Potential Predictive Biomarkers Research Articles

Identification of key necroptosis-related genes and immune landscape in patients with immunoglobulin A nephropathy

BackgroundImmunoglobulin A nephropathy (IgAN) is a major cause of chronic kidney disease (CKD) and kidney failure. Necroptosis is a novel type of programmed cell death that has been proved to be associated with the pathogenesis of infectious disease, cardiovascular disease, neurological disorders and so on. However, the role of necroptosis in IgAN remains unclear.MethodsIn this study, we explored the role of necroptosis-related genes in the pathogenesis of IgAN using a comprehensive bioinformatics method. Microarray datasets GSE93798 and GSE115857 were downloaded from Gene Expression Omnibus (GEO). “limma” package of R software was employed to identify necroptosis-related differentially expressed genes (NRDEGs) between IgAN and healthy controls. GO and KEGG functional enrichment analysis was performed by Clusterprofiler. Least absolute shrinkage and selection operator (LASSO) regression analysis identified hub NRDEGs. We further established a diagnostic model consisting of 7 diagnostic hub NRDEGs and validated the efficacy by an external dataset. The expression of hub genes was confirmed in sc-RNA dataset GSE171314. Immune infiltration, gene set enrichment analysis and transcription factor binding motifs enrichment analysis were conducted to further uncover their roles.Results1076 differentially expressed genes were identified between healthy individuals and IgAN patients from RNA-seq dataset GSE9379. Then we cross-linked them with necroptosis-related genes to obtain 9 NRDEGs. LASSO regression analysis screened out 7 hub genes (JUN, CD274, SERTAD1, NFKBIA, H19, UCHL1 and EZH2) of IgAN. We further conducted functional enrichment analysis and constructed the diagnostic model based on dataset GSE93798. GSE115857 was used as the independent validation cohort and indicated a great predictive efficacy. Immune infiltration, gene set enrichment analysis and transcription factor binding motifs enrichment analysis revealed their potential function. Finally, we screened out four drugs that were predicted to have therapeutic value of IgAN.ConclusionsIn summary, we identified 7 hub necroptosis-associated genes, which can be used as potential genetic biomarkers for IgAN prediction and treatment. Four drugs were predicted as the potential therapeutic solutions. Collectively, we provided insights into the necroptosis-related mechanisms and treatment of IgAN at the transcriptome level.

Advances in biomarkers for immunotherapy in small-cell lung cancer

Small-cell lung cancer (SCLC) is a refractory cancer with rapid growth and high aggressiveness. Extensive-stage SCLC is initially sensitive to chemotherapy; however, drug resistance and recurrence occur rapidly, resulting in a poor survival outcome due to lack of subsequently efficient therapy. The emergence of immune checkpoint inhibitors (ICIs) generated a new landscape of SCLC treatment and significantly prolonged the survival of patients. However, the unselected immunotherapy restrains both beneficiary population and responsive period in SCLC compared to the other tumors. The complex tumor origin, high heterogeneity, and immunosuppressive microenvironment may disturb the value of conventional biomarkers in SCLC including programmed cell death 1 ligand 1 and tumor mutation burden. Transcriptional regulator–based subtypes of SCLC are current research hotspot, revealing that Y (I) subtype can benefit from ICIs. Additionally, molecules related to immune microenvironment, immunogenicity, epigenetics, and SCLC itself also indicated the therapeutic benefits of ICIs, becoming potential predictive biomarkers. In this review, we discussed the advances of biomarkers for prediction and prognosis of immunotherapy, promising directions in the future, and provide reference and options for precision immunotherapy and survival improvement in patients with SCLC.

Bioinformatics analysis of colorectal cancer transcriptomic data reveals novel prognostic signature and potential biomarker genes

