16S rRNA seq-identified Corynebacterium promotes pyroptosis to aggravate diabetic foot ulcer.

BACKGROUND: Diabetic foot ulcer (DFU) is one of the main chronic complications caused by diabetes, leading to amputation in severe cases. Bacterial infection affects the wound healing in DFU. METHODS: DFU patients who met the criteria were selected, and the clinical data were recorded in detail. The pus exudate from the patient's foot wound and venous blood were collected for biochemical analysis. The distribution of bacterial flora in pus exudates of patients was analyzed by 16S rRNA sequencing, and the correlation between DFU and pathogenic variables, pyroptosis and immunity was analyzed by statistical analysis. Then, the effects of key bacteria on the inflammation, proliferation, apoptosis, and pyroptosis of polymorphonuclear leukocytes were investigated by ELISA, CCK-8, flow cytometry, RT-qPCR and western blot. RESULTS: Clinical data analysis showed that Wagner score was positively correlated with the level of inflammatory factors, and there was high CD3+, CD4+, and low CD8+ levels in DFU patients with high Wagner score. Through alpha, beta diversity analysis and species composition analysis, Corynebacterium accounted for a large proportion in DFU. Logistics regression model and Person correlation analysis demonstrated that mixed bacterial infections could aggravate foot ulcer, and the number of bacteria was closely related to inflammatory factors PCT, PRT, immune cells CD8+, and pyroptosis-related proteins GSDMD and NLRP3. Through in vitro experiments, Corynebacterium inhibited cell proliferation, promoted inflammation (TNF-α, PCT, CRP), apoptosis and pyroptosis (IL-1ß, LDH, IL-18, GSDMD, NLRP3, and caspase-3). CONCLUSION: Mixed bacterial infections exacerbate DFU progression with a high predominance of Corynebacterium, and Corynebacterium promotes inflammation, apoptosis and pyroptosis to inhibit DFU healing.

RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility.

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

Multilevel prioritization of gene regulators associated with consensus molecular subtypes of colorectal cancer.

Consensus molecular subtypes (CMSs) are emerging as critical factor for prognosis and treatment of colorectal cancer. Gene regulators, including chromatin regulator, RNA-binding protein and transcriptional factor, are critical modulators of cancer hallmark, yet little is known regarding the underlying functional mechanism in CMSs. Herein, we identified a core set of 235 functional gene regulators (FGRs) by integrating genome, epigenome, transcriptome and interactome of CMSs. FGRs exhibited significant multi-omics alterations and impacts on cell lines growth, as well as significantly enriched cancer driver genes and pathways. Moreover, common FGRs played different roles in the context of CMSs. In accordance with the immune characteristics of CMSs, we found that the anti-tumor immune pathways were mainly activated by FGRs (e.g. STAT1 and CREBBP) in CMS1, while inhibited by FGRs in CMS2-4. FGRs mediated aberrant expression of ligands, which bind to receptor on immune cells, and modulated tumor immune microenvironment of subtypes. Intriguingly, systematic exploration of datasets using genomic and transcriptome co-similarity reveals the coordinated manner in FGRs act in CMSs to orchestrate their pathways and patients' prognosis. Expression signatures of the FGRs revealed an optimized CMS classifier, which demonstrated 88% concordance with the gold-standard classifier, but avoiding the influence of sample composition. Overall, our integrative analysis identified FGRs to regulate core tumorigenic processes/pathways across CMSs.

An absolute human stemness index associated with oncogenic dedifferentiation.

The progression of cancer is accompanied by the acquisition of stemness features. Many stemness evaluation methods based on transcriptional profiles have been presented to reveal the relationship between stemness and cancer. However, instead of absolute stemness index values-the values with certain range-these methods gave the values without range, which makes them unable to intuitively evaluate the stemness. Besides, these indices were based on the absolute expression values of genes, which were found to be seriously influenced by batch effects and the composition of samples in the dataset. Recently, we have showed that the signatures based on the relative expression orderings (REOs) of gene pairs within a sample were highly robust against these factors, which makes that the REO-based signatures have been stably applied in the evaluations of the continuous scores with certain range. Here, we provided an absolute REO-based stemness index to evaluate the stemness. We found that this stemness index had higher correlation with the culture time of the differentiated stem cells than the previous stemness index. When applied to the cancer and normal tissue samples, the stemness index showed its significant difference between cancers and normal tissues and its ability to reveal the intratumor heterogeneity at stemness level. Importantly, higher stemness index was associated with poorer prognosis and greater oncogenic dedifferentiation reflected by histological grade. All results showed the capability of the REO-based stemness index to assist the assignment of tumor grade and its potential therapeutic and diagnostic implications.

