUND

Colorectal cancer (CRC) is an aggressive tumor of the gastrointestinal tract, which is a major public health concern worldwide. Despite numerous studies, the precise mechanism of metastasis behind its progression remains elusive. As a member of the containing olfactomedin domains protein family, olfactomedin 2 (OLFM2) may play a role in tumor metastasis. It is highly expressed in colorectal cancer, and its role in the metastasis of CRC is still unclear. As such, this study seeks to explore the function of OLFM2 on CRC metastasis and its potential mechanisms.

METHODS

Real-time fluorescence quantitative PCR and western blotting were used to study the expression of OLFM2 in human CRC and adjacent normal tissues. Knockdown and overexpression OLFM2 cell lines were constructed using siRNA and overexpression plasmids to explore the role of OLFM2 in the migration and invasion of CRC through transwell, and wound healing experiments. Finally, the expression of epithelial-mesenchymal transition (EMT) -related proteins and TGF-β/Smad signaling pathway-related proteins was investigated using western blotting.

RESULTS

In this study, we observed an elevation of OLFM2 expression levels in CRC tissues. To investigate the function of OLFM2, we overexpressed and knocked down OLFM2. We discovered that OLFM2 knockdown inhibited migration and invasion of colon cancer cells. Furthermore, E-cadherin expression increased while N-cadherin and Vimentin expression were opposite. It is no surprise that overexpressing OLFM2 had the opposite effects. We also identified that OLFM2 knockdown resulted in reduced TGF-βR1 and downstream molecules p-Smad2 and p-Smad3, which are related to the TGF-β / Smad pathway. In contrast, overexpressing OLFM2 significantly boosted their expression levels.

CONCLUSION

The protein OLFM2 has been identified as a crucial determinant in the progression of CRC. Its mechanism of action involves the facilitation of EMT through the TGF-β/Smad signaling pathway. Given its pivotal role in CRC, OLFM2 has emerged as a promising diagnostic and therapeutic target for the disease. These results indicate the potential of OLFM2 as a valuable biomarker for CRC diagnosis and treatment and highlight the need for further research exploring its clinical significance.

The Role of Olfactomedin 2 in the Adipose Tissue–Liver Axis and Its Implication in Obesity-Associated Nonalcoholic Fatty Liver Disease

This study’s objective was to assess the involvement of olfactomedin 2 (OLFM2), a secreted glycoprotein related to lipid metabolism regulation, in nonalcoholic fatty liver disease (NAFLD) mediated by the adipose-tissue–liver axis. OLFM2 mRNA expression was analyzed in subcutaneous (SAT) and visceral (VAT) adipose tissue by RT–qPCR. The cohort included women with normal weight (n = 16) or morbid obesity (MO, n = 60) who were subclassified into normal liver (n = 20), simple steatosis (n = 21), and nonalcoholic steatohepatitis (NASH, n = 19) groups. The results showed that OLFM2 expression in SAT was enhanced in MO individuals and in the presence of NAFLD. Specifically, OLFM2 expression in SAT was increased in mild and moderate degrees of steatosis in comparison to the absence of it. Moreover, OLFM2 expression in SAT was negatively correlated with interleukin-6 levels. On the other hand, OLFM2 expression in VAT decreased in the presence of NASH and exhibited a positive correlation with adiponectin levels. In conclusion, OLFM2 in SAT seems to be implicated in hepatic lipid accumulation. Additionally, since we previously suggested the possible implication of hepatic OLFM2 in NAFLD progression, now we propose a possible interaction between the liver and SAT, reinforcing the potential implication of this tissue in NAFLD development.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Genetic Analysis of Platelet-Related Genes in Hepatocellular Carcinoma Reveals a Novel Prognostic Signature and Determines PRKCD as the Potential Molecular Bridge

BACKGROUND

Hepatocellular carcinoma (HCC) belongs to a representative lethality gastrointestinal malignancy, and comprehensive management of HCC remains intractable at present on account of its invasive biological feature that is easy to relapse and early metastasis. The intimate connection between platelets and tumor progression has been widely reported, and platelet-related indicators are also used in the clinical practice of carcinoma. This work is designed to investigate the significance of platelet-related genes in the prognostic prediction of patients with HCC and their potential role in the cross-talk between HCC cells and platelets in the tumor microenvironment.

