Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer. METHOD: First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy. RESULTS: The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response. CONCLUSION: These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

[Comparison of two superparamagnetic purification magnetic beads-based screening and enrichment techniques for isolating cell-free fetal DNA from maternal plasma for non-invasive prenatal screening].

OBJECTIVE: To assess the efficiency of modified enrichment method for cell-free fetal DNA (cffDNA) through purified superparamagnetic beads during non-invasive prenatal testing (NIPT). METHODS: A total of 26 252 pregnant women undergoing NIPT at the Maternal and Child Health Care Hospital of Haidian District from December 2017 to September 2022 were recruited and randomly assigned into the conventional group (n = 10 573) and the modified enrichment group (n = 15 679), who were then subjected to the screening and enrichment of the cffDNA using a conventional and a modified technique, respectively. High-risk pregnant women detected by NIPT were subjected to invasive prenatal diagnosis. All women were followed up for their pregnancy outcomes, and the detection efficacy of the two methods was compared in terms of fragment size, concentration of cffDNA, duplicate detection rate, and indices of clinical laboratory tests. RESULTS: The fragment size of the main peak of the cell-free DNA library of the modified enrichment group was significantly lower than that of the conventional group [267 (264, 269) bp vs. 294 (292, 296) bp, P < 0.01], while the concentration of cffDNA was significantly higher [21.86% (17.61%, 26.36%) vs. 9.08% (6.87%, 11.87%), P < 0.01]. In addition, the duplicate detection rate (0.740% vs. 2.02%, X2 = 83.90, P < 0.01) and detection failure rate (0.006% vs. 0.057%, P < 0.05) in the modified enrichment group were significantly lower than those of the conventional group. The combined positive predictive value (PPV) in both high-risk (64.3% vs. 76.1%) and low-risk (35.3% vs. 45.5%) pregnant women from the modified enrichment group was slightly lower than those from the conventional group, though no significant difference was detected. There was one false negative case for trisomy 21 among the high-risk pregnant women from the conventional group, and no false negative case was found in the modified enrichment group. CONCLUSION: The modified technique to screen and enrich the cffDNA has significantly enhanced the relative concentration of cffDNA and reduced the failure and duplication detection rate of NIPT, which has significantly reduced the incidence of false negative cases due to the low concentration of cffDNA, and greatly increased the overall detection efficacy of NIPT.

A dual-gene panel of two fragments of methylated IRF4 and one of ZEB2 in plasma cell-free DNA for gastric cancer detection.

Early detection is crucial for increasing the survival rate of gastric cancer (GC). We aimed to identify a methylated cell-free DNA (cfDNA) marker panel for detecting GC. The differentially methylated CpGs (DMCs) were selected from datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The selected DMCs were validated and further selected in tissue samples (40 gastric cancer and 36 healthy white blood cell samples) and in a quarter sample volume of plasma samples (37 gastric cancer, 12 benign gastric disease, and 43 healthy individuals). The marker combination selected was then evaluated in a normal sample volume of plasma samples (35 gastric cancer, 39 control diseases, and 40 healthy individuals) using real-time methylation-specific PCR (MSP). The analysis of the results compared methods based on 2-ΔΔCt values and Ct values. In the results, 30 DMCs were selected through bioinformatics methods, and then 5 were selected for biological validation. The marker combination of two fragments of IRF4 (IRF4-1 and IRF4-2) and one of ZEB2 was selected due to its good performance. The Ct-based method was selected for its good results and practical advantages. The assay, IRF4-1 and IRF4-2 in one fluorescence channel and ZEB2 in another, obtained 74.3% sensitivity for the GC group at any stage, at 92.4% specificity. In conclusion, the panel of IRF4 and ZEB2 in plasma cfDNA demonstrates good diagnostic performance and application potential in clinical settings.

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

PURPOSE: Simultaneous profiling of cell-free DNA (cfDNA) methylation and fragmentation features to improve the performance of cfDNA-based cancer detection is technically challenging. We developed a method to comprehensively analyze multimodal cfDNA genomic features for more sensitive esophageal squamous cell carcinoma (ESCC) detection. MATERIALS AND METHODS: Enzymatic conversion-mediated whole-methylome sequencing was applied to plasma cfDNA samples extracted from 168 patients with ESCC and 251 noncancer controls. ESCC characteristic cfDNA methylation, fragmentation, and copy number signatures were analyzed both across the genome and at accessible cis-regulatory DNA elements. To distinguish ESCC from noncancer samples, a first-layer classifier was developed for each feature type, the prediction results of which were incorporated to construct the second-layer ensemble model. RESULTS: ESCC plasma genome displayed global hypomethylation, altered fragmentation size, and chromosomal copy number alteration. Methylation and fragmentation changes at cancer tissue-specific accessible cis-regulatory DNA elements were also observed in ESCC plasma. By integrating multimodal genomic features for ESCC detection, the ensemble model showed improved performance over individual modalities. In the training cohort with a specificity of 99.2%, the detection sensitivity was 81.0% for all stages and 70.0% for stage 0-II. Consistent performance was observed in the test cohort with a specificity of 98.4%, an all-stage sensitivity of 79.8%, and a stage 0-II sensitivity of 69.0%. The performance of the classifier was associated with the disease stage, irrespective of clinical covariates. CONCLUSION: This study comprehensively profiles the epigenomic landscape of ESCC plasma and provides a novel noninvasive and sensitive ESCC detection approach with genome-scale multimodal analysis.

Casting a wider net: The clinical potential of EV transcriptomics in multi-analyte liquid biopsy.

Cancer cells release cell-free DNA (cfDNA) and extracellular vesicles (EVs) into the bloodstream, allowing disease non-invasive monitoring. In this issue of Cancer Cell, Casanova-Salas et al. analyze cfDNA, EV-DNA, and EV-RNA in prostate cancer longitudinal cohorts treated with androgen receptor signaling inhibitors and taxanes, identifying signals reflecting tumor adaptation processes.

Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid.

Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

European Society for Organ Transplantation (ESOT) Consensus Statement on the Use of Non-invasive Biomarkers for Cardiothoracic Transplant Rejection Surveillance.

While allograft rejection (AR) continues to threaten the success of cardiothoracic transplantation, lack of accurate and repeatable surveillance tools to diagnose AR is a major unmet need in the clinical management of cardiothoracic transplant recipients. Endomyocardial biopsy (EMB) and transbronchial biopsy (TBBx) have been the cornerstone of rejection monitoring since the field's incipience, but both suffer from significant limitations, including poor concordance of biopsy interpretation among pathologists. In recent years, novel molecular tools for AR monitoring have emerged and their performance characteristics have been evaluated in multiple studies. An international working group convened by ESOT has reviewed the existing literature and provides a series of recommendations to guide the use of these biomarkers in clinical practice. While acknowledging some caveats, the group recognized that Gene-expression profiling and donor-derived cell-free DNA (dd-cfDNA) may be used to rule out rejection in heart transplant recipients, but they are not recommended for cardiac allograft vasculopathy screening. Other traditional biomarkers (NT-proBNP, BNP or troponin) do not have sufficient evidence to support their use to diagnose AR. Regarding lung transplant, dd-cfDNA could be used to rule out clinical rejection and infection, but its use to monitor treatment response is not recommended.

Cell free DNA in patients with pancreatic adenocarcinoma: clinicopathologic correlations.

Detection of circulating tumor DNA (ctDNA) from plasma cell free DNA (cfDNA) has shown promise for diagnosis, therapeutic targeting, and prognosis. This study explores ctDNA detection by next generation sequencing (NGS) and associated clinicopathologic factors in patients with pancreatic adenocarcinoma (PDAC). Patients undergoing surgical exploration or resection of pancreatic lesions were enrolled with informed consent. Plasma samples (4-6 ml) were collected prior to surgery and cfDNA was recovered from 95 plasma samples. Adequate cfDNA for NGS (20 ng) was obtained from 81 patients. NGS was performed using the Oncomine Lung cfDNA assay on the Ion Torrent S5 sequencing platform. Twenty-five patients (30.9%) had detectable mutations in KRAS and/or TP53 with allele frequencies ranging from 0.05 to 8.5%, while mutations in other genes were detected less frequently and always along with KRAS or TP53. Detectable ctDNA mutations were more frequent in patients with poorly differentiated tumors, and patients without detectable ctDNA mutations showed longer survival (medians of 10.5 months vs. 18 months, p = 0.019). The detection of circulating tumor DNA in pancreatic adenocarcinomas is correlated with worse survival outcomes.

Detection of minimal residual disease in acute myeloid leukemia: evaluating utility and challenges.

