Rank Aggregation Research Articles

Effective signal reconstruction from multiple ranked lists via convex optimization

The ranking of objects is widely used to rate their relative quality or relevance across multiple assessments. Beyond classical rank aggregation, it is of interest to estimate the usually unobservable latent signals that inform a consensus ranking. Under the only assumption of independent assessments, which can be incomplete, we introduce indirect inference via convex optimization in combination with computationally efficient Poisson Bootstrap. Two different objective functions are suggested, one linear and the other quadratic. The mathematical formulation of the signal estimation problem is based on pairwise comparisons of all objects with respect to their rank positions. Sets of constraints represent the order relations. The transitivity property of rank scales allows us to reduce substantially the number of constraints associated with the full set of object comparisons. The key idea is to globally reduce the errors induced by the rankers until optimal latent signals can be obtained. Its main advantage is low computational costs, even when handling n<<p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n < < p$$\\end{document} data problems. Exploratory tools can be developed based on the bootstrap signal estimates and standard errors. Simulation evidence, a comparison with the state-of-the-art rank centrality method, and two applications, one in higher education evaluation and the other in molecular cancer research, are presented.

Heuristic Search for Rank Aggregation with Application to Label Ranking

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and an enhanced late acceptance local search method reinforced by a relaxed acceptance and replacement strategy and a fast incremental evaluation mechanism. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on both synthetic and real-world benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, a well-established machine learning task. We additionally analyze several key algorithmic components to gain insight into their operation. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search &amp; Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72371157] and Shanghai Pujiang Programme [Grant 23PJC069]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0019 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0019 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Rank aggregation of independent genetic screen results highlights new strategies for adoptive cellular transfer therapy of cancer.

Efficient intratumoral infiltration of adoptively transferred cells is a significant barrier to effectively treating solid tumors with adoptive cellular transfer (ACT) therapies. Our recent forward genetic, whole-genome screen identified T cell-intrinsic gene candidates that may improve tumor infiltration of T cells. Here, results are combined with five independent genetic screens using rank aggregation to improve rigor. This resulted in a combined total of 1,523 candidate genes - including 1,464 genes not currently being evaluated as therapeutic targets - that may improve tumor infiltration of T cells. Gene set enrichment analysis of a published human dataset shows that these gene candidates are differentially expressed in tumor infiltrating compared to circulating T cells, supporting translational potential. Importantly, adoptive transfer of T cells overexpressing gain-of-function candidates (AAK1ΔN125, SPRR1B, and EHHADH) into tumor-bearing mice resulted in increased T cell infiltration into tumors. These novel gene candidates may be considered as potential therapeutic candidates that can aid adoptive cellular therapy in improving T cell infiltration into solid tumors.

In silico prioritiziation of drug repositioning candidates for Alzheimer’s disease using signature search meta‐analysis

AbstractBackgroundAlzheimer’s disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder with no effective treatment. Due to the genetic, pathological, and clinical heterogeneity among patients with AD, traditional drug development has struggled to target AD‐related pathways. Drug repositioning is a promising alternative to de novo drug development that investigates the potential of existing drugs to be effective in AD. Transcriptome signature matching is one repositioning approach that relies on the signature reversion principle to identify candidate drugs. In this study, we report on several potential candidate drugs for AD obtained by meta‐analyzing transcriptome‐based signature search methods on AMP‐AD nominated target genes for AD that are differentially expressed across seven brain regions.MethodAs search input, we used genes that are nominated as AD targets by the AMP‐AD initiative and significantly differentially expressed in at least one brain region. We compared their expression signatures against the CMAP and LINCS reference databases via four signature search methods (CMAP, LINCS, gCMAP, and correlation‐based methods). Significant candidate drugs were ranked based on their score for each search method and then meta‐analyzed using an ensemble rank aggregation approach employing a modified version of the Dowdall rule. Top‐scoring candidate drugs for AD were annotated using literature search and functional enrichment analysis results.ResultHighest‐scoring drugs in the meta‐analysis included psycholeptics and psychoanaleptics (including acetylcholinesterase inhibitors), antibiotics, antivirals, histone deacetylase (HDAC) inhibitors, and anti‐inflammatory agents. Among those were drugs that are already used for symptomatic treatment of AD or are currently investigated in clinical trials. Novel candidate drugs reported here include antipsychotics, corticosteroids, and calcium channel blockers. Functional enrichment analysis of known targets of these candidate drugs showed shared targeting of biological processes related to dopaminergic synaptic transmission, calcium ion transport regulation, and alkaloid metabolic processConclusionGene expression signature matching is a promising approach for in silico drug repositioning. Our results describe several novel potential candidate drugs for AD obtained through the signature reversion principle, demonstrating the utility of meta‐analyzing data across brain regions and signature search strategies in prioritizing drug repositioning candidates for AD.

CRISPR-Based Functional Transcriptomics Defines the Tumor-Dependency and Molecular Determinants of Long Noncoding RNAs in Multiple Myeloma

Long noncoding RNAs (lncRNAs) constitute most elements in the human transcriptome, yet we lack a comprehensive understanding of their role in tumor cells. Here, in multiple myeloma (MM), we exploited the RNA-targeting activity of the CRISPR-Cas13d endonuclease to develop a functional transcriptomics screening platform that enabled not only the transcriptome-wide identification of tumor-promoting-lncRNAs (tp-lncRNAs) in MM cells - outperforming any prior attempt - but also provided mechanistic information about their sub-cellular site-of-function, the isoform-conferring activity, and their downstream molecular pathways. Using RNA-seq data from CD138+ plasma cells from 319 clinically annotated, homogeneously treated, newly diagnosed MM patients, we first identified 5,304 lncRNAs that were expressed in ≥10% of patients, and then designed and generated a pooled lentiviral library with 9 sgRNAs per identified lncRNA, ~500 non-targeting sgRNAs as negative controls, and 9 sgRNAs targeting known protein-coding oncogenes in MM, such as IRF4, as positive controls. We infected this library at low MOI (&amp;lt;0.3) in five MM cell lines (AMO1, H929, KMS11, OPM2, R8226), each expressing a Cas13d-GFP fusion protein. After 3 weeks, we tested for sgRNAs that were relatively depleted or enriched in the MM cell population using deep sequencing and the MAGeCK robust rank aggregation algorithm. From this, we identified ~600 tp-lncRNAs upon which MM cell lines exhibit significant growth dependency, making up ~12% of the identified lncRNA transcriptome, significantly larger than the previous estimate of 3-5%. The tp-lncRNAs overlapped across MM cell lines: 17% were observed in all cell lines tested, 26% in four cell lines, 34% in at least three cell lines, and 53% in at least two cell lines. The dependency score of many tp-lncRNAs was on a par or even superior to the dependency score of the positive control IRF4 and, as expected, the top-scoring tp-lncRNAs included RROL, MALAT1, and NEAT1. We used a small library of 3,500 gRNAs to validate the dependency on 314 tp-lncRNAs and further validated the dependency on 12 tp-lncRNAs by individually targeting them using antisense oligonucleotides or CRISPR-Cas13d. These studies were followed by downstream transcriptomic analyses using either qRT-PCR or RNA-seq, which validated the growth phenotypes and informed on function. Interestingly, when comparing CRISPR-Cas13d to the current state-of-the-art CRISPR interference screens, we found that the CRISPR-Cas13d platform was superior, with a 4- to 30-fold increase in identifying tp-lncRNAs among 913 lncRNAs of intergenic origin in the same cell lines, and with the additional ability to identify tp-lncRNAs generated at gene loci that are antisense to protein-coding genes. We further investigated the sub-cellular localization and site-of-function of the tp-lncRNAs by using a Cas13d that accumulates either in the cytosol or in the nucleus as well as sub-cellular RNA-seq and single molecule RNA FISH. This way, while confirming that most tp-lncRNAs were functional and enriched in the cell nuclei, we also identified 20 hits providing dependency in the cytosol, including the small nucleolar RNA host genes 6 and 10. We observed that the lncRNA transcriptome investigated in this study contained several transcript variants produced from the same lncRNA genes, which differ in their sequences and likely in their structures and functions. Importantly, our screening platform was able to precisely identify tp-lncRNAs isoform(s) conferring dependencies even among many isoforms, a result that was corroborated by RNA-seq and/or qRT-PCR analysis after individually targeting the tp-lncRNA isoforms with Cas13d. Finally, from our screens, we identified and validated the MM dependency on the novel lncRNA Plasma Cell Specific RNA 1 ( PLASP-1), which is one of the most abundant lncRNAs in MM and normal plasma cells (median TPM&amp;gt;25), whereas it has significantly lower expression or is absent in 54 normal tissues and cell types. PLASP-1 is an un-spliced transcript that directly interacts with spliceosome proteins, as established in living cells. We are currently investigating its functional impact on the splicing machinery and the results will be presented. Overall, this novel platform comprehensively defines tumor dependency on lncRNAs in MM and provides information to guide further mechanistic investigation.

