Search

Background: With the rapid development of Internet technology and social media, a growing number of companies now not only transfer their traditional offline business interactions to online but also engage in online customer relationship management, market share expansion and brand building. Consumer interaction in the virtual brand community has become progressively more important in consumers’ purchase decision. Objectives: The purpose of establishing virtual brand community is to obtain brand loyalty, but the existing research rarely discusses the impact of member interaction on brand loyalty. To address this research gap, we explore the influence mechanism between virtual brand community member interaction and brand loyalty. Method: To verify the research hypothesis, we distributed and collected 393 valid questionnaires on wenjuan.com, one of the largest online questionnaire service companies in China. Results: Our results reveal that virtual brand community members’ interaction has a positive impact on brand loyalty. In addition, community identification also has a partial mediating effect. Moreover, media richness regulates the relationship between member interaction and community identification. Conclusion: This article mainly investigates the virtual brand community member interaction behaviours. We also explore the influences of different dimensions of virtual brand community interaction on community identification and member brand loyalty. Contribution: This article deepens our understanding of online platform members’ participance behaviours.

Abstract Tumor heterogeneity, the presence of multiple distinct subpopulations of cancer cells between patients or among the same tumors, poses a major challenge to current targeted therapies. The way these different subpopulations interact among themselves and the stromal niche environment, and how such interactions affect cancer stem cell behavior has remained largely unknown. Here, it is shown that an FGF‐BMP7‐INHBA signaling positive feedback loop integrates interactions among different cell populations, including mammary gland stem cells, luminal epithelial and stromal fibroblast niche components not only in organ regeneration but also, with certain modifications, in cancer progression. The reciprocal dependence of basal stem cells and luminal epithelium is based on basal‐derived BMP7 and luminal‐derived INHBA, which promote their respective expansion, and is regulated by stromal‐epithelial FGF signaling. Targeting this interaction loop, for example, by reducing the function of one or more of its components, inhibits organ regeneration and breast cancer progression. The results have profound implications for overcoming drug resistance because of tumor heterogeneity in future targeted therapies.

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

Abstract As one of the leading causes of irreversible blindness worldwide, glaucoma is characterized by structural damage and functional loss. Glaucoma patients often have a long follow-up and prognosis prediction is an important part in treatment. However, existing public glaucoma datasets are almost cross-sectional, concentrating on segmentation on optic disc (OD) and glaucoma diagnosis. With the development of artificial intelligence (AI), the deep learning model can already provide accurate prediction of future visual field (VF) and its progression with the support of longitudinal datasets. Here, we proposed a public longitudinal glaucoma real-world appraisal progression ensemble (GRAPE) dataset. The GRAPE dataset contains 1115 follow-up records from 263 eyes, with VFs, fundus images, OCT measurements and clinical information, and OD segmentation and VF progression are annotated. Two baseline models demonstrated the feasibility in prediction of VF and its progression. This dataset will advance AI research in glaucoma management.

Summary: Here, we present a protocol for exploring the effects of PPP1R15A inhibitor, Sephin1, on antitumor immunity of B16F1 subcutaneous tumor in mice. We describe steps for constructing single-cell transcriptome and TCR libraries, sequencing, and using sequencing data for the integration of expression and TCR data. We then detail procedures for gene differentiation, regulon and cell-cell communication analysis, and validation of single-cell analysis results.For complete details on the use and execution of this protocol, please refer to Wang et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Phosphatidylinositol 3-kinase alpha (PI3Kα) inhibitors are currently evaluated for the therapy of esophageal squamous cell carcinoma (ESCC). It is of great importance to identify potential biomarkers to predict or monitor the efficacy of PI3Kα inhibitors in an aim to improve the clinical responsive rate in ESCC. Here, ESCC PDXs with CCND1 amplification were found to be more sensitive to CYH33, a novel PI3Kα-selective inhibitor currently in clinical trials for the treatment of advanced solid tumors including ESCC. Elevated level of cyclin D1, p21 and Rb was found in CYH33-sensitive ESCC cells compared to those in resistant cells. CYH33 significantly arrested sensitive cells but not resistant cells at G1 phase, which was associated with accumulation of p21 and suppression of Rb phosphorylation by CDK4/6 and CDK2. Hypo-phosphorylation of Rb attenuated the transcriptional activation of SKP2 by E2F1, which in turn hindered SKP2-mediated degradation of p21 and reinforced accumulation of p21. Moreover, CDK4/6 inhibitors sensitized resistant ESCC cells and PDXs to CYH33. These findings provided mechanistic rationale to evaluate PI3Kα inhibitors in ESCC patients harboring amplified CCND1 and the combined regimen with CDK4/6 inhibitors in ESCC with proficient Rb.

