Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.

BACKGROUND: Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules. METHODS: Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules. RESULTS: Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones. CONCLUSIONS: Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic. CLINICALTRIALS: gov/ct2/show/NCT05432128 .

Non-invasive diagnosis of pulmonary nodules by circulating tumor DNA methylation: A prospective multicenter study.

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).

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study.

BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy. METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes. RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies. CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

MeIS: DNA Methylation-Based Immune Response Signatures for Thyroid Nodule Diagnostics.

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.

Cross-platform comparisons for targeted bisulfite sequencing of MGISEQ-2000 and NovaSeq6000.

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.

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma.

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).

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

BACKGROUND AND AIM: Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS: A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. RESULTS: We grouped 35 HCC patients into 2 categories, including the MVI- group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI- group than that in the predicted MVI + group. CONCLUSIONS: In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.

Noninvasive detection of pancreatic ductal adenocarcinoma using the methylation signature of circulating tumour DNA.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has the lowest overall survival rate primarily due to the late onset of symptoms and rapid progression. Reliable and accurate tests for early detection are lacking. We aimed to develop a noninvasive test for early PDAC detection by capturing the circulating tumour DNA (ctDNA) methylation signature in blood. METHODS: Genome-wide methylation profiles were generated from PDAC and nonmalignant tissues and plasma. Methylation haplotype blocks (MHBs) were examined to discover de novo PDAC markers. They were combined with multiple cancer markers and screened for PDAC classification accuracy. The most accurate markers were used to develop PDACatch, a targeted methylation sequencing assay. PDACatch was applied to additional PDAC and healthy plasma cohorts to train, validate and independently test a PDAC-discriminating classifier. Finally, the classifier was compared and integrated with carbohydrate antigen 19-9 (CA19-9) to evaluate and maximize its accuracy and utility. RESULTS: In total, 90 tissues and 546 plasma samples were collected from 232 PDAC patients, 25 chronic pancreatitis (CP) patients and 323 healthy controls. Among 223 PDAC cases with known stage information, 43/119/38/23 cases were of Stage I/II/III/IV. A total of 171 de novo PDAC-specific markers and 595 multicancer markers were screened for PDAC classification accuracy. The top 185 markers were included in PDACatch, from which a 56-marker classifier for PDAC plasma was trained, validated and independently tested. It achieved an area under the curve (AUC) of 0.91 in both the validation (31 PDAC, 26 healthy; sensitivity = 84%, specificity = 89%) and independent tests (74 PDAC, 65 healthy; sensitivity = 82%, specificity = 88%). Importantly, the PDACatch classifier detected CA19-9-negative PDAC plasma at sensitivities of 75 and 100% during the validation and independent tests, respectively. It was more sensitive than CA19-9 in detecting Stage I (sensitivity = 80 and 68%, respectively) and early-stage (Stage I-IIa) PDAC (sensitivity = 76 and 70%, respectively). A combinatorial classifier integrating PDACatch and CA19-9 outperformed (AUC=0.94) either PDACatch (0.91) or CA19-9 (0.89) alone (p < 0.001). CONCLUSIONS: The PDACatch assay demonstrated high sensitivity for early PDAC plasma, providing potential utility for noninvasive detection of early PDAC and indicating the effectiveness of methylation haplotype analyses in discovering robust cancer markers.

DNA methylation patterns at and beyond the histological margin of early-stage invasive lung adenocarcinoma radiologically manifested as pure ground-glass opacity.

