Network pharmacology-based exploration of molecular mechanisms underlying therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure with spleen Qi deficiency syndrome.

BACKGROUND: Chronic heart failure is a complex clinical syndrome. The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure; however, the underlying molecular mechanism is still not clear. AIM: To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure. METHODS: The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The target genes of chronic heart failure were searched in the Genecards database. The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drug-disease targets, which were then used to construct a key chemical component-target network using Cytoscape 3.7.2 software. The protein-protein interaction network was constructed using the String database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database. Finally, our previously published relevant articles were searched to verify the results obtained via network pharmacology. RESULTS: A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified, of which quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3, MAPK3, AKT1, JUN, MAPK1, TP53, TNF, HSP90AA1, p65, MAPK8, MAPK14, IL6, EGFR, EDN1, FOS, and other proteins. The pathways identified by KEGG enrichment analysis include pathways in cancer, IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, cAMP signaling pathway, NF-kappaB signaling pathway, AMPK signaling pathway, etc. Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α, IL-6, MAPK, cAMP, and AMPK pathways to affect the mitochondrial structure of myocardial cells, oxidative stress, and energy metabolism, thus achieving the therapeutic effects on chronic heart failure. CONCLUSION: The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes, oxidative stress, energy metabolism, and other processes. Future studies are warranted to investigate the role of the IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.

Research on the shared function of central neurons and breast cancer based on gene expression profile data mining: The role of EMID1 protein antibody expression.

In recent years, the incidence of breast cancer has gradually increased, and the research on it has become a hot spot in the scientific community. Central neurons play an important role in breast cancer. This study aims to explore the application of gene expression profile data mining in the study of shared function between central neurons and breast cancer, and focuses on the expression of EMID1 protein antibody. The study collected biomedical images and gene expression profile data of breast cancer patients. Then, we use image processing and analysis technology to extract and analyze features of biomedical images to obtain quantitative features of breast cancer. Gene expression profile data were preprocessed and analyzed to obtain information about breast cancer related genes. Integrating and fusing biomedical images and gene expression profile data, and exploring the sharing function between central neurons and breast cancer through data mining algorithms and statistical analysis methods. The results showed that the expression of EMID1 protein was high in breast cancer tissues, and the expression pattern was similar to that of central neurons. Further functional studies have shown that EMID1 protein is involved in the regulation of proliferation and invasion of breast cancer cells. By regulating the expression level of EMID1 protein, we observed that the proliferation and invasion ability of breast cancer cells were significantly affected. The research results show that through the comprehensive analysis of biomedical images and gene expression profile data, we found the sharing function between central neurons and breast cancer. The central neuronal cell marker genes EMID1 and GREB1L may be used as key biomarkers to regulate the pathogenesis of breast cancer and affect the occurrence and development of breast cancer.

Cytokine release syndrome associated with immune checkpoint inhibitors: a pharmacovigilance study based on spontaneous reports in FAERS.

OBJECTIVE: To describe cytokine release syndrome (CRS) associated with immune checkpoint inhibitors (ICIs) reported in the FDA Adverse Event Reporting System (FAERS). METHODS: We obtained ICIs adverse event (AE) reports from January 2011 to September 2023 from the FAERS database. The preferred term (PT) 'cytokine release syndrome' from the Medical Dictionary for Regulatory Activities (MedDRA) 26.1 was used to identify cases with ICIs-related CRS. The reporting odds ratio (ROR) of the disproportionality method was performed to quantify the association between CRS and ICIs treatment strategy. RESULTS: Three hundred and ninety-five cases were gathered. 42.03% of the patients were aged 18 to 65. Male patients outnumbered female patients (53.67% vs. 34.94%). The prevalent potential cancer types were lung cancer (33.42%) and skin cancer (20.51%). Japanese were responsible for the majority of ICIs-related CRS cases (176 cases). The combination of nivolumab and ipilimumab resulted in the most CRS cases (138 cases), and the ICIs combination therapy had the highest ROR signal value (ROR = 11.95 [10.14-14.06]). ICIs-related CRS had a median time to onset of 14 days (interquartile range [IQR] 7-43.25). CONCLUSIONS: ICIs-related CRS is an increasingly important immune-related AE. Our study provided helpful information to help medical professionals learn more about ICIs-related CRS.

