Deciphering the protein–protein interaction network regulating hepatocellular carcinoma metastasis

High-throughput proteomics-guided biomarker discovery of hepatocellular carcinoma

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

Bioinformatic Deconstruction of Differentially Expressed Sequence Tags in Hepatocellular Carcinoma Based on Artificial Neural Network

Traditional medical imaging methods for diagnosing hepatocellular carcinoma can only provide information for differential diagnosis in terms of morphology and blood supply of the lesion, and the determination of the nature of the lesion still relies on tissue biopsy. Although ultrasound or CT-guided biopsy has become an effective method for the diagnosis of liver cancer in recent years, the puncture has the possibility of tumor irritation, liver tumor rupture, or needle tract metastasis. In this paper, the use of bioinformatics method is to gradually screen potentially high-risk genes associated with HCC recurrence on a genome-wide scale would help to discover the key target molecules. The ANN method was used to establish a gene prediction model that can predict the recurrence and survival of HCC, so as to construct a tool to identify patients at risk of HCC recurrence. It provided a certain therapeutic basis for future clinical work, thereby improving the prognosis of patients with HCC. Using the "survfit" function of the "survival" package in the R language, the log-rank test (the log-rank test was a common method for comparing two survival curves) was performed on all genes with posthoc recurrence of hepatocellular carcinoma as the outcome event. Then, the BLAST tool (Basic Local Alignment Search Tool) was used to search the similarity of each hepatocellular carcinoma database to find out the genes with similar sequences to each hepatocellular carcinoma, so as to determine the function of each differentially expressed sequence tag. This paper found that the AUC of the ANN model was greater than that of the discriminant analysis model (P < 0.05). This paper promoted the development of new therapeutic measures for hepatocellular carcinoma and provided important theoretical guidance for human beings to fight cancer.

HSSG: Identification of Cancer Subtypes Based on Heterogeneity Score of A Single Gene

Cancer is a highly heterogeneous disease, which leads to the fact that even the same cancer can be further classified into different subtypes according to its pathology. With the multi-omics data widely used in cancer subtypes identification, effective feature selection is essential for accurately identifying cancer subtypes. However, the feature selection in the existing cancer subtypes identification methods has the problem that the most helpful features cannot be selected from a biomolecular perspective, and the relationship between the selected features cannot be reflected. To solve this problem, we propose a method for feature selection to identify cancer subtypes based on the heterogeneity score of a single gene: HSSG. In the proposed method, the sample-similarity network of a single gene is constructed, and pseudo-F statistics calculates the heterogeneity score for cancer subtypes identification of each gene. Finally, we construct gene-gene networks using genes with higher heterogeneity scores and mine essential genes from the networks. From the seven TCGA data sets for three experiments, including cancer subtypes identification in single-omics data, the performance in feature selection of multi-omics data, and the effectiveness and stability of the selected features, HSSG achieves good performance in all. This indicates that HSSG can effectively select features for subtypes identification.

lncRNA TPT1‑AS1 knockdown inhibits liver cancer cell proliferation, migration and invasion

Long non-coding RNA (lncRNA) tumor protein translationally controlled 1 antisense RNA 1 (TPT1-AS1) serves as an oncogene in several tumors, including ovarian and cervical cancer. However, the functional role of TPT1-AS1 in liver cancer (LC) is not completely understood. The present study aimed to explore the role of TPT1-AS1 in LC. In this study, the reverse transcription-quantitative PCR results demonstrated that TPT1-AS1 expression was significantly upregulated in LC tissues and cell lines compared with adjacent paracancerous tissues and THLE-3 cells, respectively. Elevated TPT1-AS1 expression was significantly associated with TNM stage lymph node metastasis and poor prognosis in patients with LC, as determined via χ² and Kaplan-Meier survival analyses. By constructing TPT1-AS1 knockdown LC cell lines (HepG2 and SNU-182), loss-of-function experiments, including Cell Counting Kit-8, colony formation, flow cytometry, wound healing and Transwell assays, were performed to explore the function role of TPT1-AS1 in LC in vitro. The results demonstrated that TPT1-AS1 knockdown inhibited LC cell proliferation, G₁/S transition, migration and invasion compared with the small interfering RNA (si)-negative control (NC) group. Mechanistically, TPT1-AS1 knockdown markedly decreased CDK4, N-cadherin and Vimentin expression levels, but notably increased p21 and E-cadherin expression levels compared with the si-NC group. Therefore, the results of the present study suggested that TPT1-AS1 might serve as a promising therapeutic target for LC treatment.