Objective Colorectal cancer (CRC) is a type of digestive system cancer. At the molecular level, some factors, including genetic and epigenetic factors, as well as various signaling pathways such as oxidative stress and inflammation, play an active role in the onset of CRC. Genetic and epigenetic mutations, particularly in oncogenes and tumor suppressor genes, occur during colorectal adenocarcinoma development as a result of a change in gastrointestinal epithelial cell proliferation and self-renewal rates. This study aimed to determine the genes and molecular mechanisms that play a role in the emergence of this disease by analyzing the CRC data. Material and methods Microarray data selected for bioinformatics analysis is Gene Expression data stored with the code GSE110224 in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene expression analysis, functional clustering analysis, enrichment analysis, and pathway analysis were performed using this data set. Results Analysis of raw transcriptomic data revealed 1770 common DEGs in CRC. While the expression level of 769 of these genes increased, the expression level of 1001 genes decreased. A Protein–protein interaction (PPI) network was created from the first 25 genes with increased expression levels and 11 signature genes were identified. Increased expression of REG1A, MMP3, FOXQ1 and CEMIP genes and decreased expression of AQP8, CA1, CLDN8, PYY, CA4, CEACAM7 and SLC30A10 genes were observed. Conclusions This approach revealed a CRC-specific molecular profile and may provide some guidance for further investigation of potential biomarkers for diagnosis and prognosis prediction of CRC patients.

From tumor microenvironment to emerging biomarkers: the reshaping of the esophageal squamous cell carcinoma tumor microenvironment by neoadjuvant chemotherapy combined with immunotherapy

Esophageal squamous cell carcinoma is a cancer with high morbidity and mortality. The advent of immune checkpoint inhibitors has significantly increased complete response rates and postoperative R0 resection rates after neoadjuvant therapy. These drugs can largely reverse the suppression of the immune system caused by the tumor microenvironment, allowing the reactivation of anti-tumor immune infiltrating cells, significantly improving the patient’s tumor microenvironment, and thus preventing tumor development. However, there are still some patients who respond poorly to neoadjuvant combined immunotherapy and cannot achieve the expected results. It is now found that exploring changes in the tumor microenvironment not only elucidates patient responsiveness to immunotherapy and identifies more reliable biomarkers, but also addresses the limitations of prediction with imaging examination such as CT and the instability of existing biomarkers. In light of these considerations, this review aims to delve into the alterations within the tumor microenvironment and identify potential predictive biomarkers ensuing from neoadjuvant immunotherapy in the context of esophageal squamous cell carcinoma.

Differences in cerebral structure among patients with amnestic mild cognitive impairment and patients with Alzheimer's disease.

Brain has been shown to undergo progressive atrophy in patients with Alzheimer's disease (AD); however, more evidence is needed to elucidate how the brain structure changes during the progression to AD. Here, we observed differences in the cerebral structure among patients with amnestic mild cognitive impairment (aMCI) and patients with AD. A total of 46 participants were selected and divided into AD, aMCI, and healthy control (HC) groups. Structural magnetic resonance imaging (sMRI) was performed on all participants. Voxel-based morphometry (VBM) and surface-based morphometry (SBM) techniques were utilized to analyze sMRI data so as to identify significant differences among the specific brain regions of these three groups. Then, a correlation analysis was performed on the characteristics of the identified brain regions and the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) cognitive assessment scores. The volume of the left precuneus region, which was identified by voxel-based morphometry, and the thickness of both sides of the inferior parietal, which was identified by surface-based morphometry, were shown to be less in AD/aMCI patients, compared to those of the HC. The correlation analysis showed that there were significant differences between the volume of the left precuneus region and the MMSE/MoCA scores, as well as between the thickness of the left and right sides of the inferior parietal region and the MMSE/MoCA scores. The sMRI characteristics of the identified brain regions were considered to be potential predictive diagnostic biomarkers for AD. Identifier: ChiCTR2400092593.

Phosphorylation-mediated conformational change regulates human SLFN11

Human Schlafen 11 (SLFN11) is sensitizing cells to DNA damaging agents by irreversibly blocking stalled replication forks, making it a potential predictive biomarker in chemotherapy. Furthermore, SLFN11 acts as a pattern recognition receptor for single-stranded DNA (ssDNA) and functions as an antiviral restriction factor, targeting translation in a codon-usage-dependent manner through its endoribonuclease activity. However, the regulation of the various SLFN11 functions and enzymatic activities remains enigmatic. Here, we present cryo-electron microscopy (cryo-EM) structures of SLFN11 bound to tRNA-Leu and tRNA-Met that give insights into tRNA binding and cleavage, as well as its regulation by phosphorylation at S219 and T230. SLFN11 phosphomimetic mutant S753D adopts a monomeric conformation, shows ATP binding, but loses its ability to bind ssDNA and shows reduced ribonuclease activity. Thus, the phosphorylation site S753 serves as a conformational switch, regulating SLFN11 dimerization, as well as ATP and ssDNA binding, while S219 and T230 regulate tRNA recognition and nuclease activity.

