Codonopsis pilosula water extract delays D-galactose-induced aging of the brain in mice by activating autophagy and regulating metabolism.

ETHNOPHARMACOLOGICAL RELEVANCE: Codonopsis pilosula (C. pilosula), also called "Dangshen" in Chinese, is derived from the roots of Codonopsis pilosula (Franch.) Nannf. (C. pilosula), Codonopsis pilosula var. Modesta (Nannf.) L.D.Shen (C. pilosula var. modesta) or Codonopsis pilosula subsp. Tangshen (Oliv.) D.Y.Hong (C. pilosula subsp. tangshen), is a well-known traditional Chinese medicine. It has been regularly used for anti-aging, strengthening the spleen and tonifying the lungs, regulating blood sugar, lowering blood pressure, strengthening the body's immune system, etc. However, the mechanism, by which, C. pilosula exerts its therapeutic effects on brain aging remains unclear. AIM OF THE STUDY: This study aimed to investigate the underlying mechanisms of the protective effects of C. pilosula water extract (CPWE) on the hippocampal tissue of D-galactose-induced aging mice. MATERIALS AND METHODS: In this research, plant taxonomy has been confirmed in the "The Plant List" database (www.theplantlist.org). First, an aging mouse model was established through the intraperitoneal injections of D-galactose solution, and low-, medium-, and high-dose CPWE were administered to mice by gavage for 42 days. Then, the learning and memory abilities of the mice were examined using the Morris water maze tests and step-down test. Hematoxylin and eosin staining was performed to visualize histopathological damage in the hippocampus. A transmission electron microscope was used to observe the ultrastructure of hippocampal neurons. Immunohistochemical staining was performed to examine the expression of glial fibrillary acidic protein (GFAP), the marker protein of astrocyte activation, and autophagy-related proteins, including microtubule-associated protein light chain 3 (LC3) and sequestosome 1 (SQSTM1)/p62, in the hippocampal tissues of mice. Moreover, targeted metabolomic analysis was performed to assess the changes in polar metabolites and short-chain fatty acids in the hippocampus. RESULTS: First, CPWE alleviated cognitive impairment and ameliorated hippocampal tissue damage in aging mice. Furthermore, CPWE markedly alleviated mitochondrial damage, restored the number of autophagosomes, and activated autophagy in the hippocampal tissue of aging mice by increasing the expression of LC3 protein and reducing the expression of p62 protein. Meanwhile, the expression levels of the brain injury marker protein GFAP decreased. Moreover, quantitative targeted metabolomic analysis revealed that CPWE intervention reversed the abnormal levels of L-asparagine, L-glutamic acid, L-glutamine, serotonin hydrochloride, succinic acid, and acetic acid in the hippocampal tissue of aging mice. CPWE also significantly regulated pathways associated with D-glutamine and D-glutamate metabolism, nitrogen metabolism, arginine biosynthesis, alanine, aspartate, and glutamate metabolisms, and aminoacyl-tRNA biosynthesis. CONCLUSIONS: CPWE could improve cognitive and pathological conditions induced by D-galactose in aging mice by activating autophagy and regulating metabolism, thereby slowing down brain aging.

DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.

Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosis study and identified a new gene, EPG5, harboring possibly pathogenic mutations.

TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS.

In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (pdfs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper, we model identifying differential density regions as a multiple testing problem. First, we partition the sample space into small bins. In each bin, we form a hypothesis to test the existence of differential pdfs. Second, we develop a novel multiple testing method, called TEAM (Testing on the Aggregation tree Method), to identify those bins that harbor differential pdfs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine- to coarse-resolution. The procedure achieves the statistical goal of pinpointing density differences to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. Competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.

The circRNA-MYLK plays oncogenic roles in the Hep-2 cell line by sponging microRNA-145-5p.

For the exploration of circular RNA light chain kinase (circRNA-MYLK), siRNA#1 and siRNA#2 targeting circRNA-MYLK as well as microRNA(miR)-145-5p inhibitor were transfected. Viability was valued with the CCK-8. The protein expression was examined relying on Western blot. The expression of circRNA-MYLK or miR-145-5p was tested depending on qRT-PCR. The apoptotic/migration/invasion rate was separately measured by the Annexin v-FITC/PI with flow cytometer or chambers assays. CircRNA-MYLK was overexpressed in tumor tissue. Silencing circRNA-MYLK induced the inhibitions of viability, invasion and migration, as well as the blocks of MEK/ERK and NF-κB cascades, however, silencing circRNA-MYLK led to provoking of apoptosis. Besides, circRNA-MYLK silencing stimulated the over-production of miR-145-5p, whose silencing abolished the effects of siRNA#1 and siRNA#2 of circRNA-MYLK on those factors above. The circRNA-MYLK had oncogenic roles via targeting miR-145-5p in the Hep-2 cell line via stimulating MEK/ERK and NF-κB cascades.

Yield of Malignant Pleural Effusion for Detection of Oncogenic Driver Mutations in Lung Adenocarcinoma.

BACKGROUND: Pleural fluid can be used to assess targetable mutations in patients with lung adenocarcinoma. The primary objective of this study was to assess the yield of pleural fluid cytology for targetable oncogenic mutations (EGFR, KRAS, BRAF, ALK, and ROS1 gene rearrangements). We also assessed pleural fluid volume necessary for molecular testing. METHODS: Retrospective review was performed of 134 consecutive patients with lung adenocarcinoma associated malignant pleural effusions. EGFR and KRAS testing was done using PCR amplification followed by DNA sequencing, or next generation sequencing in more recent cases that included BRAF assessment. Fluorescence in situ hybridization employing break-apart probes was used to test for ALK and ROS1 rearrangements. RESULTS: Mutation analysis on pleural fluid cell-block was performed on 56 patients. It was adequate for complete analysis ordered including EGFR, KRAS, BRAF, ALK, and ROS1 rearrangements on 40 (71.4%) samples. For individual mutations, EGFR testing was possible in 38 of 49 (77.6%); KRAS 22 of 28 (78.6%); BRAF 10 of 13 (76.9%), ALK gene rearrangement 42 of 51 (82.4%) and ROS1 gene rearrangement in 21 of 28 (75%) pleural fluid specimens. The analysis was satisfactory in 13 of 19 (68.4%) samples with ≤100 mL versus 27 of 37 (72.9%) with >100 mL of fluid tested (P-value=0.7). CONCLUSION: Genetic mutation analysis can be performed on malignant pleural effusions secondary to lung adenocarcinoma, independent of fluid volume.

The Value of MRI in Distinguishing Subtypes of Lipomatous Extremity Tumors Needs Reassessment in the Era of MDM2 and CDK4 Testing.

INTRODUCTION: Extremity lipomas and well-differentiated liposarcomas (WDLs) are difficult to distinguish on MR imaging. We sought to evaluate the accuracy of MRI interpretation using MDM2 amplification, via fluorescence in-situ hybridization (FISH), as the gold standard for pathologic diagnosis. Furthermore, we aimed to investigate the utility of a diagnostic formula proposed in the literature. METHODS: We retrospectively collected 49 patients with lipomas or WDLs utilizing MDM2 for pathologic diagnosis. Four expert readers interpreted each patient's MRI independently and provided a diagnosis. Additionally, a formula based on imaging characteristics (i.e. tumor depth, diameter, presence of septa, and internal cystic change) was used to predict the pathologic diagnosis. The accuracy and reliability of imaging-based diagnoses were then analyzed in comparison to the MDM2 pathologic diagnoses. RESULTS: The accuracy of MRI readers was 73.5% (95% CI 61-86%) with substantial interobserver agreement (κ=0.7022). The formula had an accuracy of 71%, which was not significantly different from the readers (p=0.71). The formula and expert observers had similar sensitivity (83% versus 83%) and specificity (64.5% versus 67.7%; p=0.659) for detecting WDLs. CONCLUSION: The accuracy of both our readers and the formula suggests that MRI remains unreliable for distinguishing between lipoma and WDLs.

Single-nucleotide polymorphisms of stemness genes predicted to regulate RNA splicing, microRNA and oncogenic signaling are associated with prostate cancer survival.

Prostate cancer (PCa) is a clinically and molecularly heterogeneous disease, with variation in outcomes only partially predicted by grade and stage. Additional tools to distinguish indolent from aggressive disease are needed. Phenotypic characteristics of stemness correlate with poor cancer prognosis. Given this correlation, we identified single-nucleotide polymorphisms (SNPs) of stemness-related genes and examined their associations with PCa survival. SNPs within stemness-related genes were analyzed for association with overall survival of PCa in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Significant SNPs predicted to be functional were selected for linkage disequilibrium analysis and combined and stratified analyses. Identified SNPs were evaluated for association with gene expression. SNPs of CD44 (rs9666607), ABCC1 (rs35605 and rs212091) and GDF15 (rs1058587) were associated with PCa survival and predicted to be functional. A role for rs9666607 of CD44 and rs35605 of ABCC1 in RNA splicing regulation, rs212091 of ABCC1 in miRNA binding site activity and rs1058587 of GDF15 in causing an amino acid change was predicted. These SNPs represent potential novel prognostic markers for overall survival of PCa and support a contribution of the stemness pathway to PCa patient outcome.

Prospective Evaluation of Lymph Node Processing at Staging Surgery for High-grade Endometrial Cancer.

To determine whether the processing of additional adipose tissue collected during lymph node (LN) dissection results in the identification of additional LNs during endometrial cancer (EC) staging and to determine if the division of LNs into nodal basin-specific specimens has an effect on the number of LNs identified during EC staging. A prospective randomized controlled trial was performed on women with high-grade EC undergoing surgical staging. Subjects were randomized to collection of LNs into nodal basin-specific containers on the randomized side versus simple labeling on the nonrandomized side. The total number of LNs and total number of LNs with metastases on the randomized versus the nonrandomized side were compared. The remaining adipose tissue from each LN specimen was submitted for histologic examination. We analyzed the number of LNs with and without metastases identified from additional adipose tissue. Of 120 consented subjects, 56 had sufficient data for analysis. The additional adipose tissue contained 7.5 additional LNs per patient on average (range: 0-26). In 2/54 total cases (3.7%) and 2/5 cases with nodal metastases (40%), the additional adipose contained LNs with metastases. In both cases, metastases were also detected in grossly identified LN candidates. The mean number of LNs identified was not significantly different based on method of collection (P=0.22). The mean number of LNs containing metastases per side was not significantly different (P=0.58). Processing of adipose tissue does increase the total number of LNs identified, however, it does not influence EC stage. No difference in LN counts was noted with basin-specific collection.

A national analysis of wedge resection versus stereotactic body radiation therapy for stage IA non-small cell lung cancer.

OBJECTIVE: Lobectomy is considered optimal therapy for early-stage non-small cell lung cancer, but sublobar wedge resection and stereotactic body radiation therapy are alternative treatments. This study compared outcomes between wedge resection and stereotactic body radiotherapy. METHODS: Overall survival of patients with cT1N0 and tumors ≤2 cm who underwent stereotactic body radiotherapy or wedge resection in the National Cancer Data Base from 2008 to 2011 was assessed via a Kaplan-Meier and propensity score-matched analysis. A center-level sensitivity analysis that used observed/expected mortality ratios was conducted to identify an association between center use of stereotactic body radiotherapy and mortality. RESULTS: Of the 6295 patients included, 1778 (28.2%) underwent stereotactic body radiotherapy, and 4517 (71.8%) underwent wedge resection. Stereotactic body radiotherapy was associated with significantly reduced 5-year survival compared with wedge resection in both unmatched analysis (30.9% vs 55.2%, P < .001) and after adjustment for covariates (31.0% vs 49.9%, P < .001). Stereotactic body radiotherapy also was associated with worse overall survival than wedge resection after 2 subgroup analyses of propensity-matched patients (P < .05 for both). Centers that used stereotactic body radiotherapy more often as opposed to surgery for patients with cT1N0 patients with tumors <2 cm were more likely to have an observed/expected mortality ratio > 1 for 3-year mortality (P = .034). CONCLUSIONS: In this national analysis, wedge resection was associated with better survival for stage IA non-small cell lung cancer than stereotactic body radiotherapy.

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC.