In Vivo and in vitro antitumor activity of tomatine in hepatocellular carcinoma

Background: There is abundant ethnopharmacological evidence the uses of regarding Solanum species as antitumor and anticancer agents. Glycoalkaloids are among the molecules with antiproliferative activity reported in these species.

Purpose: To evaluate the anticancer effect of the Solanum glycoalkaloid tomatine in hepatocellular carcinoma (HCC) in vitro (HepG2 cells) and in vivo models.

Methods: The resazurin reduction assay was performed to detect the effect of tomatine on cell viability in human HepG2 cell lines. Programmed cell death was investigated by means of cellular apoptosis assays using Annexin V. The expression of cancer related proteins was detected by Western blotting (WB). Reactive oxygen species (ROS) and calcium were determined by 2,7-dichlorodihydrofluorescein diacetate and Fluo-4, respectively. Intrahepatic HepG2 xenograft mouse model was used to elucidate the effect of tomatine on tumor growth in vivo.

Results and Discussion: Tomatine reduced HepG2 cell viability and induced the early apoptosis phase of cell death, consistently with caspase-3, -7, Bcl-2 family, and P53 proteins activation. Furthermore, tomatine increased intracellular ROS and cytosolic Ca⁺² levels. Moreover, the NSG mouse xenograft model showed that treating mice with tomatine inhibited HepG2 tumor growth.

Conclusion: Tomatine inhibits in vitro and in vivo HCC tumorigenesis in part via modulation of p53, Ca⁺², and ROS signalling. Thus, the results suggest the potential cancer therapeutic use of tomatine in HCC patients.

New Perspectives on the Importance of Cell-Free DNA Biology

Body fluids are constantly replenished with a population of genetically diverse cell-free DNA (cfDNA) fragments, representing a vast reservoir of information reflecting real-time changes in the host and metagenome. As many body fluids can be collected non-invasively in a one-off and serial fashion, this reservoir can be tapped to develop assays for the diagnosis, prognosis, and monitoring of wide-ranging pathologies, such as solid tumors, fetal genetic abnormalities, rejected organ transplants, infections, and potentially many others. The translation of cfDNA research into useful clinical tests is gaining momentum, with recent progress being driven by rapidly evolving preanalytical and analytical procedures, integrated bioinformatics, and machine learning algorithms. Yet, despite these spectacular advances, cfDNA remains a very challenging analyte due to its immense heterogeneity and fluctuation in vivo. It is increasingly recognized that high-fidelity reconstruction of the information stored in cfDNA, and in turn the development of tests that are fit for clinical roll-out, requires a much deeper understanding of both the physico-chemical features of cfDNA and the biological, physiological, lifestyle, and environmental factors that modulate it. This is a daunting task, but with significant upsides. In this review we showed how expanded knowledge on cfDNA biology and faithful reverse-engineering of cfDNA samples promises to (i) augment the sensitivity and specificity of existing cfDNA assays; (ii) expand the repertoire of disease-specific cfDNA markers, thereby leading to the development of increasingly powerful assays; (iii) reshape personal molecular medicine; and (iv) have an unprecedented impact on genetics research.

Biological Random Walks: multi-omics integration for disease gene prioritization

MOTIVATION

Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration.

RESULTS

In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines.

AVAILABILITY AND IMPLEMENTATION

All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

When does hepatitis B virus meet long-stranded noncoding RNAs?

Hepatitis B virus (HBV) infection in humans and its associated diseases are long-standing problems. HBV can produce a large number of non-self-molecules during its life cycle, which acts as targets for innate immune recognition and initiation. Among these, interferon and its large number of downstream interferon-stimulated gene molecules are important early antiviral factors. However, the development of an effective antiviral immune response is not simple and depends not only on the delicate regulation of the immune response but also on the various mechanisms of virus-related immune escape and immune tolerance. Therefore, despite there being a relatively well-established consensus on the major pathways of the antiviral response and their component molecules, the complete clearance of HBV remains a challenge in both basic and clinical research. Long-noncoding RNAs (lncRNAs) are generally >200 bp in length and perform different functions in the RNA strand encoding the protein. As an important part of the IFN-inducible genes, interferon-stimulated lncRNAs are involved in the regulation of several HBV infection-related pathways. This review traces the basic elements of such pathways and characterizes the various recent targets of lncRNAs, which not only complement the regulatory mechanisms of pathways related to chronic HBV infection, fibrosis, and cancer promotion but also present with new potential therapeutic targets for controlling HBV infection and the malignant transformation of hepatocytes.

The diagnostic value of circulating tumor DNA in hepatitis B virus induced hepatocellular carcinoma: a systematic review and meta-analysis

Background/Aim

New biomarkers are urgently needed to aid in the diagnosis of early stage hepatocellular carcinoma (HCC). We performed a meta-analysis on the diagnostic utility of circulating tumor DNA (ctDNA) levels in patients with hepatitis B virus-induced HCC.

Methods

We retrieved relevant articles from PubMed, Embase, and the Cochrane Library up to February 8, 2022. Two subgroups were defined; one subset of studies analyzed the ctDNA methylation status, and the other subset combined tumor markers and ctDNA assays. Pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC) were analyzed.

