Identification of key pathways and biomarkers in sorafenib-resistant hepatocellular carcinoma using bioinformatics analysis

Endoplasmic Reticulum Membrane Protein Complex Regulates Cancer Stem Cells and is Associated with Sorafenib Resistance in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, underscoring the need for novel therapeutic targets. This study aimed to elucidate the role of endoplasmic reticulum membrane protein complex subunit 1 (EMC1) in HCC progression and its therapeutic potential.

Methods

Publicly available sequencing data and biopsy specimens were analyzed to assess EMC's clinical value and functions in HCC. In vitro experiments validated EMC functions, and multiplex immunofluorescence analysis examined EMC-associated sorafenib resistance mechanisms. EMC1 expression was knocked down in HCC cell lines, followed by cell viability, wound healing, and transwell migration assays. Tumor growth and response to sorafenib treatment were evaluated in mouse models. Metabolomic analysis assessed changes in the TCA cycle.

Results

EMC genes were aberrantly expressed in HCC, and high EMC1 expression correlated with poorer survival rates. EMC1 disruption enhanced HCC cells' sensitivity to sorafenib, reducing cell viability, increasing apoptosis, and decreasing tumor size and weight. EMC1 maintained cancer cell stemness and promoted M2 macrophage infiltration. Metabolomic analysis revealed significant changes in the TCA cycle, indicating EMC1's role in HCC metabolic reprogramming. Importantly, EMC1 is highly associated with sorafenib resistance, potentially linked to CTNNB1 mutation or activation.

Conclusion

EMC1 plays a critical role in regulating the sorafenib resistance in HCC. Targeting EMC1 may improve HCC treatment efficacy.

Multi-Omics Analysis Revealed a Significant Alteration of Critical Metabolic Pathways Due to Sorafenib-Resistance in Hep3B Cell Lines

Hepatocellular carcinoma (HCC) is the second prominent cause of cancer-associated death worldwide. Usually, HCC is diagnosed in advanced stages, wherein sorafenib, a multiple target tyrosine kinase inhibitor, is used as the first line of treatment. Unfortunately, resistance to sorafenib is usually encountered within six months of treatment. Therefore, there is a critical need to identify the underlying reasons for drug resistance. In the present study, we investigated the proteomic and metabolomics alterations accompanying sorafenib resistance in hepatocellular carcinoma Hep3B cells by employing ultra-high-performance liquid chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS). The Bruker Human Metabolome Database (HMDB) library was used to identify the differentially abundant metabolites through MetaboScape 4.0 software (Bruker). For protein annotation and identification, the Uniprot proteome for Homo sapiens (Human) database was utilized through MaxQuant. The results revealed that 27 metabolites and 18 proteins were significantly dysregulated due to sorafenib resistance in Hep3B cells compared to the parental phenotype. D-alanine, L-proline, o-tyrosine, succinic acid and phosphatidylcholine (PC, 16:0/16:0) were among the significantly altered metabolites. Ubiquitin carboxyl-terminal hydrolase isozyme L1, mitochondrial superoxide dismutase, UDP-glucose-6-dehydrogenase, sorbitol dehydrogenase and calpain small subunit 1 were among the significantly altered proteins. The findings revealed that resistant Hep3B cells demonstrated significant alterations in amino acid and nucleotide metabolic pathways, energy production pathways and other pathways related to cancer aggressiveness, such as migration, proliferation and drug-resistance. Joint pathway enrichment analysis unveiled unique pathways, including the antifolate resistance pathway and other important pathways that maintain cancer cells' survival, growth, and proliferation. Collectively, the results identified potential biomarkers for sorafenib-resistant HCC and gave insights into their role in chemotherapeutic drug resistance, cancer initiation, progression and aggressiveness, which may contribute to better prognosis and chemotherapeutic outcomes.

Differential gene expression analysis after DAPK1 knockout in hepatocellular carcinoma cells

Background

The mechanism through which death-associated protein kinase 1 (DAPK1) causes hepatocellular carcinoma (HCC) progression remains unclear. In this study, we aimed to identify key proteins that were altered after DAPK1 knockout.

Methods

Stable DAPK1 knockout HCC cell lines were established, then the differentially expressed genes (DEGs) of HCC were screened using the NetworkAnalyst database and enriched using the Metascape software. Protein-protein interaction networks (PPIs) were analyzed and visualized using the STRING database expansion.

