Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Genetic insights into superior grain number traits: a QTL analysis of wheat-Agropyron cristatum derivative pubing3228

BACKGROUND

Agropyron cristatum (L.) is a valuable genetic resource for expanding the genetic diversity of common wheat. Pubing3228, a novel wheat-A. cristatum hybrid germplasm, exhibits several desirable agricultural traits, including high grain number per spike (GNS). Understanding the genetic architecture of GNS in Pubing3228 is crucial for enhancing wheat yield. This study aims to analyze the specific genetic regions and alleles associated with high GNS in Pubing3228.

METHODS

The study employed a recombination inbred line (RIL) population derived from a cross between Pubing3228 and Jing4839 to investigate the genetic regions and alleles linked to high GNS. Quantitative Trait Loci (QTL) analysis and candidate gene investigation were utilized to explore these traits.

RESULTS

A total of 40 QTLs associated with GNS were identified across 16 chromosomes, accounting for 4.25-17.17% of the total phenotypic variation. Five QTLs (QGns.wa-1D, QGns.wa-5 A, QGns.wa-7Da.1, QGns.wa-7Da.2 and QGns.wa-7Da.3) accounter for over 10% of the phenotypic variation in at least two environments. Furthermore, 94.67% of the GNS QTL with positive effects originated from Pubing3228. Candidate gene analysis of stable QTLs identified 11 candidate genes for GNS, including a senescence-associated protein gene (TraesCS7D01G148000) linked to the most significant SNP (AX-108,748,734) on chromosome 7D, potentially involved in reallocating nutrients from senescing tissues to developing seeds.

CONCLUSION

This study provides new insights into the genetic mechanisms underlying high GNS in Pubing3228, offering valuable resources for marker-assisted selection in wheat breeding to enhance yield.

Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects

Breast cancer responds variably to anticancer therapies, often leading to significant off-target effects. This study proposes that the variability in tumour responses and drug-induced adverse events is linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues. We analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues. Our findings correlate the drug responses of breast cancer cell lines with the expression levels of their targeted CSRs. Notably, we identified distinct differences in CSR expression between primary breast tumour subtypes and corresponding cell lines, which may influence drug response predictions. Additionally, we used clinical trial data to uncover associations between CSR gene expression in healthy tissues and the incidence of adverse drug reactions. This integrative approach facilitates the selection of optimal CSR targets for therapy, leveraging cell line dose-responses, CSR expression in normal tissues, and patient adverse event profiles.

Ribosomal protein S6 kinase 2 (RPS6KB2) is a potential immunotherapeutic target for cancer that upregulates proinflammatory cytokines

BACKGROUND

Cancer is still a leading cause of mortality. Over the years, cancer therapy has undergone significant advances driven by advancements in science and technology. A promising area of drug discovery in this field involves the development of therapeutic targets for cancer treatment. The urgent need to identify new pharmacological targets arises from the impact of tumor resistance on the effectiveness of current medications. Specifically, the RPS6KB2 gene on chromosome 11 has been implicated in cell cycle regulation and exhibits higher expression levels in tumor tissue. Given this association, there is a potential for this gene to serve as a target for cancer treatment.

METHODS

We conducted an analysis using the GTEx, TCGA, and CCLE databases to explore the relationship between RPS6KB2 and immune infiltration, the tumor microenvironment (TME), microsatellite instability (MSI), and more. Cell proliferation was assessed using EDU detection, while cell invasion and migration were evaluated via wound healing and Transwell assays. Additionally, western blot analysis was employed to measure expression of Bax, Bcl-2, MMP2, MMP9, PCNA, and proinflammatory factors.

RESULTS

Through data analysis and molecular biology methods, our study carefully examined the potential role of RPS6KB2 in cancer therapy. The data revealed that RPS6KB2 is aberrantly expressed in most cancers and is associated with poor prognosis. Further analysis indicated its involvement in cancer cell apoptosis and migration, as well as its role in cancer immune processes. We validated the significance of RPS6KB2 in hepatocellular carcinoma (HCC), highlighting its capacity to upregulate proinflammatory cytokines.

CONCLUSION

Our research indicates that RPS6KB2 is a prognostic biomarker associated with immune infiltration in cancer that can affect antitumor immunity by increasing secretion of proinflammatory factors, providing a potential drug target for cancer treatment.