Natural Killer Cells Are Active Against Myeloma, Leukemia or Lymphoma Cells with Loss of Tumor Suppressor Genes

The therapeutic targeting of tumor cells with loss of function (LOF) for tumor suppressor genes (TSGs) is challenging across cancers, including hematologic neoplasias, because pharmacological mechanisms to restore the function(s) of such genes are not readily feasible, in contrast to e.g., inhibition of oncogenic drivers. We reasoned, however, that, although LOF for TSGs leads to de-repressed growth of neoplastic hematopoietic cells, it may not necessarily protect them from immune attack. We thus explored the hypothesis that CRISPR-based loss of function of TSGs in cells from multiple myeloma (MM), leukemias or lymphoma may still be associated with substantial response to immune effector cells such as NK cells, which have the advantage to kill tumor cells across HLA barriers. We have conducted CRISPR-based studies in 7 cell lines that represent different hematologic malignancies and different levels of sensitivity to natural killer (NK) cells, namely the B-cell lymphoma (SUDHL4), precursor B cell acute lymphoblastic leukemia (NALM6), multiple myeloma (MM1.S, LP1, KMS11), chronic myeloid leukemia (K562), and acute myeloid leukemia (MOLM14). In these genome-scale or focused CRISPR screens for LOF (CRISPR-based gene editing) or gain of function (GOF, CRISPR activation), the blood cancer lines were exposed to allogeneic donor-derived NK cells (vs. control cultures without NK cells). We evaluated the performance of genes known to represent recurrent TSGs based on genomic data of patient samples or cell lines; as well as candidate TSGs, based on results from genome-scale CRISPR gene editing screens (e.g., CERES or CHRONOS scores &amp;gt;0.4 in multiple DepMap releases and TPM&amp;gt;1 [RNA-seq]) in the same cell lines as the NK cell resistance screens. These analyses sought to identify any TSGs whose LOF may potentially alter the response of blood cancer cells to NK cells. We also evaluated genes identified as top recurrent TSGs in patient samples from MM and other hematologic neoplasias (e.g., based on prior genomic studies). On aggregate, we evaluated a collection of known and recurrent TSGs (e.g., PTEN, TP53, RB1, CDKN2C, CDKN1B, TENT5C/FAM46C) as well as other, previously underappreciated candidate genes with TSG properties in hematologic neoplasias (e.g., HIF1A, DEPDC5). Perturbation of none of these genes was identified to meet criteria for association with significant resistance to allogeneic donor-derived NK cells (e.g., log2FC&amp;gt;1.0, at least 3-4 sgRNAs with enrichment upon CRISPR KO or depletion with CRISPR activation, p-value &amp;lt;0.05, enrichment rank &amp;lt;100, based on rank aggregation algorithm) in any of the MM, leukemia or lymphoma cell lines examined in LOF or GOF CRISPR screens for NK cell resistance. In fact, for a limited set of cases (e.g., PTEN in KMS11 cells), KO of a TSG was associated with sensitization to NK cell treatment. To probe the mechanistic basis for this lack of effect of TSG loss, we examined the molecular sequelae of CRISPR-based KO for one of these recurrent TSGs, PTEN, by performing scRNAseq using the CROP-seq platform. Pools of MM1.S and LP1 cells expressing sgRNAs targeting select hits from our CRISPR screens and also PTEN were co-cultured with NK cells for 24 h or left untreated, followed by scRNA-seq and sgRNA detection, differential gene-expression analysis. We observed in these studies that the transcriptional changes induced in MM cells by KO of PTEN included limited, if any, changes in the expression of key genes involved in regulation of NK cell responses of tumor cells, e.g., ligands for activating or inhibitor receptors in NK cells, death receptors (e.g., TRAIL, Fas) or their downstream effectors/regulators or other molecules identified from our aforementioned genome-scale and focused CRISPR LOF and GOF studies. Notably, our in-house results from studies of NK exposure of cell lines from hematologic neoplasias are concordant with results for these TSGs in CRISPR screens of non-hematological cancer cells treated with cytotoxic T-cells (e.g., 4T1 or RENCA cells; GSE149933). Overall, our observations indicate that MM, leukemia or lymphoma cells that become deficient for diverse TSGs based on CRISPR-based gene editing are equally responsive to NK cells as their TSG-proficient counterparts. NK cell-based therapies may thus be a promising approach to target TSG-deficient hematologic malignancies for which specific pharmacological therapies are not currently available.