Results

In total, 732 differentially expressed genes were identified, including 415 upregulated genes and 317 downregulated genes. Through Cytoscape software scoring, 10 pivotal genes were found to be closely related to changes in DAPK1 expression; Kininogen-1 (KNG1), Complement C3 (C3), Metalloproteinase inhibitor 1 (TIMP1), and Alpha-2-HS-glycoprotein (AHSG) were the most strongly associated with DAPK1 expression changes. Moreover, western blot analysis results revealed that changes in the levels of proteins encoded by the four key genes after DAPK1 knockout were consistent with those seen in the database screening.

Conclusions

These results provide a direction for further studies on the DAPK1 gene and on the mechanism through which DAPK1 leads to hepatocellular carcinoma development.

Identification of gene signature in RNA-Seq hepatocellular carcinoma data by Pareto-optimal cluster algorithm

Aim

This study aimed to detect gene signatures in RNA-sequencing (RNA-seq) data using Pareto-optimal cluster size identification.

Background

RNA-seq has emerged as an important technology for transcriptome profiling in recent years. Gene expression signatures involving tens of genes have been proven to be predictive of disease type and patient response to treatment.

Methods

Data related to the liver cancer RNA-seq dataset, which included 35 paired hepatocellular carcinoma (HCC) and non-tumor tissue samples, was used in this study. The differentially expressed genes (DEGs) were identified after performing pre-filtering and normalization. After that, a multi-objective optimization technique, namely multi-objective optimization for collecting cluster alternatives (MOCCA), was used to discover the Pareto-optimal cluster size for these DEGs. Then, the k-means clustering method was performed on the RNA-seq data. The best cluster, as a signature for the disease, was found by calculating the average Spearman's correlation score of all genes in the module in a pair-wise manner. All analyses were performed in the R 4.1.1 package in virtual space with 100 Gb of RAM memory.

Results

Using MOCCA, eight Pareto-optimal clusters were obtained. Ultimately, two clusters with the greatest average Spearman's correlation coefficient scores were chosen as gene signatures. Eleven prognostic genes involved in HCC's abnormal metabolism were identified. In addition, three differentially expressed pathways were identified between tumor and non-tumor tissues.

Conclusion

These identified metabolic prognostic genes help us to provide more powerful prognostic information and enhance survival prediction for HCC patients. In addition, Pareto-optimal cluster size identification is suggested for gene signature in other RNA-Seq data.

Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), a high mortality malignancy, has become a worldwide public health concern. Acquired resistance to the multikinase inhibitor sorafenib challenges its clinical efficacy and the survival benefits it provides to patients with advanced HCC. This study aimed to identify critical genes and pathways associated with sorafenib resistance in HCC using integrated bioinformatics analysis. Differentially expressed genes (DEGs) were identified using four HCC gene expression profiles (including 34 sorafenib-resistant and 29 sorafenib-sensitive samples) based on the robust rank aggregation method and R software. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and small molecules reversing sorafenib resistance were searched for using the connectivity map (CMAP) database. Pearson correlation and survival analyses of hub genes were performed using cBioPortal and Gene Expression Profiling and Interactive Analysis (GEPIA). Finally, the expression levels of hub genes in sorafenib-resistant HCC cells were verified using quantitative polymerase chain reaction (q-PCR). A total of 165 integrated DEGs (66 upregulated and 99 downregulated in sorafenib resistant samples compared sorafenib sensitive ones) primarily enriched in negative regulation of endopeptidase activity, extracellular exosome, and protease binding were identified. Some pathways were commonly shared between the integrated DEGs. Seven promising therapeutic agents and 13 hub genes were identified. These findings provide a strategy and theoretical basis for overcoming sorafenib resistance in HCC patients.

Underlying mechanism of sorafenib resistance in hepatocellular carcinoma: a bioinformatics study based on validated resistance-related genes

Background

Sorafenib, the first approved targeted therapy for advanced hepatocellular carcinoma (HCC), is often reported to comprised survival-benefit due to resistance. An underlying mechanism of resistance was proposed using bioinformatics analysis based on differentially expressed genes (DEGs) from microarrays. However, most DEGs were invalidated at both the expression level, and the role in causing resistance. Therefore, we conducted a bioinformatics analysis based on experimentally determined sorafenib-resistance-related genes (SRRGs) to elucidate the mechanism of sorafenib resistance.

