Identification Of Biomarkers Research Articles

The Role of circHIPK3 in Tumorigenesis and Its Potential as a Biomarker in Lung Cancer.

Lung cancer treatment and detection can be improved by the identification of new biomarkers. Novel approaches in investigating circular RNAs (circRNAs) as biomarkers have yielded promising results. A circRNA molecule circHIPK3 was found to be widely expressed in non-small-cell lung cancer (NSCLC) cells, where it plays a crucial role in lung cancer tumorigenesis. CircHIPK3 promotes lung cancer progression by sponging oncosuppressive miRNAs such as miR-124, miR-381-3p, miR-149, and miR-107, which results in increased cell proliferation, migration, and resistance to therapies. Inhibiting circHIPK3 has been demonstrated to suppress tumour growth and induce apoptosis, which suggests its potential use in the development of new lung cancer treatment strategies targeting circHIPK3-related pathways. As a biomarker, circHIPK3 shows promise for early detection and monitoring of lung cancer. CircHIPK3 increased expression levels in lung cancer cells, and its potential link to metastasis risk highlights its clinical relevance. Given the promising preliminary findings, more clinical trials are needed to validate circHIPK3 efficacy as a biomarker. Moreover, future research should determine if the mechanisms discovered in NSCLC apply to small cell lung cancer (SCLC) to investigate circHIPK3-targeted therapies for SCLC.

Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning.

Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the specific molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS. BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identified. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosis-related genes (DE-ASRGs). The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune infiltration was performed in BD. Overall, 67 DE-ASRGs were found to be overlapping between the DEGs and AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identified seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1). Eventually, two hub genes (CX3CR1 and ST6GAL1) were identified as BD and AS co-biomarkers by using machine learning algorithms. Immune infiltration had revealed the disorder of immunocytes. This study identified the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice.

Comparative assessment of microbiome and resistome of influent and effluent of sewage treatment plant and common effluent treatment plant located in Delhi, India using shotgun approach

Antimicrobial resistance (AMR) is a significant threat that demands surveillance to identify and analyze trends of the emerging antibiotic resistance genes (ARGs) and potential microbial carriers. The influent of the wastewater treatment plants (WWTPs) reflects the microbes derived from the population and effluent being the source of dissemination of potential pathogenic microbes and AMR. The present study aimed to monitor microbial communities and antibiotic resistance genes in WWTPs employing a whole metagenome shotgun sequencing approach. The samples were collected from a sewage treatment plant (STP) and a common effluent treatment plant (CETP) in Delhi, India. The results showed the influent of STP to be rich in Bifidobacterium, Bacteroides, Escherichia, Arcobacter, and Pseudomonas residents of gut microbiota and known to cause diseases in humans and animals; whereas the CETP sample was abundant in Aeromonas, Escherichia, and Shewanella known to be involved in the degradation of different compounds. Interestingly, the effluent samples from both STPs and CETP were rich in microbial diversity, comprising organic and xenobiotic compound degrading and disease-causing bacteria, indicating the effluent being the source of dissemination of concerning bacteria to the environment. The functional profile at both sites displayed similarity with an abundance of housekeeping function genes as analyzed by Clusters of Orthologous Genes (COG), KEGG Orthology (KO), and subsystem databases. Resistome profiling by MEGARes showed the dominance of ARGs corresponding to beta-lactams having relative abundance ranging from 16% to 34% in all the metagenome datasets, followed by tetracycline (8%–16%), aminoglycosides (7%–9%), multi-drug (5%–9%), and rifampin (3%–9%). Also, AMR genes oxa, ant3-DPRIME, and rpoB, which are of clinical importance were predominantly and most prevalently present in all the samples. The presence of AMR in effluents from both types of treatment plants indicates that wastewater from both sources contributes to the spread of pathogenic bacteria and resistance genes, increasing the environmental AMR burden and therefore requires tertiary treatment before discharge. This work will facilitate further research towards the identification of suitable biomarkers for monitoring antibiotic resistance.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation.

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis.

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (n = 24), osteopenia group (n = 20), and normal bone mass group (n = 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Shared Pathophysiology of Inflammatory Bowel Disease and Psoriasis: Unraveling the Connection.

Psoriasis (PS) and inflammatory bowel disease (IBD) are immune-mediated chronic conditions that share pathophysiological processes, including immune system dysfunction, microbiome dysbiosis, and inflammatory pathways. These pathways result in increased turnover of epithelial cells and compromised barrier function. The assessment of the literature suggests that immunopathogenic mechanisms, such as tumor necrosis factor (TNF)-α signaling and IL-23/IL-17 axis dysregulation, are shared by PS and IBD. Clinical characteristics and diagnostic approaches overlap significantly, and advances in biomarker identification benefit both conditions. Current treatments, namely biologics that target TNF-α, IL-17, and IL-23, show promising results in decreasing inflammation and controlling symptoms. Precision medicine approaches are prioritized in prospective therapeutic procedures to tailor pharmaceuticals based on specific biomarkers, perhaps improving outcomes and minimizing side effects. This study thoroughly examines and evaluates the body of research on PS and IBD. Several papers were examined to compile data on clinical features, diagnosis, therapies, pathophysiology, epidemiology, and potential future therapeutic developments. The selection of articles was based on three methodological qualities: relevance and addition to the knowledge of IBD and PS. The retrieved data were combined to provide a coherent summary of the state of the knowledge and to spot new trends. The overview of the latest studies demonstrates that both PS and IBD share pathophysiological foundations and therapeutic approaches. With a spotlight on particular biomarkers, advances in precision medicine provide a promising path toward enhancing therapeutic effectiveness and minimizing side effects.

Identification of potential diagnostic biomarkers and therapeutic targets in patients with hypoxia pulmonary hypertension

BackgroundPulmonary hypertension is a serious disease. Emerging studies have shown that M2 macrophages play an essential role in pulmonary hypertension; however, their mechanism of action is uncertain. MethodsFour GEO datasets were downloaded. The differentially expressed genes (DEGs) were obtained using the limma package. Simultaneously, the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and weighted gene co-expression network analysis (WGCNA) were used to get the information about M2 macrophage–related modules. Potential key genes were obtained by intersecting DEGs with M2 macrophage–related module genes (M2MRGs), and finally the area under the curve (AUC) was calculated. Rats were exposed to hypoxia condition (10 % O2) for 4 weeks to induce PH. Subsequently, potential key genes with AUC>0.7 were analyzed by quantitative real-time polymerase chain reaction and Western blot using normoxia and hypoxia rat lungs. We knocked down EPHA3 in Raw264.7 cells and detected the protein expression of M2 macrophage markers including arginase 1 (ARG1) and interleukin 10 (IL-10), phospho-protein kinase B (P-Akt), and protein kinase B (Akt) to explore the downstream pathways of EPHA3. ResultsSeven potential hub genes were detected by intersecting M2MRGs and DEGs. Six genes with AUC values above 0.7 were used for further exploration. The expression of EPHA3 mRNA and protein was significantly more upregulated in rats with hypoxia than in rats with normoxia. The expression levels of IL10, ARG1, and P-Akt/Akt decreased after knocking down EPHA3. ConclusionsThis study suggested that the activation of the P-Akt/Akt signaling pathway promoted by EPHA3 played an essential role in the progression of pulmonary hypertension.

Identification and validation of potential prognostic biomarkers in glioblastoma via the mesenchymal stem cell infiltration level.

Mesenchymal stem cells (MSCs) are key components in promoting glioblastoma (GBM) progression. This study aimed to explore new therapeutic targets and related pathogenic mechanisms based on different MSCs infiltration levels in GBM patients. We estimated the relationship between cell infiltration and prognosis of GBM. Subsequently, key risk genes were identified and prognostic models were constructed by LASSO-Cox analysis. The risk genes were validated by five independent external cohorts, single-cell RNA analysis, and immunohistochemistry of human GBM tissues. TIDE analysis predicted responsiveness to immune checkpoint inhibitors in different risk groups. The MSCs infiltration level was negatively associated with survival in GBM patients. LOXL1, LOXL4, and GUCA1A are key risk genes that promote GBM progression and may act through complex intercellular communication. This research has provided a comprehensive study for exploring the MSCs infiltration environment on GBM progression, which could shed light on novel biomarkers and mechanisms involved in GBM progression.

Volatilomics: An emerging discipline within Omics Sciences - A systematic review

Introduction Volatilomics is an omics science that is characterized as being a specific subbranch of metabolomics, which studies the different types of volatile organic compounds that may be present in a certain biological matrix. It has had impacts on the identification of new natural compounds and food safety processes, since it allows the evaluation of emerging contaminants that are present on food matrices, through the identification of biomarkers generated in response to this type of xenobiotic compounds through xenovolatilomic studies. Objective In this way, this review seeks to understand the scientific advances reported towards volatilomic studies, for which different types of primary research are reported depending on the main instrumental techniques used for the characterization of different types of VOCs that have been reported in our country between 2012 and 2022. Methodology Using a qualitative methodology, a search was carried out in the Scopus database, from which the bibliometric information of the primary research reported during this time was obtained, to later analyze the different research concerning the use of volatilomic studies and the fields of action that are currently used, as well as the different techniques for obtaining these compounds and the data analysis methodologies established for the processing of this type of research. Conclusions Finally, it can be concluded that, from the present review, the applicability of volatilomic studies is shown. The incursion carried out by this type of science on the verification of food safety in different types of matrices, in addition to allowing the study of the volatile profile formed by the different volatile organic compounds expressed by said matrix and the respective ecological role expressed by these compounds with the environment in which they are found.

