Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

microRNA 21 and long non-coding RNAs interplays underlie cancer pathophysiology: A narrative review

Non-coding RNAs (ncRNAs) are a diverse group of functional RNA molecules that lack the ability to code for proteins. Despite missing this traditional role, ncRNAs have emerged as crucial regulators of various biological processes and have been implicated in the development and progression of many diseases, including cancer. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two prominent classes of ncRNAs that have emerged as key players in cancer pathophysiology. In particular, miR-21 has been reported to exhibit oncogenic roles in various forms of human cancer, including prostate, breast, lung, and colorectal cancer. In this context, miR-21 overexpression is closely associated with tumor proliferation, growth, invasion, angiogenesis, and chemoresistance, whereas miR-21 inactivation is linked to the regression of most tumor-related processes. Accordingly, miR-21 is a crucial modulator of various canonical oncogenic pathways such as PTEN/PI3K/Akt, Wnt/β-catenin, STAT, p53, MMP2, and MMP9. Moreover, interplays between lncRNA and miRNA further complicate the regulatory mechanisms underlying tumor development and progression. In this regard, several lncRNAs have been found to interact with miR-21 and, by functioning as competitive endogenous RNAs (ceRNAs) or miRNA sponges, can modulate cancer tumorigenesis. This work presents and discusses recent findings highlighting the roles and pathophysiological implications of the miR-21-lncRNA regulatory axis in cancer occurrence, development, and progression. The data collected indicate that specific lncRNAs, such as MEG3, CASC2, and GAS5, are strongly associated with miR-21 in various types of cancer, including gastric, cervical, lung, and glioma. Indeed, these lncRNAs are well-known tumor suppressors and are commonly downregulated in different types of tumors. Conversely, by modulating various mechanisms and oncogenic signaling pathways, their overexpression has been linked with preventing tumor formation and development. This review highlights the significance of these regulatory pathways in cancer and their potential for use in cancer therapy as diagnostic and prognostic markers.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl β-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 μg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

Association between CYP1A2 gene variants -163 C/A (rs762551) and -3860 G/A (rs2069514) and bladder cancer susceptibility

BACKGROUND

Bladder cancer (BLCA) poses a significant global health challenge due to its high incidence, poor prognosis, and limited treatment options.

AIMS AND OBJECTIVES

This study aims to investigate the association between two specific polymorphisms, CYP1A2-163 C/A and CYP1A2-3860G/A, within the Cytochrome P450 1A2 (CYP1A2) gene and susceptibility to BLCA.

METHODS

The study employed a case-control design, genotyping 340 individuals using Polymerase Chain Reaction-High-Resolution Melting Curve (PCR-HRM). Various genetic models were applied to evaluate allele and genotype frequencies. Genetic linkage analysis was facilitated using R packages.

RESULTS

The study reveals a significant association with the - 163 C/A allele, particularly in the additive model. Odds ratio (OR) analysis links CYP1A2-163 C/A (rs762551) and CYP1A2-3860G/A(rs2069514) polymorphisms to BLCA susceptibility. The rs762551 C/A genotype is prevalent in 55% of BLCA cases and exhibits an OR of 2.21. The A/A genotype has an OR of 1.54. Regarding CYP1A2-3860G/A, the G/A genotype has an OR of 1.54, and the A/A genotype has an OR of 2.08. Haplotype analysis shows a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%. This study underscores associations between CYP1A2 gene variants, particularly rs762551 (CYP1A2-163 C/A), and an increased susceptibility to BLCA. Haplotype analysis of 340 individuals reveals a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%.

CONCLUSION

In conclusion, the - 163 C/A allele, C/A genotype of rs762551, and G/A genotype of rs2069514 emerge as potential genetic markers associated with elevated BLCA risk.

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph

BACKGROUND

Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery.

RESULTS

We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs.

CONCLUSIONS

GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

CD38/NAD+ glycohydrolase and associated antigens in chronic lymphocytic leukaemia: From interconnected signalling pathways to therapeutic strategies

Chronic lymphocytic leukaemia (CLL) is a heterogenous disease characterized by the accumulation of neoplastic CD5+/CD19+ B lymphocytes. The spreading of the leukaemia relies on the CLL cell's ability to survive in the blood and migrate to and proliferate within the bone marrow and lymphoid tissues. Some patients with CLL are either refractory to the currently available therapies or relapse after treatment; this emphasizes the need for novel therapeutic strategies that improving clinical responses and overcome drug resistance. CD38 is a marker of a poor prognosis and governs a set of survival, proliferation and migration signals that contribute to the pathophysiology of CLL. The literature data evidence a spatiotemporal association between the cell surface expression of CD38 and that of other CLL antigens, such as the B-cell receptor (BCR), CD19, CD26, CD44, the integrin very late antigen 4 (VLA4), the chemokine receptor CXCR4, the vascular endothelial growth factor receptor-2 (VEGF-R2), and the neutrophil gelatinase-associated lipocalin receptor (NGAL-R). Most of these proteins contribute to CLL cell survival, proliferation and trafficking, and cooperate with CD38 in multilayered signal transduction processes. In general, these antigens have already been validated as therapeutic targets in cancer, and a broad repertoire of specific monoclonal antibodies and derivatives are available. Here, we review the state of the art in this field and examine the therapeutic opportunities for cotargeting CD38 and its partners in CLL, e.g. by designing novel bi-/trispecific antibodies.

Discovery of a Covalent Inhibitor That Overcame Resistance to Venetoclax in AML Cells Overexpressing BFL-1

Clinical and biological studies have shown that overexpression of BFL-1 is one contributing factor to venetoclax resistance. The resistance might be overcome by a potent BFL-1 inhibitor, but such an inhibitor is rare. In this study, we show that 56, featuring an acrylamide moiety, inhibited the BFL-1/BID interaction with a Ki value of 105 nM. More interestingly, 56 formed an irreversible conjugation adduct at the C55 residue of BFL-1. 56 was a selective BFL-1 inhibitor, and its MCL-1 binding affinity was 10-fold weaker, while it did not bind BCL-2 and BCL-xL. Mechanistic studies showed that 56 overcame venetoclax resistance in isogenic AML cell lines MOLM-13-OE and MV4-11-OE, which both overexpressed BFL-1. More importantly, 56 and venetoclax combination promoted stronger apoptosis induction than either single agent. Collectively, our data show that 56 overcame resistance to venetoclax in AML cells overexpressing BFL-1. These attributes make 56 a promising candidate for future optimization.