METHODS

By integrating the RNA-seq data and clinicopathological information of HCC patients, we extracted prognosis-associated platelet-related genes based on the univariate cox analysis and further established a relevant prognostic signature via the lasso cox regression analysis, and two independent HCC cohorts were used as external validation. Multiple bioinformatics methods were utilized to explore the underlying functional discrepancy between different risk groups classified by the risk model. And in vitro proliferation, invasion, and migration assays were conducted to investigate the effect of platelet stimulation on HCC cells' viability and motility, and flow cytometric analysis was exerted to demonstrate the influence of HCC cells on platelet activation.

RESULTS

A novel platelet-related risk model was developed and patients both in the training and testing cohorts were divided into distinct risk subgroups according to the median risk score. It was observed that the high-risk status was closely associated with poor prognosis and worse clinicopathological parameters. Meanwhile, an obvious discrepancy in the constitution of the immune microenvironment also indicated that distinct immune status might be a potential determinant affecting prognosis as well as immunotherapy reactiveness. Moreover, in vitro experiments demonstrated that PRKCD could act as a molecular bridge between tumor cells and platelets, which could either participate in regulating tumor malignant phenotype or mediating platelet activation.

CONCLUSIONS

In brief, this work reveals a novel platelet-related risk signature for prognostic evaluation of HCC patients and confirms that PRKCD is a key messenger in HCC cell-platelet interaction and plays a crucial role in mediating platelet-induced tumor progression.

Astragalus mongholicus Bunge and Curcuma aromatica Salisb. inhibits liver metastasis of colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway

BACKGROUND

One of the most challenging aspects of colon cancer (CC) prognosis and treatment is liver-tropic metastasis. Astragalus mongholicus Bunge-Curcuma aromatica Salisb. (AC) is a typical medication combination for the therapy of many malignancies. Our previous studies found that AC intervention inhibits liver metastasis of colon cancer (LMCC). Nevertheless, the comprehensive anti-metastasis mechanisms of AC have not been uncovered.

METHODS

In bioinformatics analysis, RNA-seq data of CC and LMCC patients were collected from TCGA and GEO databases, and differentially expressed genes (DEGs) were identified. The biological processes and signaling pathways involved in DEGs were enriched by GO and KEGG. The protein-protein interaction (PPI) network of DEGs was established and visualized using the Cytocape software, followed by screening Hub genes in the PPI network using Degree value as the criterion. Subsequently, the expression and survival relevance of Hub gene in COAD patients were verified. In the experimental study, the effects of AC on the inhibition of colon cancer growth and liver metastasis were comprehensively evaluated by cellular and animal models. Finally, based on the results of bioinformatics analysis, the possible mechanisms of AC inhibition of colon cancer EMT and liver metastasis were explored by in vivo and in vitro pharmacological experiments.

RESULTS

In this study, we obtained 2386 DEGs relevant to LMCC from the COAD (colon adenocarcinoma) and GSE38174 datasets. Results of GO gene function and KEGG signaling pathway enrichment analysis suggested that cellular EMT (Epithelial-mesenchymal transition) biological processes, Cytokine-cytokine receptor interaction and PI3K/Akt signaling pathways might be closely related to LMCC mechanism. We then screened for CXCL8, the core hub gene with the highest centrality within the PPI network of DEGs, and discovered that CXCL8 expression was negatively correlated with the prognosis of COAD patients. In vitro and in vivo experimental evidence presented that AC significantly inhibited colon cancer cell proliferation, migration and invasion ability, and suppressed tumor growth and liver metastasis in colon cancer orthotopic transplantation mice models. Concomitantly, AC significantly reduced CXCL8 expression levels in cell supernatants and serum. Moreover, AC reduced the expression and transcription of genes related to the PI3K/AKT pathway while suppressing the EMT process in colon cancer cells and model mice.

CONCLUSIONS

In summary, our research predicted the potential targets and pathways of LMCC, and experimentally demonstrated that AC might inhibit the growth and liver metastasis in colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway, which may facilitate the discovery of mechanisms and new therapeutic strategies for LMCC.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.

Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.

Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.

Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.

Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification

Background

Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC.