The efficacy of plasma exosomal miRNAs as predictive biomarkers for PD-1 blockade plus chemotherapy in gastric cancer.

The response of gastric cancer (GC) patients to first-line programmed cell death 1 (PD-1) blockade and S-1 plus oxaliplatin (SOX) chemotherapy varies considerably, and the underlying mechanisms driving this variability remain elusive. Exosomal microRNAs (miRNAs or miRs) have emerged as potential biomarkers for efficacy prediction due to their roles in GC biology and stable expression in serum. In this study, we aimed to identify biomarkers to predict patients' response to anti-PD-1 therapy and further elucidate the potential mechanisms by which these exosomal miRNAs modulate the immune response in GC. Serum exosomes were extracted from 11 GC patients (five in the primary cohort and six in the validation cohort) treated with SOX and camrelizumab (a PD-1 inhibitor). High-throughput sequencing was performed to identify miRNA expression profiles, after which hierarchical clustering and a differential expression analysis were conducted. Functional enrichment analyses of the target genes for the significantly upregulated miRNAs were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The validation of the candidate miRNAs was carried out by quantitative polymerase chain reaction (qPCR) in an independent cohort. MiRNA sequencing identified 3,083 miRNAs, of which 74 (42 upregulated and 32 downregulated) were differentially expressed between the responders and non-responders. The GO and KEGG pathway analyses of the top 20 upregulated miRNAs indicated that the target genes were significantly involved in transcription regulation, cytoplasmic processes, and protein binding, and that key pathways included the PI3K-AKT, MAPK, RAP1, and RAS signaling pathways. Consistent with the sequencing findings, the qPCR validation results showed significant differences in the expression levels of miRNA451a and miRNA142-5p between the responders and non-responders. This study identified specific plasma exosomal miRNAs in GC patients that differ between responders and non-responders to PD-1 monoclonal antibody therapy combined with chemotherapy. These miRNAs could serve as predictive biomarkers, paving the way for precision medicine and personalized therapy in the treatment of GC.

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

TRIM47 promotes hypopharyngeal and laryngeal cancers progression through promoting K63-linked ubiquitination of vimentin.

Hypopharyngeal and laryngeal cancers which belong to head and neck squamous cell carcinoma (HNSCC) are the two most malignant types of head and neck cancer, characterized by a low 5-year survival rate, high recurrence and metastasis rate. It is vital to explore strategies to suppress metastasis and improve prognosis for patients with these cancers. In this research, we analyzed the clinical data and found that E3 ubiquitin ligase TRIM47 was upregulated in cancer tissues of hypopharyngeal cancer and was closely associated with poor survival outcomes. In terms of mechanism, we performed tandem affinity chromatography and denatured Ni-NTA Agarose pulldown. As a result, TRIM47 was found to interact with vimentin and control vimentin stabilization through ubiquitination, specifically in the form of K63 chains. Importantly, through experiments of cancer cell viability and migration, we found that TRIM47 could enhance the proliferation and metastasis abilities of cancer cells in a vimentin-dependent manner, thus promoting the advancement of hypopharyngeal and laryngeal cancers. TRIM47 was verified to regulate cancer cells metastasis invivo using metastasis models. All these results imply that TRIM47 emerges as a potential biomarker for early diagnosis and metastasis prediction of hypopharyngeal and laryngeal cancers and represents a promising therapeutic target.

Toripalimab plus anlotinib in patients with recurrent or metastatic nasopharyngeal carcinoma: A multicenter, single-arm phase 2 trial (TORAL).

Treatment options for patients with recurrent or metastatic nasopharyngeal carcinoma (RM-NPC) after failure of platinum-based therapy are limited. In this phase 2 trial, 40 patients with RM-NPC who failed platinum-based chemotherapy receive toripalimab plus anlotinib regimen. The objective response rate is 37.5%, and the disease control rate is 85.0%. With a median follow-up of 17.4months, the median progression-free survival (PFS) is 9.5months and 1-year overall survival rate is 73.3%. The most common treatment-related grade 3-4 adverse events are hand-foot syndrome (22.5%) and oral mucositis (17.5%). Analyses of plasma circulating tumor DNA (ctDNA) demonstrate that the blood tumor mutation burden at cycle 1/2 is associated with response and PFS, and disease progression indicated by ctDNA precedes radiological progression by a median of 2.3months. In conclusion, toripalimab plus anlotinib is well tolerated and shows promising efficacy in patients with RM-NPC, and ctDNA could be a potential predictive biomarker. The trial is registered at ClinicalTrials.gov (NCT04996758).

Obesity-Specific improvement of lung cancer outcomes and immunotherapy efficacy with metformin.

Pre-clinical cancer studies ascribe promising anticancer properties to metformin. Yet, clinical findings vary, casting uncertainty on its therapeutic value for non-small cell lung cancer (NSCLC) patients. We hypothesized that metformin could benefit obese and overweight patients with NSCLC. We retrospectively analyzed two clinical cohorts and employed complementary mouse models to test our hypothesis. One cohort included NSCLC patients with overweight BMI (≥25, n = 511) and non-overweight BMI (<25, n = 232) who underwent lobectomy, evaluating metformin's impact on clinical outcomes. Another cohort examined metformin's effect on progression-free survival (PFS) after immune checkpoint inhibitors (ICI) in overweight (n = 284) vs non-overweight (n = 184) NSCLC patients. Metformin's effects on tumor progression, antitumor immunity, and ICI response in obese and normal-weight mice were assessed with lung cancer models. Metformin is associated with increased recurrence-free survival in overweight patients (HR = 0.47 [95%CI = 0.24-0.94], p = .035) after lobectomy. It also corrected accelerated tumor growth in diet-induced obese mouse models in a lymphocyte-specific manner while reversing several mechanisms of immune suppression potentiated by obesity. PD-1 blockade coupled with metformin was more effective at limiting tumor burden in obese mice and correlated with PFS only in overweight patients on immunotherapy (HR = 0.60, [95%CI = 0.39-0.93], p = .024). Metformin may improve lung cancer-specific clinical outcomes in obese and overweight lung cancer patients and enhance immunotherapy efficacy in this growing population as well. This work identifies obesity as a potential predictive biomarker of metformin's anticancer and immunotherapy-enhancing properties in lung cancer while shedding light on the underlying immunological phenomena.

Role of gut microbiome in developing necrotizing enterocolitis.

Necrotizing enterocolitis (NEC) is one of the most devastating intestinal diseases observed in preterm in the first days of life. Researchers have recently focused on potential predictive biomarkers for early and concomitant diagnoses. Thus, we inquired about the linkage of intestinal dysbiosis, one of the most important factors in NEC development to the gut microbiota. In this study, the systematic differences in the bacterial composition between neonates affected by NEC and healthy newborns were highlighted by metagenomic analysis. The next-generation sequencing of the V3-V4 variable region of the 16S rRNA gene and gene-specific qPCR analyzed the untargeted gut microbiota. Total bacteria, total and fecal coliform loads in stool samples with NEC were higher than control. OTU-level relative abundances of NEC infant was characterized by Firmicutes and Bacteroidetes at phylum levels. At the genus level, NEC stool was identified by the lack of Klebsiella and the presence of Roseburia, Blautia, and Parasutterella. Finally, Clostridium fessum was the predominant species of Clostridium genus in disease and healthy specimens at the species level, whereas Clostridium jeddahitimonense was at NEC diagnosis. Despite a strong relationship between pathophysiology and characterization of gut microbiota at a clinical diagnosis of NEC, our results emphasize the broad difficulty in identifying potential biomarkers.

CNSC-08. PROGNOSTIC BIOMARKER DISCOVERY THROUGH PROTEOMIC ANALYSIS OF GLIOBLASTOMA MULTIFORME

Abstract BACKGROUND Glioblastoma multiforme (GBM) is an aggressively malignant brain tumor known for its high recurrence rates and dismal survival outcomes. Despite advancements in surgical and therapeutic strategies, the prognosis for GBM patients remains poor. This study applies proteomic analysis to postoperative GBM tumor specimens to identify molecular markers that could enhance prognosis prediction. Through the analysis of protein expression profiles before and after tumor recurrence, we identified significant alterations in protein pathways via Gene Set Enrichment Analysis (GSEA), highlighting potential biomarkers for disease outcome prediction. METHODS We employed Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (SWATH-MS) proteomics to analyze protein samples from both primary and recurrent GBM patients. Gene Set Enrichment Analysis (GSEA) was utilized to compare protein expression profiles and discern significantly altered pathways. RESULTS Analysis revealed 778 differentially expressed proteins (DEPs), with notable alterations in pathways such as glycolysis, interleukin signaling, and MAPK signaling. Proteins such as TOLLIP, ENO1, and ITGA2B emerged as potential prognostic markers. Validation through an online tumor prognostic analysis platform (ToPP), integrated with The Cancer Genome Atlas (TCGA) data, facilitated the development of a prognostic risk score model. This model effectively categorized patients into high and low-risk groups based on their protein expression profiles, enhancing the prediction of patient outcomes. The proteins and pathways identified offer new insights into the molecular dynamics of GBM postoperatively. The association between protein expression profiles and patient prognosis underscores the potential of these markers in developing customized therapeutic strategies. Additionally, the prognostic risk score model serves as a valuable tool for improving prognostic accuracy and could guide personalized treatment plans. CONCLUSION Proteomic profiling of GBM tumors significantly contributes to uncovering the molecular mechanisms driving this aggressive cancer and facilitates the development of dependable prognostic tools. These findings not only affirm the critical role of proteomics in cancer research but also advance personalized medicine strategies in GBM management.

Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches

BackgroundTo compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) and further evaluate the stratified effect of the radiomics model on disease-free survival (DFS) and overall survival (OS) of LSCC patients.MethodsIn all, 106 LSCC patients (40 with inflamed and 66 with non-inflamed immunophenotyping) were randomly assigned into a training (n = 53) and testing (n = 53) cohort. Briefly, 750 radiomics features from contrast-enhanced CT images were respectively extracted from the whole tumor and two Otsu method-derived subregions. Intraclass correlation coefficients (ICCs) were calculated to evaluate the reproducibility. The radiomics models for predicting immunophenotyping were respectively created using K-nearest neighbors (KNN), logistic regression (LR), and Naive bayes (NB) classifiers. The performance of models in the testing cohort were compared using area under the curve (AUC). The prognostic value of the optimal model was determined by survival analysis.ResultsThe radiomics features derived from whole tumor showed better reproducibility than those derived from habitats. The best model for the whole tumor (LR classifier) showed superior performance than that for the habitats (KNN classifier) in the testing cohort, but there were no significant differences (AUC: 0.741 vs. 0.611, p = 0.112). Multivariable Cox regression analysis showed that the immunophenotyping predicted by the optimal model was an independent risk factor of unfavorable DFS (p = 0.009) and OS (p = 0.008) in LSCC patients.ConclusionsWhole tumor-based CT radiomics could serve as a potential predictive biomarker of immunophenotyping and outcome prediction in LSCC patients.

The correlation between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with non-alcoholic fatty liver disease: an analysis of the population-based NHANES (2017-2018).

Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver disorders, from benign steatosis to more severe conditions like non-alcoholic steatohepatitis, with risks of progressing to fibrosis, cirrhosis, and hepatocellular carcinoma. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) indicates lipid metabolic dysregulation and is associated with increased risks of various diseases. This study examines the relationship between NHHR and NAFLD to evaluate NHHR as a potential predictive biomarker for NAFLD. Data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) were used for cross-sectional analysis. After excluding individuals with incomplete data, hepatitis infections, heavy alcohol use, and those under 18, the study included 2,757 adults. The relationship between NHHR and NAFLD was analyzed using multivariable logistic regression, including subgroup analysis and interaction testing. Among the 2,757 participants (mean age 49.91 years), 44.9% had NAFLD. NHHR showed a significant positive association with NAFLD, with an unadjusted odds ratio (OR) of 1.71 and a fully adjusted OR of 1.45. Quartile analysis revealed a 228% higher prevalence of NAFLD in the highest NHHR quartile, with an OR of 3.28. This positive association was consistent across various subgroups. Our findings suggest that elevated NHHR is positively correlated with the prevalence of NAFLD and possesses predictive value. We recommend that future research validate the clinical utility of NHHR, particularly for early detection of high-risk individuals and guiding personalized interventions.

A Long-Term Follow-Up Study of Serum NFATc3 Levels in Pediatric Patients with Bronchial Asthma: A Prospective Observational Case-Control Investigation.

