BACKGROUND:Diabetes mellitus (DM) and complications such as chronic kidney disease and cardiovascular symptoms pose a substantial public health burden. Increasing studies have shown that circular RNAs (circRNAs) regulate many gene expressions that are essential in diverse pathological and biological procedures. However, the roles of particular circRNAs in DM are unclear. METHODS:In the current investigation, endothelial progenitor cells (EPCs) were used to search for abnormal expression of circRNAs by using high-throughput sequencing under high glucose (HG) conditions. The regulatory mechanisms and targets were then studied through bioinformatics analysis, luciferase reporter analysis, angiogenic differentiation experiments, flow cytometry detection of apoptosis and RT-qPCR analysis. RESULTS:The circ-Astn1 expression in EPCs decreased after HG treatment. Overexpression or circ-Astn1 suppressed HG induced endothelial cell damage. MicroRNA (miR)-138-5p and SIRT5 were found to be the downstream targets of circ-Astn1 through luciferase reporter analysis. SIRT5 downregulation or miR-138-5p overexpression reversed circ-Astn1's protective effect against HG induced endothelial cell dysfunction, including apoptosis and abnormal vascular differentiation. Furthermore, circ-Astn1 overexpression promoted autophagy activation by increasing SIRT5 expression under HG conditions. Our findings suggest that circ-Astn1 mediated promotion of SIRT5 facilitates autophagy by sponging miR-138-5p. CONLUSION:Together, our findings show that the overexpression of circ-Astn1 suppresses HG induced endothelial cell damage by targeting miR-138-5p/SIRT5 axis.

BACKGROUND:With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs. METHODS:A blood-based assay ("LUNG-TRAC") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort. RESULTS:The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets. CONCLUSION:The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer. CLINICAL TRIAL REGISTRATION:www. CLINICALTRIALS:gov (NCT03989219).

Context Accurately distinguishing between benign thyroid nodules (BTNs) and papillary thyroid cancers (PTCs) with current conventional methods poses a significant challenge.Objective We identify DNA methylation markers of immune response-related genes for distinguishing BTNs and PTCs.Methods In this study, we analyzed a public reduced representative bisulfite sequencing dataset and revealed distinct methylation patterns associated with immune signals in PTCs and BTNs. Based on these findings, we developed a diagnostic classifier named the Methylation-based Immune Response Signature (MeIS), which was composed of 15 DNA methylation markers associated with immune response-related genes. We validated MeIS's performance in 2 independent cohorts: Z.S.'s retrospective cohort (50 PTC and 18 BTN surgery-leftover samples) and Z.S.'s preoperative cohort (31 PTC and 30 BTN fine-needle aspiration samples).Results The MeIS classifier demonstrated significant clinical promise, achieving areas under the curve of 0.96, 0.98, 0.89, and 0.90 in the training set, validation set, Z.S.'s retrospective cohort, and Z.S.'s preoperative cohort, respectively. For the cytologically indeterminate thyroid nodules, in Z.S.'s retrospective cohort, MeIS exhibited a sensitivity of 91% and a specificity of 82%; in Z.S.'s preoperative cohort, MeIS achieved a sensitivity of 84% and a specificity of 74%. Additionally, combining MeIS and BRAF V600E detection improved the detecting performance of cytologically indeterminate thyroid nodules, yielding sensitivities of 98% and 87%, and specificities of 82% and 74% in Z.S.'s retrospective cohort and Z.S.'s preoperative cohort, respectively.Conclusion The 15 markers we identified can be employed to improve the diagnostic of cytologically indeterminate thyroid nodules.

m6A demethylase FTO is confirmed to be involved in pancreatic cancer progression. FTO regulates miRNA processing. To investigate the regulatory effect of FTO on miR-383-5p and its role in pancreatic cancer. The expression of miR-383-5p, ITGA3, and FTO was predicted using bioinformatic analysis in tissues and was measured using qPCR in cells. Cell biological functions were investigated using MTT assay, Transwell assay, sphere formation assay, and qPCR. The targeting relationship between miR-383-5p and ITGA3 was evaluated using the dual-luciferase reporter assay. The effect of FTO on miR-383-5p processing was evaluated using RIP and MeRIP assay. FTO expression was upregulated in pancreatic cancer and silencing of FTO promoted the processing of miR-383-5p in an m6A-dependent manner. m6A-modified miRNA processing was recognized by IGF2BP1. Downregulation of miR-383-5p reversed FTO knockdown-induced inhibition of cellular processes. The FTO/miR-383-5p/ITGA3 axis facilitated cell viability, metastasis, and stemness in pancreatic cancer.

