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Genetic and clinical insights into the coexistence of multiple myeloma and diffuse large B cell lymphoma from a case report and systematic review with bioinformatics analysis.

BACKGROUND: Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are B-cell malignancies that rarely coexist in a single patient, presenting significant diagnostic and therapeutic challenges. While MM primarily involves clonal plasma cells, DLBCL is an aggressive lymphoid neoplasm. Investigating shared genetic mutations and understanding their clinical relevance in both cancers could provide novel insights into their pathogenesis and underlying molecular mechanisms, thereby informing future translational research. MATERIALS AND METHODS: A case report was conducted on a 52-year-old male who presented with abdominal pain and anemia. Imaging revealed lymphadenopathy, and biopsy confirmed high-grade DLBCL with concurrent bone marrow involvement suggestive of MM. Laboratory tests identified monoclonal IgM gammopathy, and the patient was treated with R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone) chemotherapy for DLBCL followed by autologous stem cell transplantation (ASCT) for MM relapse. A systematic review of the literature was performed using PubMed, Scopus, and Web of Science databases to identify cases of patients diagnosed with both MM and DLBCL. Data on patient demographics, clinical features, treatment regimens, and outcomes were extracted. Additionally, bioinformatics analysis was conducted using publicly available genomic data from cBioPortal and IntOGen to identify driver gene mutations in MM and DLBCL. Functional and pathway enrichment analysis was performed with KEGG and Gene Ontology (GO) databases. RESULTS: The case report highlighted a complex clinical course where the patient initially responded well to R-CHOP chemotherapy for DLBCL, achieving remission, but later relapsed with MM, treated with ASCT and lenalidomide. The systematic review revealed 14 eligible studies in which MM and DLBCL often occur in older patients, either simultaneously or sequentially, with variable treatment responses, including complete remission, partial remission, or relapse. The bioinformatics analysis identified several shared function and cancer-related pathways between two cancers including interleukin and cytokine-mediated signaling pathways, regulation of cell cycle, neurotrophin signaling pathway, FOXO signaling pathway, Epstein Barr virus infection, and viral carcinogenesis. CONCLUSION: This study provides valuable insights into the dual occurrence of MM and DLBCL, emphasizing the importance of tailored treatment approaches. The driver mutations identified highlight overlapping oncogenic pathways rather than implying a shared clonal origin, and may inform future studies exploring their biological and clinical implications. Further research into these shared molecular mechanisms could lead to more effective treatments for patients with coexisting MM and DLBCL.

Bioinformatics analysis↗

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

Integrative proteomics and bioinformatics pipelines for PTM profiling.

Post-translational modifications (PTMs) regulate protein function across all life forms and allow plants to respond rapidly to biotic and abiotic stress. Over 450 PTM types have been described across organisms, of which 23-33 have been experimentally confirmed in plants, including phosphorylation, acetylation, methylation, glycosylation, ubiquitination, and sumoylation. These modifications are highly dynamic and often reversible, and frequently act in combination, or "crosstalk," to fine-tune cellular processes. Advances in high-resolution mass spectrometry and large-scale genome sequencing continue to expand the catalogue of known PTM sites, while machine learning and deep learning approaches increasingly support prediction of PTM site localization and function. Unlike broader surveys of plant PTMs, this review focuses specifically on O-phosphorylation and Lys-N(ε)-acetylation, the two best-characterized and most extensively crosstalking PTMs in plants, and integrates four perspectives: the historical development of proteomic and bioinformatics approaches to these modifications; current mass spectrometry-based workflows and enrichment strategies; the bioinformatics tools and databases available for their analysis; and the technical and species-related challenges, particularly in non-model plants, that currently limit their study. We close by outlining priority directions for future research, including multi-omics integration, AI-based prediction, and the translation of PTM knowledge into crop stress resilience and breeding applications.

Protein Processing, Post-Translational↗

Public web-based services from the European Bioinformatics Institute.