Henagliflozin monotherapy in patients with type 2 diabetes inadequately controlled on diet and exercise: A randomized, double-blind, placebo-controlled, phase 3 trial.

AIM: To evaluate henagliflozin, a novel sodium-glucose co-transporter-2 inhibitor, as monotherapy in patients with type 2 diabetes and inadequate glycaemic control with diet and exercise. MATERIALS AND METHODS: This multicentre trial included a 24-week, randomized, double-blind, placebo-controlled period, followed by a 28-week extension period. Four hundred and sixty-eight patients with an HbA1c of 7.0%-10.5% were randomly assigned (1:1:1) to receive once-daily placebo, or 5 or 10 mg henagliflozin. After 24 weeks, patients on placebo were switched to 5 or 10 mg henagliflozin, and patients on henagliflozin maintained the initial therapy. The primary endpoint was the change in HbA1c from baseline after 24 weeks. RESULTS: At Week 24, the placebo-adjusted least squares (LS) mean changes from baseline in HbA1c were -0.91% (95% CI: -1.11% to -0.72%; P < .001) and -0.94% (-1.13% to -0.75%; P < .001) with henagliflozin 5 and 10 mg, respectively; the placebo-adjusted LS mean changes were -1.3 (-1.8 to -0.9) and -1.5 (-2.0 to -1.1) kg in body weight, and -5.1 (-7.2 to -3.0) and -4.4 (-6.5 to -2.3) mmHg in systolic blood pressure (all P < .05). The trends of these improvements were sustained for an additional 28 weeks. Adverse events occurred in 81.0%, 78.9% and 78.9% of patients in the placebo, henagliflozin 5 and 10 mg groups, respectively. No diabetic ketoacidosis or major episodes of hypoglycaemia occurred. CONCLUSIONS: Henagliflozin 5 mg and 10 mg as monotherapy provided effective glycaemic control, reduced body weight and blood pressure, and was generally well tolerated.

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients.

BACKGROUND: Histological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients. RESULTS: From the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples. CONCLUSIONS: The transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.

End-of-life (EoL) mobile phone management in Hong Kong households.

A questionnaire survey and interviews were conducted in households and end-of-life (EoL) mobile phone business centres in Hong Kong. Widespread Internet use, combined with the rapid evolution of modern social networks, has resulted in the more rapid obsolescence of mobile phones, and thus a tremendous increase in the number of obsolete phones. In 2013, the volume of EoL mobile phones generated in Hong Kong totalled at least 330 tonnes, and the amount is rising. Approximately 80% of electronic waste is exported to Africa and developing countries such as mainland China or Pakistan for recycling. However, the material flow of the large number of obsolete phones generated by the territory's households remains unclear. Hence, the flow of EoL mobile phones in those households was analysed, with the average lifespan of a mobile phone in Hong Kong found to be just under two years (nearly 23 months). Most EoL mobile phones are transferred to mainland China for disposal. Current recycling methods are neither environmentally friendly nor sustainable, with serious implications for the environment and human health. The results of this analysis provide useful information for planning the collection system and facilities needed in Hong Kong and mainland China to better manage EoL mobile phones in the future.

Characteristics of PM2.5, CO2 and particle-number concentration in mass transit railway carriages in Hong Kong.