e16338 Background: Many cancers are symptoms free in early clinical stages, resulting in nearly half of cancer patients diagnosed in advanced-stages when therapeutic options are limited. Early cancer detection is key to improve clinical outcomes. We developed PanSeer7, a multi-cancer detection assay based on targeted bisulfite sequencing of circulating cell-free DNA (cfDNA) and evaluated its technical performances of reproducibility and sensitivity. Methods: The panel of PanSeer7 consists of 2447 markers which were either differentially methylated between healthy and cancer samples, or distinctively methylated in a specific cancer. We assessed PanSeer7’s reproducibility by using it to sequence technical replicates of cfDNA samples. For its limit of detection (LOD), we prepared samples mimicking cancer plasma DNA by diluting fragmented cancer cell line DNA, which represent 7 common cancer types, into GM12878 control at ratios of 1/10,000 to 1/100. We further tested PanSeer7’s ability to identify tissue of origin (TOO) by analyzing DNA samples from formalin-fixed paraffin-embedded (FFPE) tissues and healthy plasma. Results: We analyzed PanSeer7’s reproducibility by sequencing 40 replicates of synthetic healthy cfDNA samples on four independent batches and with different inputs (from 2ng to 20ng). Results show that at a minimum of 10ng input, PanSeer7 produced highly consistent methylation levels among replicates. As to cancer signal detection, we found that PanSeer7’s technical LOD was 1/10,000 for lung cancer cell line H1650, liver cancer line HepG2, gastric cancer line HGC27, esophageal cancer line KYSE150 and colorectal cancer line SW480; it was slightly lower as 5/10,000 for pancreatic cancer line PANC1 and breast cancer line MDA-MB-231. To evaluate PanSeer7’s accuracy of TOO identification, we sequenced 38 healthy plasma and 121 FFPE tissues (17 of liver cancer, 13 of pancreatic cancer, 21 of gastric cancer, 18 of esophageal cancer, 16 of colorectal cancer, 14 of lung cancer, and 22 of breast cancer). Clustering analysis showed that they were segregated according to their TOO based on methylation levels. We also generated 3500 sets of simulated data by mixing the reads of cancer tissue into those of healthy plasma at ratios of 1/10,000 to 1/100, and trained TOO-predicting models with train data sets. At a ratio of 5/10,000, the model predicted TOO of test data sets with an accuracy of over 95%. Conclusions: PanSeer7 required as low as 10ng input DNA for high reproducibility. Its technical LOD in detecting cancer signal was no lower than 5/10,000 for all 7 cancer cell lines tested, and has an in silico TOO detection LOD of 5/10,000. Thus, PanSeer7 had excellent performances in both cancer signal detection and TOO identification, showing promise to be clinically applied for non-invasive multi-cancer detection after future optimization and validation.

Background Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN).Methods 220 PTC-and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy.Findings From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity = 0.957, specificity = 0.708).Interpretation The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC.Funding This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850). Copyright (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

BACKGROUND:An accurate and reproducible next-generation sequencing platform is essential to identify malignancy-related abnormal DNA methylation changes and translate them into clinical applications including cancer detection, prognosis, and surveillance. However, high-quality DNA methylation sequencing has been challenging because poor sequence diversity of the bisulfite-converted libraries severely impairs sequencing quality and yield. In this study, we tested MGISEQ-2000 Sequencer's capability of DNA methylation sequencing with a published non-invasive pancreatic cancer detection assay, using NovaSeq6000 as the benchmark.RESULTS:We sequenced a series of synthetic cell-free DNA (cfDNA) samples with different tumor fractions and found MGISEQ-2000 yielded data with similar quality as NovaSeq6000. The methylation levels measured by MGISEQ-2000 demonstrated high consistency with NovaSeq6000. Moreover, MGISEQ-2000 showed a comparable analytic sensitivity with NovaSeq6000, suggesting its potential for clinical detection. As to evaluate the clinical performance of MGISEQ-2000, we sequenced 24 clinical samples and predicted the pathology of the samples with a clinical diagnosis model, PDACatch classifier. The clinical model performance of MGISEQ-2000's data was highly consistent with that of NovaSeq6000's data, with the area under the curve of 1. We also tested the model's robustness with MGISEQ-2000's data when reducing the sequencing depth. The results showed that MGISEQ-2000's data showed matching robustness of the PDACatch classifier with NovaSeq6000's data.CONCLUSIONS:Taken together, MGISEQ-2000 demonstrated similar data quality, consistency of the methylation levels, comparable analytic sensitivity, and matching clinical performance, supporting its application in future non-invasive early cancer detection investigations by detecting distinct methylation patterns of cfDNAs.

