Immunohistochemical expression of cytochrome P4A11 (CYP4A11), carbonic anhydrase 9 (CAIX) and Ki67 in renal cell carcinoma; diagnostic relevance and relations to clinicopathological parameters

BACKGROUND

Cytochrome P4A11 (CYP4A11) is a member of cytochrome p450 family, which is involved in arachidonic acid metabolism that participates in promoting malignant cell proliferation, progression, and angiogenetic capacity. Carbonic Anhydrase 9 (CAIX) is a transmembrane protein that plays an integral part in regulating hypoxia which affects cancer cell metabolism, proliferation and promotes metastasis. The aim of this study was to evaluate the immunohistochemical expression of CYP4A11, CAIX and ki67 in RCC subtypes in relation to clinicopathological parameters and to evaluate the diagnostic significance of CYP4A11 and CAIX in differentiating renal cell carcinoma (RCC) subtypes.

MATERIALS AND METHODS

one hundred primary RCC cases, collected from Pathology Department, Faculty of Medicine, Tanta University and from private laboratories, were evaluated for immunohistochemical expression of CYP4A11, CAIX and ki67.

RESULTS

CYP4A11 was expressed in 59% of RCC; with 91.7% sensitivity and 90% specificity in differentiating clear cell and non-clear cell subtypes. CAIX was expressed in 50% of RCC; with 95% sensitivity, 80% specificity. High expression of CYP4A11 was statistically positively associated with higher tumor grade, high expression of CAIX was statistically positively associated with lower tumor grade and absence of necrosis and high ki67 labeling index was significantly associated with clear cell subtype, larger tumor sizes, higher tumor grade, advanced tumor stage, fat invasion and vascular invasion.

CONCLUSIONS

CYP4A11 and CAIX can be used as diagnostic markers to differentiate clear cell RCC from other subtypes. CYP4A11 is more diagnostically accurate and specific than CAIX. High expression of CYP4A11, low CAIX expression and high ki67 labeling index were related to features of aggressive tumor behavior.

Advances in nanotechnology-based targeted-contrast agents for computed tomography and magnetic resonance

X-ray computed tomography (CT) and magnetic resonance (MR) imaging are essential tools in modern medical diagnosis and treatment. However, traditional contrast agents are inadequate in the diagnosis of various health conditions. Consequently, the development of targeted nano-contrast agents has become a crucial area of focus in the development of medical image-enhancing contrast agents. To fully understand the current development of nano-contrast agents, this review provides an overview of the preparation methods and research advancements in CT nano-contrast agents, MR nano-contrast agents, and CT/MR multimodal nano-contrast agents described in previous publications. Due to the physicochemical properties of nanomaterials, such as self-assembly and surface modifiability, these specific nano-contrast agents can greatly improve the targeting of lesions through various preparation methods and clearly highlight the distinction between lesions and normal tissues in both CT and MR. As a result, they have the potential to be used in the early stages of disease to improve diagnostic capacity and level in medical imaging.

Targeting Receptor Tyrosine Kinases as a Novel Strategy for the Treatment of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) comprises a group of aggressive and heterogeneous breast carcinoma. Chemotherapy is the mainstay for the treatment of triple-negative tumors. Nevertheless, the success of chemotherapeutic treatments is limited by their toxicity and development of acquired resistance leading to therapeutic failure and tumor relapse. Hence, there is an urgent need to explore novel targeted therapies for TNBC. Receptor tyrosine kinases (RTKs) are a family of transmembrane receptors that are key regulators of intracellular signaling pathways controlling cell proliferation, differentiation, survival, and motility. Aberrant activity and/or expression of several types of RTKs have been strongly connected to tumorigenesis. RTKs are frequently overexpressed and/or deregulated in triple-negative breast tumors and are further associated with tumor progression and reduced survival in patients. Therefore, targeting RTKs could be an appealing therapeutic strategy for the treatment of TNBC. This review summarizes the current evidence regarding the antitumor activity of RTK inhibitors in preclinical models of TNBC. The review also provides insights into the clinical trials evaluating the use of RTK inhibitors for the treatment of patients with TNBC.

Rutin is a potent senomorphic agent to target senescent cells and can improve chemotherapeutic efficacy

Aging is a major risk factor for most chronic disorders, for which cellular senescence is one of the central hallmarks. Senescent cells develop the pro-inflammatory senescence-associated secretory phenotype (SASP), which significantly contributes to organismal aging and age-related disorders. Development of senotherapeutics, an emerging class of therapeutic agents to target senescent cells, allows to effectively delay aging and alleviate chronic pathologies. Here we report preliminary outputs from screening of a natural medicinal agent (NMA) library for senotherapeutic candidates and validated several agents with prominent potential as senomorphics. Rutin, a phytochemical constituent found in a number of plants, showed remarkable capacity in targeting senescent cells by dampening expression of the full spectrum SASP. Further analysis indicated that rutin restrains the acute stress-associated phenotype (ASAP) by specifically interfering with the interactions of ATM with HIF1α, a master regulator of cellular and systemic homeostasis activated during senescence, and of ATM with TRAF6, part of a key signaling axis supporting the ASAP development toward the SASP. Conditioned media produced by senescent stromal cells enhanced the malignant phenotypes of prostate cancer cells, including in vitro proliferation, migration, invasion, and more importantly, chemoresistance, while rutin remarkably downregulated these gain-of-functions. Although classic chemotherapy reduced tumor progression, the treatment outcome was substantially improved upon combination of a chemotherapeutic agent with rutin. Our study provides a proof of concept for rutin as an emerging natural senomorphic agent, and presents an effective therapeutic avenue for alleviating age-related pathologies including cancer.

AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

Human Mycobiota and Its Role in Cancer Progression, Diagnostics and Therapeutics: A Link Lesser-Known

Although not as well studied as the bacterial component of the human microbiota, the commensal fungi or mycobiota play important roles in maintaining our health by augmenting our immune system. This mycobiota is also associated with various fatal diseases like opportunistic mycoses, and even cancer, with different cancers having respective type-specific mycobiota. The different fungal species which comprise these different intratumoral mycobiota play important roles in cancer progression. The aim of this review paper is to decipher the association between mycobiota and cancer, and shed light on new avenues in cancer diagnosis, and the development of new anti-cancer therapeutics.

