Discovery Set Research Articles

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy.

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers. A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry. Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively. Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR. Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Deciphering epigenetic regulation of enhancers in high‐risk prostate cancer

AbstractProstate cancer (PCa) is associated with widespread promoter hypermethylation. We hypothesized that aberrant DNA methylation also targets gene enhancers, modulating their activity and contributing to disease etiology. A patient discovery set (n = 37) was used for differential methylation analysis and biomarker identification using the Infinium methylation EPIC array, on high‐risk (n = 13), low‐risk (n = 11), and histologically benign (n = 13) tissues. Enhancers were primarily hypermethylated. However proportionally, hypomethylated enhancers were more prominent in high‐risk (n = 385, 15%) than low‐risk (n = 105, 10%) disease, primarily targeting genes involved in development and enriched for oncoprotein binding motifs, including FOXA1. The clinical significance of enhancer methylation was evaluated by identifying a 17 enhancer differentially methylated probe (DMP) signature using a Least Absolute Shrinkage and Selection Operator model in the discovery set. A large external dataset (n = 746) obtained from four publicly available prostate tissue methylation array studies was used to assess the enhancer signature through logistic regression models trained on a 2/3 training set and tested in a 1/3 test set. This delivered an area under the curve of 0.81 (95% bootstrapped CI 0.78–0.9) for selective detection of high‐risk PCa, achieving a 0.71 sensitivity and 0.76 specificity. Array‐wide aberrant DNA methylation at enhancers highlighted their epigenetic perturbance in high‐risk disease. A clinically significant enhancer signature from this study could be used for detecting high‐risk PCa.

Abstract WMP117: Altered Patterns of microRNA 9-3p in Acute Ischemic Stroke With Large Vessel Occlusion

Background: Stroke is a significant health issue in the United States, and identifying biomarkers for preventing acute ischemic stroke (AIS) remains the highest priority. This study aims to identify microRNA (miRNA) associated with AIS pathophysiology through longitudinal and case-control studies. Methods: We examined global miRNA profiles of serum exosome vesicles of 15 patients with a large vessel occlusion (LVO) using NextGen sequencing. Discovery cohort consisted of miRNA profiles collected during acute (<72 hours, T1) and chronic (3-5 months, T2) stroke stages. The preliminary results were validated through Validation set-1 included 55 LVO cases and 53 controls. and Validation set-2 comprised 15 LVO patients collected at different time points <24h-A1, 48-72h-A2, 5-7 days-A3, >3 months-A4. Results: Our study identified four miRNAs, miR-9-3p, miR-129-5p, miR-223-3p, and miR-423-5p, with differential expressions in LVOs compared to controls. Exosomal miR-9-3p showed a 14.7-fold increase in T1 (Bonferroni p= 9.4x10 -6 ) in the Discovery set and a 9.39-fold increase (Bonferroni p= 1.3x10 -4 ) in stroke cases compared to controls in the Validation-set-1. In the longitudinal analysis of Validation set-2, miR-9-3p levels peaked significantly at A2 and then gradually decreased over A3 and A4 time points. Though miR-129-5p and miR-423-5p were also significantly upregulated during the T1, their association did not survive the stringent Bonferroni correction in any sets. Discussion and Conclusion: MiR-9-3p triggers brain injury via caspase pathway in the murine stroke model. Its role in human AIS pathophysiology is not available. For the first time, we report the possible contribution of miR-9-3p during the acute state in LVO strokes. The pathway analysis suggests miR-9-3p may lead to neuronal cell death and apoptosis in AIS. If confirmed, therapeutic targeting of miRNA 9-3p may become clinically useful biomarkers for treating LVO in humans. Funding: College of Medicine Alumni Association, the Presbyterian Health Foundation, Leinbach Foundation grants, and Dr. Geoffrey Altshuler Endowment funds from the Children’s Health Foundation of OUHSC, and partly supported by National Institute of Health (NIH/NIDDK) R01DK118427 grant.

ZNF326 as a potential prognostic and predictive biomarker in stage II colorectal cancer.