The most important variables associated with death due to COVID-19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross-sectional study.

Introduction: Death due to covid-19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID-19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID-19. Methods: To describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID-19, chi-square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID-19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over-sampling examples, rpart, rpartplot packages was done. Results: Out of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9 ± 21.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid-19 patients showed (p < 0.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model. Conclusion: The results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.

RNA-binding protein AZGP1 inhibits epithelial cell proliferation by regulating the genes of alternative splicing in COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is characterized by high morbidity, disability, and mortality rates worldwide. RNA-binding proteins (RBPs) might regulate genes involved in oxidative stress and inflammation in COPD patients. Single-cell transcriptome sequencing (scRNA-seq) offers an accurate tool for identifying intercellular heterogeneity and the diversity of immune cells. However, the role of RBPs in the regulation of various cells, especially AT2 cells, remains elusive. MATERIALS AND METHODS: A scRNA-seq dataset (GSE173896) and a bulk RNA-seq dataset acquired from airway tissues (GSE124180) were employed for data mining. Next, RNA-seq analysis was performed in both COPD and control patients. Differentially expressed genes (DEGs) were identified using criteria of fold change (FC ≥ 1.5 or ≤ 1.5) and P value ≤ 0.05. Lastly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and alternative splicing identification analyses were carried out. RESULTS: RBP genes exhibited specific expression patterns across different cell groups and participated in cell proliferation and mitochondrial dysfunction in AT2 cells. As an RBP, AZGP1 expression was upregulated in both the scRNA-seq and RNA-seq datasets. It might potentially be a candidate immune biomarker that regulates COPD progression by modulating AT2 cell proliferation and adhesion by regulating the expression of SAMD5, DNER, DPYSL3, GBP5, GBP3, and KCNJ2. Moreover, AZGP1 regulated alternative splicing events in COPD, particularly DDAH1 and SFRP1, holding significant implications in COPD. CONCLUSION: RBP gene AZGP1 inhibits epithelial cell proliferation by regulating genes participating in alternative splicing in COPD.

A novel technology for harmonizing and analyzing cancer data. Observations from integrating health connect in Newfoundland and Labrador, Canada.

Objective: This article aims to describe the implementation of a new health information technology system called Health Connect that is harmonizing cancer data in the Canadian province of Newfoundland and Labrador; explain high-level technical details of this technology; provide concrete examples of how this technology is helping to improve cancer care in the province, and to discuss its future expansion and implications. Methods: We give a technical description of the Health Connect architecture, how it integrated numerous data sources into a single, scalable health information system for cancer data and highlight its artificial intelligence and analytics capacity. Results: We illustrated two practical achievements of Health Connect. First, an analytical dashboard that was used to pinpoint variations in colon cancer screening uptake in small defined geographic regions of the province; and second, a natural language processing algorithm that provided AI-assisted decision support in interpreting appropriate follow-up action based on assessments of breast mammography reports. Conclusion: Health Connect is a cutting-edge, health systems solution for harmonizing cancer screening data for practical decision-making. The long term goal is to integrate all cancer care data holdings into Health Connect to build a comprehensive health information system for cancer care in the province.

Data mining study on adverse events of tirzepatide based on FAERS database.

BACKGROUND: Tirzepatide is a novel dual gastric inhibitory polypeptide (GIP) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) for type 2 diabetes or obesity. To explore the safety profile of tirzepatide in real-world clinical applications. RESEARCH DESIGN AND METHODS: A retrospective analysis of adverse events (AEs) reports associated with tirzepatide was conducted from the second quarter of 2022 through the fourth quarter of 2023, utilizing the FDA Adverse Event Reporting System (FAERS) database. Signal mining utilized the reporting odds ratio (ROR) method, and onset time was analyzed utilizing the Weibull Shape Parameter (WSP). RESULTS: We identified 25,215 AE reports related to tirzepatide, predominantly in the 65 to 85 age group. Four positive signals were found at the system organ classes level. Additionally,109 AEs at the preferred terms level with positive signals were indicated. Included among these are novel signals, such as the presence of thyroid mass, medullary thyroid carcinoma, and conditions affecting the reproductive system and breast. Most AEs occurred within the first 30 days. The WSP was 0.66, indicating a propensity for early failure type. CONCLUSIONS: This study identified several novel AE signals for tirzepatide, highlighting the need for careful monitoring, especially in the early stages of treatment.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer.