A comprehensive review of cancer therapies mediated by conjugated gold nanoparticles with nucleic acid

Nucleic acids provide a promising therapeutic platform by targeting various cell signaling pathways involved in cancer and genetic disorders. However, maintaining optimal stability during delivery limits their utility. Nucleic acid delivery vehicles are generally categorized into biological and synthetic carriers. Regardless of the efficiency of biological vectors, such as viral vectors, issues related to their immunogenicity and carcinogenesis are very important and vital for clinical applications. On the other hand, synthetic vectors such as lipids or polymers, have been widely used for nucleic acid delivery. Despite their transfection efficiency, low storage stability, targeting inefficiency, and tracking limitations are among the limitations of the clinical application of these vectors. In the past decades, gold nanoparticles with unique properties have been shown to be highly efficient mineral vectors for overcoming these obstacles. In this review, we focus on gold nanoparticle-nucleic acid combinations and highlight their use in the treatment of various types of cancers. Furthermore, by stating the biological applications of these structures, we will discuss their clinical applications.

Nanobody-loaded nanobubbles targeting the G250 antigen with ultrasound/photoacoustic/fluorescence multimodal imaging capabilities for specifically enhanced imaging of RCC

Clinicians have attempted to discover a noninvasive, easy-to-perform, and accurate method to distinguish benign and malignant renal masses. The targeted nanobubbles (NBs) we constructed that target the specific membrane antigen of renal cell carcinoma (RCC), G250, and contain indocyanine green (ICG) provide multimodal enhanced imaging capability in ultrasound/photoacoustic/fluorescence for RCC which may possibly solve this problem. In this study, we encapsulated ICG in the lipid shell of the NBs by mechanical oscillation, then anti-G250 nanobodies (AGN) were coupled to the surfaces by the biotin-streptavidin bridge method, and the nanobubble named AGN/ICG-NB was completely constructed. The average particle diameter of the prepared AGN/ICG-NBs was (427.2 ± 4.50) nm, and the zeta potential was (-13.33 ± 1.01) mV. Immunofluorescence and flow cytometry confirmed the specific binding capability of AGN/ICG-NBs to G250-positive cells. In vitro imaging experiments confirmed the multimodal imaging capability of AGN/ICG-NBs, and the in vivo imaging experiments demonstrated the specifically enhanced ability of AGN/ICG-NBs for ultrasound/photoacoustic/fluorescence imaging of human-derived RCC tumors. The biosafety of AGN/ICG-NB was verified by CCK-8 assay, organ H&E staining and blood biochemical indices. In conclusion, the targeted nanobubbles we prepared with ultrasound/photoacoustic/fluorescence multimodal imaging capabilities provide a potentially feasible approach to address the need for early diagnosis and differential diagnosis of renal masses.

Gut microbiota and immunotherapy of renal cell carcinoma

The gut microbiome has recently been proposed as a key player in cancer development and progression. Several studies have reported that the composition of the gut microbiome plays a role in the response to immune checkpoint inhibitors (ICIs). The gut microbiome modulation has been investigated as a potential therapeutic strategy for cancer, mainly in patients undergoing therapy with ICIs. In particular, modulation through probiotics, FMT or other microbiome-related approaches have proven effective to improve the response to ICIs. In this review, we examine the role of the gut microbiome in enhancing clinical responses to ICIs in the treatment of renal cancer.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.

Physical interaction between STAT3 and AP1 in cervical carcinogenesis: Implications in HPV transcription control

The constitutive activation and aberrant expression of Signal Transducer and Activator of Transcription 3 (STAT3) plays a key role in initiation and progression of cervical cancer (CaCx). How STAT3 influences HPV transcription is poorly defined. In the present study, we probed direct and indirect interactions of STAT3 with HPV16/18 LCR. In silico assessment of cis-elements present on LCR revealed the presence of potential STAT3 binding motifs. However, experimental validation by ChIP-PCR could not confirm any specific STAT3 binding on HPV16 LCR. Protein-protein interaction (PPI) network analysis of STAT3 with other host transcription factors that bind LCR, highlighted the physical association of STAT3 with c-FOS and c-JUN. This was further confirmed in vitro by co-immunoprecipitation, where STAT3 co-immunoprecipitated with c-FOS and c-JUN in CaCx cells. The result was supported by immunocytochemical analysis and colocalization of STAT3 with c-FOS and c-JUN. Positive signals in proximity ligation assay validated physical interaction and colocalization of STAT3 with AP1. Colocalization of STAT3 with c-FOS and c-JUN increased upon IL-6 treatment and decreased post-Stattic treatment. Alteration of STAT3 expression affected the subcellular localization of c-FOS and c-JUN, along with the expression of viral oncoproteins (E6 and E7) in CaCx cells. High expression of c-JUN in tumor tissues correlated with poor prognosis in both HPV16 and HPV18 CaCx cohort whereas high expression of STAT3 correlated with poor prognosis in HPV18 CaCx lesions only. Overall, the data suggest an indirect interaction of STAT3 with HPV LCR via c-FOS and c-JUN and potentiate transcription of viral oncoproteins.

Thinking small to win big? A critical review on the potential application of extracellular vesicles for biomarker discovery and new therapeutic approaches in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly form of cancer, with limited progress in 5-year survival rates despite significant research efforts. The main challenges in treating PDAC include difficulties in early detection, and resistance to current therapeutic approaches due to aggressive molecular and microenvironment features. These challenges emphasize the importance of identifying clinically validated biomarkers for early detection and clinical management. Extracellular vesicles (EVs), particularly exosomes, have emerged as crucial mediators of intercellular communication by transporting molecular cargo. Recent research has unveiled their role in initiation, metastasis, and chemoresistance of PDAC. Consequently, utilizing EVs in liquid biopsies holds promise for the identification of biomarkers for early detection, prognosis, and monitoring of drug efficacy. However, numerous limitations, including challenges in isolation and characterization of homogeneous EVs populations, as well as the absence of standardized protocols, can affect the reliability of studies involving EVs as biomarkers, underscoring the necessity for a prudent approach. EVs have also garnered considerable attention as a promising drug delivery system and novel therapy for tumors. The loading of biomolecules or chemical drugs into exosomes and their subsequent delivery to target cells can effectively impede tumor progression. Nevertheless, there are obstacles that must be overcome to ensure the accuracy and efficacy of therapies relying on EVs for the treatment of tumors. In this review, we examine both recent advancements and remaining obstacles, exploring the potential of utilizing EVs in biomarker discovery as well as for the development of drug delivery vehicles.

