Independent Sample Set Research Articles

Untargeted metabolomics-based approach using UHPLC-HRMS to authenticate carrots (Daucus carota L.) based on geographical origin and production mode

Carrots produced in different agricultural regions with organic or conventional mode were analyzed by untargeted UHPLC-HRMS using reversed-phase and HILIC modes. Data were first treated separately, and further combined to possibly improve results. An in-house data processing workflow was applied to identify relevant features after peak detection. Based on these features, discrimination models were built using chemometrics. A tentative annotation of chemical markers was performed using online databases and UHPLC-HRMS/MS analyses. An independent set of samples was analyzed to assess the discrimination potential of these markers. Carrots produced in the New Aquitaine region could be successfully discriminated from carrots originating from the Normandy region by an OLPS-DA model. Arginine and 6-methoxymellein could be identified as potential markers with the C18-silica column. Additional markers (N-acetylputrescine, l-carnitine) could be identified thanks to the polar column. Discrimination based on production mode was more challenging: some trend was observed but model metrics remained unsatisfactory.

Use of near-infrared reflectance spectroscopy on feces to estimate digestibility and dry matter intake of dietary nutritional characteristics under grazing conditions in Colombian creole steers.

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Patient outcomes after neonatal tracheotomy: A retrospective case-control study.

Patient outcomes after neonatal tracheotomy: A retrospective case-control study.

Abstract 3744: A circulating microRNA panel predicts recurrence and survival in early-stage lung adenocarcinoma

Abstract Background: Early-stage lung adenocarcinoma (LUAD) patients have substantial risk for recurrence and disease-related death. Cisplatin-based adjuvant chemotherapy remains the standard for care of LUAD patients who have undergone surgical resection with a high risk of recurrence. However, adjuvant chemotherapy is associated with increased risk of toxicity including chemotherapy-related death, with only a modest survival benefit. Therefore, there is an unmet need of biomarkers for assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. Circulating miRNAs are stably present in blood and potentially reflect different expressions in cancerous and non-cancerous tissues, making them attractive biomarkers. The purpose of this study was to identify circulating miRNAs useful for predicting recurrence in early-stage LUAD. Materials and Methods: miRNA microarray analysis was performed with pooled pretreatment plasma samples from stage I LUAD patients who developed recurrence within two years after curative surgery or remained recurrence free over a six-year follow-up period, as well as from healthy controls. miRNA biomarker candidates were assayed in two independent plasma sample sets from 85 stage I LUAD (validation set) and from 57 stage I and II LUAD (test set) patients. Results: Based on miRNA microarray data and previous reports, predictive performance of miR-23a-3p, miR-23b-3p, miR-191-5p, miR-185-5p, miR-151a-3p, miR-320c, miR-21-5p, miR-125b-5p, miR-30d-5p, and miR-197-3p was evaluated in the validation set. Plasma levels of miR-23a-3p, miR-185-5p, miR-320c, miR-21-5p, miR-125b-5p, miR-30d-5p, and miR-197-3p were significantly higher in those with recurrence as compared to those without. A miRNA panel comprised of miR-23a-3p, miR-320c, and miR-125b-5p was developed based on a logistic regression, with yielding an AUC of 0.776 (95% confidence interval [CI] = 0.660 to 0.893). The three-miRNA panel with fixed coefficients yielded an AUC of 0.804 (95% CI = 0.688 to 0.920) with a sensitivity of 45.8% at 95% specificity in the test set. The miRNA panel score was a significant and independent factor for predicting disease-free survival (DFS; P < 0.001, HR = 1.64, 95% CI = 1.51-4.22) and overall survival (OS; P = 0.001, HR = 1.51, 95% CI = 1.17-1.94). Conclusion: This circulating miRNA panel may serve as a noninvasive blood test for predicting DFS and OS in early-stage LUAD patients. Our findings provide rationale for further investigation to stratify early-stage LUAD patients using blood-based biomarkers to increase the ability to provide more personalized care. Citation Format: Mei-Chee Tai, Leonidas E. Bantis, Gargy Parhy, Taketo Kato, Ichidai Tanaka, Chi-Wan Chow, Junya Fujimoto, Carmen Behrens, Tetsunari Hase, Koji Kawaguchi, Johannes F. Fahrmann, Edwin Ostrin, Kohei Yokoi, Toyofumi F. Chen-Yoshikawa, Yoshinori Hasegawa, Samir M. Hanash, Ignacio I. Wistuba, Ayumu Taguchi. A circulating microRNA panel predicts recurrence and survival in early-stage lung adenocarcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3744.