330 Background: Gastrointestinal (GI) cancers totally account for more than one third of the cancerous deaths, yet there is no cost-effective blood-based assay for the early detection of GI cancers. We sought to develop GutSeer, a noninvasive test based on cell-free DNA (cfDNA) methylation and fragmentation signatures derived from one single targeted DNA methylation sequencing panel, for early detection and localization of five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: A DNA methylation targeted sequencing panel with 1656 target regions was designed. It was then verified in a large cohort of retrospective cancer and control plasma samples for feature selection and modeling. The participants were randomly divided into a training cohort and a validation cohort in a 1:1 ratio. DNA methylation and fragmentomic features were calculated based on GutSeer sequencing data. An ensemble stacked machine learning approach was built to classify cancer and healthy samples in training cohort and tested in validation cohort. We also constructed a TOO model to predict the tissue of origin of detected cancer samples. Results: To develop GutSeer assay, we have enrolled and tested a total of 1844 retrospective plasma samples (787 healthy, 342 LC, 239 GC, 209 EC, 180 CC, and 87 PC), over half of the cancer samples were diagnosed with early-stage disease (TNM stage I 35.6%; stage II 23.3%; stage III 21.7%; stage IV 12.5%). Cancer- vs-healthy model was built on training cohort and tested in validation cohort, achieving an AUC of 0.94 (sensitivity=77.7%, specificity=96.4%) with methylation features, and 0.95 (sensitivity=77.1%, specificity=95.9%) with fragmentomic features. Combining these features could achieve AUC of 0.963 (sensitivity = 86.2%, specificity = 96.7%). For individual cancer types, the sensitivity was 93.3% (CC), 81.1% (EC), 70.3% (GC), 96.5% (LC) and 86.4% (PC), respectively. For predicted cancer samples, we achieved an 82% top-one (66.7% CC, 87.0% GC/EC, 89.0% LC, 63.2% PC) and 95.2% top-two (86,9% CC, 98.2% GC/EC, 97.6% LC, 89.5% PC) TOO accuracy (ACC, accuracy of predicting the most likely, and the top 2 most likely tissue or organ types where the identified cancer was located, respectively) in validation cohort with TOO model combined all features. Conclusions: Based on a single targeted DNA methylation sequencing assay, GutSeer, which combined cfDNA methylation and fragmentomic signatures, could detect and localize the major five GI cancers with high accuracy but low cost. Although this is a pilot study with limited sample size, GutSeer demonstrated the potential to be further optimized into non-invasive diagnostics for blood-based early screening and diagnosis for GI cancers.

4128 Background: Hepatocellular carcinoma (HCC) is one of the most common cancers in China, and one of the leading causes of cancer-related deaths in the country. With a 5-year survival rate of only 15-20%, early detection is crucial to improve the treatment and survival of HCC patients. Currently, alpha-fetoprotein (AFP) is commonly used as a serum marker for HCC, but it is not a sufficiently specific and can cause false positive readings due to elevated levels caused by other liver conditions. An alternative method is to use circulating free DNA (cfDNA) released by tumor cells as cancer-screening targets, which has been shown to be a more sensitive and specific biomarkers for HCC detection. This study aims to develop a non-invasive screening assay based on cfDNA features to improve the detection of early-stage HCC. Methods: Candidate methylation markers for HCC detection were collected and evaluated using GEO, TCGA and in-house datasets, 1601 of which were incorporated into a targeted sequencing panel named HcSeer. Multiple types of cfDNA features were constructed from the sequencing data, which included methylation-related features such as methylation haplotype blocks (MHBs) and methylated haplotype fraction (MHF), and fragmentomics features such as end motif and CNV. For model building, Cancer and healthy plasma samples were randomly divided into a training and a testing set at a 2:1 ratio. A two-step deep neural network model was built to classify HCC using selected features of both types. Results: We previously enrolled a total of 401 plasma samples (200 healthy, 201 HCC) for model construction and the performance of the HcSeer model have been documented. An independent validation cohort of 421 plasma samples (280 healthy, 141 HCC) was currently collected from different centers. In this independent validation, the HcSeer model achieved an AUC of 0.98 with a sensitivity of 96.5% at a specificity of 96.4%. Importantly, HcSeer maintained a high sensitivity for HCC across all stages: 94.3%, 96%, 100% and 100% for stage I – IV cases, respectively. When compared to AFP, HcSeer achieved a significantly higher sensitivity of 94% than AFP’s 55% in 137 HCC cases having AFP level tested. When AFP level was combined with the HcSeer model, the sensitivity for HCC further increased to 96%. Conclusions: This study demonstrated that the DNA methylation and fragmentomics patterns of cfDNA can accurately distinguish HCC and healthy plasma samples, particularly in the early stages of HCC. The combination of the HcSeer and AFP further improved the accuracy of the prediction. Although this study was limited in sample size, it clearly showed the potential of the HcSeer assay for accurate HCC detection in blood.