Fine particulate matter (PM2.5) levels, carbon dioxide (CO2) levels and particle-number concentrations (PNC) were monitored in train carriages on seven routes of the mass transit railway in Hong Kong between March and May 2014, using real-time monitoring instruments. The 8-h average PM2.5 levels in carriages on the seven routes ranged from 24.1 to 49.8 µg/m3, higher than levels in Finland and similar to those in New York, and in most cases exceeding the standard set by the World Health Organisation (25 µg/m3). The CO2 concentration ranged from 714 to 1801 ppm on four of the routes, generally exceeding indoor air quality guidelines (1000 ppm over 8 h) and reaching levels as high as those in Beijing. PNC ranged from 1506 to 11,570 particles/cm3, lower than readings in Sydney and higher than readings in Taipei. Correlation analysis indicated that the number of passengers in a given carriage did not affect the PM2.5 concentration or PNC in the carriage. However, a significant positive correlation (p < 0.001, R 2 = 0.834) was observed between passenger numbers and CO2 levels, with each passenger contributing approximately 7.7-9.8 ppm of CO2. The real-time measurements of PM2.5 and PNC varied considerably, rising when carriage doors opened on arrival at a station and when passengers inside the carriage were more active. This suggests that air pollutants outside the train and passenger movements may contribute to PM2.5 levels and PNC. Assessment of the risk associated with PM2.5 exposure revealed that children are most severely affected by PM2.5 pollution, followed in order by juveniles, adults and the elderly. In addition, females were found to be more vulnerable to PM2.5 pollution than males (p < 0.001), and different subway lines were associated with different levels of risk.

Occurrence and risk assessment of antibiotics in river water in Hong Kong.

The occurrence and distribution of six typical antibiotics in the main rivers of Hong Kong were investigated using high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ES-MS/MS). The results revealed that the antibiotics were widely distributed in the area studied. Of the target antibiotics, ofloxacin was the most frequently detected in the rivers, with a detection rate of 69.6% and a median concentration of 0.7ng/L. Sulfadimidine (n.d-580.4ng/L) and doxycycline (n.d-82.2ng/L), with detection frequencies of 65.2% and 30.4%, respectively, were found at the same level as in rivers in North America, Spain, France, Australia, and in the Yangtze and Pearl Rivers of China, while the other target antibiotics were found at lower levels. According to the ratios of the measured environmental concentration to the predicted no-effect concentration, ofloxacin and doxycycline could present a medium to low ecological risk to algae, while sulfonamides posed no obvious ecological risk to the relevant aquatic organisms (algae, Daphnia magna, and fish). A high detection rate of antibiotics occurred in densely populated areas, revealing that population activities might be greatly contributing to the increasing levels of antibiotics in the area. Thus, the residues of antibiotics present in the waters of Hong Kong need to be closely monitored.

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature.

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

Tailored machine learning for evaluating the long-term diabetes risk in older individuals: findings from the Irish Longitudinal Study on Ageing (TILDA).

OBJECTIVES: The prevalence of diabetes has increased globally, leading to a significant disease burden and financial cost. Early prediction is crucial to control its prevalence. DESIGN: A prospective cohort study. SETTING: National representative study on Irish. PARTICIPANTS: 8504 individuals aged 50 years or older were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Surveys were conducted to collect over 40 000 variables related to social, financial, health, mental and family status. Feature selection was performed using logistic regression. Different machine/deep learning algorithms were trained, including distributed random forest, extremely randomised trees, a generalised linear model with regularisation, a gradient boosting machine and a deep neural network. These algorithms were integrated into a stacked ensemble to generate the best model. The model was tested using various metrics, such as the area under the curve (AUC), log loss, mean per classification error, mean square error (MSE) and root MSE (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the established model. RESULTS: After 2 years, 105 baseline features were identified as major contributors to diabetes risk, including sex, low-density lipoprotein cholesterol and cirrhosis. The best model achieved high accuracy, robustness and discrimination in predicting diabetes risk, with an AUC of 0.854, log loss of 0.187, mean per classification error of 0.267, RMSE of 0.229 and MSE of 0.052 in the independent test set. The model was also shown to be well calibrated. The SHAP algorithm provided insights into the decision-making process of the model. CONCLUSIONS: These findings could help physicians in the early identification of high-risk patients and implement targeted interventions to reduce diabetes incidence.

Protective effect of D-(-)-quinic acid as food supplement in modulating AMP-activated protein kinase signalling pathway activation in HFD induced obesity.