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Mapping the influence of hydrocarbons mixture on molecular mechanisms, involved in breast and lung neoplasms: in silico toxicogenomic data-mining

BACKGROUND

Exposure to chemical mixtures inherent in air pollution, has been shown to be associated with the risk of breast and lung cancers. However, studies on the molecular mechanisms of exposure to a mixture of these pollutants, such as hydrocarbons, in the development of breast and lung cancers are scarce. We utilized in silico toxicogenomic analysis to elucidate the molecular pathways linked to both cancers that are influenced by exposure to a mixture of selected hydrocarbons. The Comparative Toxicogenomics Database and Cytoscape software were used for data mining and visualization.

RESULTS

Twenty-five hydrocarbons, common in air pollution with carcinogenicity classification of 1 A/B or 2 (known/presumed or suspected human carcinogen), were divided into three groups: alkanes and alkenes, halogenated hydrocarbons, and polyaromatic hydrocarbons. The in silico data-mining revealed 87 and 44 genes commonly interacted with most of the investigated hydrocarbons are linked to breast and lung cancer, respectively. The dominant interactions among the common genes are co-expression, physical interaction, genetic interaction, co-localization, and interaction in shared protein domains. Among these genes, only 16 are common in the development of both cancers. Benzo(a)pyrene and tetrachlorodibenzodioxin interacted with all 16 genes. The molecular pathways potentially affected by the investigated hydrocarbons include aryl hydrocarbon receptor, chemical carcinogenesis, ferroptosis, fluid shear stress and atherosclerosis, interleukin 17 signaling pathway, lipid and atherosclerosis, NRF2 pathway, and oxidative stress response.

CONCLUSIONS

Within the inherent limitations of in silico toxicogenomics tools, we elucidated the molecular pathways associated with breast and lung cancer development potentially affected by hydrocarbons mixture. Our findings indicate adaptive responses to oxidative stress and inflammatory damages are instrumental in the development of both cancers. Additionally, ferroptosis-a non-apoptotic programmed cell death driven by lipid peroxidation and iron homeostasis-was identified as a new player in these responses. Finally, AHR potential involvement in modulating IL-8, a critical gene that mediates breast cancer invasion and metastasis to the lungs, was also highlighted. A deeper understanding of the interplay between genes associated with these pathways, and other survival signaling pathways identified in this study, will provide invaluable knowledge in assessing the risk of inhalation exposure to hydrocarbons mixture. The findings offer insights into future in vivo and in vitro laboratory investigations that focus on inhalation exposure to the hydrocarbons mixture.

Exosomal NAT10 from esophageal squamous cell carcinoma cells modulates macrophage lipid metabolism and polarization through ac4C modification of FASN

N-acetyltransferase 10 (NAT10) is acknowledged as a tumor promoter in various cancers due to its role as a regulator of acetylation modification. Tumor-associated macrophages (TAMs) play a pivotal role in the tumor microenvironment (TME). However, the intercellular communication between esophageal squamous cell carcinoma (ESCC) cells and TAMs involving NAT10 remains poorly understood. This study aimed to elucidate the regulatory mechanism of NAT10 in modulating macrophage lipid metabolism and polarization. Experimental evidence was derived from in vitro and in vivo analyses. We explored the association between upregulated NAT10 in ESCC tissues, macrophage polarization, and the therapeutic efficacy of PD-1. Furthermore, we investigated the impact of methyltransferase 3 (METTL3)-induced m6A modification on the increased expression of NAT10 in ESCC cells. Additionally, we examined the role of exosomal NAT10 in stabilizing the expression of fatty acid synthase (FASN) and promoting macrophage M2 polarization through mediating the ac4C modification of FASN. Results indicated that NAT10, packaged by exosomes derived from ESCC cells, promotes macrophage M2 polarization by facilitating lipid metabolism. In vivo animal studies demonstrated that targeting NAT10 could enhance the therapeutic effect of PD-1 on ESCC by mediating macrophage reprogramming. Our findings offer novel insights into improving ESCC treatment through NAT10 targeting.

Mass spectrometry for the study of adipocyte cell secretome in cardiovascular diseases

Adipose tissue is classically considered the primary site of lipid storage, but in recent years has garnered appreciation for its broad role as an endocrine organ, capable of remotely signaling to other tissues to alter their metabolic program. The adipose tissue is now recognized as a crucial regulator of cardiovascular health, mediated by the secretion of several bioactive products, with a wide range of endocrine and paracrine effects on the cardiovascular system. Thanks to the development and improvement of high-throughput mass spectrometry, the size and components of the human secretome have been characterized. In this review, we summarized the recent advances in mass spectrometry-based studies of the cell and tissue secretome for the understanding of adipose tissue biology, which may help to decipher the complex molecular mechanisms controlling the crosstalk between the adipose tissue and the cardiovascular system, and their possible clinical translation.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

A promising strategy of surface-modified nanoparticles targeting CXCR4 for precision cancer therapy

Nanoparticle (NP) functionalization with specific ligands enhances targeted cancer therapy and imaging by promoting receptor recognition and improving cellular uptake. This review focuses on recent research exploring the interaction between cancer cell-expressed chemokine receptor 4 (CXCR4) and ligand-conjugated NPs, utilising small molecules, peptides, and antibodies. Active NP targeting has shown improved tumour targeting and reduced toxicity, enabling precision therapy and diagnosis. However, challenges persist in the clinical translation of targeted NPs due to issues with biological response, tumour accumulation, and maintaining NP quality at an industrial scale. Biological and intratumoral barriers further hinder efficient NP accumulation in tumours, hampering translatability. To address these challenges, the academic community is refocusing efforts on understanding NP biological fate and establishing robust preclinical models. Future studies should investigate NP-body interactions, develop computational models, and identify optimal preclinical models. Establishing central NP research databases and fostering collaboration across disciplines is crucial to expediting clinical translation. Overcoming these hurdles will unlock the transformative potential of CXCR4-ligand-NP conjugates in revolutionising cancer treatment.

PTEN deficiency induces an extrahepatic cholangitis-cholangiocarcinoma continuum via aurora kinase A in mice

BACKGROUND & AIMS

The PTEN-AKT pathway is frequently altered in extrahepatic cholangiocarcinoma (eCCA). We aimed to evaluate the role of PTEN in the pathogenesis of eCCA and identify novel therapeutic targets for this disease.

METHODS

The Pten gene was genetically deleted using the Cre-loxp system in biliary epithelial cells. The pathologies were evaluated both macroscopically and histologically. The characteristics were further analyzed by immunohistochemistry, reverse-transcription PCR, cell culture, and RNA sequencing. Some features were compared to those in human eCCA samples. Further mechanistic studies utilized the conditional knockout of Trp53 and Aurora kinase A (Aurka) genes. We also tested the effectiveness of an Aurka inhibitor.

RESULTS

We observed that genetic deletion of the Pten gene in the extrahepatic biliary epithelium and peri-ductal glands initiated sclerosing cholangitis-like lesions in mice, resulting in enlarged and distorted extrahepatic bile ducts in mice as early as 1 month after birth. Histologically, these lesions exhibited increased epithelial proliferation, inflammatory cell infiltration, and fibrosis. With aging, the lesions progressed from low-grade dysplasia to invasive carcinoma. Trp53 inactivation further accelerated disease progression, potentially by downregulating senescence. Further mechanistic studies showed that both human and mouse eCCA showed high expression of AURKA. Notably, the genetic deletion of Aurka completely eliminated Pten deficiency-induced extrahepatic bile duct lesions. Furthermore, pharmacological inhibition of Aurka alleviated disease progression.

CONCLUSIONS

Pten deficiency in extrahepatic cholangiocytes and peribiliary glands led to a cholangitis-to-cholangiocarcinoma continuum that was dependent on Aurka. These findings offer new insights into preventive and therapeutic interventions for extrahepatic CCA.

IMPACT AND IMPLICATIONS

The aberrant PTEN-PI3K-AKT signaling pathway is commonly observed in human extrahepatic cholangiocarcinoma (eCCA), a disease with a poor prognosis. In our study, we developed a mouse model mimicking cholangitis to eCCA progression by conditionally deleting the Pten gene via Pdx1-Cre in epithelial cells and peribiliary glands of the extrahepatic biliary duct. The conditional Pten deletion in these cells led to cholangitis, which gradually advanced to dysplasia, ultimately resulting in eCCA. The loss of Pten heightened Akt signaling, cell proliferation, inflammation, fibrosis, DNA damage, epigenetic signaling, epithelial-mesenchymal transition, cell dysplasia, and cellular senescence. Genetic deletion or pharmacological inhibition of Aurka successfully halted disease progression. This model will be valuable for testing novel therapies and unraveling the mechanisms of eCCA tumorigenesis.

PTPN23[Thr] variant reduces susceptibility and tumorigenesis in esophageal squamous cell carcinoma through dephosphorylation of EGFR

Post-translational modifications (PTMs) have emerged as pivotal regulators of the development of cancers, including esophageal squamous cell carcinoma (ESCC). Here, we conducted a comprehensive analysis of PTM-related genetic variants associated with ESCC risk using large-scale genome-wide and exome-wide association datasets. We observed significant enrichment of PTM-related variants in the ESCC risk loci and identified five variants that were significantly associated with ESCC risk. Among them, rs6780013 in PTPN23 exhibited the highest level of significance in ESCC susceptibility in 9,728 ESCC cases and 10,977 controls (odds ratio [OR] = 0.85, 95 % confidence interval [CI] = 0.81- 0.89, P = 9.77 × 10-14). Further functional investigations revealed that PTPN23[Thr] variant binds to EGFR and modulates its phosphorylation at Thr699. PTPN23[Thr] variant substantially inhibited ESCC cell proliferation both in vitro and in vivo. Our findings underscore the critical role of PTPN23[Thr]-EGFR interaction in ESCC development, providing more insights into the pathogenesis of this cancer.

Clinical efficacy and identification of factors confer resistance to afatinib (tyrosine kinase inhibitor) in EGFR-overexpressing esophageal squamous cell carcinoma

Epidermal growth factor receptor (EGFR) is reportedly overexpressed in most esophageal squamous cell carcinoma (ESCC) patients, but anti-EGFR treatments offer limited survival benefits. Our preclinical data showed the promising antitumor activity of afatinib in EGFR-overexpressing ESCC. This proof-of-concept, phase II trial assessed the efficacy and safety of afatinib in pretreated metastatic ESCC patients (n = 41) with EGFR overexpression (NCT03940976). The study met its primary endpoint, with a confirmed objective response rate (ORR) of 39% in 38 efficacy-evaluable patients and a median overall survival of 7.8 months, with a manageable toxicity profile. Transcriptome analysis of pretreatment tumors revealed that neurotrophic receptor tyrosine kinase 2 (NTRK2) was negatively associated with afatinib sensitivity and might serve as a predictive biomarker, irrespective of EGFR expression. Notably, knocking down or inhibiting NTRK2 sensitized ESCC cells to afatinib treatment. Our study provides novel findings on the molecular factors underlying afatinib resistance and indicates that afatinib has the potential to become an important treatment for metastatic ESCC patients.

Predictive and Experimental Motif Interaction Analysis Identifies Functions of the WNK-OSR1/SPAK Pathway

The WNK-OSR1/SPAK protein kinase signaling pathway regulates ion homeostasis and cell volume, but its other functions are poorly understood. To uncover undefined signaling functions of the pathway we analyzed the binding specificity of the conserved C-terminal (CCT) domains of OSR1 and SPAK to find all possible interaction motifs in human proteins. These kinases bind the core consensus sequences R-F-x-V/I and R-x-F-x-V/I. Motifs were ranked based on sequence, conservation, cellular localization, and solvent accessibility. Out of nearly 3,700 motifs identified, 90% of previously published motifs were within the top 2% of those predicted. Selected candidates (TSC22D1, CAVIN1, ATG9A, NOS3, ARHGEF5) were tested. Upstream kinases WNKs 1-4 and their close relatives, the pseudokinases NRBP1/2, contain CCT-like domains as well. We identified additional distinct motif variants lacking the conserved arginine previously thought to be required, and found that the NRBP1 CCT-like domain binds TSC22D1 via the same motif as OSR1 and SPAK. Our results further highlight the rich and diverse functionality of CCT and CCT-like domains in connecting WNK signaling to cellular processes.

Circular RNAs and the JAK/STAT pathway: New frontiers in cancer therapeutics

Circular RNAs, known as circRNAs, have drawn more attention to cancer biology in the last few years. Novel functions of circRNAs in cancer therapy open promising prospects for personalized medicine. This review focuses on the molecular properties and potential of circRNAs as biomarkers or therapeutic targets in cancer treatment. Unique properties of circular RNAs associated with a circular form provide stability and resilience to RNA exonuclease degradation. Circular RNAs' most important characteristic is that they are involved in the JAK/STAT pathway associated with oncogenesis. Notably, their deregulation has been reported in multiple carcinomas due to involvement in JAK/STAT signaling cascade modulation. Increased knowledge about circRNAs' interaction with the JAK/STAT pathway leads to the emergence of new possibilities for targeted cancer therapy. In addition, since circRNAs demonstrate tissue-relatedness of expression, they may be a reliable biomarker for predicting and diagnosing cancer. With the development of new technologies for targeting circRNAs, novel therapeutics can be produced that offer more personalized cancer treatment options based on the nature of the patient. The present review explores the exciting prospects of circRNAs for transforming cancer treatment into personalized medicine. It describes the current understanding of circRNA biology, its relationship to tumorigenesis, and possible targeting methods.