X chromosome dosage compensation (XDC) refers to the process by which X-linked genes acquire expression equivalence between two sexes. Ohno proposed that XDC is achieved by two-fold upregulations of X-linked genes in both sexes and by silencing one X chromosome (X chromosome inactivation, XCI) in females. However, genes subject to two-fold upregulations as well as the underlying mechanism remain unclear. It’s reported that gene dosage changes may only affect X-linked dosage-sensitive genes, such as protein complex coding genes (PCGs). Our results showed that in human PCGs are more likely to escape XCI and escaping PCGs (EsP) show two-fold higher expression than inactivated PCGs (InP) or other X-linked genes at RNA and protein levels in both sexes, which suggest that EsP may achieve upregulations and XDC. The higher expressions of EsP possibly result from the upregulations of the single active X chromosome (Xa), rather than escaping expressions from the inactive X chromosome (Xi). EsP genes have relatively high expression levels in humans and lower dN/dS ratios, suggesting that they are likely under stronger selection pressure over evolutionary time. Our study also suggests that SP1 transcription factor is significantly enriched in EsP and may be involved in the up-regulations of EsP on the active X. Finally, human EsP genes in this study are enriched in the toll-like receptor pathway, NF-kB pathway, apoptotic pathway, and abnormal mental, developmental and reproductive phenotypes. These findings suggest misregulations of EsP may be involved in autoimmune, reproductive, and neurological diseases, providing insight for the diagnosis and treatment of these diseases.

Objective: To assess the thorium exposure of the mine workers and the public to a typical rare earth mine and estimate the resultant committed effective dose to them. Methods: A total of 79 volunteers were selected in this survey, including 69 mine workers and 10 local residents living about 4 ​km away from the mine site. Urinary samples were collected from 79 volunteers, with 65 hair samples from 57 workers and 8 local residents. The thorium concentrations in urinary and hair samples were determined by using inductively coupled plasma mass spectrometry. The committed effective doses were estimated based on the urinary samples collected and the parameters as recommended by ICRP Publication 137. Results: Thorium concentrations in the samples from the workers ranged from 19.0 to 2388.8 ​ng/g in hair with median of 149.8 ​ng/g, and in urine 18.3–906.1 ​ng/L with median of 59.6 ​ng/L, respectively. The median values of thorium concentrations were 11.8 ​ng/g in hair and 32.40 ​ng/L in urine for the public respectively. The thorium concentrations in urinary and hair samples of the workers were not only higher than those of the residents in the local area, but also than those of non-occupationally exposed populations as reported. The median values of committed effective dose were estimated to be 1.51 ​mSv for the workers and 781 ​μSv for the local residents, respectively. Conclusions: The residents in the local area, and especially the workers, have been subject to long term thorium exposure. More attention should be paid to the radiological hazards of thorium to the workers and local residents.

Abstract Niche for stem cells profoundly influences their maintenance and fate during tissue homeostasis and pathological disorders; however, the underlying mechanisms and tissue‐specific features remain poorly understood. Here, it is reported that fatty acid desaturation catabolized by stearoyl‐coenzyme A desaturase 1 (SCD1) regulates hair follicle stem cells (HFSCs) and hair growth by maintaining the bulge, niche for HFSCs. Scd1 deletion in mice results in abnormal hair growth, an effect exerted directly on keratin K14+ keratinocytes rather than on HFSCs. Mechanistically, Scd1 deficiency impairs the level of integrin α6β4 complex and thus the assembly of hemidesmosomes (HDs). The disruption of HDs allows the aberrant activation of focal adhesion kinase and PI3K in K14+ keratinocytes and subsequently their differentiation and proliferation. The overgrowth of basal keratinocytes results in downward extension of the outer root sheath and interruption of bulge formation. Then, inhibition of PI3K signaling in Scd1−/− mice normalizes the bulge, HFSCs, and hair growth. Additionally, supplementation of oleic acid to Scd1−/− mice reestablishes HDs and the homeostasis of bulge niche, and restores hair growth. Thus, SCD1 is critical in regulating hair growth through stabilizing HDs in basal keratinocytes and thus sustaining bulge for HFSC residence and periodic activity.