BACKGROUND: Dietary quinic acid given as the nutritional supplement, which may leads to tryptophan and nicotinamide production in the intestinal tract and NAD+ precursor which can prevent from the negative consequences of high fat diet (HFD) consumption. OBJECTIVE: The present study was designed to assess in vivo and in vitro effect of D-(-)-Quinic acid in high-fat diet induced hyperlipidemia in mice. MATERIAL AND METHODS: Thirty six albino mice were randomly divided in six groups and each group had six mice. Group I, controlled mice given normal pellet diet, Group-II mice, administered with high fat diet (HFD), Group-III mice given standard drug, Atorvastatin (20 mg/kg, p.o.) along with HFD to mice and Group IV, V and VI mice received D-(-)-Quinic acid at a dose of 75, 150 and 300 mg/kg, respectively in separate group along with HFD to mice. After completion of trial (49 days) the animals were sacrificed and evaluated for body weight, organ fat pad weight, and changes in weight of liver, heart and kidney and also for biochemical parameters, expression of adipogenic and inflammation markers in adipose tissues, and histology examination of liver tissue. RESULTS: In vitro testing results showed, D-(-)-Quinic acid potentially inhibit α-glucosidase enzyme activity as compared to acarbose. The D-(-)-Quinic acid showed significant hypolipidemic activity by decreasing the increased level of cholesterol, triglyceride level, LDL, VLDL and other hepatic parameters like SGOT and SGPT in serum. D-(-)-Quinic acid reduces the mRNA expression level of PPAR-γ2, TNF-α, IL-1ß and IL-6 in adipose tissue in hyperlipidemic mice.

Correlation between Insulin Resistance and Microalbuminuria Creatinine Ratio in Postmenopausal Women.

Objective: To study the relationship between insulin resistance and urinary microalbumin creatinine ratio in postmenopausal women. Methods: The selected research group comprised 104 postmenopausal women with type 2 diabetes who were admitted to the Department of Endocrinology in the green card center at the First Affiliated Hospital of Hainan Medical University between 2017 and 2019 inclusive. Ninety-eight postmenopausal women with the normal blood glucose metabolism hospitalized in the same period were used as the control group. The age, body mass index, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, fasting blood glucose, fasting insulin (FINS), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and urinary albumin-creatinine ratio (UACR) were analyzed. The insulin resistance index (HOMR-IR) was calculated, and the correlation between IR and UACR was analyzed. Results: Levels of HOMA-IR, SBP, HbA1c, HDL-C, LDL-C, TC, TG, FPG, FINS, and UACR in the study group were higher than those in the control group, and a significant difference was found between the groups (P < 0.05). The level of DBP in the study group was lower than that in the control group, and the difference was statistically significant (P < 0.05). Pearson correlation analysis showed that UACR was positively correlated with HOMA-IR and HbA1c (r = 0.254, r = 0.565, P < 0.01). Multiple linear stepwise regression analysis further showed that HOMA-IR and age were positively correlated with UACR (P < 0.05). Conclusion: There is a correlation between IR and UACR in postmenopausal women. IR is an independent risk factor for UACR.

StemSC: a cross-dataset human stemness index for single-cell samples.

BACKGROUND: Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can't leverage the existing experimentally validated stem cells to recognize the stem-like cells. METHODS: Here, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time. Then, we used 13 RNA-seq datasets from both the bulk and single-cell embryonic stem cell (ESC) samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples. RESULTS: We validated the StemSC by its higher negative correlations with differentiation time in eight normal datasets and its higher positive correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. Besides, the robust of StemSC to batch effect enabled us to leverage the existing experimentally validated cancer stem cells to recognize the stem-like cells in other independent tumor datasets. And the recognized stem-like tumor cells had fewer interactions with anti-tumor immune cells. Further survival analysis showed the immunotherapy-treated patients with high stemness had worse survival than those with low stemness. CONCLUSIONS: StemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells.

Tumor-infiltrating immune cells are important components in the tumor microenvironment (TME) and different types of these cells exert different effects on tumor development and progression; these effects depend upon the type of cancer involved. Several methods have been developed for estimating the proportion of immune cells using bulk transcriptome data. However, there is a distinct lack of methods that are capable of predicting the immune contexture in specific types of cancer. Furthermore, the existing methods are based on absolute gene expression and are susceptible to experimental batch effects, thus resulting in incomparability across different datasets. In this study, we considered two common neoplasms as examples (colorectal cancer [CRC] and melanoma) and introduced the Tumor-infiltrating Immune Cell Proportion Estimator (TICPE), a cancer-specific qualitative method for estimating the proportion of tumor-infiltrating immune cells. The TICPE was based on the relative expression orderings (REOs) of gene pairs within a sample and is notably insensitive to batch effects. Performance evaluation using public expression data with mRNA mixtures, single-cell RNA-Seq (scRNA-Seq) data, immunohistochemistry data, and simulated bulk RNA-seq samples, indicated that the TICPE can estimate the proportion of immune cells with levels of accuracy that are clearly superior to other methods. Furthermore, we showed that the TICPE could effectively detect prognostic signals in patients with tumors and changes in the fractions of immune cells during immunotherapy in melanoma. In conclusion, our work presented a unique novel method, TICPE, to estimate the proportion of immune cells in specific cancer types and explore the effect of the infiltration of immune cells on the efficacy of immunotherapy and the prognosis of cancer. The source code for TICPE is available at https://github.com/huitingxiao/TICPE.

The protective effects of S14G-humanin (HNG) against streptozotocin (STZ)-induced cardiac dysfunction.

Excessive oxidative stress, inflammation, and myocardial hypertrophy have been associated with diabetic cardiomyopathy (DCM). S14G-humanin (HNG) is a potent humanin analogue that has demonstrated cytoprotective effects in a variety of cells and tissues. However, the pharmacological function of HNG in diabetic cardiomyopathy has not yet been reported. In the present study, we investigated the protective effects of HNG against streptozotocin (STZ)-induced cardiac dysfunction in diabetic mice. Myocardial hypertrophy in diabetic mice was determined using Wheat Gem Agglutinin (WGA) staining. The heart function was measured with Echocardiographic imaging. Levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) proteins in plasma were measured using enzyme-linked immunosorbent assay (ELISA) kits. Protein expression of Phosphorylated p38/p38 was determined using western blots. We found that HNG treatment attenuated the STZ-induced myocardial hypertrophy and significantly improved heart function. Also, its treatment proved effective as it reduced the levels of several myocardial injury indicators, including creatine kinase-MB (CK-MB), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and both the cardiac and plasma levels of TNF-α and IL-6, highlighting its effect on the STZ-induced myocardial injury. Lastly, HNG suppressed the activation of the p38/nuclear factor kappa-B (NF-κB) signaling pathway. S14G humanin possesses protective effects against streptozotocin-induced cardiac dysfunction through inhibiting the activation of the p38/NF-κB signaling pathway.

A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer.

Bevacizumab is the molecular-targeted agent used for the antiangiogenic therapy of metastatic colorectal cancer. But some patients are resistant to bevacizumab, it needs an effective biomarker to predict the prognosis and responses of metastatic colorectal cancer (mCRC) to bevacizumab therapy. In this work, we developed a qualitative transcriptional signature to individually predict the response of bevacizumab in patients with mCRC. First, using mCRC samples treated with bevacizumab, we detected differentially expressed genes between response and nonresponse groups. Then, the gene pairs, consisting of at least one differentially expressed gene, with stable relative expression orderings in the response samples but reversal stable relative expression orderings in the nonresponse samples were identified, denoted as pairs-bevacizumab. Similarly, we screened the gene pairs significantly associated with primary tumor locations, donated as pairs-LR. Among the overlapped gene pairs between the pairs-bevacizumab and pairs-LR, we adopted a feature selection process to extract gene pairs that reached the highest F-score for predicting bevacizumab response status in mCRC as the final gene pair signature (GPS), denoted as 64-GPS. In two independent datasets, the predicted response group showed significantly better overall survival than the nonresponse group (P = 6.00e-4 in GSE72970; P = 0.04 in TCGA). Genomic analyses showed that the predicted response group was characterized by frequent copy number alternations, whereas the nonresponse group was characterized by hypermutation. In conclusion, 64-GPS was an objective and robust predictive signature for patients with mCRC treated with bevacizumab, which could effectively assist in the decision of clinical therapy.

Selection of Reference Genes for Quantitative Real-Time PCR in Aquatica leii (Coleoptera: Lampyridae) Under Five Different Experimental Conditions.

Aquatic fireflies are important indicators of the quality of freshwater environments and key models for research on insect adaptation to freshwater environments. For these investigations, gene expression analyses using quantitative real-time PCR are heavily dependent on reliable reference genes. In this study, based on a transcriptome assembly and annotation for the aquatic firefly Aquatica leii at the adult and larval stages, 10 candidate reference genes (α-tubulin, ß-tubulin, ß-actin, EF1A, SDHA, UBQ, GST, GAPDH, RPS31, and RPL13A) were identified for analyses of expression stability. Quantitative real-time PCR analyses for each candidate reference genes in A. leii was conducted for four developmental stages, four adult tissue types, two adult sexes, and two ecological stressors [adults exposed to five temperatures and larvae exposed to four concentrations of benzo(a)pyrene]. Results were evaluated by three independent algorithms (geNorm, NormFinder, and BestKeeper) and one comparative algorithm (RefFinder). The expression stability of candidate reference genes in A. leii differed under various conditions. Reference genes with the most stable expressions levels in different tissues, temperatures, sexes, developmental stages, and concentrations of benzo(a)pyrene were α-tubulin, GST, ß-actin, ß-tubulin, and α-tubulin, respectively. Furthermore, the optimal normalization factors (NFs) for the quantification of the expression levels of target genes by quantitative real-time PCR analyses of A. leii were identified for each experimental group. In particular, NF = 2 for different tissues (α-tubulin + ß-tubulin), different sexes (ß-actin + EF1A), and larvae exposed to different concentrations of benzo(a)pyrene (α-tubulin + EF1A); NF = 3 for developmental stages (GST + GAPDH + SDHA) and adults exposed to different temperatures (ß-tubulin + EFA + GST). In addition, we surveyed the expression profiles of two target genes (CYP3A and CSP8) in larvae exposed to different concentrations of benzo(a)pyrene and in different adult tissues. The results further validated the reliability of the reference genes. The optimal reference genes for various experimental conditions identified in these analyses provide a useful tool for ecological studies of aquatic fireflies.

A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer.

A part of colorectal cancer which is characterized by simultaneous numerous hypermethylation CpG islands sites is defined as CpG island methylator phenotype (CIMP) status. Stage II and III CIMP-positive (CIMP+) right-sided colon cancer (RCC) patients have a better prognosis than CIMP-negative (CIMP-) RCC treated with surgery alone. However, there is no gold standard available in defining CIMP status. In this work, we selected the gene pairs whose relative expression orderings (REOs) were associated with the CIMP status, to develop a qualitative transcriptional signature to individually predict CIMP status for stage II and III RCC. Based on the REOs of gene pairs, a signature composed of 19 gene pairs was developed to predict the CIMP status of RCC through a feature selection process. A sample is predicted as CIMP+ when the gene expression orderings of at least 12 gene pairs vote for CIMP+; otherwise the CIMP-. The difference of prognosis between the predicted CIMP+ and CIMP- groups was more significantly different than the original CIMP status groups. There were more differential methylation and expression characteristics between the two predicted groups. The hierarchical clustering analysis showed that the signature could perform better for predicting CIMP status of RCC than current methods. In conclusion, the qualitative transcriptional signature for classifying CIMP status at the individualized level can predict outcome and guide therapy for RCC patients.

Qualitative Ras pathway signature for cetuximab therapy reveals resistant mechanism in colorectal cancer.

Cetuximab therapy, which heavily relies on the activation of Ras pathway, has been used in KRAS, NRAS, BRAF, and PIK3CA wild-type colorectal cancer (CRC) (Ras-normal). However, the response rate only reached 60%, due to false-negative mutation detection and mutation-like transcriptome features in wild-type patients. Herein, by integrating RNA-seq, microarray, and mutation data, we developed a Ras pathway signature by characterizing KRAS/NRAS/BRAF/PIK3CA mutations to identify the hidden nonresponders from the Ras-normal patients by mutation detection. Using public and in-house data of CRC patients treated with cetuximab, discovery of the signature could identify cetuximab-resistant samples from the Ras-normal samples. Cetuximab resistance-related genes, such as PTEN, were significantly and frequently mutated in the identified Ras-activated samples, whereas two cetuximab sensitivity-related genes, APC and TP53, showed comutation and significantly higher mutation frequencies in the remaining Ras-normal samples. Furthermore, all the NF1- and BCL2L1-mutated samples were identified as Ras-activated from the Ras-normal samples by the Ras pathway signature with significantly under-regulated expression. Genes co-expressed with the two genes were both involved in Ras signaling pathway, the out-of-control of which could be attributed by the genes' loss-of-function mutations. To improve the treatment of cetuximab in CRC, NF1 and BCL2L1 could be used as complementary detection technique to those applied in clinical. In conclusion, the proposed Ras pathway signature could identify the hidden CRC patients resistant to cetuximab therapy and help to reveal resistance mechanisms.