The early and precise diagnosis of asthma significantly impacts the long-term health outcomes of pediatric patients. The sensitivity and specificity of current biomarkers, however, are frequently limited. Our study aimed to evaluate the clinical significance of nuclear factor of activated T cells, cytoplasmic 3 (NFATc3), in pediatric bronchial asthma, focusing on its diagnostic and prognostic value for disease severity and recurrence. This observational, prospective case-control study involved 200 pediatric patients with bronchial asthma and 200 age- and sex-matched healthy controls, from January 2020 to January 2023. Follow-up varied from 1 to 3 years. We measured levels of NFATc3 and inflammatory cytokines interleukin-1β (IL-1β), IL-6, and TNF-α via enzyme-linked immunosorbent assay. NFATc3 and IL-1β levels at enrollment were markedly higher in patients with acute exacerbations and those classified as severe, compared with their less severe counterparts. Throughout the study, NFATc3, IL-1β, and IL-6 levels significantly increased in severe or acutely exacerbating cases. The diagnostic value of NFATc3 was assessed through receiver operating characteristic curve analysis, which showed its potential in diagnosing bronchial asthma and identifying severe cases. Spearman's analysis confirmed positive associations between peak NFATc3 and cytokine levels. Importantly, disease type, NFATc3 values at enrollment, as well as peak IL-6 levels were identified as independent risk factors for severe bronchial asthma. Elevated NFATc3 is linked with the severity of pediatric bronchial asthma and serves as a potential biomarker for diagnosis and severity prediction, emphasizing its role in guiding treatment strategies.

The role of leukocyte activation in suspected Non-IgE excipient-related COVID-19 vaccine reactions: An exploratory hypothesis-driven study of pathogenesis.

Background: Adverse allergic reactions due to the administration of vaccines developed for the protection of coronavirus disease 2019 (COVID-19) have been reported since the initiation of the vaccination campaigns in December 15, 2020. Current analyses provided by the Centers for Disease Control and Prevention and the U.S. Food and Drug Administration in the United States have estimated the rates of anaphylactic reactions in 2.5 and 11.1 per million of messenger RNA (mRNA) 1273 and BNT162b2 vaccines administered, respectively. The mechanisms by which these mRNA vaccines induce adverse vaccine reactions have been the subject of conflicting reports. Although skin testing with excipient components found in mRNA-1273 and BNT162b2 vaccines, such as polyethylene glycol (PEG) and related vaccine lipid products, were originally recommended to identify potential predictive biomarkers of adverse allergic reactions, more recent evidence has suggested that routine skin testing with these vaccine excipients have poor predictability and do not correlate with susceptibility to vaccine injury. Objective: The goal of this proof-of-concept (POC) exploratory study was to investigate the role of leukocyte activation (LA) induced by lipid excipients found in mRNA COVID-19 vaccines in the pathogenesis of COVID-19 mRNA vaccine-associated adverse reactions by using an LA assay developed in our laboratory. Results: An LA assay was performed on blood samples obtained from 30 study subjects who were assigned to three study groups: group 1 consisted of 10 subjects who had received an mRNA COVID-19 vaccine and developed a serious vaccine adverse reaction; group 2 consisted of 10 subjects who had received a COVID-19 vaccine and developed a mild adverse reaction; and group 3 consisted of 10 subjects who had not received a COVID-19 vaccine and were asymptomatic. Five excipients were tested in each of the 10 subjects; hence, a potential of 50 reactions could be expressed in each of the three groups. In the subjects in group 1 who had shown clinically severe vaccine effects, 8 of 50 (16%) had severe LA index (LAI) responses (>144.83), 12 of 50 (24%) had moderate LAI responses (87.62 -144.82), and 30 of 50 (60%) had no reaction (0 - 87.61). In the subjects in group 2 who had shown clinically mild vaccine effects, 4 of 50 (8%) had severe LAI responses (>144.83), 9 of 50 (18%) had moderate LAI responses (87.62 -144.82), and 37 of 50 (74%) had no reaction. In the subjects in group 3 who had not received the vaccine and, therefore, had no clinical vaccine effects, 2 of 50 (4%) had severe LAI responses (>144.83), 10 of 50 (20%) had moderate LAI responses (87.62 -144.82), and 38 of 50 (76%) had no reaction LA index (LAI) responses. Conclusion: The results of this exploratory POC study suggest that the measurement of LA induced by PEG and other vaccine-related lipid excipients found in mRNA COVID-19 vaccines may provide a novel and useful predictive biomarker associated with adverse non-immunoglobulin E (IgE) related allergic reactions to these vaccines. The study results also underscore growing concerns related to these non-IgE hypersensitivity reactions and their potential for pathogenesis of adverse vaccine reactions. This is particularly noteworthy because, with the continuing emergence of novel and evolving variants of severe acute respiratory syndrome corona-virus 2 mutants, yearly immunization with mRNA vaccines will most likely be recommended. Although the study was not sufficiently powered to draw definitive conclusions with regard to associations between vaccine-associated COVID-19 reactions and LA, the trends of a more severe set of clinical reactions seen associated with LAI reactivity scores, particularly with ALC-0159 (2-[{polyethylene glycol} 2000]-N,N-ditetradecylacetamide), suggest a potential benefit worthy of exploration in future randomized controlled trials.

