Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

Acute pancreatitis (AP) is a type of digestive system disease with high mortality. Previous studies have shown that gut microbiota can participate in developing and treating acute pancreatitis by affecting the host’s metabolism. In this study, we followed 20 AP patients to generate longitudinal gut microbiota profiles and activity during disease (before treatment, on the third day of treatment, and 1 month after discharge). We analyzed species composition and metabolic pathways’ changes across the treatment phase, severity, and etiology. The diversity of the gut microbiome of patients with AP did not show much variation with treatment. In contrast, the metabolic functions of the gut microbiota, such as the essential chemical reactions that produce energy and maintain life, were partially reinstated after treatment. The severe AP (SAP) patients contained less beneficial bacteria (i.e., Bacteroides xylanisolvens , Clostridium lavalense, and Roseburia inulinivorans ) and weaker sugar degradation function than mild AP patients before treatment. Moreover, etiology was one of the drivers of gut microbiome composition and explained the 3.54% variation in species’ relative abundance. The relative abundance of pathways related to lipid synthesis was higher in the gut of hyperlipidemia AP patients than in biliary AP patients. The composition and functional profiles of the gut microbiota reflect the severity and etiology of AP. Otherwise, we also identified bacterial species associated with SAP, i.e., Oscillibacter sp . 57_20 , Parabacteroides johnsonii, Bacteroides stercoris, Methanobrevibacter smithii, Ruminococcus lactaris, Coprococcus comes, and Dorea formicigenerans , which have the potential to identify the SAP at an early stage. Importance: Acute pancreatitis (AP) is a type of digestive system disease with high mortality. Previous studies have shown that gut microbiota can participate in the development and treatment of acute pancreatitis by affecting the host’s metabolism. However, fewer studies acquired metagenomic sequencing data to associate species to functions intuitively and performed longitudinal analysis to explore how gut microbiota influences the development of AP. We followed 20 AP patients to generate longitudinal gut microbiota profiles and activity during disease and studied the differences in intestinal flora under different severities and etiologies. We have two findings. First, the gut microbiota profile has the potential to identify the severity and etiology of AP at an early stage. Second, gut microbiota likely acts synergistically in the development of AP. This study provides a reference for characterizing the driver flora of severe AP to identify the severity of acute pancreatitis at an early stage.

Hepatocellular carcinoma (HCC) genomic research has discovered actionable genetic changes that might guide treatment decisions and clinical trials. Nonetheless, due to a lack of large-scale multicenter clinical validation, these putative targets have not been converted into patient survival advantages. So, it's crucial to ascertain whether genetic analysis is clinically feasible, useful, and whether it can be advantageous for patients. We sequenced tumour tissue and blood samples (as normal controls) from 111 Chinese HCC patients at Qingdao University Hospital using the 508-gene panel and the 688-gene panel, respectively. Approximately 95% of patients had gene variations related to targeted treatment, with 50% having clinically actionable mutations that offered significant information for targeted therapy. Immune cell infiltration was enhanced in individuals with TP53 mutations but decreased in patients with CTNNB1 and KMT2D mutations. More notably, we discovered that SPEN, EPPK1, and BRCA2 mutations were related to decreased median overall survival, although MUC16 mutations were not. Furthermore, we found mutant MUC16 as an independent protective factor for the prognosis of HCC patients after curative hepatectomy. In conclusion, this study connects genetic abnormalities to clinical practice and potentially identifies individuals with poor prognoses who may benefit from targeted treatment or immunotherapy.

Gastric carcinoma (GC) is a high heterogeneity and malignant tumor with a poor prognosis. The current implementation of immunotherapy in GC is limited due to the insufficient exploration of immune-related mutations and speculated early mutation events. Therefore, we performed whole-exome sequencing on 40 patients with GC to explore their genetic characteristics, shedding light on the order of genetic events, somatic mutations impacting the immune microenvironment, and potential biomarkers for immunotherapy. Regarding genetic events, TP53 disruptions were identified as frequent and early events in GC progression, often occurring alongside other gene mutations. The mutations occurring in GANS , SMAD4 , and POLE were early independent events. Patients harboring CSMD3 , FAT4 , FLG , KMT2C , LRP1B , MUC5B , MUC16 , PLEC , RNF43 , SYNE1 , TP53 , TTN , XIRP2 , and ZFHX4 mutations tended to have decreased B cells, T cells, macrophage, neutrophil, and dendritic cells infiltration, except for the ARID1A gene mutations. We also found patients with microsatellite instability-high tumors had higher homologous recombination deficiency (HRD) scores. HRD showed a positive correlation with tumor mutational burden, which might serve as indirect evidence supporting the potential of HRD as a biomarker for GC. These findings highlighted GC 's high heterogeneity and complexity and provided valuable insights into the somatic mutations that affect early genetic progression and immune microenvironment.

Endometrial cancer (EC) is a major gynecological malignancy with rising morbidity and mortality worldwide. The aim of this study was to explore a safe and readily available sample and a sensitive and effective detection method and its biomarkers for early diagnosis of EC, which is critical for patient prognosis. This study designed a panel targeting variants for EC-related genes, assessed its technical performance by comparing it with whole-exon sequencing, and explored the diagnostic potential of endometrial biopsies using the Pipelle aspirator, cervical samples using the Pap brush, and vaginal specimens using the swab from 38 EC patients and 208 women with risk factors for EC by applying targeted panel sequencing (TPS). TPS produced high-quality data (Q30 > 85% and mapping ratios > 99.35%) and was found to have strong consistency with whole-exome sequencing (WES) in detecting pathogenic mutations (92.11%), calculating homologous recombination deficiency (HRD) scores (r = 0.65), and assessing the microsatellite instability (MSI) status of EC (100%). The sensitivity of TPS in detection of EC is slightly better than that of WES (86.84% vs. 84.21%). Of the three types of samples detected using TPS, endometrial biopsy using the Pipelle aspirator had the highest sensitivity in detection of pathogenic mutations (81.87%) and the best consistency with surgical tumor specimens in MSI (85.16%). About 84% of EC patients contained pathogenic mutations in PIK3CA, PTEN, TP53, ARID1A, CTNNB1, KRAS, and MTOR, suggesting that this small gene set can achieve an excellent pathogenic mutation detection rate in Chinese EC patients. The custom panel combined with ultra-deep sequencing serves as a sensitive method for detecting genetic lesions from endometrial biopsy using the Pipelle aspirator.