Results

Nine articles including 2,161 participants were included. The overall SEN and SPE were 0.705 (95% confidence interval [CI], 0.629-0.771) and 0.833 (95% CI, 0.769-0.882), respectively. The DOR, PLR, and NLR were 11.759 (95% CI, 7.982-17.322), 4.285 (95% CI, 3.098-5.925), and 0.336 (0.301-0.366), respectively. The ctDNA assay subset exhibited an AUC of 0.835. The AUC of the combined tumor marker and ctDNA assay was 0.848, with an SEN of 0.761 (95% CI, 0.659-0.839) and an SPE of 0.828 (95% CI, 0.692-0.911).

Conclusions

Circulating tumor DNA has promising diagnostic potential for HCC. It can serve as an auxiliary tool for HCC screening and detection, especially when combined with tumor markers.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

Pancreatic Cancer: Pathogenesis, Screening, Diagnosis, and Treatment

Pancreatic ductal adenocarcinoma (PDAC) is a clinically challenging cancer, due to both its late stage at diagnosis and its resistance to chemotherapy. However, recent advances in our understanding of the biology of PDAC have revealed new opportunities for early detection and targeted therapy of PDAC. In this review, we discuss the pathogenesis of PDAC, including molecular alterations in tumor cells, cellular alterations in the tumor microenvironment, and population-level risk factors. We review the current status of surveillance and early detection of PDAC, including populations at high risk and screening approaches. We outline the diagnostic approach to PDAC and highlight key treatment considerations, including how therapeutic approaches change with disease stage and targetable subtypes of PDAC. Recent years have seen significant improvements in our approaches to detect and treat PDAC, but large-scale, coordinated efforts will be needed to maximize the clinical impact for patients and improve overall survival.

Tracing the Origin of Cell-Free DNA Molecules through Tissue-Specific Epigenetic Signatures

All cell and tissue types constantly release DNA fragments into human body fluids by various mechanisms including programmed cell death, accidental cell degradation and active extrusion. Particularly, cell-free DNA (cfDNA) in plasma or serum has been utilized for minimally invasive molecular diagnostics. Disease onset or pathological conditions that lead to increased cell death alter the contribution of different tissues to the total pool of cfDNA. Because cfDNA molecules retain cell-type specific epigenetic features, it is possible to infer tissue-of-origin from epigenetic characteristics. Recent research efforts demonstrated that analysis of, e.g., methylation patterns, nucleosome occupancy, and fragmentomics determined the cell- or tissue-of-origin of individual cfDNA molecules. This novel tissue-of origin-analysis enables to estimate the contributions of different tissues to the total cfDNA pool in body fluids and find tissues with increased cell death (pathologic condition), expanding the portfolio of liquid biopsies towards a wide range of pathologies and early diagnosis. In this review, we summarize the currently available tissue-of-origin approaches and point out the next steps towards clinical implementation.

Integrative Analyses of Transcriptomes to Explore Common Molecular Effects of Antipsychotic Drugs

There is little understanding of the underlying molecular mechanism(s) involved in the clinical efficacy of antipsychotics for schizophrenia. This study integrated schizophrenia-associated transcriptional perturbations with antipsychotic-induced gene expression profiles to detect potentially relevant therapeutic targets shared by multiple antipsychotics. Human neuronal-like cells (NT2-N) were treated for 24 h with one of the following antipsychotic drugs: amisulpride, aripiprazole, clozapine, risperidone, or vehicle controls. Drug-induced gene expression patterns were compared to schizophrenia-associated transcriptional data in post-mortem brain tissues. Genes regulated by each of four antipsychotic drugs in the reverse direction to schizophrenia were identified as potential therapeutic-relevant genes. A total of 886 genes were reversely expressed between at least one drug treatment (versus vehicle) and schizophrenia (versus healthy control), in which 218 genes were commonly regulated by all four antipsychotic drugs. The most enriched biological pathways include Wnt signaling and action potential regulation. The protein-protein interaction (PPI) networks found two main clusters having schizophrenia expression quantitative trait loci (eQTL) genes such as PDCD10, ANK2, and AKT3, suggesting further investigation on these genes as potential novel treatment targets.

The Cell Type-Specific 5hmC Landscape and Dynamics of Healthy Human Hematopoiesis and TET2-Mutant Preleukemia

The conversion of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) is a key step in DNA demethylation that is mediated by ten-eleven translocation (TET) enzymes, which require ascorbate/vitamin C. Here, we report the 5hmC landscape of normal hematopoiesis and identify cell type-specific 5hmC profiles associated with active transcription and chromatin accessibility of key hematopoietic regulators. We utilized CRISPR/Cas9 to model TET2 loss-of-function mutations in primary human hematopoietic stem and progenitor cells (HSPC). Disrupted cells exhibited increased colonies in serial replating, defective erythroid/megakaryocytic differentiation, and in vivo competitive advantage and myeloid skewing coupled with reduction of 5hmC at erythroid-associated gene loci. Azacitidine and ascorbate restored 5hmC abundance and slowed or reverted the expansion of TET2-mutant clones in vivo. These results demonstrate the key role of 5hmC in normal hematopoiesis and TET2-mutant phenotypes and raise the possibility of utilizing these agents to further our understanding of preleukemia and clonal hematopoiesis.

SIGNIFICANCE

We show that 5-hydroxymethylation profiles are cell type-specific and associated with transcriptional abundance and chromatin accessibility across human hematopoiesis. TET2 loss caused aberrant growth and differentiation phenotypes and disrupted 5hmC and transcriptional landscapes. Treatment of TET2 KO HSPCs with ascorbate or azacitidine reverted 5hmC profiles and restored aberrant phenotypes. This article is highlighted in the In This Issue feature, p. 265.

Niclosamide-A promising treatment for COVID-19

Vaccines have reduced the transmission and severity of COVID-19, but there remains a paucity of efficacious treatment for drug-resistant strains and more susceptible individuals, particularly those who mount a suboptimal vaccine response, either due to underlying health conditions or concomitant therapies. Repurposing existing drugs is a timely, safe and scientifically robust method for treating pandemics, such as COVID-19. Here, we review the pharmacology and scientific rationale for repurposing niclosamide, an anti-helminth already in human use as a treatment for COVID-19. In addition, its potent antiviral activity, niclosamide has shown pleiotropic anti-inflammatory, antibacterial, bronchodilatory and anticancer effects in numerous preclinical and early clinical studies. The advantages and rationale for nebulized and intranasal formulations of niclosamide, which target the site of the primary infection in COVID-19, are reviewed. Finally, we give an overview of ongoing clinical trials investigating niclosamide as a promising candidate against SARS-CoV-2.

Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers

BACKGROUND

Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identification of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identified. The relationship between modules was assessed using the Pearson correlation coefficient (p-value < 0.05). An elastic network classifier was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efficiency of the model, the Area Under the Curve (AUC) was computed using five-fold cross-validation in an external dataset.

RESULTS

The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organization, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73-1.00) was observed for the predictive model including 11 genes.

CONCLUSION

The intricate association between biological pathways of identified modules may reveal the hidden significance of 5-hmC in GI cancers. The identified predictive model and new biomarkers may be beneficial in cancer detection and precision medicine using liquid biopsy in the early stages.

Liquid biopsies to predict CDK4/6 inhibitor efficacy and resistance in breast cancer

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors combined with endocrine therapy have transformed the treatment of estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2 negative (HER2-) metastatic breast cancer. However, some patients do not respond to this treatment, and patients inevitably develop resistance, such that novel biomarkers are needed to predict primary resistance, monitor treatment response for acquired resistance, and personalize treatment strategies. Circumventing the spatial and temporal limitations of tissue biopsy, newly developed liquid biopsy approaches have the potential to uncover biomarkers that can predict CDK4/6 inhibitor efficacy and resistance in breast cancer patients through a simple blood test. Studies on circulating tumor DNA (ctDNA)-based liquid biopsy biomarkers of CDK4/6 inhibitor resistance have focused primarily on genomic alterations and have failed thus far to identify clear and clinically validated predictive biomarkers, but emerging epigenetic ctDNA methodologies hold promise for further discovery. The present review outlines recent advances and future directions in ctDNA-based biomarkers of CDK4/6 inhibitor treatment response.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual’s Disease-Related Signature

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening

Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified in vitro to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of “success stories,” we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.

Medicinal Chemistry Perspective on Targeting Mono-ADP-Ribosylating PARPs with Small Molecules

Major advances have recently defined functions for human mono-ADP-ribosylating PARP enzymes (mono-ARTs), also opening up potential applications for targeting them to treat diseases. Structural biology combined with medicinal chemistry has allowed the design of potent small molecule inhibitors which typically bind to the catalytic domain. Most of these inhibitors are at the early stages, but some have already a suitable profile to be used as chemical tools. One compound targeting PARP7 has even progressed to clinical trials. In this review, we collect inhibitors of mono-ARTs with a typical "H-Y-Φ" motif (Φ = hydrophobic residue) and focus on compounds that have been reported as active against one or a restricted number of enzymes. We discuss them from a medicinal chemistry point of view and include an analysis of the available crystal structures, allowing us to craft a pharmacophore model that lays the foundation for obtaining new potent and more specific inhibitors.

A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies

Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials.

TMEM16A as a potential treatment target for head and neck cancer

Transmembrane protein 16A (TMEM16A) forms a plasma membrane-localized Ca²⁺-activated Cl- channel. Its gene has been mapped to an area on chromosome 11q13, which is amplified in head and neck squamous cell carcinoma (HNSCC). In HNSCC, TMEM16A overexpression is associated with not only high tumor grade, metastasis, low survival, and poor prognosis, but also deterioration of clinical outcomes following platinum-based chemotherapy. Recent study revealed the interaction between TMEM16A and transforming growth factor-β (TGF-β) has an indirect crosstalk in clarifying the mechanism of TMEM16A-induced epithelial-mesenchymal transition. Moreover, human papillomavirus (HPV) infection can modulate TMEM16A expression along with epidermal growth factor receptor (EGFR), whose phosphorylation has been reported as a potential co-biomarker of HPV-positive cancers. Considering that EGFR forms a functional complex with TMEM16A and is a co-biomarker of HPV, there may be crosstalk between TMEM16A expression and HPV-induced HNSCC. EGFR activation can induce programmed death ligand 1 (PD-L1) synthesis via activation of the nuclear factor kappa B pathway and JAK/STAT3 pathway. Here, we describe an interplay among EGFR, PD-L1, and TMEM16A. Combination therapy using TMEM16A and PD-L1 inhibitors may improve the survival rate of HNSCC patients, especially those resistant to anti-EGFR inhibitor treatment. To the best of our knowledge, this is the first review to propose a biological validation that combines immune checkpoint inhibition with TMEM16A inhibition.