Integrative analysis of the immunological significances of guanylate binding protein family genes in microsatellite stability colorectal cancer

BackgroundMicrosatellite stability (MSS) colorectal cancer (CRC) has poor sensitivity to immunotherapy and its underlying mechanisms are still unclear. Guanylate binding proteins (GBPs) are a family of GTPase involving innate immune responses by providing defense against invading microbes and pathogens. However, the immunological significances of GBPs in MSS CRC remain unknown. MethodsWe utilized bioinformatic tools to comprehensively analysis the expression pattern, clinical relevance, prognostic value, biological function, and immunoregulation effect of distinct GBP members in MSS CRC. ResultsThe expression of all seven GBPs in MSS samples are remarkably decreased compared to microsatellite instability-high (MSI-H) samples. Among them, GBP1/2/4/5 are obviously correlated with distant metastasis status. High expression of GBP1/4/5/6 was remarkably related to favorable overall survival (OS) and progression-free survival (PFS) in CRC patients with MSS tumor. Subsequent enrichment analysis revealed that Interferon-gamma (IFN-γ) and NOD-like receptor signaling are the most relevant functions. Besides, the expression patterns of GBPs are remarkably associated with several tumor infiltrated immune cells (e.g. regulatory T cells, CD4+ T cells, and macrophages) and diverse immunoregulatory molecules (e.g. immune checkpoint biomarkers (ICBs) and major histocompatibility complex (MHC) molecules). Moreover, high GBP1/2/4/5 expression predicted better immunotherapy responsiveness in immunotherapy cohorts. ConclusionThese findings might provide novel insights for the identification of therapeutic targets and potential prognostic biomarkers of GBP family in CRC with MSS samples.

Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning.

This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing an integrated analysis of metadata encompassing six gene expression omnibus (GEO) microarray datasets, 987 differentially expressed genes were identified. Further gene ontology and pathway enrichment analyses revealed the enrichment of these genes across various biological processes and pathways. Moreover, a systematic integration of two machine learning algorithms along with pathway-gene correlations identified six candidate biomarkers, which were validated in a separate cohort comprising six additional microarray datasets, ultimately identifying ADD3 and GNAS as diagnostic biomarkers for COPD. Subsequently, the diagnostic efficacy of ADD3 and GNAS was assessed, and the impact of their expression levels on overall survival was further evaluated and quantified in the validation cohort. Examination of immune cell subtype infiltration found increased proportions of cytotoxic CD8+ T cells, resting and activated NK cells, along with decreased M0 and M2 macrophages, in COPD versus control samples. Correlation analyses also uncovered significant associations between ADD3 and GNAS expression and infiltration of various immune cell types. In conclusion, this study elucidates crucial COPD diagnostic biomarkers and immune cell profiles which may illuminate the immunopathological drivers of COPD progression, representing personalized therapeutic targets warranting further investigation.

94P A one-tube multiplex methylation-specific droplet digital PCR assay for identification of ctDNA biomarkers in anal squamous cell carcinoma

94P A one-tube multiplex methylation-specific droplet digital PCR assay for identification of ctDNA biomarkers in anal squamous cell carcinoma

Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.

Graph Representation Learning Based on Specific Subgraphs for Biomedical Interaction Prediction.

Discovering the novel associations of biomedical entities is of great significance and can facilitate not only the identification of network biomarkers of disease but also the search for putative drug targets.Graph representation learning (GRL) has incredible potential to efficiently predict the interactions from biomedical networks by modeling the robust representation for each node.> However, the current GRL-based methods learn the representation of nodes by aggregating the features of their neighbors with equal weights. Furthermore, they also fail to identify which features of higher-order neighbors are integrated into the representation of the central node. In this work, we propose a novel graph representation learning framework: a multi-order graph neural network based on reconstructed specific subgraphs (MGRS) for biomedical interaction prediction. In the MGRS, we apply the multi-order graph aggregation module (MOGA) to learn the wide-view representation by integrating the multi-hop neighbor features. Besides, we propose a subgraph selection module (SGSM) to reconstruct the specific subgraph with adaptive edge weights for each node. SGSM can clearly explore the dependency of the node representation on the neighbor features and learn the subgraph-based representation based on the reconstructed weighted subgraphs. Extensive experimental results on four public biomedical networks demonstrate that the MGRS performs better and is more robust than the latest baselines.