Aberrant spliceosome activity via elevated intron retention and upregulation and phosphorylation of SF3B1 in chronic lymphocytic leukemia

Splicing factor 3b subunit 1 (SF3B1) is the largest subunit and core component of the spliceosome. Inhibition of SF3B1 was associated with an increase in broad intron retention (IR) on most transcripts, suggesting that IR can be used as a marker of spliceosome inhibition in chronic lymphocytic leukemia (CLL) cells. Furthermore, we separately analyzed exonic and intronic mapped reads on annotated RNA-sequencing transcripts obtained from B cells (n = 98 CLL patients) and healthy volunteers (n = 9). We measured intron/exon ratio to use that as a surrogate for alternative RNA splicing (ARS) and found that 66% of CLL-B cell transcripts had significant IR elevation compared with normal B cells (NBCs) and that correlated with mRNA downregulation and low expression levels. Transcripts with the highest IR levels belonged to biological pathways associated with gene expression and RNA splicing. A >2-fold increase of active pSF3B1 was observed in CLL-B cells compared with NBCs. Additionally, when the CLL-B cells were treated with macrolides (pladienolide-B), a significant decrease in pSF3B1, but not total SF3B1 protein, was observed. These findings suggest that IR/ARS is increased in CLL, which is associated with SF3B1 phosphorylation and susceptibility to SF3B1 inhibitors. These data provide additional support to the relevance of ARS in carcinogenesis and evidence of pSF3B1 participation in this process.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Identification of hub genes and potential therapeutic mechanisms related to HPV positive head and neck squamous carcinoma based on full transcriptomic detection and ceRNA network construction

Infection with human papillomavirus (HPV) is a major risk factor for head and neck squamous cell carcinoma (HNSCC). The objective of this study is to investigate the gene expression profiles and signaling pathways that are specific to HPV-positive HNSCC (HPV+ HNSCC). Moreover, a competing endogenous RNA (ceRNA) network analysis was utilized to identify the core gene of HPV+ HNSCC and potential targeted therapeutic drugs. Transcriptome sequencing analysis identified 3,253 coding RNAs and 3,903 non-coding RNAs (ncRNAs) that exhibited preferentially expressed in HPV+ HNSCC. Four key signaling pathways were selected through pathway enrichment analysis. By combining ceRNA network and protein-protein interaction (PPI) network topology analysis, RNA Polymerase II Associated Protein 2 (RPAP2), which also exhibited high expression in HPV+ HNSCC based on the TCGA database, was identified as the hub gene. Gene set enrichment analysis (GSEA) results revealed RPAP2's involvement in various signaling pathways, encompassing basal transcription factors, ubiquitin-mediated proteolysis, adherens junction, other glycan degradation, ATP-binding cassette (ABC) transporters, and oglycan biosynthesis. Five potential small molecule targeted drugs (enzastaurin, brequinar, talinolol, phenylbutazone, and afuresertib) were identified using the cMAP database, with enzastaurin showing the highest affinity for RPAP2. Cellular functional experiments confirmed the inhibitory effect of enzastaurin on cell viability of HPV+ HNSCC and RPAP2 expression levels. Additionally, enzastaurin treatment suppressed the expression levels of the top-ranked long non-coding RNA (lncRNA), circular RNA (circRNA), and microRNA (miRNA) in the ceRNA network. This study based on the ceRNA network provides valuable insights into the molecular mechanisms and potential therapeutic strategies for HPV+ HNSCC, and provide theoretical basis for the exploration of HPV+ HNSCC biomarkers and the development of targeted drugs.

Current knowledge on therapeutic, diagnostic, and prognostics applications of exosomes in multiple myeloma: Opportunities and challenges

Interactions between the plasma cells and the BM microenvironment of Multiple myeloma (MM) take place through factors such as exosomes. Many studies have confirmed the role of exosomes in these interactions. By carrying proteins, cytokines, lipids, microRNAs, etc. as their cargo, exosomes can regulate the interactions between MM plasma cells and neighboring cells and participate in the signaling between cancer cells and the environment. It has been shown that MM-derived exosomes can induce angiogenesis, enhance osteoblast activity, confer drug resistance, and have immunosuppressive properties. Abnormal cargos in endosomes originating from MM patients, can be used as a cancer biomarker to detect or screen early prognosis in MM patients. The native nanostructure of exosomes, in addition to their biocompatibility, stability, and safety, make them excellent candidates for therapeutic, drug delivery, and immunomodulatory applications against MM. On the other hand, exosomes derived from dendritic cells (DC) may be used as vaccines against MM. Thanks to the development of new 'omics' approaches, we anticipate to hear more about exosomes in fight against MM. In the present review, we described the most current knowledge on the role of exosomes in MM pathogenesis and their potential role as novel biomarkers and therapeutic tools in MM.

A protein-protein interaction network aligner study in the multi-objective domain

BACKGROUND AND OBJECTIVE

The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain.

METHODS

Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes.

RESULTS

Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments.

CONCLUSIONS

The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.

Meta-analysis of the efficacy of neoadjuvant chemotherapy for locally advanced cervical cancer

BACKGROUND

Neither improvements in surgical techniques and methods nor advances in radiotherapy equipment and techniques have significantly improved cervical cancer survival rates for quite some time.

AIM

By comparing the effectiveness of neoadjuvant chemotherapy in the treatment of locally advanced cervical cancer, this study aimed to explore effective treatment methods for locally advanced cervical cancer, and provide a theoretical basis to guide clinical practice.

METHODS

A search of PubMed, Embase, Scopus, Web of Science and Cochrane databases was undertaken to identify randomized controlled trials on the efficacy of neoadjuvant chemotherapy for locally advanced cervical cancer, where the intervention in the experimental group was neoadjuvant chemotherapy. Based on the inclusion and exclusion criteria, the studies were evaluated for quality according to the Cochrane Quality Rating Scale. Baseline information, intervention information and outcome indicators of the included studies were extracted. Meta-analysis was performed using RevMan 5.4.