Protein aggregation and associated amyloid formation is linked with several harmful human pathophysiologies including Alzheimer's, Parkinson's, and cerebrovascular diseases. A potential approach for modulating and exploring amyloid fibrillization is the control of protein self-assembly. Herein, anti-aggregation effects of salidroside, its influence on the kinetics of amyloid fibrillization of Aβ1-42 peptide and its cytotoxicity against cerebrovascular endothelial cells (bEnd.3) were assessed by using a wide range of spectroscopic and cellular techniques. The present outcome of Thioflavin T (ThT) and 8-anilino-1-naphthalenesulfonic acid (ANS) fluorescence, Congo red (CR), and circular dichroism (CD) analyses indicated that salidroside potentially inhibits protein fibril formation. The cellular studies inferred that salidroside protects bEnd.3 cells against Aβ1-42 oligomers -triggered cytotoxicity through modulation of oxidative stress [reactive oxygen species (ROS), superoxide dismutase (SOD) and catalase (CAT) activities] and apoptosis (caspase-3 activity). Therefore, the data signifies the role of salidroside as a promising small molecule in inhibiting Aβ1-42 aggregation and associated cerebrovascular endothelial cell toxicity. Hence, salidroside can serve as a potential inhibitor in the therapeutic advancement to combat cerebrovascular diseases.

4069 Background: Esophageal and gastric cancer (EC and GC) are two common cancer types that severely impact patients’ health. The 5-year survival rate for EC and GC is as low as 19% and 31%, respectively. However, early detection will significantly increase the survival rate: stage-1 EC has a 5-year survival rate of 51%, while for stage-1 GC it’s 69%. Invasive screening methods, such as endoscopy and biopsy, caused low compliance. Computational tomography and carcinoembryonic antigen were limited by low sensitivity. To address this problem, we developed GaEsSeer, a non-invasive targeted-sequencing-based assay that utilizes multiple methylation and fragmentomics features of cell-free DNA (cfDNA) to accurately detect EC and GC signals in blood. Methods: cfDNA was tested using the GaEsSeer panel, which was developed using in-house genome-wide sequencing data on EC and GC samples, and public datasets from databases and literature. Methylation features, which was quantified as methylation haplotypes or methylation encoding score, and fragmentomics features including copy number and end motif ratio were taken for modeling. Separate sub-models were trained utilizing each type of feature, which were eventually combined via logistic regression to establish the final predicting model. Results: A total of 1770 participants were recruited from multiple centers. This included 787 healthy individuals, 448 cancers (209 EC, 239 GC; stage I:156, -II:120, -III:78, and -IV:58), 174 benign esophageal diseases, and 361 benign gastric diseases. For cancer detection, the methylation-only model had an AUC of 0.909 and 0.897 in training (618 total) and test sets (617 total), respectively; while the AUC of the fragmentomics-based model was 0.885 and 0.911, respectively. The combinatorial model further improved performances, which achieves an AUC of 0.940 and 0.931 in the training and test cohorts, respectively. While the specificity remained at 96.7%, GaEsSeer detected 81.1% EC and 70.3% GC cases in the test cohort. It had a sensitivity of 74.2% and 48.9% for stage-I EC and GC, respectively. GaEsSeer also has high specificities of 87.9% and 89.8% for benign esophageal and gastric diseases, respectively. Additionally, the performance of GaEsSeer was compared with known serum cancer markers such as CEA, CA19-9, and CA72-4; and the results show that it had significantly higher sensitivity than any of these serum markers (54.8% vs 6.4% when against CEA; 53.5% vs 7.1% when against CA19-9; 50% and 16.7% when against CA72-4). Conclusions: In this pilot study, we developed the blood-based GaEsSeer assay and a model for EC and GC detection with high accuracy by stacking multiple methylation- and fragmentomics-based submodules together. Further optimization and validation of GaEsSeer using larger prospective cohorts are needed to validate its potentials for clinical application.

Abstract Background Microvascular dysfunction is one of the most common pathological characteristics in Type 2 diabetes. Human mesenchymal stem cell-derived exosomes (hUCMSCs-Exo) have diverse functions in improving microcirculation; however, the molecular mechanism of hUCMSCs-Exo in regulating burn-induced inflammation is not well understood. Methods hUCMSCs-Exo were extracted by hypervelocity centrifugation method, and exosome morphology was observed by transmission electron microscopy, exosome diameter distribution was detected by particle size analysis, and exosome specific proteins were identified by Western blot.2. DB/DB mice were randomly divided into exosomes group and PBS group. Exosomes and PBS were injected into the tail vein, respectively, and the calf muscle tissue was taken 28 days later. 0.5% Evans blue fluorescence assessment microvascular permeability. The expression of CD31 was detected by immunofluorescence.The morphology and function of microvessels in muscle tissue of lower limbs was evaluated by transmission electron microscopy.3. TMT proteomics was used to detect the changes of differential protein expression in lower limb muscle tissues of the PBS group and the exosome group, and data analysis was performed to screen key signal molecules and their involved biological pathways. Key signal molecules CD105 were verified by Western blot. The expression of TGF-β1 in exosomes were evaluated by Western blot. Results Electron microscopy showed that hUCMSCs-Exo presented a uniform vesicle structure, and NTA showed that its diameter was about 160 nm. Western blot showed positive expression of specific proteins CD9, CD81 and TSG101 on exosomes.2. There is no significant change in blood glucose and body weight before and after the exosome treatment. The exosome group can significantly reduce the exudation of Evans blue. Compared with the PBS group. Meanwhile, CD31 immunofluorescence showed that the red fluorescence of exosome treatment was significantly increased, which was higher than that of PBS group. Transmission electron microscopy showed smooth capillary lumen and smooth and complete surface of endothelial cells in the exosome group, while narrow capillary lumen and fingerlike protrusion of endothelial cells in the PBS group.3.Quantitative analysis of TMT proteomics showed that there were 82 differential proteins, including 49 down-regulated proteins and 33 up-regulated proteins. Go enrichment analysis showed that the differential proteins were involved in molecular function, biological process, cell components,among which CD105 was one of the up-regulated proteins. Through literature search, CD105 was found to be related to endothelial cell proliferation. Therefore, this study verified the changes of CD105 in the exosome group, and it was used as the mechanism study of this study. 4. Western blot analysis showed that the expression of CD105 protein in lower limb muscle tissue of exosome group was significantly increased compared with that of PBS group. Based on the fact that CD105 is a component of the TGF-β1 receptor complex and exosomes are rich in growth factors and cytokines, this study further examined the expression of TGF-β1 in exosomes, and the results showed that exosomes had high expression of TGF-β1. Conclusion By improving the integrity of microvascular endothelial cells, hUCMSCs-Exo can improve the permeability of microvessels in diabetic lower muscle tissue, further promote the proliferation of lower limb muscle cells and inhibit the apoptosis of tissue cells. The mechanism may be associated with exosomes rich in TGF-β1, which is likely to promote endothelial cell proliferation and improve permeability through binding to the endothelial CD105/TβR-II receptor complex, while promoting angiogenesis and protecting skeletal muscle cells from apoptosis.

4103 Background: Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide, especially in Asian counties. Patients can be treated more effectively if detected earlier, however the current screening strategies with alpha-fetoprotein (AFP) or ultrasound it is largely suboptimal. We aimed to develop non-invasive and cost-effective assay to improve HCC early detection. Methods: HCC-specific DNA methylation markers were screened from tissue and plasma samples through a modified reduce representation bisulfite sequencing assaay,and optimized by a targeted methylation sequencing assay. The most informative markers were then integrated in a multi-locus qPCR assay, HepaQ. Results: Profiling DNA methylation pattern on 61 tissue samples (31 HCC tumor and 30 normal tissues) and 663 plasma samples (276 HCC and 393 control plasma samples) achieved an AUC of 0.99, which corresponds to 91% sensitivity at 94% specificity. The best-performance markers were further screened and analytically verified in additional tissues and plasmas after several rounds of marker selection. A multi-locus qPCR assay, designated as HepaQ, was then developed to incorporate the most effective markers. A cohort of 559 plasma samples including 293 HCC (84% of them at stage 0/A), 60 liver cirrhosis (LC), 36 chronic hepatitis B (CHB) and 170 healthy controls (CTRL), were used to train a classifier for HCC early detection. HepaQ classifier enables to detect 85.3% of HCC under a specificity of 88.3%, 91.7% and 92.4% in LC, CHB and CTRL, respectively. Finally, HepaQ classifier was validated in 374 plasma samples independently collected from multiple clinical centers to confirm its performance of 87.2% sensitivity in HCC and 86.8%,90.5% and 93.4% specificities in LC, CHB and CTRL respectively. Conclusions: We have developed and demonstrated a blood-based ctDNA methylation assay, HepaQ, that can detect early-stage HCC at high sensitivity and specificity. We proposed that HepaQ assay, a cost-effective qPCR assay, has the great potential to benefit the population at-risk for HCC early detection and screening.

