Placenta‐origin pregnancy complications, including preeclampsia (PE), gestational diabetes mellitus (GDM), fetal growth restriction (FGR), and macrosomia (MA) are common occurrences in pregnancy, resulting in significant morbidity and mortality for both mother and fetus. However, despite their frequency, there are no reliable methods for the early diagnosis of these complications. Since cfDNA is mainly derived from placental trophoblasts and maternal hematopoietic cells, it might have information for gene expression which can be used for disease prediction. Here, low coverage whole‐genome sequencing on plasma DNA from 2,199 pregnancies is performed based on retrospective cohorts of 3,200 pregnant women. Read depth in the promoter regions is examined to define read‐depth distribution patterns of promoters for pregnancy complications and controls. Using machine learning methods, classifiers for predicting pregnancy complications are developed. Using these classifiers, complications are successfully predicted with an accuracy of 80.3%, 78.9%, 72.1%, and 83.0% for MA, FGR, GDM, and PE, respectively. The findings suggest that promoter profiling of cfDNA may be used as a biological biomarker for predicting pregnancy complications at early gestational age.

DNA hypomethylation and/or hypermethylation are presumed to be early events in carcinogenesis, and one or more DNA methyltransferases (DNMTs) have been suggested to play roles in carcinogenesis of gastric cancer (GC). However, there have been no systematic studies regarding the association between DNMT gene polymorphisms and GC risk. Here, we examined the associations of 16 single nucleotide polymorphisms (SNPs) from DNMT1 (rs2114724, rs2228611, rs2228612, rs8101866, rs16999593), DNMT2 (rs11695471, rs11254413), DNMT3A (rs1550117, rs11887120, rs13420827, rs13428812, rs6733301), DNMT3B (rs2424908, rs2424913, rs6087990) and DNMT3L (rs113593938) with GC in the Southern Chinese population. We assessed the associations of these 16 SNPs with GC in a case-control study that consisted of 242 GC cases and 294 controls, using the Sequenom MALDI-TOF-MS platform. Association analyses based on the χ² test and binary logistic regression were performed to determine the odds ratio (OR) and 95% confidence interval (95%CI) for each SNP. We found that rs16999593 in DNMT1, rs11254413 in DNMT2 and rs13420827 in DNMT3A were significantly associated with GC susceptibility (OR 1.45, 0.15, 0.66, respectively; 95% CI 1.00–2.11, p = 0.047; 0.08–0.27, p < 0.01; 0.45–0.97, p = 0.034, respectively, overdominant model). These results suggested that DNMT1, DNMT2 and DNMT3A may play important roles in GC carcinogenesis. However, further studies are required to elucidate the mechanism.

Patterns of DNA methylation are established and maintained by a family of DNA methyltransferases (DNMTs). Aberrant promoter DNA methylation of tumor suppressor genes is found in breast cancer. Association studies between DNMT gene polymorphisms and breast cancer in various populations have reported inconsistent results. This study assessed the associations of single nucleotide polymorphisms (SNPs) in DNMT1, DNMT3A, DNMT3B, DNMT3L, and DNMT2 with breast cancer among Han Chinese women from South China. Sixteen SNPs (rs2114724, rs2228611, rs2228612, rs8101866, and rs16999593 in DNMT1; rs13420827, rs11887120, rs13428812, rs1550117, rs11695471, and rs6733301 in DNMT3A; rs2424908, rs2424913, and rs6087990 in DNMT3B; rs113593938 in DNMT3L, and rs11254413 in DNMT2) in 408 women with breast cancer and 469 controls were genotyped using a MassARRAY matrix-assisted laser desorption/ionization time-of-flight mass spectrometry platform. Two SNPs, rs16999593 in DNMT1 and rs2424908 in DNMT3B, were significantly associated with breast cancer risk. The heterozygous genotype CT of rs16999593 was associated with increased breast cancer risk [odds ratio (OR) = 1.60; 95% confidence interval (95% CI) = 1.20-2.14; P = 0.0052], whereas rs2424908 was associated with decreased risk (OR = 0.62; 95% CI = 0.46-0.84; P = 0.0061). Other DNMT polymorphisms showed no significant associations with breast cancer risk in the study population. Haplotype CGTC of rs2114724, rs2228611, rs8101866, and rs16999593 in DNMT1 differed significantly as a risk factor between the case and control groups (OR = 1.51; 95% CI = 1.18-1.93; P = 0.0012). The heterozygous genotypes of rs16999593 in DNMT1 and rs2424908 in DNMT3B were strongly associated with breast cancer risk.