Methods

In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro.

Results

Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2.

Conclusions

This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03530-3.

A novel prognostic model for hepatocellular carcinoma based on 5 microRNAs related to vascular invasion

BACKGROUND

Hepatocellular carcinoma (HCC) is prevalent worldwide with a high mortality rate. Prognosis prediction is crucial for improving HCC patient outcomes, but effective tools are still lacking. Characteristics related to vascular invasion (VI), an important process involved in HCC recurrence and metastasis, may provide ideas on prognosis prediction.

METHODS

Tools, including R 4.0.3, Funrich version 3, Cytoscape 3.8.2, STRING 11.5, Venny 2.1.0, and GEPIA 2, were used to perform bioinformatic analyses. The VI-related microRNAs (miRNAs) were identified using Gene Expression Omnibus HCC miRNA dataset GSE67140, containing 81 samples of HCC with VI and 91 samples of HCC without VI. After further evaluated the identified miRNAs based on The Cancer Genome Atlas database, a prognostic model was constructed via Cox regression analysis. The miRNAs in this model were also verified in HCC patients. Moreover, a nomogram was developed by integrating risk score from the prognostic model with clinicopathological parameters. Finally, a potential miRNA-mRNA network related to VI was established through weighted gene co-expression network analysis of HCC mRNA dataset GSE20017, containing 40 samples of HCC with VI and 95 samples of HCC without VI.

RESULTS

A prognostic model of 5 VI-related miRNAs (hsa-miR-126-3p, hsa-miR-148a-3p, hsa-miR-15a-5p, hsa-miR-30a-5p, hsa-miR-199a-5p) was constructed. The area under receiver operating characteristic curve was 0.709 in predicting 5-year survival rate, with a sensitivity of 0.74 and a specificity of 0.63. The nomogram containing risk score could also predict prognosis. Moreover, a VI-related miRNA-mRNA network covering 4 miRNAs and 15 mRNAs was established.

CONCLUSION

The prognostic model and nomogram might be potential tools in HCC management, and the VI-related miRNA-mRNA network gave insights into how VI was developed.

A New Risk Score Based on Eight Hepatocellular Carcinoma- Immune Gene Expression Can Predict the Prognosis of the Patients

Background

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism.

Methods

We analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database.

Results

The IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells.

Conclusion

Our results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients.

Deficiency of GFRα1 promotes hepatocellular carcinoma progression but enhances oxaliplatin-mediated anti-tumor efficacy

Neurotrophic factors and their receptors have been identified to promote tumor progression. GFRα1, the receptor for glial cell line-derived neurotrophic factor (GDNF), has been demonstrated to be predominantly expressed in adult liver tissue. Our preliminary data showed that GFRα1 is significantly downregulated in hepatocellular carcinoma (HCC) tissue, compared to the matched non-neoplastic tissue. However, the role of GFRα1 in HCC progression remains unknown. Here we found that the expression of GFRα1 in HCC tissue is inversely correlated with the poorer prognosis of HCC patients. Silencing of GFRα1 expression markedly enhances HCC cell growth, tumor metastasis, as well as shortens the survival of HCC tumor-bearing mice. Forced expression of GFRα1 in HCC cells significantly reverses the tumor-promoting effects of GFRα1 silencing, and AAV8-mediated GFRα1 transfection in HCC tumor tissues significantly impedes tumor growth and prolongs the survival of HCC tumor-bearing mice. These results are also verified in vivo in GFRα1 knock-out mice model, with increased DEN-induced HCC carcinogenesis. Mechanistically, GFRα1 could inhibit epithelial-to-mesenchymal transition (EMT) of HCC cells, by upregulating expression of Claudin-1 and ZO-1. Of note, silencing of GFRα1 expression promotes oxaliplatin-mediated HCC cell apoptosis resulting in prolonged survival of HCC-bearing mice, and forced expression of GFRα1 markedly increased oxaliplatin resistance of HCC cells. These results demonstrate that deficiency of GFRα1 promotes HCC progression but enhances chemotherapeutic anti-tumor efficacy, suggesting that GFRα1 may be a candidate prognostic biomarker and a potential therapeutic target in HCC.