Testing the hypothesis of the normality of the falling number of oatmeal in small samples

Verification of the hypothesis of the normality of small samples is required to establish whether the empirical distribution obtained belongs to the theoretical distribution. The condition for testing the hypothesis of normality for a set of small independent samples is the presence of a sufficient number of them with the same volume. In this case, it is possible to test the hypothesis of the normality of the general aggregates from which the studied samples were taken, assuming that the parameters of these aggregates have different values. When testing the hypothesis of normality for a large number of small samples, only one value of the first, second, etc. measurements is randomly selected from each sample, thereby allowing simplification and random selection of data. The object of this study is small samples of the falling number of oat flour used in bakery production in the development of bakery products. The purpose of this work was to test the hypothesis of normality for small samples of the experiment using the nonparametric criterion of agreement ω2 of the smallest of each of the four definitions of the incidence number, since rounding the values of direct measurements excludes the random nature of the quantity or its normal distribution in favor of a uniform one. It was found that at a significance level of p = 0?05, the table value (nω2)1-p is greater than the calculated value of nω2 for all four definitions, hence the hypothesis of the normal distribution of small samples for all four definitions (as random variables) of the falling number of oatmeal does not deviate. The results obtained in this work are consistent with the generally accepted classical concepts of testing the statistical hypothesis of the normal distribution of samples. The statistical method provides sufficient accuracy of the studied indicator in technical systems and does not require the synthesis of a statistical criterion to test the hypothesis of the normality of small sample.

DNA Methylation of Window of Implantation Genes in Cervical Secretions Predicts Ongoing Pregnancy in Infertility Treatment

Window of implantation (WOI) genes have been comprehensively identified at the single cell level. DNA methylation changes in cervical secretions are associated with in vitro fertilization embryo transfer (IVF-ET) outcomes. Using a machine learning (ML) approach, we aimed to determine which methylation changes in WOI genes from cervical secretions best predict ongoing pregnancy during embryo transfer. A total of 2708 promoter probes were extracted from mid-secretory phase cervical secretion methylomic profiles for 158 WOI genes, and 152 differentially methylated probes (DMPs) were selected. Fifteen DMPs in 14 genes (BMP2, CTSA, DEFB1, GRN, MTF1, SERPINE1, SERPINE2, SFRP1, STAT3, TAGLN2, TCF4, THBS1, ZBTB20, ZNF292) were identified as the most relevant to ongoing pregnancy status. These 15 DMPs yielded accuracy rates of 83.53%, 85.26%, 85.78%, and 76.44%, and areas under the receiver operating characteristic curves (AUCs) of 0.90, 0.91, 0.89, and 0.86 for prediction by random forest (RF), naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (KNN), respectively. SERPINE1, SERPINE2, and TAGLN2 maintained their methylation difference trends in an independent set of cervical secretion samples, resulting in accuracy rates of 71.46%, 80.06%, 80.72%, and 80.68%, and AUCs of 0.79, 0.84, 0.83, and 0.82 for prediction by RF, NB, SVM, and KNN, respectively. Our findings demonstrate that methylation changes in WOI genes detected noninvasively from cervical secretions are potential markers for predicting IVF-ET outcomes. Further studies of cervical secretion of DNA methylation markers may provide a novel approach for precision embryo transfer.

Contributions of NR1H3 genetic polymorphisms to susceptibility and effects of narrowband UVB phototherapy to nonsegmental vitiligo

Vitiligo is the most common depigmenting disorder to which both genetic and environmental factors contribute. The aim of the current work was to evaluate the relationship between polymorphisms of the gene nuclear receptor subfamily 1 Group H member 3 (NR1H3) and the risk of vitiligo and phototherapy effects in the Chinese Han population. Two independent samples were enrolled to form the discovery set (comprised of 1668 nonsegmental vitiligo [NSV] patients and 2542 controls) and the validation set (comprised of 745 NSV patients and 1492 controls). A total of 13 tag single nucleotide polymorphisms (SNPs) were genotyped in the samples from the discovery stage. SNPs that achieved nominal significance were validated in another independent sample set. The serum level of NR1H3 protein was assayed using enzyme-linked immunosorbent assay kits in the validation set. Genetic association analysis was carried out at allelic and genotypic levels. The therapeutic effects of significant SNPs were examined in the validation set. The SNP rs3758672 was significantly associated with NSV. The A allele was correlated with NSV risk and poorer therapeutic effects. The A allele was strongly correlated with the increased level of serum NR1H3 in both controls and patients. In summary, SNP rs3758672 in NR1H3 was significantly associated with both disease susceptibility and individualized therapeutic effects of NSV in study participants with Han Chinese ancestry.

Development and validation of a multigenomic liquid biopsy (PROSTest) for prostate cancer detection.

252 Background: A crucial requirement in prostate cancer (PCa) management is an accurate, easily measurable, liquid biopsy that can define the molecular pathology of an individual PCa. We report the development and clinical validation of a novel PCa-specific, multi-genomic biomarker. Methods: We identified candidate mRNA biomarkers in PCa-Adeno transcriptomes ( n=1,159) using several strategies: co-expression networks, differential expression, and functional enrichment. mRNA transcripts were screened in an independent tumor tissue ( n=50) set and validated as biomarkers in the TCGA-PRAD ( n=500) dataset. An amalgam of Random Forest, Gradient Boosted Machines and Support Vector Machines, all standard machine learning classifiers, was used to develop a classification algorithm and probability score in a peripheral blood gene expression test cohort ( n=430). This multigene biomarker was validated in two independent clinical blood sample sets (Set I: PCa n=77, controls n=54; Set II: PCa n=132, controls/BPH n=99) to determine as PCa-specificity and diagnostic efficacy Clinical utility was evaluated versus Gleason scores, T-staging and PSA ( n=209) and in a prostatectomy cohort ( n=47). Results: The pipeline identified 27 of PCa gene markers in the tumor tissue set and TCGA-PRAD dataset. Gene expression was significantly correlated ( r=0.72, p<0.0001) in matched tissue/blood samples. The PROSTest (scale: 0-100) ensemble algorithm (developed in blood) had a sensitivity for PCa of 92.2% (95% CI: 83.8-97.1%; Set I) and 95.0% (95% CI: 89.9-98%. Set II). The specificity was 100% for Set I (95% CI: 93.4-100%) and 100% for Set II (95% CI: 96.3-100%). PCa scores were significantly ( p<0.0001) lower for controls (Set I: 17±4; Set II: 18±4) and BPH (19±6) to PCa; 82±19 (Set I) and 80±19 (Set II). The AUROC was 0.98±0.01. PROSTest scores were elevated ( p<0.05) in T2-4 and were significantly correlated with Gleason ( r=0.93, p<0.02). In contrast, PSA from matched samples was not associated ( p=NS) with clinically significant disease (Gleason 7-10 or T2-4 tumors). In head-to-head comparisons, the PROSTest was considerably more accurate than PSA for detecting significant disease (z-statistic: 2.43, p=0.015). In the R0 prostatectomy cohort, all scores were elevated (72±7) and significantly decreased post-surgery (26±8, p<0.0001, n=37). Individuals with residual disease ( n=10) exhibited elevated (60±4) post-surgical scores. Conclusions: The PROSTEst is a multigenomic blood-based PCR tool that accurately (>90%) identifies prostate cancer. It is significantly more accurate than PSA for the detection and stratification of clinically significant prostate disease. A multigenomic liquid biopsy for PCA provides a real-time, non-invasive method for detection of a PCa and may facilitate the early identification of residual/recurrent disease.

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.

Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologicmethods. Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.

Soybean sorting based on protein content using X-ray fluorescence spectrometry

The purpose of this research was to evaluate performance of an energy-dispersive X-ray fluorescence (XRF) sensor to classify soybean based on protein content. The hypothesis was that sulfur signals and other XRF spectral features can be used as proxies to infer soybean protein content. Sample preparation and equipment settings to optimize detection of S and other specific emission lines were tested for this application. A logistic regression model for classifying soybean as high- or low-protein was developed based on XRF spectra and protein contents. Additionally, the model was validated with an independent set of samples. Global accuracies of the method were 0.83 (training set) and 0.81 (test set) and the corresponding kappa indices were 0.66 and 0.61, respectively. These numbers indicated satisfactory performance of the sensor, suggesting that XRF spectral features can be applied for screening protein content in soybean.

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models.

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness-lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the "CST2 + OCLN + pT" model (AUC = 0.863) based on the CatBoost Classifier algorithm.

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BackgroundLow-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.MethodsSingle cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.ResultsAutomated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83–0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89–0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71–0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.ConclusionCyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis.Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Intraoperative plasma proteomic changes in cardiac surgery: In search of biomarkers of post-operative delirium.

Delirium presents a significant healthcare burden. It complicates post-operative care in up to 50% of cardiac surgical patients with worse outcomes, longer hospital stays and higher cost of care. Moreover, the nature of delirium following cardiac surgery with cardiopulmonary bypass (CPB) remains unclear, the underlying pathobiology is poorly understood, status quo diagnostic methods are subjective, and diagnostic biomarkers are currently lacking. To identify diagnostic biomarkers of delirium and for insights into possible neuronal pathomechanisms. Comparative proteomic analyses were performed on plasma samples from a nested matched cohort of patients who underwent cardiac surgery. Validation by targeted proteomics was performed in an independent set of samples. Biomarkers were assessed for biological functions and diagnostic accuracy. Forty-seven percent of subjects demonstrated delirium. Of 3803 proteins identified from patient samples by multiplexed quantitative proteomics, 16 were identified as signatures of exposure to CPB, and 11 biomarkers distinguished delirium cases from non-cases (AuROC=93%). Notable among these biomarkers are C-reactive protein, serum amyloid A-1 and cathepsin-B. The interplay of systemic and central inflammatory markers sheds new light on delirium pathogenesis. This work suggests that accurate identification of cases may be achievable using panels of biomarkers.

Development of a Machine Learning Algorithm for Drug Screening Analysis on High-Resolution UPLC-MSE/QTOF Mass Spectrometry.

Ultra-performance liquid chromatography (UPLC)-MSE/quadrupole time-of-flight (QTOF) high-resolution mass spectrometry employs untargeted, data-independent acquisition in a dual mode that simultaneously collects precursor ions and product ions at low and ramped collision energies, respectively. However, algorithmic analysis of large-scale multivariate data of comprehensive drug screening as well as the positivity criteria of drug identification have not been systematically investigated. It is also unclear whether ion ratio (IR), the intensity ratio of a defined product ion divided by the precursor ion, is a stable parameter that can be incorporated into the MSE/QTOF data analysis algorithm. IR of 91 drugs were experimentally determined and variation of IR was investigated across 5 concentrations measured on 3 different days. A data-driven machine learning approach was employed to develop multivariate linear regression (MLR) models incorporating mass error, retention time, number of detected fragment ions and IR, accuracy of isotope abundance, and peak response using drug-supplemented urine samples. Performance of the models was evaluated in an independent data set of unknown clinical urine samples in comparison with the results of manual analysis. IR of most compounds acquired by MSE/QTOF were low and concentration-dependent (i.e., IR increased at higher concentrations). We developed an MLR model with composite score outputs incorporating 7 parameters to predict positive drug identification. The model achieved a mean accuracy of 89.38% in the validation set and 87.92% agreement in the test set. The MLR model incorporating all contributing parameters can serve as a decision-support tool to facilitate objective drug identification using UPLC-MSE/QTOF.

Identification of adolescent patients with depression via assessment of the niacin skin flushing response

BackgroundDepressive disorder (DD) affects approximately 20 % of adolescents worldwide, but it is underdiagnosed due to the lack of objective biomarkers. Niacin skin flushing response (NSFR) is an objective and noninvasive biomarker of adult depression; however, its effectiveness has not been assessed in adolescents. MethodsThis study included 198 adolescents with 50 % healthy controls (HC). Linear mixed-effects model and multiple linear regression analyses were performed to assess differences in NSFR between the DD and HC groups. Logistic regression models based on NSFR were constructed, and the area under curve (AUC) was calculated to evaluate the performance of models. Spearman correlations were calculated to assess the relationships between NSFR and disease duration and hormone levels associated with puberty. ResultsAdolescents with DD displayed significantly attenuated and delayed NSFR compared to HC. NSFR effectively distinguished DD patients from HC with AUC values of 0.719 (sensitivity = 0.844) and 0.721 (sensitivity = 0.829) determined in the discovery and validation sets, respectively. Within the DD group, the maximum degree of NSFR was negatively correlated with the disease duration (r = −0.28, p = 0.011), and the overall degree of NSFR was positively associated with prolactin (r = 0.29, p = 0.039) and thyroxine (r = 0.29, p = 0.027) levels. LimitationsFuture investigations will be necessary to confirm our results in an independent sample set. ConclusionsThis study provides the first evidence of the utility of NSFR as an objective auxiliary diagnostic biomarker for adolescent depression. It provides new clues to understand the pathophysiology of the disease, and helps promote precise diagnosis, treatment, and prognostic evaluation of adolescent depression.

Abstract PR007: Candidate antigens for a ductal carcinoma in situ vaccine, essential for breast cancer cell survival across multiple subtypes, are immunogenic in DCIS and IBC

Abstract Vaccine therapy may to destroy the current ductal carcinoma in situ (DCIS), prevent recurrence, and have little risk of long-term complications. One of the limitations for a DCIS vaccine is identifying appropriate antigens which, when targeted, may be able to treat DCIS and prevent development of IBC. In this study, we identified proteins that were overexpressed in both DCIS and IBC tumors across fifteen human Geo and Array Express data sets. From the 68 candidate proteins, twelve proteins expression was necessary for cancer cell survival across breast cancer subtypes using a high throughput siRNA screen. We chose candidates that increased apoptosis and decreased cell survival in human HER2 positive (HER2), triple negative (TN), and hormone receptor positive HER2 negative (HR) human breast cancer cell lines with decreased expression of the target protein. Twelve proteins (AURKA, KIF11, NDC80, RRM2, SDC1, UBE2C, HJURP, CENPA, CENPF, HIST2H2AA3, KRT8, and TOP2A) were the twelve antigens developed for the vaccine. All twelve targets were immunogenic in women with DCIS and IBC as compared to age matched control women. For example, autoantibodies to four of the antigens (AURKA, NDC80, KRT8, and RRM2) predicted women with DCIS with AUC 0.69 (p=0.005 95% CI 0.56 to 0.80) and IBC with AUC 0.79 (p&amp;lt;0.001 95% CI 0.66 to 0.90) as compared to age matched control women in a discovery set of 59 DCIS patients, 37 IBC patients, and 43 control women. In an independent validation set of serum samples of 50 DCIS, 60 IBC, and 50 control patients, autoantibodies to the four candidate antigens could predict women with DCIS with AUC 0.62 (p=0.04, 95% CI 0.51 to 0.73) and IBC with AUC 0.69 (p&amp;lt;0.001, 95% CI 0.60 to 0.79). Autoantibodies to the four DCIS antigens could identify each subtype of breast cancer subtype as compared to control, HR+(n=20 patients) with AUC 0.69 (p=0.01, 95% CI 0.55 to 0.83), HER2+ (n=20 patients) with AUC 0.70 (p=0.01, 95% CI 0.55 to 0.84), and TN (n=20 patients) with AUC 0.74 (p=0.002, 95% CI 0.61 to 0.87). All these were IgG autoantibodies suggest pre-existing T cells that recognize the candidate antigens because Ig class switching from IgM to IgG requires antigen-specific T cells. These twelve candidate antigens, overexpressed in human breast cancer, are also overexpressed in the TgMMTV-neu and C3(1)Tag transgenic mouse mammary tumor models of breast cancer. Vaccination with human MHC Class II predicted epitopes from KRT8, conserved between mouse and human, inhibited tumor growth in TgMMTV-neu mice by 47% and C3(1)Tag by 62%. The KRT8- specific IFN-g immune response developed was associated with lower tumor volume with mice vaccinated with the human MHC Class II Th1 KRT8 epitopes with the KRT8-specific IFN-g immune response associated with smaller tumors in TgMMTV-neu mice (R2= 0.64, p=0.02). We plan to develop these antigens in a multi-antigen DCIS vaccine that may be able to destroy DCIS and prevent breast cancer across all breast cancer subtypes. Citation Format: Sasha E. Stanton, Jason Schlumbohm, Mary L. Disis. Candidate antigens for a ductal carcinoma in situ vaccine, essential for breast cancer cell survival across multiple subtypes, are immunogenic in DCIS and IBC [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr PR007.

Bone-Metabolism-Related Serum microRNAs to Diagnose Osteoporosis in Middle-Aged and Elderly Women.

Objective: Postmenopausal osteoporosis (PMOP), a chronic systemic metabolic disease prevalent in middle-aged and elderly women, heavily relies on bone mineral density (BMD) measurement as the diagnostic indicator. In this study, we investigated serum microRNAs (miRNAs) as a possible screening tool for PMOP. Methods: This investigation recruited 83 eligible participants from 795 community-dwelling postmenopausal women between June 2020 and August 2021. The miRNA expression profiles in the serum of PMOP patients were evaluated via miRNA microarray (six PMOP patients and four postmenopausal women without osteoporosis (n-PMOP) as controls). Subsequently, results were verified in independent sample sets (47 PMOP patients and 26 n-PMOP controls) using quantitative real-time PCR. In addition, the target genes and main functions of the differentially expressed miRNAs were explored by bioinformatics analysis. Results: Four highly expressed miRNAs in the serum of patients (hsa-miR-144-5p, hsa-miR-506-3p, hsa-miR-8068, and hsa-miR-6851-3p) showed acceptable disease-independent discrimination performance (area under the curve range: 0.747-0.902) in the training set and verification set, outperforming traditional bone turnover markers. Among four key miRNAs, hsa-miR-144-5p is the only one that can simultaneously predict changes in BMD in lumbar spine 1-4, total hip, and femoral neck (β = -0.265, p = 0.022; β = -0.301, p = 0.005; and β = -0.324, p = 0.003, respectively). Bioinformatics analysis suggested that the differentially expressed miRNAs were targeted mainly to YY1, VIM, and YWHAE genes, which are extensively involved in bone metabolism processes. Conclusions: Bone-metabolism-related serum miRNAs, such as hsa-miR-144-5p, hsa-miR-506-3p, hsa-miR-8068, and hsa-miR-6851-3p, can be used as novel biomarkers for PMOP diagnosis independent of radiological findings and traditional bone turnover markers. Further study of these miRNAs and their target genes may provide new insights into the epigenetic regulatory mechanisms of the onset and progression of the disease.

DNA methylation biomarkers for noninvasive detection of triple-negative breast cancer using liquid biopsy.

Noninvasive detection of aberrant DNA methylation could provide invaluable biomarkers for earlier detection of triple-negative breast cancer (TNBC) which could help clinicians with easier and more efficient treatment options. We evaluated genome-wide DNA methylation data derived from TNBC and normal breast tissues, peripheral blood of TNBC cases and controls and reference samples of sorted blood and mammary cells. Differentially methylated regions (DMRs) between TNBC and normal breast tissues were stringently selected, verified and externally validated. A machine-learning algorithm was applied to select the top DMRs, which then were evaluated on plasma-derived circulating cell-free DNA (cfDNA) samples of TNBC patients and healthy controls. We identified 23 DMRs accounting for the methylation profile of blood cells and reference mammary cells and then selected six top DMRs for cfDNA analysis. We quantified un-/methylated copies of these DMRs by droplet digital PCR analysis in a plasma test set from TNBC patients and healthy controls and confirmed our findings obtained on tissues. Differential cfDNA methylation was confirmed in an independent validation set of plasma samples. A methylation score combining signatures of the top three DMRs overlapping with the SPAG6, LINC10606 and TBCD/ZNF750 genes had the best capability to discriminate TNBC patients from controls (AUC=0.78 in the test set and AUC=0.74 in validation set). Our findings demonstrate the usefulness of cfDNA-based methylation signatures as noninvasive liquid biopsy markers for the diagnosis of TNBC.

Establishment of evaluation criterion based on starch dyeing method and implementation of optical and acoustic techniques for postharvest determination of “HongYang” kiwifruit ripeness

Realizing online detection of ripeness is meaningful and profitable in existing postharvest sorting system. This paper aims to establish evaluation criterion of kiwifruit ripeness based on starch dyeing method and realize online detection of kiwifruit ripeness based on spectroscopy and acoustic vibration. Considering storage time, flavor, and edible status of kiwifruit, evaluation criterion of kiwifruit ripeness was established based on starch conversion rate obtained from starch staining images. Differences were found in quality attributes (i.e., SSC and firmness), microstructure, absorbance spectra, and frequency-domain signals of kiwifruit at different ripeness levels. Based on spectral and acoustic vibration data collected by self-designed online systems, ripeness determination models were established. Linear PLSDA model performed better in spectral classification, while nonlinear SVM model performed better in acoustic vibration classification. Deep 1D-CNN models were proposed to further improve performance of ripeness determination models. Further novelty of this work was that it externally tested robustness of ripeness determination models using independent sample set with biological variability. For new batch of samples, overall accuracy of ripeness determination model based on spectral data (Accuracy = 93.08%) was slightly better than that of ripeness determination model based on acoustic vibration data (Accuracy = 92.31%). External test results showed that recognition performance of deep 1D-CNN model based on spectral data and deep 1D-CNN model based on acoustic vibration data was satisfactory. Deep 1D-CNN models proposed in this study can be used for determination of kiwifruit at different ripeness levels and have strong practicability in commercial online sorting.