KF-finder: identification of key factors from host-microbial networks in cervical cancer

Cervical Squamous Intraepithelial Lesions Are Associated with Differences in the Vaginal Microbiota of Mexican Women

Cervical cancer is an important health concern worldwide and is one of the leading causes of death in Mexican women. Previous studies have shown changes in the female genital tract microbe community related to human papillomavirus (HPV) infection and cervical cancer; yet, this link remains unexplored in many human populations. This study evaluated the vaginal bacterial community among Mexican women with precancerous squamous intraepithelial lesions (SIL). We sequenced the V3 region of the 16S rRNA gene in cervical samples from 228 Mexican women, including 121 participants with SIL, most of which were HPV positive, and 107 healthy women without HPV infection or SIL. The presence of SIL was associated with changes in composition (beta diversity) and with a higher species richness (Chao1). A comparison of HPV-positive women with and without SIL showed that microbiota changes occurred even in the absence of SIL. Multivariate association with linear models (MaAsLin) analysis yielded independent associations between HPV infection and an increase in the relative abundance of Brachybacterium conglomeratum and Brevibacterium aureum as well as a decrease in two Lactobacillus iners operational taxonomic units (OTUs). We also identified a positive independent association between HPV-16, the most common HPV subtype linked to SIL, and Brachybacterium conglomeratum. Our work indicates that HPV infection leading to SIL is primarily associated with shifts in vaginal microbiota composition, some of which may be specific to this human population.

IMPORTANCE Human papillomavirus (HPV) plays a critical role in cervical carcinogenesis but is not sufficient for cervical cancer development, indicating the involvement of other factors. The vaginal microbiota is an important factor in controlling infections caused by HPV, and, depending on its composition, it can modulate the microenvironment in vaginal mucosa against viral infections. Ethnic and sociodemographic factors influence differences in vaginal microbiome composition, which underlies the dysbiotic patterns linked to HPV infection and cervical cancer across different populations of women. Here, we provide evidence for associations between vaginal microbiota patterns and HPV infection linked to ethnic and sociodemographic factors. To our knowledge, this is the first report of the species Brevibacterium aureum and Brachybacterium conglomeratum linked to HPV infection or squamous intraepithelial lesions (SIL).

The effect of psychological nursing on the short- and long-term negative emotions and quality of life of cervical cancer patients undergoing postoperative chemotherapy

OBJECTIVE

The purpose of this study was to analyze the effect of psychological nursing intervention on the short- and long-term negative emotions and changes in the quality of life in patients with cervical cancer who underwent postoperative chemotherapy.

METHODS

141 patients with cervical cancer who received postoperative chemotherapy in our hospital were recruited as the study cohort. They were divided into the study group (80 cases) and the control group (61 cases) according to the different nursing methods each underwent. The patients in the control group underwent routine nursing, and the study group also underwent psychological nursing. The changes in the quality of life and the negative emotions of the patients in the two groups before and after the intervention were compared, and the correlation between the quality of life and the negative emotions were explored.

RESULTS

The patients' Quality of Life Questionnaire (EROTC-QLQ-C30) and Self-rating Anxiety Scale (SAS) scores in the two groups before the intervention were not significantly different (P > 0.05). A re-evaluation at the end of the 90 day-intervention showed that the EROTC-QLQ-C30 scores in the study group were significantly higher than they were in the control group (P < 0.05). A dynamic evaluation showed that the proportion of patients with mild anxiety in the study group was higher than it was in the control group at 30, 60, and 90 days of intervention (P < 0.05). A Spearman correlation analysis showed that the SAS scale and EROTC-QLQ-C30 scores were negatively correlated (r=-0.4438, P < 0.05).

CONCLUSION

The implementation of psychological intervention can help alleviate the short- and long-term negative emotions of cervical cancer patients who underwent postoperative chemotherapy, and it is feasible and conducive to the patients' quality of life. We recommend carrying out the clinical promotion and application of this psychological intervention.

Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks

BACKGROUND

Network alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world systems. The dynamic characteristic of PPI networks is essential for understanding the evolution and regulation mechanism at the molecular level and there is still much room to improve the alignment quality in dynamic networks.

RESULTS

In this paper, we proposed a novel alignment algorithm, Twadn, to align dynamic PPI networks based on a strategy of time warping. We compare Twadn with the existing dynamic network alignment algorithm DynaMAGNA++ and DynaWAVE and use area under the receiver operating characteristic curve and area under the precision-recall curve as evaluation indicators. The experimental results show that Twadn is superior to DynaMAGNA++ and DynaWAVE. In addition, we use protein interaction network of Drosophila to compare Twadn and the static network alignment algorithm NetCoffee2 and experimental results show that Twadn is able to capture timing information compared to NetCoffee2.