This study discusses the importance of minimal residual disease (MRD) detection in acute myeloid leukemia (AML) patients using liquid biopsy and next-generation sequencing (NGS). AML prognosis is based on various factors, including genetic alterations. NGS has revealed the molecular complexity of AML and helped refine risk stratification and personalized therapies. The long-term survival rates for AML patients are low, and MRD assessment is crucial in predicting prognosis. Currently, the most common methods for MRD detection are flow cytometry and quantitative PCR, but NGS is being incorporated into clinical practice due to its ability to detect genomic aberrations in the majority of AML patients. Typically, bone marrow samples are used for MRD assessment, but using peripheral blood samples or liquid biopsies would be less invasive. Leukemia originates in the bone marrow, along with the cfDNA obtained from peripheral blood. This study aimed to assess the utility of cell-free DNA (cfDNA) from peripheral blood samples for MRD detection in AML patients. A cohort of 20 AML patients was analyzed using NGS, and a correlation between MRD assessment by cfDNA and circulating tumor cells (CTCs) in paired samples was observed. Furthermore, a higher tumor signal was detected in cfDNA compared to CTCs, indicating greater sensitivity. Challenges for the application of liquid biopsy in MRD assessment were discussed, including the selection of appropriate markers and the sensitivity of certain markers. This study emphasizes the potential of liquid biopsy using cfDNA for MRD detection in AML patients and highlights the need for further research in this area.

Kinetics of plasma cell-free DNA as a prospective biomarker to predict the prognosis and radiotherapy effect of esophageal cancer.

PURPOSE: The lack of reliable biomarkers for the prognosis and radiotherapy efficacy in esophageal cancer (EC) necessitates further research. The aim of our study was to investigate the predictive utility of plasma cell-free DNA (cfDNA) kinetics in patients with EC. MATERIALS AND METHODS: We retrospectively analyzed the clinical data and cfDNA levels (pre-radiotherapy [pre-RT] and post-radiotherapy [post-RT]) and the cfDNA kinetics (cfDNA ratio: post-RT cfDNA/pre-RT cfDNA) of 88 patients. We employed Kaplan-Meier curves to examine the relationship between cfDNA and overall survival (OS) as well as progression-free survival (PFS). Univariate and multivariate Cox regression analyses were executed to ascertain the independent risk factors in EC. RESULTS: The pre-RT cfDNA levels were positively correlated with clinical stage (P=0.001). The pre-RT cfDNA levels (cutoff value=16.915ng/mL), but not the post-RT cfDNA levels, were linked to a diminished OS (P<0.001) and PFS (P=0.0137). CfDNA kinetics (cutoff value=0.883) were positively associated with OS (P=0.0326) and PFS (P=0.0020). Notably, we identified independent risk factors for OS in EC treated with RT, including cfDNA ratio (high/low) (HR=0.447 [0.221-0.914] P=0.025), ECOG (0/1/2) (HR=0.501 [0.285-0.880] p=0.016), and histological type (esophagal squamous cell carcinoma [ESCC]/non-ESCC) (HR=3.973 [1.074-14.692] P=0.039). CONCLUSION: Plasma cfDNA kinetics is associated with prognosis and radiotherapy effect in EC undergoing RT, suggesting potential clinical application of a cheap and simple blood-based test.

Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.

Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we sought to develop a deep learning model incorporating cfDNA methylation and fragment size profiles. To achieve this, we utilized methylation data collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Then we generated methylated DNA immunoprecipitation sequencing and genome-wide Enzymatic Methyl-seq (EM-seq) form lung cancer tissue and plasma. Using these data, we selected 366 methylation markers. A targeted EM-seq panel was designed using the selected markers, and 142 lung cancer and 56 healthy samples were produced with the panel. Additionally, cfDNA samples from healthy individuals and lung cancer patients were diluted to evaluate sensitivity. Its lung cancer detection performance reached an accuracy of 81.5% and an area under the receiver operating characteristic curve of 0.87. In the serial dilution experiment, we achieved tumor fraction detection of 1% at 98% specificity and 0.1% at 80% specificity. In conclusion, we successfully developed and validated a combination of methylation panel and a deep learning model that can distinguish between patients with lung cancer and healthy individuals.

Accurate Early Detection and EGFR Mutation Status Prediction of Lung Cancer Using Plasma cfDNA Coverage Patterns: A Proof-of-Concept Study.

Lung cancer is a major global health concern with a low survival rate, often due to late-stage diagnosis. Liquid biopsy offers a non-invasive approach to cancer detection and monitoring, utilizing various features of circulating cell-free DNA (cfDNA). In this study, we established two models based on cfDNA coverage patterns at the transcription start sites (TSSs) from 6X whole-genome sequencing: an Early Cancer Screening Model and an EGFR mutation status prediction model. The Early Cancer Screening Model showed encouraging prediction ability, especially for early-stage lung cancer. The EGFR mutation status prediction model exhibited high accuracy in distinguishing between EGFR-positive and wild-type cases. Additionally, cfDNA coverage patterns at TSSs also reflect gene expression patterns at the pathway level in lung cancer patients. These findings demonstrate the potential applications of cfDNA coverage patterns at TSSs in early cancer screening and in cancer subtyping.

[Development and validation of predictive models for esophageal squamous cell carcinoma and its precancerous lesions using terminal motif analysis in circulating cell-free DNA].

Objectives: To develop and validate predictive models for esophageal squamous cell carcinoma (ESCC) using circulating cell-free DNA (cfDNA) terminal motif analysis. The goal was to improve the non-invasive detection of early-stage ESCC and its precancerous lesions. Methods: Between August 2021 and November 2022, we prospectively collected plasma samples from 448 individuals at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences for cfDNA extraction, library construction, and sequencing. We analyzed 201 cases of ESCC, 46 high-grade intraepithelial neoplasia (HGIN), 46 low-grade intraepithelial neoplasia (LGIN), 176 benign esophageal lesions, and 29 healthy controls. Participants, including ESCC patients and control subjects, were randomly assigned to a training set (n=284) and a validation set (n=122). The training cohort underwent z-score normalization of cfDNA terminal motif matrices and a selection of distinctive features differentiated ESCC cases from controls. The random forest classifier, Motif-1 (M1), was then developed through principal component analysis, ten-fold cross-validation, and recursive feature elimination. M1's efficacy was then validated in the validation and precancerous lesion sets. Subsequently, individuals with precancerous lesions were included in the dataset and participants were randomly allocated to newly formed training (n=243), validation (n=105), and test (n=150) cohorts. Using the same procedure as M1, we trained the Motif-2 (M2) random forest model with the training cohort. The M2 model's accuracy was then confirmed in the validation cohort to establish the optimal threshold and further tested by performing validation in the test cohort. Results: We developed two cfDNA terminal motif-based predictive models for ESCC and associated precancerous conditions. The first model, M1, achieved a sensitivity of 90.0%, a specificity of 77.4%, and an area under the curve (AUC) of 0.884 in the validation cohort. For LGIN, HGIN, and T1aN0 stage ESCC, M1's sensitivities were 76.1%, 80.4%, and 91.2% respectively. Notably, the sensitivity for jointly predicting HGIN and T1aN0 ESCC reached 85.0%. Both the predictive accuracy and sensitivity increased in line with the cancer's progression (P＜0.001). The second model, M2, exhibited a sensitivity of 87.5%, a specificity of 77.4%, and an AUC of 0.857 in the test cohort. M2's sensitivities for detecting precancerous lesions and ESCC were 80.0% and 89.7%, respectively, and it showed a combined sensitivity of 89.4% for HGIN and T1aN0 stage ESCC. Conclusions: Two predictive models based on cfDNA terminal motif analysis for ESCC and its precancerous lesions are developed. They both show high sensitivity and specificity in identifying ESCC and its precancerous stages, indicating its potential for early ESCC detection.

Evaluating circulating cell-free DNA and DNA integrity index as biomarkers in non-small cell lung cancer.

BACKGROUND: Analysis of free DNA molecules shed from tumour cells in plasma of patients referred as circulating tumour DNA (ctDNA) with reference to physiological circulating cell-free DNA (cfDNA) is nowadays exploited as liquid biopsy and is considered a new emerging promising biomarker for diagnosis, selection of proper treatment, and prognosis of cancer. DNA integrity index (DII) is assessed by calculating the ratio between the concentration of long cfDNA strands released from tumour cells (ALU247) and the short strands released from normal cells (ALU115). The aim of the current study was to evaluate DII as a potential diagnostic and prognostic biomarker of NSCLC. METHODS: Our study included 48 NSCLC patients diagnosed as primary NSCLC before starting treatment, 30 COPD patients diagnosed clinically, radiologically, and subjected to chest high-resolution computerized tomography, and 40 healthy controls. cfDNA concentration and DII were measured by quantitative real-time polymerase chain reaction (qPCR). RESULTS: ALU115, ALU247, and DII were significantly higher in NSCLC compared to COPD patients (p < 0.0001) and controls (p < 0.0001) and in COPD patients compared to control subjects (p < 0.0001). DII positively correlated with the stage of tumour (p = 0.01), tumour metastasis (p = 0.004), and with adenocarcinoma compared to other histopathological types (p = 0.02). To evaluate clinical utility of DII in NSCLC, ROC curve analysis demonstrated an AUC of 0.91 at a cut-off value of 0.44 with total accuracy = 85.6%, sensitivity = 90%, specificity = 83%, PPV = 78.1%, and NPV = 92.1%. CONCLUSION: cfDNA and DII represent a promising diagnostic and prognostic tool in NSCLC. This type of noninvasive liquid biopsy revealed its chance in the screening, early diagnosis, and monitoring of NSCLC.

LABS: linear amplification-based bisulfite sequencing for ultrasensitive cancer detection from cell-free DNA.

Methylation-based liquid biopsies show promises in detecting cancer using circulating cell-free DNA; however, current limitations impede clinical application. Most assays necessitate substantial DNA inputs, posing challenges. Additionally, underrepresented tumor DNA fragments may go undetected during exponential amplification steps of traditional sequencing methods. Here, we report linear amplification-based bisulfite sequencing (LABS), enabling linear amplification of bisulfite-treated DNA fragments in a genome-wide, unbiased fashion, detecting cancer abnormalities with sub-nanogram inputs. Applying LABS to 100 patient samples revealed cancer-specific patterns, copy number alterations, and enhanced cancer detection accuracy by identifying tissue-of-origin and immune cell composition.

Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top.

Acute respiratory distress syndrome (ARDS) poses a significant and widespread public health challenge. Extensive research conducted in recent decades has considerably improved our understanding of the disease pathophysiology. Nevertheless, ARDS continues to rank among the leading causes of mortality in intensive care units and its management remains a formidable task, primarily due to its remarkable heterogeneity. As a consequence, the syndrome is underdiagnosed, prognostication has important gaps and selection of the appropriate therapeutic approach is laborious. In recent years, the noncoding transcriptome has emerged as a new area of attention for researchers interested in biomarker development. Numerous studies have confirmed the potential of long noncoding RNAs (lncRNAs), transcripts with little or no coding information, as noninvasive tools for diagnosis, prognosis and prediction of the therapeutic response across a broad spectrum of ailments, including respiratory conditions. This article aims to provide a comprehensive overview of lncRNAs with specific emphasis on their role as biomarkers. We review current knowledge on the circulating lncRNAs as potential markers that can be used to enhance decision making in ARDS management. Additionally, we address the primary limitations and outline the steps that will be essential for integration of the use of lncRNAs in clinical laboratories. Our ultimate objective is to provide a framework for the implementation of lncRNAs in the management of ARDS.

Noninvasive prenatal screening in a pregnant woman with a history of stem cell transplant from a male donor: A case report and literature review.

BACKGROUND: As a screening method, inaccuracies in noninvasive prenatal screening (NIPS) exist, which are often attributable to biological factors. One such factor is the history of transplantation. However, there are still limited reports on such NIPS cases. METHODS: We report an NIPS case of a pregnant woman who had received a stem cell transplant from a male donor. To determine the karyotype in the woman's original cell, we performed chromosome microarray analysis (CMA) on her postnatal blood and oral mucosa. To comprehensively estimate the cell-free DNA (cfDNA) composition, we further performed standard NIPS procedures on the postnatal plasma. Moreover, we reviewed all published relevant NIPS case reports about pregnant women with transplantation history. RESULTS: NIPS showed a low-risk result for common trisomies with a fetal fraction of 65.80%. CMA on maternal white blood cells showed a nonmosaic male karyotype, while the oral mucosa showed a nonmosaic female karyotype. The proportion of donor's cfDNA in postnatal plasma was 94.73% based on the Y-chromosome reads ratio. The composition of cfDNA in maternal plasma was estimated as follows: prenatally, 13.60% maternal, 65.80% donor, and 20.60% fetal/placental, whereas postnatally, 5.27% maternal and 94.73% donor. CONCLUSIONS: This study expanded our understanding of the influence of stem cell transplantation on NIPS, allowing us to optimize NIPS management for these women.

Release of CD36-associated cell-free mitochondrial DNA and RNA as a hallmark of space environment response.

A detailed understanding of how spaceflight affects human health is essential for long-term space exploration. Liquid biopsies allow for minimally-invasive multi-omics assessments that can resolve the molecular heterogeneity of internal tissues. Here, we report initial results from the JAXA Cell-Free Epigenome Study, a liquid biopsy study with six astronauts who resided on the International Space Station (ISS) for more than 120 days. Analysis of plasma cell-free RNA (cfRNA) collected before, during, and after spaceflight confirms previously reported mitochondrial dysregulation in space. Screening with 361 cell surface marker antibodies identifies a mitochondrial DNA-enriched fraction associated with the scavenger receptor CD36. RNA-sequencing of the CD36 fraction reveals tissue-enriched RNA species, suggesting the plasma mitochondrial components originated from various tissues. We compare our plasma cfRNA data to mouse plasma cfRNA data from a previous JAXA mission, which had used on-board artificial gravity, and discover a link between microgravity and the observed mitochondrial responses.