Super-enhancer RNA m6A promotes local chromatin accessibility and oncogene transcription in pancreatic ductal adenocarcinoma

The biological functions of noncoding RNA N6-methyladenosine (m6A) modification remain poorly understood. In the present study, we depict the landscape of super-enhancer RNA (seRNA) m6A modification in pancreatic ductal adenocarcinoma (PDAC) and reveal a regulatory axis of m6A seRNA, H3K4me3 modification, chromatin accessibility and oncogene transcription. We demonstrate the cofilin family protein CFL1, overexpressed in PDAC, as a METTL3 cofactor that helps seRNA m6A methylation formation. The increased seRNA m6As are recognized by the reader YTHDC2, which recruits H3K4 methyltransferase MLL1 to promote H3K4me3 modification cotranscriptionally. Super-enhancers with a high level of H3K4me3 augment chromatin accessibility and facilitate oncogene transcription. Collectively, these results shed light on a CFL1-METTL3-seRNA m6A-YTHDC2/MLL1 axis that plays a role in the epigenetic regulation of local chromatin state and gene expression, which strengthens our knowledge about the functions of super-enhancers and their transcripts.

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

Association of hypertension in pregnancy with serum electrolyte disorders in late pregnancy among Cameroonian women

Multiple electrolyte disorders, including sodium, potassium and calcium disorders, have been associated with hypertension in pregnancy. Most of these studies failed to evaluate the combined effect of low and high sodium, potassium, calcium and chloride ion concentrations on hypertension in pregnancy. This study evaluates the combined effect of these ion categories (low, normal, high) on hypertension in pregnancy. Biochemical ion assays and blood pressure measurements were carried out on 1074 apparently healthy pregnant women in late third trimester. Serum potassium, sodium, chloride, and ionised calcium were measured by ion-selective electrode potentiometry, while total plasma calcium was measured by absorption spectrophotometry. Hypertension in pregnancy was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg. The prevalence of hyponatraemia, hypokalaemia, hypochloraemia, ionised hypocalcaemia and total hypocalcaemia in late pregnancy was 1.30 [0.78-2.18]%, 3.55 [2.60-4.84]%, 1.96 [1.28-2.97]%, 1.49 [0.92-2.21]% and 43.58 [40.64-46.56]%, respectively. Hypernatraemia, hyperkalaemia, hyperchloraemia, ionised hypercalcaemia and total hypercalcaemia were found in 1.49 [0.92-2.41]%, 2.34 [1.59-3.43]%, 4.38 [3.31-5.77]%, 39.94 [37.06-42.90]%, 2.79 [1.96-3.96]% of the participants, respectively. The prevalence of hypertension in pregnancy was 7.17 [5.77-8.87]%. When ion categories were considered in multiple logistic regression, only ionised and total calcium had significant associations with hypertension in pregnancy. Women with ionised hypercalcaemia had lower odds of hypertension in pregnancy (AOR = 0.50 [0.29-0.87], p-value = 0.015), and women with total hypocalcaemia had higher odds of hypertension in pregnancy (AOR = 1.99 [1.21-3.29], p-value = 0.007), compared to women with ionised and total normocalcaemia, respectively. Increasing kalaemia was associated significantly with higher odds of hypertension in pregnancy; however, kalaemia below and above the normal concentrations had no significant association with hypertension. Nonetheless, participants with kalaemia ≤ 3.98 mmol/L, had lower odds of hypertension in pregnancy compared with those with higher kalaemia (OR = 0.40 [0.24-0.66], p-value = 0.0003). Calcium disorders remain the most frequent electrolyte disorders in pregnancy. When normal cut-offs are considered for calcium and other ions, only ionised and total calcium influence the occurrence of hypertension in pregnancy. Kalaemia seems to affect hypertension in pregnancy but primarily within its normal concentrations. Serum electrolyte follow-up is indispensable for a proper pregnancy follow-up.

Inhibition of SF3B1 improves the immune microenvironment through pyroptosis and synergizes with αPDL1 in ovarian cancer

Ovarian cancer is resistant to immune checkpoint blockade (ICB) treatment. Combination of targeted therapy and immunotherapy is a promising strategy for ovarian cancer treatment benefit from an improved immune microenvironment. In this study, Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) cohorts were used to screen prognosis and cytotoxic lymphocyte infiltration-associated genes in upregulated genes of ovarian cancer, tissue microarrays were built for further verification. In vitro experiments and mouse (C57/BL6) ovarian tumor (ID8) models were built to evaluate the synergistic effect of the combination of SF3B1 inhibitor and PD-L1 antibody in the treatment of ovarian cancer. The results show that SF3B1 is shown to be overexpressed and related to low cytotoxic immune cell infiltration in ovarian cancer. Inhibition of SF3B1 induces pyroptosis in ovarian cancer cells and releases mitochondrial DNA (mtDNA), which is englobed by macrophages and subsequently activates them (polarization to M1). Moreover, pladienolide B increases cytotoxic immune cell infiltration in the ID8 mouse model as a SF3B1 inhibitor and increases the expression of PD-L1 which can enhance the antitumor effect of αPDL1 in ovarian cancer. The data suggests that inhibition of SF3B1 improves the immune microenvironment of ovarian cancer and synergizes ICB immunotherapy, which provides preclinical evidence for the combination of SF3B1 inhibitor and ICB to ovarian cancer treatment.

Drug-drug interaction prediction: databases, web servers and computational models

In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach

The significant research carried out on medical healthcare networks is giving computing innovations lots of space to produce the most recent innovations. Pancreatic cancer, which ranks among of the most common tumors that are thought to be fatal and unsuspected since it is positioned in the region of the abdomen beyond the stomach and can't be adequately treated once diagnosed. In radiological imaging, such as MRI and CT, computer-aided diagnosis (CAD), quantitative evaluations, and automated pancreatic cancer classification approaches are routinely provided. This study provides a dynamic weighted ensemble framework for pancreatic cancer classification inspired by game theory. Grey Level Co-occurrence Matrix (GLCM) is utilized for feature extraction, together with Gaussian kernel-based fuzzy rough sets theory (GKFRST) for feature reduction and the Random Forest (RF) classifier for categorization. The ResNet50 and VGG16 are used in the transfer learning (TL) paradigm. The combination of the outcomes from the TL paradigm and the RF classifier paradigm is suggested using an innovative ensemble classifier that relies on the game theory method. When compared with the current models, the ensemble technique considerably increases the pancreatic cancer classification accuracy and yields exceptional performance. The study improves the categorization of pancreatic cancer by using game theory, a mathematical paradigm that simulates strategic interactions. Because game theory has been not frequently used in the discipline of cancer categorization, this research is distinctive in its methodology.