A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia

BACKGROUND

Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.

METHODS

A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms.

RESULTS

In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81-1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively.

CONCLUSIONS

A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.

Tumor microenvironment diversity and plasticity in cancer multidrug resistance

Multidrug resistance (MDR) poses a significant obstacle to effective cancer treatment, and the tumor microenvironment (TME) is crucial for MDR development and reversal. The TME plays an active role in promoting MDR through several pathways. However, a promising therapeutic approach for battling MDR involves targeting specific elements within the TME. Therefore, this comprehensive review elaborates on the research developments regarding the dual role of the TME in promoting and reversing MDR in cancer. Understanding the complex role of the TME in promoting and reversing MDR is essential to developing effective cancer therapies. Utilizing the adaptability of the TME by targeting novel TME-specific factors, utilizing combination therapies, and employing innovative treatment strategies can potentially combat MDR and achieve personalized treatment outcomes for patients with cancer.

Nanocarriers overcoming biological barriers induced by multidrug resistance of chemotherapeutics in 2D and 3D cancer models

Multidrug resistance (MDR) is currently a big challenge in cancer therapy and limits its success in several patients. Tumors use the MDR mechanisms to colonize the host and reduce the efficacy of chemotherapeutics that are injected as single agents or combinations. MDR mechanisms are responsible for inactivation of drugs and formbiological barriers in cancer like the drug efflux pumps, aberrant extracellular matrix, hypoxic areas, altered cell death mechanisms, etc. Nanocarriers have some potential to overcome these barriers and improve the efficacy of chemotherapeutics. In fact, they are versatile and can deliver natural and synthetic biomolecules, as well as RNAi/DNAi, thus providing a controlled release of drugs and a synergistic effect in tumor tissues. Biocompatible and safe multifunctional biopolymers, with or without specific targeting molecules, modify the surface and interface properties of nanocarriers. These modifications affect the interaction of nanocarriers with cellular models as well as the selection of suitable models for in vitro experiments. MDR cancer cells, and particularly their 2D and 3D models, in combination with anatomical and physiological structures of tumor tissues, can boost the design and preparation of nanomedicines for anticancer therapy. 2D and 3D cancer cell cultures are suitable models to study the interaction, internalization, and efficacy of nanocarriers, the mechanisms of MDR in cancer cells and tissues, and they are used to tailor a personalized medicine and improve the efficacy of anticancer treatment in patients. The description of molecular mechanisms and physio-pathological pathways of these models further allow the design of nanomedicine that can efficiently overcome biological barriers involved in MDR and test the activity of nanocarriers in 2D and 3D models of MDR cancer cells.

Catalytically Active Metal-Organic Frameworks Elicit Robust Immune Response to Combination Chemodynamic and Checkpoint Blockade Immunotherapy

Chemodynamic therapy (CDT) strategies rely on the generation of reactive oxygen species (ROS) to kill tumor cells, with hydroxyl radicals (^•OH) serving as the key mediators of cytotoxicity in this setting. However, the efficacy of CDT approaches is often hampered by the properties of the tumor microenvironment (TME) and associated limitations to the Fenton reaction that constrains ROS generation. As such, there is a pressing need for the design of new nanoplatforms capable of improving CDT outcomes. In this study, an Fc-based metal-organic framework (MOF) vitamin k3 (Vk3)-loaded cascade catalytic nanoplatform (Vk3@Co-Fc) was developed. This platform was capable of undergoing TME-responsive degradation without impacting normal cells. After its release, Vk3 was processed by nicotinamide adenine dinucleotide hydrogen phosphate (NAD(P)H) quinone oxidoreductase-1 (NQO1), which is highly expressed in tumor cells, thereby yielding large quantities of H₂O₂ that in turn interact with Fe ions via the Fenton reaction to facilitate in situ cytotoxic ^•OH production. This process leads to immunogenic cell death (ICD) of the tumor, which then promotes dendritic cell maturation and ultimately increases T cell infiltration into the tumor site. When this nanoplatform was combined with programmed death 1 (PD-1) checkpoint blockade approaches, it was sufficient to enhance tumor-associated immune responses in breast cancer as evidenced by increases in the frequencies of CD45⁺ leukocytes and CD8⁺ cytotoxic T lymphocytes, thereby inhibiting tumor metastasis to the lungs and improving murine survival outcomes. Together, this Vk3@Co-Fc cascading catalytic nanoplatform enables potent cancer immunotherapy for breast cancer regression and metastasis prevention.