A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression

Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by k-means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS2) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.

Sorafenib and nitazoxanide disrupt mitochondrial function and inhibit regrowth capacity in three-dimensional models of hepatocellular and colorectal carcinoma

Quiescent cancer cells in malignant tumors can withstand cell-cycle active treatment and cause cancer spread and recurrence. Three-dimensional (3D) cancer cell models have led to the identification of oxidative phosphorylation (OXPHOS) as a context-dependent vulnerability. The limited treatment options for advanced hepatocellular carcinoma (HCC) and colorectal carcinoma (CRC) metastatic to the liver include the multikinase inhibitors sorafenib and regorafenib. Off-target effects of sorafenib and regorafenib are related to OXPHOS inhibition; however the importance of this feature to the effect on tumor cells has not been investigated in 3D models. We began by assessing global transcriptional responses in monolayer cell cultures, then moved on to multicellular tumor spheroids (MCTS) and tumoroids generated from a CRC patient. Cells were treated with chemotherapeutics, kinase inhibitors, and the OXPHOS inhibitors. Cells grown in 3D cultures were sensitive to the OXPHOS inhibitor nitazoxanide, sorafenib, and regorafenib and resistant to other multikinase inhibitors and chemotherapeutic drugs. Furthermore, nitazoxanide and sorafenib reduced viability, regrowth potential and inhibited mitochondrial membrane potential in an additive manner at clinically relevant concentrations. This study demonstrates that the OXPHOS inhibition caused by sorafenib and regorafenib parallels 3D activity and can be further investigated for new combination strategies.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Plasma cell-free RNA profiling distinguishes cancers from pre-malignant conditions in solid and hematologic malignancies

Cell-free RNA (cfRNA) in plasma reflects phenotypic alterations of both localized sites of cancer and the systemic host response. Here we report that cfRNA sequencing enables the discovery of messenger RNA (mRNA) biomarkers in plasma with the tissue of origin-specific to cancer types and precancerous conditions in both solid and hematologic malignancies. To explore the diagnostic potential of total cfRNA from blood, we sequenced plasma samples of eight hepatocellular carcinoma (HCC) and ten multiple myeloma (MM) patients, 12 patients of their respective precancerous conditions, and 20 non-cancer (NC) donors. We identified distinct gene sets and built classification models using Random Forest and linear discriminant analysis algorithms that could distinguish cancer patients from premalignant conditions and NC individuals with high accuracy. Plasma cfRNA biomarkers of HCC are liver-specific genes and biomarkers of MM are highly expressed in the bone marrow compared to other tissues and are related to cell cycle processes. The cfRNA level of these biomarkers displayed a gradual transition from noncancerous states through precancerous conditions and cancer. Sequencing data were cross-validated by quantitative reverse transcription PCR and cfRNA biomarkers were validated in an independent sample set (20 HCC, 9 MM, and 10 NC) with AUC greater than 0.86. cfRNA results observed in precancerous conditions require further validation. This work demonstrates a proof of principle for using mRNA transcripts in plasma with a small panel of genes to distinguish between cancers, noncancerous states, and precancerous conditions.

Subclass-specific Prognosis and Treatment Efficacy Inference in Head and Neck Squamous Carcinoma

Exploring the prognostic classification and biomarkers in Head and Neck Squamous Carcinoma (HNSC) is of great clinical significance. We hybridized three prominent strategies to comprehensively characterize the molecular features of HNSC. We constructed a 15-gene signature to predict patients death risk with an average AUC of 0.744 for 1-, 3-, and 5-year on TCGA-HNSC training set, and average AUCs of 0.636, 0.584, 0.755 in GSE65858, GSE-112026, CPTAC-HNSCC datasets, respectively. By combined with NMF clustering and consensus clustering of fraction of tumor immune cell infiltration (ICI) in the tumor microenvironment (TME), we captured a more refined biological characteristics of HNSC, and observed a prognosis heterogeneity in high tumor immunity patients. By matching tumor subset-specific expression signatures to drug-induced cell line expression profiles from large-scale pharmacogenomic databases in the OCTAD workspace, we identified a group of HNSC patients featured with poor prognosis and demonstrated that the individuals in this group are likely to receive increased drug sensitivity to reverse differentially expressed disease signature genes. This trend is especially highlighted among those with higher death risk and tumour immunity.

Target Therapy for Hepatocellular Carcinoma: Beyond Receptor Tyrosine Kinase Inhibitors and Immune Checkpoint Inhibitors

Simple Summary

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and its incidence is steadily increasing. The development of HCC is a complex, multi-step process that is accompanied by alterations in multiple signaling cascades. Recent years have seen advancement in understanding molecular signaling pathways that play central roles in hepatocarcinogenesis. Aberrant activation of YAP/TAZ, Hedgehog, or Wnt/β-catenin signaling is frequently found in a subset of HCC patients. Targeting the signaling pathway via small molecule inhibitors could be a promising therapeutic option for the subset of patients. In this review, we will introduce the signaling pathways, discuss their roles in the development of HCC, and propose a therapeutic approach targeting the signaling pathways in the context of HCC.

Abstract

Hepatocellular carcinoma (HCC) is a major health concern worldwide, and its incidence is increasing steadily. To date, receptor tyrosine kinases (RTKs) are the most favored molecular targets for the treatment of HCC, followed by immune checkpoint regulators such as PD-1, PD-L1, and CTLA-4. With less than desirable clinical outcomes from RTK inhibitors as well as immune checkpoint inhibitors (ICI) so far, novel molecular target therapies have been proposed for HCC. In this review, we will introduce diverse molecular signaling pathways that are aberrantly activated in HCC, focusing on YAP/TAZ, Hedgehog, and Wnt/β-catenin signaling pathways, and discuss potential therapeutic strategies targeting the signaling pathways in HCC.

Deubiquitination of TNKS1 Regulates Wnt/β-Catenin to Affect the Expression of USP25 to Promote the Progression of Glioma

Objective

To explore the regulatory effect of ubiquitin specific protease 25 (USP25) on glioma cell proliferation, migration, invasion, and its underlying mechanism.

Methods

The USP25-overexpressed and USP25-knockdown glioma cells were established on U251 and U87 cells, respectively. Glioma cell proliferation ability was evaluated by CCK-8 assay. Cell apoptosis and cell cycle were determined utilizing flow cytometry. The Transwell assay measured cell invasion with wound healing used for cell migration detection. Western blotting established key protein expression levels in the Wnt/β-catenin pathway. The coimmunoprecipitation was used to check Thankyrase 1 (TNKS1) ubiquitination levels.

Results

TNKS1 expression levels were found to be considerably repressed in USP25-knockdown glioma cells and elevated in USP25-overexpressed glioma cells, accompanied by Wnt/β-catenin pathway key protein downregulation and upregulation, respectively. Glioma cell invasion, migration, and proliferation activity were dramatically inhibited in USP25-knockdown glioma cells and promoted in USP25-overexpressed glioma cells. TNKS1 ubiquitination level was knowingly increased in USP25-knockdown glioma cells and reduced in USP25-overexpressed glioma cells, suggesting TNKS1 ubiquitination levels were negatively regulated by USP25.

Conclusion

USP25 facilitated glioma cell invasion, migration, and proliferation by regulating Wnt/β-catenin through the deubiquitination on TNKS1.

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity-and not inverse similarity-between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.

Bioinformatics analysis methods for cell-free DNA

As a kind of novel non-invasive marker for molecular detection, cell-free DNA (cfDNA) has potential value for the early diagnosis of diseases, prognosis assessment, and efficacy monitoring. The constant developments in molecular biology detection technologies have led to an increase in clinical studies on the use of cfDNA detection methods for patients, and many gratifying outcomes have been achieved. In this review, the contributions of bioinformatics tools to the study of cfDNA are well discussed. The focus of the review is on cfDNA identification signals, cfDNA identification methods, and the relationship of cfDNA with human diseases such as hepatic cancer, lung cancer, end-stage kidney disease, and ischemic stroke. The research significance and existing problems of using cfDNA as a biomarker for diseases are also discussed.

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

Combinatorial Power of cfDNA, CTCs and EVs in Oncology

Liquid biopsy is a promising technique for clinical management of oncological patients. The diversity of analytes circulating in the blood useable for liquid biopsy testing is enormous. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and extracellular vesicles (EVs), as well as blood cells and other soluble components in the plasma, were shown as liquid biopsy analytes. A few studies directly comparing two liquid biopsy analytes showed a benefit of one analyte over the other, while most authors concluded the benefit of the additional analyte. Only three years ago, the first studies to examine the value of a characterization of more than two liquid biopsy analytes from the same sample were conducted. We attempt to reflect on the recent development of multimodal liquid biopsy testing in this review. Although the analytes and clinical purposes of the published multimodal studies differed significantly, the additive value of the analytes was concluded in almost all projects. Thus, the blood components, as liquid biopsy reservoirs, are complementary rather than competitive, and orthogonal data sets were even shown to harbor synergistic effects. The unmistakable potential of multimodal liquid biopsy testing, however, is dampened by its clinical utility, which is yet to be proven, the lack of methodical standardization and insufficiently mature reimbursement, logistics and data handling.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Integrative Analysis of 5-Hydroxymethylcytosine and Transcriptional Profiling Identified 5hmC-Modified lncRNA Panel as Non-Invasive Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer

Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown. In this study, we performed the genome-wide investigation of lncRNA-associated plasma cfDNA 5hmC changes in PAAD by plotting 5hmC reads against lncRNA genes, and identified six PAAD-specific lncRNAs with abnormal 5hmC modifications compared with healthy individuals. Then we applied machine-learning and Cox regression approaches to develop predictive diagnostic (5hLRS) and prognostic (5hLPS) models using the 5hmC-modified lncRNAs. The 5hLRS demonstrated excellent performance in discriminating PAAD from healthy controls with an area under the curve (AUC) of 0.833 in the training cohort and 0.719 in the independent testing cohort. The 5hLPS also effectively divides PAAD patients into high-risk and low-risk groups with significantly different clinical outcomes in the training cohort (log-rank test p = 0.04) and independent testing cohort (log-rank test p = 0.0035). Functional analysis based on competitive endogenous RNA (ceRNA) and enrichment analysis suggested that these differentially regulated 5hmC modified lncRNAs were associated with angiogenesis, circulatory system process, leukocyte differentiation and metal ion homeostasis that are known important events in the development and progression of PAAD. In conclusion, our study indicated the potential clinical utility of 5hmC profiles at lncRNA loci as valuable biomarkers for non-invasive diagnosis and prognostication of cancers.

Small-Molecule Inhibitors of Tankyrases as Prospective Therapeutics for Cancer

Tankyrases are multifunctional poly(adenosine diphosphate-ribose) polymerases that regulate diverse biological processes including telomere maintenance and cellular signaling. These processes are often implicated in a number of human diseases, with cancer being the most prevalent example. Accordingly, tankyrase inhibitors have gained increasing attention as potential therapeutics. Since the discovery of XAV939 and IWR-1 as the first tankyrase inhibitors over two decades ago, tankyrase-targeted drug discovery has made significant progress. This review starts with an introduction of tankyrases, with emphasis placed on their cancer-related functions. Small-molecule inhibitors of tankyrases are subsequently delineated based on their distinct modes of binding to the enzymes. In addition to inhibitors that compete with oxidized nicotinamide adenine dinucleotide (NAD⁺) for binding to the catalytic domain of tankyrases, non-NAD⁺-competitive inhibitors are detailed. This is followed by a description of three clinically trialled tankyrase inhibitors. To conclude, some of challenges and prospects in developing tankyrase-targeted cancer therapies are discussed.

Computational Methods for Drug Repurposing

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

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.

The role of complement in the clinical course of hepatocellular carcinoma

Background

The complement system, an innate immune system, may either play an antitumor role, or promote tumorigenesis and cancer progression in different kinds of cancer. The function of complement in hepatocellular carcinoma (HCC) is unclear.

Methods

The gene expressions of the complement system were based on data obtained from TCGA and GEO. We explored gene expressions, mutation, enrichment analysis, clinicopathology, patients' outcome, and immune infiltration via Gepia2, cBioPortal, Metascape, UALCAN, Kaplan–Meier Plotter, and TIMER 2.

Results

Five complement genes, including C1R, C6, C7, CFP, and CFHR3, were not only found to be significantly downregulated in HCC samples compared with normal liver samples, but also found to be significantly associated with overall survival, disease‐free survival, and progress‐free survival in HCC patients. In addition, lower mRNA expression of C1R, C6, C7, and CFHR3 were found correlated with advanced cancer stages and higher tumor grades in HCC patients. Also, the expression levels of CFP were correlated with many sets of immune markers of tumor immune cells, such as those of CD8+ T cells, CD4+ T cells, B cells, M2 macrophages, neutrophils, DCs, Th1 cells, Th2 cells, and T cell exhaustion in HCC. Based on that, we developed a prognostic model for HCC patients—Riskscore = (−0.0053)*C6+(−0.0498)*C7+(−0.1045)*CFHR3.

Conclusion

C1R, C6, C7, CFP, and CFHR3 could be prognostic biomarkers for patients with HCC.

DNA Hydroxymethylation in Smoking-Associated Cancers

5-hydroxymethylcytosine (5-hmC) was first detected in mammalian DNA five decades ago. However, it did not take center stage in the field of epigenetics until 2009, when ten-eleven translocation 1 (TET1) was found to oxidize 5-methylcytosine to 5-hmC, thus offering a long-awaited mechanism for active DNA demethylation. Since then, a remarkable body of research has implicated DNA hydroxymethylation in pluripotency, differentiation, neural system development, aging, and pathogenesis of numerous diseases, especially cancer. Here, we focus on DNA hydroxymethylation in smoking-associated carcinogenesis to highlight the diagnostic, therapeutic, and prognostic potentials of this epigenetic mark. We describe the significance of 5-hmC in DNA demethylation, the importance of substrates and cofactors in TET-mediated DNA hydroxymethylation, the regulation of TETs and related genes (isocitrate dehydrogenases, fumarate hydratase, and succinate dehydrogenase), the cell-type dependency and genomic distribution of 5-hmC, and the functional role of 5-hmC in the epigenetic regulation of transcription. We showcase examples of studies on three major smoking-associated cancers, including lung, bladder, and colorectal cancers, to summarize the current state of knowledge, outstanding questions, and future direction in the field.

A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer

Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL₄)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.

Utility of Perioperative Measurement of Cell-Free DNA and Circulating Tumor DNA in Informing the Prognosis of GI Cancers: A Systematic Review

PURPOSE

Current surveillance imaging and tumor markers lack sensitivity for the early detection of recurrence in GI cancers. This study critically evaluates the current literature on the role of sequential measurement of circulating tumor DNA (ctDNA) before and after curative resection in informing recurrence.

METHODS

A systematic search using a predefined, registered protocol was conducted for studies published between January 2010 and May 2020. Included studies described patients with GI cancers treated with curative-intent surgical resection and measurement of ctDNA both before and after surgery. Patients were divided into three groups on the basis of the presence or absence of ctDNA at these time points. The primary outcome was recurrence-free survival (RFS).

RESULTS

The search yielded 3,873 articles; five met the inclusion criteria and collectively evaluated 57 patients. Pooled median RFS was 62 months (interquartile range 19 to not reached). Although median RFS was not reached in group 1 (- to -) or group 2 (+ to -), median RFS in group 3 (+ to +) was 15 months (interquartile range 9.6-60.4 months). Cox hazard ratio was 4.46 (95% CI, 1.17 to 16.99; P = .028) between group 1 and group 2, and 10.47 (95% CI, 2.91 to 37.74; P < .001) between group 2 and group 3.

CONCLUSION

Detectable ctDNA, either preoperatively or postoperatively, and its persistence after curative surgery are associated with a greater risk of recurrence and decreased RFS in GI cancers. Thus, perioperative measurement of ctDNA may be a useful postoperative risk stratification tool and guide additional therapies.

β-Catenin signaling in hepatocellular carcinoma

Deregulated Wnt/β-catenin signaling is one of the main genetic alterations in human hepatocellular carcinoma (HCC). Comprehensive genomic analyses have revealed that gain-of-function mutation of CTNNB1, which encodes β-catenin, and loss-of-function mutation of AXIN1 occur in approximately 35% of human HCC samples. Human HCCs with activation of the Wnt/β-catenin pathway demonstrate unique gene expression patterns and pathological features. Activated Wnt/β-catenin synergizes with multiple signaling cascades to drive HCC formation, and it functions through its downstream effectors. Therefore, strategies targeting Wnt/β-catenin have been pursued as possible therapeutics against HCC. Here, we review the genetic alterations and oncogenic roles of aberrant Wnt/β-catenin signaling during hepatocarcinogenesis. In addition, we discuss the implication of this pathway in HCC diagnosis, classification, and personalized treatment.

Active demethylation upregulates CD147 expression promoting non-small cell lung cancer invasion and metastasis

Non-small cell lung cancer (NSCLC) is a fatal disease, and its metastatic process is poorly understood. Although aberrant methylation is involved in tumor progression, the mechanisms underlying dynamic DNA methylation remain to be elucidated. It is significant to study the molecular mechanism of NSCLC metastasis and identify new biomarkers for NSCLC early diagnosis. Here, we performed MeDIP-seq and hMeDIP-seq analyses to detect the genes regulated by dynamic DNA methylation. Comparison of the 5mC and 5hmC sites revealed that the CD147 gene underwent active demethylation in NSCLC tissues compared with normal tissues, and this demethylation upregulated CD147 expression. Significantly high levels of CD147 expression and low levels of promoter methylation were observed in NSCLC tissues. Then, we identified the CD147 promoter as a target of KLF6, MeCP2, and DNMT3A. Treatment of cells with TGF-β triggered active demethylation involving loss of KLF6/MeCP2/DNMT3A and recruitment of Sp1, Tet1, TDG, and SMAD2/3 transcription complexes. A dCas9-SunTag-DNMAT3A-sgCD147-targeted methylation system was constructed to reverse CD147 expression. The targeted methylation system downregulated CD147 expression and inhibited NSCLC proliferation and metastasis in vitro and in vivo. Accordingly, we used cfDNA to detect the levels of CD147 methylation in NSCLC tissues and found that the CD147 methylation levels exhibited an inverse relationship with tumor size, lymphatic metastasis, and TNM stage. In conclusion, this study clarified the mechanism of active demethylation of CD147 and suggested that the targeted methylation of CD147 could inhibit NSCLC invasion and metastasis, providing a highly promising therapeutic target for NSCLC.

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was originally approved for. Repurposing non-oncology small-molecule drugs has been increasingly becoming an attractive approach to improve cancer therapy, with potentially lower overall costs and shorter timelines. Several non-oncology drugs approved by FDA have been recently reported to treat different types of human cancers, with the aid of some new emerging technologies, such as omics sequencing and artificial intelligence to overcome the bottleneck of drug repurposing. Therefore, in this review, we focus on summarizing the therapeutic potential of non-oncology drugs, including cardiovascular drugs, microbiological drugs, small-molecule antibiotics, anti-viral drugs, anti-inflammatory drugs, anti-neurodegenerative drugs, antipsychotic drugs, antidepressants, and other drugs in human cancers. We also discuss their novel potential targets and relevant signaling pathways of these old non-oncology drugs in cancer therapies. Taken together, these inspiring findings will shed new light on repurposing more non-oncology small-molecule drugs with their intricate molecular mechanisms for future cancer drug discovery.

Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays

Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridization to Infinium HumanMethylation BeadChips allows nucleotide resolution of 5-hydroxymethylcytosine genome-wide. Here, the authors compared data quality acquired from BS-treated and oxBS-treated samples. Materials & methods: Raw BeadArray data from 417 pairs of samples across 12 independent datasets were included in the study. Probe call rates were compared between paired BS and oxBS treatments controlling for technical variables. Results: oxBS-treated samples had a significantly lower call rate. Among technical variables, DNA-specific extraction kits performed better with higher call rates after oxBS conversion. Conclusion: The authors emphasize the importance of quality control during oxBS conversion to minimize information loss and recommend using a DNA-specific extraction kit for DNA extraction and an oxBSQC package for data preprocessing.