Differential Modulation of Markers of Oxidative Stress and DNA Damage in Arterial Hypertension

Patients with arterial hypertension have an increased risk of developing tumors, particularly renal cell carcinoma. Arterial hypertension is linked to DNA damage via the generation of oxidative stress, in which an upregulated renin-angiotensin-aldosterone system plays a crucial role. The current study investigated surrogates of oxidative stress and DNA damage in a group of hypertensive patients (HypAll, n = 64) and subgroups of well (HypWell, n = 36) and poorly (HypPoor, n = 28) controlled hypertensive patients compared to healthy controls (n = 8). In addition, a longitudinal analysis was performed with some of the hypertensive patients. Markers for oxidative stress in plasma (SHp, D-ROM, and 3-nitrotyrosine) and urine (8-oxodG, 15-F2t-isoprostane, and malondialdehyde) and markers for DNA damage in lymphocytes (γ-H2AX and micronuclei) were measured. In HypAll, all markers of oxidative stress except malondialdehyde were increased compared to the controls. After adjustment for age, this association was maintained for the protein stress markers SHp and 3-nitrotyrosine. With regard to the markers for DNA damage, there was no difference between HypAll and the controls. Further, no significant differences became apparent in the levels of both oxidative stress and DNA damage between HypWell and HypPoor. Finally, a positive correlation between the development of blood pressure and oxidative stress was observed in the longitudinal study based on the changes in D-ROM and systolic blood pressure. In conclusion, we found increased oxidative stress in extensively treated hypertensive patients correlating with the level of blood-pressure control but no association with DNA damage.

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.

Unraveling the Aurora kinase A and Epstein-Barr nuclear antigen 1 axis in Epstein Barr virus associated gastric cancer

Aurora kinase A (AURKA) is one of the crucial cell cycle regulators associated with gastric cancer. Here, we explored Epstein Barr Virus-induced gastric cancer progression through EBV protein EBNA1 with AURKA. We found that EBV infection enhanced cell proliferation and migration of AGS cells and upregulation of AURKA levels. AURKA knockdown markedly reduced the proliferation and migration of the AGS cells even with EBV infection. Moreover, MD-simulation data deciphered the probable connection between EBNA1 and AURKA. The in-vitro analysis through the transcript and protein expression showed that AURKA knockdown reduces the expression of EBNA1. Moreover, EBNA1 alone can enhance AURKA protein expression in AGS cells. Co-immunoprecipitation and NMR analysis between AURKA and EBNA1 depicts the interaction between two proteins. In addition, AURKA knockdown promotes apoptosis in EBV-infected AGS cells through cleavage of Caspase-3, -9, and PARP1. This study demonstrates that EBV oncogenic modulators EBNA1 possibly modulate AURKA in EBV-mediated gastric cancer progression.

Magnesium sulphate in patients with thrombotic thrombocytopenic purpura (MAGMAT): a randomised, double-blind, superiority trial

PURPOSE

Studies have suggested benefits from magnesium sulphate in thrombotic thrombocytopenic purpura (TTP). We aimed to measure the effects of magnesium sulphate supplementation on TTP recovery.

METHODS

In this multicenter, randomised, double-blind, controlled, superiority study, we enrolled adults with a clinical diagnosis of TTP. Patients were randomly allocated to receive magnesium sulphate (6 g intravenously followed by a continuous infusion of 6 g/24 h for 3 days) or placebo, in addition to the standard treatment. The primary outcome was the median time to platelet normalisation (defined as a platelet count ≥ 150 G/L). Efficacy and safety were assessed by intention-to-treat.

RESULTS

Overall, we enrolled 74 participants, including one who withdrew his/her consent. Seventy-three patients were further analyzed, 35 (48%) allocated to magnesium sulphate and 38 (52%) to placebo. The median time to platelet normalisation was 4 days (95% confidence interval [CI], 3-4) in the magnesium sulphate group and 4 days (95% CI 3-5) in the placebo group. The cause-specific hazard ratio of response was 0.93 (95% CI 0.58-1.48, p = 0.75). The number of patients with ≥ 1 serious adverse reactions was similar in the two groups. By day 90, four patients in the magnesium sulphate group and two patients in the placebo group had died (p = 0.42). The most frequent adverse event was low blood pressure occurring in 34% in the magnesium sulphate group and 29% in the placebo group (p = 0.80).

CONCLUSION

Among patients with TTP, the addition of magnesium sulphate to the standard of care did not result in a significant improvement in time to platelet normalisation.

Circ_0058063 regulates cell vitality and proliferation in oesophageal squamous-cell carcinomas

Oesophageal squamous-cell carcinoma (ESCC) is a malignant tumor of the digestive system with a poor prognosis. Recent studies have shown the promoting effect of hsa_circ_0058063 (circ_0058063) on ESCC, but the potential regulatory mechanisms of circ_0058063 in ESCC remain largely unclear. The levels of circ_0058063, microRNA-4319 (miR-4319) and mRNA of thrombospondin-1 (THBS1) were indicated by quantitative real-time polymerase chain reaction in ESCC tissues and cells. Meanwhile, the level of THBS1 was quantified by western blot analysis. In addition, the cell functions were examined by CCK8 assay, Edu assay, flow cytometry assay and transwell assay. Furthermore, the interplay between miR-4319 and circ_0058063 or THBS1 was detected by dual-luciferase reporter assay. Finally, an in vivo experiment was implemented to confirm the effect of circ_0058063. The level of circ_0058063 and THBS1 were increased, and the miR-4319 level was decreased in ESCC tissues in contrast to that in normal tissues and cells. For functional analysis, circ_0058063 deficiency inhibited cell vitality, cell proliferation, migration and invasion in ESCC cells, whereas promoted cell apoptosis. Moreover, miR-4319 was confirmed to repress the progression of ESCC cells by suppressing THBS1. In mechanism, circ_0058063 acted as a miR-4319 sponge to regulate the level of THBS1. Besides, circ_0058063 knockdown also attenuated tumour growth in vivo. Circ_0058063 facilitates the development of ESCC through increasing THBS1 expression by regulating miR-4319, which also offered an underlying targeted therapy for ESCC treatment.

