A m6A regulators-related classifier for prognosis and tumor microenvironment characterization in hepatocellular carcinoma.

Background: Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification. Method: RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC). Results: Two distinct m6A modification patterns were identified based on 23 m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit. Conclusion: Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.

Ultrasensitive Method Enables Liquid Biopsy for the Precise Detection of Circulating MicroRNAs.

Due to invasive and serial examinations of bioactive molecules, liquid biopsy (LB) has emerged as a rapid and reliable solution for early disease detection and monitoring. Developing portable devices with high specificity and sensitivity for LB is highly valuable. To realize a generalized approach to increase the sensitivity of LB, we developed an ultrasensitive diagnostic biochip based on the amplification of miRNA by recombinase polymerase amplification and the significant enhancement of fluorescence signals by photonic crystal (PC) materials. The PCs-RPA biochip has a detection limit as low as 0.24 aM, a wide linear range of 8 orders of magnitude, and excellent specificity. Such advantages realize the accurate detection of circulating miRNAs with very low content in clinical serum samples for the precise diagnosis of nonsmall cell lung cancer.

Method for the extraction of circulating nucleic acids based on MOF reveals cell-free RNA signatures in liver cancer.

Cell-free RNA (cfRNA) allows assessment of health, status, and phenotype of a variety of human organs and is a potential biomarker to non-invasively diagnose numerous diseases. Nevertheless, there is a lack of highly efficient and bias-free cfRNA isolation technologies due to the low abundance and instability of cfRNA. Here, we developed a reproducible and high-efficiency isolation technology for different types of cell-free nucleic acids (containing cfRNA and viral RNA) in serum/plasma based on the inclusion of nucleic acids by metal-organic framework (MOF) materials, which greatly improved the isolation efficiency and was able to preserve RNA integrity compared with the most widely used research kit method. Importantly, the quality of cfRNA extracted by the MOF method is about 10-fold that of the kit method, and the MOF method isolates more than three times as many different RNA types as the kit method. The whole transcriptome mapping characteristics of cfRNA in serum from patients with liver cancer was described and a cfRNA signature with six cfRNAs was identified to diagnose liver cancer with high diagnostic efficiency (area under curve = 0.905 in the independent validation cohort) using this MOF method. Thus, this new MOF isolation technique will advance the field of liquid biopsy, with the potential to diagnose liver cancer.

Altered bile metabolome and its diagnostic potential for biliopancreatic malignancies.

BACKGROUND: Due to the difficulty of pathological sampling, the clinical differentiation between benign and malignant biliopancreatic diseases remains challenging. Endoscopic retrograde cholangiopancreatography (ERCP) is used to investigate biliary diseases, enabling the collection of bile. This study assessed potential metabolic alterations in biliopancreatic malignancies by exploring changes in the bile metabolome and the diagnostic potential of bile metabolome analysis. METHODS: A total of 264 bile samples were collected from patients who were divided into a discovery cohort (n = 85) and a validation cohort (n = 179). Untargeted metabolomic analysis was used in the discovery cohort, while targeted metabolomic analysis was used in the validation cohort for further investigation of the differentially abundant metabolites. RESULTS: The untargeted metabolomic analysis revealed that the metabolic changes associated with biliopancreatic malignancies occurred mainly in lipid metabolites, among which fatty acid metabolism was most significantly altered, and differentially abundant metabolites identified in the discovery cohort were mainly enriched in unsaturated fatty acid synthesis and linolenic acid synthesis pathways. Analysis of free fatty acid (FFA) metabolism in the validation cohort revealed that the FFA levels and related indicators verified the abnormal fatty acid metabolism associated with biliopancreatic malignancies. The combined model for biliopancreatic malignancies based on the fatty acid indexes and clinical test results improved the diagnostic performance of current clinical level. Then, we used machine learning to define three different FFA metabolic clusters of biliopancreatic malignancies, and survival analysis showed significant differences in prognostic outcomes among the three clusters. CONCLUSIONS: This study found metabolic alterations in biliopancreatic malignancies based on bile samples, which may provide new insights for the clinical diagnosis and prognostic assessment of biliopancreatic malignancies.

Bile liquid biopsy in biliary tract cancer.