BACKGROUND: Early-stage lung cancers radiologically manifested as ground-glass opacities (GGOs) have been increasingly identified, among which pure GGO (pGGO) has a good prognosis after local resection. However, the optimal surgical margin is still under debate. Precancerous lesions exist in tumor-adjacent tissues beyond the histological margin. However, potential precancerous epigenetic variation patterns beyond the histological margin of pGGO are yet to be discovered and described. RESULTS: A genome-wide high-resolution DNA methylation analysis was performed on samples collected from 15 pGGO at tumor core (TC), tumor edge (TE), para-tumor tissues at the 5 mm, 10 mm, 15 mm, 20 mm beyond the tumor, and peripheral normal (PN) tissue. TC and TE were tested with the same genetic alterations, which were also observed in histologically normal tissue at 5 mm in two patients with lower mutation allele frequency. According to the difference of methylation profiles between PN samples, 2284 methylation haplotype blocks (MHBs), 1657 differentially methylated CpG sites (DMCs), and 713 differentially methylated regions (DMRs) were identified using reduced representation bisulfite sequencing (RRBS). Two different patterns of methylation markers were observed: Steep (S) markers sharply changed at 5 mm beyond the histological margin, and Gradual (G) markers changed gradually from TC to PN. S markers composed 86.2% of the tumor-related methylation markers, and G markers composed the other 13.8%. S-marker-associated genes enriched in GO terms that were related to the hallmarks of cancer, and G-markers-associated genes enriched in pathways of stem cell pluripotency and transcriptional misregulation in cancer. Significant difference in DNA methylation score was observed between peripheral normal tissue and tumor-adjacent tissues 5 mm further from the histological margin (p < 0.001 in MHB markers). DNA methylation score at and beyond 10 mm from histological margin is not significantly different from peripheral normal tissues (p > 0.05 in all markers). CONCLUSIONS: According to the methylation pattern observed in our study, it was implied that methylation alterations were not significantly different between tissues at or beyond P10 and distal normal tissues. This finding explained for the excellent prognosis from radical resections with surgical margins of more than 15 mm. The inclusion of epigenetic characteristics into surgical margin analysis may yield a more sensitive and accurate assessment of remnant cancerous and precancerous cells in the surgical margins.

Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements.

BACKGROUND: Early diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results for stratifying acute pancreatitis (AP) severity. RESULTS: We identified 11 markers predictive of AP severity and trained an AP stratification model called APSAVE, which classified AP cases within 24 hours at an average area under the curve (AUC) of 0.74 +/- 0.04. It was further validated in 568 validation cases, achieving an AUC of 0.73, which is similar to that of Ranson's criteria (AUC = 0.74) and higher than APACHE II and BISAP (AUC = 0.69 and 0.66, respectively). CONCLUSIONS: We developed and validated a venous blood marker-based AP severity stratification model with higher accuracy and broader applicability, which holds promises for reducing SAP mortality and improving its clinical outcomes. MATERIALS AND METHODS: Nine hundred and forty-five AP patients were enrolled into this study. Clinical venous blood tests covering 65 biomarkers were performed on AP patients within 24 hours of admission. An SAP prediction model was built with statistical learning to select biomarkers that are most predictive for AP severity.

DNA Methylation Haplotype Block Markers Efficiently Discriminate Follicular Thyroid Carcinoma from Follicular Adenoma.

CONTEXT: Follicular thyroid carcinoma (FTC) is the second most common type of thyroid carcinoma and must be pathologically distinguished from benign follicular adenoma (FA). Additionally, the clinical assessment of thyroid tumors with uncertain malignant potential (TT-UMP) demands effective indicators. OBJECTIVE: We aimed to identify discriminating DNA methylation markers between FA and FTC. METHODS: DNA methylation patterns were investigated in 33 FTC and 33 FA samples using reduced representation bisulfite sequencing and methylation haplotype block-based analysis. A prediction model was constructed and validated in an independent cohort of 13 FTC and 13 FA samples. Moreover, 36 TT-UMP samples were assessed using this model. RESULTS: A total of 70 DNA methylation markers, approximately half of which were located within promoters, were identified to be significantly different between the FTC and FA samples. All the Gene Ontology terms enriched among the marker-associated genes were related to "DNA binding," implying that the inactivation of DNA binding played a role in FTC development. A random forest model with an area under the curve of 0.994 was constructed using those markers for discriminating FTC from FA in the validation cohort. When the TT-UMP samples were scored using this model, those with fewer driver mutations also exhibited lower scores. CONCLUSION: An FTC-predicting model was constructed using DNA methylation markers, which distinguished between FA and FTC tissues with a high degree of accuracy. This model can also be used to help determine the potential of malignancy in TT-UMP.

Non-invasive early detection of cancer four years before conventional diagnosis using a blood test.