Adjuvant chemotherapy is considered for stage II colorectal cancer (CRC) patients with poor prognostic risk factors. However, current stratification algorithms are still insufficient to identify high-risk patients. We conducted a screening strategy to define ZNF326 based on quantitative proteomics in 11 paired CRC patients selected by a nested case-control design, and tested the association between ZNF326 expression level with the prognosis of stage II CRC patients and the benefit from adjuvant chemotherapy in public datasets; further investigation was conducted through subgroup analyses. We found that low ZNF326 expression was significantly associated with a lower 5-year overall survival (OS) rate among stage II patients in both the discovery [P=0.008; hazard ratio (HR): 3.13, 95% confidence interval (CI): 1.29-7.58] and validation (P=0.025; HR: 1.98, 95% CI: 1.08-3.65) cohorts. In the Cox multivariable analysis, low ZNF326 expression was both associated with shorter OS after adjustment for age, sex, and adjuvant chemotherapy in the discovery and validation data sets. Subgroup analyses yielded largely similar results. In a pooled database, the rate of 5-year OS was higher among stage II ZNF326-high tumors who were treated with adjuvant chemotherapy than it was among those who were not treated with adjuvant chemotherapy (P=0.011; HR: 0.28, 95% CI: 0.10-0.80). ZNF326 has the potential to be used in clinical practice for risk classification. ZNF326-low expression level identified a subgroup of patients with high-risk stage II CRC who appeared to less benefit from adjuvant chemotherapy.

Exploration of a hypoxia-immune-related microenvironment gene signature and prediction model for hepatitis C-induced early-stage fibrosis

BackgroundLiver fibrosis contributes to significant morbidity and mortality in Western nations, primarily attributed to chronic hepatitis C virus (HCV) infection. Hypoxia and immune status have been reported to be significantly correlated with the progression of liver fibrosis. The current research aimed to investigate the gene signature related to the hypoxia-immune-related microenvironment and identify potential targets for liver fibrosis.MethodSequencing data obtained from GEO were employed to assess the hypoxia and immune status of the discovery set utilizing UMAP and ESTIMATE methods. The prognostic genes were screened utilizing the LASSO model. The infiltration level of 22 types of immune cells was quantified utilizing CIBERSORT, and a prognosis-predictive model was established based on the selected genes. The model was also verified using qRT-PCR with surgical resection samples and liver failure samples RNA-sequencing data.ResultsElevated hypoxia and immune status were linked to an unfavorable prognosis in HCV-induced early-stage liver fibrosis. Increased plasma and resting NK cell infiltration were identified as a risk factor for liver fibrosis progression. Additionally, CYP1A2, CBS, GSTZ1, FOXA1, WDR72 and UHMK1 were determined as hypoxia-immune-related protective genes. The combined model effectively predicted patient prognosis. Furthermore, the preliminary validation of clinical samples supported most of the conclusions drawn from this study.ConclusionThe prognosis-predictive model developed using six hypoxia-immune-related genes effectively predicts the prognosis and progression of liver fibrosis. The current study opens new avenues for the future prediction and treatment of liver fibrosis.

The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population.

Polygenic risk scores (PRSs) predict individuals' genetic risk of developing complex diseases. They summarize the effect of many variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to other ethnicities. Genetic profiling of individuals in the discovery set (on which the GWAS was performed) and target set (on which the PRS is applied) is typically done by SNP arrays that genotype a fraction of common SNPs. Therefore, a key step in GWAS analysis and PRS calculation is imputing untyped SNPs using a panel of fully sequenced individuals. The imputation results depend on the ethnic composition of the imputation panel. Imputing genotypes with a panel of individuals of the same ethnicity as the genotyped individuals typically improves imputation accuracy. However, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS. We estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery and the target sets come from different ethnic groups. We analyzed binary phenotypes on ethnically distinct sets from the UK Biobank and other resources. We generated ethnically homogenous panels, imputed the target sets, and generated PRSs. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnicity of the target population yields only a marginal improvement and only under specific conditions. The source code used for executing the analyses is this paper is available at https://github.com/Shamir-Lab/PRS-imputation-panels.