BackgroundPancreatic ductal adenocarcinoma is a cancer with high mortality and low survival, the early detection of which is hampered by the absence of specific symptoms until an advanced stage is reached. No reliable early screening tool for pancreatic ductal adenocarcinoma is currently available. Circulating tumour DNA (ctDNA) methylation has emerged as a promising new type of biomarker for blood-based early detection of multiple types of cancer. As a part of the PanSeerX study, which aims to discover robust ctDNA methylation markers for multicancer early screening, a customised pancreatic ductal adenocarcinoma-specific targeted methylation sequencing panel has been preliminarily developed and validated for its accuracy in classifying pancreatic ductal adenocarcinoma from retrospective clinical plasma samples.MethodsWe led a single-centre retrospective pilot study at the Changhai Hospital, Navy Medical University, Shanghai, China, where 62 adult patients with pancreatic ductal adenocarcinoma and 393 age-matched and sex-matched healthy controls were enrolled after written consent was obtained from each participant. Individuals with previous cancer diagnosis were excluded. Blood samples were drawn from all participants. A methylation target sequencing panel covering 1601 potential pancreatic ductal adenocarcinoma methylation markers, which were collected from our previous PanSeer study and other related studies, identified by mining the public methylomic datasets The Cancer Genome Atlas and Gene Expression Omnibus, or discovered from the analysis of in-house reduced-representation bisulfite sequencing data, was designed and technically validated. The panel was then applied to test the collected plasma samples at mean a sequencing depth of 1000× per target to quantify the methylation levels and patterns on the targets. A two-layer deep neural network model was built to classify cancer and healthy samples from the sequencing data. The robustness of entire approach was verified with a 3× cross-validation by randomly splitting samples into training set and test set at a 2:1 ratio. This study was approved by the Changhai Hospital, Navy Medical University (reference number CHEC2021–165).FindingsAll blood samples were used in the development and validation of the pancreatic ductal adenocarcinoma panel. The average area under the curve of the training dataset during the 3× validation was 1·000 (95% CI 0·999–1·000; sensitivity 100·0% [95% CI 100·0–100·0; specificity 99·0%, [98·6–99·5]). The average area under the curve of the testing dataset during the 3× validation was 0·987 (95% CI 0·971–0·996; sensitivity 89·0% [95% CI 75·0–100·0]; specificity 96·0% [92·3–100·0]). Notably, this model is highly sensitive to pancreatic ductal adenocarcinoma of stages I and II, achieving a sensitivity of 81% for stage I and 88% for stage II, showing its ability to detect early stage pancreatic ductal adenocarcinoma.InterpretationOur preliminary results show that our pancreatic ductal adenocarcinoma-detecting panel is highly accurate in classifying pancreatic ductal adenocarcinoma plasma from healthy controls using ctDNA methylation markers. Its high sensitivity for stages I and II pancreatic cancer is especially promising for further optimisation into diagnostics for blood-based, early pancreatic ductal adenocarcinoma screening. On the basis of these results, a larger, multicentre study is currently underway, which not only enrolled a higher number of patients with pancreatic ductal adenocarcinoma cases and healthy controls, but also included samples from acute and chronic pancreatitis to comprehensive evaluate our pancreatic ductal adenocarcinoma-detecting panel's accuracy in classifying pancreatic ductal adenocarcinoma plasma against non-malignant controls. The results of this study are expected to be reported later in 2022.FundingThe National Key Research and Development Project of China (grant 2019YFC1315904), the 234 Discipline Climbing Plan Project of the First Affiliated Hospital of Naval Military Medical University (grant 2019YXK033), and the Shanghai Science and Technology Committee.

Background: Lung cancer is one of the deadliest types of cancer in China. Its 5-year survival rates at early stages are significantly higher than advanced stages. LDCT has been adopted for lung cancer screening in high-risk individuals; however, debate regarding its accuracy is still ongoing. Mutation detection on cell free DNA (cfDNA) has traditionally been used to monitor DNA molecular changes derived from lung cancer cells in blood, while recently fragmentation pattern profiling of cfDNA has been shown as a promising alternative for early cancer detection. We aimed to combine mutation detection and fragmentation pattern analysis on cfDNA to develop a non-invasive assay to screen early lung cancer. Methods: Candidate DNA mutations were curated from literature and public databases, including COSMIC and TCGA. DNA fragmentation markers were collected from literature and whole genome sequencing (WGS) datasets. A panel of 407 primers covering selected mutation and fragmentation markers was developed to distinguish lung adenocarcinoma (ADC) plasma and normal plasma. We enrolled a total of 122 plasma samples (64 normal, 58 ADC) for this study. 49 normal samples and 44 ADC samples were used to construct a mutation-based classification model and a fragmentation-based classification model separately. The tuning parameters and features were determined by inner 4-fold cross validation. For the mutation-based model, baseline was set using normal samples in training set. Maximum allele frequency was calculated for each sample in test data (15 normal, 14 ADC), which was filtered by the background baseline. For the fragmentation-based model, we used the DELFI fragment score to construct fragmentation profiles, which was the ratio between short fragments (100-150bp) and long fragments (151-220bp). After optimization, the two models were integrated by Logistic Regression to create a combined model, which was validated by 4-fold nested cross validation. Results: ADC and normal plasma were sequenced by the aforementioned panel at an average depth of 2,000X to ensure the reliability of model construction and classification results. In classifying normal and ADC plasma, the mutation model alone is only modestly accurate as it produced an AUC of 0.69. But the fragmentation model demonstrated significantly higher accuracy, achieving AUC of 0.85. Furthermore, the combined model performed better than either model along, achieving an elevated AUC of 0.87. Conclusions: We demonstrate that DNA mutation and fragmentation pattern of cfDNA can classify lung cancer and normal plasmas separately, but fragmentation pattern are more accurate than mutation in this task. Combining the two models further improved prediction accuracy, suggesting they complement each other. Although this is a pilot study of limited cases, it demonstrated the potential of combining markers for accurate lung ADC detection in plasma. Citation Format: Zhoufeng Wang, Minjie Xu, Hua Chen, Kehui Xie, Minyang Su, Qiye He, Zhixi Su, Rui Liu, Weimin Li. Plasma cell-free DNA fragmentation patterns combined with tumor mutation detection in diagnosis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6202.

Hepatocellular carcinoma is one of the deadliest cancers worldwide. Early detection has been shown to enable more effective treatments, thus decreasing morbidity and mortality. Non-invasive cancer detection via circulating tumour DNA (ctDNA) has emerged as a promising approach to monitor the molecular changes in liver tumour cells. We aimed to use ctDNA methylation and fragmentation signals to develop a blood-based assay for hepatocellular carcinoma early detection, named HcSeer.A targeted methylation sequencing panel was designed to integrate 1601 liver cancer-informative methylation markers, on the basis of in-house data and data from the public databases The Cancer Genome Atlas and Gene Expression Omnibus. Various methylation features were constructed from methylation sequencing data, including methylation haplotype load and methylated haplotype fraction. Low-pass whole-genome sequencing data from plasma samples were also analysed at a mean sequencing depth of 10×. Fragmentomic features, such as end motif, breakpoint motif, fragmentation size ratio, and copy number variation, were extracted from whole-genome sequencing data. A two-step deep neural network model was built to classify cancer and healthy samples with selected features of both types. The robustness of entire approach was verified with a 3× cross-validation by randomly splitting samples into training set and test set at a 2:1 ratio.In the discovery phase, a case-control study was designed to develop the HcSeer assay for the early detection of hepatocellular carcinoma. A total of 401 participants were recruited (200 healthy individuals and 201 patients with hepatocellular carcinoma). Most patients with cancer were at early stages standardised by Chinese liver cancer staging (109 [54%] at stage I and 25 [12%] at stage II). The classification model of HcSeer assay was built and cross-validated to achieve an average area under the curve of 0·99 (sensitivity 94% [189 of 201; 95% CI 90-97%] and specificity 96% [192 of 200; 92-98%]). The detection accuracy was observed to increase with cancer stages, with 91% (99 of 109; 95% CI 83-95%) sensitivity for stage I, 96% (24 of 25; 77-100%) for stage II, 98% (43 of 44; 86-100%) for stage III, and 100% (23 of 23, 84-100%) for stage IV. The validation cohort is ongoing, with the aim of reaching 510 plasma samples from multiple centres, including a full spectrum of liver diseases and age-matched healthy controls. The validation phase is expected to be completed in June, 2022.We have developed the HcSeer assay to combine the signatures of DNA methylation and genome-wide fragmentome. In a case-control study, we showed its feasibility to detect early-stage hepatocellular carcinoma with high accuracy. We propose it as a potential aid for non-invasive diagnostics of hepatocellular carcinoma. The performance of the assay is being validated in an independent sample set collected through a multicentre study, which was approved by the Ethical Committee of Zhongshan Hospital affiliated to Fudan University (number B2020-299R).This study was supported by the National Key Research and Development Program of China (grant number 2019YFC1315800).