Biliary tract cancers are heterogeneous in etiology, morphology and molecular characteristics thus impacting disease management. Diagnosis is complex and prognosis poor. The advent of liquid biopsy has provided a unique approach to more thoroughly understand tumor biology in general and biliary tract cancers specifically. Due to their minimally invasive nature, liquid biopsy can be used to serially monitor disease progression and allow real-time monitoring of tumor genetic profiles as well as therapeutic response. Due to the unique anatomic location of biliary tract cancer, bile provides a promising biologic fluid for this purpose. This review focuses on the composition of bile and the use of these various components, ie, cells, extracellular vesicles, nucleic acids, proteins and metabolites as potential biomarkers. Based on the disease characteristics and research status of biliary tract cancer, considerable effort should be made to increase understanding of this disease, promote research and development into early diagnosis, develop efficient diagnostic, therapeutic and prognostic markers.

Antibody-Free Fluorine-Assisted Metabolic Sequencing of RNA N4-Acetylcytidine.

N4-Acetylcytidine (ac4C) has been found to affect a variety of cellular and biological processes. For a mechanistic understanding of the roles of ac4C in biology and disease, we present an antibody-free, fluorine-assisted metabolic sequencing method to detect RNA ac4C, called "FAM-seq". We successfully applied FAM-seq to profile ac4C landscapes in human 293T, HeLa, and MDA cell lines in parallel with the reported acRIP-seq method. By comparison with the classic ac4C antibody sequencing method, we found that FAM-seq is a convenient and reliable method for transcriptome-wide mapping of ac4C. Because this method holds promise for detecting nascent RNA ac4C modifications, we further investigated the role of ac4C in regulating chemotherapy drug resistance in chronic myeloid leukemia. The results indicated that drug development or combination therapy could be enhanced by appreciating the key role of ac4C modification in cancer therapy.

Cuproptosis-related signature for clinical prognosis and immunotherapy sensitivity in hepatocellular carcinoma.

BACKGROUND: Copper homeostasis imbalance has been implicated in tumor progression, aggressiveness, and treatment response. However, the precise roles of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) remain poorly understood. METHODS: In this study, we employed a consensus clustering algorithm to identify distinct molecular subtypes. We then performed Kaplan-Meier analysis and univariate Cox regression analysis to identify prognostic differentially expressed genes. The expression of these genes was subsequently validated using qPCR on fresh-frozen tissues obtained from HCC patients. Moreover, leveraging the TCGA-HCC cohort, we constructed a CRGs-related risk prediction model using the LASSO and multivariate Cox regression analysis. RESULTS: By analyzing the data, we successfully established a CRGs risk prognostic model for HCC patients, comprising five differential genes (CAD, SGCB, TXNRD1, KDR, and MTND4P20). Cox regression analysis revealed that the CRGs risk score could serve as an independent prognostic factor for overall survival (hazard ratio [HR] = 1.308, 95% confidence interval [CI] = 1.200 - 1.426, P < 0.001). The area under the curve (AUC) values of the CRGs-score for predicting 1-year, 3-year, and 5-year survival rates were 0.785, 0.724, and 0.723, respectively. Notably, the expression levels of immune checkpoints (including PD-1, PD-L1, and CTLA4) significantly differed between the low- and high-risk score groups. Furthermore, the low-risk score group displayed increased sensitivity to sorafenib, cisplatin, cyclopamine, nilotinib, salubrinal, and gemcitabine, whereas the high-risk score group exhibited heightened sensitivity to lapatinib, erlotinib, and gefitinib. CONCLUSIONS: Our findings highlight the potential of the CRGs risk score as an independent and promising biomarker for clinical prognosis and immunotherapy sensitivity in HCC patients.

Serum Lipoprotein(a) and High-Density Lipoprotein Cholesterol Associate with Diabetic Nephropathy: Evidence from Machine Learning Perspectives.

