Expression of human dCTP pyrophosphatase 1 (DCTPP1) and its association with cisplatin resistance characteristics in ovarian cancer.

Cisplatin (DDP) resistance is a major challenge in treating ovarian cancer patients. A recently discovered enzyme called dCTP pyrophosphatase 1 (DCTPP1) has been implicated in regulating cancer characteristics, including drug responses. In this study, we aimed to understand the role of DCTPP1 in cancer progression and cisplatin response. Using publicly available databases, we analysed the expression and clinical significance of DCTPP1 in ovarian cancer. Our bioinformatics analysis confirmed that DCTPP1 is significantly overexpressed in ovarian cancer and is closely associated with tumour progression and poor prognosis after cisplatin treatment. We also found that DCTPP1 located in oxidoreductase complex and may be involved in various biological processes related to cisplatin resistance, including pyrimidine nucleotide metabolism, the P53 signalling pathway and cell cycle signalling pathways. We observed higher expression of DCTPP1 in cisplatin-resistant cells (SKOV3/DDP) and samples compared to their sensitive counterparts. Additionally, we found that DCTPP1 expression was only enhanced in SKOV3/S cells when treated with cisplatin, indicating different expression patterns of DCTPP1 in cisplatin-sensitive and cisplatin-resistant cancer cells. Our study further supports the notion that cisplatin induces intracellular reactive oxygen species (ROS) and triggers cancer cell death through excessive oxidative stress. Knocking out DCTPP1 reversed the drug resistance of ovarian cancer cells by enhancing the intracellular antioxidant stress response and accumulating ROS. Based on our research findings, we conclude that DCTPP1 has prognostic value for ovarian cancer patients, and targeting DCTPP1 may be clinically significant in overcoming cisplatin resistance in ovarian cancer.

Development of Cas13a-based therapy for cancer treatment.

Gene therapy has become a major focus of current biomedical research. CRISPR (Clustered Regularly Inter spaced Short Palindromic Repeats) systems have been extensively researched for disease treatment applications through genome editing specificity. Compared with Cas9 (CRISPR-associated proteins, Cas), a commonly used tool enzyme for genome editing, Cas13a exhibits RNA-dependent endonuclease activity, including collateral cleavage without obvious potential genetic risks. With its high specificity, Cas13a has significantly improved the sensitivity of viral diagnosis and shown potential to eliminate viruses. However, its efficacy in tumor therapy has not been determined. This review introduces the mechanism and research developments associated with the CRISPR-Cas13a system in tumor treatments and its potential to be used as a new tool for gene therapy. We hope more research would apply Cas13a-based therapy in cancer treatment in the future.

A time-resolved fluorescence immunoassay for rapid and precise automatic quality control of human papillomavirus type 68 VLPs in human papillomavirus vaccine.

The effectiveness and necessity of human papillomavirus (HPV) vaccination to prevent HPV infection and cervical cancer are increasingly recognized by people. The 15-valent HPV vaccine, which protects against almost high-risk types of HPV viruses identified by WHO, has attracted much attention. However, as the valence of vaccines increases, quality control in the HPV vaccine production process is facing more challenges. The precise quality control of the HPV type 68 virus-like particles (VLPs), one of the unique components of the 15-valent HPV vaccine that distinguishes it from existing vaccines, is the new requirement for vaccine manufacturers. Here we developed a novel time-resolved fluorescence immunoassay (TRFIA) for rapid and precise automatic quality control of HPV68 VLPs in HPV vaccine. Two murine monoclonal antibodies specifically targeting the HPV68 L1 protein were used to establish a classical sandwich assay. Except for pretreating the vaccine sample, the whole analysis process was performed by a fully automated machine, which saves detection time and gets rid of manual error. Multiple experiments established that the current novel TRFIA can efficiently and reliably analyses HPV68 VLPs. Present novel TRFIA has exhibited merits with speed, robustness, high sensitivity with a minimum detection value of 0.08 ng/mL, considerable accuracy, a wide detection range (up to 1000 ng/mL) and excellent specificity. It is also expected to provide a new detection method for quality control for each HPV type VLPs. To summarize, the novel TRFIA is of great interest for application in HPV vaccine quality control.