Summary: Protein phosphatase 1 regulatory subunit 15A (PPP1R15A) is an important factor in the integrated stress response (ISR) in mammals and may play a crucial role in tumorigenesis. In our studies, we found an inhibitor of PPP1R15A, Sephin1, plays a protumorigenic role in mouse tumor models. By analyzing the single-cell transcriptome data of the mouse tumor models, we found that in C57BL/6 mice, Sephin1 treatment could lead to higher levels of ISR activity and lower levels of antitumor immune activities. Specifically, Sephin1 treatment caused reductions in antitumor immune cell types and lower expression levels of cytotoxicity-related genes. In addition, T cell receptor (TCR) repertoire analysis demonstrated that the clonal expansion of tumor-specific T cells was inhibited by Sephin1. A special TCR + macrophage subtype in tumor was identified to be significantly depleted upon Sephin1 treatment, implying its key antitumor role. These results suggest that PPP1R15A has the potential to be an effective target for tumor therapy.

Summary: KRAS is widely mutated in human cancers, resulting in unchecked tumor proliferation and metastasis, which makes identifying KRAS-targeting therapies a priority. Herein, we observe that mutant KRAS specifically promotes the formation of the ERK2-p53 complex in stomach/colorectal tumor cells. Disruption of this complex by applying MEK1/2 and ERK2 inhibitors elicits strong apoptotic responses in a p53-dependent manner, validated by genome-wide knockout screening. Mechanistically, p53 physically associates with phosphorylated ERK2 through a hydrophobic interaction in the presence of mutant KRAS, which suppresses p53 activation by preventing the recruitment of p300/CBP; trametinib disrupts the ERK2-p53 complex by reducing ERK2 phosphorylation, allowing the acetylation of p53 protein by recruiting p300/CBP; acetylated p53 activates PUMA transcription and thereby kills KRAS-mutant tumors. Our study shows an important role for the ERK2-p53 complex and provides a potential therapeutic strategy for treating KRAS-mutant cancer.

Guo, Zhang, Guo et al. investigate microbiome alterations in different molecular subtypes of PDAC and report that distinct signatures exist in basal-like tumours, characterized by an increase in certain bacteria, such as Acinetobacter, Pseudomonas and Sphingopyxis. The authors associate increases in these bacteria with increased inflammation and carcinogenic potential, as suggested by pathway analyses.

Abstract Phosphoinositide‐3 kinase alpha (PI3Kα) has been confirmed to be a potential therapeutic target for esophageal squamous cell carcinoma (ESCC), while the potency of PI3Kα inhibitors is often attenuated by concurrent oncogenic signalling pathways. We performed genome‐wide gain‐of‐function screening with a CRISPR‐SAM library and identified enhancer of zeste homolog 2 (EZH2) rendering ESCC cells resistant to the PI3Kα inhibitor CYH33. Enhanced expression of EZH2 frequently occurs in ESCC and is related to poor prognosis. Overexpression of full‐length EZH2 but not methyltransferase‐deficient EZH2 conferred resistance to CYH33, while downregulating EZH2 expression restored sensitivity. EZH2 expression was negatively related to the activity of CYH33 against the proliferation of ESCC cell lines and patient‐derived cells. Transcriptomic analysis revealed that EZH2 abrogated CYH33‐mediated cell cycle regulation. EZH2 epigenetically suppressed the transcription of CDKN1A, promoting RB phosphorylation and cell cycle progression. Concurrently targeting EZH2 significantly potentiated CYH33 to inhibit the growth of ESCC cells and patient‐derived xenografts accompanied by enhanced cell cycle arrest. Taken together, our study demonstrated that an EZH2‐p21‐RB axis remodeled cell cycle regulation and rendered resistance to PI3Kα inhibitors in ESCC. Simultaneously targeting PI3Kα and EZH2 may provide an effective strategy for ESCC therapy with high expression of EZH2.