Early detection has the potential to reduce cancer mortality, but an effective screening test must demonstrate asymptomatic cancer detection years before conventional diagnosis in a longitudinal study. In the Taizhou Longitudinal Study (TZL), 123,115 healthy subjects provided plasma samples for long-term storage and were then monitored for cancer occurrence. Here we report the preliminary results of PanSeer, a noninvasive blood test based on circulating tumor DNA methylation, on TZL plasma samples from 605 asymptomatic individuals, 191 of whom were later diagnosed with stomach, esophageal, colorectal, lung or liver cancer within four years of blood draw. We also assay plasma samples from an additional 223 cancer patients, plus 200 primary tumor and normal tissues. We show that PanSeer detects five common types of cancer in 88% (95% CI: 80-93%) of post-diagnosis patients with a specificity of 96% (95% CI: 93-98%), We also demonstrate that PanSeer detects cancer in 95% (95% CI: 89-98%) of asymptomatic individuals who were later diagnosed, though future longitudinal studies are required to confirm this result. These results demonstrate that cancer can be non-invasively detected up to four years before current standard of care.

Pseudostellaria heterophylla Extract Polysaccharide H-1-2 Suppresses Pancreatic Cancer by Inhibiting Hypoxia-Induced AG2.

We aimed to examine the therapeutic potential of polysaccharide H-1-2, a bioactive component of Pseudostellaria heterophylla, against pancreatic cancer, as well as to demonstrate the underlying molecular mechanisms. Invasion and migration of pancreatic cells treated with H-1-2 were evaluated. A xenograft tumor mouse model was established to assess the effect of H-1-2 on tumor growth. Expression levels of hypoxic inducible factor-1α (HIF1α) and anterior gradient 2 (AGR2) were measured in pancreatic cells after H-1-2 treatment. Luciferase report and chromatin immunoprecipitation assays were conducted to investigate HIF1α regulation on AGR2. AGR2 expression was re-introduced into pancreatic cells to assess the role of AGR2 as a downstream effector of hypoxia after H-1-2 treatment. H-1-2 inhibited invasion and migration of pancreatic cancer cells, repressed xenograft pancreatic tumor growth, and increased survival of mice. H-1-2 repressed AGR2 expression in pancreatic cancer cells through the hypoxia response element (HRE) in its promoter region. Ectopic AGR2 expression partially negated the H-1-2 inhibitory effect on invasion and migration of pancreatic cells and on xenograft pancreatic tumors growth, and it also compromised the H-1-2 promotional effect on survival of mice. We conclude that H-1-2 suppresses pancreatic cancer by inhibiting hypoxia-induced AGR2 expression, supporting further investigation into its efficacy against pancreatic cancer in clinical settings.

Corrigendum: Natural Killer Cell-Derived Exosomal miR-3607-3p Inhibits Pancreatic Cancer Progression by Targeting IL-26.

[This corrects the article DOI: 10.3389/fimmu.2019.02819.].

Illuminating the Activated Brain: Emerging Activity-Dependent Tools to Capture and Control Functional Neural Circuits.

Immediate-early genes (IEGs) have long been used to visualize neural activations induced by sensory and behavioral stimuli. Recent advances in imaging techniques have made it possible to use endogenous IEG signals to visualize and discriminate neural ensembles activated by multiple stimuli, and to map whole-brain-scale neural activation at single-neuron resolution. In addition, a collection of IEG-dependent molecular tools has been developed that can be used to complement the labeling of endogenous IEG genes and, especially, to manipulate activated neural ensembles in order to reveal the circuits and mechanisms underlying different behaviors. Here, we review these techniques and tools in terms of their utility in studying functional neural circuits. In addition, we provide an experimental strategy to measure the signal-to-noise ratio of IEG-dependent molecular tools, for evaluating their suitability for investigating relevant circuits and behaviors.

Natural Killer Cell-Derived Exosomal miR-3607-3p Inhibits Pancreatic Cancer Progression by Targeting IL-26.

Increasing evidences have suggested that natural killer (NK) cells in the tumor microenvironment are involved in the regulation of cancer development. However, the potential biological roles and regulatory mechanisms of NK cells in pancreatic cancer (PC) remain unclear. Co-culture system of NK cells with PC cells is used to test the ability of cancer cell proliferation, migration and invasion both in vitro and in vivo. And tail vein intravenous transfer was used to test metastasis in vivo. Meanwhile, extracellular vesicles (EVs) were separated and examined. Furthermore, reporter assay and Biotin-RNA pull down assay were performed to verify the interaction between molecules. NK cells can inhibit the malignant transformation of co-cultured PC cells both in vivo and in vitro, which requires miR-3607-3p. miR-3607-3p is found enriched in the EVs of NK cells and transmitted to PC cells, and low level of miR-3607-3p predicts poor prognosis in PC patients. It can also inhibit proliferation, migration and invasion of PC cells in vitro. Importantly, IL-26 is found to be a direct target of miR-3607-3p in PC cells. miR-3607-3p enriched in EVs derived from NK cells can inhibit the malignant transformation of PC probably through directly targeting of IL-26.