Development and characterization of reference materials for EGFR, KRAS, NRAS, BRAF, PIK3CA, ALK, and MET genetic testing.

BACKGROUND: Along with the dramatic development of molecular diagnostic testing for the detection of oncogene variations, reference materials (RMs) have become increasingly important in performance evaluation of genetic testing. OBJECTIVE: In this study, we built a set of RMs for genetic testing based on next-generation sequencing (NGS). METHOD: Solid tumor tissues were selected as the samples of RMs for preparation. NGS was used to determine and validate the variants and the mutation frequency in DNA samples. Digital PCR was used to determine the copy numbers of RNA samples. The performance of the RMs was validated by six laboratories. RESULTS: Thirty common genetic alterations were designed based on these RMs. RMs consisted of a positive reference, a limit of detection reference, and a negative reference. The validation results confirmed the performance of the RMs. CONCLUSION: These RMs may be an attractive tool for the development, validation, and quality monitoring of molecular genetic testing.

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.

Dual-color quantum dot-loaded nanoparticles based lateral flow biosensor for the simultaneous detection of gastric cancer markers in a single test line.

A dual-color quantum dots-loaded nanoparticles (QPs) based lateral flow biosensor with single test line has been developed. Red and green emitted QPs were conjugated with antibodies and served as detecting probes in assays respectively, while the mixture of various antibodies were immobilized on nitrocellulose membranes as one detection line. Benefit from eliminating the heterogeneity caused by different position on the membrane, current biosensor achieved higher accuracy comparing with prevalent multi-lines or multi-strips lateral flow systems, which is of great significance for analyzing ratio-related diagnostics. The capability and reliability of the multiplex biosensor are also demonstrated by utilizing pepsinogen I (PG I) and pepsinogen II (PG II) as the model analytes. Under the optimal conditions, quantitative detection was achieved with ultra-low limits of detection at 6.9 pM (0.29 ng mL-1, PG I) and 15.7 pM (0.66 ng mL-1, PG II) respectively. The spectra crosstalk was negligible and no apparent cross-reaction was found in simultaneous detection. Furthermore, a good linear correlation of the QPs based lateral flow biosensor and commercial time-resolved fluoroimmunoassay was obtained in the detection of clinical samples, indicating the high reliability of the proposed biosensor.

Europium (III) chelate nanoparticle-based lateral flow immunoassay strips for rapid and quantitative detection of cystatin C in serum.

The concentration of Cys C in the patient's serum can reflect the level of glomerular filtration rate and indicate the occurrence of renal failure. The establishment of a simple and rapid analytical method to quantitatively monitor the concentration of Cys C in serum could help timely detection of renal failure. In this study, we have developed an Eu (III) chelate nanoparticles based lateral flow immunoassay to fulfill real-time monitoring of Cys C concentration in serum within 15 min. This method was performed as a sandwich immunoassay with a wide detection range (0.05-10 µg/mL) and a low limit of detection (24.54 ng/mL). The intra and inter-assay coefficients of variation were 8.31-8.61% and 8.92-9.95%, respectively. Furthermore, the application of this method was evaluated by comparing the determined results with those obtained by chemiluminescence immunoassay, exhibiting a satisfactory correlation (R2 = 0.9830). The developed LFIA method with satisfactory analytical performance has great potential for real-time monitoring of renal failure and self-detection for the high-risk population.

ROS-Induced DCTPP1 Upregulation Contributes to Cisplatin Resistance in Ovarian Cancer.