Lipid droplets (LDs) have increasingly been recognized as an essential organelle for eukaryotes. Although the biochemistry of lipid synthesis and degradation is well characterized, the regulation of LD dynamics, including its formation, maintenance, and secretion, is poorly understood. Here, we report that mice lacking Occludin (Ocln) show defective lipid metabolism. We show that LDs were larger than normal along its biogenesis and secretion pathway in Ocln null mammary cells. This defect in LD size control did not result from abnormal lipid synthesis or degradation; rather, it was because of secretion failure during the lactation stage. We found that OCLN was located on the LD membrane and was bound to essential regulators of lipid secretion, including BTN1a1 and XOR, in a C-terminus-dependent manner. Finally, OCLN was a phosphorylation target of Src kinase, whose loss causes lactation failure. Together, we demonstrate that Ocln is a downstream target of Src kinase and promotes LD secretion by binding to BTN1a1 and XOR.

Background The phosphatidylinositol 3-kinase (PI3K) is frequently hyperactivated in cancer and plays important roles in both malignant and immune cells. The effect of PI3Kα inhibitors on the tumor microenvironment (TME) remains largely unknown. Here, we investigated the modulation of the TME by a clinical PI3Kα-specific inhibitor CYH33.Methods The activity of CYH33 against a panel of murine tumors in the immune-competent context or athymic mice was detected. Single-cell RNA sequencing and multi-parameter flow cytometry were performed to determine the immune profiling of TME. The effect of CYH33 on immune cells was conducted with primary murine cells.Results CYH33 exhibited more potent antitumor activity in immune-competent context. CYH33 enhanced the infiltration and activation of CD8+T and CD4+T cells, while attenuating M2-like macrophages and regulatory CD4+T cells. Increase in memory T cells was confirmed by the induction of long-term immune memory on CYH33 treatment. Mechanistically, CYH33 relieved the suppressed expansion of CD8+T cells via preferential polarization of the macrophages to the M1 phenotype. CYH33 promoted fatty acid (FA) metabolism in the TME, while FA enhanced the activity of CD8+T cells in vitro. The combination of CYH33 with the FA synthase (FASN) inhibitor C75 synergistically inhibited tumor growth with enhanced host immunity.Conclusions CYH33 induces immune activation and synergizes with FASN inhibitor to further promote the antitumor immunity, which gains novel insights into how PI3K inhibitors exert their activity by modulating TME and provides a rationale for the concurrent targeting of PI3K and FASN in breast cancer treatment.

This study revealed the microbial compositions in children with bacterial meningitis who were treated with antibiotics and made a comprehensive comparison between blood and CSF specimens for risk and prognosis assessment. We found that microbiome signatures could distinguish patient subsets and were correlated with the CRP level in blood and granulocyte percentage in CSF.

BackgroundWith the advent of large-scale molecular profiling, an increasing number of oncogenic drivers contributing to precise medicine and reshaping classification of lung adenocarcinoma (LUAD) have been identified. However, only a minority of patients archived improved outcome under current standard therapies because of the dynamic mutational spectrum, which required expanding susceptible gene libraries. Accumulating evidence has witnessed that understanding gene regulatory networks as well as their changing processes was helpful in identifying core genes which acted as master regulators during carcinogenesis. The present study aimed at identifying key genes with differential correlations between normal and tumor status.MethodsWeighted gene co-expression network analysis (WGCNA) was employed to build a gene interaction network using the expression profile of LUAD from The Cancer Genome Atlas (TCGA). R package DiffCorr was implemented for the identification of differential correlations between tumor and adjacent normal tissues. STRING and Cytoscape were used for the construction and visualization of biological networks.ResultsA total of 176 modules were detected in the network, among which yellow and medium orchid modules showed the most significant associations with LUAD. Then genes in these two modules were further chosen to evaluate their differential correlations. Finally, dozens of novel genes with opposite correlations including ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 were identified. Further biological and survival analyses highlighted their potential values in the diagnosis and treatment of LUAD. Moreover, real-time qPCR confirmed the expression patterns of ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 in LUAD tissues and cell lines.ConclusionOur study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.