Simulated neutron dose for two cell culture configurations

One of the major hurdles for long duration interplanetary space missions is space radiation-induced carcinogenesis. The need for early identification of relevant biomarkers of elevated space radiation risk is crucial for ensuring the health of astronauts after returning from long-duration exploration missions. The longer the duration of the mission, the greater the risk of space-radiation induced carcinogenesis. It is vital to have an accurate understanding of the absorbed dose to sensitive cells comprising the liver and colon cell models for identifying biomarkers that should be further investigated in more human-like systems exposed to space-like radiation. The specific liver and colon cell lines were chosen to build on previous experience with them and to establish a proof of concept for investigation of individual variability impact on biomarkers. Monte Carlo N-Particle transport code 6.2 was used in the simulation of two-dimensional and three-dimensional cell cultures exposed to deuterium–tritium fusion neutrons. Fast neutrons were chosen as most space neutron dose is delivered by neutrons with energy greater than 100MeV. Deuterium–tritium fusion neutrons posses the greatest possible neutron energy outside of large accelerator facilities. Deuterium–tritium neutron-induced charged particles closely resemble the high-LET portion of the GCR spectrum. The absorbed dose was recorded over the cell layer (target volume) using an F6 tally in MCNP. From foil activation analysis, the experimental source strength of 1.2 × 108 neutrons s−1 was used to determine the absorbed dose rate to the cells, which ranged from 69 to 73 mGy h−1. This simulated dose rate will be used to determine the total dose received by the cells in experiments conducted to identify biomarkers related to increased risk of carcinogenesis from fast neutron exposure.

Omalizumab reduces allergic rhinitis symptoms due to Japanese cedar pollen by improving eosinophilic inflammation.

Seasonal allergic rhinitis caused by Japanese cedar pollen (SAR-JCP) is a serious social problem in Japan, affecting 38.8% of the population (1). Omalizumab, a recombinant humanised monoclonal anti-immunoglobulin (Ig)E antibody, reduces serum-free IgE levels by 84-99% (2). The reduction of serum-free IgE levels induced by omalizumab ultimately downregulates FcεRI expression in basophils and mast cells (3). Omalizumab significantly reduces nasal symptoms and improves the quality of life in patients with allergic rhinitis (4,5); however, other than a decrease in free IgE, its biomarker activity is unclear. Allergic rhinitis reactions are more pronounced in nasal secretions and mucosa than in serum; however, no studies have examined the changes in proteins in nasal secretions after omalizumab administration. In this study, we aimed to elucidate the pathophysiology of the effect of omalizumab. This may serve as a basis for the identification of new biomarkers through the examination of proinflammatory proteins in nasal secretions, which may reflect the pathophysiology more accurately than peripheral blood.

Elevated tissue expression of RANKL and RANK is associated with poorer survival rates in pancreatic cancer patients.

Pancreatic cancer is a highly lethal malignancy with a growing incidence reported worldwide. Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which is often diagnosed at advanced stages, making its prognosis and medical management difficult. The identification of histopathological biomarkers has allowed a more precise stratification of pancreatic cancer patients, providing additional information about their prognosis and offering possible therapeutic targets to be explored. The prognostic value of the receptor activator of nuclear factor-kappa B (RANK) and its ligand (RANKL) has been evaluated in breast and prostate tumors, however, their usefulness has not been assessed in pancreatic cancer. In the present work, we analyzed the relationship between the protein expression of RANK and RANKL with the survival of 41 patients with pancreatic cancer followed for 60 months, by performing immunohistochemistry and Kaplan-Meier curves. Our results demonstrate a direct association of high expression levels of RANK and RANKL with poorer survival of pancreatic cancer patients in comparison to those with low/medium and null expression levels of both markers. Further studies should be conducted to explore the carcinogenic role of both components in this type of tumor, as well as additional promising translational uses.

Number of Blastodisc Cells During the Laying Period for Two Successive Generations of Layer and Broiler Breeder.