RESULTS

Significant differences in overall survival [relative risk (RR) 1.63, 95 % confidence interval (CI) 0.69-2.57; p = 0.0007] and complete remission rate (RR 0.37, 95 % CI -0.49 to 1.23; p = 0.041) were found between the two groups. Heterogeneity of the objective response rate showed p < 0.0001 and I2 = 99 % (I2 = 99 > 50 % and p > 0.1 for the Q-test suggested strong heterogeneity). The fixed effects model was chosen for the integration statistic [standardized mean difference (SMD) 0.81, 95 % CI -0.21 to 1.83; p = 0.12]; the difference was not significant (p > 0.05). Heterogeneity of the adverse effects of neoadjuvant chemotherapy showed p < 0.0001 and I2 = 98 % (I2 = 98 %>50 % and p > 0.1 for the Q-test suggested strong heterogeneity). The fixed effects model was chosen for the integration statistic (SMD -0.023, 95 % CI -0.95 to 0.49; p = 0.53); the difference was not significant (p > 0.05).

CONCLUSIONS

The use of neoadjuvant chemotherapy for the treatment of locally advanced cervical cancer improved the objective response rate and the complete remission rate of patients, but failed to improve overall survival and adverse effects.

Causal relationship between lipid-lowering drugs and ovarian cancer, cervical cancer: a drug target mendelian randomization study

BACKGROUND

The causal impact of lipid-lowering drugs on ovarian cancer (OC) and cervical cancer (CC) has received considerable attention, but its causal relationship is still a subject of debate. Hence, the objective of this study is to evaluate the impact of lipid-lowering medications on the occurrence risk of OC and CC through Mendelian randomization (MR) analysis of drug targets.

METHODS

This investigation concentrated on the primary targets of lipid-lowering medications, specifically, 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) and proprotein convertase kexin 9 (PCSK9). Genetic variations associated with HMGCR and PCSK9 were derived from published genome-wide association study (GWAS) findings to serve as substitutes for HMGCR and PCSK9 inhibitors. Employing a MR approach, an analysis was conducted to scrutinize the impact of inhibitors targeting HMGCR and PCSK9 on the occurrence of OC and CC. Coronary heart disease (CHD) risk was utilized as a positive control, and the primary outcomes encompassed OC and CC.

RESULTS

The findings of the study suggest a notable elevation in the risk of OC among patients treated with HMGCR inhibitors (OR [95%CI] = 1.815 [1.316, 2.315], p = 0.019). In contrast, no significant correlation was observed between PCSK9 inhibitors and the occurrence of OC. Additionally, the analysis did not reveal any noteworthy connection between HMGCR inhibitors, PCSK9 inhibitors, and CC.

CONCLUSION

HMGCR inhibitors significantly elevate the risk of OC in patients, but their mechanism needs further investigation, and no influence of PCSK9 inhibitors on OC has been observed. There is no significant relationship between HMGCR inhibitors, PCSK9 inhibitors, and CC.

IGFBP7 promotes gastric cancer by facilitating epithelial-mesenchymal transition of gastric cells

Gastric cancer (GC) with high morbidity and mortality is one major cause of tumor-related death. Mechanisms underlying GC invasion and metastasis remain unclear. IGFBP7 exerted variable effects in different cancers and its role in GC is controversial. Here, IGFBP7 was found to be upregulated and elevated IGFBP7 expression represented a poorer overall survival in GC using bioinformatics analysis. Moreover, IGFBP7 was up-regulated in human GC specimens and promoted tumor growth in xenograft tumor animals. For GC cell lines, we found that IGFBP7 was also upregulated and facilitated the cell malignant behavior and EMT of GC cells, which may involve NF-κB and ERK signaling pathways. This research may provide new avenues for GC therapy.

Down-regulation of RTEL1 Improves M1/M2 Macrophage Polarization by Promoting SFRP2 in Fibroblasts-derived Exosomes to Alleviate COPD

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease worldwide. Macrophage polarization plays a substantial role in the pathogenesis of COPD. This study is aimed to explore the regulatory mechanism of regulator of telomere elongation 1 (RTEL1) in COPD. COPD model mouse was conducted by cigarette smoke (CS). The pathological features of lung in mice were observed by histological staining. After extracting exosomes, macrophages were co-cultured with fibroblasts-derived exosomes. Then, the effects of RTEL1 and exosomal secreted frizzled-related protein 2 (SFRP2) on macrophage proliferation, inflammation, apoptosis, and M1, M2 macrophage polarization (iNOS and CD206) were evaluated by cell counting kit-8, EdU assay, enzyme-linked immuno sorbent assay, and western blotting, respectively. CS-induced COPD model mouse was successfully constructed. Through in vitro experiments, knockdown of RTEL1 inhibited macrophage proliferation, inflammation (MMP9, IL-1β and TNF-α), and promoted apoptosis (Bax, cleaved-caspase3, Bcl-2) in CS extract-induced lung fibroblasts. Meanwhile, RTEL1 knockdown promoted M1 and suppressed M2 macrophage polarization in COPD. Additionally, silencing SFRP2 in fibroblasts-derived exosomes reversed the effects of RTEL1 knockdown on proliferation, inflammation, apoptosis, and M1, M2 macrophage polarization. Collectively, down-regulation of RTEL1 improved M1/M2 macrophage polarization by promoting SFRP2 in fibroblasts-derived exosomes to alleviate CS-induced COPD.

Assessing the validity of driver gene identification tools for targeted genome sequencing data

Motivation

Most cancer driver gene identification tools have been developed for whole-exome sequencing data. Targeted sequencing is a popular alternative to whole-exome sequencing for large cancer studies due to its greater depth at a lower cost per tumor. Unlike whole-exome sequencing, targeted sequencing only enables mutation calling for a selected subset of genes. Whether existing driver gene identification tools remain valid in that context has not previously been studied.

Results

We evaluated the validity of seven popular driver gene identification tools when applied to targeted sequencing data. Based on whole-exome data of 14 different cancer types from TCGA, we constructed matching targeted datasets by keeping only the mutations overlapping with the pan-cancer MSK-IMPACT panel and, in the case of breast cancer, also the breast-cancer-specific B-CAST panel. We then compared the driver gene predictions obtained on whole-exome and targeted mutation data for each of the seven tools. Differences in how the tools model background mutation rates were the most important determinant of their validity on targeted sequencing data. Based on our results, we recommend OncodriveFML, OncodriveCLUSTL, 20/20+, dNdSCv, and ActiveDriver for driver gene identification in targeted sequencing data, whereas MutSigCV and DriverML are best avoided in that context.