Modulation of the Mucosa-Associated Microbiome Linked to the PTPN2 Risk Gene in Patients with Primary Sclerosing Cholangitis and Ulcerative Colitis

Gut microbiota appears to be involved in the pathogenesis of primary sclerosing cholangitis (PSC). The protein tyrosine phosphatase nonreceptor 2 (PTPN2) gene risk variant rs1893217 is associated with gut dysbiosis in inflammatory bowel disease (IBD), and PTPN2 was mentioned as a possible risk gene for PSC. This study assessed the microbial profile of ulcerative colitis (UC) patients with PSC and without PSC (non-PSC). Additionally, effects of the PTPN2 risk variant were assessed. In total, 216 mucosal samples from ileum, colon, and rectum were collected from 7 PSC and 42 non-PSC patients, as well as 28 control subjects (non-IBD). The microbial composition was derived from 16S rRNA sequencing data. Overall, bacterial richness was highest in PSC patients, who also had a higher relative abundance of the genus Roseburia compared to non-PSC, as well as Haemophilus, Fusobacterium, Bifidobacterium, and Actinobacillus compared to non-IBD, as well as a lower relative abundance of Bacteroides compared to non-PSC and non-IBD, respectively. After exclusion of patients with the PTPN2 risk variant, Brachyspira was higher in PSC compared to non-PSC, while, solely in colon samples, Eubacterium and Tepidimonas were higher in PSC vs. non-IBD. In conclusion, this study underlines the presence of gut mucosa-associated microbiome changes in PSC patients and rather weakens the role of PTPN2 as a PSC risk gene.

Novel role of LINC01013/miR-6795-5p/FMNL3 axis in the regulation of hepatocellular carcinoma stem cell features

Cancer stem cells (CSCs) are major contributors to tumor initiation, recurrence, and metastasis of hepatocellular carcinoma (HCC). Some long non-coding RNAs have been reported as modulators of stem-like properties in cancer cells. However, the role of LINC01013 in liver CSCs has not yet been clarified. In this study, we aimed to elucidate the expression pattern and functions of LINC01013 in HCC. HCC tissues and normal controls were collected, and the expression pattern of LINC01013 and miR-6795-5p was identified by quick real-time polymerase chain reaction. Cell counting kit-8 assay, colony formation, and spheroid formation were performed to measure cell viability, proliferation, and self-renewal of HCC cell lines. The expression of stem markers was detected by western blot analysis. The effect of LINC01013 on viability, proliferation, and stem-like properties was detected through gain-of-function and loss-of-function experiments. The direct interaction among LINC01013, miR-6795-5p, and FMNL3 was testified by dual-luciferase reporter gene assay. Tumor-bearing mice were constructed to ascertain the functions of LINC01013 in vivo. HCC tissues showed increased LINC01013 and FMNL3 expression, while it showed a decreased miR-6795-5p expression as compared to the relative controls. Moreover, the high level of LINC01013 was closely related to the poor prognosis of HCC patients. LINC01013 directly binds to miR-6795-5p and subsequently relieves FMNL3. Silencing LINC01013, FMNL3, or overexpression of miR-6795-5p could suppress spheroid and colony formation, proliferation, as well as expression of stemness markers in HepG2 and SNU-182 cells. LINC01013 knockdown suppressed growth and stem-like traits of HCC cells in vivo by reducing FMNL3 expression. LINC01013/miR-6795-5p/FMNL3 axis may be a novel therapeutic target for HCC.

Research Progress on Regulating LncRNAs of Hepatocellular Carcinoma Stem Cells

Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies around the world. The self-renewal, proliferation, differentiation, and tumorigenic potential of liver cancer stem cells (LCSCs) may account for the high recurrence rate and the refractory feature of HCC. Despite extensive researches, the underlying regulatory mechanism of LCSCs has not been fully disclosed. Long nonprotein coding RNAs (lncRNAs) may exert an essential role in regulating various biological functions of LCSCs, such as maintaining the stemness of cancer stem cells (CSCs) and promoting tumor development. Therefore, it is highly critical to determine which lncRNAs can control LCSCs functions and understand how LCSCs are regulated by lncRNAs. Herein, we summarized lncRNAs and the main signaling pathways involved in the regulation of LCSCs found in recent years. Moreover, we shed light on the existence of the network system of lncRNAs and LCSCs, which may provide valuable clues on targeting LCSCs.

Novel therapeutic compounds for prostate adenocarcinoma treatment: An analysis using bioinformatic approaches and the CMap database

INTRODUCTION

Prostate adenocarcinoma is the most frequently diagnosed malignancy, particularly for people >70 years old. The main challenge in the treatment of advanced neoplasm is bone metastasis and therapeutic resistance for known oncology drugs. Novel treatment methods to prolong the survival time and improve the life quality of these specific patients are required. The present study attempted to screen potential therapeutic compounds for the tumor through bioinformatics approaches, in order to provide conceptual treatment for this malignant disease.

METHODS

Differentially expressed genes were obtained from the Gene Expression Omnibus database and submitted into the Connectivity Map database for the detection of potentially associated compounds. Target genes were extracted from the search results. Functional annotation and pathway enrichment were performed for the confirmation. Survival analysis was used to measure potential therapeutic effects.

RESULTS

It was revealed that 3 compounds (vanoxerine, tolnaftate, and gabexate) may help to prolong the disease-free survival time from tumor metastasis of patients with the tumor. A total of 6 genes [also-keto reductase family 1 member C3 (AKR1C3), collagen type III α 1 chain (COL3A1), lipoprotein lipase (LPL), glucuronidase, β pseudogene 11 (GUSBP11), apolipoprotein E (APOE), and collagen type I α 1 chain (COL1A1)] were identified to be the potential therapeutic targets for the aforementioned compounds.

CONCLUSION

In the present study, it was speculated that 3 compounds may function as the potential therapeutic drugs of bone metastatic prostate adenocarcinoma; however, further studies verifying vitro and in vivo are necessary.

Protein kinase function of pyruvate kinase M2 and cancer

Pyruvate kinase is a terminal enzyme in the glycolytic pathway, where it catalyzes the conversion of phosphoenolpyruvate to pyruvate and production of ATP via substrate level phosphorylation. PKM2 is one of four isoforms of pyruvate kinase and is widely expressed in many types of tumors and associated with tumorigenesis. In addition to pyruvate kinase activity involving the metabolic pathway, increasing evidence demonstrates that PKM2 exerts a non-metabolic function in cancers. PKM2 has been shown to be translocated into nucleus, where it serves as a protein kinase to phosphorylate various protein targets and contribute to multiple physiopathological processes. We discuss the nuclear localization of PKM2, its protein kinase function and association with cancers, and regulation of PKM2 activity.

Derlin-1 Promotes the Progression of Human Hepatocellular Carcinoma via the Activation of AKT Pathway

Introduction

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. In the present research, we explored a new oncogene, derlin-1 (DERL1), and studied its role and mechanism in human HCC.

Methods

We assessed the expression and prognosis value of DERL1 in human HCC by using GEPIA dataset analysis and immunohistochemistry. To elucidate the specific function of DERL1, we suppressed its expression in two HCC cell lines, HuH7 and Hep3B, and overexpressed DERL1 in Hep3B cells. Cell proliferation and migration was detected by CCK8 and transwell assays. Cell flow cytometry was used to evaluate cell apoptosis.

Results

Our results demonstrated that DERL1 was highly expressed in HCC samples (n = 369) than in normal samples (n = 160). Similar results were obtained in 60 clinical samples that we collected from the local hospital. The high expression rate of DERL1 reached 78.3% (47/60). DERL1 overexpression samples were concentrated in patients with tumor diameters >5cm or lymph node metastases. Thus, we speculated that DERL1 operated as a tumor promotor in HCC, and its expression might be proposed as a predictor for tumor metastasis of human HCC. Interference of DERL1 markedly blocked cell proliferation and migration, and induced the apoptosis of HCC cells in vitro. Phosphorylation of Akt was significantly inhibited in cells transfected with DERL1 siRNA compared to their control cells in HuH7 and Hep3B cell lines. The opposite result was observed in the DERL1 overexpression cells.

Conclusion

Our findings prove that DERL1 promotes tumor progression via AKT pathway and provide a new potential target for the clinical treatment and diagnosis of human HCC.

Using Deep Neural Networks to Improve the Performance of Protein–Protein Interactions Prediction

Protein–protein interactions (PPIs) help to elucidate the molecular mechanisms of life activities and have a certain role in promoting disease treatment and new drug development. With the advent of the proteomics era, some PPIs prediction methods have emerged. However, the performances of these PPIs prediction methods still need to be optimized and improved. In order to optimize the performance of the PPIs prediction methods, we used the dropout method to reduce over-fitting by deep neural networks (DNNs), and combined with three types of feature extraction methods, conjoint triad (CT), auto covariance (AC) and local descriptor (LD), to build DNN models based on amino acid sequences. The results showed that the accuracy of the CT, AC and LD increased from 97.11% to 98.12%, 96.84% to 98.17%, and 95.30% to 95.60%, respectively. The loss values of the CT, AC and LD decreased from 27.47% to 14.96%, 65.91% to 17.82% and 36.23% to 15.34%, respectively. Experimental results show that dropout can optimize the performances of the DNN models. The results can provide a resource for scholars in future studies involving the prediction of PPIs. The experimental code is available at https://github.com/smalltalkman/hppi-tensorflow .

Silencing Long Non-coding RNA LINC01224 Inhibits Hepatocellular Carcinoma Progression via MicroRNA-330-5p-Induced Inhibition of CHEK1

Hepatocellular carcinoma (HCC) accounts for approximately 85%–90% of primary liver cancers. Based on in silico analysis, differentially expressed long non-coding RNA (lncRNA) LINC01224 in HCC, the downstream microRNA (miRNA) miR-330-5p, and its target gene checkpoint kinase 1 (CHEK1) were selected as research subjects. Herein, this study was designed to evaluate their interaction effects on the malignant phenotypes of HCC cells. LINC01224 and CHEK1 were upregulated and miR-330-5p was downregulated in HCC cells. miR-330-5p shared negative correlations with LINC01224 and CHEK1, and LINC01224 shared a positive correlation with CHEK1. Notably, LINC01224 could specifically bind to miR-330-5p, and CHEK1 was identified as a target gene of miR-330-5p. When LINC01224 was silenced or miR-330-5p was elevated, the sphere and colony formation abilities and proliferative, migrative, and invasive potentials of HCC cells were diminished, while cell cycle arrest and apoptosis were enhanced. Moreover, LINC01224 induced HCC progression in vitro and accelerated tumor formation in nude mice by increasing CHEK1 expression. The key findings of the present study demonstrated that silencing LINC01224 could downregulate the expression of CHEK1 by competitively binding to miR-330-5p, thus inhibiting HCC progression. This result highlights the LINC01224/miR-330-5p/CHEK1 axis as a novel molecular mechanism involved in the pathology of HCC.

Liver cancer-associated changes to the proteome: what deserves clinical focus?

Hepatocellular carcinoma (HCC) is recognized as the fifth most common neoplasm and currently represents the second leading form of cancer-related death worldwide. Despite great progress has been done in the understanding of its pathogenesis, HCC represents a heavy societal and economic burden as most patients are still diagnosed at advanced stages and the 5-year survival rate remain below 20%. Early detection and revolutionary therapies that rely on the discovery of new molecular biomarkers and therapeutic targets are therefore urgently needed to develop precision medicine strategies for a more efficient management of patients. Areas covered: This review intends to comprehensively analyse the proteomics-based research conducted in the last few years to address some of the principal still open riddles in HCC biology, based on the identification of molecular drivers of tumor progression and metastasis. Expert commentary: The technical advances in mass spectrometry experienced in the last decade have significantly improved the analytical capacity of proteome wide studies. Large-scale protein and protein variant (post-translational modifications) identification and quantification have allowed detailed dissections of molecular mechanisms underlying HCC progression and are already paving the way for the identification of clinically relevant proteins and the development of their use on patient care.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA

Hepatocellular carcinoma (HCC) is a devastating disease worldwide. Though many efforts have been made to elucidate the process of HCC, its molecular mechanisms of development remain elusive due to its complexity. To explore the stepwise carcinogenic process from pre-neoplastic lesions to the end stage of HCC, we employed weighted gene co-expression network analysis (WGCNA) which has been proved to be an effective method in many diseases to detect co-expressed modules and hub genes using eight pathological stages including normal, cirrhosis without HCC, cirrhosis, low-grade dysplastic, high-grade dysplastic, very early and early, advanced HCC and very advanced HCC. Among the eight consecutive pathological stages, five representative modules are selected to perform canonical pathway enrichment and upstream regulator analysis by using ingenuity pathway analysis (IPA) software. We found that cell cycle related biological processes were activated at four neoplastic stages, and the degree of activation of the cell cycle corresponded to the deterioration degree of HCC. The orange and yellow modules enriched in energy metabolism, especially oxidative metabolism, and the expression value of the genes decreased only at four neoplastic stages. The brown module, enriched in protein ubiquitination and ephrin receptor signaling pathways, correlated mainly with the very early stage of HCC. The darkred module, enriched in hepatic fibrosis/hepatic stellate cell activation, correlated with the cirrhotic stage only. The high degree hub genes were identified based on the protein-protein interaction (PPI) network and were verified by Kaplan-Meier survival analysis. The novel five high degree hub genes signature that was identified in our study may shed light on future prognostic and therapeutic approaches. Our study brings a new perspective to the understanding of the key pathways and genes in the dynamic changes of HCC progression. These findings shed light on further investigations.