Methods

The SRRGs, which have been experimentally determined to promote or inhibit resistance, were collected from published studies. The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform Gene Ontology (GO) and pathway enrichment analysis, respectively. A corresponding protein-protein interaction network (PPI) was created using the Cytoscape software program, and network hub genes were proposed.

Results

A total of 145 SRRGs, with 117 promoting and 28 inhibiting resistance, were identified. Cell proliferation, migration, development, response to oxygen levels, epithelial-to-mesenchymal transition (EMT), cell skeleton, protein function, and autophagy were all proposed as crucial gene functions related to resistance. The pathways related to cell proliferation or apoptosis, immune function, endocrine metabolism, stem cell function, and differentiation were identified as key resistance-related pathways. A total of 81 hub genes were proposed, including the following top 10 genes: TP53, AKT1, EGFR, STAT3, VEGFA, JUN, MAPK1, IL6, PTEN, and CTNNB1.

Conclusions

In conclusion, this study gathered experimentally validated genes that determine sorafenib resistance in HCC, provided an overview of the underlying mechanisms of resistance, and further validated sorafenib resistance in HCC.

Fibrinogen-Like Protein 1 Modulates Sorafenib Resistance in Human Hepatocellular Carcinoma Cells

Despite liver cancer being the second-leading cause of cancer-related death worldwide, few systemic drugs have been approved. Sorafenib, the first FDA-approved systemic drug for unresectable hepatocellular carcinoma (HCC), is limited by resistance. However, the precise mechanisms underlying this phenomenon are unknown. Since fibrinogen-like 1 (FGL1) is involved in HCC progression and upregulated after anticancer therapy, we investigated its role in regulating sorafenib resistance in HCC. FGL1 expression was assessed in six HCC cell lines (HepG2, Huh7, Hep3B, SNU387, SNU449, and SNU475) using western blotting. Correlations between FGL1 expression and sorafenib resistance were examined by cell viability, colony formation, and flow cytometry assays. FGL1 was knocked-down to confirm its effects on sorafenib resistance. FGL1 expression was higher in HepG2, Huh7, and Hep3B cells than in SNU387, SNU449, and SNU475 cells; high FGL1-expressing HCC cells showed a lower IC₅₀ and higher sensitivity to sorafenib. In Huh7 and Hep3B cells, FGL1 knockdown significantly increased colony formation by 61% (p = 0.0013) and 99% (p = 0.0002), respectively, compared to that in controls and abolished sorafenib-induced suppression of colony formation, possibly by modulating ERK and autophagy signals. Our findings demonstrate that sorafenib resistance mediated by FGL1 in HCC cells, suggesting FGL1 as a potential sorafenib-resistance biomarker and target for HCC therapy.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

LncRNA NEAT1 modulates sorafenib resistance in hepatocellular carcinoma through regulating the miR-149-5p/AKT1 axis

BACKGROUND/AIMS

The purpose of this study is to explore the expression characteristics of lncRNA NEAT1 in hepatocellular carcinoma (HCC) and the molecular mechanism of its regulation on sorafenib resistance.

MATERIALS AND METHODS

This experimental study was performed from June 2013 to June 2019. The level of NEAT1 was determined using RT-PCR in HCC and matched adjacent tissues from 79 HCC patients in Linyi central hospital. The patients were divided into two groups to compare their prognosis based on the median NEAT1 expressions as a cutoff value. HCC cell line HepG2 negative control (HepG2-NC), sorafenib-resistant HepG2 cells (HepG2-SR) were transfected with or without NEAT1 siRNA, followed by subsequent molecular analysis, to determine the function of NEAT1 on sorafenib resistance in HCC cells. The cell transcripts were determined by RNA-sequencing analysis. The binding site of the NEAT1 and microRNA-149-5p (miR-149-5p) was verified by luciferase assay.

RESULTS

We found that NEAT1 was significantly increased in HCC tissues. Furthermore, NEAT1 expressions were significantly associated with HCC prognosis and chemoresistance patterns against sorafenib. Subsequently, the sorafenib-resistant HCC cell lines, together with the controls, were used to determine the regulatory effect of NEAT1 on HCC cells' progression and sorafenib resistance. NEAT1 targets the miR-149-5p, and therefore, decrease the activity of sorafenib against HCC cells. NEAT1 functions were demonstrated to be triggered by the regulation of miR-149-5p/AKT1 axis.