Low expression of KIF20A suppresses cell proliferation, promotes chemosensitivity and is associated with better prognosis in HCC

This study analysed the microarray datasets from Gene Expression Omnibus (GEO) database, and aimed to identify novel potential hub genes associated with the progression of HCC via bioinformatics analysis and experimental validation. The common differentially expressed genes (DEGs) from five GEO datasets were screened using GEO2R tool. The expression and survival analysis of hub genes in HCC were performed using Gene Expression Profiling Interactive Analysis, UALCAN and Kaplan-Meier plotter tools. In vitro functional assays were used to determine the caspase-3, -9, cell proliferation and chemo-sensitivity of HCC cells. A total of 177 common DEGs were identified between normal liver and HCC tissues among these datasets. Functional enrichment and PPI network analysis identified 22 hub genes from the common DEGs. The mRNA expression of 22 hub genes was all significantly up-regulated in HCC tissues compared to that in normal liver tissues. Further survival analysis showed that 10 hub genes predicted poor prognosis of patients with HCC. More importantly, the in vitro functional studies demonstrated that KIF20A knockdown suppressed the HCC cell proliferation and promoted the chemosensitivity of HCC cells to cisplatin and sorafenib. In conclusion, the present study identified a total of 177 common DEGs among 5 GEO microarray datasets and found that 10 hub genes could predict the poor prognosis of patients with HCC using the comprehensive bioinformatics analysis. Furthermore, KIF20A silence suppressed cell proliferation and enhanced chemosensitivity in HCC cells. Further studies may be required to determine the mechanistic role of these hub genes in HCC progression.

Traditional versus Microsphere Embolization for Hepatocellular Carcinoma: An Effectiveness Evaluation Using Data Mining

Background: For hepatocellular carcinoma (“HCC”), the current standard of treatment is hepatic artery embolization, generally through trans-catheter arterial chemoembolization (“TACE”). There are two types: traditional (“conventional” or “cTACE”) and microsphere (“DC bead TACE”). Unfortunately, the literature comparing the relative effectiveness of cTACE versus DC bead TACE is inconclusive, partially due to the complexity of HCC and its response to treatment. Data mining is an excellent method to extract meaning from complex data sets. Purpose: Through the application of data mining techniques, to compare the relative effectiveness of cTACE and DC bead TACE using a large patient database and to use said comparison to establish usable guidelines for developing treatment plans for HCC patients. Materials and Methods: The data of 372 HCC patients who underwent TACE in Taichung Veterans General Hospital were analyzed. The chi-square test was used to compare the difference in the effectiveness of the two therapies was compared. Logistic regression was used to calculate the odds ratios. Furthermore, using the C4.5 decision tree, the two therapies were classified into applicable fields. Chi-square test, the t-test, and logistic regression were used to verify the classification results. Results: In Barcelona Clinic Stages A and B cancers, cTACE was found to be 22.7% more effective than DC bead TACE. By using the decision tree C4.5 as a classifier, the effectiveness of either treatment for small tumors was 8.475 times than that for large tumors. DC bead TACE was 3.39 times more successful in treating patients with a single tumor than with multiple tumors. For patients with a single tumor, the chi-square test showed that 100–300 μm microspheres were significantly more effective than 300–500 μm. While these findings provide a reference for the selection of an appropriate TACE approach, we noted that overall accuracy was somewhat low, possibly due to the limited population. Conclusions: We found that data mining could be applied to develop clear guidelines for physician and researcher use in the case of complex pathologies such as HCC. However, some of our results contradicted those elsewhere in the literature, possibly due to a relatively small sample size. Significantly larger data sets with appropriate levels of granularity could produce more accurate results.