Availability and implementation

Code for the analyses is available at https://github.com/SchmidtGroupNKI/TGSdrivergene_validity.

FOXP4-mediated induction of PTK7 activates the Wnt/β-catenin pathway and promotes ovarian cancer development

Ovarian cancer (OV) poses a significant challenge in clinical settings due to its difficulty in early diagnosis and treatment resistance. FOXP4, belonging to the FOXP subfamily, plays a pivotal role in various biological processes including cancer, cell cycle regulation, and embryonic development. However, the specific role and importance of FOXP4 in OV have remained unclear. Our research showed that FOXP4 is highly expressed in OV tissues, with its elevated levels correlating with poor prognosis. We further explored FOXP4's function through RNA sequencing and functional analysis in FOXP4-deficient cells, revealing its critical role in activating the Wnt signaling pathway. This activation exacerbates the malignant phenotype in OV. Mechanistically, FOXP4 directly induces the expression of protein tyrosine kinase 7 (PTK7), a Wnt-binding receptor tyrosine pseudokinase, which causes abnormal activation of the Wnt signaling pathway. Disrupting the FOXP4-Wnt feedback loop by inactivating the Wnt signaling pathway or reducing FOXP4 expression resulted in the reduction of the malignant phenotype of OV cells, while restoring PTK7 expression reversed this effect. In conclusion, our findings underscore the significance of the FOXP4-induced Wnt pathway activation in OV, suggesting the therapeutic potential of targeting this pathway in OV treatment.

DNA methylation markers in esophageal cancer

Background

Esophageal cancer (EC) is a prevalent malignancy characterized by a low 5-year survival rate, primarily attributed to delayed diagnosis and limited therapeutic options. Currently, early detection of EC heavily relies on endoscopy and pathological examination, which pose challenges due to their invasiveness and high costs, leading to low patient compliance. The detection of DNA methylation offers a non-endoscopic, cost-effective, and secure approach that holds promising prospects for early EC detection.

Methods

To identify improved methylation markers for early EC detection, we conducted a comprehensive review of relevant literature, summarized the performance of DNA methylation markers based on different input samples and analytical methods in EC early detection and screening.

Findings

This review reveals that blood cell free DNA methylation-based method is an effective non-invasive method for early detection of EC, although there is still a need to improve its sensitivity and specificity. Another highly sensitive and specific non-endoscopic approach for early detection of EC is the esophageal exfoliated cells based-DNA methylation analysis. However, while there are substantial studies in esophageal adenocarcinoma, further more validation is required in esophageal squamous cell carcinoma.

Conclusion

In conclusion, DNA methylation detection holds significant potential as an early detection and screening technology for EC.

Ribosome-binding protein-1 (RRBP1) expression in prostate carcinomas and its relationship with clinicopathological prognostic factors

INTRODUCTION

Studies in recent years have shown that ribosome-binding protein-1 (RRBP1) is expressed at high rates in many cancers and that it may be a potential prognostic biomarker. The objective of the present study is to determine the RRBP1 expression level in prostatic carcinoma and neighboring non-neoplastic prostate tissue, the relationship between its expression level with prognostic factors, and the role of RRBP1 in the development of prostate cancer.

MATERIALS AND METHODS

The study included 45 patients who were diagnosed with prostatic carcinoma and underwent radical prostatectomy in our center between the years 2010 and 2021. Pathology reports were reviewed. Mann-Whitney U test was used for the comparison of RRBP1 and GADPH values of the cases (control and tumoral tissue) between the primary tumor stage (pT) and Gleason score (GS) groups. Hierarchical regression analysis was used to explain the effective variables in explaining the RRBP1 value of the research cases.

RESULTS

According to the Mann-Whitney U test, mean and median RRBP1-T values of the cases with GS ≥ 8 were detected to be statistically significantly higher than the mean and median RRBP1-T values of the cases with GS < 8.

CONCLUSION

We found out that RRBP1 was expressed at higher rates in patients with high GS and advanced-stage patients. This result indicated that RRBP1 expression may be important in predicting the prognosis of prostate carcinoma.

Unwinding circular RNA's role in inflammatory pulmonary diseases

Circular RNAs (circRNAs) have emerged as pivotal regulators of gene expression and cellular processes in various physiological and pathological conditions. In recent years, there has been a growing interest in investigating the role of circRNAs in inflammatory lung diseases, owing to their potential to modulate inflammation-associated pathways and contribute to disease pathogenesis. Inflammatory lung diseases, like asthma, chronic obstructive pulmonary disease (COPD), and COVID-19, pose significant global health challenges. The dysregulation of inflammatory responses demonstrates a pivotal function in advancing these diseases. CircRNAs have been identified as important players in regulating inflammation by functioning as miRNA sponges, engaging with RNA-binding proteins, and participating in intricate ceRNA networks. These interactions enable circRNAs to regulate the manifestation of key inflammatory genes and signaling pathways. Furthermore, emerging evidence suggests that specific circRNAs are differentially expressed in response to inflammatory stimuli and exhibit distinct patterns in various lung diseases. Their involvement in immune cell activation, cytokine production, and tissue remodeling processes underscores their possible capabilities as therapeutic targets and diagnostic biomarkers. Harnessing the knowledge of circRNA-mediated regulation in inflammatory lung diseases could lead to the development of innovative strategies for disease management and intervention. This review summarizes the current understanding of the role of circRNAs in inflammatory lung diseases, focusing on their regulatory mechanisms and functional implications.

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. Several studies have identified predictive gene lists for patient prognosis in breast cancer, but these lists are unstable and have only a few genes in common. Instability of feature selection impedes biological interpretability: genes that are relevant for cancer pathology should be members of any predictive gene list obtained for the same clinical type of patients. Stability and interpretability of selected features can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are methods to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to construct feature sets by aggregating individual explanations. We suggest a methodology to systematically and quantitatively analyze the stability, the impact on the classification performance, and the interpretability of the selected feature sets. We used this methodology to compare GCNN+LRP to GCNN+SHAP and to more classical ML-based feature selection approaches. Utilizing a large breast cancer gene expression dataset we show that, while feature selection with SHAP is useful in applications where selected features have to be impactful for classification performance, among all studied methods GCNN+LRP delivers the most stable (reproducible) and interpretable gene lists.