CONCLUSIONS

NEAT1/miR-149-5p/AKT1 pathway-based therapy might be a potential clinical application for HCC patients.

Multi-omic Pathway and Network Analysis to Identify Biomarkers for Hepatocellular Carcinoma

The threat of Hepatocellular Carcinoma (HCC) is a growing problem, with incidence rates anticipated to near double over the next two decades. The increasing burden makes discovery of novel diagnostic, prognostic, and therapeutic biomarkers distinguishing HCC from underlying cirrhosis a significant focus. In this study, we analyzed tissue and serum samples from 40 HCC cases and 25 patients with liver cirrhosis (CIRR) to better understand the mechanistic differences between HCC and CIRR. Through pathway and network analysis, we are able to take a systems biology approach to conduct multi-omic analysis of transcriptomic, glycoproteomic, and metabolomic data acquired through various platforms. As a result, we are able to identify the FXR/RXR Activation pathway as being represented by molecules spanning multiple molecular compartments in these samples. Specifically, serum metabolites deoxycholate and chenodeoxycholic acid and serum glycoproteins C4A/C4B, KNG1, and HPX are biomarker candidates identified from this analysis that are of interest for future targeted studies. These results demonstrate the integrative power of multi-omic analysis to prioritize clinically and biologically relevant biomarker candidates that can increase understanding of molecular mechanisms driving HCC and make an impact in patient care.

Sorafenib-loaded hydroxyethyl starch-TG100-115 micelles for the treatment of liver cancer based on synergistic treatment

Tumor microenvironment is closely related to the occurrence and development of liver cancer. Tumor-associated macrophages (TAMs) are an important part of tumor microenvironment promoting tumor deterioration and metastasis by inhibiting immune cells. Previous studies showed that PI3Kγ inhibitor could reverse the phenotype of TAMs, relieve immunosuppression and sensitize chemotherapy drugs, suggesting that the combination of PI3Kγ inhibitor and chemotherapeutics is likely to bring new breakthroughs in the treatment of liver cancer. Based on it, this paper builds HES-TG100-115-CDM-PEG micelles with tumor microenvironment responsiveness that simultaneously loaded sorafenib and TG100-115 to synergistically treat liver cancer. Pharmacokinetic study showed that the prepared micelles had longer half-life than that of the free drug solutions, which was favorable for high propensity of extravasation through tumor vascular fenestrations. Under low pH and high α-amylasereductive conditions, micelles could depolymerize quickly due to the sensitivity of bonds and enhance significantly cytotoxic activity against Hep-3B liver cancer cell. Additionally, micelles demonstrated higher levels of antitumor efficiency and better tolerance against nude mouse with Hep-3B cell than the free drug solutions. These findings reveal that HES-TG100-115-CDM-PEG micelles are a promising drug delivery system in clinical comprehensive therapy of liver cancer.

Diagnostic value of apolipoprotein C-I, transthyretin and apolipoprotein C-III in gastric cancer

Diagnostic value of apolipoprotein C-I (ApoC-I), transthyretin (TTR) and ApoC-III in gastric cancer were evaluated. Retrospective analysis methods were used to collect 60 patients with gastric cancer first diagnosed in The First Affiliated Hospital of Jiaxing University. There were 60 patients with chronic atrophic gastritis in the benign lesion group and 60 healthy individuals in the control group. The expression levels of serum ApoC-I, TTR and ApoC-III was detected by enzyme-linked immunosorbent assay. Differences existed in the expression levels of ApoC-I, TTR and ApoC-III in the gastric cancer group, benign lesion group and control group (P<0.001), with the expression levels of ApoC-I, TTR and ApoC-III in the gastric cancer group being lower than that of the benign lesion group (P<0.05), and the expression levels of ApoC-I, TTR and ApoC-III in the benign lesion group being lower than that of the control group (P<0.05). The expression levels of ApoC-I, TTR and ApoC-III in the gastric cancer group were to a certain degree correlated with the clinical stage, lymph node metastasis and differentiation of patients in the gastric cancer group (P<0.05). The specificity and negative predictive value of combined detection were proven to be higher than the separate detection of the three factors (P<0.05). The detection of serum ApoC-I, TTR and ApoC-III was of great significance in the diagnosis of gastric cancer and the estimation of its severity. The method of combined detection is worth a further in-depth study as it could improve the specificity of diagnosis and have a higher negative predictive value.