Identification and validation of EPHX2 as a prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common types of cancer, which is associated with a poor prognosis. It is necessary to identify novel prognostic biomarkers and therapeutic targets to improve the survival of patients with HCC. In the present study, a seven-gene signature associated with HCC progression was identified using weighted gene co-expression network analysis and least absolute shrinkage and selection operator, and its prognostic prediction value was confirmed in The Cancer Genome Atlas-liver HCC and International Cancer Genome Consortium liver cancer-RIKEN, Japan cohorts. Subsequently, a rarely reported gene, epoxide hydrolase 2 (EPHX2), was selected for further validation. Downregulation of EPHX2 in HCC was revealed using multiple expression datasets. Furthermore, reduced expression of EPHX2 was confirmed in HCC tissue samples and cell lines using reverse transcription-quantitative polymerase chain reaction and western blotting. Additionally, Kaplan-Meier survival curves indicated that patients with higher EPHX2 expression exhibited better prognosis, and clinicopathological analysis also revealed elevated EPHX2 levels in patients with early-stage HCC. Notably, EPHX2 was identified as an independent prognostic biomarker for overall survival of patients with HCC. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis and gene set enrichment analysis were performed to elucidate the functions of EPHX2. The results suggested that EPHX2 expression was closely associated with metabolic reprogramming. Finally, the prognostic value of EPHX2 was evaluated using HCC tissue microarrays. In conclusion, downregulation of EPHX2 was significantly associated with the development of HCC; therefore, EPHX2 may be considered a putative therapeutic candidate for the targeted treatment of HCC.

Accurate transcriptome assembly by Nanopore RNA sequencing reveals novel functional transcripts in hepatocellular carcinoma

The long reads of Nanopore sequencing permit accurate transcript assembly and ease in discovering novel transcripts with potentially important functions in cancers. The wide adoption of Nanopore sequencing for transcript quantification, however, is largely limited by high costs. To address this issue, we developed a bioinformatics software, NovelQuant, that can specifically quantify long-read-assembled novel transcripts with short-read sequencing data. Nanopore Direct RNA Sequencing was carried out on three hepatocellular carcinoma (HCC) patients' tumor, matched portal vein tumor thrombus, and peritumor to reconstruct the HCC transcriptome. Then, based on the reconstructed transcriptome, NovelQuant was applied on Illumina RNA sequencing data of 59 HCC patients' tumor and paired peritumor to quantify novel transcripts. Our further analysis revealed 361 novel transcripts dysregulated in HCC and that 101 of them were significantly associated with prognosis. There were 19 novel prognostic transcripts predicted to be long noncoding RNAs (lncRNAs), and some of them had regulatory targets that were reported to be associated with HCC. Additionally, 42 novel prognostic transcripts were predicted to be protein-coding mRNAs, and many of them could be involved in xenobiotic metabolism. Moreover, the tumor-suppressive roles of two representative novel prognostic transcripts, CDO1-novel (lncRNA) and CYP2A6-novel (protein-coding mRNA), were further functionally validated during HCC progression. Overall, the current study shows a possibility of combining long- and short-read sequencing to explore functionally important novel transcripts in HCC with accuracy and cost-efficiency, which expands the pool of molecular biomarkers that could enhance our understanding of the molecular mechanisms of HCC.

Construction of a ceRNA Network and a Prognostic lncRNA Signature associated with Vascular Invasion in Hepatocellular Carcinoma based on Weighted Gene Co-Expression Network Analysis

Background: Understanding risk factors for vascular invasion (VI) is crucial for assessing the risk of recurrence and overall prognosis of hepatocellular carcinoma (HCC). This study aimed to construct a prognostic long non-coding RNA (lncRNA) signature and a ceRNA Network associated with vascular invasion in HCC. Methods: Differentially expressed genes (DEGs) of HCC patients associated with VI were identified by analyzing data from TCGA. Weighted gene co-expression network analysis (WGCNA) was used to identify associations between gene expression modules and clinical features. A VI-related prognostic lncRNA signature was then established using univariate, LASSO and multivariate Cox proportional hazards regression analyses. Based on the hub modules identified by the WGCNA, we constructed a VI-related lncRNA-miRNA-mRNA ceRNA network and screened hub lncRNAs for further research. Finally, we conducted in vitro and in vivo experiments to determine the biological roles of the identified hub gene BBOX1-AS1. Results: The key module related to VI and OS was identified using WGCNA, after which a prognostic model consisting of eight lncRNAs was established, and verified using time-dependent receiver operating characteristic (ROC) curve analysis. BBOX1-AS1 was confirmed to be highly expressed in HCC tissues, and its expression was significantly correlated with a poor prognosis. Silencing BBOX1-AS1 in vitro significantly suppressed the proliferation, migration and invasion of HCC cells. In vivo experiments demonstrated that knocking down of BBOX1-AS1 could result in significant decrease of tumor volume and tumor weight. Conclusions: The VI-related lncRNA signature established in this study can be used to predict the clinical outcomes of HCC patients. In addition, we constructed a VI-related lncRNA-miRNA-mRNA ceRNA network and demonstrated that BBOX1-AS1 might be a novel biomarker associated with VI in HCC.