A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1)

Thousand and one amino acid kinase 1 (TAOK1) is a sterile 20 family Serine/Threonine kinase linked to microtubule dynamics, checkpoint signaling, DNA damage response, and neurological functions. Molecular-level alterations of TAOK1 have been associated with neurodevelopment disorders and cancers. Despite their known involvement in physiological and pathophysiological processes, and as a core member of the hippo signaling pathway, the phosphoregulatory network of TAOK1 has not been visualized. Aimed to explore this network, we first analyzed the predominantly detected and differentially regulated TAOK1 phosphosites in global phosphoproteome datasets across diverse experimental conditions. Based on 709 qualitative and 210 quantitative differential cellular phosphoproteome datasets that were systematically assembled, we identified that phosphorylation at Ser421, Ser9, Ser965, and Ser445 predominantly represented TAOK1 in almost 75% of these datasets. Surprisingly, the functional role of all these phosphosites in TAOK1 remains unexplored. Hence, we employed a robust strategy to extract the phosphosites in proteins that significantly correlated in expression with predominant TAOK1 phosphosites. This led to the first categorization of the phosphosites including those in the currently known and predicted interactors, kinases, and substrates, that positively/negatively correlated with the expression status of each predominant TAOK1 phosphosites. Subsequently, we also analyzed the phosphosites in core proteins of the hippo signaling pathway. Based on the TAOK1 phosphoregulatory network analysis, we inferred the potential role of the predominant TAOK1 phosphosites. Especially, we propose pSer9 as an autophosphorylation and TAOK1 kinase activity-associated phosphosite and pS421, the most frequently detected phosphosite in TAOK1, as a significant regulatory phosphosite involved in the maintenance of genome integrity. Considering that the impact of all phosphosites that predominantly represent each kinase is essential for the efficient interpretation of global phosphoproteome datasets, we believe that the approach undertaken in this study is suitable to be extended to other kinases for accelerated research.

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

Multi-omics characterization of esophageal squamous cell carcinoma identifies molecular subtypes and therapeutic targets

Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal cancer and is characterized by an unfavorable prognosis. To elucidate the distinct molecular alterations in ESCC and investigate therapeutic targets, we performed a comprehensive analysis of transcriptomics, proteomics, and phosphoproteomics data derived from 60 paired treatment-naive ESCC and adjacent nontumor tissue samples. Additionally, we conducted a correlation analysis to describe the regulatory relationship between transcriptomic and proteomic processes, revealing alterations in key metabolic pathways. Unsupervised clustering analysis of the proteomics data stratified patients with ESCC into 3 subtypes with different molecular characteristics and clinical outcomes. Notably, subtype III exhibited the worst prognosis and enrichment in proteins associated with malignant processes, including glycolysis and DNA repair pathways. Furthermore, translocase of inner mitochondrial membrane domain containing 1 (TIMMDC1) was validated as a potential prognostic molecule for ESCC. Moreover, integrated kinase-substrate network analysis using the phosphoproteome nominated candidate kinases as potential targets. In vitro and in vivo experiments further confirmed casein kinase II subunit α (CSNK2A1) as a potential kinase target for ESCC. These underlying data represent a valuable resource for researchers that may provide better insights into the biology and treatment of ESCC.

Dexrazoxane inhibits the growth of esophageal squamous cell carcinoma by attenuating SDCBP/MDA-9/syntenin-mediated EGFR-PI3K-Akt pathway activation

Syndecan-binding protein (SDCBP) was reported to stimulate the advancement of esophageal squamous cell carcinoma (ESCC) and could potentially be a target for ESCC treatment. There is a growing corpus of research on the anti-tumor effects of iron chelators; however, very few studies have addressed the involvement of dexrazoxane in cancer. In this study, structure-based virtual screening was employed to select drugs targeting SDCBP from the Food and Drug Administration (FDA)-approved drug databases. The sepharose 4B beads pull-down assay revealed that dexrazoxane targeted SDCBP by interacting with its PDZ1 domain. Additionally, dexrazoxane inhibited ESCC cell proliferation and anchorage-independent colony formation via SDCBP. ESCC cell apoptosis and G2 phase arrest were induced as measured by the flow cytometry assay. Subsequent research revealed that dexrazoxane attenuated the binding ability between SDCBP and EGFR in an immunoprecipitation assay. Furthermore, dexrazoxane impaired EGFR membrane localization and inactivated the EGFR/PI3K/Akt pathway. In vivo, xenograft mouse experiments indicated that dexrazoxane suppressed ESCC tumor growth. These data indicate that dexrazoxane might be established as a potential anti-cancer agent in ESCC by targeting SDCBP.

Doxorubicin loaded thermostable nanoarchaeosomes: a next-generation drug carrier for breast cancer therapeutics

Breast cancer has a poor prognosis due to the toxic side effects associated with high doses of chemotherapy. Liposomal drug encapsulation has resulted in clinical success in enhancing chemotherapy tolerability. However, the formulation faces severe limitations with a lack of colloidal stability, reduced drug efficiency, and difficulties in storage conditions. Nanoarchaeosomes (NA) are a new generation of highly stable nanovesicles composed of the natural ether lipids extracted from archaea. In our study, we synthesized and characterized the NA, evaluated their colloidal stability, drug release potential, and anticancer efficacy. Transmission electron microscopy images have shown that the NA prepared from the hyperthermophilic archaeon Aeropyrum pernix K1 was in the size range of 61 ± 3 nm. The dynamic light scattering result has confirmed that the NA were stable at acidic pH (pH 4) and high temperature (70 °C). The NA exhibited excellent colloidal stability for 50 days with storage conditions at room temperature. The cell viability results have shown that the pure NA did not induce cytotoxicity in NIH 3T3 fibroblast cells and are biocompatible. Then NA were loaded with doxorubicin (NAD), and FTIR and UV-vis spectroscopy results have confirmed high drug loading efficiency of 97 ± 1% with sustained drug release for 48 h. The in vitro cytotoxicity studies in MCF-7 breast cancer cell lines showed that NAD induced cytotoxicity at less than 10 nM concentration. Fluorescence-activated cell sorting (FACS) results confirmed that NAD induced late apoptosis in nearly 92% of MCF-7 cells and necrosis in the remaining cells with cell cycle arrest at the G0/G1 phase. Our results confirmed that the NA could be a potential next-generation carrier with excellent stability, high drug loading efficiency, sustained drug release ability, and increased therapeutic efficacy, thus reducing the side effects of conventional drugs.