Comparison of next-generation sequencing and immunohistochemistry analysis for targeted therapy-related genomic status in lung cancer patients.

BACKGROUND: Some drugs that target molecular pathways are available for the targeted treatment of lung cancer. Multiple tests are needed to detect the status of the known molecular targets to determine whether the patients can respond to the drugs. An integrated platform for various gene alteration detection including both mutations and rearrangements is necessary for patients, especially those without enough tissue. METHODS: In our study, detections of EGFR mutations, ALK rearrangement, ROS1 rearrangement, and alterations of other nine important lung cancer-related genes were integrated into a single next-generation sequencing (NGS) platform. The NGS analysis was performed in 107 cases of non-small cell lung cancer (NSCLC). Meanwhile, hot spots such as EGFR L858R, EGFR E746-A750Del mutations and gene rearrangement of ALK and ROS1 were detected by immunohistochemical (IHC) staining. RESULTS: NGS could explore various gene mutations and gene rearrangements with a reduced experiment time and lower amounts of tumor tissues than multiple IHC staining experiments. NGS results were more informative and reliable than IHC staining for EGFR gene alterations, especially for the exon 19 region. NGS could also increase the positive rate of ALK rearrangement and decrease the false positive results of ROS1 rearrangements detected by IHC staining. CONCLUSIONS: NGS is effective for confirmation the status of various important lung cancer-related gene alterations. Furthermore, NGS is necessary for the confirmation of the IHC results of ALK and ROS1 rearrangements.

Use of TAI-FISH to visualize neural ensembles activated by multiple stimuli.

Researchers in behavioral neuroscience have long sought imaging techniques that can identify and distinguish neural ensembles that are activated by sequentially applied stimuli at single-cell resolution across the whole brain. Taking advantage of the different kinetics of immediate-early genes' mRNA and protein expression, we addressed this problem by developing tyramide-amplified immunohistochemistry-fluorescence in situ hybridization (TAI-FISH), a dual-epoch neural-activity-dependent labeling protocol. Here we describe the step-by-step procedures for TAI-FISH on brain sections from mice that were sequentially stimulated by morphine (appetitive first stimulus) and foot shock (aversive second stimulus). We exemplify our approach by FISH-labeling the neural ensembles that were activated by the second stimulus for the mRNA expression of c-fos, a well-established marker of neural activation. We labeled neuronal ensembles activated by the first stimulus using fluorescence immunohistochemistry (IHC) for the c-fos protein. To further improve the temporal separation of the c-fos mRNA and protein signals, we provide instructions on how to enhance the protein signals using tyramide signal amplification (TSA). Compared with other dual-epoch labeling techniques, TAI-FISH provides better temporal separation of the activated neural ensembles and is better suited to investigation of whole-brain responses. TAI-FISH has been used to investigate neural activation patterns in response to appetitive and aversive stimuli, and we expect it to be more broadly utilized for visualizing brain responses to other types of stimuli, such as sensory stimuli or psychiatric drugs. From first stimulation to image analysis, TAI-FISH takes ∼9 d to complete.

Drosophila poised enhancers are generated during tissue patterning with the help of repression.

Histone modifications are frequently used as markers for enhancer states, but how to interpret enhancer states in the context of embryonic development is not clear. The poised enhancer signature, involving H3K4me1 and low levels of H3K27ac, has been reported to mark inactive enhancers that are poised for future activation. However, future activation is not always observed, and alternative reasons for the widespread occurrence of this enhancer signature have not been investigated. By analyzing enhancers during dorsal-ventral (DV) axis formation in the Drosophila embryo, we find that the poised enhancer signature is specifically generated during patterning in the tissue where the enhancers are not induced, including at enhancers that are known to be repressed by a transcriptional repressor. These results suggest that, rather than serving exclusively as an intermediate step before future activation, the poised enhancer state may be a mark for spatial regulation during tissue patterning. We discuss the possibility that the poised enhancer state is more generally the result of repression by transcriptional repressors.