The mission of the European Bioinformatics Institute (EBI), an outstation of the European Molecular Biology Laboratory (EMBL) in Heidelberg, is to ensure that the growing body of information from molecular biology and genome research is placed in the public domain and is accessible freely to all parts of the scientific community in ways that promote scientific progress. To fulfil this mission, the EBI provides a wide variety of free, publicly available bioinformatics services. These can be divided into data submissions processing; access to query, analysis and retrieval systems and tools; ftp downloads of software and databases; training and education and user support. All of these services are available at the EBI website: http://www.ebi.ac.uk/services. This paper provides a detailed introduction to the interactive analysis systems that are available from the EBI and a brief introduction to other, related services.

Computational Biology↗

Identification of key genes related to bone metastasis of breast cancer using bioinformatics methods and construction of a prognostic model.

Breast cancer (BC) ranks among the most prevalent cancers in females, with bone metastasis significantly compromising patients' quality of life and survival rates. Enhancing our comprehension of BC bone metastasis mechanisms at the molecular level holds promise for improving BC treatment and prognosis. Leveraging bioinformatics tools, we integrated multiple datasets, conducted comprehensive analyses across various databases, identified biomarkers associated with BC bone metastasis, and constructed a prognostic model. Firstly, 3 BC bone metastasis-related datasets were downloaded from gene expression omnibus, the data were merged, and batch effects were removed, followed by identification of differentially expressed genes (DEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed using the STRING database to screen hub genes. Then, survival analysis of hub genes was performed using the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using key genes with survival differences, and the model was evaluated. Two hundred ninety-two DEGs were identified. Gene ontology and KEGG pathway enrichment analysis yielded 769 biological processes (BPs), 78 cellular components, 43 molecular functions, and 50 KEGG pathways. Fifteen hub genes were selected from the protein-protein interaction network. Survival analysis revealed 6 genes related to BC survival. The prognostic model identified 4 genes with important predictive value for BC prognosis. Our study utilized bioinformatics analysis to identify a series of DEGs related to BC bone metastasis. Based on further selection of hub genes, we constructed a relatively ideal prognostic model for BC, and identified 4 genes (DLGAP5, TPX2, PLK1, and CENPN) with valuable predictive value for BC prognosis.

Humans↗

Integrative analyses of mendelian randomization and bioinformatics reveal casual relationship and genetic links between COVID-19 and knee osteoarthritis.

BACKGROUND: Clinical and epidemiological analyses have found an association between coronavirus disease 2019 (COVID-19) and knee osteoarthritis (KOA). Infection with COVID-19 may increase the risk of developing KOA. OBJECTIVES: This study aimed to investigate the potential causal relationship between COVID-19 and KOA using Mendelian randomization (MR) and to explore the underlying mechanisms through a systematic bioinformatics approach. METHODS: Our investigation focused on exploring the potential causal relationship between COVID-19, acute upper respiratory tract infection (URTI) and KOA utilizing a bidirectional MR approach. Additionally, we conducted differential gene expression analysis using public datasets related to these three conditions. Subsequent analyses, including transcriptional regulation analysis, immune cell infiltration analysis, single-cell analysis, and druggability evaluation, were performed to explore potential mechanisms and prioritize therapeutic targets. RESULTS: The results indicate that COVID-19 has a one-way impact on KOA, while URTI does not play a causal role in this association. Ribosomal dysfunction may serve as an intermediate factor connecting COVID-19 with KOA. Specifically, COVID-19 has the potential to influence the metabolic processes of the extracellular matrix, potentially impacting the joint homeostasis. A specific group of genes (COL10A1, BGN, COL3A1, COMP, ACAN, THBS2, COL5A1, COL16A1, COL5A2) has been identified as a shared transcriptomic signature in response to KOA with COVID-19. Imatinib, Adiponectin, Myricetin, Tranexamic acid, and Chenodeoxycholic acid are potential drugs for the treatment of KOA patients with COVID-19. CONCLUSIONS: This study uniquely combines Mendelian randomization and bioinformatics tools to explore the possibility of a causal relationship and genetic association between COVID-19 and KOA. These findings are expected to provide novel perspectives on the underlying biological mechanisms that link COVID-19 and KOA.

Humans↗

Investigating the molecular mechanisms, drug prediction, and validation of CCNA2 and MAD2L1 in esophageal squamous cell carcinoma based on bioinformatics.

OBJECTIVE: Aims to comprehensively investigate the expression patterns of CCNA2 and MAD2L1 in esophageal squamous cell carcinoma using bioinformatics methods. METHODS: Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression and ESCC-related genes in public databases, were employed for investigating potential biomarkers for prognosis of esophageal squamous cell carcinoma(ESCC).Finally,. performing qRT-PCR and immunohistochemistry to validate. RESULTS: Ultimately identified 4 hub genes: CDK1, CCNA2,TOP2A and MAD2L1. Bioinformatics analysis showed high expression of these four genes in ESCC (P&#x2009;<&#x2009;0.05). CCNA2 and MAD2L1 were selected for subsequent analysis based on literature.3.Single gene enrichment analysis revealed significant enrichment of CCNA2 and MAD2L1 in pathways related to splicing, bladder cancer, non-homologous end joining and homologous recombination, glycosaminoglycan biosynthesis chondroitin sulfate, progesterone-mediated oocyte maturation and mismatch repair. PASTAA database indicated the involvement of transcription factors such as Roralpha1, Pou6f1, Roralpha2, Atf-1, Pax-3, C/ebpalpha, Nkx2-1 in the regulation of CCNA2, while no transcription factors were predicted for MAD2L1..Immune infiltration analysis revealed a close association between ESCC and plasma cells, CD8&#x2009;+&#x2009;T cells, monocytes, M0 macrophages, M1 macrophages, dendritic cells, and resting mast cells.Drug prediction for CCNA2 included 7 drugs such as ETHINYL ESTRADIOL, Seliciclib and TAMOXIFEN, while no drugs were predicted for MAD2L1.qRT-PCR and immunohistochemistry demonstrated high expression of CCNA2 in ESCC, while MAD2L1 showed no significant difference between ESCC and normal esophageal squamous epithelial tissues. CONCLUSION: CCNA2 and MAD2L1 may be potential biomarkers for ESCC, providing a novel basis for understanding the molecular mechanisms underlying ESCC pathogenesis.Additionally, the potential drugs predicted for CCNA2 may emerge as a new hope for ESCC patients in the future.

Humans↗

Large language models in bioinformatics: a comprehensive survey.

The emergence of foundation models with trillion-level parameters has redefined the landscape of artificial intelligence. Various fields are developing their own large-scale models, which can solve many problems within the field and improve work efficiency. Biological large-scale models are a cross-disciplinary research field that combines mathematics, computer science, and biology, aiming to simulate and understand the structure, function, and dynamic changes of biological systems through the establishment of complex computational models. This field covers multiple levels such as biological pathways, population dynamics, protein folding, etc., providing us with tools for deep exploration of the mysteries of life and applications in medicine, ecology, and other fields. This article reviews the background and research status of biological large-scale models, and discusses future directions. Large language models (LLMs) and other large-scale foundation models have rapidly advanced in recent years, enabling powerful representation learning and generation across text, sequences, and multimodal data. In bioinformatics and biomedicine, these models are increasingly used to analyze genomic sequences, infer protein properties and structures, support drug discovery, and integrate heterogeneous biomedical evidence. This survey reviews the basic principles of LLMs and summarizes representative applications in (i) gene and genome sequence analysis, (ii) protein structure and function prediction, and (iii) drug design, including virtual screening and personalized medicine. We also discuss emerging multi-model modeling approaches, as well as key challenges such as data quality and privacy, interpretability, generalization to new organisms and tasks, and responsible deployment in health-related settings. Finally, we outline future directions for developing reliable, scalable, and explainable bioinformatics foundation models.

bioinformatics↗

Bioinformatic Analysis of Bacillus pacificus B630: Molecular Understanding of Biofilm Production.

The aim of this study was to determine biofilm production and motility in Bacillus pacificus B630 and Bacillus cereus ATCC 14579, and to perform a comparative genome analysis using bioinformatic tools to understand the differences between the two strains. Biofilm production was performed in glass tubes stained with safranin; motility was determined on soft agar. Bioinformatic analysis was performed using genomic information from both strains, including the identification of orthologous genes, the similarity between genes of the eps1 and sipW-tasA-calY operons, and the SipW and TasA model prediction. B. pacificus B630 produces a greater amount of biofilm on glass than B. cereus ATCC 14579 (p < 0.01). Furthermore, B. pacificus B630 shows lower motility than B. cereus ATCC 14579 (p < 0.001). B. pacificus B630 contains 45 unshared genes, whereas B. cereus ATCC 14579 has 27 unshared genes. Differences in similarity were observed between the genes of the eps1 and sipW-tasA-calY operons. These differences between SipW and TasA may affect protein structural predictions. In SipW, the differences may affect the C-terminal region. In TasA, the number of B-sheets differed between the two proteins, and amino acid substitutions were found in regions of high protein aggregation. Genomic differences in genes associated with biofilm production may explain differences in biofilm production between the strains studied.

Biofilms↗

A combination of chemical derivatisation and improved bioinformatic tools optimises protein identification for proteomics.

The identification of individual protein species within an organism's proteome has been optimised by increasing the information produced from mass spectral analysis through the chemical derivatisation of tryptic peptides and the development of new software tools. Peptide fragments are subjected to two forms of derivatisation. First, lysine residues are converted to homoarginine moieties by guanidination. This procedure has two advantages, first, it usually identifies the C-terminal amino acid of the tryptic peptide and also greatly increases the total information content of the mass spectrum by improving the signal response of C-terminal lysine fragments. Second, an Edman-type phenylthiocarbamoyl (PTC) modification is carried out on the N-terminal amino acid. The renders the first peptide bond highly susceptible to cleavage during mass spectrometry (MS) analysis and consequently allows the ready identification of the N-terminal residue. The utility of the procedure has been demonstrated by developing novel bioinformatic tools to exploit the additional mass spectral data in the identification of proteome proteins from the yeast Saccharomyces cerevisiae. With this combination of novel chemistry and bioinformatics, it should be possible to identify unambiguously any yeast protein spot or band from either two-dimensional or one-dimensional electropheretograms.

Databases, Factual↗

Identification of a Nonribosomal Peptide Analog With Activity Against Multiple Gram-Positive Bacteria via a Synthetic Bioinformatic Natural Product Discovery Approach.

Nonribosomal peptide (NRP) antibiotics exhibit potent biological activities and are broadly used in clinical therapy. Because most microorganisms are difficult to culture and many antibiotic biosynthetic genes are silent, traditional activity tracking approaches face major limitations in the discovery of novel NRPs. Here, based on a synthetic bioinformatic natural product (syn-BNP) discovery approach that integrates bioinformatics and chemical synthesis, a novel nonribosomal peptide synthetase (NRPS) gene cluster from the genome of Rhodococcus erythropolis D-1 was mined. A putative NRP scaffold synthesized by the NRPS encoded by this cluster was predicted. Through chemical synthesis and four rounds of structure-activity relationship (SAR) studies, 37 NRP analogs were ultimately generated. Among these analogs, ZURJC28 shows activity against multiple Gram-positive bacteria, including two drug-resistant strains. Mechanistic studies and metabolomics analyses revealed that ZURJC28 exerts membrane-disruptive activity associated with interaction with phosphatidylglycerol (PG)-enriched Gram-positive membranes, leading to membrane damage and widespread metabolic dysregulation. ZURJC28 also shows low cytotoxicity and low hemolytic activity, suggesting its preliminary in vitro safety profile.

Gram-Positive Bacteria↗

Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: identification by mass spectrometry and bioinformatics.

Weakly bound cell wall proteins of Arabidopsis thaliana were identified using a proteomic and bioinformatic approach. An efficient protocol of extraction based on vacuum-infiltration of the tissues was developed. Several salts and a chelating agent were compared for their ability to extract cell wall proteins without releasing cytoplasmic contaminants. Of the 93 proteins that were identified, a large proportion (60%) was released by calcium chloride. From bioinformatics analysis, it may be predicted that most of them (87 out of 93) had a signal peptide, whereas only six originated from the cytoplasm. Among the putative apoplastic proteins, a high proportion (67 out of 87) had a basic pI. Numerous glycoside hydrolases and proteins with interacting domains were identified, in agreement with the expected role of the extracellular matrix in polysaccharide metabolism and recognition phenomena. Ten proteinases were also found as well as six proteins with unknown functions. Comparison of the cell wall proteome of rosettes with the previously published cell wall proteome of cell suspension cultures showed a high level of cell specificity, especially for the different members of several large multigenic families.

Arabidopsis↗

HUPO Brain Proteome Project Pilot Studies: bioinformatics at work.

The data acquisition phase of initial pilot studies (human and mouse brain samples) of the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) is now complete and the data generated by the participating laboratories has been submitted to the central Data Collection Center. The BPP Bioinformatics Group met on 8th April 2005 at the European Bioinformatics Institute (Hinxton, UK) to discuss strategies for the reanalysis of the pooled data from all the participating laboratories. A summary of the results of the data reprocessing will be presented at the 4th HUPO World Congress that will be held in August/September 2005.

Animals↗

Proteome informatics I: bioinformatics tools for processing experimental data.

Bioinformatics tools for proteomics, also called proteome informatics tools, span today a large panel of very diverse applications ranging from simple tools to compare protein amino acid compositions to sophisticated software for large-scale protein structure determination. This review considers the available and ready to use tools that can help end-users to interpret, validate and generate biological information from their experimental data. It concentrates on bioinformatics tools for 2-DE analysis, for LC followed by MS analysis, for protein identification by PMF, by peptide fragment fingerprinting and by de novo sequencing and for data quantitation with MS data. It also discloses initiatives that propose to automate the processes of MS analysis and enhance the quality of the obtained results.

Algorithms↗

The bioinformatics challenges in comparative analysis of cereal genomes-an overview.

Comparative genomic analysis is the cornerstone of in silico-based approaches to understanding biological systems and processes across cereal species, such as rice, wheat and barley, in order to identify genes of agronomic interest. The size of the genomic repositories is nearly doubling every year, and this has significant implications on the way bioinformatics analyses are carried out. In this overview the concepts and technology underpinning bioinformatics as applied to comparative genomic analysis are considered in the context of other manuscripts appearing in this issue of Functional and Integrative Genomics.

Computational Biology↗

Bioinformatics: The philosophical and ethical issues at stake in a new modality of research practices.

This article deals with the integration of ethical reflection into the research practices of the project at the Lille Nord-Pas-de-Calais genopole: "Multifactorial genetic pathologies and therapeutic innovations". The general hypothesis of this text is that changes in research practices in biology (mainly through the use of bioinformatics) imply changes in medical practices, which require critical reflection. This hypothesis could be broken down into three sub-hypotheses: (1) Research in biology is undergoing a complete transformation; (2) Research in biology is a cultural practice, which cannot be reduced to a simple cognitive action; (3) Research in biology is a techno-scientific practice. As for the method, the aim of our research at the Medical Ethics Centre is to elucidate the philosophical and ethical range of biomedical practices. This work entails a double task for reflection. On the one hand, from the revelation of ethical tensions present in these practices, we have to think about what is at stake in these practices, and more broadly in society. On the other hand, we have to analyse the conditions enabling the actors to assume the significance of ethical reflection in their practices. The method set up to undertake this double task could be qualified as "narrative hermeneutics", as its aim is to attempt to interpret the stakes in practices from proximity with these practices and from what their actors have to say about them. The text then goes on to analyse more specifically the emergence and place of bioinformatics in present-day biomedical research.

Biomedical Research↗

Bioinformatics and cancer: an essential alliance.

Modern research in cancer has been revolutionized by the introduction of new high-throughput methodologies such as DNA microarrays. Keeping the pace with these technologies, the bioinformatics offer new solutions for data analysis and, what is more important, it permits to formulate a new class of hypothesis inspired in systems biology, more oriented to blocks of functionally-related genes. Although software implementations for this new methodologies is new there are some options already available. Bioinformatic solutions for other high-throughput techniques such as array-CGH of large-scale genotyping is also revised.

Chromosome Aberrations↗

Bioinformatics--principles and potential of a new multidisciplinary tool.

The materials of bioinformatics are biological data, and its methods are derived from a wide variety of computational techniques. Recent years have seen an explosive growth in biological data, and the development of novel computational methods. These methods have become essential to research progress in structural biology, genomics, structure-based drug design and molecular evolution. The development and maintenance of a robust infrastructure of biological data is of equal importance if biotechnology is to take maximum advantage of research advances in a wide variety of fields. While bioinformatics has already made important contributions, it faces significant challenges as it matures.

Animals↗