In silico drug discovery of SIRT2 inhibitors from natural source as anticancer agents

Sirtuin 2 (SIRT2) is a member of the sirtuin protein family, which includes lysine deacylases that are NAD⁺-dependent and organize several biological processes. Different forms of cancer have been associated with dysregulation of SIRT2 activity. Hence, identifying potent inhibitors for SIRT2 has piqued considerable attention in the drug discovery community. In the current study, the Natural Products Atlas (NPAtlas) database was mined to hunt potential SIRT2 inhibitors utilizing in silico techniques. Initially, the performance of the employed docking protocol to anticipate ligand-SIRT2 binding mode was assessed according to the accessible experimental data. Based on the predicted docking scores, the most promising NPAtlas molecules were selected and submitted to molecular dynamics (MD) simulations, followed by binding energy computations. Based on the MM-GBSA binding energy estimations over a 200 ns MD course, three NPAtlas compounds, namely NPA009578, NPA006805, and NPA001884, were identified with better ΔG_binding towards SIRT2 protein than the native ligand (SirReal2) with values of - 59.9, - 57.4, - 53.5, and - 49.7 kcal/mol, respectively. On the basis of structural and energetic assessments, the identified NPAtlas compounds were confirmed to be steady over a 200 ns MD course. The drug-likeness and pharmacokinetic characteristics of the identified NPAtlas molecules were anticipated, and robust bioavailability was predicted. Conclusively, the current results propose potent inhibitors for SIRT2 deserving more in vitro/in vivo investigation.

Interferon-α2b-Induced RARRES3 Upregulation Inhibits Hypertrophic Scar Fibroblasts' Proliferation and Migration Through Wnt/β-Catenin Pathway Suppression

Hypertrophic scar (HS) is a severe skin fibrotic disorder with unclear pathogenesis. Interferon-α2b (IFN-α2b) exerts inhibitory effects on HS in vivo and in vitro; however, the exact mechanism remains unclear. In this study, we aimed to evaluate the inhibitory effects of IFN-α2b on hypertrophic scar fibroblasts' (HSFs) proliferation and migration, and to further investigate the associated molecular mechanism. Cell Counting Kit-8 and CyQUANT assays were used to assess HSFs' proliferation; wound healing and Transwell assays were used to assess HSFs' migration; real-time quantitative polymerase chain reaction and Western blotting were used to detect messenger RNA and protein levels, respectively, of related genes; bioinformatics analysis was performed to predict the downstream target of IFN-α2b. Our findings are as follows: (1) IFN-α2b inhibited HSFs' proliferation and migration in a dose-dependent manner. (2) IFN-α2b inhibited HSFs' proliferation and migration by suppressing the Wnt/β-catenin pathway. (3) Retinoic-acid receptor responder 3 (RARRES3) was predicted as a functional downstream molecule of IFN-α2b, which was low in HSFs. (4) IFN-α2b inhibited HSF phenotypes and the Wnt/β-catenin pathway by upregulating RARRES3 expression. (5) RARRES3 restrained HSFs' proliferation and migration by repressing the Wnt/β-catenin pathway. In conclusion, IFN-α2b-induced RARRES3 upregulation inhibited HSFs' proliferation and migration through Wnt/β-catenin pathway suppression.

Special Issue: "New Diagnostic and Therapeutic Tools against Multidrug-Resistant Tumors (STRATAGEM Special Issue, EU-COST CA17104)"

Cancer drug resistance, either intrinsic or acquired, often causes treatment failure and increased mortality [...].

Machine learning for distinguishing right from left premature ventricular contraction origin using surface electrocardiogram features

BACKGROUND

Precise localization of the site of origin of premature ventricular contractions (PVCs) before ablation can facilitate the planning and execution of the electrophysiological procedure.

OBJECTIVE

The purpose of this study was to develop a predictive model that can be used to differentiate PVCs between the left ventricular outflow tract and right ventricular outflow tract (RVOT) using surface electrocardiogram characteristics.