Purpose: Diabetic nephropathy (DN) is a common complication of type 2 diabetes mellitus (T2DM) that significantly impacts the quality of life for affected patients. Dyslipidemia is a known risk factor for developing cardiovascular complications in T2DM patients. However, the association between serum lipoprotein(a) (Lp(a)) and high-density lipoprotein cholesterol (HDL-C) with DN requires further investigation. Patients and Methods: For this cross-sectional study, we randomly selected T2DM patients with nephropathy (DN, n = 211) and T2DM patients without nephropathy (T2DM, n = 217) from a cohort of 142,611 patients based on predefined inclusion and exclusion criteria. We collected clinical data from the patients to identify potential risk factors for DN using binary logistic regression and machine learning. After obtaining the feature importance score of clinical indicators by building a random forest classifier, we examined the correlations between Lp(a), HDL-C and the top 10 indicators. Finally, we trained decision tree models with top 10 features using training data and evaluated their performance with independent testing data. Results: Compared to the T2DM group, the DN group had significantly higher serum levels of Lp(a) (p < 0.001) and lower levels of HDL-C (p = 0.028). Lp(a) was identified as a risk factor for DN, while HDL-C was found to be protective. We identified the top 10 indicators that were associated with Lp(a) and/or HDL-C, including urinary albumin (uALB), uALB to creatinine ratio (uACR), cystatin C, creatinine, urinary ɑ1-microglobulin, estimated glomerular filtration rate (eGFR), urinary ß2-microglobulin, urea nitrogen, superoxide dismutase and fibrinogen. The decision tree models trained using the top 10 features and with uALB at a cut-off value of 31.1 mg/L showed an average area under the receiver operating characteristic curve (AUC) of 0.874, with an AUC range of 0.870 to 0.890. Conclusion: Our findings indicate that serum Lp(a) and HDL-C are associated with DN and we have provided a decision tree model with uALB as a predictor for DN.

Associations of MTHFR Polymorphisms and Cytosine Modifications with Early-Gestational Diabetes Mellitus in Chinese Pregnant Women.

Early-Gestational Diabetes Mellitus (Early-GDM) is a complex condition that may cause complications in infants of affected mothers. The aim of this case-control study was to analyze the effects of genetic-epigenetic interaction on Early-GDM and fetal development with respect to cytosine modifications (i.e., 5mC, 5-methylcytosines; and 5hmC, 5-hydroxymethylcytosines) and single nucleotide polymorphisms (SNPs) of MTHFR, a key gene involving cytosine modifications. Peripheral blood samples were collected from 92 women in their first or second trimester of pregnancy (Early-GDM, n = 14; Controls, n = 78). Global DNA 5mC and 5hmC were quantified by HPLC-MS/MS, and MTHFR SNPs (rs1801133 C > T and rs1801131 A > C) were determined by TaqMan-qPCR. Association analysis suggested that MTHFR rs1801133 TT genotype was a risk factor of Early-GDM (OR [odds ratio] = 4.00; 95% CI [confidence interval]: 1.24, 12.86; p = 0.02). The C allele of rs1801131 appeared to be a protective factor for the 2-h OGTT (oral glucose tolerance test) (OR = -0.79; 95% CI: -1.48, -0.10; p = 0.03). Patients with Early-GDM had higher global 5mC and lower global 5hmC. The reduction of global 5hmC and the TT genotype of rs1801133 were associated with higher level of the 1st-FBG (fasting blood glucose in the first trimester) (p < 0.05). Additionally, global 5mC showed a positive correlation with birth weight, body length and head circumference of newborns, while global 5hmC showed a negative correlation with birth weight. The current study implicated MTHFR SNPs and cytosine modifications in the development of Early-GDM and potential complications in their newborns.

Reduction of mRNA m6A associates with glucose metabolism via YTHDC1 in human and mice.

AIMS: N6-methyladenosine (m6A) in mRNA is involved in glucose metabolism. Our goal is to investigate the relationship of glucose metabolism, m6A and YTH domain-containing protein 1 (YTHDC1), a binding protein to m6A, in the development of type 2 diabetes (T2D). METHODS: HPLC-MS/MS and qRT-PCR were used to quantify m6A and YTHDC1 levels in white blood cells from patients with T2D and healthy individuals. MIP-CreERT and tamoxifen treatment were used to create ß-cell Ythdc1 knockout mice (ßKO). m6A sequencing and RNA sequencing were performed in wildtype/ßKO islets and MIN6 cells to identify the differential genes. RESULTS: In T2D patients, both of m6A and YTHDC1 levels were reduced and associated with fasting glucose. Deletion of Ythdc1 resulted in glucose intolerance and diabetes due to decreased insulin secretion, even though ß-cell mass in ßKO mice was comparable to wildtype mice. Moreover, Ythdc1 was shown to bind to SRSF3 (serine/arginine-rich splicing factor 3) and CPSF6 (cleavage and polyadenylation specific factor 6) in ß-cells. CONCLUSIONS: Our data suggested that YTHDC1 may regulate mRNA splicing and export by interacting with SRSF3 and CPSF6 to modulate glucose metabolism via regulating insulin secretion, implying YTHDC1 might be a novel potential target for lowing glucose.

Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study.

Objective: This study aims to develop and evaluate a predictive nomogram for early assessment risk factors of gestational diabetes mellitus (GDM) during early pregnancy term, so as to help early clinical management and intervention. Methods: A total of 824 pregnant women at Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province from 1 February 2020 to 30 April 2020 were enrolled in a retrospective observational study and comprised the training dataset. Routine clinical and laboratory information was collected; we applied least absolute shrinkage and selection operator (LASSO) logistic regression and multivariate ROC risk analysis to determine significant predictors and establish the nomogram, and the early pregnancy files (gestational weeks 12-16, n = 392) at the same hospital were collected as a validation dataset. We evaluated the nomogram via the receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis (DCA). Results: We conducted LASSO analysis and multivariate regression to establish a GDM nomogram during the early pregnancy term; the five selected risk predictors are as follows: age, blood urea nitrogen (BUN), fibrinogen-to-albumin ratio (FAR), blood urea nitrogen-to-creatinine ratio (BUN/Cr), and blood urea nitrogen-to-albumin ratio (BUN/ALB). The calibration curve and DCA present optimal predictive power. DCA demonstrates that the nomogram could be applied clinically. Conclusion: An effective nomogram that predicts GDM should be established in order to help clinical management and intervention at the early gestational stage.

Advances in biomarker discovery using circulating cell-free DNA for early detection of hepatocellular carcinoma.

The past several decades have witnessed unprecedented progress in basic and clinical cancer research, and our understanding of the molecular mechanisms and pathogenesis of cancers have been greatly improved. More recently, with the availability of high-throughput sequencing and profiling platforms as well as sophisticated analytical tools and high-performance computing capacity, there have been tremendous advances in the development of diagnostic approaches in clinical oncology, especially the discovery of novel biomarkers for cancer early detection. Although tissue biopsy-based pathology has been the "gold standard" for cancer diagnosis, notable limitations such as the risk due to invasiveness and the bias due to intra-tumoral heterogeneity have limited its broader applications in oncology (e.g., screening, regular disease monitoring). Liquid biopsy analysis that exploits the genetic and epigenetic information contained in DNA/RNA materials from body fluids, particularly circulating cell-free DNA (cfDNA) in the blood, has been an intriguing alternative approach because of advantageous features such as sampling convenience and minimal invasiveness. Taking advantage of innovative enabling technologies, cfDNA has been demonstrated for its clinical potential in cancer early detection, including hepatocellular carcinoma (HCC), the most common liver cancer that causes serious healthcare burden globally. Hereby, we reviewed the current advances in cfDNA-based approaches for cancer biomarker discovery, with a focus on recent findings of cfDNA-based early detection of HCC. Future clinical investigations and trials are warranted to further validate these approaches for early detection of HCC, which will contribute to more effective prevention, control, and intervention strategies with the ultimate goal of reducing HCC-associated mortality. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics.

Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method.

BACKGROUND: Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically. PURPOSE: To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method. METHODS: In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively. RESULTS: Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use. CONCLUSION: This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.

Live-cell RNA imaging using the CRISPR-dCas13 system with modified sgRNAs appended with fluorescent RNA aptamers.

The development of RNA imaging strategies in live cells is essential to improve our understanding of their role in various cellular functions. We report an efficient RNA imaging method based on the CRISPR-dPspCas13b system with fluorescent RNA aptamers in sgRNA (CasFAS) in live cells. Using modified sgRNA attached to fluorescent RNA aptamers that showed reduced background fluorescence, this approach provides a simple, sensitive way to image and track endogenous RNA with high accuracy and efficiency. In addition, color switching can be easily achieved by changing the fluorogenic dye analogues in living cells through user-friendly washing and restaining operations. CasFAS is compatible with orthogonal fluorescent aptamers, such as Broccoli and Pepper, enabling multiple colors RNA labeling or intracellular RNA-RNA interaction imaging. Finally, the visualization of severe fever with thrombocytopenia syndrome virus (SFTSV) was achieved by CasFAS, which may facilitate further studies on this virus.

Angiotensin-Converting Enzyme 2 SNPs as Common Genetic Loci and Optimal Early Identification Genetic Markers for COVID-19.

Background: Angiotensin-converting enzyme 2 (ACE2) is implicated as a host cell receptor that causes infection in the pathogenesis of coronavirus disease 2019 (COVID-19), and its genetic polymorphisms in the ACE2 gene may promote cardiovascular disease and systemic inflammatory injury in COVID-19 patients. Hence, the genetic background may potentially explain the broad interindividual variation in disease susceptibility and/or severity. Methods: Genetic susceptibility to COVID-19 was analyzed by examining single-nucleotide polymorphisms (SNPs) of ACE2 in 246 patients with COVID-19 and 210 normal controls using the TaqMan genotyping assay. Results: We demonstrated that the ACE2 SNPs rs4646142, rs6632677, and rs2074192 were associated with COVID-19 (for all, p < 0.05), and the differences in the ACE2 SNPs rs4646142 and rs6632677 were correlated with COVID-19-related systemic inflammatory injury and cardiovascular risk. Specifically, rs4646142 was associated with high-sensitivity C-reactive protein (hs-CRP), prealbumin (PAB), apolipoprotein A (APOA), high-density lipoprotein (HDL), and acid glycoprotein (AGP) levels. Rs6632677 was also associated with elevated CRP, acid glycoprotein (AGP), and haptoglobin (HPT). Conclusions: Our results suggest that the ACE2 SNPs rs4646142 and rs6632677 may be common genetic loci and optimal early identification genetic markers for COVID-19 with cardiovascular risk.

Clinical Diagnostic and Prognostic Potential of NDRG1 and NDRG2 in Hepatocellular Carcinoma Patients.

Background: Primary liver cancer is still the most common lethal malignancy. The N-myc downstream-regulated gene family (NDRG1-4) is a group of multifunctional proteins associated with carcinogenesis. However, systematic evaluation of the diagnostic and prognostic values of NDRG1 or NDRG2 expression in liver cancer is poorly investigated. Method: The gene expression matrix of liver hepatocellular carcinoma (LIHC) was comprehensively analyzed by the "limma" and "Dseq2" R packages. The Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functional differences. A single-sample GSEA (ssGSEA) was conducted to quantify the extent of immune cell infiltration. Finally, the clinical and prognostic information of LIHC patients was systematically investigated using Kaplan-Meier analysis and logistic and Cox regression analysis. Results: Compared with normal tissues, NDRG1 expression was higher, whereas NDRG2 expression was lower in tumor tissues (P <0.001). The area under the receiver operator characteristic curve (AUROC) of NDRG1 and NDRG2 for LIHC was 0.715 and 0.799, respectively. Kaplan-Meier analysis revealed that NDRG1 and NDRG2 were independent clinical prognostic biomarkers for the overall survival (OS, P = 0.001 and 2.9e-06), progression-free interval (PFI, P = 0.028 and 0.005) and disease-specific survival (DSS, P = 0.027 and P <0.001). The C-indexes and calibration plots of the nomogram suggest that NDRG1 and NDRG2 have an effective predictive performance for OS (C-index: 0.676), DSS (C-index: 0.741) and PFI (C-index: 0.630) of liver cancer patients. The mutation rate of NDRG1 in liver cancer reached up to 14%, and DNA methylation levels of NDRG1 and NDRG2 promoters correlated significantly with clinical prognosis. Conclusions: The mRNA expression and DNA methylation of NDRG superfamily members have the potential for LIHC diagnosis and prognosis via integrative analysis from multiple cohorts.

The diagnostic and prognostic implications of PRKRA expression in HBV-related hepatocellular carcinoma.

BACKGROUND: Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) accounts for more than half of total HCC patients in developing countries. Currently, HBV-related HCC diagnosis and prognosis still lack specific biomarkers. Here, we investigated if PRKRA expression in peripheral blood could be a potential biomarker for the diagnosis/prognosis of HBV-related HCC. METHODS: The expression of PRKRA in HBV-related HCC was firstly analyzed using TCGA and GEO databases. The results were confirmed in a validation cohort including 152 blood samples from 77 healthy controls and 75 HCC patients, 60 of which were infected with HBV. The potential diagnostic and prognostic values of PRKRA were also evaluated by the area under the receiver operator characteristic curve (AUROC) and Kaplan-Meier method, respectively. RESULTS: PRKRA was significantly upregulated in HCC patients, especially in those with HBV infections. In addition, the combination of PRKRA expression in peripheral blood with serum AFP and CEA levels displayed a better diagnostic performance (AUROC = 0.908, 95% CI 0.844-0.972; p < 0.001). Notably, when serum AFP is less than 200 ng/mL, PRKRA expression demonstrated better diagnostic capability. Furthermore, PRKRA expression levels were associated with expression of EIF2AK2 and inflammatory cytokine genes. CONCLUSIONS: Triple combination testing of blood PRKRA expression, serum AFP and CEA levels could be a noninvasive strategy for diagnosis; and the elevation of PRKRA expression could predicate poor prognosis for HBV-related HCC.

Genome-wide Analysis Reflects Novel 5-Hydroxymethylcytosines Implicated in Diabetic Nephropathy and the Biomarker Potential.

Aim: Diabetic nephropathy (DN) has become the most common cause of end-stage renal disease (ESRD) in most countries. Elucidating novel epigenetic contributors to DN can not only enhance our understanding of this complex disorder, but also lay the foundation for developing more effective monitoring tools and preventive interventions in the future, thus contributing to our ultimate goal of improving patient care. Methods: The 5hmC-Seal, a highly selective, chemical labeling technique, was used to profile genome-wide 5-hydroxymethylcytosines (5hmC), a stable cytosine modification type marking gene activation, in circulating cell-free DNA (cfDNA) samples from a cohort of patients recruited at Zhongnan Hospital, including T2D patients with nephropathy (DN, n = 12), T2D patients with non-DN vascular complications (non-DN, n = 29), and T2D patients without any complication (controls, n = 14). Differentially analysis was performed to find DN-associated 5hmC features, followed by the exploration of biomarker potential of 5hmC in cfDNA for DN using a machine learning approach. Results: Genome-wide analyses of 5hmC in cfDNA detected 427 and 336 differential 5hmC modifications associated with DN, compared with non-DN individuals and controls, and suggested relevant pathways such as NOD-like receptor signaling pathway and tyrosine metabolism. Our exploration using a machine learning approach revealed an exploratory model comprised of ten 5hmC genes showing the possibility to distinguish DN from non-DN individuals or controls. Conclusion: Genome-wide analysis suggests the possibility of exploiting novel 5hmC in patient-derived cfDNA as a non-invasive tool for monitoring DN in high risk T2D patients in the future.

CT-Guided Percutaneous Core Needle Biopsy in Typing and Subtyping Lung Cancer: A Comparison to Surgery.

Background: Lung cancer histologic types and subtypes are closely associated with treatment selection and prognosis prediction. In this study, we aim to evaluate the suitability of computed tomography-guided percutaneous core needle biopsy (CT-guided PCNB) in typing and subtyping lung cancer. Methods: From August 2007 to December 2015, the patients who underwent CT-guided PCNB and lung lesion resection were retrospectively collected and analyzed. All pathological sections were reassessed in consensus by 2 junior pathologists (group A) and 2 senior pathologists (group B), respectively. All cases were diagnosed on 3 levels: first, malignant and benign diagnosis; second, histologic types diagnosis; and third, histologic subtypes diagnosis and compared with surgery results. Pearson chi-square test was used to compare the differences of diagnostic accuracy between pathologists in group A and group B. Results: A cohort of 160 patients was included in this study. On the first level, the diagnostic accuracy was 90.63% (group A) and 94.38% (group B), (P = .20). On the second level, the diagnostic accuracy for malignant lesions, adenocarcinoma (ADC), and squamous cell carcinoma (SQC) were, respectively, 72.66%, 84.72%, and 69.05% (group A) and 76.98%, 90.28%, and 71.43% (group B) (P > .05). On the third level, the diagnostic accuracy for ADC subtypes were 26.39% (group A) and 55.56% (group B) (P < 0.01); for SQC subtypes were 28.57% (group A) and 38.10% (group B) (P = 0.36). Conclusion: Small specimens obtained by CT-guided PCNB were suitable for the diagnosis of lung cancer histologic types, which may contribute to the selection of a suitable treatment strategy for the unresectable lung cancers. While for the diagnosis of subtypes, discussion with experienced pathologists was recommended.