Metabolomic Approach to Identify Potential Biomarkers in KRAS-Mutant Pancreatic Cancer Cells

Pancreatic cancer is characterized by its high mortality rate and limited treatment options, often driven by oncogenic RAS mutations. In this study, we investigated the metabolomic profiles of pancreatic cancer cells based on their KRAS genetic status. Utilizing both KRAS-wildtype BxPC3 and KRAS-mutant PANC1 cell lines, we identified 195 metabolites differentially altered by KRAS status through untargeted metabolomics. Principal component analysis and hierarchical condition trees revealed distinct separation between KRAS-wildtype and KRAS-mutant cells. Metabolite set enrichment analysis highlighted significant pathways such as homocysteine degradation and taurine and hypotaurine metabolism. Additionally, lipid enrichment analysis identified pathways including fatty acyl glycosides and sphingoid bases. Mapping of identified metabolites to KEGG pathways identified nine significant metabolic pathways associated with KRAS status, indicating diverse metabolic alterations in pancreatic cancer cells. Furthermore, we explored the impact of TRPML1 inhibition on the metabolomic profile of KRAS-mutant pancreatic cancer cells. TRPML1 inhibition using ML-SI1 significantly altered the metabolomic profile, leading to distinct separation between vehicle-treated and ML-SI1-treated PANC1 cells. Metabolite set enrichment analysis revealed enriched pathways such as arginine and proline metabolism, and mapping to KEGG pathways identified 17 significant metabolic pathways associated with TRPML1 inhibition. Interestingly, some metabolites identified in PANC1 compared to BxPC3 were oppositely regulated by TRPML1 inhibition, suggesting their potential as biomarkers for KRAS-mutant cancer cells. Overall, our findings shed light on the distinct metabolite changes induced by both KRAS status and TRPML1 inhibition in pancreatic cancer cells, providing insights into potential therapeutic targets and biomarkers for this deadly disease.

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC

Background

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice.

Methods

We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments.

Results

We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment.

Conclusions

We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Informed by Cancer Stem Cells of Solid Tumors: Advances in Treatments Targeting Tumor-Promoting Factors and Pathways

Cancer stem cells (CSCs) represent a subpopulation within tumors that promote cancer progression, metastasis, and recurrence due to their self-renewal capacity and resistance to conventional therapies. CSC-specific markers and signaling pathways highly active in CSCs have emerged as a promising strategy for improving patient outcomes. This review provides a comprehensive overview of the therapeutic targets associated with CSCs of solid tumors across various cancer types, including key molecular markers aldehyde dehydrogenases, CD44, epithelial cellular adhesion molecule, and CD133 and signaling pathways such as Wnt/β-catenin, Notch, and Sonic Hedgehog. We discuss a wide array of therapeutic modalities ranging from targeted antibodies, small molecule inhibitors, and near-infrared photoimmunotherapy to advanced genetic approaches like RNA interference, CRISPR/Cas9 technology, aptamers, antisense oligonucleotides, chimeric antigen receptor (CAR) T cells, CAR natural killer cells, bispecific T cell engagers, immunotoxins, drug-antibody conjugates, therapeutic peptides, and dendritic cell vaccines. This review spans developments from preclinical investigations to ongoing clinical trials, highlighting the innovative targeting strategies that have been informed by CSC-associated pathways and molecules to overcome therapeutic resistance. We aim to provide insights into the potential of these therapies to revolutionize cancer treatment, underscoring the critical need for a multi-faceted approach in the battle against cancer. This comprehensive analysis demonstrates how advances made in the CSC field have informed significant developments in novel targeted therapeutic approaches, with the ultimate goal of achieving more effective and durable responses in cancer patients.

MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework

The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods.

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network

Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Loss of MTA2-mediated downregulation of PTK7 inhibits hepatocellular carcinoma metastasis progression by modulating the FAK-MMP7 axis

The expression of metastasis tumor-associated protein 2 (MTA2) and protein tyrosine kinase 7 (PTK7) is associated with hepatocellular carcinoma (HCC) progression. However, the functional effect and mechanism through which MTA2 regulates PTK7-mediated HCC progression remains unclear. Here, we found that MTA2 knockdown significantly down-regulated PTK7 expression in HCC cells (SK-Hep-1 and PLC/PRF/5). Data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases show that the PTK7 expression level was higher in HCC tissues than in normal liver tissues. In HCC patients, the PTK7 expression level clearly correlated with tumor stage and grade, lower overall survival (OS) correlated positively with MTA2 level, and PTK7 expression acted as a downstream factor for MTA2 expression. In addition, matrix metalloproteinase 7 (MMP7) expression was closely regulated by PTK7, and the mRNA and protein expression levels of MTA2 and PTK7 correlated positively with lower OS. MMP7 downregulation by PTK7 knockdown clearly decreased the migration and invasion abilities of HCC cells. In HCC cells, recombinant human MMP7 reversed the PTK7 knockdown-induced suppression of migration and invasion. Furthermore, deactivation of FAK using siFAK or FAK inhibitor (PF-573228, PF) synergistically contributed to PTK7 knockdown-inhibited FAK activity, MMP7 expression, and the migration and invasion abilities of HCC cells. Collectively, our findings show that PTK7 mediates HCC progression by regulating the MTA2-FAK-MMP7 axis and may be a diagnostic value for HCC patients.

A probabilistic knowledge graph for target identification

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

The acute phase reactant orosomucoid-2 directly promotes rheumatoid inflammation

Acute phase proteins involved in chronic inflammatory diseases have not been systematically analyzed. Here, global proteome profiling of serum and urine revealed that orosomucoid-2 (ORM2), an acute phase reactant, was differentially expressed in rheumatoid arthritis (RA) patients and showed the highest fold change. Therefore, we questioned the extent to which ORM2, which is produced mainly in the liver, actively participates in rheumatoid inflammation. Surprisingly, ORM2 expression was upregulated in the synovial fluids and synovial membranes of RA patients. The major cell types producing ORM2 were synovial macrophages and fibroblast-like synoviocytes (FLSs) from RA patients. Recombinant ORM2 robustly increased IL-6, TNF-α, CXCL8 (IL-8), and CCL2 production by RA macrophages and FLSs via the NF-κB and p38 MAPK pathways. Interestingly, glycophorin C, a membrane protein for determining erythrocyte shape, was the receptor for ORM2. Intra-articular injection of ORM2 increased the severity of arthritis in mice and accelerated the infiltration of macrophages into the affected joints. Moreover, circulating ORM2 levels correlated with RA activity and radiographic progression. In conclusion, the acute phase protein ORM2 can directly increase the production of proinflammatory mediators and promote chronic arthritis in mice, suggesting that ORM2 could be a new therapeutic target for RA.