Cell-free DNA technologies for the analysis of brain cancer

Survival for glioma patients has shown minimal improvement over the past 20 years. The ability to detect and monitor gliomas relies primarily upon imaging technologies that lack sensitivity and specificity, especially during the post-surgical treatment phase. Treatment-response monitoring with an effective liquid-biopsy paradigm may also provide the most facile clinical scenario for liquid-biopsy integration into brain-tumour care. Conceptually, liquid biopsy is advantageous when compared with both tissue sampling (less invasive) and imaging (more sensitive and specific), but is hampered by technical and biological problems. These problems predominantly relate to low concentrations of tumour-derived DNA in the bloodstream of glioma patients. In this review, we highlight methods by which the neuro-oncological scientific and clinical communities have attempted to circumvent this limitation. The use of novel biological, technological and computational approaches will be explored. The utility of alternate bio-fluids, tumour-guided sequencing, epigenomic and fragmentomic methods may eventually be leveraged to provide the biological and technological means to unlock a wide range of clinical applications for liquid biopsy in glioma.

Integrated 5-hydroxymethylcytosine and fragmentation signatures as enhanced biomarkers in lung cancer

Background

Lung cancer is one of most common cancers worldwide, with a 5-year survival rate of less than 20%, which is mainly due to late-stage diagnosis. Noninvasive methods using 5-hydroxymethylation of cytosine (5hmC) modifications and fragmentation profiles from 5hmC cell-free DNA (cfDNA) sequencing provide an opportunity for lung cancer detection and management.

Results

A total of 157 lung cancer patients were recruited to generate the largest lung cancer cfDNA 5hmC dataset, which mainly consisted of 62 lung adenocarcinoma (LUAD), 48 lung squamous cell carcinoma (LUSC) and 25 small cell lung cancer (SCLC) patients, with most patients (131, 83.44%) at advanced tumor stages. A 37-feature 5hmC model was constructed and validated to distinguish lung cancer patients from healthy controls, with areas under the curve (AUCs) of 0.8938 and 0.8476 (sensitivity = 87.50% and 72.73%, specificity = 83.87% and 80.60%) in two distinct validation sets. Furthermore, fragment profiles of cfDNA 5hmC datasets were first explored to develop a 48-feature fragmentation model with good performance (AUC = 0.9257 and 0.822, sensitivity = 87.50% and 78.79%, specificity = 80.65% and 76.12%) in the two validation sets. Another diagnostic model integrating 5hmC signals and fragment profiles improved AUC to 0.9432 and 0.8639 (sensitivity = 87.50% and 83.33%, specificity = 90.30% and 77.61%) in the two validation sets, better than models based on either of them alone and performing well in different stages and lung cancer subtypes. Several 5hmC markers were found to be associated with overall survival (OS) and disease-free survival (DFS) based on gene expression data from The Cancer Genome Atlas (TCGA).

Conclusions

Both the 5hmC signal and fragmentation profiles in 5hmC cfDNA data are sensitive and effective in lung cancer detection and could be incorporated into the diagnostic model to achieve good performance, promoting research focused on clinical diagnostic models based on cfDNA 5hmC data.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01233-7.

NucPosDB: a database of nucleosome positioning in vivo and nucleosomics of cell-free DNA

Nucleosome positioning is involved in many gene regulatory processes happening in the cell, and it may change as cells differentiate or respond to the changing microenvironment in a healthy or diseased organism. One important implication of nucleosome positioning in clinical epigenetics is its use in the “nucleosomics” analysis of cell-free DNA (cfDNA) for the purpose of patient diagnostics in liquid biopsies. The rationale for this is that the apoptotic nucleases that digest chromatin of the dying cells mostly cut DNA between nucleosomes. Thus, the short pieces of DNA in body fluids reflect the positions of nucleosomes in the cells of origin. Here, we report a systematic nucleosomics database — NucPosDB — curating published nucleosome positioning datasets in vivo as well as datasets of sequenced cell-free DNA (cfDNA) that reflect nucleosome positioning in situ in the cells of origin. Users can select subsets of the database by a number of criteria and then obtain raw or processed data. NucPosDB also reports the originally determined regions with stable nucleosome occupancy across several individuals with a given condition. An additional section provides a catalogue of computational tools for the analysis of nucleosome positioning or cfDNA experiments and theoretical algorithms for the prediction of nucleosome positioning preferences from DNA sequence. We provide an overview of the field, describe the structure of the database in this context, and demonstrate data variability using examples of different medical conditions. NucPosDB is useful both for the analysis of fundamental gene regulation processes and the training of computational models for patient diagnostics based on cfDNA. The database currently curates ~ 400 publications on nucleosome positioning in cell lines and in situ as well as cfDNA from > 10,000 patients and healthy volunteers. For open-access cfDNA datasets as well as key MNase-seq datasets in human cells, NucPosDB allows downloading processed mapped data in addition to the regions with stable nucleosome occupancy. NucPosDB is available at https://generegulation.org/nucposdb/.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Background

LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.

Results

Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.

Conclusions

The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04530-9.

Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy

Background

In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them.

Methods

In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses.

Results

We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined.

Conclusion

Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-021-01462-z.