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

A real-world pharmacovigilance study of FDA adverse event reporting system events for atogepant.

BACKGROUND: Atogepant, an orally administered, small-molecule, calcitonin gene-related peptide (CGRP) receptor antagonist, is being investigated for the treatment of migraine. METHODS: We collected data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Four algorithms (ROR, PRR, BCPNN, and EBGM) were used as measures to detect signals of atogepant-associated adverse events (AEs) in real-world data. RESULT: Of the 3,552,072 reports, 2876 expressly stated the use of atogepant. Women accounted for the majority of adverse events (AEs), with a notable age concentration of 45-65 years. The percentage of reported adverse events was the highest in the United States. Significant system organ categories (SOC) included nervous system disorders, gastrointestinal disorders, nervous system disorders, surgical and medical procedures, ear and labyrinth disorders. Notably, preferred terms (PTs) related to atogepant include migraine, constipation, nausea, vertigo, somnolence, decreased appetite, dizziness and fatigue. Unexpected adverse events such as abnormal dreams, self-injurious ideation, brain fog, tension headache, nightmare, brain neoplasm, feeling abnormal, euphoric mood, hyperacusis and post concussion syndrome were also identified. CONCLUSIONS: The present investigation has detected new and unexpected signals of atogepant-related adverse drug reactions (ADRs). In order to confirm these solve safety issues that were previously overlooked, more research is necessary.

Rules of acupoint selection in treatment of cancer-related insomnia with acupuncture and moxibustion based on data mining technology. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŠ€æœ¯æŽ¢æžé’ˆç¸æ²»ç–—è‚¿ç˜¤ç›¸å ³æ€§å¤±çœ çš„é€‰ç©´è§„å¾‹.

OBJECTIVES: To analyze the rules of acupoint selection in treatment of cancer-related insomnia with acupuncture and moxibustion by data mining technology. METHODS: The articles of cancer-related insomnia treated with acupuncture and moxibustion were searched from CNKI, Wanfang, VIP, SinoMed, PubMed, WOS, Cochrane, and Embase databases, from the inception of each database to January 5, 2024. The prescription database of acupuncture and moxibustion for cancer-related insomnia was established. The descriptive analysis was conducted on the use frequency, meridian tropism and distribution of acupoints. Using SPSS Modeler 18.0 Apriori algorithm, the association rules of acupoint prescriptions were analyzed. With Cytoscape3.9.1 software used, the complex network diagram was plotted, and the cluster analysis of high-frequency acupoints was performed by SPSS26.0 software. RESULTS: Forty-one articles were included, and 67 prescriptions were extracted with 89 acupoints involved, and the total use frequency was 447 times. The top 4 acupoints of the high use frequency were Baihui (GV20), Sanyinjiao (SP6), Shenmen (HT7) and Shenting (GV24). The included meridians were the governor vessel, the spleen meridian, the bladder meridian, the conception vessel, the heart meridian and the stomach meridian. The selected acupoints were mostly distributed on the head, the neck and and the upper and lower limbs. The special acupoints of the high use frequency included the five-Shu points, the crossing points and yuan-primordial points. Regarding acupoint combination, GV24, SP6, HT7, and GV20 were highly correlated. The three effective clusters were categorized among the top 12 acupoints of the high use frequency. CONCLUSIONS: In treatment of cancer-related insomnia with acupuncture and moxibustion, the principle focuses on supporting the healthy qi, eliminating pathogens, regulating yin and yang, promoting the circulation of the governor vessel for regulating the spirit, and tranquilizing the mind. The core acupoint prescription may includes GV24, SP6, HT7 and GV20；combined with Zusanli (ST36) and Yintang (GV4+) to enhance the therapeutic effect.

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data.

BACKGROUND: Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions. OBJECTIVE: This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support. METHODS: We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support. RESULTS: We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4. CONCLUSIONS: When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

Deciphering the molecular landscape of ionising radiation-induced eye damage with the help of genomic data mining.