In the poultry industry, genetic selection for growth performance is associated with poor reproductive efficiency and an increase in embryo mortality. The identification of new biomarkers is essential to improve these parameters. The blastodisc, composed of blastodermal cells, undergoes cellular events to achieve embryo development. Factors such as hen's age, temperature and time of egg storage could influence the number of blastodermal cells and impair embryo development. In this study, we investigated the variability of the number of viable cells of blastodisc (NVCB) that could be dependent on the stage of laying and on the breed and potentially associated with reproductive parameters. In experimental breeds, eggs were collected during the whole cycle of laying. Then, the protocol was repeated on industrial breeds (breeder hens) during five successive days at three stages of laying (before, after laying peak and at the end of laying period) for two generations (mothers and offsprings). For each egg, the blastodisc was dissected in order to count viable cells. For both experiments, the NVCB increased during the laying cycle. The NVCB was higher in broiler blastodisc compared to layer blastodisc for both generations. For layer breed, the NVCB were negatively correlated with laying rate for the first generation while positively associated for offsprings. However, the NVCB was positively correlated with laying rates in both generations for broiler hens and with fertility and hatchability rates. The NVCB from fresh oviposited fertilised eggs could be a potential tool in predicting on reproductive performances in poultry.

LINC01857 promotes cell proliferation and migration while dampening cell apoptosis in pancreatic cancer by upregulating CDC42EP3 via miR-450b-5p

BackgroundPancreatic cancer (PC) is a devastating human malignancy with a poor survival outcome (5-year survival less than 10%). In recent years, the regulatory roles of long non-coding RNAs (lncRNAs) in various types of cancers have been widely reported. Based on bioinformatics analysis, LINC01857 is shown to be highly expressed in PC tissue. Nevertheless, the role of LINC01857 in PC is limitedly reported. Hence, this study aimed to explore the effects of lncRNA LINC01857 on PC cell process and the related mechanism. MethodsRT-qPCR and fluorescence in situ hybridization (FISH) assay were conducted to measure LINC01857 expression and distribution in PANC-1 and MIA PaCa-2 cells. Colony formation and wound healing assays as well as flow cytometry analyses were employed to estimate the proliferation, migration, and apoptosis of PC cells transfected with pcDNA3.1-LINC01857 or si-LINC01857 compared with the behavior of PC cells transfected with empty pcDNA3.1 vector (control) or si-negative control (NC). Furthermore, RNA pulldown and luciferase reporter assays were utilized to demonstrate the interaction of LINC01857 and miR-450b-5p or to validate the binding of miR-450b-5p and cell division cycle 42 effector protein 3 (CDC42EP3). ResultsLINC01857 was highly expressed in PANC-1 and MIA PaCa-2 cells in contrast to its expression in pancreatic ductal epithelial cells (8.9 folds and 7.1 folds, p<0.001). Silencing LINC01857 significantly reduced cell proliferation and migration while enhancing apoptosis (p<0.0005). In contrast, overexpression of LINC01857 markedly (p<0.05) accelerated these malignant behavior of PC cells. MiR-450b-5p was targeted and inversely regulated by LINC01857. Moreover, CDC42EP3 was verified to be targeted by miR-450b-5p, and CDC42EP3 was correlated to LINC01857 in a positive manner (p<0.001). Rescue experiments manifested that silencing CDC42EP3 effectively (p<0.05) reversed the promoting effect of LINC01857 on malignant behavior of PC cells. ConclusionLINC01857 promotes PC cell proliferation and migration while obstructing cell apoptosis by binding to miR-450b-5p and thus regulating CDC42EP3 expression. The study presents a novel and promising regulatory axis, which holds potential for the identification of biomarkers and development of therapeutic strategies for PC treatment.

Identification of Biomarkers for Endometrial Cancer Based on Natural Killer Cell-Related Genes

Endometrial cancer (EC) is a malignant tumor. Natural killer (NK) cells play a crucial role in various cancers, but their role in EC is unclear. To this purpose, in this paper, differential expression analysis was performed on transcriptome data from the TCGA database, and the obtained DEGs and the collected NRGs were intersected, and single-factor Cox regression analysis and Lasso-Cox regression analysis were performed on the intersected genes to obtain prognosis-related genes and risk model, respectively. These genes and models were validated by Kaplan-Meier (KM) survival curve analysis and ROC analysis on the internal and external test sets. In addition, nomogram models were constructed based on prognosis-associated genes, sample risk scores, and clinical factors. Finally, we explored the immune landscape of high- and low-risk groups of EC. The results showed that the risk models constructed in this paper exhibited excellent predictive effects, which will facilitate research on the precision treatment of EC. There were significant differences in prognosis, immune cell infiltration abundance, immune checkpoint-associated genes, and HLA gene expression between high- and low-risk groups of EC. The risk model in this paper can provide a reference for the personalized treatment of EC. In addition, we performed RT-qPCR to validate the levels of genes significantly associated with prognosis.