SF3B1 mutation-mediated sensitization to H3B-8800 splicing inhibitor in chronic lymphocytic leukemia

Splicing factor 3B subunit 1 (SF3B1) is involved in pre-mRNA branch site recognition and is the target of antitumor-splicing inhibitors. Mutations in SF3B1 are observed in 15% of patients with chronic lymphocytic leukemia (CLL) and are associated with poor prognosis, but their pathogenic mechanisms remain poorly understood. Using deep RNA-sequencing data from 298 CLL tumor samples and isogenic SF3B1 WT and K700E-mutated CLL cell lines, we characterize targets and pre-mRNA sequence features associated with the selection of cryptic 3' splice sites upon SF3B1 mutation, including an event in the MAP3K7 gene relevant for activation of NF-κB signaling. Using the H3B-8800 splicing modulator, we show, for the first time in CLL, cytotoxic effects in vitro in primary CLL samples and in SF3B1-mutated isogenic CLL cell lines, accompanied by major splicing changes and delayed leukemic infiltration in a CLL xenotransplant mouse model. H3B-8800 displayed preferential lethality towards SF3B1-mutated cells and synergism with the BCL2 inhibitor venetoclax, supporting the potential use of SF3B1 inhibitors as a novel therapeutic strategy in CLL.

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Canonical and non-canonical functions of NLRP3

BACKGROUND

Since its discovery, NLRP3 is almost never separated from its major role in the protein complex it forms with ASC, NEK7 and Caspase-1, the inflammasome. This key component of the innate immune response mediates the secretion of proinflammatory cytokines IL-1β and IL-18 involved in immune response to microbial infection and cellular damage. However, NLRP3 has also other functions that do not involve the inflammasome assembly nor the innate immune response. These non-canonical functions have been poorly studied. Nevertheless, NLRP3 is associated with different kind of diseases probably through its inflammasome dependent function as through its inflammasome independent functions.

AIM OF THE REVIEW

The study and understanding of the canonical and non-canonical functions of NLRP3 can help to better understand its involvement in various pathologies. In parallel, the description of the mechanisms of action and regulation of its various functions, can allow the identification of new therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

NLRP3 functions have mainly been studied in the context of the inflammasome, in myeloid cells and in totally deficient transgenic mice. However, for several year, the work of different teams has proven that NLRP3 is also expressed in other cell types where it has functions that are independent of the inflammasome. If these studies suggest that NLRP3 could play different roles in the cytoplasm or the nucleus of the cells, the mechanisms underlying NLRP3 non-canonical functions remain unclear. This is why we propose in this review an inventory of the canonical and non-canonical functions of NLRP3 and their impact in different pathologies.

Reversing pathological cell states: the road less travelled can extend the therapeutic horizon

Acquisition of omics data advances at a formidable pace. Yet, our ability to utilize these data to control cell phenotypes and design interventions that reverse pathological states lags behind. Here, we posit that cell states are determined by core networks that control cell-wide networks. To steer cell fate decisions, core networks connecting genotype to phenotype must be reconstructed and understood. A recent method, cell state transition assessment and regulation (cSTAR), applies perturbation biology to quantify causal connections and mechanistically models how core networks influence cell phenotypes. cSTAR models are akin to digital cell twins enabling us to purposefully convert pathological states back to physiologically normal states. While this capability has a range of applications, here we discuss reverting oncogenic transformation.

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

SUMMARY

Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA).

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).

Hands-Free Proteomic Profiling of Urinary Extracellular Vesicles with a High-Throughput Automated Workflow

Extracellular vesicles (EVs) have emerged as a promising source of disease biomarkers for noninvasive early stage diagnoses, but a bottleneck in EV sample processing restricts their immense potential in clinical applications. Existing methods are limited by a low EV yield and integrity, slow processing speeds, low sample capacity, and poor recovery efficiency. We aimed to address these issues with a high-throughput automated workflow for EV isolation, EV lysis, protein extraction, and protein denaturation. The automation can process clinical urine samples in parallel, resulting in protein-covered beads ready for various analytical methods, including immunoassays, protein quantitation assays, and mass spectrometry. Compared to the standard manual lysis method for contamination levels, efficiency, and consistency of EV isolation, the automated protocol shows reproducible and robust proteomic quantitation with less than a 10% median coefficient of variation. When we applied the method to clinical samples, we identified a total 3,793 unique proteins and 40,380 unique peptides, with 992 significantly upregulated proteins in kidney cancer patients versus healthy controls. These upregulated proteins were found to be involved in several important kidney cancer metabolic pathways also identified with a manual control. This hands-free workflow represents a practical EV extraction and profiling approach that can benefit both clinical and research applications, streamlining biomarker discovery, tumor monitoring, and early cancer diagnoses.

Writers and readers of H3K9me2 form distinct protein networks during the cell cycle that include candidates for H3K9 mimicry

Histone H3 lysine 9 methylation (H3K9me), which is written by the Euchromatic Histone Lysine Methyltransferases EHMT1 and EHMT2 and read by the heterochromatin protein 1 (HP1) chromobox (CBX) protein family, is dysregulated in many types of cancers. Approaches to inhibit regulators of this pathway are currently being evaluated for therapeutic purposes. Thus, knowledge of the complexes supporting the function of these writers and readers during the process of cell proliferation is critical for our understanding of their role in carcinogenesis. Here, we immunopurified each of these proteins and used mass spectrometry to define their associated non-histone proteins, individually and at two different phases of the cell cycle, namely G1/S and G2/M. Our findings identify novel binding proteins for these writers and readers, as well as corroborate known interactors, to show the formation of distinct protein complex networks in a cell cycle phase-specific manner. Furthermore, there is an organizational switch between cell cycle phases for interactions among specific writer-reader pairs. Through a multi-tiered bioinformatics-based approach, we reveal that many interacting proteins exhibit histone mimicry, based on an H3K9-like linear motif. Gene ontology analyses, pathway enrichment, and network reconstruction inferred that these comprehensive EHMT and CBX-associated interacting protein networks participate in various functions, including transcription, DNA repair, splicing, and membrane disassembly. Combined, our data reveals novel complexes that provide insight into key functions of cell cycle-associated epigenomic processes that are highly relevant for better understanding these chromatin-modifying proteins during cell cycle and carcinogenesis.

Transcriptional Regulation of the Human 5-HT1A Receptor Gene by Lithium: Role of Deaf1 and GSK3β

Serotonin 1A (5-HT1A) autoreceptors located on serotonin neurons inhibit their activity, and their upregulation has been implicated in depression, suicide and resistance to antidepressant treatment. Conversely, post-synaptic 5-HT1A heteroreceptors are important for antidepressant response. The transcription factor deformed epidermal autoregulatory factor 1 (Deaf1) acts as a presynaptic repressor and postsynaptic enhancer of 5-HT1A transcription, but the mechanism is unclear. Because Deaf1 interacts with and is phosphorylated by glycogen synthase kinase 3β (GSK3β)-a constitutively active protein kinase that is inhibited by the mood stabilizer lithium at therapeutic concentrations-we investigated the role of GSK3β in Deaf1 regulation of human 5-HT1A transcription. In 5-HT1A promoter-reporter assays, human HEK293 kidney and 5-HT1A-expressing SKN-SH neuroblastoma cells, transfection of Deaf1 reduced 5-HT1A promoter activity by ~45%. To identify potential GSK3β site(s) on Deaf1, point mutations of known and predicted phosphorylation sites on Deaf1 were tested. Deaf1 repressor function was not affected by any of the mutants tested except the Y300F mutant, which augmented Deaf1 repression. Both lithium and the selective GSK3 inhibitors CHIR-99021 and AR-014418 attenuated and reversed Deaf1 repression compared to vector. This inhibition was at concentrations that maximally inhibit GSK3β activity as detected by the GSK3β-sensitive TCF/LEF reporter construct. Our results support the hypothesis that GSK3β regulates the activity of Deaf1 to repress 5-HT1A transcription and provide a potential mechanism for actions of GSK3 inhibitors on behavior.

Unraveling the Bone Tissue Microenvironment in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is the most frequent leukemia in Western countries. Although characterized by the progressive expansion and accumulation of leukemic B cells in peripheral blood, CLL cells develop in protective niches mainly located within lymph nodes and bone marrow. Multiple interactions between CLL and microenvironmental cells may favor the expansion of a B cell clone, further driving immune cells toward an immunosuppressive phenotype. Here, we summarize the current understanding of bone tissue alterations in CLL patients, further addressing and suggesting how the multiple interactions between CLL cells and osteoblasts/osteoclasts can be involved in these processes. Recent findings proposing the disruption of the endosteal niche by the expansion of a leukemic B cell clone appear to be a novel field of research to be deeply investigated and potentially relevant to provide new therapeutic approaches.

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.

Identification and validation of PCSK9 as a prognostic and immune-related influencing factor in tumorigenesis: a pan-cancer analysis

Introduction

Proprotein convertase subtilisin/kexin-9 (PCSK9) has been primarily studied in the cardiovascular field however, its role in cancer pathophysiology remains incompletely defined. Recently, a pivotal role for PCSK9 in cancer immunotherapy was proposed based on the finding that PCSK9 inhibition was associated with enhancing the antigen presentation efficacy of target programmed cell death-1 (PD-1). Herein, we provide results of a comprehensive pan-cancer analysis of PCSK9 that assessed its prognostic and immunological functions in cancer.

Methods

Using a variety of available online cancer-related databases including TIMER, cBioPortal, and GEPIA, we identified the abnormal expression of PCSK9 and its potential clinical associations in diverse cancer types including liver, brain and lung. We also validated its role in progression-free survival (PFS) and immune infiltration in neuroblastoma.

Results

Overall, the pan-cancer survival analysis revealed an association between dysregulated PCSK9 and poor clinical outcomes in various cancer types. Specifically, PCSK9 was extensively genetically altered across most cancer types and was consistently found in different tumor types and substages when compared with adjacent normal tissues. Thus, aberrant DNA methylation may be responsible for PCSK9 expression in many cancer types. Focusing on liver hepatocellular carcinoma (LIHC), we found that PCSK9 expression correlated with clinicopathological characteristics following stratified prognostic analyses. PCSK9 expression was significantly associated with immune infiltrate since specific markers of CD8+ T cells, macrophage polarization, and exhausted T cells exhibited different PCSK9-related immune infiltration patterns in LIHC and lung squamous cell carcinoma. In addition, PCSK9 was connected with resistance of drugs such as erlotinib and docetaxel. Finally, we validated PCSK9 expression in clinical neuroblastoma samples and concluded that PCSK9 appeared to correlate with a poor PFS and natural killer cell infiltration in neuroblastoma patients.

Conclusion

PCSK9 could serve as a robust prognostic pan-cancer biomarker given its correlation with immune infiltrates in different cancer types, thus potentially highlighting a new direction for targeted clinical therapy of cancers.

Neuroanatomical zones of human traumatic brain injury reveal significant differences in protein profile and protein oxidation: Implications for secondary injury events

Traumatic brain injury (TBI) causes significant neurological deficits and long-term degenerative changes. Primary injury in TBI entails distinct neuroanatomical zones, i.e., contusion (Ct) and pericontusion (PC). Their dynamic expansion could contribute to unpredictable neurological deterioration in patients. Molecular characterization of these zones compared with away from contusion (AC) zone is invaluable for TBI management. Using proteomics-based approach, we were able to distinguish Ct, PC and AC zones in human TBI brains. Ct was associated with structural changes (blood-brain barrier (BBB) disruption, neuroinflammation, axonal injury, demyelination and ferroptosis), while PC was associated with initial events of secondary injury (glutamate excitotoxicity, glial activation, accumulation of cytoskeleton proteins, oxidative stress, endocytosis) and AC displayed mitochondrial dysfunction that could contribute to secondary injury events and trigger long-term degenerative changes. Phosphoproteome analysis in these zones revealed that certain differentially phosphorylated proteins synergistically contribute to the injury events along with the differentially expressed proteins. Non-synaptic mitochondria (ns-mito) was associated with relatively more differentially expressed proteins (DEPs) compared to synaptosomes (Syn), while the latter displayed increased protein oxidation including tryptophan (Trp) oxidation. Proteomic analysis of immunocaptured complex I (CI) from Syn revealed increased Trp oxidation in Ct > PC > AC (vs. control). Oxidized W272 in the ND1 subunit of CI, revealed local conformational changes in ND1 and the neighboring subunits, as indicated by molecular dynamics simulation (MDS). Taken together, neuroanatomical zones in TBI show distinct protein profile and protein oxidation representing different primary and secondary injury events with potential implications for TBI pathology and neurological status of the patients.