METHODS

A total of 851 patients undergoing radiofrequency ablation of premature ventricular beats from January 2015 to March 2022 were enrolled. Ninety-two patients were excluded. The other 759 patients were enrolled into the development (n = 605), external validation (n = 104), or prospective cohort (n = 50). The development cohort consisted of the training group (n = 423) and the internal validation group (n = 182). Machine learning algorithms were used to construct predictive models for the origin of PVCs using body surface electrocardiogram features.

RESULTS

In the development cohort, the Random Forest model showed a maximum receiver operating characteristic curve area of 0.96. In the external validation cohort, the Random Forest model surpasses 4 reported algorithms in predicting performance (accuracy 94.23%; sensitivity 97.10%; specificity 88.57%). In the prospective cohort, the Random Forest model showed good performance (accuracy 94.00%; sensitivity 85.71%; specificity 97.22%).

CONCLUSION

Random Forest algorithm has improved the accuracy of distinguishing the origin of PVCs, which surpasses 4 previous standards, and would be used to identify the origin of PVCs before the interventional procedure.

DRESIS: the first comprehensive landscape of drug resistance information

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

Transcriptomic profiling on localized gastric cancer identified CPLX1 as a gene promoting malignant phenotype of gastric cancer and a predictor of recurrence after surgery and subsequent chemotherapy

BACKGROUND

Localized gastric cancer (GC) becomes fatal once recurring. We still have room for improving their prognoses.

METHODS

Transcriptomic analysis was done on surgically resected specimens of 16 patients with UICC stage III GC who underwent curative gastrectomy and adjuvant oral fluoropyrimidine monotherapy. Four of them were free from disease for longer than 5 years, and the others experienced metachronous metastasis within 2 years after surgery. Quantitative RT-PCR determined mRNA expression levels of primary gastric cancer tissues, which were collected from 180 patients who underwent gastric resection for stage II-III GC without preoperative treatment between 2001 and 2014. We tested alteration of malignant phenotypes including drug resistance of GC cell lines by siRNA and shRNA-mediated knockdown and forced expression experiments.

RESULTS

CPLX1 was identified as a candidate biomarker for GC recurrence among 57,749 genes. Inhibiting and forced expression experiments indicated that CPLX1 promotes proliferation, motility, and invasiveness of GC cells, and decreases apoptosis and sensitivity to fluorouracil. Subcutaneous xenograft mouse models revealed that shRNA-mediated knockdown of CPLX1 also attenuated tumor growth of MKN1 cells in vivo. Overexpression of CPLX1 in gastric cancer tissue correlated with worse prognosis and was an independent risk factor for peritoneal recurrence in subgroups receiving adjuvant chemotherapy.

CONCLUSIONS

CPLX1 may represent a biomarker for recurrence of gastric cancer and a target for therapy.

A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.

Drug Discovery in Spinal Cord Injury With Ankylosing Spondylitis Identified by Text Mining and Biomedical Databases

Spinal cord injury (SCI) and ankylosing spondylitis (AS) are common inflammatory diseases in spine surgery. However, it is a project where the relationship between the two diseases is ambiguous and the efficiency of drug discovery is limited. Therefore, the study aimed to investigate new drug therapies for SCI and AS. First, text mining was used to obtain the interacting genes related to SCI and AS, and then, the functional analysis was conducted. Protein–protein interaction (PPI) networks were constructed by STRING online and Cytoscape software to identify hub genes. Last, hub genes and potential drugs were performed after undergoing drug–gene interaction analysis, and MicroRNA and transcription factors regulatory networks were also analyzed. Two hundred five genes common to “SCI” and “AS” identified by text mining were enriched in inflammatory responses. PPI network analysis showed that 30 genes constructed two significant modules. Ultimately, nine (SST, VWF, IL1B, IL6, CXCR4, VEGFA, SERPINE1, FN1, and PROS1) out of 30 genes could be targetable by a total of 13 drugs. In conclusion, the novel core genes contribute to a novel insight for latent functional mechanisms and present potential prognostic indicators and therapeutic targets in SCI and AS.

Supervised Methods for Biomarker Detection from Microarray Experiments

Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.