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Background

Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.

Methods

The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.

Results

Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.

Conclusions

The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-020-02116-y.

Selecting the first line treatment in non-metastatic hepatocellular carcinoma - comparing clinical practice guidelines

Primary malignancy of the liver or hepatocellular carcinoma (HCC) is unique in its presentation, disease process, and management. Unlike breast or colon cancer, the staging of HCC depends on performance status and baseline liver function along with pathological characteristics. Apart from traditional options like surgery and systemic therapy, effective management can be achieved in selected cases with liver transplant and locoregional therapy (LRT) like transarterial chemoembolization (TACE), transarterial radioembolization (TARE), and ablation. Liver study societies and cancer groups across the globe proposed guidelines to aid the treating physicians in choosing first-line treatment for liver cancer. It is tough to compare these guidelines as they differ not only in treatment recommendations but also in risk assessment (and staging). The approach to the same patient may be different in the country he or she is managed. In clinical practice, decisions are usually taken on the consensus of multidisciplinary tumor boards and do not necessarily adhere to any guidelines. In the early (and very early) stage HCC, curative options like surgery, transplant, and ablation are recommended. In intermediate stage HCC, LRT (TACE and TARE) is preferred in the first line and systemic therapy for treatment failure or residual disease. Systemic therapy, including the atezolizumab/bevacizumab combination and tyrosine kinase inhibitors (TKI) like sorafenib and lenvatinib, is used for advanced stages. Supportive care is advised for terminal stage HCC.

Hypomethylation of GDNF family receptor alpha 1 promotes epithelial-mesenchymal transition and predicts metastasis of colorectal cancer

Tumor metastasis is the major cause of poor prognosis and mortality in colorectal cancer (CRC). However, early diagnosis of highly metastatic CRC is currently difficult. In the present study, we screened for a novel biomarker, GDNF family receptor alpha 1 (GFRA1) based on the expression and methylation data in CRC patients from The Cancer Genome Altlas (TCGA), followed by further analysis of the correlation between the GFRA1 expression, methylation, and prognosis of patients. Our results show DNA hypomethylation-mediated upregulation of GFRA1 in invasive CRC, and it was found to be correlated with poor prognosis of CRC patients. Furthermore, GFRA1 methylation-modified sequences were found to have potential as methylation diagnostic markers of highly metastatic CRC. The targeted demethylation of GFRA1 by dCas9-TET1CD and gRNA promoted CRC metastasis in vivo and in vitro. Mechanistically, demethylation of GFRA1 induces epithelial-mesenchymal transition (EMT) by promoting AKT phosphorylation and increasing c-Jun expression in CRC cells. Collectively, our findings indicate that GFRA1 hypomethylation can promote CRC invasion via inducing EMT, and thus, GFRA1 methylation can be used as a biomarker for the early diagnosis of highly metastasis CRC.

Abnormal DNA methylation, one of important characteristics in tumor cells, is exploited as biomarkers for cancer diagnosis and prognosis prediction. Early diagnosis of highly metastatic CRC will be helpful for the clinical management, thus prolongs patient survival. However, it is currently difficult to make early diagnosis of highly metastatic CRC in clinical practice. Currently, we screened a novel biomarker gene, GFRA1, which associated with the invasion and poor prognosis of CRC. The targeted demethylation of GFRA1 exerted a significant promoting effect on CRC metastasis, and GFRA1 methylation-modified sequences are valuable diagnostic biomarker for CRC metastasis risk assessment. Mechanically, demethylation of GFRA1 induced epithelial-mesenchymal transition (EMT) by upregulating AKT phosphorylation and c-Jun expression in CRC cells. Our results demonstrate the promoting effect of GFRA1 demethylation on CRC invasion and GFRA1 methylation may be a potential prognostic marker for predicting metastasis of CRC.