Abstract Background Men are at higher risk of developing chronic lymphocytic leukemia (CLL) than women. DNA methylation has been shown to play important roles in a number of cancers. There are differences in the DNA methylation pattern between men and women. In this study, we investigated whether this contributes to the sex-related difference of B cell CLL risk. Methods Using the HumanMethylation450 BeadChip, we profiled the genome-wide DNA methylation pattern of CD19+ B cells from 48 CLL patients (29 female patients and 19 male patients) and 28 healthy people (19 women and 9 men). Results We identified 1043 sex-related differentially methylated positions (DMPs) related to CLL, 56 of which are located on autosomes and 987 on the X chromosome. Using published B cell RNA-sequencing data, we found 18 genes covered by the DMPs also have different expression levels in male and female CLL patients. Among them, TRIB1, an autosome gene, has been shown to promote tumor growth by suppressing apoptosis. Conclusions Our study represents the first epigenome-wide association study (EWAS) that investigates the sex-related differences in cancer, and indicated that DNA methylation differences might contribute to the sex-related difference in CLL risk.

Tumors are driven by a sequence of genetic and epigenetic alterations. Previous studies have mostly focused on the roles of somatic mutations in tumorigenesis, but how germline variants act is largely unknown. In this study, we hypothesized that allelic expression imbalance (AEI) participated in the process of germline variants on tumorigenesis. We screened single-nucleotide polymorphisms (SNPs) as representative germline variants. By using 127 patients’ RNA sequencing data from paired lung cancer and adjacent normal tissues from public databases, we analyzed the effects of the functional consequence of SNPs, function and conservativeness on genes with AEI. We found that natural selection can affect AEI. Functional adaptability of genes with a high frequency of AEI and a correlation of the incidence of AEI with conservativeness were observed in both adjacent tissues and tumor tissues. Moreover, we observed a higher incidence of AEI in genes with non-synonymous SNPs than in those with synonymous SNPs. However, we also found that AEI was affected by allele expression noise, especially in tumor tissues, which led to an increased proportion of AEI, weakened the effect of natural selection and eliminated the influence of the functional consequence of SNPs on AEI. We unveiled a previously unknown adaptive regulatory mechanism in which the effect of natural selection on SNPs can be reflected in allelic expression, which provides insight into a better understanding of cancer evolution.

Abstract Background Inhibition of nonsense-mediated mRNA decay (NMD) in tumor cells can suppress tumor growth through expressing new antigens whose mRNAs otherwise are degraded by NMD. Thus NMD inhibition is a promising approach for developing cancer therapies. Apparently, the success of this approach relies on the basal NMD activity in cancer cells. If NMD is already strongly inhibited in tumors, the approach would not work. Therefore, it is crucial to assess NMD activity in cancers to forecast the efficacy of NMD-inhibition based therapy. Methods Here we develop three metrics using RNA-seq data to measure NMD activity, and apply them to a dataset consisting of 72 lung cancer (adenocarcinoma) patients. Results We show that these metrics have good correlations, and that the NMD activities in adenocarcinoma samples vary among patients: some cancerous samples show significantly stronger NMD activities than the normal tissues while some others show the opposite pattern. The variation of NMD activities among these samples may be partly explained by the varying expression of NMD effectors. Conclusions In sum, NMD activity varies among lung cancerous samples, which forecasts varying efficacies of NMD-inhibition based therapy. The developed metrics can be further used in other cancer types to assess NMD activity.

Through linkage and candidate gene screening, many breast cancer (BC) predisposition genes have been identified in the past 20 years. However, the majority of genetic risks that contribute to familial BC remains undetermined. In this study, we revisited whole exome sequencing datasets from non-BRCA1/2 familial BC patients, to search for novel BC predisposition genes. Based on the infinite mutation model, we supposed that rare non-silent variants that cooccurred between familial and TCGA-germline datasets, might play a predisposition contributing role. In our analysis, we not only identified novel potential pathogenic variants from known cancer predisposition genes, such as MRE11, CTR9 but also identified novel candidate predisposition genes, such as NCK1. According to the TCGA mRNA expression dataset of BC, NCK1 was significantly upregulated in basal-like subtypes and downregulated in luminal subtypes. In vitro, NCK1 mutants (D73H and R42Q) transfected MCF7 cell lines, which attributed to the luminal subtype, were much more viable and invasive than the wild type. On the other side, our results also showed that overall survival and disease-free survival of patients with NCK1 variations might be dependent on the genomic context. In conclusion, genetic heterogeneity exists among non-BRCA1/2 BC pedigrees and NCK1 could be a novel BC predisposition gene.

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.

As a chronic illness derived from hair cells of the inner ear, Menière's disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1st-order predictions in the leave-one-out and ten-fold cross-validations. For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in reality.

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

The human brain is one of the most mysterious tissues in the body. Our knowledge of the human brain is limited due to the complexity of its structure and the microscopic nature of connections between brain regions and other tissues in the body. In this study, we analyzed the gene expression profiles of three brain regions-the brain stem, cerebellum and cerebral cortex-to identify genes that are differentially expressed among these different brain regions in humans and to obtain a list of robust, region-specific, differentially expressed genes by comparing the expression signatures from different individuals. Feature selection methods, specifically minimum redundancy maximum relevance and incremental feature selection, were employed to analyze the gene expression profiles. Sequential minimal optimization, a machine-learning algorithm, was employed to examine the utility of selected genes. We also performed a literature search, and we discuss the experimental evidence for the important physiological functions of several highly ranked genes, including NR2E1, DAO, and LRRC7, and we give our analyses on a gene (TFAP2B) that have not been investigated or experimentally validated. As a whole, the results of our study will improve our ability to predict and understand genes related to brain regionalization and function.

Lung cancer causes over one million deaths every year worldwide. However, prevention and treatment methods for this serious disease are limited. The identification of new chemicals related to lung cancer may aid in disease prevention and the design of more effective treatments. This study employed a weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer. Then, a randomization test as well as chemical-chemical interaction and chemical structure information were utilized to make further selections. A final analysis of these new chemicals in the context of the current literature indicates that several chemicals are strongly linked to lung cancer.

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

Human brain development is a dramatic process composed of a series of complex and fine-tuned spatiotemporal gene expressions. A good comprehension of this process can assist us in developing the potential of our brain. However, we have only limited knowledge about the genes and gene functions that are involved in this biological process. Therefore, a substantial demand remains to discover new brain development-related genes and identify their biological functions. In this study, we aimed to discover new brain-development related genes by building a computational method. We referred to a series of computational methods used to discover new disease-related genes and developed a similar method. In this method, the shortest path algorithm was executed on a weighted graph that was constructed using protein-protein interactions. New candidate genes fell on at least one of the shortest paths connecting two known genes that are related to brain development. A randomization test was then adopted to filter positive discoveries. Of the final identified genes, several have been reported to be associated with brain development, indicating the effectiveness of the method, whereas several of the others may have potential roles in brain development.

Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR)" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions.

RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and MCC (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and MCC were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.

Congenital heart disease (CHD) is the most common birth defect affecting the structure and function of fetal hearts. Despite decades of extensive studies, the genetic mechanism of sporadic CHD remains obscure. Deleted in liver cancer 1 (DLC1) gene, encoding a GTPase-activating protein, is highly expressed in heart and essential for heart development according to the knowledge of Dlc1-deficient mice. To determine whether DLC1 is a susceptibility gene for sporadic CHD, we sequenced the coding region of DLC1 isoform 1 in 151 sporadic CHD patients and identified 13 non-synonymous rare variants (including 6 private variants) in the case cohort. Importantly, these rare variants (8/13) were enriched in the N-terminal region of the DLC1 isoform 1 protein. Seven of eight amino acids at the N-terminal variant positions were conserved among the primates. Among the 9 rare variants that were predicted as "damaging", five were located at the N-terminal region. Ensuing in vitro functional assays showed that three private variants (Met360Lys, Glu418Lys and Asp554Val) impaired the ability of DLC1 to inhibit cell migration or altered the subcellular location of the protein compared to wild-type DLC1 isoform 1. These data suggest that DLC1 might act as a CHD-associated gene in addition to its role as a tumor suppressor in cancer.