Cisplatin resistance hinders the improvement of the prognosis of patients with ovarian cancer. Cisplatin induces cancer cell apoptosis by inducing reactive oxygen species (ROS). dCTP pyrophosphatase 1 (DCTPP1) is a newly discovered dNTP pyrophosphatase. This study aimed to identify the role of DCTPP1 in oxidative stress and cisplatin response of ovarian cancer. Our results indicates cisplatin-induced ROS generation was responsible for the upregulation of DCTPP1 in ovarian cancer cells, whereas DCTPP1 knockdown significantly enhanced the sensitivity of ovarian cancer cells to cisplatin, reflect in reactive oxygen species (ROS) generation, double-strand DNA breaks, and cell apoptosis. The expression of redox-related genes and the activation of the PI3/Akt signaling pathway were also inhibited by DCTPP1 knockdown. Our data proposes that the development of therapeutic approaches targeting DCTPP1 may be useful in the treatment of ovarian cancer.

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis.

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing.

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer.

BACKGROUND: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed. METHODS: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions. We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method. RESULTS: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOBmRNA, exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOBmRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOBmRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer. CONCLUSIONS: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.

Noninvasive discrimination of benign and malignant breast lesions using genome-wide nucleosome profiles of plasma cell-free DNA.

BACKGROUND: Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. METHODS: We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. RESULTS: cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. CONCLUSIONS: Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.

Tspan5 promotes epithelial-mesenchymal transition and tumour metastasis of hepatocellular carcinoma by activating Notch signalling.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide due to a high rate of tumour metastasis and disease recurrence. In physiological conditions, tetraspanins interact with specific partner proteins in tetraspanin-enriched microdomains and regulate their subcellular localization and function. However, the function of Tspan5 in pathological processes, particularly in cancer biology and its clinical significance, are still unclear. Here, we describe that a high expression of Tspan5 is significantly associated with some clinicopathological features including invasive length, vascular invasion, clinical stage and poor overall survival of HCC patients. Alterations of Tspan5 expression by lentivirus transductions in HCC cells demonstrated that Tspan5 promotes wound healing and cell migration in vitro and tumour metastasis of HCC cells in vivo. Mechanistic studies revealed that Tspan5 promoted cell migration and tumour metastasis by increasing the enzymatic maturation of ADAM10 and activating Notch signalling via the increase of the cleavage of the Notch1 receptor catalysed by the γ-secretase complex. Activation of Notch signalling by Tspan5 was shown further to enhance the epithelial-mesenchymal transition (EMT) and actin skeleton rearrangement of tumour cells. In clinical HCC samples, Tspan5 expression is strongly correlated with many key molecules acting in Notch signalling and EMT, highlighting the role of Tspan5 in the regulation of Notch signalling, EMT and tumour metastasis of HCC. Our findings provide new insights into the mechanism of tumour metastasis and disease progression of HCC and may facilitate the development of novel clinical intervention strategies against HCC.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer.

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

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.

Systematic Analyses of the Role of the Reader Protein of N 6-Methyladenosine RNA Methylation, YTH Domain Family 2, in Liver Hepatocellular Carcinoma.