Micropeptides (≤100 amino acids) are essential regulators of physiological and pathological processes, which can be encoded by small open reading frames (smORFs) derived from long non-coding RNAs (lncRNAs). Recently, lncRNA-encoded micropeptides have been shown to have essential roles in tumorigenesis. Since translated smORF identification remains technically challenging, little is known of their pathological functions in cancer. Therefore, we created classifiers to identify translated smORFs derived from lncRNAs based on ribosome-protected fragment sequencing and machine learning methods. In total, 537 putative translated smORFs were identified and the coding potential of five smORFs was experimentally validated via green fluorescent protein-tagged protein generation and mass spectrometry. After analyzing 11 lncRNA expression profiles of seven cancer types, we identified one validated translated lncRNA, ZFAS1, which was significantly up-regulated in hepatocellular carcinoma (HCC). Functional studies revealed that ZFAS1 can promote cancer cell migration by elevating intracellular reactive oxygen species production by inhibiting nicotinamide adenine dinucleotide dehydrogenase expression, indicating that translated ZFAS1 may be an essential oncogene in the progression of HCC. In this study, we systematically identified translated smORFs derived from lncRNAs and explored their potential pathological functions in cancer to improve our comprehensive understanding of the building blocks of living systems

The fraction of circulating cell-free fetal (cff) DNA in maternal plasma is a critical parameter for aneuploidy screening with non-invasive prenatal testing, especially for those samples located in equivocal zones. We developed an approach to quantify cff DNA fractions directly with sequencing data, and increased cff DNAs by optimizing library construction procedure. Artificial DNA mixture samples (360), with known cff DNA fractions, were used to develop a method to determine cff DNA fraction through calculating the proportion of Y chromosomal unique reads, with sequencing data generated by Ion Proton. To validate our method, we investigated cff DNA fractions of 2,063 pregnant women with fetuses who were diagnosed as high risk of fetal defects. The z-score was calculated to determine aneuploidies for chromosomes 21, 18 and 13. The relationships between z-score and parameters of pregnancies were also analyzed. To improve cff DNA fractions in our samples, two groups were established as follows: in group A, the large-size DNA fragments were removed, and in group B these were retained, during library construction. A method to determine cff DNA fractions was successfully developed using 360 artificial mixture samples in which cff DNA fractions were known. A strong positive correlation was found between z-score and fetal DNA fraction in the artificial mixture samples of trisomy 21, 18 and 13, as well as in clinical maternal plasma samples. There was a positive correlation between gestational age and the cff DNA fraction in the clinical samples, but no correlation for maternal age. Moreover, increased fetal DNA fractions were found in group A compared to group B. A relatively accurate method was developed to determine the cff DNA fraction in maternal plasma. By optimizing, we can improve cff DNA fractions in sequencing samples, which may contribute to improvements in detection rate and reliability.

Oxford Nanopore Technology (ONT) third-generation sequencing (TGS) is a versatile genetic diagnostic platform. However, it is nonetheless challenging to prepare long-template libraries for long-read TGS, particularly the ONT method for analysis of hemoglobinopathy variants involving complex structures and occurring in GC-rich and/or homologous regions. A multiplex long PCR was designed to prepare library templates, including the whole-gene amplicons for HBA2/1, HBG2/1, HBD, and HBB, as well as the allelic amplicons for targeted deletions and special structural variations. Library construction was performed using long-PCR products, and sequencing was conducted on an Oxford Nanopore MinION instrument. Genotypes were identified based on integrative genomics viewer (IGV) plots. This novel long-read TGS method distinguished all single nucleotide variants and structural variants within HBA2/1, HBG2/1, HBD, and HBB based on the whole-gene sequence reads. Targeted deletions and special structural variations were also identified according to the specific allelic reads. The result of 158 α-/β-thalassemia samples showed 100% concordance with previously known genotypes. This ONT TGS method is high-throughput, which can be used for molecular screening and genetic diagnosis of hemoglobinopathies. The strategy of multiplex long PCR is an efficient strategy for library preparation, providing a practical reference for TGS assay development.