Risk stratification of cutaneous melanoma reveals carcinogen metabolism enrichment and immune inhibition in high-risk patients

Cutaneous melanoma (CM) is the most lethal form of skin cancer. Risk assessment should facilitate stratified surveillance and guide treatment selection. Here, based on the mRNA-seq data from 419 CM patients in the Cancer Genome Atlas (TCGA), we developed a prognostic 21-gene signature to distinguish the outcomes of high- and low-risk patients, which was further validated in two external cohorts. The signature achieved a higher C-index as compared with other known biomarkers and clinical characteristics in both the TCGA and validation cohorts. Notably, in high-risk patients the expression levels of three driver genes, BRAF, NRAS, and NF1 in the MAPK pathway, were lower but exhibited a stronger positive correlation as compared with low-risk patients. Moreover, the genes involved in nicotinamide adenine dinucleotide metabolism were negatively correlated with the expression of BRAF in the high-risk group. Function analysis revealed that the upregulated genes in the high-risk group were enriched in the cytochrome P450-mediated metabolism of chemical carcinogens. Furthermore, the low-risk group had high levels of gamma delta T cells infiltration, while regulatory T cells were accumulated in the high-risk group. The present study offers a promising new prognostic signature for CM, and provides insight into the mechanisms of melanoma progression.

A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels

Objective

Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy.

Methods

Microarray datasets related with FAP were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes were identified by FAP vs. normal samples and FAP and duodenal cancer vs. normal samples. Furthermore, functional enrichment analyses of these differentially expressed genes were performed. A support vector machine (SVM) was performed to train and validate cancer risk prediction model.

Results

A total of 196 differentially expressed genes were identified between FAP compared with normal samples. 177 similarly expressed genes were identified both in FAP and duodenal cancer, which were mainly enriched in pathways in cancer and metabolic-related pathway, indicating that these genes in patients with FAP could contribute to duodenal cancer. Among them, Cyclin D1, SDF-1, AXIN, and TCF were significantly upregulated in FAP tissues using qRT-PCR. Based on the 177 genes, an SVM model was constructed for prediction of the risk of cancer in patients with FAP. After validation, the model can accurately distinguish FAP patients with high risk from those with low risk for duodenal cancer.

Conclusion

This study proposed a cancer risk prediction model based on an SVM at the transcript levels.

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy, which is a major cause of cancer morbidity and mortality worldwide. Thus, the aim of the present study was to identify the hub genes and underlying pathways of HCC via bioinformatics analyses. The present study screened three datasets, including GSE112790, GSE84402 and GSE74656 from the Gene Expression Omnibus (GEO) database, and downloaded the RNA-sequencing of HCC from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) in both the GEO and TCGA datasets were filtered, and the screened DEGs were subsequently analyzed for functional enrichment pathways. A protein-protein interaction (PPI) network was constructed, and hub genes were further screened to create the Kaplan-Meier curve using cBioPortal. The expression levels of hub genes were then validated in different datasets using the Oncomine database. In addition, associations between expression and tumor grade, hepatitis virus infection status, satellites and vascular invasion were assessed. A total of 126 DEGs were identified, containing 70 upregulated genes and 56 downregulated genes from the GEO and TCGA databases. By constructing the PPI network, the present study identified hub genes, including cyclin B1 (CCNB1), cell-division cycle protein 20 (CDC20), cyclin-dependent kinase 1, BUB1 mitotic checkpoint serine/threonine kinase β (BUB1B), cyclin A2, nucleolar and spindle associated protein 1, ubiquitin-conjugating enzyme E2 C (UBE2C) and ZW10 interactor. Furthermore, upregulated CCNB1, CDC20, BUB1B and UBE2C expression levels indicated worse disease-free and overall survival. Moreover, a meta-analysis of tumor and healthy tissues in the Oncomine database demonstrated that BUB1B and UBE2C were highly expressed in HCC. The present study also analyzed the data of HCC in TCGA database using univariate and multivariate Cox analyses, and demonstrated that BUB1B and UBE2C may be used as independent prognostic factors. In conclusion, the present study identified several genes and the signaling pathways that were associated with tumorigenesis using bioinformatics analyses, which could be potential targets for the diagnosis and treatment of HCC.