Validity of the cell-extracted proteome as a substrate pool for exploring phosphorylation motifs of kinases

Three representative protein kinases with different substrate preferences, ERK1 (Pro-directed), CK2 (acidophilic), and PKA (basophilic), were used to investigate phosphorylation sequence motifs in substrate pools consisting of the proteomes from three different cell lines, MCF7 (human mammary carcinoma), HeLa (human cervical carcinoma), and Jurkat (human acute T-cell leukemia). Specifically, recombinant kinases were added to the cell-extracted proteomes to phosphorylate the substrates in vitro. After trypsin digestion, the phosphopeptides were enriched and subjected to nanoLC/MS/MS analysis to identify their phosphorylation sites on a large scale. By analyzing the obtained phosphorylation sites and their surrounding sequences, phosphorylation motifs were extracted for each kinase-substrate proteome pair. We found that each kinase exhibited the same set of phosphorylation motifs, independently of the substrate pool proteome. Furthermore, the identified motifs were also consistent with those found using a completely randomized peptide library. These results indicate that cell-extracted proteomes can provide kinase phosphorylation motifs with sufficient accuracy, even though their sequences are not completely random, supporting the robustness of phosphorylation motif identification based on phosphoproteome analysis of cell extracts as a substrate pool for a kinase of interest.

Alternative splicing: a bridge connecting NAFLD and HCC

Non-alcoholic fatty liver disease (NAFLD) is becoming the most important risk factor for hepatocellular carcinoma (HCC). Understanding the progression of benign diseases to HCC is crucial for early prevention and reversal of malignant transformation. Alternative splicing (AS) of RNA plays a role in the pathogenicity, initiation, and transformation of liver disease. We summarize the changes or mutations in the activity of splicing factors in NAFLD and HCC, as well as the impact of AS mediated by epigenetic modifications such as DNA methylation, RNA methylation, histone modification, and protein phosphorylation on liver cell fate. We also summarize therapeutic methods and drugs that are helpful for treating NAFLD, HCC, and the early stages of NAFLD progression to HCC.

Microfluidic Immuno-Serolomic Assay Reveals Systems Level Association with COVID-19 Pathology and Vaccine Protection

How to develop highly informative serology assays to evaluate the quality of immune protection against coronavirus disease-19 (COVID-19) has been a global pursuit over the past years. Here, a microfluidic high-plex immuno-serolomic assay is developed to simultaneously measure50 plasma or serum samples for50 soluble markers including 35proteins, 11 anti-spike/receptor binding domian (RBD) IgG antibodies spanningmajor variants, and controls. This assay demonstrates the quintuplicate test in a single run with high throughput, low sample volume, high reproducibilityand accuracy. It is applied to the measurement of 1012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein analysis reveals distinct immune mediator modules that exhibit a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies or receiving B cell depletion therapy. Serological analysis identifies that COVID-infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which can be associated with limited clonotype diversity and functional deficiency in B cells. These findings underscore the importance to individualize immunization strategies for these high-risk patients and provide an informative tool to monitor their responses at the systems level.

Cross-view contrastive representation learning approach to predicting DTIs via integrating multi-source information

Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive learning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.

Survival and Enrichment Analysis of Epithelial-Mesenchymal Transition Genes in Bladder Urothelial Carcinoma

The escalating prevalence of bladder cancer, particularly urothelial carcinoma, necessitates innovative approaches for prognosis and therapy. This study delves into the significance of genes related to epithelial-mesenchymal transition (EMT), a process inherently linked to carcinogenesis and comparatively better studied in other cancers. We examined 1184 EMT-related gene expression levels in bladder urothelial cancer cases through the TCGA dataset. Genes shown to be differentially expressed in relation to survival underwent further network and enrichment analysis to uncover how they might shape disease outcomes. Our in silico analysis revealed a subset of 32 genes, including those significantly represented in biological pathways such as VEGF signaling and bacterium response. In addition, these genes interact with genes involved in the JAK-STAT signaling pathway. Additionally, some of those 32 genes have been linked to immunomodulators such as chemokines CCL15 and CCL18, as well as to various immune cell infiltrates. Our findings highlight the prognostic utility of various EMT-related genes and identify possible modulators of their effect on survival, allowing for further targeted wet lab research and possible therapeutic intervention.

Critical assessment of on-premise approaches to scalable genome analysis

BACKGROUND

Plummeting DNA sequencing cost in recent years has enabled genome sequencing projects to scale up by several orders of magnitude, which is transforming genomics into a highly data-intensive field of research. This development provides the much needed statistical power required for genotype-phenotype predictions in complex diseases.

METHODS

In order to efficiently leverage the wealth of information, we here assessed several genomic data science tools. The rationale to focus on on-premise installations is to cope with situations where data confidentiality and compliance regulations etc. rule out cloud based solutions. We established a comprehensive qualitative and quantitative comparison between BCFtools, SnpSift, Hail, GEMINI, and OpenCGA. The tools were compared in terms of data storage technology, query speed, scalability, annotation, data manipulation, visualization, data output representation, and availability.

RESULTS

Tools that leverage sophisticated data structures are noted as the most suitable for large-scale projects in varying degrees of scalability in comparison to flat-file manipulation (e.g., BCFtools, and SnpSift). Remarkably, for small to mid-size projects, even lightweight relational database.

CONCLUSION

The assessment criteria provide insights into the typical questions posed in scalable genomics and serve as guidance for the development of scalable computational infrastructure in genomics.

Serum Cytokine and miRNA Levels Are Differently Expressed in Right- and Left-Sided Colon Cancer

The tumor location in colorectal cancer (right- or left-sided colon cancer) is a key factor in determining disease progression. Right- and left-sided colon tumors are different in their clinical and molecular characteristics. Dysregulation of serum levels of proinflammatory cytokines, such as Transforming Growth Factor β (TGF-β) and Tumor Necrosis Factor-α (TNF-α), and Peroxisome Proliferator Activated Receptor-γ (PPAR-γ), known to be a growth-limiting and differentiation-promoting factor, as well as changes in miRNAs expression, are the major signaling pathways involved in the pathogenesis of this neoplasia. In the serum from 60 colorectal cancer (CRC) patients, we compared the differences in the expression of the levels of TGF-β, TNF-α, and PPAR-γ and in the expression of the main human miRNAs between right and left CRC. A significant over-expression in the TGF-β and TNF-α levels was observed in the serum from right-sided colon cancer patients. For the PPAR-γ, the patients with CRC located on the right-side showed lower levels than those detected in the serum from left-sided CRC subjects. Furthermore, significant differences also existed in the expression of specific circulating miRNAs between right- and left-sided CRC. In particular, the right upregulated miRNAs were all involved in the cell growth and proliferation related pathways. These findings confirm that the analysis of circulating levels of TGF-β, TNF-α, and PPAR-γ, as well as the study of the specific miRNAs in the serum, are able to identify specific characteristics of CRC patients, useful for choosing a personalized treatment protocol.

Inhibition of FNDC1 suppresses gastric cancer progression by interfering with Gβγ-VEGFR2 complex formation

Gastric cancer (GC) is a prevalent digestive tract malignant tumor characterized by an insidious onset, ease of metastasis, rapid growth, and poor prognosis. Here, we report that fibronectin type III domain containing 1 (FNDC1) has high expression in GC and indicates poor outcomes in patients with GC. FNDC1 over-expression or knockdown promotes or inhibits tumorigenesis and metastasis, respectively. The expression of FNDC1 is upregulated by TWIST1, strengthening its interaction with Gβγ and VEGFR2. The formation of the trimers, TWIST1 plus Gβγ and VEGFR2, increases VEGFR2 phosphorylation and Gβγ trafficking, which activates RAS-MAPK and PI3K-AKT signaling, benefiting GC progression. In this study, we demonstrated that arsenite can efficiently suppress FNDC1 expression, attenuating the formation of the trimers and downstream pathways. Altogether, our results indicate that FNDC1 might be a promising target for clinical treatment and prognostic judgment, while FNDC1 inhibition by arsenite provides a new opportunity for overcoming this fatal disease.

Precision oncology strategy in cetuximab-resistant ERRFI1-mutant colon cancer: case report of disease progression on afatinib

Despite the existence of effective first and second line therapy options for patients with colorectal cancer, heavily treated patients have limited additional therapies. Genomic profiling is a promising tool for guiding subsequent treatment selection. Here, we describe the results of treating a colorectal cancer patient with molecularly-matched therapy based on the results of genomic profiling. The patient received a combination of afatinib and bevacizumab due to the presence of ERRFI1 variant. To our knowledge, this is the first report on the effect of EGFR inhibitors in patients with ERRFI1-altered RAS/BRAF wild-type colorectal adenocarcinoma.

Proteome analysis of CD5-positive diffuse large B cell lymphoma FFPE tissue reveals downregulation of DDX3X, DNAJB1, and B cell receptor signaling pathway proteins including BTK and Immunoglobulins

BACKGROUND

The molecular pathology of diffuse large B cell lymphoma (DLBCL) has been extensively studied. Among DLBCL subtypes, the prognosis of CD5-positive DLBCL is worse than that of CD5-negative DLBCL, considering the central nervous system relapse and poor response to R-CHOP therapy. However, the molecular mechanisms underlying the tumorigenesis and progression of CD5-positive DLBCL remain unknown.

METHODS

To identify molecular markers that can be targeted for treating DLBCL, a proteomic study was performed using liquid chromatography-mass spectrometry with chemically pretreated formalin-fixed paraffin-embedded specimens from CD5-positive (n = 5) and CD5-negative DLBCL patients (n = 6).

RESULTS

Twenty-one proteins showed significant downregulation in CD5-positive DLBCL compared to CD5-negative DLBCL. Principal component analysis of protein expression profiling in CD5-positive and CD5-negative DLBCL revealed that DNAJB1, DDX3X, and BTK, which is one of the B cell phenotypic proteins, were the most significantly downregulated proteins and served as biomarkers that distinguished both groups. Additionally, a set of immunoglobulins, including IgG4, exhibited significant downregulation. Immunohistochemistry analysis for BTK demonstrated reduced staining in CD5-positive DLBCL compared to CD5-negative DLBCL.

CONCLUSIONS

In conclusion, DNAJB1 and DDX3X, BTK, and a set of immunoglobulins are promising biomarkers. Probably, the suppression of BCR signaling is the unique phenotype of CD5-positive DLBCL. This formalin-fixed paraffin-embedded (FFPE)-based profiling may help to develop novel therapeutic molecularly targeted drugs for treating DLBCL.

Dynamic Interplay in Tumor Ecosystems: Communication between Hepatoma Cells and Fibroblasts

Tumors are intricate ecosystems where cancer cells and non-malignant stromal cells, including cancer-associated fibroblasts (CAFs), engage in complex communication. In this study, we investigated the interaction between poorly (HLE) and well-differentiated (HuH7) hepatoma cells and LX2 fibroblasts. We explored various communication channels, including soluble factors, metabolites, extracellular vesicles (EVs), and miRNAs. Co-culture with HLE cells induced LX2 to produce higher levels of laminin β1, type IV collagen, and CD44, with pronounced syndecan-1 shedding. Conversely, in HuH7/LX2 co-culture, fibronectin, thrombospondin-1, type IV collagen, and cell surface syndecan-1 were dominant matrix components. Integrins α6β4 and α6β1 were upregulated in HLE, while α5β1 and αVβ1 were increased in HuH7. HLE-stimulated LX2 produced excess MMP-2 and 9, whereas HuH7-stimulated LX2 produced excess MMP-1. LX2 activated MAPK and Wnt signaling in hepatoma cells, and conversely, hepatoma-derived EVs upregulated MAPK and Wnt in LX2 cells. LX2-derived EVs induced over tenfold upregulation of SPOCK1/testican-1 in hepatoma EV cargo. We also identified liver cancer-specific miRNAs in hepatoma EVs, with potential implications for early diagnosis. In summary, our study reveals tumor type-dependent communication between hepatoma cells and fibroblasts, shedding light on potential implications for tumor progression. However, the clinical relevance of liver cancer-specific miRNAs requires further investigation.

Review of Predicting Synergistic Drug Combinations

The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.

Combining old and new concepts in targeting telomerase for cancer therapy: transient, immediate, complete and combinatory attack (TICCA)

Telomerase can overcome replicative senescence by elongation of telomeres but is also a specific element in most cancer cells. It is expressed more vastly than any other tumor marker. Telomerase as a tumor target inducing replicative immortality can be overcome by only one other mechanism: alternative lengthening of telomeres (ALT). This limits the probability to develop resistance to treatments. Moreover, telomerase inhibition offers some degree of specificity with a low risk of toxicity in normal cells. Nevertheless, only one telomerase antagonist reached late preclinical studies. The underlying causes, the pitfalls of telomerase-based therapies, and future chances based on recent technical advancements are summarized in this review. Based on new findings and approaches, we propose a concept how long-term survival in telomerase-based cancer therapies can be significantly improved: the TICCA (Transient Immediate Complete and Combinatory Attack) strategy.