Higher Serum Galectin‑3 Levels Were Associated with More Severe Motor Performance in Parkinson's Disease.

Parkinson's disease (PD) is a movement disorder that lacks proven biomarkers. Case-control genome-wide association studies revealed the potential effect of galectin‑3 (GAL3) on motor progression in PD patients. Based on this finding, our study aimed to explore the correlation between serum GAL3 levels and motor performance in PD patients. Five hundred PD patients and 200healthy controls were recruited. The serum levels of GAL3 were measured in participants by enzyme linked immunosorbent assay (ELISA). The baseline characteristics of the participants were collected, and the associated scale scores were obtained. Compared with healthy controls, the serum levels of GAL3 were greatly increased in PD patients. These levels could distinguish between PD patients and healthy controls with a sensitivity of 0.798 and a specificity of 0.815 (AUC = 0.795, 95% CI 0.757-0.834, P < 0.001). Patients with age >60 years tended to have higher serum GAL3 levels, disease duration, Hoehn-Yahr stage, MDS-UPDRS III total score, tremor subscores, rigid subscores, and bradykinesia subscores than those with age ≤60 years. When adjusting for confounders, higher GAL3 level was significantly correlated with MDS-UPDRS III total score and rigid subscores. In men with PD, GAL3 was significantly correlated with MDS-UPDRS III total score; but the association between GAL3 and bradykinesia subscores was found in women. Moreover, the associations between GAL3 with MDS-UPDRS III total score and bradykinesia subscores were significant in patients with age >60 years. Higher GAL3 level was related to more severe motor performance in patients with age >60 years, and it may be a potential predictive biomarker for motor performance in PD patients.

Metabolomic Profiling of Cerebrospinal Fluid Reveals Metabolite Biomarkers in Tick-Borne Encephalitis Patient.

Tick-borne encephalitis virus (TBEV) can cause life-threatening central nervous system infection. Changes in cerebrospinal fluid (CSF) metabolites may reflect critical aspects of host responses and end-organ damage in neuro infection and neuroinflammation. In this study, we applied an untargeted metabolomics screen of CSF samples to investigate the metabolites profile and explore biomarkers for TBEV infection. By analyzing CSF samples from 77 patients with TBEV infection and 23 without TBEV infection, tryptophan metabolism and Citrate cycle were found to be the top important metabolic pathways in differentiating the control and case groups; acetoacetate, 5'-deoxy-5'-(methylthio)-adenosine, 3-methyl-2-oxobutanoic acid, and so forth. were identified to be metabolic biomarkers (| FC|> 1, VIP > 1, FDR < 0.05) in CSF and clearly separated the TBEV infection from the noninfected samples. Moreover, four metabolites were identified to be associated with fatal outcome, including kynurenic acid, 5-hydroxyindole-3-acetic acid, DL-tryptophan, indole-3-acrylic acid, demonstrating the potential predictive biomarkers for severe TBEV infection. This study explored the metabolic profile of TBEV infection in CSF samples and identified candidate biomarkers for TBEV infection, which might be useful in target screening for differential diagnosis and therapeutic inter-vention.

Repair-assisted damage detection as a potential predictive biomarker for immunotherapy response in ovarian cancer

Repair-assisted damage detection as a potential predictive biomarker for immunotherapy response in ovarian cancer