Abnormal circulating steroids refine risk of progression to heart failure in ischemic heart disease.

Patients with ischemic heart disease (IHD) experience a high incidence of progression to heart failure (HF) despite current therapies. We speculated that steroid hormone metabolic disorders distinct adverse phenotypes and contribute to HF. We measured 18 steroids using liquid chromatography with tandem mass spectrometry in 2023 patients from the Registry Study of Biomarkers in Ischemic Heart Disease (BIOMS-IHD), including 1091 patients with IHD in a retrospective discovery set and 932 patients with IHD in a multicentre validation set. Our outcomes included incident HF after a median follow-up of 4 years. We demonstrated steroid-based signatures of inflammation, coronary microvascular dysfunction and left ventricular hypertrophy that were associated with subsequent HF events in patients with IHD. In both cohorts, patients with a high steroid-heart failure score (SHFS) (>1) exhibited a greater risk of incident HF than patients with a low SHFS (≤1). The SHFS further improved the prognostic accuracy beyond clinical variables (net reclassification improvement of 0.628 in the discovery set and 0.299 in the validation set) and demonstrated the maximal effect of steroid signatures in patients with IHD who had lower B-type natriuretic peptide levels (pinteraction = 0.038). A steroid-based strategy can simply and effectively identify individuals at higher HF risk who may derive benefit from more intensive follow-ups.

Genome-wide functional integration identified MAZ-controlled RPS14 dysregulation in hepatocellular carcinoma.

Chronic infection with Hepatitis B virus (HBV) significantly increases the risk of hepatocellular carcinoma (HCC), particularly in Eastern Asia. However, only a subset of individuals with chronic HBV infection develop HCC, suggesting the role for genetic factors in HCC etiology. Despite genome-wide association studies (GWASs) identifying multiple single nucleotide polymorphisms (SNPs) associated with HBV-related HCC susceptibility, the underlying mechanisms and causal genetic polymorphisms remain largely unclear. To address this, we developed The Updated Integrative Functional Genomics Approach (TUIFGA), an methodology that combines data from transcription factor (TF) cistromics, ATAC-seq, DNAase-seq, and the 1000 Genomes Project to identify cancer susceptibility SNPs within TF-binding sites across human genome. Using TUIFGA, we discovered SNP rs13170300 which located in the TF MAZ binding motif of RPS14. The RPS14 rs13170300 was significantly associated with HCC risk in two case-control sets, with the T allele as the protective allele (Shandong discovery set: TT OR = 0.60, 95% CI = 0.49-0.74, P = 1.0 × 10-6; CT OR = 0.69, 95% CI = 0.55-0.86, P = 0.001; Jiangsu validation set: TT OR = 0.70, 95% CI = 0.56-0.87, P = 0.001; CT OR = 0.65, 95% CI = 0.53-0.82, P = 1.6 × 10-4). SNP rs13170300 affected MAZ binding in the RPS14 promoter, resulting in allele-specific changes in gene expression. RPS14 functions as a novel oncogene in HCC, specifically via activating the AKT signaling. Our findings present important insights into the functional genetics underlying HBV-related HCC development and may contribute to personalized approaches for cancer prevention and novel therapeutics.

Genetic prediction of antihyperglycemic drug targets and risk of epilepsy: a mendelian randomisation study

A connection between diabetes and an increased risk of epilepsy has been suggested by observational studies. Animal studies have also shown that antihyperglycemic drugs can improve seizures. However, it is unclear whether antihyperglycemic drugs have a causal role in epilepsy in humans. To investigate this potential causal relationship, a Mendelian randomisation study was conducted using International League Against Epilepsy data as the discovery set and FinnGen data as the replication set. It was discovered that three antidiabetic drug target genes, ETFDH, CYP21A2 and CYP2D6, were involved in the occurrence of epilepsy. In particular, ETFDH was identified as a target gene in both the discovery set (inverse variance weighting [IVW], odds ratio [OR] = 1.018, 95% confidence interval [CI], 1.004–1.033, p = 0.009) and replication set (IVW, OR = 1.074, 95% CI, 1.034–1.114, p = 0.00016), and CYP21A2 was identified in the discovery set (IVW, OR = 1.029, 95% CI, 1.005–1.053, p = 0.016) and replication set (IVW, OR = 1.057, 95% CI, 1.001–1.116, p = 0.045) as having a causal association with an increased risk of epilepsy. Conversely, the CYP2D6 gene was found to be a protective factor for epilepsy in both the discovery set (IVW, OR = 0.0984, 95% CI, 0.969–0.998, p = 0.025) and replication set (IVW, OR = 0.977, 95% CI, 0.955–1.000, p = 0.046). A search of DrugBank revealed that metformin, an anti-glucose drug, is an inhibitor of the ETFDH gene and may have a potential therapeutic effect on epilepsy.