Even at low levels, exposure to ionising radiation can lead to eye damage. However, the underlying molecular mechanisms are not yet fully understood. We aimed to address this gap with a comprehensive in silico approach to the issue. For this purpose we relied on the Comparative Toxicogenomics Database (CTD), ToppGene Suite, Cytoscape, GeneMANIA, and Metascape to identify six key regulator genes associated with radiation-induced eye damage (ATM, CRYAB, SIRT1, TGFB1, TREX1, and YAP1), all of which have physical interactions. Some of the identified molecular functions revolve around DNA repair mechanisms, while others are involved in protein binding, enzymatic activities, metabolic processes, and post-translational protein modifications. The biological processes are mostly centred on response to DNA damage, the p53 signalling pathway in particular. We identified a significant role of several miRNAs, such as hsa-miR-183 and hsamiR-589, in the mechanisms behind ionising radiation-induced eye injuries. Our study offers a valuable method for gaining deeper insights into the adverse effects of radiation exposure.

Performance comparison between multi-level gene expression data in cancer subgroup classification.

Cancer is a serious disease that can affect various parts of the body such as breast, colon, lung or stomach. Each of these cancers has their own treatment dependent historical subgroups. Hence, the correct identification of cancer subgroup has almost same importance as the timely diagnosis of cancer. This is still a challenging task and a system with highest accuracy is essential. Current researches are moving towards analyzing the gene expression data of cancer patients for various purposes including biomarker identification and studying differently expressed genes, using gene expression data measured in a single level (selected from different gene levels including genome, transcriptome or translation). However, previous studies showed that information carried by one level of gene expression is not similar to another level. This shows the importance of integrating multi-level omics data in these studies. Hence, this study uses tumor gene expression data measured from various levels of gene along with the integration of those data in the subgroup classification of nine different cancers. This is a comprehensive analysis where four different gene expression data such as transcriptome, miRNA, methylation and proteome are used in this subgrouping and the performances between models are compared to reveal the best model.

A disproportionality analysis of adverse events caused by GnRHas from the FAERS and JADER databases.

Background: Gonadotrophin-releasing hormone analogs (GnRHas) play a significant role in addressing gynecological diseases, central precocious puberty, and cancer. However, ensuring the safety of GnRHas in real-world applications requires continuous vigilance. In light of this, we undertook a disproportionality analysis focused on adverse events (AEs) associated with GnRHas using data from both the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER). We evaluated GnRHas-associated AEs and characterized the clinical priority of unlisted AEs caused by each GnRHa from the different databases. Methods: In the disproportionality analysis, we applied two adjusted algorithms to identify signals related to GnRHas in the FAERS and JADER databases from 2004 to 2023. Additionally, we utilized the Statistical Analysis System (SAS, 9.4) to examine potential and high-aROR (adjusted reporting odds ratio) signals associated with GnRHas. We performed clinical priority assessment for suspicious PTs and an analysis of serious/non-serious outcomes. We also gathered information on the onset times of AEs linked with GnRHas from both databases. Results: From January 2004 to September 2023, FAERS and JADER recorded a total of 50,360,413 and 1,440,200 AEs, respectively. Employing two algorithms, the suspicious preferred terms (PTs) related to leuprolide (Leu) were 562 potential PTs (44 unlisted in specifications), followed by goserelin (Gos) with 189 PTs (28 unlisted), triptorelin (Tri) with 172 PTs (28 unlisted), and Leu-JADER with 85 PTs (10 unlisted). At the same PT level, the differences in GnRHas between the two databases were observed, such as cardiac failure, diabetes mellitus, liver disorder, dementia, suicidal ideation, interstitial lung disease, urinary disorders, and hypertensive crisis. In an analysis of serious vs. non-serious outcomes, a total of 43 AEs of Leu were more likely to be reported as serious AEs with p < 0.05 (such as asthenia, urinary retention, diabetes mellitus, interstitial lung disease, gait disturbance, and so on), following by Tri (6 AEs), and Gos (4 AEs). Based on the clinical priority score, 41 PTs of Leu, 26 PTs of Tri, 24 PTs of Gos, and 8 PTs of Leu-JADER were graded as weak. There were 3 PTs of Leu, 2 PTs of Tri, 4 PTs of Gos, and 2 PTs of Leu-JADER that were graded as moderate. Notably, in the assessment of the relevant evidence, 2 PTs (loss of libido and urinary tract toxicity caused by Leu), 1 PT (electrolyte imbalance caused by Tri), and 2 PTs (anorexia and suicidal ideation caused by Gos) showed a strong level of evidence with "++." The differences in the signal strength of the same PTs from two databases were also worth noting. Moreover, the median onset time for GnRHas (Leu, Tri, and Gos) was 23 days (0, 298), 22 days (0, 181), and 217 days (29, 706), respectively, as median (Q1, Q3). Conclusion: An examination of two databases revealed suspicious AEs associated with GnRHas. Our study found potential new AE signals of GnRHas and supported continuous clinical monitoring, pharmacovigilance, regional differences, and further studies of GnRHas.

