CRISPR/Cas13a-based supersensitive circulating tumor DNA assay for detecting EGFR mutations in plasma.

Despite recent technological advancements in cell tumor DNA (ctDNA) mutation detection, challenges persist in identifying low-frequency mutations due to inadequate sensitivity and coverage of current procedures. Herein, we introduce a super-sensitivity and specificity technique for detecting ctDNA mutations, named HiCASE. The method utilizes PCR-based CRISPR, coupled with the restriction enzyme. In this work, HiCASE focuses on testing a series of EGFR mutations to provide enhanced detection technology for non-small cell lung cancer (NSCLC), enabling a detection sensitivity of 0.01% with 40 ng cell free DNA standard. When applied to a panel of 140 plasma samples from 120 NSCLC patients, HiCASE exhibits 88.1% clinical sensitivity and 100% specificity with 40 µL of plasma, higher than ddPCR and Super-ARMS assay. In addition, HiCASE can also clearly distinguish T790M/C797S mutations in different positions at a 1% variant allele frequency, offering valuable guidance for drug utilization. Indeed, the established HiCASE assay shows potential for clinical applications.

Asymmetric Supercapacitors based on 1,10-phenanthroline-5,6-dione Molecular Electrodes Paired with MXene.

An efficient approach to increase the energy density of supercapacitors is to prepare electrode materials with larger specific capacitance and increase the potential difference between the positive and negative electrodes in the device. Herein, an organic molecular electrode (OME) is prepared by anchoring 1,10-phenanthroline-5,6-dione (PD), which possesses two pyridine rings and an electron-deficient conjugated system, onto reduced graphene oxide (rGO). Because of the electron-deficient conjugated structure of PD molecule, PD/rGOs exhibit a more positive redox peak potential along with the advantages of high capacitance-controlled behaviour and fast reaction kinetics. Additionally, the small energy gap between the lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) leads to increased conductivity in PD/rGO. To assemble the asymmetric supercapacitor (ASC), a two-dimensional metal carbide, as known as MXene, with a chemical composition of Ti3C2Tx is selected as the negative electrode due to its exceptional performance, and PD/rGO-0.5 is employed as the positive electrode. Consequently, the working voltage is expanded up to 1.8 V. Through further electrochemical measurements, the assembled ASC (PD/rGO-0.5//Ti3C2Tx) achieves a remarkable energy density of 36.8 Wh kg-1. Remarkably, connecting two ASCs in series can power 73 LEDs, showcasing its promising potential for energy storage applications.

Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing.

Introduction: Metagenomic next-generation sequencing (mNGS) has emerged as a powerful tool for rapid pathogen identification in clinical practice. However, the parameters used to interpret mNGS data, such as read count, genus rank, and coverage, lack explicit performance evaluation. In this study, the developed indicators as well as novel parameters were assessed for their performance in bacterium detection. Methods: We developed several relevant parameters, including 10M normalized reads, double-discard reads, Genus Rank Ratio, King Genus Rank Ratio, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank. These parameters, together with frequently used read indicators including raw reads, reads per million mapped reads (RPM), transcript per kilobase per million mapped reads (TPM), Genus Rank, and coverage were analyzed for their diagnostic efficiency in bronchoalveolar lavage fluid (BALF), a common source for detecting eight bacterium pathogens: Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Hemophilus influenzae, Stenotrophomonas maltophilia, Pseudomonas aeruginosa, and Aspergillus fumigatus. Results: The results demonstrated that these indicators exhibited good diagnostic efficacy for the eight pathogens. The AUC values of all indicators were almost greater than 0.9, and the corresponding sensitivity and specificity values were almost greater than 0.8, excepted coverage. The negative predictive value of all indicators was greater than 0.9. The results showed that the use of double-discarded reads, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank exhibited better diagnostic efficiency than that of raw reads, RPM, TPM, and in Genus Rank. These parameters can serve as a reference for interpreting mNGS data of BALF. Moreover, precision filters integrating our novel parameters were built to detect the eight bacterium pathogens in BALF samples through machine learning. Summary: In this study, we developed a set of novel parameters for pathogen identification in clinical mNGS based on reads and ranking. These parameters were found to be more effective in diagnosing pathogens than traditional approaches. The findings provide valuable insights for improving the interpretation of mNGS reports in clinical settings, specifically in BALF analysis.