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.

Data-lndependent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Salivary Biomarkers of Primary Sjogren's Syndrome

Objective As primary Sj?gren's syndrome (pSS) primarily affects the salivary glands, saliva can serve as an indicator of the glands' pathophysiology and the disease's status. This study aims to illustrate the salivary proteomic profiles of pSS patients and identify potential candidate biomarkers for diagnosis.Methods The discovery set contained 49 samples (24 from pSS and 25 from age- and gender-matched healthy controls [HCs]) and the validation set included 25 samples (12 from pSS and 13 from HCs). Totally 36 pSS patients and 38 HCs were centrally randomized into the discovery set or to the validation set at a 2:1 ratio. Unstimulated whole saliva samples from pSS patients and HCs were analyzed using a data-independent acquisition (DIA) strategy on a 2D LC?HRMS/MS platform to reveal differential proteins. The crucial proteins were verified using DIA analysis and annotated using gene ontology (GO) and International Pharmaceutical Abstracts (IPA) analysis. A prediction model for SS was established using random forests.Results A total of 1,963 proteins were discovered, and 136 proteins exhibited differential representation in pSS patients. The bioinformatic research indicated that these proteins were primarily linked to immunological functions, metabolism, and inflammation. A panel of 19 protein biomarkers was identified by ranking order based on P-value and random forest algorichm, and was validated as the predictive biomarkers exhibiting good performance with area under the curve (AUC) of 0.817 for discovery set and 0.882 for validation set.Conclusions The candidate protein panel discovered may aid in pSS diagnosis. Salivary proteomic analysis is a promising non-invasive method for prognostic evaluation and early and precise treatments for pSS patients. DIA offers the best time efficiency and data dependability and may be a suitable option for future research on the salivary proteome.

A-409 - The Dutch Early-Stage Melanoma Study (D-ESMEL): a Discovery Set and Validation Cohort to Predict the Absolute Risk of Distant Metastases in Stage I/II Cutaneous Melanoma

A-409 - The Dutch Early-Stage Melanoma Study (D-ESMEL): a Discovery Set and Validation Cohort to Predict the Absolute Risk of Distant Metastases in Stage I/II Cutaneous Melanoma

SLC11A1 predicts the overall survival of patients with colorectal cancer.

Colorectal cancer (CRC) remains a significant contributor to cancer-related mortality, emphasizing the critical need for identifying biomarkers that can improve clinical management and patient outcomes. In this retrospective study, we analyzed tumor samples from 25 patients with metastatic CRC, categorized based on long-term (> 50 months) or short-term (< 10 months) survival. Employing the PanCancer Immune Profile Panel, encompassing 770 genes, in the discovery dataset, we identified 54 differentially expressed genes (DEGs) within the tumor microenvironment of metastatic CRC. Validation of potential biomarkers was performed using two publicly available RNA-based sequencing datasets (TCGA 1 (n=371) and TCGA 2 (n=566)). Univariate COX regression unveiled that three significant biomarkers were associated with overall survival in CRC within the discovery dataset, which were SLC11A1 (hazard ratio (HR): 4.09, P=0.012), TNFSF11 (HR: 3.67, P=0.02), and MEF2C (HR: 0.34, P=0.037). Kaplan-Meier survival curve analyses confirmed the correlation between SLC11A1 expression and overall survival in CRC across the discovery set (P=0.0071) and the two independent datasets (TCGA 1 (P=0.0016) and TCGA 2 (P=0.025)). Receiver operating characteristic curve analysis demonstrated an area under the curve ranging from 0.64 to 0.76, with sensitivity of 59% to 87% and specificity of 60% to 73% for predicting CRC overall survival. Immunohistochemistry staining further validated the strong expression of SLC11A1 protein in CRC tumor cells, with high expression correlating with short-term survival. These findings suggest that SLC11A1 serves as a predictive biomarker for overall survival in CRC patients.

Expression profile of microRNAs in patients with decompensated cirrhosis by small RNA deep sequencing

Introduction and objectiveDecompensated cirrhosis (DCC) is a more advanced stage of liver cirrhosis (LC). It is important to identify biomarkers to predict DCC progression. The aim of this study was to analyze microRNA (miRNA) profiles of whole blood involved in the DCC process to gain a better understanding of the molecular mechanisms underlying its development. Materials and methodsRNA-Seq analysis of blood samples from a discovery set, including four DCC patients and four LC individuals, was performed to identify differentially expressed miRNAs. The selected differentially expressed miRNAs were validated by using an independent validation set. ResultsIn this study, a total of 1,036 miRNAs were identified in whole blood samples. Forty differentially expressed miRNAs were identified, including 24 upregulated and 16 downregulated miRNAs. The expression levels of three upregulated miRNAs (hsa-miR-20b-5p, hsa-miR-421, and hsa-miR-1307-3p) and two downregulated miRNAs (hsa-miR-139-5p and hsa-miR-150-5p) were validated by quantitative reverse transcriptase polymerase chain reaction. The receiver operator characteristic curve for the logistic regression model based on hsa-miR-20b-5p, hsa-miR-421, and hsa-miR-150-5p could distinguish DCC patients with excellent diagnostic accuracy (area under the curve: 0.981, p < 0.01). ConclusionThe miRNA expression profiles in patients with DCC and LC controls suggested that miR-20b-5p, miR-421, and miR-150-5p could be potential biomarkers and therapeutic targets for this condition.

Endothelial Response to Type I Interferon Contributes to Vasculopathy and Fibrosis and Predicts Disease Progression of Systemic Sclerosis.

Interferon (IFN)-1 signatures are a hallmark of patients with systemic sclerosis (SSc). However, its significance in clinical stratification and contribution to deterioration still need to be better understood. For hypothesis generation, we performed single-cell RNA sequencing (scRNA-seq) on skin biopsies (four patients with SSc and two controls) using the BD Rhapsody platform. Two publicly available data sets of skin scRNA-seq were used for validation (GSE138669: 12 patients with diffuse cutaneous SSc [dcSSc] and 10 controls; GSE195452: 52 patients with dcSSc and 41 patients with limited cutaneous SSc [lcSSc] and 54 controls). The IFN-1 signature was mapped, functionally investigated in a bleomycin plus IFNα-2 adenovirus-associated virus (AAV)-induced model and verified in an SSc cohort (n=61). The discovery and validation data sets showed similar findings. Endothelial cells (ECs) had the most prominent IFN-1 signature among dermal nonimmune cells. The EC IFN-1 signature was increased both in patients with SSc versus controls and in patients with dcSSc versus those with lcSSc. Among EC subclusters, the IFN-1 signature was statistically higher in the capillary ECs of patients with dcSSc, which was higher than those in patients with lcSSc, which in turn was higher than those in healthy controls (HCs). Endothelial-to-mesenchymal transition (EndoMT) scores increased in parallel. Deteriorated bleomycin-induced dermal fibrosis, EndoMT, and perivascular fibrosis and caused blood vessel loss with EC apoptosis. Vascular myxovirus resistance (MX) 1, an IFN-1 response protein, was significantly increased both in total SSc versus HC skin and in dcSSc versus lcSSc skin. Baseline vascular MX1 performed similarly to skin score in predicting disease progression over 6 to 34 months in total SSc and was superior in the dcSSc subpopulation. The EC IFN-1 signature distinguished SSc skin subtypes and disease progression and may contribute to vasculopathy and fibrosis.

Molecular and clinical characterization of ICOS expression in breast cancer through large-scale transcriptome data.

ICOS (Inducible T Cell Costimulator), one of the co-stimulatory B7 superfamily members, was characterized as a co-stimulatory receptor for T-cell enhancement. However, the role of ICOS in breast cancer remains largely unknown. The present study systematically investigated the expression pattern and its relation to clinical characteristics and immunotherapy by integrating multiple clinical cohorts and large-scale gene expression data. This study included 2994 breast tumor samples with transcriptome data and matched clinical data. To make our findings more reliable, we set the TCGA cohort as the discovery set and the METABRIC cohort as the validation set. The expression of ICOS in breast cancer is strongly associated with major clinical and molecular characteristics. There is an association between higher ICOS expression and malignant subtypes and grades of tumors. In addition, gene ontology analysis based on genes significantly correlated with ICOS expression indicated that the expression of ICOS is mainly associated with immune responses and inflammation. We also observed strong correlations between ICOS and other promising immune-checkpoint molecules, including PD1, PDL1, CTLA4, and IDO1. Furthermore, we found that ICOS expression is associated with the response to anti-PDL1 immunotherapy and may serve as a biomarker for immunotherapy prediction. Our results indicated higher ICOS expression is significantly associated with favorable survival in triple-negative breast cancer (TNBC) patients, but not for all subtypes of breast cancer patients. In summary, ICOS correlates with higher malignant breast cancers, and it contributes to the regulation of the immune microenvironment of breast tumors, making it a potential biomarker and immunotherapy target.

Deep Brain Stimulation for Obsessive-Compulsive Disorder: Optimal Stimulation Sites

BackgroundDeep brain stimulation (DBS) is a promising treatment option for treatment-refractory obsessive-compulsive disorder (OCD). Several stimulation targets have been used, mostly in and around the anterior limb of the internal capsule and ventral striatum. However, the precise target within this region remains a matter of debate. MethodsHere, we retrospectively studied a multicenter cohort of 82 patients with OCD who underwent DBS of the ventral capsule/ventral striatum and mapped optimal stimulation sites in this region. ResultsDBS sweet-spot mapping performed on a discovery set of 58 patients revealed 2 optimal stimulation sites associated with improvements on the Yale-Brown Obsessive Compulsive Scale, one in the anterior limb of the internal capsule that overlapped with a previously identified OCD–DBS response tract and one in the region of the inferior thalamic peduncle and bed nucleus of the stria terminalis. Critically, the nucleus accumbens proper and anterior commissure were associated with beneficial but suboptimal clinical improvements. Moreover, overlap with the resulting sweet- and sour-spots significantly estimated variance in outcomes in an independent cohort of 22 patients from 2 additional DBS centers. Finally, beyond obsessive-compulsive symptoms, stimulation of the anterior site was associated with optimal outcomes for both depression and anxiety, while the posterior site was only associated with improvements in depression. ConclusionsOur results suggest how to refine targeting of DBS in OCD and may be helpful in guiding DBS programming in existing patients.

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics.

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Real-world application of PBPK in drug discovery.

The utility of PBPK models in support of drug development has been well documented. During the discovery stage, PBPK has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection and early formulation assessment. Previous review articles have proposed model building and application strategies for PBPK-based first in human predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees, based on comprehensive literature reviews, to guide the application of PBPK in the discovery setting. The goal of this mini review is to provide additional guidance on the real-world application of PBPK, in support of the discovery stage of drug development. In this mini review, our goal is to provide guidance on the typical steps involved in the development and application of a PBPK model during drug discovery to assist in decision making. We have illustrated our recommended approach through description of case examples, where PBPK has been successfully applied to aid in human PK projection, candidate selection and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including pre-verification in preclinical species, the application of empirical scalars in the prediction of in vivo clearance from in vitro systems, in silico prediction of permeability and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. Significance Statement Through description of three case studies, we have highlighted the fundamental principles of PBPK application during drug discovery. These include pre-verification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed.