CancerLivER: a database of liver cancer gene expression resources and biomarkers

LiverSCA: A comprehensive and user-friendly cell atlas in human hepatocellular carcinoma

We developed a cell atlas named LiverSCA on human liver cancer single-cell RNA sequencing data. It has a user-friendly web interface and comprehensive functionalities aiming to help researchers to make easy access to cellular and molecular landscapes of the tumor microenvironment in liver cancer. LiverSCA includes a complete analytical pipeline that allow mechanistic exploration on a wide variety of functionalities, such as cell clustering, cell annotation, identification of differentially expressed genes, functional enrichment analysis, analysis of cellular crosstalk, and pseudo-time trajectory analysis. Notably, our intuitive web interface allows users, particularly wet-lab researchers, to easily explore and undertake data discovery, without the need to handle any of the raw data.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

Impact of COVID-19 national response on primary care utilisation in Singapore: an interrupted time-series analysis

Since the start of the pandemic, many national responses, such as nationwide lockdowns, have been implemented to curb the spread of COVID-19. We aim to assess the impact of Singapore's national responses on primary care utilisation. We performed an interrupted time series using acute and chronic primary care data of 3 168 578 visits between 1 September 2019 and 31 August 2020 over four periods: before any measures were put in place, during Disease Outbreak Response System Condition (DORSCON) Orange, when Circuit Breaker was instituted, and when Circuit Breaker was lifted. We found significant mean reductions in acute and chronic primary care visits immediately following DORSCON Orange and Circuit Breaker. DORSCON Orange was associated with - 2020 mean daily visits (95% CI - 2890 to - 1150). Circuit Breaker was associated with a further - 2510 mean daily visits (95% CI - 3660 to - 1360). Primary care utilisation for acute visits remained below baseline levels even after the Circuit Breaker was lifted. These significant reductions were observed in both acute and chronic visits, with acute visits experiencing a steeper drop during DORSCON Orange. Understanding the impact of COVID-19 measures on primary care utilisation will be useful for future public health planning.

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma

Background & Aims

Intratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC).

Methods

We interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients.

Results

An average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality.

Conclusions

We constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC.

Impact and Implications

Intratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.

Translational Control of Metabolism and Cell Cycle Progression in Hepatocellular Carcinoma

The liver is a metabolic hub characterized by high levels of protein synthesis. Eukaryotic initiation factors, eIFs, control the first phase of translation, initiation. Initiation factors are essential for tumor progression and, since they regulate the translation of specific mRNAs downstream of oncogenic signaling cascades, may be druggable. In this review, we address the issue of whether the massive translational machinery of liver cells contributes to liver pathology and to the progression of hepatocellular carcinoma (HCC); it represents a valuable biomarker and druggable target. First, we observe that the common markers of HCC cells, such as phosphorylated ribosomal protein S6, belong to the ribosomal and translational apparatus. This fact is in agreement with observations that demonstrate a huge amplification of the ribosomal machinery during the progression to HCC. Some translation factors, such as eIF4E and eIF6, are then harnessed by oncogenic signaling. In particular, the action of eIF4E and eIF6 is particularly important in HCC when driven by fatty liver pathologies. Indeed, both eIF4E and eIF6 amplify at the translational level the production and accumulation of fatty acids. As it is evident that abnormal levels of these factors drive cancer, we discuss their therapeutic value.

Targeting Lin28 axis enhances glypican-3-CAR T cell efficacy against hepatic tumor initiating cell population

Overexpression of Lin28 is detected in various cancers with involvement in the self-renewal process and cancer stem cell generation. In the present study, we evaluated how the Lin28 axis plays an immune-protective role for tumor-initiating cancer cells in hepatocellular carcinoma (HCC). Our result using HCC patient samples showed a positive correlation between indoleamine 2,3-dioxygenase-1 (IDO1), a kynurenine-producing enzyme with effects on tumor immune escape, and Lin28B. Using in silico prediction, we identified a Sox2/Oct4 transcriptional motif acting as an enhancer for IDO1. Knockdown of Lin28B reduced Sox2/Oct4 and downregulated IDO1 in tumor-initiating hepatic cancer cells. We further observed that inhibition of Lin28 by a small-molecule inhibitor (C1632) suppressed IDO1 expression. Suppression of IDO1 resulted in a decline in kynurenine production from tumor-initiating cells. Inhibition of the Lin28 axis also impaired PD-L1 expression in HCC cells. Consequently, modulating Lin28B enhanced in vitro cytotoxicity of glypican-3 (GPC3)-chimeric antigen receptor (CAR) T and NK cells. Next, we observed that GPC3-CAR T cell treatment together with C1632 in a HCC xenograft mouse model led to enhanced anti-tumor activity. In conclusion, our results suggest that inhibition of Lin28B reduces IDO1 and PD-L1 expression and enhances immunotherapeutic potential of GPC3-CART cells against HCC.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

LiqBioer: a manually curated database of cancer biomarkers in body fluid

Cancer biomarkers are measurable indicators that play vital roles in clinical applications. Biomarkers in body fluids have gained considerable attention since the development of liquid biopsy, and their data volume is rapidly increasing. Nevertheless, current research lacks the compilation of published cancer body fluid biomarkers into a centralized and sustainable repository for researchers and clinicians, despite a handful of small-scale and specific data resources. To fulfill this purpose, we developed liquid biomarker (LiqBioer) containing 6231 manually curated records from 3447 studies, covering 3056 biomarkers and 74 types of cancer in 22 tissues. LiqBioer allows users to browse and download comprehensive information on body liquid biomarkers, including cancer types, source studies and clinical usage. As a comprehensive resource for body fluid biomarkers of cancer, LiqBioer is a powerful tool for researchers and clinicians to query and retrieve biomarkers in liquid biopsy.

GlioMarker: An integrated database for knowledge exploration of diagnostic biomarkers in gliomas

Gliomas are the most frequent malignant and aggressive tumors in the central nervous system. Early and effective diagnosis of glioma using diagnostic biomarkers can prolong patients' lives and aid in the development of new personalized treatments. Therefore, a thorough and comprehensive understanding of the diagnostic biomarkers in gliomas is of great significance. To this end, we developed the integrated and web-based database GlioMarker (http://gliomarker.prophetdb.org/), the first comprehensive database for knowledge exploration of glioma diagnostic biomarkers. In GlioMarker, accurate information on 406 glioma diagnostic biomarkers from 1559 publications was manually extracted, including biomarker descriptions, clinical information, associated literature, experimental records, associated diseases, statistical indicators, etc. Importantly, we integrated many external resources to provide clinicians and researchers with the capability to further explore knowledge on these diagnostic biomarkers based on three aspects. (1) Obtain more ontology annotations of the biomarker. (2) Identify the relationship between any two or more components of diseases, drugs, genes, and variants to explore the knowledge related to precision medicine. (3) Explore the clinical application value of a specific diagnostic biomarker through online analysis of genomic and expression data from glioma cohort studies. GlioMarker provides a powerful, practical, and user-friendly web-based tool that may serve as a specialized platform for clinicians and researchers by providing rapid and comprehensive knowledge of glioma diagnostic biomarkers to subsequently facilitates high-quality research and applications.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

Targeting of eIF6-driven translation induces a metabolic rewiring that reduces NAFLD and the consequent evolution to hepatocellular carcinoma

A postprandial increase of translation mediated by eukaryotic Initiation Factor 6 (eIF6) occurs in the liver. Its contribution to steatosis and disease is unknown. In this study we address whether eIF6-driven translation contributes to disease progression. eIF6 levels increase throughout the progression from Non-Alcoholic Fatty Liver Disease (NAFLD) to hepatocellular carcinoma. Reduction of eIF6 levels protects the liver from disease progression. eIF6 depletion blunts lipid accumulation, increases fatty acid oxidation (FAO) and reduces oncogenic transformation in vitro. In addition, eIF6 depletion delays the progression from NAFLD to hepatocellular carcinoma, in vivo. Mechanistically, eIF6 depletion reduces the translation of transcription factor C/EBPβ, leading to a drop in biomarkers associated with NAFLD progression to hepatocellular carcinoma and preserves mitochondrial respiration due to the maintenance of an alternative mTORC1-eIF4F translational branch that increases the expression of transcription factor YY1. We provide proof-of-concept that in vitro pharmacological inhibition of eIF6 activity recapitulates the protective effects of eIF6 depletion. We hypothesize the existence of a targetable, evolutionarily conserved translation circuit optimized for lipid accumulation and tumor progression.

Non-Coding RNA-Based Biosensors for Early Detection of Liver Cancer

Primary liver cancer is an aggressive, lethal malignancy that ranks as the fourth leading cause of cancer-related death worldwide. Its 5-year mortality rate is estimated to be more than 95%. This significant low survival rate is due to poor diagnosis, which can be referred to as the lack of sufficient and early-stage detection methods. Many liver cancer-associated non-coding RNAs (ncRNAs) have been extensively examined to serve as promising biomarkers for precise diagnostics, prognostics, and the evaluation of the therapeutic progress. For the simple, rapid, and selective ncRNA detection, various nanomaterial-enhanced biosensors have been developed based on electrochemical, optical, and electromechanical detection methods. This review presents ncRNAs as the potential biomarkers for the early-stage diagnosis of liver cancer. Moreover, a comprehensive overview of recent developments in nanobiosensors for liver cancer-related ncRNA detection is provided.

Phosphodiesterase 4D Depletion/Inhibition Exerts Anti-Oncogenic Properties in Hepatocellular Carcinoma

Simple Summary

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. Drug resistance is a serious problem in the treatment of HCC. Therefore, it is of high clinical impact to discover targeted therapies that may overcome drug-related resistance and improve the survival of patients affected by HCC. In the present study, we investigated the role of Isoform D of type 4 phosphodiesterase (PDE4D) in HCC development and progression. We found that PDE4D is over-expressed HCCs in vitro and in vivo and the depletion of the gene by silencing or the pharmacological inhibition of protein activity exerted anti-tumorigenic activities.

Abstract

Isoform D of type 4 phosphodiesterase (PDE4D) has recently been associated with several human cancer types with the exception of human hepatocellular carcinoma (HCC). Here we explored the role of PDE4D in HCC. We found that PDE4D gene/protein were over-expressed in different samples of human HCCs compared to normal livers. Accordingly, HCC cells showed higher PDE4D activity than non-tumorigenic cells, accompanied by over-expression of the PDE4D isoform. Silencing of PDE4D gene and pharmacological inhibition of protein activity by the specific inhibitor Gebr-7b reduced cell proliferation and increased apoptosis in HCC cells, with a decreased fraction of cells in S phase and a differential modulation of key regulators of cell cycle and apoptosis. PDE4D silencing/inhibition also affected the gene expression of several cancer-related genes, such as the pro-oncogenic insulin growth factor (IGF2), which is down-regulated. Finally, gene expression data, available in the CancerLivER data base, confirm that PDE4D over-expression in human HCCs correlated with an increased expression of IGF2, suggesting a new possible molecular network that requires further investigations. In conclusion, intracellular depletion/inhibition of PDE4D prevents the growth of HCC cells, displaying anti-oncogenic effects. PDE4D may thus represent a new biomarker for diagnosis and a potential adjuvant target for HCC therapy.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.