Cancer is a serious disease that causes many deaths every year. We urgently need to design effective treatments to cure this disease. Tumor suppressor genes (TSGs) are a type of gene that can protect cells from becoming cancerous. In view of this, correct identification of TSGs is an alternative method for identifying effective cancer therapies. In this study, we performed gene ontology (GO) and pathway enrichment analysis of the TSGs and non-TSGs. Some popular feature selection methods, including minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS), were employed to analyze the enrichment features. Accordingly, some GO terms and KEGG pathways, such as biological adhesion, cell cycle control, genomic stability maintenance and cell death regulation, were extracted, which are important factors for identifying TSGs. We hope these findings can help in building effective prediction methods for identifying TSGs and thereby, promoting the discovery of effective cancer treatments.

Integrating high-throughput data obtained from different molecular levels is essential for understanding the mechanisms of complex diseases such as cancer. In this study, we integrated the methylation, microRNA and mRNA data from lung cancer tissues and normal lung tissues using functional gene sets. For each Gene Ontology (GO) term, three sets were defined: the methylation set, the microRNA set and the mRNA set. The discriminating ability of each gene set was represented by the Matthews correlation coefficient (MCC), as evaluated by leave-one-out cross-validation (LOOCV). Next, the MCCs in the methylation sets, the microRNA sets and the mRNA sets were ranked. By comparing the MCC ranks of methylation, microRNA and mRNA for each GO term, we classified the GO sets into six groups and identified the dysfunctional methylation, microRNA and mRNA gene sets in lung cancer. Our results provide a systematic view of the functional alterations during tumorigenesis that may help to elucidate the mechanisms of lung cancer and lead to improved treatments for patients.

A very small proportion of people remain negative for HIV infection after repeated HIV-1 viral exposure, which is called HIV-1 resistance. Understanding the mechanism of HIV-1 resistance is important for the development of HIV-1 vaccines and Acquired Immune Deficiency Syndrome (AIDS) therapies. In this study, we analyzed the gene expression profiles of CD4+ T cells from HIV-1-resistant individuals and HIV-susceptible individuals. One hundred eighty-five discriminative HIV-1 resistance genes were identified using the Minimum Redundancy-Maximum Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The virus protein target enrichment analysis of the 185 HIV-1 resistance genes suggested that the HIV-1 protein nef might play an important role in HIV-1 infection. Moreover, we identified 29 infection information exchanger genes from the 185 HIV-1 resistance genes based on a virus-host interaction network analysis. The infection information exchanger genes are located on the shortest paths between virus-targeted proteins and are important for the coordination of virus infection. These proteins may be useful targets for AIDS prevention or therapy, as intervention in these pathways could disrupt communication with virus-targeted proteins and HIV-1 infection.

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named "pair-wise interaction-based association mapping" (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P < 0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P < 0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09 × 10⁻⁷). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P < 0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.

Deubiquitinating enzymes (DUBs) are important regulators of cell proliferation. Here we identified a functional deubiquitinating enzyme, ovarian tumor domain-containing 6B (OTUD-6B). Mutation of the conserved Cys residue abolished its deubiquitinating activity in vitro. Otud-6b expression was induced with cytokine stimulation in both mouse Ba/F3 cells and primary B lymphocytes followed a rapid decrease. This rapid decrease was partially facilitated by tristetraprolin (TTP) destabilization of Otud-6b mRNA through AU-rich motifs. Enforced expression of OTUD-6B in Ba/F3 cells could block cell proliferation by arresting cells in G1 phase. In addition, cyclin D2 level was down-regulated when OTUD-6B WT was overexpressed. Therefore, down-regulation of Otud-6b expression after prolonged cytokine stimulation may be required for cell proliferation in B lymphocytes.

Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5'UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale.

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism.