Q-rung orthopair fuzzy decision-making method of multi-source information based on the compression mapping and inverse score function

Intelligent computing is an independent algorithm system that imitates the laws and principles of nature's biological chain to design and solve practical problems in modern society, it can help people better handle some big data problems through artificial intelligence technology and different cognitive characteristics. Especially, when dealing with group decision problems, it is often considered as human behavior based on cognitive computing. The q-rung orthopair fuzzy set (q-ROFS) is not only a generalization of Pythagorean fuzzy set (PFS), but also can deal with the decision problem of multi-source information in a wider area. In this paper, we first give the detailed explanations of the relationship between the q-ROFS and q-rung orthopair fuzzy number (q-ROFN)by an example, and itis pointed out that there are some defects in the existing two ranking methods for the Pythagorean fuzzy numbers (PFNs) and intuitionistic fuzzy numbers (IFNs) through counterexamples, and then all q-ROFNs are mapped into the unit triangle of the first quadrant through q-compression mapping, and the concept of distance factor for q-ROFNs is proposed by Euclidean distance between each q-ROFN and the maximum element (1,0).Secondly, a novel inverse score function for q-ROFNs are put forward by the distance factor and hesitancy degree, and the monotonicity of the inverse score function are discussed. Finally, a new ranking criterion of q-ROFNs is given by the new score formula, and then the rationality of the ranking criterion is proved, and through the method of solving the conditional extreme value of multivariate function (Lagrange multiplier method), there is indeed an extreme value point (corresponding to the maximum q-ROFN) on υ=μ. Besides, a q-rung orthopair fuzzy decision-making method for multi-source information is given by the new ranking criterion and aggregation operations, and the comparison of examples shows that the inverse score function and decision-making method do have many advantages in the q-ROFN environment.

Integrating a microRNA signature as a liquid biopsy-based tool for the early diagnosis and prediction of potential therapeutic targets in pancreatic cancer