Estimating Venous Thromboembolism Risk in Metastatic Colorectal Cancer Inpatients: Validation of Existing Risk Scores and Development of New Risk Scores.

Metastatic colorectal cancer (mCRC) patients are predisposed to venous thromboembolism (VTE). This study aimed to (1) evaluate the efficacy of 4 existing cancer-specific VTE models in predicting VTE incidence among hospitalized mCRC patients, and (2) examine the influence of incorporating mCRC molecular subtypes into these models. We conducted an evaluation of 4 cancer-specific VTE models, including Khorana, Vienna CATS, Protecht, and CONKO in a dataset involving 1392 mCRC patients. To evaluate the predictive performance, we utilized receiver operating characteristic (ROC) curves for both the original models and the modified models that incorporated microsatellite instability status or KRAS/NRAS/BRAF mutations. Moreover, we computed the net reclassification improvement (NRI) to quantify the enhancements made to the modified VTE risk models. All models demonstrated a moderate area under the ROC curve (ROC-AUC) when predicting the occurrence of VTE: Khorana (0.550), Vienna CATS (0.671), Protecht (0.652), and CONKO (0.578). The incorporation of KRAS and BRAF mutations significantly improved the ROC-AUC of all 4 existing models (modified Khorana: 0.796, modified Vienna CATS: 0.832, modified Protecht: 0.834, and modified CONKO: 0.809). After dichotomizing the risk using a threshold of 3 points and comparing them with the original models, NRI values for the 4 modified models were 0.97, 0.95, 1.11, and 0.98, respectively. All 4 cancer-specific VTE models exhibit moderate performance when identifying mCRC patients at high risk of VTE. Incorporating KRAS and BRAF mutations may enhance the prediction of VTE in hospitalized mCRC patients.

A study on the effects of college students' knowledge-sharing behavior on group performance and individual social status.

Introduction: Universities, as typical knowledge-based organizations, engage in various knowledge management activities, including knowledge acquisition, storage, application, and innovation. This research focuses on applying organizational knowledge management principles to college student groups in universities, aiming to explore the current state of knowledge-sharing behaviors within these groups and investigate the relationship between group performance, individual social status, and knowledge-sharing behaviors. Methods: A sample of 497 college students from six universities in China was randomly selected, and an econometric analysis using structural equation modeling was conducted with SPSS21.0 and AMOS21.0 to examine their knowledge-sharing behaviors, individual social status, and group performance. Results: The findings reveal that individual knowledge-sharing behavior significantly influences the knowledge sharing behavior of others and the recognition received by the sharer. Moreover, the knowledge-sharing behavior of others positively contributes to group performance, while recognition from others enhances the social status of the sharer. Furthermore, the knowledge-sharing behaviors of others mediate the relationship between individual knowledge-sharing behaviors and group performance, while others' recognition of the sharer mediates the relationship between individual knowledge-sharing behaviors and the sharer's social status. This study provides valuable theoretical guidance for organizational knowledge management and the development of college students' learning abilities, establishing a crucial foundation for comprehensive, scientific, and standardized student management. Conclusion: Overall, this research contributes to understanding the dynamics of knowledge sharing among college students and highlights the importance of incorporating knowledge management principles in educational settings. The findings underscore the positive impact of knowledge sharing on group performance and individual social status, emphasizing the need for effective knowledge sharing practices to enhance student management in higher education institutions.

Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.

Background: Colorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations. Methods: Data were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks. Results: The incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana). Conclusions: This study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.

Cobalt-Catalyzed Diastereo- and Enantioselective Carbon-Carbon Bond Forming Reactions of Cyclobutenes.

Catalytic enantioselective functionalization of cyclobutenes constitutes a general and modular strategy for construction of enantioenriched complex cyclobutanes bearing multiple stereogenic centers, as chiral four-membered rings are common motifs in biologically active molecules and versatile intermediates in organic synthesis. However, enantioselective synthesis of cyclobutanes through such a strategy remained significantly limited. Herein, we report a series of unprecedented cobalt-catalyzed carbon-carbon bond forming reactions of cyclobutenes that are initiated through enantioselective carbometalation. The protocols feature diastereo- and enantioselective introduction of allyl, alkynyl, and functionalized alkyl groups. Mechanistic studies indicated an unusual 1,3-cobalt migration and subsequent ß-carbon elimination cascade process occurred in the allyl addition. These new discoveries established a new elementary process for cobalt catalysis and an extension of diversity of nucleophiles for enantioselective transformations of cyclobutenes.

Noninvasive prediction of axillary lymph node status in breast cancer using promoter profiling of circulating cell-free DNA.

BACKGROUND: Lymph node metastasis (LNM) is one of the most important factors affecting the prognosis of breast cancer. The accurate evaluation of lymph node status is useful to predict the outcomes of patients and guide the choice of cancer treatment. However, there is still lack of a low-cost non-invasive method to assess the status of axillary lymph node (ALN). Gene expression signature has been used to assess lymph node metastasis status of breast cancer. In addition, nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of its original tissues, so it may be used to evaluate the axillary lymph node status in breast cancer. METHODS: In this study, we found that the cfDNA nucleosome footprints between the ALN-positive patients and ALN-negative patients showed different patterns by implementing whole-genome sequencing (WGS) to detect 15 ALN-positive and 15 ALN-negative patients. In order to further evaluate its potential for assessing ALN status, we developed a classifier with multiple machine learning models by using 330 WGS data of cfDNA from 162 ALN-positive and 168 ALN-negative samples to distinguish these two types of patients. RESULTS: We found that the promoter profiling between the ALN-positive patients and ALN-negative patients showed distinct patterns. In addition, we observed 1071 genes with differential promoter coverage and their functions were closely related to tumorigenesis. We found that the predictive classifier based on promoter profiling with a support vector machine model, named PPCNM, produced the largest area under the curve of 0.897 (95% confidence interval 0.86-0.93). CONCLUSIONS: These results indicate that promoter profiling can be used to distinguish ALN-positive patients from ALN-negative patients, which may be helpful to guide the choice of cancer treatment.

[Advantages and limitations of COVID-19 detection techniques].

The occurrence and persistent pandemic of 2019 coronavirus pneumonia (COVID-19), caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has taken a big toll on global public health. The development of virus detection techniques and its application played an important role in health management, including screening, identification and treatment of patients, and slowing down the spread of virus. This review briefly summarizes the biological characteristics of SARS-CoV-2, and introduces in detail the SARS-CoV-2 detection techniques developed and used worldwide. Perspectives on the follow-up development of virus detection techniques were presented, with the aim to facilitate medical diagnosis, public health protection, disease prevention and control.

Simulated confined placental mosaicism proportion (SCPMP) based on cell-free fetal DNA fraction enrichment can reduce false-positive results in non-invasive prenatal testing.

OBJECTIVE: To decrease the false-positive rate of NIPT using cell-free fetal DNA (cffDNA) fraction enrichment and the simulated confined placental mosaicism proportion (SCPMP) threshold application via cffDNA quantification. METHOD: Using a cffDNA enrichment method, 303 plasma samples with positive NIPT results (Z-score > 3.0; 200 true-positive and 103 false-positive cases) were re-sequenced. A method to calculate the SCPMP based on the quantified cffDNA fraction was developed; the SCPMP threshold between true- and false-positive NIPT results was determined and used for re-analyses. RESULTS: With enrichment, the fetal fraction of the 303 samples was 26.9 ± 8.4%, compared to 11.0 ± 3.2% without enrichment. The optimized threshold method with double determination using the Z-value-defined SCPMP can reduce the false-positive rates for trisomies 21, 18, and 13 by 87%, 80%, and 88.9%, respectively. CONCLUSION: Our optimized method can decrease the false-positive rate of NIPT results.

Multicenter assessment of shotgun metagenomics for pathogen detection.