The DNA of all eukaryotic organisms is packaged into nucleosomes, the basic repeating units of chromatin. The nucleosome consists of a histone octamer around which a DNA core is wrapped and the linker histone H1, which is associated with linker DNA. By altering the accessibility of DNA sequences, the nucleosome has profound effects on all DNA-dependent processes. Understanding the factors that influence nucleosome positioning is of great importance for the study of genomic control mechanisms. Transcription factors (TFs) have been suggested to play a role in nucleosome positioning in vivo. Here, the minimum redundancy maximum relevance (mRMR) feature selection algorithm, the nearest neighbor algorithm (NNA), and the incremental feature selection (IFS) method were used to identify the most important TFs that either favor or inhibit nucleosome positioning by analyzing the numbers of transcription factor binding sites (TFBSs) in 53,021 nucleosomal DNA sequences and 50,299 linker DNA sequences. A total of nine important families of TFs were extracted from 35 families, and the overall prediction accuracy was 87.4% as evaluated by the jackknife cross-validation test. Our results are consistent with the notion that TFs are more likely to bind linker DNA sequences than the sequences in the nucleosomes. In addition, our results imply that there may be some TFs that are important for nucleosome positioning but that play an insignificant role in discriminating nucleosome-forming DNA sequences from nucleosome-inhibiting DNA sequences. The hypothesis that TFs play a role in nucleosome positioning is, thus, confirmed by the results of this study.

Copy number variations (CNV) are important causal genetic variations for human disease; however, the lack of a statistical model has impeded the systematic testing of CNVs associated with disease in large-scale cohort.Here, we developed a novel integrated strategy to test CNV-association in genome-wide case-control studies. We converted the single-nucleotide polymorphism (SNP) signal to copy number states using a well-trained hidden Markov model. We mapped the susceptible CNV-loci through SNP site-specific testing to cope with the physiological complexity of CNVs. We also ensured the credibility of the associated CNVs through further window-based CNV-pattern clustering. Genome-wide data with seven diseases were used to test our strategy and, in total, we identified 36 new susceptible loci that are associated with CNVs for the seven diseases: 5 with bipolar disorder, 4 with coronary artery disease, 1 with Crohn's disease, 7 with hypertension, 9 with rheumatoid arthritis, 7 with type 1 diabetes and 3 with type 2 diabetes. Fifteen of these identified loci were validated through genotype-association and physiological function from previous studies, which provide further confidence for our results. Notably, the genes associated with bipolar disorder converged in the phosphoinositide/calcium signaling, a well-known affected pathway in bipolar disorder, which further supports that CNVs have impact on bipolar disorder.Our results demonstrated the effectiveness and robustness of our CNV-association analysis and provided an alternative avenue for discovering new associated loci of human diseases.

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies.

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: "short", "medium", "long", and "extra-long" half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.

BackgroundStudy of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.Methods/principal findingsTo realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.Conclusion/significanceOur results indicate that the network prediction system thus established is quite promising and encouraging.

Nonsense‐mediated mRNA decay (NMD) is a surveillance system that eliminates transcripts with premature termination codons. In this study, we show that mRNAs targeted by NMD are also suppressed at the translational level. The low translational efficiency (TE) is a consequence of multiple features acting in concert, including low translation initiation rate, mediated by 5′ secondary structure and by use of weak initiation sites, and low translation elongation speed, mediated by low codon usage bias. Despite low elongation rates, NMD transcripts show low ribosome density in the coding sequence, probably owing to low initiation rates, high abortion rates or rapid transit of the ribosome following initiation failure. The low TE is observed in the absence of NMD and is not explained by low transcript abundance. Translational inefficiency is flexible, such that NMD targets have increased TE upon starvation. We propose that the low TE predisposes to NMD and/or that it is part of a mechanism for regulation of NMD transcripts.

BACKGROUND: More and more experiments have shown that transcription and mRNA processing are not two independent events but are tightly coupled to each other. Both promoter and transcription rate were found to influence alternative splicing. More than half of human genes have alternative promoters, but it is still not clear why there are so many alternative promoters and what their biological roles are. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we explored whether there is a functional correlation between alternative promoters and alternative splicing by a genome-wide analysis of human and mouse genes. We constructed a large data set of genes with alternative promoter and alternative splicing annotations. By analyzing these genes, we showed that genes with alternative promoters tended to demonstrate alternative splicing compare to genes with single promoter, and, genes with more alternative promoters tend to have more alternative splicing variants. Furthermore, transcripts from different alternative promoters tended to splice differently. CONCLUSIONS/SIGNIFICANCE: Thus at the genomic level, alternative promoters are positively correlated with alternative splicing.