IntroductionPancreatic cancer is a highly aggressive cancer, and early diagnosis significantly improves patient prognosis due to the early implementation of curative-intent surgery. Our study aimed to implement machine-learning algorithms to aid in early pancreatic cancer diagnosis based on minimally invasive liquid biopsies.Materials and methodsThe analysis data were derived from nine public pancreatic cancer miRNA datasets and two sequencing datasets from 26 pancreatic cancer patients treated in our medical center, featuring small RNAseq data for patient-matched tumor and non-tumor samples and serum. Upon batch-effect removal, systematic analyses for differences between paired tissue and serum samples were performed. The robust rank aggregation (RRA) algorithm was used to reveal feature markers that were co-expressed by both sample types. The repeatability and real-world significance of the enriched markers were then determined by validating their expression in our patients’ serum. The top candidate markers were used to assess the accuracy of predicting pancreatic cancer through four machine learning methods. Notably, these markers were also applied for the identification of pancreatic cancer and pancreatitis. Finally, we explored the clinical prognostic value, candidate targets and predict possible regulatory cell biology mechanisms involved.ResultsOur multicenter analysis identified hsa-miR-1246, hsa-miR-205-5p, and hsa-miR-191-5p as promising candidate serum biomarkers to identify pancreatic cancer. In the test dataset, the accuracy values of the prediction model applied via four methods were 94.4%, 84.9%, 82.3%, and 83.3%, respectively. In the real-world study, the accuracy values of this miRNA signatures were 82.3%, 83.5%, 79.0%, and 82.2. Moreover, elevated levels of these miRNAs were significant indicators of advanced disease stage and allowed the discrimination of pancreatitis from pancreatic cancer with an accuracy rate of 91.5%. Elevated expression of hsa-miR-205-5p, a previously undescribed blood marker for pancreatic cancer, is associated with negative clinical outcomes in patients.ConclusionA panel of three miRNAs was developed with satisfactory statistical and computational performance in real-world data. Circulating hsa-miRNA 205-5p serum levels serve as a minimally invasive, early detection tool for pancreatic cancer diagnosis and disease staging and might help monitor therapy success.

Abstract 13918: Improvement of Pulmonary Arterial Hypertension (PAH) Risk Assessment Model Using Cardiac Magnetic Resonance Imaging Variables

Introduction: PAH is a deadly disease without cure. Formalized risk stratification allows therapeutic adjustments that optimize drug utilization. Risk scores, like REVEAL 2.0 recommended by PAH guidelines only offer good discrimination. Our goal was to create risk models with excellent discrimination (C-Index over 0.8), using modern statistical techniques and expanded variable pools including imaging and genomics. The AIM of this study was to demonstrate the improved performance with the addition of cardiac MRI variables. Methods: PAH patients from the ASPIRE cardiac MRI database were analyzed. Imaging variable (IMV) selection was performed using three machine learning methods: logistic regression, Lasso, and Random Forest. Rankings of the IMVs from these sources were aggregated to arrive at a consensus list. The selected IMVs were added to the set of variables for deriving the REVEAL 2.0 composite score. Bayesian networks (BN) were then built to predict 1-year survival based on the Tree-Augmented naïve Bayes (TAN) algorithm. Five-fold cross-validation was performed to assess the improvement in survival prediction from adding the selected imaging variables. Results: A total of 343 PAH previously diagnosed subjects were included in this analysis. The rank aggregation algorithm identified several IMV, including LVSVI, RVCO, LVEDVI, and RVESVI, that were predictive of survival but not the REVEAL 2.0 composite score. Adding these IMV to the REVEAL 2.0 variables, we built a BN model that depicts the non-linear relationships among the predictors and one-year survival (Fig 1). We obtained an average AUC of 0.83 over the five cross-validation test sets, an improvement over the AUC of 0.78 using only the REVEAL 2.0 variables. A Mann-Whitney non-parametric test shows the improvement is statistically significant at the 0.1 level. Conclusion: Advanced statistical models that include cardiac IMVs improve performance of PAH risk assessment models.

Explainable Predictive Maintenance is Not Enough: Quantifying Trust in Remaining Useful Life Estimation

Machine learning (ML)/deep learning (DL) has shown tremendous success in data-driven predictive maintenance (PdM). However, operators and technicians often require insights to understand what is happening, why it is happening, and how to react, which these black-box models cannot provide. This is a major obstacle in adopting PdM as it cannot support experts in making maintenance decisions based on the problems it detects. Motivated by this, several researchers have recently utilized various post-hoc explanation methods and tools, such as LIME, SHAP, etc., for explaining the predicted RUL from these black-box models. Unfortunately, such (post-hoc) explanation methods often suffer from the \emph{disagreement problem}, which occurs when multiple explainable AI (XAI) tools differ in their feature ranking. Hence, explainable PdM models that rely on these methods are not trustworthy, as such unstable explanations may lead to catastrophic consequences in safety-critical PdM applications. This paper proposes a novel framework to address this problem. Specifically, first, we utilize three state-of-the-art explanation methods: LIME, SHAP, and Anchor, to explain the predicted RUL from three ML-based PdM models, namely extreme gradient boosting (XGB), random forest (RF), logistic regression (LR), and one feed-forward neural network (FFNN)-based PdM model using the C-MAPSS dataset. We show that the ranking of dominant features for RUL prediction differs for different explanation methods. Then, we propose a new metric \emph{trust score} for selecting the proper explanation method. This is achieved by evaluating the XAI methods using four evaluation metrics: fidelity, stability, consistency, and identity, and then combining them into a single \emph{trust score} metric through utilizing Kenny and Borda rank aggregation methods. Our results show that the proposed method effectively selects the most appropriate explanation method from a set of explanation methods for estimated RULs. To the best of our knowledge, this is the first work that attempts to address and solve the disagreement problem in explainable PdM.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

Hybrid feature ranking and classifier aggregation based on multi-criteria decision-making

This study introduces an ensemble methodology, namely, hybrid feature ranking and classifier aggregation (HyFraCa), to integrate ensemble feature selection and ensemble classification in a composite framework. The proposed HyFraCa is embedded in a multi-criteria decision-making (MCDM)-based scheme for feature ranking and classifier weighting, with an effective aggregation rule that yields a consensus feature ranking from ensembles of heterogeneous classifiers and feature selectors. Experimental evaluations on 20 public UCI datasets demonstrated the superiority of HyFraCa in producing a more accurate and generalizable classification compared with state-of-the-art benchmark ensemble methods. HyFraCa also provides robust and reliable consensus feature rankings, which are favorable for real-world classification problems in which feature interpretability is emphasized.

Fusobacterium nucleatum upregulates MMP7 to promote metastasis-related characteristics of colorectal cancer cell via activating MAPK(JNK)-AP1 axis

BackgroundColorectal cancer (CRC) is the third most common malignant tumor. Fusobacterium nucleatum (F. nucleatum) is overabundant in CRC and associated with metastasis, but the role of F. nucleatum in CRC cell migration and metastasis has not been fully elucidated.MethodsDifferential gene analysis, protein−protein interaction, robust rank aggregation analysis, functional enrichment analysis, and gene set variation analysis were used to figure out the potential vital genes and biological functions affected by F. nucleatum infection. The 16S rDNA sequencing and q-PCR were used to detect the abundance of F. nucleatum in tissues and stools. Then, we assessed the effect of F. nucleatum on CRC cell migration by wound healing and transwell assays, and confirmed the role of Matrix metalloproteinase 7 (MMP7) induced by F. nucleatum in cell migration. Furthermore, we dissected the mechanisms involved in F. nucleatum induced MMP7 expression. We also investigated the MMP7 expression in clinical samples and its correlation with prognosis in CRC patients. Finally, we screened out potential small molecular drugs that targeted MMP7 using the HERB database and molecular docking.ResultsF. nucleatum infection altered the gene expression profile and affected immune response, inflammation, biosynthesis, metabolism, adhesion and motility related biological functions in CRC. F. nucleatum was enriched in CRC and promoted the migration of CRC cell by upregulating MMP7 in vitro. MMP7 expression induced by F. nucleatum infection was mediated by the MAPK(JNK)-AP1 axis. MMP7 was highly expressed in CRC and correlated with CMS4 and poor clinical prognosis. Small molecular drugs such as δ-tocotrienol, 3,4-benzopyrene, tea polyphenols, and gallic catechin served as potential targeted therapeutic drugs for F. nucleatum induced MMP7 in CRC.ConclusionsOur study showed that F. nucleatum promoted metastasis-related characteristics of CRC cell by upregulating MMP7 via MAPK(JNK)-AP1 axis. F. nucleatum and MMP7 may serve as potential therapeutic targets for repressing CRC advance and metastasis.

RGS5 as a Biomarker of Pericytes, Involvement in Vascular Remodeling and Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by a sustained rise in mean pulmonary artery pressure. Pulmonary vascular remodeling serves an important role in PAH. Identifying a key driver gene to regulate vascular remodeling of the pulmonary microvasculature is critical for PAH management. Differentially expressed genes were identified using the Gene Expression Omnibus (GEO) GSE117261, GSE48149, GSE113439, GSE53408 and GSE16947 datasets. A co-expression network was constructed using weighted gene co-expression network analysis. Novel and key signatures of PAH were screened using four algorithms, including weighted gene co-expression network analysis, GEO2R analysis, support vector machines recursive feature elimination and robust rank aggregation rank analysis. Regulator of G-protein signaling 5 (RGS5), a pro-apoptotic/anti-proliferative protein, which regulate arterial tone and blood pressure in vascular smooth muscle cells. The expression of RGS5 was determined using reverse transcription-quantitative PCR (RT-qPCR) in PAH and normal mice. The location of RGS5 and pericytes was detected using immunofluorescence. Compared with that in the normal group, RGS5 expression was upregulated in the PAH group based on GEO and RT-qPCR analyses. RGS5 expression in single cells was enriched in pericytes in single-cell RNA sequencing analysis. RGS5 co-localization with pericytes was detected in the pulmonary microvasculature of PAH. RGS5 regulates vascular remodeling of the pulmonary microvasculature and the occurrence of PAH through pericytes, which has provided novel ideas and strategies regarding the occurrence and innovative treatment of PAH.

Common Molecular Signatures Between Coronavirus Infection and Alzheimer's Disease Reveal Targets for Drug Development.

Cognitive decline is a common consequence of COVID-19, and studies suggest a link between COVID-19 and Alzheimer's disease (AD). However, the molecular mechanisms underlying this association remain unclear. To understand the potential molecular mechanisms underlying the association between COVID-19 and AD development, and identify the potential genetic targets for pharmaceutical approaches to reduce the risk or delay the development of COVID-19-related neurological pathologies. We analyzed transcriptome datasets of 638 brain samples using a novel Robust Rank Aggregation method, followed by functional enrichment, protein-protein, hub genes, gene-miRNA, and gene-transcription factor (TF) interaction analyses to identify molecular markers altered in AD and COVID-19 infected brains. Our analyses of frontal cortex from COVID-19 and AD patients identified commonly altered genes, miRNAs and TFs. Functional enrichment and hub gene analysis of these molecular changes revealed commonly altered pathways, including downregulation of the cyclic adenosine monophosphate (cAMP) signaling and taurine and hypotaurine metabolism, alongside upregulation of neuroinflammatory pathways. Furthermore, gene-miRNA and gene-TF network analyses provided potential up- and downstream regulators of identified pathways. We found that downregulation of cAMP signaling pathway, taurine metabolisms, and upregulation of neuroinflammatory related pathways are commonly altered in AD and COVID-19 pathogenesis, and may make COVID-19 patients more susceptible to cognitive decline and AD. We also identified genetic targets, regulating these pathways that can be targeted pharmaceutically to reduce the risk or delay the development of COVID-19-related neurological pathologies and AD.

A unifying rank aggregation framework to suitably and efficiently aggregate any kind of rankings

The aggregation of multiple rankings into a consensus ranking is a crucial task in various domains such as search engine results or user-based ratings. This task poses significant challenges due to its inherent complexity. The complexity of the problem stems not only from the need for exactness and efficiency, but also from the diversity of real-world scenarios, which often involve incomplete rankings and ties.Most existing methods propose a specific way to aggregate rankings. However, these methods often do not take into account different real use-case scenarios which can impact the relevance of their final result, as the congruence between the aggregated output and the expected outcome inherently depends on the context. To address the issue of context-dependency in ranking aggregation, we introduce a unifying framework that subsumes a variety of generalizations of the Kemeny score for incomplete rankings with ties and enables the design of new ones if a specific context requires it. Our framework is parameterized, allowing for different behaviors depending on the specific use case.We provide a broader scope of application to the methods encompassed by our approach, augmenting them with a larger theoretical and algorithmic structure. We establish an axiomatic study to better understand each method within our framework and present an algorithmic approach that includes exact methods, partitioning algorithms, and heuristics.Finally, we demonstrate the practical relevance of our approach through an empirical study on both real and synthetic datasets. Notably, the synthetic datasets are generated based on devised real-world scenarios, highlighting the context-dependent applicability of different Kemeny-based rank aggregation methods within our framework.

Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19.

Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin-angiotensin-aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.

Construction of lncRNA-miRNA-mRNA regulatory network in severe asthmatic bronchial epithelial cells: A bioinformatics study.

Asthma is a chronic respiratory disease caused by environment-host interactions. Bronchial epithelial cells (BECs) are the first line of defense against environmental toxins. However, the mechanisms underlying the role of BECs in severe asthma (SA) are not yet fully understood. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been shown to play important roles in the regulation of gene expression in the pathogenesis of SA. In this study, bioinformatics was used for the first time to reveal the lncRNA-miRNA-mRNA regulatory network of BECs in SA. Five mRNA datasets of bronchial brushing samples from patients with SA and healthy controls (HC) were downloaded from the Gene Expression Omnibus (GEO) database. A combination of the Venn diagram and robust rank aggregation (RRA) method was used to identify core differentially expressed genes (DEGs). Protein-protein interaction (PPI) analysis of core DEGs was performed to screen hub genes. The miRDB, miRWalk, and ENCORI databases were used to predict the miRNA-mRNA relationships, and the ENCORI and starBase v2.0 databases were used to predict the upstream lncRNAs of the miRNA-mRNA relationships. Four core DEGs were identified: carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), interleukin-1 receptor type 2 (IL1R2), trefoil factor 3 (TFF3), and vascular endothelial growth factor A (VEGFA). These 4 core DEGs indicated that SA was not significantly associated with sex. Enrichment analysis showed that the MAPK, Rap1, Ras, PI3K-Akt and Calcium signaling pathways may serve as the principal pathways of BECs in SA. A lncRNA-miRNA-mRNA regulatory network of the severe asthmatic bronchial epithelium was constructed. The top 10 competing endogenous RNAs (ceRNAs) were FGD5 antisense RNA 1 (FGD5-AS1), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), X inactive specific transcript (XIST), HLA complex group 18 (HCG18), small nucleolar RNA host gene 16 (SNHG16), has-miR-20b-5p, has-miR-106a-5p, hsa-miR-106b-5p, has-miR-519d-3p and Fms related receptor tyrosine kinase 1 (FLT1). Our study revealed a potential mechanism for the lncRNA-miRNA-mRNA regulatory network in BECs in SA.

Shared biomarkers of multi-tissue origin for primary Sjogren's syndrome and their importance in immune microenvironment alterations

With the recent advancement in omics and molecular techniques, a wealth of new molecular biomarkers have become available for the diagnosis and classification of primary Sjögren's syndrome (pSS) patients. However, whether these biomarkers are universal is of great interest to us. In this study, we used various methods to obtain shared biomarkers derived from multiple tissue in pSS patients and to explore their relationship with immune microenvironment alterations. First we identified differentially expressed genes (DEGs) between pSS and healthy controls utilizing nine mRNA microarray datasets obtained from the Gene Expression Omnibus (GEO). Then, shared biomarkers were filtered out using robust rank aggregation (RRA), data integration analysis, weighted gene co-expression network analysis (WGCNA), and least absolute selection and shrinkage operator (LASSO) regression; their roles in pSS and association with changes in the immune microenvironment were also analyzed. In addition, these biomarkers were further confirmed with both the testing set and immunohistochemistry (IHC). As a result, ten biomarkers, i.e., EPSTI1, IFI44, IFIT1, IFIT2, IFIT3, MX1, OAS1, PARP9, SAMD9L and TRIM22, were identified. Receiver operating characteristic (ROC) curves showed that the ten genes could discriminate pSS from controls. Gene set enrichment analysis (GSEA) showed that the enrichment of immune-related gene sets was significant in pSS patients with high expression of either biomarker. Furthermore, the association between some immunocytes and these biomarkers was identified. In the two distinct molecular patterns of pSS patients based on the expressions of these biomarkers, the proportions of immunocytes were significantly different. Our study identified shared biomarkers of multi-tissue origin and revealed their relationship with altered immune microenvironment in pSS patients. These markers not only have diagnostic implications but also provide potential immunotherapeutic targets for the clinical treatment of pSS patients.

Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson's Disease.

Parkinson's disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa-carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.