Decipher the Helicobacter pylori Protein Targeting in the Nucleus of Host Cell and their Implications in Gallbladder Cancer: An insilico approach

Curcumin-gold nanoshell mediated near-infrared irradiation on human ovarian cancer cell: in vitro study

Ovarian cancer is considered a predominant female reproductive malignancy and poses a significant threat due to its 80-90% fatality rate. The typical approach involves surgery and chemotherapy, which due to problems such as drug resistance, encourage researchers to use new methods such as nanotechnology. The current study introduces a novel strategy: leveraging Curcumin-Gold Nanoshells (Cur-AuNShs) to combat chemotherapy's adverse effects and overcome drug resistance through hyperthermia mediation. Gold-based nanoparticles that absorb laser have shown the potential to target and treat cancer selectively through highly efficient light-to-heat conversion. This experimental study focused on the synthesis of AuNShs and their subsequent conjugation with Cur. The gold shell coverage on the surfaces of silica nanoparticles was examined using UV-VIS spectroscopy and transmission electron microscopy (TEM). Dynamic light scattering (DLS) and Zeta potential analysis were employed to evaluate the stability of particle size and surface charge. Human ovarian carcinoma cell lines (SKOV-3) were treated with a combination of Cur (15 μM) and AuNShs (75 μM), under the activation of near-infrared (NIR) laser irradiation at a power of 2.5 W/cm3 for 5 or 10 min. Cell viability was then assessed using the MTT assay. Lastly, the expression levels of Bax, Bcl2, and HSPB1 genes were analyzed using the real-time polymerase chain reaction (real-time PCR) technique. The average diameter of the AuNShs was measured at 70 ± 7.1 nm. Findings revealed that after a 48 h incubation with Cur-AuNShs followed by 10 min of laser irradiation, cell viability decreased significantly from 44.3 ± 1.7 to 14.4 ± 1. Analysis using real-time PCR showed an increase in Bax expression alongside a decrease in Bcl2 expression. Additionally, the expression of the HSPB1 gene was reduced from 1.35 ± 1 to 0.9 ± 0.65 in the laser-treated Cur-AuNShs-NIR group. The AuNShs, when combined with hyperthermia at 43 °C, demonstrated potential as an effective carrier for Cur administration. This combination was associated with a greater activation of apoptosis compared to the free drug.

The effects of flavonoid baicalein on miRNA expressions in cancer: a systematic review

Baicalein from Scutellaria baicalensis influences miRNA expression in various cancers, affecting key signaling pathways (PI3K/AKT, Wnt/β-catenin, mTOR) and processes like tumor growth, apoptosis, and metastasis. miRNAs, as small non-coding RNAs, play crucial roles in the cancer pathogenesis-associated gene regulations. This study is aimed at systematically reviewing the effects of baicalein on miRNA expression in various cancers. A comprehensive systematic review was conducted following PRISMA guidelines to investigate the impact of baicalein on miRNA expression in cancer. Databases including PubMed, Scopus, and Web of Science were systematically searched using key search terms. Inclusion criteria encompassed studies reporting changes in miRNA expression following baicalein treatment in cancer cell lines and animal models. Data extraction and risk of bias assessment based on SYRCLE's risk of bias tool were performed to ensure methodological rigor and reliability of the findings. Fifteen studies meeting the inclusion criteria were included in the systematic review. Baicalein impacts miRNA expression in cancers like hepatocellular carcinoma, breast, cervical, ovarian, and gastric cancers, suggesting its potential as a multi-cancer therapeutic. Baicalein regulates tumor-related genes (HDAC10, MDM2, Bcl-2/Bax, and Cyclin E1) and signaling molecules (AKT, FOXO3α), affecting cell viability, apoptosis, and cell cycle, indicating targeted therapeutic potential. In vitro and in vivo studies show baicalein inhibits tumor growth, enhances apoptosis, and regulates cell proliferation, supporting its anticancer effects. Baicalein exhibits potential in modulating miRNA expression in cancer, offering avenues for therapeutic intervention. However, methodological rigor in future studies is essential to enhance the reliability and validity of findings. Comprehensive understanding of baicalein's effects on miRNA expression holds promise for developing novel cancer treatment strategies.

scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by providing gene expression data at the single-cell level. Unlike bulk RNA-seq, scRNA-seq allows identification of different cell types within a given tissue, leading to a more nuanced comprehension of cell functions. However, the analysis of scRNA-seq data presents challenges due to its sparsity and high dimensionality. Since bioinformatics plays an important role in the analysis of big data and its utility for the welfare of living beings, it has been widely applied in analyzing scRNA-seq data. To address these challenges, we introduce the scMUG computational pipeline, which incorporates gene functional module information to enhance scRNA-seq clustering analysis. The pipeline includes data preprocessing, cell representation generation, cell-cell similarity matrix construction, and clustering analysis. The scMUG pipeline also introduces a novel similarity measure that combines local density and global distribution in the latent cell representation space. As far as we can tell, this is the first attempt to integrate gene functional associations into scRNA-seq clustering analysis. We curated nine human scRNA-seq datasets to evaluate our scMUG pipeline. With the help of gene functional information and the novel similarity measure, the clustering results from scMUG pipeline present deep insights into functional relationships between gene expression patterns and cellular heterogeneity. In addition, our scMUG pipeline also presents comparable or better clustering performances than other state-of-the-art methods. All source codes of scMUG have been deposited in a GitHub repository with instructions for reproducing all results (https://github.com/degiminnal/scMUG).

PathSynergy: a deep learning model for predicting drug synergy in liver cancer

Cancer is a major public health problem while liver cancer is the main cause of global cancer-related deaths. The previous study demonstrates that the 5-year survival rate for advanced liver cancer is only 30%. Few of the first-line targeted drugs including sorafenib and lenvatinib are available, which often develop resistance. Drug combination therapy is crucial for improving the efficacy of cancer therapy and overcoming resistance. However, traditional methods for discovering drug synergy are costly and time consuming. In this study, we developed a novel predicting model PathSynergy by integrating drug feature data, cell line data, drug-target interactions, and signaling pathways. PathSynergy combined the advantages of graph neural networks and pathway map mapping. Comparing with other baseline models, PathSynergy showed better performance in model classification, accuracy, and precision. Excitingly, six Food and Drug Administration (FDA)-approved drugs including pimecrolimus, topiramate, nandrolone_decanoate, fluticasone propionate, zanubrutinib, and levonorgestrel were predicted and validated to show synergistic effects with sorafenib or lenvatinib against liver cancer for the first time. In general, the PathSynergy model provides a new perspective to discover synergistic combinations of drugs and has broad application potential in the fields of drug discovery and personalized medicine.

Scaling up drug combination surface prediction

Drug combinations are required to treat advanced cancers and other complex diseases. Compared with monotherapy, combination treatments can enhance efficacy and reduce toxicity by lowering the doses of single drugs-and there especially synergistic combinations are of interest. Since drug combination screening experiments are costly and time-consuming, reliable machine learning models are needed for prioritizing potential combinations for further studies. Most of the current machine learning models are based on scalar-valued approaches, which predict individual response values or synergy scores for drug combinations. We take a functional output prediction approach, in which full, continuous dose-response combination surfaces are predicted for each drug combination on the cell lines. We investigate the predictive power of the recently proposed comboKR method, which is based on a powerful input-output kernel regression technique and functional modeling of the response surface. In this work, we develop a scaled-up formulation of the comboKR, which also implements improved modeling choices: we (1) incorporate new modeling choices for the output drug combination response surfaces to the comboKR framework, and (2) propose a projected gradient descent method to solve the challenging pre-image problem that is traditionally solved with simple candidate set approaches. We provide thorough experimental analysis of comboKR 2.0 with three real-word datasets within various challenging experimental settings, including cases where drugs or cell lines have not been encountered in the training data. Our comparison with synergy score prediction methods further highlights the relevance of dose-response prediction approaches, instead of relying on simple scoring methods.

LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks

Introduction

The identification of peptides eluted from HLA complexes by mass spectrometry (MS) can provide critical data for deep learning models of antigen presentation prediction and promote neoantigen vaccine design. A major challenge remains in determining which HLA allele eluted peptides correspond to.

Methods

To address this, we present a tool for prediction of multiple allele (MA) presentation called LRMAHpan, which integrates LSTM network and ResNet_CA network for antigen processing and presentation prediction. We trained and tested the LRMAHpan BA (binding affinity) and the LRMAHpan AP (antigen processing) models using mass spectrometry data, subsequently combined them into the LRMAHpan PS (presentation score) model. Our approach is based on a novel pHLA encoding method that enables the integration of neoantigen prediction tasks into computer vision methods. This method aggregates MA data into a multichannel matrix and incorporates peptide sequences to efficiently capture binding signals.

Results

LRMAHpan outperforms standard predictors such as NetMHCpan 4.1, MHCflurry 2.0, and TransPHLA in terms of positive predictive value (PPV) when applied to MA data. Additionally, it can accommodate peptides of variable lengths and predict HLA class I and II presentation. We also predicted neoantigens in a cohort of metastatic melanoma patients, identifying several shared neoantigens.

Discussion

Our results demonstrate that LRMAHpan significantly improves the accuracy of antigen presentation predictions.

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

The footprint of gut microbiota in gallbladder cancer: a mechanistic review

Gallbladder cancer (GBC) is the most common malignant tumor of the biliary system with the worst prognosis. Even after radical surgery, the majority of patients with GBC have difficulty achieving a clinical cure. The risk of tumor recurrence remains more than 65%, and the overall 5-year survival rate is less than 5%. The gut microbiota refers to a variety of microorganisms living in the human intestine, including bacteria, viruses and fungi, which profoundly affect the host state of general health, disease and even cancer. Over the past few decades, substantial evidence has supported that gut microbiota plays a critical role in promoting the progression of GBC. In this review, we summarize the functions, molecular mechanisms and recent advances of the intestinal microbiota in GBC. We focus on the driving role of bacteria in pivotal pathways, such as virulence factors, metabolites derived from intestinal bacteria, chronic inflammatory responses and ecological niche remodeling. Additionally, we emphasize the high level of correlation between viruses and fungi, especially EBV and Candida spp., with GBC. In general, this review not only provides a solid theoretical basis for the close relationship between gut microbiota and GBC but also highlights more potential research directions for further research in the future.

Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer

Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4, BOP1, RPLP0P6 and UTP23, were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy.

Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

The link between Helicobacter pylori infection and gallbladder and biliary tract diseases: A review

Helicobacter pylori is a gram-negative pathogen commonly associated with peptic ulcer disease and gastric cancer. H. pylori infection has also been reported in cholelithiasis, cholecystitis, gallbladder polyps, and biliary tract cancers. However, the association between H. pylori and gallbladder and biliary tract pathologies remains unclear due to the paucity of literature. In response to the current literature gap, we aim to review and provide an updated summary of the association between H. pylori with gallbladder and biliary tract diseases and its impact on their clinical management. Relevant peer-reviewed studies were retrieved from Medline, PubMed, Embase, and Cochrane databases. We found that H. pylori infection was associated with cholelithiasis, chronic cholecystitis, biliary tract cancer, primary sclerosing cholangitis, and primary biliary cholangitis but not with gallbladder polyps. While causal links have been reported, prospective longitudinal studies are required to conclude the association between H. pylori and gallbladder pathologies. Clinicians should be aware of the implications that H. pylori infection has on the management of these diseases.

Urinary Exosomes: A Promising Biomarker for Disease Diagnosis

Exosomes are nanoscale vesicles derived from endocytosis, formed by fusion of multivesicular bodies with membranes and secreted into the extracellular matrix or body fluids. Many studies have shown that exosomes can be present in a variety of biological fluids, such as plasma, urine, saliva, amniotic fluid, ascites, and sweat, and most types of cells can secrete exosomes. Exosomes play an important role in many aspects of human development, including immunity, cardiovascular diseases, neurodegenerative diseases, and neoplasia. Urine can be an alternative to blood or tissue samples as a potential source of disease biomarkers because of its simple, noninvasive, sufficient, and stable characteristics. Therefore, urinary exosomes have valuable potential for early screening, monitoring disease progression, prognosis, and treatment. The method for isolating urinary exosomes has been perfected, and exosome proteomics is widely used. Therefore, we review the potential use of urinary exosomes for disease diagnosis and summarize the related literature.

Helicobacter Species and Hepato-Biliary Tract Malignancies: A Systematic Review and Meta-Analysis