Background: Thyroid cancer (TC) account for 90% of endocrine malignant tumor. The association between thyroid inflammation and carcinogenesis has long been recognized. However, the differential immunological mechanisms underlying pathogenesis of papillary thyroid cancers (PTCs) and benign thyroid nodules (BTNs) have not been well characterized. In this study, we aimed to explore the immunological differences between PTCs and BTNs. Further, we attempted to develop a novel classifier based on DNA methylation of immune response genes for thyroid cancer precise diagnosis. Methods: A publicly available tissue Reduced Representative Bisulfite Sequencing dataset composed of 80 PTCs and 65 BTNs was collected (GSE107738), including a training cohort (39 PTCs, 28 BTNS) and a testing cohort (41 PTCs, 37 BTNs). We identified differentially methylated regions (DMRs) between PTCs and BTNs. Functional enrichment analyses of DMR-associated genes were conducted by hypergeometric test. DNA methylation markers of immune response genes were extracted. A diagnostic classifier based on these markers was developed using a training cohort and was validated in an independent testing cohort. These markers were further confirmed by two independent datasets (GSE53051: 20 PTCs, 32 BTNs; GSE97466: 60 PTCs, 17 BTNs) generated by the Infinium Methylation 450K array. A new classifier based on the corresponding 15 array-based methylation markers was built using these two datasets, and was validated in the 450K array data of 499 thyroid malignant tissue samples in the TCGA database. Results: We identified 220 DMRs between PTCs and BTNs. DMR-associated genes were significantly enriched in immune response biological processes. The classifier comprised of 15 DNA methylation markers from immune response genes achieved 100% sensitivity, 76% specificity, 82% positive predictive value (PPV), 100% negative predictive value (NPV) and 88% accuracy in the testing cohort. This classifier also demonstrated high accuracy for cytologically indeterminate thyroid nodules (25 PTCs, 29 BTNs). Leave-one-out cross validation using the corresponding array methylation markers in two independent datasets achieved prediction accuracy of 92% and 95%, respectively. An array-based classifier trained on all samples of the methylation array data achieved a sensitivity of 89% in classifying 499 thyroid malignant tissue samples in the TCGA database. Conclusion: Our study demonstrated that DNA methylation signature of immune response gene could be used to differentiate PTCs from BTNs with high accuracy, which may be caused by the different immunological microenvironments in response to PTCs and BTNs. Citation Format: Yiying Li, Minjie Xu, Jin Sun, Qiye He, Zhixi Su, Rui Liu. DNA methylation-based immune response signature for thyroid nodule diagnostics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6194.

Purpose GAS41 is a YEATS domain protein that binds to acetylated histone H3 to promote the chromatin deposition of H2A.Z in non-small cell lung cancer. The role of GAS41 in pancreatic cancer is still unknown. Here, we aimed to reveal this role. Methods GAS41 expression in pancreatic cancer tissues and cell lines was examined using qRT-PCR, Western blotting and immunohistochemistry. MTT, colony formation, spheroid formation and in vivo tumorigenesis assays were performed to assess the proliferation, tumorigenesis, stemness and gemcitabine (GEM) resistance of pancreatic cancer cells. Mechanistically, co-immunoprecipitation (co-IP) and chromatin immunoprecipitation (ChIP) assays were used to evaluate the roles of GAS41, H2A.Z.2 and Notch1 in pancreatic cancer. Results We found that GAS41 is overexpressed in human pancreatic cancer tissues and cell lines, and that its expression increases following the acquisition of GEM resistance. We also found that GAS41 up-regulates Notch, as well as pancreatic cancer cell stemness and GEM resistance in vitro and in vivo. We show that GAS41 binds to H2A.Z.2 and activates Notch and its downstream mediators, thereby regulating stemness and drug resistance. Depletion of GAS41 or H2A.Z.2 was found to down-regulate Notch and to sensitize pancreatic cancer cells to GEM. Conclusion Our data indicate that GAS41 mediates proliferation and GEM resistance in pancreatic cancer cells via H2A.Z.2 and Notch1.

Lung adenocarcinomas (LUAD) arise from precancerous lesions such as atypical adenomatous hyperplasia, which progress into adenocarcinoma in situ and minimally invasive adenocarcinoma, then finally into invasive adenocarcinoma. The cellular heterogeneity and molecular events underlying this stepwise progression remain unclear. In this study, we perform single-cell RNA sequencing of 268,471 cells collected from 25 patients in four histologic stages of LUAD and compare them to normal cell types. We detect a group of cells closely resembling alveolar type 2 cells (AT2) that emerged during atypical adenomatous hyperplasia and whose transcriptional profile began to diverge from that of AT2 cells as LUAD progressed, taking on feature characteristic of stem-like cells. We identify genes related to energy metabolism and ribosome synthesis that are upregulated in early stages of LUAD and may promote progression. MDK and TIMP1 could be potential biomarkers for understanding LUAD pathogenesis. Our work shed light on the underlying transcriptional signatures of distinct histologic stages of LUAD progression and our findings may facilitate early diagnosis.

糖尿病足的治疗主要包括介入治疗、药物治疗和外科手术等.中国糖尿病足细胞与介入治疗技术联盟根据国内外最新研究进展,结合我国实际情况制定并发布《糖尿病足介入综合诊治临床指南》(第五版).该指南汇集国内外综合诊治的临床方案,突出反映我国糖尿病足介入综合诊治策略的研究进展.文中针对新订指南,从国内前沿研究内容,诊治技术拓展以及相关护理与防治的核心内容进行解读.