BACKGROUND: Shotgun metagenomics has been used clinically for diagnosing infectious diseases. However, most technical assessments have been limited to individual sets of reference standards, experimental workflows, and laboratories. METHODS: A reference panel and performance metrics were designed and used to examine the performance of shotgun metagenomics at 17 laboratories in a coordinated collaborative study. We comprehensively assessed the reliability, key performance determinants, reproducibility, and quantitative potential. FINDINGS: Assay performance varied significantly across sites and microbial classes, with a read depth of 20 millions as a generally cost-efficient assay setting. Results of mapped reads by shotgun metagenomics could indicate relative and intra-site (but not absolute or inter-site) microbial abundance. INTERPRETATION: Assay performance was significantly impacted by the microbial type, the host context, and read depth, which emphasizes the importance of these factors when designing reference reagents and benchmarking studies. Across sites, workflows and platforms, false positive reporting and considerable site/library effects were common challenges to the assay's accuracy and quantifiability. Our study also suggested that laboratory-developed shotgun metagenomics tests for pathogen detection should aim to detect microbes at 500 CFU/mL (or copies/mL) in a clinically relevant host context (10^5 human cells/mL) within a 24h turn-around time, and with an efficient read depth of 20M. FUNDING: This work was supported by National Science and Technology Major Project of China (2018ZX10102001).

Comprehensive analysis of the prognostic value of the chemokine-like factor-like MARVEL transmembrane domain-containing family in gastric cancer.

BACKGROUND: The chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing (CMTM) family refers to a family of transcriptional repressor genes. CMTMs are closely associated with the epigenetic regulatory mechanisms and development of multiple malignancies, including gastric cancer. However, their specific biological functions and prognostic values in gastric cancer have yet to be elucidated. METHODS: Tumor sample datasets were retrieved and analyzed using databases including Oncomine, STRING, GEPIA2, cBioportal, and Kaplan-Meier plotter. To investigate the prognostic role of CMTMs in gastric cancer, we applied unsupervised hierarchical clustering analysis of CMTM gene expression patterns. RESULTS: While the mRNA levels of CMTM1/3/6/7/8 were upregulated in gastric cancer, CMTM2/4/5 showed no statistically significant difference at the mRNA level in patients with gastric cancer. Moreover, the mRNA expressions of different CMTMs exhibited strong correlations with various clinical parameters of patients with gastric cancer, including tumor stage, metastatic lymph node status, H. pylori status, and tumor grade. Also, the results suggested that elevated levels of CMTM3/5 mRNA had a significant association (P<0.05) with poor overall survival, progression-free survival, and post-progression survival. Conversely, elevated expression of CMTM2/4/6 mRNA had a significant association with better overall survival, progression-free survival, and post-progression survival. Unsupervised hierarchical clustering analysis successfully identified 2 major clusters of patients as follows: signature #1: CMTM4/6/8 and signature #2: CMTM1/2/3/5/7. Signature #2 was closely correlated with poorer overall survival, which indicated that the expression pattern of the CMTM family could be a novel prognostic factor for patients with gastric cancer. CONCLUSIONS: These results suggest that the expression levels of CMTM genes possibly have prognostic value as a biomarker of gastric cancer.

Peroxiredoxin alleviates the fitness costs of imidacloprid resistance in an insect pest of rice.

Chemical insecticides have been heavily employed as the most effective measure for control of agricultural and medical pests, but evolution of resistance by pests threatens the sustainability of this approach. Resistance-conferring mutations sometimes impose fitness costs, which may drive subsequent evolution of compensatory modifier mutations alleviating the costs of resistance. However, how modifier mutations evolve and function to overcome the fitness cost of resistance still remains unknown. Here we show that overexpression of P450s not only confers imidacloprid resistance in the brown planthopper, Nilaparvata lugens, the most voracious pest of rice, but also leads to elevated production of reactive oxygen species (ROS) through metabolism of imidacloprid and host plant compounds. The inevitable production of ROS incurs a fitness cost to the pest, which drives the increase or fixation of the compensatory modifier allele T65549 within the promoter region of N. lugens peroxiredoxin (NlPrx) in the pest populations. T65549 allele in turn upregulates the expression of NlPrx and thus increases resistant individuals' ability to clear the cost-incurring ROS of any source. The frequent involvement of P450s in insecticide resistance and their capacity to produce ROS while metabolizing their substrates suggest that peroxiredoxin or other ROS-scavenging genes may be among the common modifier genes for alleviating the fitness cost of insecticide resistance.

Identification of ANXA1 as a potential prognostic biomarker and correlating with immune infiltrates in colorectal cancer.

BACKGROUND: ANXA1 is a calcium-dependent phospholipid-binding protein and is frequently associated with inflammation, cell proliferation and apoptosis. However, the relationship between ANXA1 and the prognosis of multiple tumours and tumour infiltrating immune cells remains unclear. METHODS: Multivariate Cox proportional regression analysis was used for signature genes exploration in the basic of colon adenocarcinoma (COAD) RNA-sequence dataset obtained from TCGA, following the identification of 267 common differentially expressed genes, including ANXA1, among three expression profile datasets (GSE41328, GSE110224, and GSE113513). The differential expression of ANXA1 in different tumours and their corresponding normal tissues were evaluated through the Tumour Immune Estimation Resource (TIMER) and Oncomine database. Subsequently, we investigated the correlation between the expression level of ANXA1 and diverse panel of infiltrating immune cells and their related gene markers in colorectal cancer using correlation analysis in TIMER and GEPIA database. RESULTS: The high expression of ANXA1 was demonstrated to be closely correlated with poor survival in patients with colorectal cancer. More importantly, we found that changes in ANXA1 expression showed a moderate to strong, and statistically significant, correlation with infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. By contrast, there are only weak correlations between ANXA1 expression and immune cell infiltration in ESCA and STAD. ANXA1 expression was considerably associated with various immune markers involving immune cell recruitment, polarization of tumour-associated macrophages, and T cell exhaustion. CONCLUSION: ANXA1 is not only an independent risk factor in the prediction of the prognosis of colorectal cancer, but also a crucial regulator in immune cell infiltration. This study may shed light on the clinical value of ANXA1, especially in the areas of early diagnosis of colorectal cancer and therapeutic target discovery.

A Genotype Signature for Predicting Pathologic Complete Response in Locally Advanced Rectal Cancer.

PURPOSE: To construct and validate a predicting genotype signature for pathologic complete response (pCR) in locally advanced rectal cancer (PGS-LARC) after neoadjuvant chemoradiation. METHODS AND MATERIALS: Whole exome sequencing was performed in 15 LARC tissues. Mutation sites were selected according to the whole exome sequencing data and literature. Target sequencing was performed in a training cohort (n = 202) to build the PGS-LARC model using regression analysis, and internal (n = 76) and external validation cohorts (n = 69) were used for validating the results. Predictive performance of the PGS-LARC model was compared with clinical factors and between subgroups. The PGS-LARC model comprised 15 genes. RESULTS: The area under the curve (AUC) of the PGS model in the training, internal, and external validation cohorts was 0.776 (0.697-0.849), 0.760 (0.644-0.867), and 0.812 (0.690-0.915), respectively, and demonstrated higher AUC, accuracy, sensitivity, and specificity than cT stage, cN stage, carcinoembryonic antigen level, and CA19-9 level for pCR prediction. The predictive performance of the model was superior to clinical factors in all subgroups. For patients with clinical complete response (cCR), the positive prediction value was 94.7%. CONCLUSIONS: The PGS-LARC is a reliable predictive tool for pCR in patients with LARC and might be helpful to enable nonoperative management strategy in those patients who refuse surgery. It has the potential to guide treatment decisions for patients with different probability of tumor regression after neoadjuvant therapy, especially when combining cCR criteria and PGS-LARC.

Identification of SARS-CoV-2 Variants and Their Clinical Significance in Hefei, China.

The ongoing coronavirus disease 2019 (COVID-19) pandemic represents one of the most exigent threats of our lifetime to global public health and economy. As part of the pandemic, from January 10 to March 10, 2020, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread in Hefei (Anhui Province, China) with a total of 174 confirmed cases of COVID-19. During this period, we were able to gather critical information on the transmission and evolution of pathogens through genomic surveillance. Particularly, the objective of our study was to track putative variants of SARS-CoV-2 circulating in Hefei for the first time and contribute to the global effort toward elucidating the molecular epidemic profile of the virus. Patients who showed symptoms of COVID-19 were routinely tested for SARS-CoV-2 infections via RT-PCR at the First Affiliated Hospital of Anhui Medical University. Whole-genome sequencing was performed on 97 clinical samples collected from 29 confirmed COVID-19 patients. As a result, we identified a local novel single-nucleotide polymorphism site (10,380) harboring a G → T mutation (Gly → Val) in Hefei. Further phylogenetic network analysis with all the sequences of SARS-CoV-2 deposited in GenBank collected in East and Southeast Asia revealed a local subtype of S-type SARS-CoV-2 (a1) harboring a C → T synonymous mutation (Leu) at position 18,060 of ORF1b, likely representing a local SARS-CoV-2 mutation site that is obviously concentrated in Hefei and the Yangtze River Delta region. Moreover, clinical investigation on the inflammatory cytokine profile of the patients suggested that mutations at positions 18,060 (the shared variable site of subtype a1) and 28,253(harboring a C → T synonymous mutation, Phe) were associated with milder immune responses in the patients.

Noninvasive inferring expressed genes and in vivo monitoring of the physiology and pathology of pregnancy using cell-free DNA.

BACKGROUND: Noninvasive monitoring of fetal development and the early detection of pregnancy-associated complications is challenging, largely because of the lack of information about the molecular spectrum during pregnancy. Recently, cell-free DNA in plasma was found to reflect the global nucleosome footprint and status of gene expression and showed potential for noninvasive health monitoring during pregnancy. OBJECTIVE: We aimed to test the relationships between plasma cell-free DNA profiles and pregnancy biology and evaluate the use of a cell-free DNA profile as a noninvasive method for physiological and pathologic status monitoring during pregnancy. STUDY DESIGN: We used genome cell-free DNA sequencing data generated from noninvasive prenatal testing in a total of 2937 pregnant women. For each physiological and pathologic condition, features of the cell-free DNA profile were identified using the discovery cohort, and support vector machine classifiers were built and evaluated using independent training and validation cohorts. RESULTS: We established nucleosome occupancy profiles at transcription start sites in different gestational trimesters, demonstrated the relationships between gene expression and cell-free DNA coverage at transcription start sites, and showed that the cell-free DNA profiles at transcription start sites represented the biological processes of pregnancy. In addition, using cell-free DNA data, nucleosome profiles of transcription factor binding sites were identified to reflect the transcription factor footprint, which may help to reveal the molecular mechanisms underlying pregnancy. Finally, by using machine-learning models on low-coverage noninvasive prenatal testing data, we evaluated the use of cell-free DNA nucleosome profiles for distinguishing gestational trimesters, fetal sex, and fetal trisomy 21 and highlighted its potential utility for predicting physiological and pathologic fetal conditions by using low-coverage noninvasive prenatal testing data. CONCLUSION: Our analyses profiled nucleosome footprints and regulatory networks during pregnancy and established a noninvasive proof-of-principle methodology for health monitoring during pregnancy.

Identification of candidate biomarkers and therapeutic drugs of colorectal cancer by integrated bioinformatics analysis.