CONCLUSIONS

Twadn is a versatile and efficient alignment tool that can be applied to dynamic network. Hopefully, its application can benefit the research community in the fields of molecular function and evolution.

A novel algorithm for alignment of multiple PPI networks based on simulated annealing

Proteins play essential roles in almost all life processes. The prediction of protein function is of significance for the understanding of molecular function and evolution. Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing genomic data, interactions and annotation data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 based on graph feature vectors to discover functionally conserved proteins and predict function for unknown proteins. To test the algorithm performance, NetCoffee2 and three other notable algorithms were applied on eight real biological datasets. Functional analyses were performed to evaluate the biological quality of these alignments. Results show that NetCoffee2 is superior to existing algorithms IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available under the GNU GPL v3 license at https://github.com/screamer/NetCoffee2.

A novel algorithm based on bi-random walks to identify disease-related lncRNAs

BACKGROUNDS

There is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate computational model can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there is still much room to improve the accuracy of the algorithm.

RESULTS

We developed a novel algorithm, BiWalkLDA, to predict disease-related lncRNAs in three real datasets, which have 528 lncRNAs, 545 diseases and 1216 interactions in total. To compare performance with other algorithms, the leave-one-out validation test was performed for BiWalkLDA and three other existing algorithms, SIMCLDA, LDAP and LRLSLDA. Additional tests were carefully designed to analyze the parameter effects such as α, β, l and r, which could help user to select the best choice of these parameters in their own application. In a case study of prostate cancer, eight out of the top-ten disease-related lncRNAs reported by BiWalkLDA were previously confirmed in literatures.

CONCLUSIONS

In this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Solving cold-start problem by using neighbors' interaction profile information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity.

AVAILABILITY

https://github.com/screamer/BiwalkLDA.

miR-1307-3p overexpression inhibits cell proliferation and promotes cell apoptosis by targeting ISM1 in colon cancer

BACKGROUND

colon adenocarcinoma (COAD) is the most common malignant tumor of gastrointestinal tract. Our study attempts to explore the effect of miR-1307-3p on biological function of COAD cells and its connection with isthmin 1 (ISM1).

METHODS

The miRNA dataset and clinical information of patients with COAD were downloaded from The Cancer Genome Atlas (TCGA) database. The survival prognosis was analyzed by GGSURV package from R. MicroRNA (miR)-1307-3p was identified by identifying overlapping miRNAs that target ISM1, across two databases (miRDB and Targetscan). Dual luciferase reporter assay was employed to scrutinize the relationship between miR-1307-3p and ISM1. RT-PCR was used to quantify miR-1307-3p and ISM1 expression of colon cancer tissues and cell lines. Western blot was performed to quantify related protein expression. Flow Cytometry, CCK8 and colony formation assays were performed to evaluate the apoptosis, cell cycle, cell viability and proliferation of COAD cells.

RESULTS

miR-1307-3p mRNA level decreased in both COAD tissues and cell lines. Overexpression of miR-1307-3p suppressed the proliferation, promoted apoptosis and arrested cell cycle at G1 phase, meanwhile, downregulation of ISM1 accelerated the proliferation, inhibited apoptosis and promote cell cycleprogression. The result of dual luciferase reporter assay indicated that miR-1307-3p targeted ISM1 directly and inhibited its expression. The functions of miR-1307-3p regulating cleaved caspase-3, cyclinD1, Ki67 protein levels and activation of Wnt3a/β-catenin signaling pathway were reversed by ISM1.

CONCLUSIONS

miR-1307-3p inhibited activation of Wnt3a/β-catenin signaling through targeting downregulation of ISM1, thereby inhibited proliferation and promote apoptosis of COAD cells.

Transcriptomic analysis to affirm the regulatory role of long non-coding RNA in horn cancer of Indian zebu cattle breed Kankrej (Bos indicus)

Long non-coding RNA (lncRNA) was previously considered as a non-functional transcript, which now established as part of regulatory elements of biological events such as chromosome structure, remodeling, and regulation of gene expression. The study presented here showed the role of lncRNA through differential expression analysis on cancer-related coding genes in horn squamous cell carcinoma of Indian zebu cattle. A total of 10,360 candidate lncRNAs were identified and further analyzed for its coding potential ability using three tools (CPC, CPAT, and PLEK) that provide 8862 common lncRNAs. Pfam analysis of these common lncRNAs gave 8612 potential candidates for lncRNA differential expression analysis. Differential expression analysis showed a total of 59 significantly differentially expressed genes and 19 lncRNAs. Pearson's correlation analysis was used to identify co-expressed mRNA-lncRNAs to established relation of the regulatory role of lncRNAs in horn cancer. We established a positive relation of seven upregulated (XLOC_000016, XLOC_002198, XLOC_002851, XLOC_ 007383, XLOC_010701, XLOC_010272, and XLOC_011517) and one downregulated (XLOC_011302) lncRNAs with eleven genes that are related to keratin family protein, keratin-associated protein family, cornifelin, corneodesmosin, serpin family protein, and metallothionein that have well-established role in squamous cell carcinoma through cellular communication, cell growth, cell invasion, and cell migration. These biological events were found to be related to the MAPK pathway of cell cycle regulation indicating the role of lncRNAs in manipulating cell cycle regulation during horn squamous cell carcinomas that will be useful in identifying molecular portraits related to the development of horn cancer.

MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites

BACKGROUND

Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these binding preference patterns is of central significance in the understanding of molecular regulation mechanism. Many algorithms have been proposed for the identification of transcription factor binding sites. However, it remains a challengeable problem.

RESULTS

Here, we proposed a novel motif discovery algorithm based on support vector machine (MD-SVM) to learn a discriminative model for TF binding sites. MD-SVM firstly obtains position weight matrix (PWM) from a set of training datasets. Then it translates the MD problem into a computational framework of multiple instance learning (MIL). It was applied to several real biological datasets. Results show that our algorithm outperforms MI-SVM in terms of both accuracy and specificity.

CONCLUSIONS

In this paper, we modeled the TF motif discovery problem as a MIL optimization problem. The SVM algorithm was adapted to discriminate positive and negative bags of instances. Compared to other svm-based algorithms, MD-SVM show its superiority over its competitors in term of ROC AUC. Hopefully, it could be of benefit to the research community in the understanding of molecular functions of DNA functional elements and transcription factors.

MiteFinderII: a novel tool to identify miniature inverted-repeat transposable elements hidden in eukaryotic genomes

Background

Miniature inverted-repeat transposable element (MITE) is a type of class II non-autonomous transposable element playing a crucial role in the process of evolution in biology. There is an urgent need to develop bioinformatics tools to effectively identify MITEs on a whole genome-wide scale. However, most of currently existing tools suffer from low ability to deal with large eukaryotic genomes.

Methods

In this paper, we proposed a novel tool MiteFinderII, which was adapted from our previous algorithm MiteFinder, to efficiently detect MITEs from genomics sequences. It has six major steps: (1) build K-mer Index and search for inverted repeats; (2) filtration of inverted repeats with low complexity; (3) merger of inverted repeats; (4) filtration of candidates with low score; (5) selection of final MITE sequences; (6) selection of representative sequences.

Results

To test the performance, MiteFinderII and three other existing algorithms were applied to identify MITEs on the whole genome of oryza sativa. Results suggest that MiteFinderII outperforms existing popular tools in terms of both specificity and recall. Additionally, it is much faster and more memory-efficient than other tools in the detection.

Conclusion

MiteFinderII is an accurate and effective tool to detect MITEs hidden in eukaryotic genomes. The source code is freely accessible at the website: https://github.com/screamer/miteFinder.

WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks

BACKGROUND

The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users' test datasets.

RESULTS

Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses.

CONCLUSION

WebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users' test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks.

AVAILABILITY

http://www.nwpu-bioinformatics.com/WebNetCoffee.

An online tool for measuring and visualizing phenotype similarities using HPO

Background

The Human Phenotype Ontology (HPO) is one of the most popular bioinformatics resources. Recently, HPO-based phenotype semantic similarity has been effectively applied to model patient phenotype data. However, the existing tools are revised based on the Gene Ontology (GO)-based term similarity. The design of the models are not optimized for the unique features of HPO. In addition, existing tools only allow HPO terms as input and only provide pure text-based outputs.

Results

We present PhenoSimWeb, a web application that allows researchers to measure HPO-based phenotype semantic similarities using four approaches borrowed from GO-based similarity measurements. Besides, we provide a approach considering the unique properties of HPO. And, PhenoSimWeb allows text that describes phenotypes as input, since clinical phenotype data is always in text. PhenoSimWeb also provides a graphic visualization interface to visualize the resulting phenotype network.

Conclusions

PhenoSimWeb is an easy-to-use and functional online application. Researchers can use it to calculate phenotype similarity conveniently, predict phenotype associated genes or diseases, and visualize the network of phenotype interactions. PhenoSimWeb is available at http://120.77.47.2:8080.