Multi-Omics Model Applied to Cancer Genetics

In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

Targeting Endothelial Cell-Specific Molecule 1 Protein in Cancer: A Promising Therapeutic Approach

Despite the dramatic advances in cancer research in the past few years, effective therapeutic strategies are urgently needed. Endothelial cell-specific molecule 1 (ESM-1), a soluble dermatan sulfate proteoglycan, also known as endocan, serves as a diagnostic and prognostic indicator due to its aberrant expression under pathological conditions, including cancer, sepsis, kidney diseases, and cardiovascular disease. Significantly, ESM-1 can promote cancer progression and metastasis through the regulation of tumor cell proliferation, migration, invasion, and drug resistant. In addition, ESM-1 is involved in the tumor microenvironment, containing inflammation, angiogenesis, and lymph angiogenesis. This article reviews the molecular and biological characteristics of ESM-1 in cancer, the underlying mechanisms, the currently clinical and pre-clinical applications, and potential therapeutic strategies. Herein, we propose that ESM-1 is a new therapeutic target for cancer therapy.

Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from non-tumour liver tissue, to identify perturbed biologic functions, co-expression modules, and potentially drugable hub genes that could give rise to novel therapeutic targets and strategies. Gene Set Variation Analysis (GSVA) showed that cell growth-related pathways were upregulated, whereas apoptosis induction, immune and inflammatory-related pathways were downregulated in tumour endothelial cells. Weighted Gene Co-expression Network Analysis (WGCNA) identified several modules strongly associated to tumour endothelial cells or angiogenic activated endothelial cells with high endoglin (ENG) expression. In tumour cells, upregulated modules were associated with cell growth, cell proliferation, and DNA-replication, whereas downregulated modules were involved in immune functions, particularly complement activation. In ENG⁺ cells, upregulated modules were associated with cell adhesion and endothelial functions. One downregulated module was associated with immune system-related functions. Querying the STRING database revealed known functional-interaction networks underlying the modules. Several possible hub genes were identified, of which some (for example FEN1, BIRC5, NEK2, CDKN3, and TTK) are potentially druggable as determined by querying the Drug Gene Interaction database. In summary, our study provides a detailed picture of the transcriptomic differences between tumour and non-tumour endothelium in the liver on a co-expression network level, indicates several potential therapeutic targets and presents an analysis workflow that can be easily adapted to other projects.

Drug responsiveness of leukemic cells detected in vitro at diagnosis correlates with therapy response and survival in patients with acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common acute leukemia in adults, and chemotherapy remains the most commonly used treatment approach for this group of hematological disorders. Drug resistance is one of the predictors of unfavorable prognosis for leukemia patients.

AIM

The purpose of this study was to perform a retrospective analysis of the survival rate in AML patients according to age, tumor status, and chemotherapy regimen received and to analyze the therapy response of AML patients depending on the treatment received, initial responsiveness of tumor cells to chemotherapeutic drugs measured in vitro at diagnosis and expression of immunological markers.

METHODS

The survival of AML patients (n = 127) was analyzed using the Kaplan-Meier method. Drug sensitivity of tumor cells of AML patients (n = 37) and the expression of immunological markers were evaluated by the WST test and flow cytometry, respectively. Correlation analysis was performed using Spearman's rank order correlation coefficient.

RESULTS

We found the treatment regimen to be the defining factor in the patient survival rate. In addition, the initial responsiveness of tumor cells to chemotherapeutic drugs measured in vitro at diagnosis correlated with the therapy response of AML: patients with high tumor cell sensitivity to particular cytotoxic drugs demonstrated a good response to treatment including these drugs, and patients with initial resistance of tumor cells to a particular chemotherapeutic agents and received it according to the clinical protocols demonstrated a poor response to antitumor therapy. Correlations of drug resistance in leukemic cells with the expression of immature and aberrant immunophenotype markers as established unfavorable prognostic factors confirm our assumption.

CONCLUSION

The evaluation of the responsiveness of tumor cells to chemotherapy in vitro at diagnosis can be a useful tool for predicting the response of leukemia patients to planned chemotherapy.

Heat Shock Protein 90 Triggers Multi-Drug Resistance of Ovarian Cancer via AKT/GSK3β/β-Catenin Signaling

Ovarian cancer is the most lethal gynaecologic tumor, with which multi-drug resistance as the major therapeutic hindrance. Heat shock protein 90 (Hsp90) has been involved in cancer malignant behaviors. However, its role and mechanism in multi-drug resistance of ovarian cancer remains poorly understood. Our results demonstrated that Hsp90 was overexpressed in multi-drug resistant ovarian cancer cells. Hsp90 downregulation by shHsp90 or inhibitor BIIB021 increased the sensitivity of multi-drug resistant ovarian cancer cells to paclitaxel and cisplatin, and augmented the drugs-induced apoptosis. Hsp90 positively regulated the expressions of multi-drug resistance protein 1 (P-gp/MDR1), breast cancer resistance protein (BCRP), Survivin and Bcl-2 expressions closely associated with multi-drug resistance. Moreover, overexpression of Hsp90 promoted β-catenin accumulation, while Hsp90 downregulation decreased the accumulation, nuclear translocation and transcriptional activity of β-catenin. We also identified that β-catenin was responsible for Hsp90-mediated expressions of P-gp, BCRP, Survivin, and Bcl-2. Furthermore, Hsp90 enhanced the AKT/GSK3β signaling, and AKT signaling played a critical role in Hsp90-induced accumulation and transcriptional activity of β-catenin, as well as multi-drug resistance to paclitaxel and cisplatin. In conclusion, Hsp90 enhanced the AKT/GSK3β/β-catenin signaling to induce multi-drug resistance of ovarian cancer. Suppressing Hsp90 chemosensitized multi-drug resistant ovarian cancer cells via impairing the AKT/GSK3β/β-catenin signaling, providing a promising therapeutic strategy for a successful treatment of ovarian cancer.

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients' clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Classifying the Linkage between Adipose Tissue Inflammation and Tumor Growth through Cancer-Associated Adipocytes

Recently, tumor microenvironment (TME) and its stromal constituents have provided profound insights into understanding alterations in tumor behavior. After each identification regarding the unique roles of TME compartments, non-malignant stromal cells are found to provide a sufficient tumorigenic niche for cancer cells. Of these TME constituents, adipocytes represent a dynamic population mediating endocrine effects to facilitate the crosstalk between cancer cells and distant organs, as well as the interplay with nearby tumor cells. To date, the prevalence of obesity has emphasized the significance of metabolic homeostasis along with adipose tissue (AT) inflammation, cancer incidence, and multiple pathological disorders. In this review, we summarized distinct characteristics of hypertrophic adipocytes and cancer to highlight the importance of an individual's metabolic health during cancer therapy. As AT undergoes inflammatory alterations inducing tissue remodeling, immune cell infiltration, and vascularization, these features directly influence the TME by favoring tumor progression. A comparison between inflammatory AT and progressing cancer could potentially provide crucial insights into delineating the complex communication network between uncontrolled hyperplastic tumors and their microenvironmental components. In turn, the comparison will unravel the underlying properties of dynamic tumor behavior, advocating possible therapeutic targets within TME constituents.

LncRNA regulation: New frontiers in epigenetic solutions to drug chemoresistance

Long-noncoding RNAs (lncRNAs) have been shown to participate in sensitizing or de-sensitizing cancer cells to chemical drugs during cancer therapeutics. Notably, a plethora of lncRNAs have been confirmed to be associated with epigenetic controllers and regulate histone protein modification or DNA methylation states in the process of gene transcription. This correlation between lncRNAs and epigenetic regulators can induce the expression of core genes to trigger drug resistance. In addition, epigenetic signatures are considered to be effective and attractive biomarkers for monitoring drug therapeutic effects because they are inheritable, dynamic, and reversible. Therefore, the regulatory mechanism between lncRNAs and epigenetic machinery can serve as a novel indicator and target to overcome or reverse drug resistance in cancer therapy. In this review, we also presented a curated selection of computational tools (including online databases and network analysis) in the area of epigenetics. A classic workflow for lncRNA expression network analysis is presented, providing guidance for non-bioinformaticians to identify significant correlation between lncRNAs and other biomolecules.

Classification of the urinary metabolome using machine learning and potential applications to diagnosing interstitial cystitis

With the advent of artificial intelligence (AI) in biostatistical analysis and modeling, machine learning can potentially be applied into developing diagnostic models for interstitial cystitis (IC). In the current clinical setting, urologists are dependent on cystoscopy and questionnaire-based decisions to diagnose IC. This is a result of a lack of objective diagnostic molecular biomarkers. The purpose of this study was to develop a machine learning-based method for diagnosing IC and assess its performance using metabolomics profiles obtained from a prior study. To develop the machine learning algorithm, two classification methods, support vector machine (SVM) and logistic regression (LR), set at various parameters, were applied to 43 IC patients and 16 healthy controls. There were 3 measures used in this study, accuracy, precision (positive predictive value), and recall (sensitivity). Individual precision and recall (PR) curves were drafted. Since the sample size was relatively small, complicated deep learning could not be done. We achieved a 76%–86% accuracy with leave-one-out cross validation depending on the method and parameters set. The highest accuracy achieved was 86.4% using SVM with a polynomial kernel degree set to 5, but a larger area under the curve (AUC) from the PR curve was achieved using LR with a l₁-norm regularizer. The AUC was greater than 0.9 in its ability to discriminate IC patients from controls, suggesting that the algorithm works well in identifying IC, even when there is a class distribution imbalance between the IC and control samples. This finding provides further insight into utilizing previously identified urinary metabolic biomarkers in developing machine learning algorithms that can be applied in the clinical setting.

Systems Biology Approaches to Understanding the Human Immune System

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

HOXB2 and FOXC1 synergistically drive the progression of Wilms tumor

OBJECTIVE

To uncover the expression patterns of HOXB2 and FOXC1 in Wilms tumor samples, and their synergistical regulations on the development of Wilms tumor.

METHODS

Expression levels of HOXB2 and FOXC1 in 58 cases of Wilms tumor tissues and paracancerous ones were detected. The influences of HOXB2 and FOXC1 on prognosis in Wilms tumor patients were analyzed. Their regulatory effects on proliferative and migratory abilities in WT-CLS1 and HFWT cells were examined by cell counting kit-8 (CCK-8) and Transwell assay, respectively. The interaction between HOXB2 and FOXC1, and their synergistical regulation on the development of Wilms tumor were finally explored.

RESULTS

HOXB2 and FOXC1 were upregulated in Wilms tumor tissues. Higher levels of HOXB2 and FOXC1 indicated higher risks of advanced stage and lymphatic metastasis, as well as worse prognosis in Wilms tumor patients. Knockdown of HOXB2 or FOXC1 weakened proliferative and migratory abilities in WT-CLS1 and HFWT cells, while the opposite trends were observed in those overexpressing HOXB2 or FOXC1. The positive interaction between HOXB2 and FOXC1 was identified, which synergistically drove the malignant development of Wilms tumor.

CONCLUSIONS

HOXB2 and FOXC1 are upregulated in Wilms tumor samples, and they are closely linked to tumor staging and lymphatic metastasis in Wilms tumor patients. HOXB2 and FOXC1 synergistically drive the malignant development of Wilms tumor by stimulating proliferative and migratory potentials.

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

OBJECTIVE

Invasive breast cancer (BRCA) is one of the prevalent types of invasive tumors with high mortality worldwide. Due to the lack of effective treatment to control the recurrence of distant metastases, the prognosis of BRCA is still very unsatisfactory. We aimed to find some biomarkers by bioinformatics analysis for survival prediction.

METHODS

Differentially expressed genes (DEGs) were screened out based on tumor group and normal group. Then, the weighted gene correlation network analysis (WGCNA) was employed to identify the clinically associated gene sets. Meanwhile, the enrichment analyses were performed for the functional annotation of the critical genes. The Kaplan Meier analysis calculated the essential genes' prognostic value.

RESULTS

After threshold screening, 1655 DEGs were obtained for subsequent analysis. 51 out of 1655 DEGs were significantly associated with BRCA patients' estrogen receptor status via WGCNA. Three genes (FABP7, CXCL3, and LOC284578) out of the 51 genes were associated with overall survival, and 3 genes were relapse-free survival associated. Finally, we obtained 5 essential prognostic associated genes (FABP7, CXCL3, LOC284578, CAPN6, and NRG2), which could be used as prognostic factors for BRCA.

CONCLUSION

Our findings obtained a gene module associated with BRCA clinical trait and several key genes that acted as essential components in the prognostic of cancer, which may improve its treatment.