Helicobacter species may cause chronic inflammation of the biliary tract, but its relationship with cancer is controversial. We performed a systematic review and meta-analysis to evaluate the association between Helicobacter species and hepatobiliary tract malignancies. Twenty-six studies (4083 patients) were included in qualitative synthesis, and 18 studies (n = 1895 qualified for meta-analysis. All studies were at high-intermediate risk of bias. Most studies combined several direct microbiological methods, mostly PCR (23 studies), culture (8 studies), and/or CLOtest (5 studies). Different specimens alone or in combination were investigated, most frequently bile (16 studies), serum (7 studies), liver/biliary tissue (8 studies), and gastric tissue (3 studies). Patients with Helicobacter species infection had an increased risk of hepatobiliary tract malignancies (OR = 3.61 [95% CI 2.18-6.00]; p < 0.0001), with high heterogeneity in the analysis (I2 = 61%; p = 0.0003). This effect was consistent when Helicobacter was assessed in bile (OR = 3.57 [95% CI 1.73-7.39]; p = 0.0006), gastric tissue (OR = 42.63 [95% CI 5.25-346.24]; p = 0.0004), liver/biliary tissue (OR = 4.92 [95% CI 1.90-12.76]; p = 0.001) and serum (OR = 1.38 [95% CI 1.00-1.90]; p = 0.05). Heterogeneity was reduced in these sub-analyses (I2 = 0-27%; p = ns), except for liver/biliary tissue (I2 = 57%; p = 0.02). In conclusion, based on low-certainty data, Helicobacter species chronic infection is associated with a tripled risk of hepatobiliary tract malignancy. Prospective studies are required to delineate public health interventions.

Deciphering the enigmatic crosstalk between prostate cancer and Alzheimer's disease: A current update on molecular mechanisms and combination therapy

Alzheimer's disease (AD) and prostate cancer (PCa) are considered the leading causes of death in elderly people worldwide. Although both these diseases have striking differences in their pathologies, a few underlying mechanisms are similar when cell survival is considered. In the current study, we employed an in-silico approach to decipher the possible role of bacterial proteins in the initiation and progression of AD and PCa. We further analyzed the molecular connections between these two life-threatening diseases. The androgen deprivation therapy used against PCa has been shown to promote castrate resistant PCa as well as AD. In addition, cell signaling pathways, such as Akt, IGF, and Wnt contribute to the progression of both AD and PCa. Besides, various proteins and genes are also common in disease progression. One such similarity is mTOR signaling. mTOR is the common downstream target for many signaling pathways and plays a vital role in both PCa and AD. Targeting mTOR can be a favorable line of treatment for both AD and PCa. However, drug resistance is one of the challenges in effective drug therapy. A few drugs that target mTOR have now become ineffective due to the development of resistance. In that regard, phytochemicals can be a rich source of novel drug candidates as they can act via multiple mechanisms. This review also presents mTOR targeting phytochemicals with promising anti-PCa, anti-AD activities, and approaches to overcome the issues associated with phytochemical-based therapies in clinical trials.

Gut microbiota correlates with antitumor activity in patients with mCRC and NSCLC treated with cetuximab plus avelumab

Gut microbiota is involved in immune modulation and immune checkpoint inhibitors (ICIs) efficacy. Single-arm phase II CAVE-mCRC and CAVE-LUNG clinical trials investigated cetuximab + avelumab combination in RAS wild-type (WT) metastatic colorectal cancer (mCRC) and chemo-refractory nonsmall cell lung cancer (NSCLC) patients, respectively. A comprehensive gut microbiota genetic analysis was done in basal fecal samples of 14 patients from CAVE-mCRC trial with circulating tumor DNA (ctDNA) RAS/BRAF WT and microsatellite stable (MSS) disease. Results were validated in a cohort of 10 patients from CAVE-Lung trial. 16S rRNA sequencing revealed 23 027 bacteria species in basal fecal samples of 14 patients from CAVE-mCRC trial. In five long-term responding patients (progression-free survival [PFS], 9-24 months) significant increases in two butyrate-producing bacteria, Agathobacter M104/1 (P = .018) and Blautia SR1/5 (P = .023) were found compared to nine patients with shorter PFS (2-6 months). A significantly better PFS was also observed according to the presence or absence of these species in basal fecal samples. For Agathobacter M104/1, median PFS (mPFS) was 13.5 months (95% confidence interval [CI], 6.5-20.5 months) vs 4.6 months (95% CI, 1.8-7.4 months); P = .006. For Blautia SR1/5, mPFS was 5.9 months (95% CI, 2.2-9.7 months) vs 3.6 months (95% CI, 3.3-4.0 months); P = .021. Similarly, in CAVE-Lung validation cohort, Agathobacter M104/1 and Blautia SR1/5 expression were associated with PFS according to their presence or absence in basal fecal samples. Agathobacter and Blautia species could be potential biomarkers of outcome in mCRC, and NSCLC patients treated with cetuximab + avelumab. These findings deserve further investigation.