A pharmacovigilance study of drug-reduced male semen quality based on the Food and Drug Administration adverse event reporting system database.

BACKGROUND: Real-world big data studies on drug-reduced male semen quality are few and far between, with most studies based on animal trials, small scale retrospective studies, or a limited number of pre-market clinical trials. METHODS: This study aimed to identify culprit drugs that reduced male semen quality based on the United States Food and Drug Administration adverse event reporting system. The Medical Dictionary for Regulatory Activities preferred terms and standardized Medical Dictionary for Regulatory Activities queries were used to define reduced male semen quality. Adverse events related to drug-reduced male semen quality were then analyzed by disproportionality analysis using the United States Food and Drug Administration adverse event reporting system data between 2004 and 2023. RESULTS: At the preferred term level, 59 drugs with risk signals were detected to be associated with drug-reduced male semen quality, with the three most frequently reported second-level Anatomical Therapeutic Chemical groups being antineoplastic agents (n = 16, 27.12%), psychoanaleptics (n = 9, 15.25%), and psycholeptics (n = 6, 10.17%). At the standardized Medical Dictionary for Regulatory Activities queries level, the five drugs with the greatest number of cases were finasteride (845 cases, IC025 = 7.72), dutasteride (163 cases, IC025 = 7.22), tamsulosin (148 cases, IC025 = 5.99), testosterone (101 cases, IC025 = 4.08), and valproic acid (54 cases, IC025 = 2.44). Additionally, clinical information about drug-reduced male semen quality is absent from the Summary of Product Characteristics of 41 drugs in our study. CONCLUSIONS: Using the United States Food and Drug Administration adverse event reporting system database, we offer a list of drugs with risk signals for reducing male semen quality. In the future, there is still a need for more studies on drugs whose effects on male semen quality are not fully understood.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

RNA-binding protein transcripts as potential biomarkers for detecting Primary Sclerosing Cholangitis and for predicting its progression to Cholangiocarcinoma.

Primary Sclerosing Cholangitis (PSC) is a persistent inflammatory liver condition that affects the bile ducts and is commonly diagnosed in young individuals. Despite efforts to incorporate various clinical, biochemical and molecular parameters for diagnosing PSC, it remains challenging, and no biomarkers characteristic of the disease have been identified hitherto. PSC is linked with an uncertain prognosis, and there is a pressing need to explore multiomics databases to establish a new biomarker panel for the early detection of PSC's gradual progression into Cholangiocarcinoma (CCA) and for the development of effective therapeutic interventions. Apart from non-coding RNAs, other components of the Ribonucleoprotein (RNP) complex, such as RNA-Binding Proteins (RBPs), also hold great promise as biomarkers due to their versatile expression in pathological conditions. In the present review, an update on the RBP transcripts that show dysregulated expression in PSC and CCA is provided. Moreover, by utilizing a bioinformatic data mining approach, we give insight into those RBP transcripts that also exhibit differential expression in liver and gall bladder, as well as in body fluids, and are promising as biomarkers for diagnosing and predicting the prognosis of PSC. Expression data were bioinformatically extracted from public repositories usingTCGA Bile Duct Cancer dataset for CCA and specific NCBI GEO datasets for both PSC and CCA; more specifically, RBPs annotations were obtained from RBP World database. Interestingly, our comprehensive analysis shows an elevated expression of the non-canonical RBPs, FANCD2, as well as the microtubule dynamics regulator, ASPM, transcripts in the body fluids of patients with PSC and CCA compared with their respective controls, with the same trend in expression being observed in gall bladder and liver cancer tissues. Consequently, the manipulation of tissue expression of RBP transcripts might be considered as a strategy to mitigate the onset of CCA in PSC patients, and warrants further experimental investigation. The analysis performed herein may be helpful in the identification of non-invasive biomarkers for the early detection of PSC and for predicting its progression into CCA. In conclusion, future clinical research should investigate in more depth the full potential of RBP transcripts as biomarkers for human pathologies.

Analysis of post-market adverse events of tafamidis base on the FDA adverse event reporting system.

Tafamidis is the world's first and only oral drug approved to treat the rare disease transthyretin amyloid cardiomyopathy (ATTR-CM). Medicines are known to have different adverse reactions during the course of treatment. However, the current limited clinical studies did not identify significant adverse drug reactions to tafamidis. Tafamidis has been on the market for 5 years now, a large number of adverse drug event (ADE) reports with tafamidis as the primary suspected drug have been reported in the United Food and Drug Administration's adverse event reporting system (FAERS). We retrieved 8170 adverse event reports in FAERS with tafamidis as the first suspected drug, and mined these reports for positive signals to perform risk warnings for potentially possible adverse events with tafamidis. We found that a large number of adverse events associated with the primary disease were reported due to insufficient awareness of ATTR among the reporters, leading to a large number of positive signals reported in the cardiac disorders system. We also found that tafamidis has the potential to cause an adverse event risks of ear and labyrinth disorders system and urinary tract infection bacterial, which deserve continued clinical attention.

Adverse event signal mining and serious adverse event influencing factor analysis of fulvestrant based on FAERS database.

Fulvestrant, as the first selective estrogen receptor degrader, is widely used in the endocrine treatment of breast cancer. However, in the real world, there is a lack of relevant reports on adverse reaction data mining for fulvestrant. To perform data mining on adverse events (AEs) associated with fulvestrant and explore the risk factors contributing to severe AEs, providing a reference for the rational use of fulvestrant in clinical practice. Retrieved adverse event report information associated with fulvestrant from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database, covering the period from market introduction to September 30, 2023. Suspicious AEs were screened using the reporting odds ratio (ROR) and proportional reporting ratio methods based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe AEs to explore the risk factors associated with fulvestrant-induced severe AEs. A total of 6947 reports related to AEs associated with fulvestrant were obtained, including 5924 reports of severe AEs and 1023 reports of non-severe AEs. Using the disproportionality analysis method, a total of 210 valid AEs were identified for fulvestrant, with 45 AEs (21.43%) not listed in the product labeling, involving 11 systems and organs. The AEs associated with fulvestrant were sorted by frequency of occurrence, with neutropenia (325 cases) having the highest number of reports. By signal strength, injection site pruritus showed the strongest signal (ROR = 658.43). The results of the logistic regression analysis showed that concurrent use of medications with extremely high protein binding (≥ 98%) is an independent risk factor for severe AEs associated with fulvestrant. Age served as a protective factor for fulvestrant-related AEs. The co-administration of fulvestrant with CYP3A4 enzyme inhibitors did not show statistically significant correlation with the occurrence of severe AEs. Co-administration of drugs with extremely high protein binding (≥ 98%) may increase the risk of severe adverse reactions of fulvestrant. Meanwhile, age (60-74 years) may reduce the risk of severe AEs of fulvestrant. However, further clinical research is still needed to explore and verify whether there is interaction between fulvestrant and drugs with high protein binding through more clinical studies.

ProstaMine: a bioinformatics tool for identifying subtype-specific co-alterations associated with aggressiveness in prostate cancer.

Background: Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multi-gene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment. Methods: We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression. Results: Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes. Conclusion: ProstaMine is a method to systematically identify novel subtype-specific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtype-specific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https://bioinformatics.cuanschutz.edu/prostamine.