BACKGROUND: YTH domain family (YTHDF) 2 acts as a "reader" protein for RNA methylation, which is important in tumor regulation. However, the effect of YTHDF2 in liver hepatocellular carcinoma (LIHC) has yet to be elucidated. METHODS: We explored the role of YTHDF2 in LIHC based on publicly available datasets [The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO)]. A bioinformatics approach was employed to analyze YTHDF2. Logistic regression analyses were applied to analyze the correlation between YTHDF2 expression and clinical characteristics. To evaluate the effect of YTHDF2 on the prognosis of LIHC patients, we used Kaplan-Meier (K-M) curves. Gene set enrichment analysis (GSEA) was undertaken using TCGA dataset. Univariate and multivariate Cox analyses were used to ascertain the correlations between YTHDF2 expression and clinicopathologic characteristics with survival. Genes co-expressed with YTHDF2 were identified and detected using publicly available datasets [LinkedOmics, University of California, Santa Cruz (UCSC), Gene Expression Profiling Interactive Analysis (GEPIA), and GEO]. Correlations between YTHDF2 and infiltration of immune cells were investigated by Tumor Immune Estimation Resource (TIMER) and GEPIA. RESULTS: mRNA and protein expression of YTHDF2 was significantly higher in LIHC tissues than in non-cancerous tissues. High YTHDF2 expression in LIHC was associated with poor prognostic clinical factors (high stage, grade, and T classification). K-M analyses indicated that high YTHDF2 expression was correlated with an unfavorable prognosis. Univariate and multivariate Cox analyses revealed that YTHDF2 was an independent factor for a poor prognosis in LIHC patients. GSEA revealed that the high-expression phenotype of YTHDF2 was consistent with the molecular pathways implicated in LIHC carcinogenesis. Analyses of receiver operating characteristic curves showed that YTHDF2 might have a diagnostic value in LIHC patients. YTHDF2 expression was associated positively with SF3A3 expression, which implied that they may cooperate in LIHC progression. YTHDF2 expression was associated with infiltration of immune cells and their marker genes. YTHDF2 had the potential to regulate polarization of tumor-associated macrophages, induce T-cell exhaustion, and activate T-regulatory cells. CONCLUSION: YTHDF2 may be a promising biomarker for the diagnosis and prognosis of LIHC and may provide new directions and strategies for LIHC treatment.

Comprehensive analysis of ALK, ROS1 and RET rearrangements in locally advanced rectal cancer.

Gene rearrangements, such as anaplastic lymphoma kinase (ALK), c-ros oncogene 1 receptor tyrosine kinase (ROS1), rearranged during transfection (RET) and neurotrophic receptor tyrosine kinase 1 (NTRK1), identified in cancer have been indicated to be robust therapeutic targets in lung carcinomas. However, a few studies have focussed on locally advanced rectal cancer (LARC). The discovery of novel gene fusions is also valuable for LARC research. We used mass spectrometry-based assays and RNA sequencing to detect both known ALK, ROS1, RET and NTRK1 rearrangements and novel gene fusions in LARC patients. FusionMap was also used to find gene fusions. None of the ALK, ROS1, RET or NTRK1 gene fusions were detected by mass spectrometry-based assays or RNA sequencing. Three fusion candidates, integrin subunit beta 7 (ITGB7)-ROS1, lamin A/C (LMNA)-NTRK1 and Golgi-associated PDZ and coiled-coil motif containing (GOPC)-keratin 8 (KRT8), showed relatively high junction-spanning reads by the FusionMap algorithm, but did not pass validation. These results suggest that no ALK, ROS1 or RET rearrangements were found in LARC.

Prognostic Value and Potential Role of Alternative mRNA Splicing Events in Cervical Cancer.

BACKGROUND: Increasing evidence suggests that aberrant alternative splicing (AS) events are associated with progression of cancer. This study evaluated the prognostic value and clarify the role of AS events in cervical cancer (CC). METHODS: Based on RNA-seq AS event data and clinical information of CC patients in The Cancer Genome Atlas (TCGA) database, we sought to identify prognosis-related AS events in this setting. We selected several survival-associated AS events to construct a prognostic predictor for CC through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Moreover, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed on genes with prognosis-related AS events and constructed an AS-splicing factors (SFs) regulatory network. RESULTS: 2770 AS events were significantly correlated with overall survival (OS). The area under the curve (AUC) values of receiver-operator characteristic curve (ROC) for the final prognostic predictor were 0.926, 0.946 and 0.902 at 3, 5, and 10 years, respectively. These values indicated efficiency in prognostic risk stratification for patients with CC. The final prognostic predictor was an independent predictor of OS (HR: 1.24; 95% CI: 1.020-1.504; P < 0.05). The AS-SFs correlation network may reveal an underlying regulatory mechanism of AS events. CONCLUSION: AS events are essential participants in the prognosis of CC and hold great potentials for the prognostic stratification and development of treatment strategy.

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.