Most colorectal cancer (CRC) patients are diagnosed with advanced stages and low prognosis. We aimed to identify potential diagnostic and prognostic biomarkers, as well as active small molecules of CRC. Microarray data (GSE9348, GSE35279, and GSE106582) were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by the GEO2R platform. Common DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Cytoscape software was used to construct protein-protein interaction networks and identify hub genes. Hub genes were evaluated by Kaplan-Meier survival analysis in the GEPIA database and validated in two independent microarray data (GSE74602 and GSE83889). Common DEGs were used to select active small molecules by the connectivity map database. A total of 166 DEGs were identified as common DEGs. GO analysis demonstrated that common DEGs were significantly enriched in the apoptotic process, cell proliferation, and cell adhesion. KEGG analysis indicated that the most enriched pathways were the PI3K-Akt signaling pathway and extracellular matrix-receptor interaction. COL1A2, THBS2, TIMP1, and CXCL8 significantly upregulated in colorectal tumor. High expressions of COL1A2, THBS2, and TIMP1 were associated with poor survival, while high expressions of CXCL8 were associated with better survival. We selected 11 small molecules for CRC therapy. In conclusion, we found key dysregulated genes associated with CRC and potential small molecules to reverse them. COL1A2, THBS2, TIMP1, and CXCL8 may act as diagnostic and prognostic biomarkers of CRC.

Detection of chromosomal abnormalities in spontaneous miscarriage by low­coverage next­generation sequencing.

Chromosomal abnormalities (CAs) can cause spontaneous miscarriage and increase the incidence of subsequent pregnancy loss and other complications. Presently, CAs are detected mainly by array comparative genomic hybridization (CGH) and single nucleotide polymorphism microarrays. The present study developed a low­coverage next­generation sequencing method to detect CAs in spontaneous miscarriage and assess its clinical performance. In total, 1,401 patients who had experienced an abortion were enrolled in the present study and divided into two groups. In group I, 437 samples that had been previously validated by array CGH were used to establish a method to detect CAs using a semiconductor sequencing platform. In group II, 964 samples, which were not verified, were assessed using established methods with respect to clinical significance. Copy number variant (CNV)­positive and euploidy samples were verified by array CGH and short tandem repeat profiling, respectively, based on quantitative fluorescent PCR. The low­coverage sequencing method detected CNVs >1 Mb in length and a total of 3.5 million unique reads. Similar results to array CGH were obtained in group I, except for six CNVs <1 Mb long. In group II, there were 341 aneuploidies, 195 CNVs, 25 mosaicisms and 403 euploidies. Overall, among the 1,401 abortion samples, there were 536 aneuploidies, 263 CNVs, 34 mosaicisms, and 568 euploidies. Trisomies were present in all autosomal chromosomes. The most common aneuploidies were T16, monosomy X, T22, T15, T21 and T13. Furthermore, one tetrasomy 21, one CNV associated with Wolf­Hirschhorn syndrome, one associated with DiGeorge syndrome and one associated with both Prader­Willi and Angelman syndromes were identified. These four cases were confirmed by short tandem repeat profiling and array CGH. Quantitative fluorescent PCR revealed nine polyploidy samples. The present method demonstrated equivalent efficacy to that of array CGH in detecting CNVs >1 Mb, with advantages of requiring less input DNA and lower cost.

An integrated Asian human SNV and indel benchmark established using multiple sequencing methods.

Sequencing technologies have been rapidly developed recently, leading to the breakthrough of sequencing-based clinical diagnosis, but accurate and complete genome variation benchmark would be required for further assessment of precision medicine applications. Despite the human cell line of NA12878 has been successfully developed to be a variation benchmark, population-specific variation benchmark is still lacking. Here, we established an Asian human variation benchmark by constructing and sequencing a stabilized cell line of a Chinese Han volunteer. By using seven different sequencing strategies, we obtained ~3.88 Tb clean data from different laboratories, hoping to reach the point of high sequencing depth and accurate variation detection. Through the combination of variations identified from different sequencing strategies and different analysis pipelines, we identified 3.35 million SNVs and 348.65 thousand indels, which were well supported by our sequencing data and passed our strict quality control, thus should be high confidence variation benchmark. Besides, we also detected 5,913 high-quality SNVs which had 969 sites were novel and  located in the high homologous regions supported by long-range information in both the co-barcoding single tube Long Fragment Read (stLFR) data and PacBio HiFi CCS data. Furthermore, by using the long reads data (stLFR and HiFi CCS), we were able to phase more than 99% heterozygous SNVs, which helps to improve the benchmark to be haplotype level. Our study provided comprehensive sequencing data as well as the integrated variation benchmark of an Asian derived cell line, which would be valuable for future sequencing-based clinical development.