Bioinformatics Databases Research Articles

Marker Ki-67 is a potential biomarker for the diagnosis and prognosis of prostate cancer based on two cohorts.

Prostate cancer (PCa) is a widespread malignancy, predominantly affecting elderly males, and current methods for diagnosis and treatment of this disease continue to fall short. The marker Ki-67 (MKI67) has been previously demonstrated to correlate with the proliferation and metastasis of various cancer cells, including those of PCa. Hence, verifying the association between MKI67 and the diagnosis and prognosis of PCa, using bioinformatics databases and clinical data analysis, carries significant clinical implications. To explore the diagnostic and prognostic efficacy of antigens identified by MKI67 expression in PCa. For cohort 1, the efficacy of MKI67 diagnosis was evaluated using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. For cohort 2, the diagnostic and prognostic power of MKI67 expression was further validated using data from 271 patients with clinical PCa. In cohort 1, MKI67 expression was correlated with prostate-specific antigen (PSA), Gleason Score, T stage, and N stage. The receiver operating characteristic (ROC) curve showed a strong diagnostic ability, and the Kaplan-Meier method demonstrated that MKI67 expression was negatively associated with the progression-free interval (PFI). The time-ROC curve displayed a weak prognostic capability for MKI67 expression in PCa. In cohort 2, MKI67 expression was significantly related to the Gleason Score, T stage, and N stage; however, it was negatively associated with the PFI. The time-ROC curve revealed the stronger prognostic capability of MKI67 in patients with PCa. Multivariate COX regression analysis was performed to select risk factors, including PSA level, N stage, and MKI67 expression. A nomogram was established to predict the 3-year PFI. MKI67 expression was positively associated with the Gleason Score, T stage, and N stage and showed a strong diagnostic and prognostic ability in PCa.

High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics.

Metaproteomics offers a direct avenue to identify microbial proteins in microbiota, enabling the compositional and functional characterization of microbiota. Due to the complexity and heterogeneity of microbial communities, in-depth and accurate metaproteomics faces tremendous limitations. One challenge in metaproteomics is the construction of a suitable protein sequence database to interpret the highly complex metaproteomic data, especially in the absence of metagenomic sequencing data. Herein, we present a high-abundance protein-guided hybrid spectral library strategy for in-depth data independent acquisition (DIA) metaproteomic analysis (HAPs-hyblibDIA). A dedicated high-abundance protein database of gut microbial species is constructed and used to mine the taxonomic information on microbiota samples. Then, a sample-specific protein sequence database is built based on the taxonomic information using Uniprot protein sequence for subsequent analysis of the DIA data using hybrid spectral library-based DIA analysis. We evaluated the accuracy and sensitivity of the method using synthetic microbial community samples and human gut microbiome samples. It was demonstrated that the strategy can successfully identify taxonomic compositions of microbiota samples and that the peptides identified by HAPs-hyblibDIA overlapped greatly with the peptides identified using a metagenomic sequencing-derived database. At the peptide and species level, our results can serve as a complement to the results obtained using a metagenomic sequencing-derived database. Furthermore, we validated the applicability of the HAPs-hyblibDIA strategy in a cohort of human gut microbiota samples of colorectal cancer patients and controls, highlighting its usability in biomedical research.

GSDMB: A novel, independent prognostic marker and potential new therapeutic target in clear cell renal cell carcinoma.

Gasdermin (GSDM) family members are involved in numerous biological processes, including pyroptosis, as well as in the initiation and progression of various types of cancer. However, the specific role of GSDM genes in clear cell renal cell carcinoma (ccRCC) has yet to be fully clarified. The present study investigated the differential expression and genetic alterations GSDM genes, their effects on prognosis and immune modulation, and their functional enrichment in ccRCC. Several bioinformatics databases were used, including UALCAN, The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, Metascape, Tumor Immune Estimation Resource, GSCALite and cBioPortal. The results revealed that the expression levels of GSDMA, GSDMB, GSDMC and GSDMD were significantly upregulated in cancer tissues compared with those in paracancerous tissues in patients with ccRCC, whereas the expression of DFNB59 exhibited the opposite trend. The results were experimentally validated in patients with ccRCC, and it was confirmed that the expression levels of GSDMA, GSDMB, GSDMC, GSDMD and GSDME (DFNA5) were significantly enhanced, whereas (PJVK, DFNB59) expression was reduced. In addition, elevated GSDMB, GSDMD and DFNA5 expression levels were clearly associated with worse pathological characteristics of ccRCC, including a high pathological stage and high tumor grade. Furthermore, the high expression levels of GSDMB, GSDMC, GSDMD, DFNA5 and PJVK were shown to be associated with worse overall survival (OS) and progression-free interval in patients with ccRCC. Both univariate and multivariate analyses indicated that the expression of GSDMB was independently associated with the OS of patients with ccRCC. Additionally, a high mutation rate of GSDM genes (33%) was observed in patients with ccRCC, and GSDM gene mutations were also significantly associated with a poor OS in patients with ccRCC. Significant associations between GSDM genes and ccRCC immunoprofiling and drug sensitivity were also determined. In conclusion, the findings of the present study indicated that GSDMB, GSDMD and DFNA5 may be considered promising therapeutic agents and potential biomarkers for patients with ccRCC. Furthermore, GSDMB could act as an independent predictor for the OS of patients with ccRCC.

Sensitive and error-tolerant annotation of protein-coding DNA with BATH.

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes. The software is available at https://github.com/TravisWheelerLab/BATH.

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

Circular RNA CircACAP2 regulates temporomandibular joint osteoarthritis via miR-21-5p/PLAG1 axis.

The pathogenesis of temporomandibular joint osteoarthritis (TMJOA) remains not fully understood. Our previous studies demonstrated that miR-21-5p may participate in the TMJOA development and the interaction between circRNA-ACAP2 (CircACAP2) and miR-21-5p. Our present study aimed to explore the biological functions and regulatory mechanisms of CircACAP2 in TMJOA. The differential expression pattern of CircACAP2 in OA and normal tissues or cells was detected. CircACAP2 biological functions experiments were performed in chondrocytes by overexpression and interference techniques. The interaction of CircACAP2 with miR-21-5p and downstream target mRNA, polymorphic adenoma gene 1 (PLAG1), was predicted by bioinformatic databases and then demonstrated by dual-luciferase reporter assay. The biological role of CircACAP2 in TMJOA was investigated and validated in a mouse model. The expression level of CircACAP2 was markedly reduced in OA cartilage and directly related to chondrocyte proliferation and apoptosis as well as ECM metabolism in the cartilage. CircACAP2 functioned in chondrocytes via targeting miR-21-5p and PLAG1. Overexpressing of CircACAP2 alleviated TMJOA in mouse models. The present study unveiled that CircACAP2/miR-21-5p/PLAG1 axis may play an important regulatory role in TMJOA progression, which may highlight a potentially effective intervention and therapeutic strategy for the treatment of TMJOA.

Gene Ontology and pathway enrichment analysis of the upregulated biological Proteins in Single-Line Transgenic Pod Borer Resistant Cowpea

Aim: This research seeks to clarify the proteomic alterations linked to PBR cowpea in comparison to its non-transgenic isoline. Gene Ontology and pathway enrichment analyses were conducted to pinpoint critical biological processes, molecular functions, and cellular components that exhibit upregulation in the transgenic cowpea. Methods: Seeds from both cowpea lines were finely ground, and proteins were extracted utilizing Tris-EDTA and LDS buffers. The proteins were then separated and identified through mass spectrometry (MS), with peptide sequences being matched against protein sequence databases. Results: The enrichment analysis indicated upregulation in pathways associated with pollen tube adhesion, cell-cell adhesion, and biological processes pertinent to both intraspecies and interspecies interactions with no significant difference. Importantly, pathways linked to defense mechanisms against fungi and other organisms were notably enriched, suggesting an enhanced ability to detect and respond to biotic stresses. The results of this study does not underscore any substantial proteomic modifications in the transgenic cowpea. Conclusion: These findings offer valuable targets for genetic engineering and breeding initiatives aimed at creating cowpea varieties with enhanced pest resistance, thus improving crop yield and sustainability. Future investigations should concentrate on validating these proteins and pathways through functional assays and examining their roles in resistance under field conditions.

CircTIAM1 overexpression promotes the progression of papillary thyroid cancer by regulating the miR-338-3p/LASP1 axis.

Papillary thyroid cancer (PTC) is the most prevalent histological type of differentiated thyroid malignancy. Circular RNAs (circRNAs) have been implicated in the pathogenesis and progression of various cancers. circTIAM1 (hsa_circ_0061406) is a novel circRNA with aberrant expression in PTC. However, its functional roles in PTC progression remain to be investigated. The expression levels of circTIAM1 in the PTC and the matched para-cancerous tissues were detected by quantitative real-time reverse-transcription PCR (qRT-PCR). The subcellular localization of circTIAM1 was examined by fluorescence in-situ hybridization (FISH). Kaplan-Meier plot was used to analyze the association of clinicopathological features with circTIAM1 expression. Bioinformatics databases were utilized to predict the target miRNAs of circTIAM1 and the downstream target mRNAs. RNA pull-down, RIP assay, and dual-luciferase reporter assay were used to confirm the interactions. Functional experiments, such as CCK-8, EDU staining, and apoptosis assays, as well as in vivo xenograft model were employed to explore the impacts of circTIAM1, miR-338-3p, and LIM/SH3 protein 1 (LASP1) on the malignant phenotype of the PTC cells. CircTIAM1 was highly expressed in PTC cells. Moreover, circTIAM1 silencing suppressed the proliferation and invasion of PTC cells in vitro and impaired tumorigenesis in vivo. Furthermore, miR-338-3p was verified as a miRNA target of circTIAM1. LASP1 was also identified as a downstream target of miR-338-3p. The anti-tumorigenic effect of miR-338-3p overexpression and the pro-tumorigenic effect of LASP1 was further explored by functional assays, which demonstrated that circTIAM1 modulated the PTC progression through targeting miR-338-3p/LASP1 axis. The overexpression of circTIAM1 is associated with the malignant progression of PTC. A high level of circTIAM1 promotes the malignancy of PTC cells via the miR-338-3p/LASP1 axis.

Molecular Docking Study on Phytochemicals of Alpinia galanga and Their Derivatives as Inhibitors of β-Ketoacyl Reductase (MabA) of Mycobacterium Tuberculosis: An In-silico Study

Introduction: Tuberculosis (TB), an infectious disease caused by the bacterium Mycobacterium Tuberculosis (MTB), continues to be a global health problem. Alpinia galanga (Linn.) of the Zingiberaceae family has antitubercular properties, and their mode of action in in-vitro as well as in-vivo conditions is well established. This knowledge of the active phytochemicals of Alpinia galanga has been utilised to identify new potent drugs for MTB. Aim: To perform molecular docking studies of various phytochemicals of Alpinia galanga and their derivatives with β-ketoacyl reductase (MabA) of MTB. Materials and Methods: The present study is an in-silico study conducted in the Bioinformatics facility of the Central Research Laboratory of Tagore Medical College and Hospital, Chennai, Tamil Nadu, India from November 2022 to April 2023. The receptor protein was downloaded from the Research Collaboratory for Structural Bioinformatics (RCSB) database. The phytochemicals in. sdf format were downloaded from the PubChem database. The derivatives were prepared by Chemsketch software. Docking was performed using AutoDock Vina with PyRx as the GUI (Graphical User Interface). Postdocking analysis was performed in LigPlot+. Results: Phytochemicals from Alpinia galanga were obtained from the PubChem database and docked with MabA of MTB. The derivatives were further subjected to docking analyses. From the docking study, two molecules, namely, (1E,6Z)-2,4-diamino6-fluoro-1,7-bis(4-hydroxyphenyl)-1-sulfanylhepta-1,6-diene3,5-dione and (2E,6Z,10E)-2,6,9,9-tetrakis(hydroxymethyl) cycloundeca-2,6,10-trien-1-one-ethane (1/1), were found to have good binding energy values. Conclusion: The present study helped us find drug-like molecules that can inhibit the MabA of MTB. Two compounds derived from the phytochemicals of A. galanga were found to have an effective binding capacity to the drug target in-silico. Hence, the outcome of present study has provided a therapeutic strategy for TB, especially for strains of MTB that are drug-resistant.

Evaluation of miRNAs regulation of BDNF and IGF1 genes in T2DM insulin resistance in experimental models: bioinformatics based approach.

microRNAs (miRNAs) are recognized as diabetes mellitus type 2 (T2DM) biomarkers useful for disease metabolism comprehension and have great potential as therapeutics targets. BDNF and IGF1 increased expression are highly involved in the benefits of insulin and glucose paths, however, they are down-regulated in insulin resistance conditions, while their expression increase is correlated to the improvement of glucose and insulin metabolism. Studies suggest the microRNA regulation of these genes in several different contexts, providing a novel investigation approach for comprehending T2DM metabolism and revealing potential therapeutic targets. In the present study, we investigate in different animal models (human, rat, and mouse) miRNAs that target BDNF and IGF1 in skeletal muscle tissue with T2DM physiological conditions. Bioinformatics tools and databases were used to miRNA prediction, molecular homology, experimental validation of interactions, expression in the studied physiological condition, and network interaction. The findings showed three miRNAs candidates for IGF1(miR-29a, miR-29b, and miR-29c) and one for BDNF (miR-206). The experimental evaluations and the search for the expression in skeletal muscle from T2DM subjects confirmed the predicted interaction between miRNA-mRNA for miR-29b and miR-206 through human, rat, and mouse models. This interaction was reaffirmed in multiple network analyses. In conclusion, our results show the regulation relationship between miR-29b and miR-206 with the investigated genes, in several tissues, suggesting an inhibition pattern. Nevertheless, these data show a large number of possible interaction physiological processes, for future biotechnological prospects.

Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds.

Network pharmacology is an emerging pioneering approach in the drug discovery process, which is used to predict the therapeutic mechanism of compounds using various bioinformatic tools and databases. Emerging studies have indicated the use of network pharmacological approaches in various research fields, particularly in the identification of possible mechanisms of herbal compounds/ayurvedic formulations in the management of various diseases. These techniques could also play an important role in the prediction of the possible mechanisms of neuroprotective compounds. The first part of the chapter includes an introduction onneuroprotective compounds based on literature. Further, network pharmacological approaches are briefly discussed. The use of network pharmacology in the prediction of the neuroprotective mechanism of compounds is discussed in detail with suitable examples. Finally, the chapter concludes with the current challenges and future prospectives.

Bioinformatic Analysis for Identifying Transcription Factors Involved in Protein Secretion and Vacuole Formation.

Protein secretion and vacuole formation are vital processes in plant cells, playing crucial roles in various aspects of plant development, growth, and stress responses. Multiple regulators have been uncovered to beinvolved in these processes. In animal cells, the transcription factor TFEB has been extensively studied and its role in lysosomal biogenesis is well understood. However, the transcription factors governing protein secretion and vacuole formation in plants remain largely unexplored. In recent years, an increasing number of bioinformatics databases and tools have been developed, facilitating computational prediction and analysis of the function of genes or proteins in specific cellular processes. Leveraging these resources, this chapter aims to provide practical guidance on how to effectively utilize these existing databases and tools for the analysis of key transcription factors involved in regulating protein secretion and vacuole formation in plants, with a particular focus on Arabidopsis and other higher plants. The findings from this analysis can serve as a valuable resource for future experimental investigations and the development of targeted strategies to manipulate protein secretion and vacuole formation in plants.

RhizoBindingSites v2.0 Is a Bioinformatic Database of DNA Motifs Potentially Involved in Transcriptional Regulation Deduced From Their Genomic Sites.

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, Rhizobium leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

Identification of Fatty Acid Metabolism-Related Subtypes in Gastric Cancer Aided by Machine Learning.

Gastric cancer, the fifth most common malignant tumor in the world, poses a serious threat to human health. However, the role of fatty acid metabolism (FAM) in gastric cancer remains incompletely understood. We aim to provide guidance for clinical decisions by utilizing public database of gastric adenocarcinoma to establish an FAM-related gene subtypes via machine learning algorithm. The intersection of FMGs from KEGG, Hallmark, and Reactome bioinformatics databases and the DEGs of the TCGA-STAD cohort was used to decompose the gene matrix related to establish FAM-related gene subtypes by NMF. Comparison of immune infiltrating differences between subtypes using ESTIMATE and Cibersort algorithms. The multifactor Cox regression to identify independent risk genes for patient prognosis based on the subtypes. A prognostic model including independent risk genes was built using random survival forest and Cox regression. IHC validation in gastric cancer and adjacent tissues confirmed the above gene expression level. 71 DEGs related to FMGs of STAD were identified, which was used to established the FAM-related gene subtypes, C1 and C2. The immune infiltrating analysis showed that most immune features of C2 were significantly upregulated compared to C1. The independent risk genes were CGβ8, UPK1B, and OR51G based on the subtypes. A gastric cancer prognostic model consisting of independent risk genes was constructed and patients were classified into high-risk and low-risk groups with survival differential analysis. Finally, IHC showed that CGβ8 and UPK1B expression were upregulated in gastric cancer, while OR51G2 did not detect differences in expression. The study developed a machine learning-based gastric cancer prognosis risk model using FMGs. This model effectively stratifies patients according to their risk levels and provides valuable insights for clinical decision-making, enabling accurate evaluation of patient prognosis.

ABCA8 Elevation Predicts the Prognosis and Exerts the Anti-oncogenic Effects on the Malignancy of Non-small Cell Lung Cancer via TCF21-Mediated Inactivation of PI3K/AKT.

The malignant growth and metastatic potential of non-small-cell lung cancer (NSCLC) are the major causes for its poor prognosis. ATP-binding cassette (ABC) subfamily A member 8 (ABCA8) exerts contradictive roles in the development of several cancers. Nevertheless, its role in NSCLC remains unclear. In this study, three GEO datasets and bioinformatics databases (GEPIA2 and UALCAN) revealed the obvious down-regulation of ABCA8 in NSCLC tissues and cells, and this expression was associated with cancer stages and lymph node metastasis. Low expression of ABCA8 predicted poor survival in NSCLC. ABCA8 elevation inhibited cell proliferation and induced cell apoptosis. Moreover, ABCA8 overexpression suppressed cancer cell invasion. Mechanistically, ABCA8 was associated with TCF21 in NSCLC specimens and its overexpression enhanced TCF21 expression. ABCA8 elevation inactivated the PI3K/AKT signaling, which was reversed after TCF21 knockdown. Additionally, targeting TCF21 overturned the anti-oncogenic effects of ABCA8 elevation on cell proliferation, apoptosis and invasion. Thus, the current findings highlight that ABCA8 may be a promising prognostic marker and may act as a suppressor gene to regulate the malignancy of NSCLC cells via TCF21-mediated inactivation of PI3K/AKT signaling, supporting a new promising target for the treatment of NSCLC.

Suppression of DNMT1 combined with ATM or ATR inhibitor as a therapeutic combination of acute myeloid leukemia.

The potential treatment option of targeting DNA methyltransferase 1 (DNMT1) has been explored, but further investigation is required to assess the efficacy of combination therapy in acute myeloid leukemia (AML). In this study, bioinformatics and online databases were utilized to select the combined therapeutic targets. The potential kinases associated with DNMT1-related genes in AML were analyzed using the Cancer Genome Atlas (TCGA) database and X2K Appyter (Expression2Kinases) database. In-vitro evaluations were conducted to assess the synergistic effects between DNMT1 and ATR/ATM in five AML cell lines (MOLM-16, NB-4, HEL 92.1.7, HEL, EOL-1). In our study, ATR and ATM are primarily the kinases associated with DNMT1-related genes in AML. We observed a significant upregulation of DNMT1, ATR, and ATM expression in AML tissues and cell lines. The five AML cell lines demonstrated sensitivity to monotherapy with GSK-368, AZD-1390, or AZD-6738 (EC50 value ranges from 5.461 to 7.349 nM, 5.821 to 10.120 nM, and 7.618 to 10.100 nM, respectively). A considerable synergistic effect was observed in AML cell lines when combining GSK-368 and AZD-1390, GSK-368 and AZD-6738, or AZD-1390 and AZD-6738, resulting in induced cell apoptosis and inhibited cell growth. DNMT1, ATM, and ATR possess potential as therapeutic targets for AML. Both individual targeting and combination targeting of these molecules have been confirmed as promising therapeutic approaches for AML.

ITGA2 as a prognostic factor of glioma promotes GSCs invasion and EMT by activating STAT3 phosphorylation.

Glioma is the most common malignant brain tumor in adults with a high mortality and recurrence rate. Integrin alpha 2 (ITGA2) is involved in cell adhesion, stem cell regulation, angiogenesis and immune cell function. The role of ITGA2 in glioma malignant invasion remains unknown. The function and clinical relevance of ITGA2 were analysed by bioinformatics databases. The expression of ITGA2 in parent cells and GSCs was detected by flow cytometry and immunofluorescence double staining. The role of ITGA2 on the malignant phenotype of GSCs and epithelial-mesenchymal transition (EMT) was identified by stem cell function assays and Western blot. The effect of ITGA2 on glioma progression in vivo was determined by the intracranial orthotopic xenograft model. Immunohistochemistry, Spearman correlation and Kaplan-Meier were used to analyse the relationship of ITGA2 with clinical features and glioma prognosis. Biological analysis showed that ITGA2 might be related to cell invasion and migration. ITGA2, enriched in GSCs and co-expressed with SOX2, promoted the invasion and migration of GSCs by activating STAT3 phosphorylation and enhancing EMT. ITGA2 knockout suppressed the intracranial orthotopic xenograft growth and prolonged the survival of xenograft mice. In addition, the expression level of ITGA2 was significantly correlated to the grade of malignancy, N-cadherin and Ki67. High expression of ITGA2 indicated a worse prognosis of glioma patients. As a biomarker for the prediction of prognosis, ITGA2 promotes the malignant invasion of GSCs by activating STAT3 phosphorylation and enhancing EMT, leading to tumor recurrence and poor prognosis.

EDITING CDKN1A AND RASD1 IN A CELL CULTURE SYSTEM

Abstract As an individual ages, there is a higher incidence of cancer and infertility. Testicular germ cell cancer rates have been rising year after year. According to the American Cancer Society 1 of every 250 males will develop testicular cancer during their lifetime. In this research project, we aim to develop a molecular toolbox to analyze gene function in male germinal cells using a CRISPR/Cas9 cell culture system. This biotechnology allows for the targeted alteration and cutting of specific DNA strands. To achieve this, we will utilize cancer cell lines (SUSA and NT2/D1 testicular cancer cell lines) and knock out two genes of interest: cyclin-dependent kinase inhibitor 1 (CDKN1A) and ras-related dexamethasone-induced 1 (RASD1). Our objective is to investigate the effects of gene knockout on cell proliferation. For this purpose, we designed primers and single-guide RNAs using bioinformatic databases: Benchling and Chop Chop. The tools we used were employed to amplify human genomic DNA and assess the effectiveness of the guide RNAs through in vitro cutting of our genes of interest, followed by gel electrophoresis observations. Based on the results obtained from these experiments, our next step is to electroporate the guide RNA and Cas9 complex to alter gene function in our cell culture system. Through a better understanding of gene function in testicular cancer cell lines, we hope to inform future therapeutic targets for both testicular cancer and age-related infertility.

BioTreasury: a community-based repository enabling indexing and rating of bioinformatics tools.

The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments. Therefore, to aid scientists in making informed choices, a community-based platform that indexes and rates bioinformatics tools is urgently needed. In this study, we introduce BioTreasury ( http://biotreasury.rjmart.cn ), an integrated community-based repository that provides an interactive platform for users and developers to share their experiences in various bioinformatics tools. BioTreasury offers a comprehensive collection of well-indexed bioinformatics software, tools, and databases, totaling over 10,000 entries. In the past two years, we have continuously improved and maintained BioTreasury, adding several exciting features, including creating structured homepages for every tool and user, a hierarchical category of bioinformatics tools and classifying tools using large language model (LLM). BioTreasury streamlines the tool submission process with intelligent auto-completion. Additionally, BioTreasury provides a wide range of social features, for example, enabling users to participate in interactive discussions, rate tools, build and share tool collections for the public. We believe BioTreasury can be a valuable resource and knowledge-sharing platform for the biomedical community. It empowers researchers to effectively discover and evaluate bioinformatics tools, fostering collaboration and advancing bioinformatics research.

RSRC2 Expression Inhibits Malignant Progression of Triple-Negative Breast Cancer by Transcriptionally Regulating SCIN Expression.

Triple-negative breast cancer (TNBC) has a shorter survival time and higher mortality rate than other molecular subtypes. RSRC2 is a newly discovered tumor suppressor gene. However, the potential functional mechanism of RSRC2 in TNBC remains unknown so far. Multiple bioinformatics databases were used. A Human Transcriptome Array 2.0 analysis, ChIP-seq analysis, ChIP-qPCR, RT-qPCR, Western blot, cell function assays in vitro and a metastatic mouse model in vivo were performed to demonstrate the role of RSRC2 in TNBC. Through the analysis of various databases, RSRC2 expression was the lowest in TNBC tissues compared to other molecular subtypes. The low expression of RSRC2 was associated with a worse prognosis for patients with breast cancer. The transcriptome array, ChIP-seq and bioinformatics analysis identified that GRHL2 and SCIN might have a close relationship with RSRC2. The functional bioinformatics enrichment analysis and functional cell experiments showed that RSRC2 was involved in cell adhesion, cell proliferation, cell migration and invasion. Furthermore, RSRC2 expression suppressed SCIN expression but not GRHL2 expression. SCIN re-expression in the RSRC2 overexpression cells or SCIN knockdown in the RSRC2 knockdown cells reversed the cellular function caused by RSRC2. Mechanistically, RSRC2 transcriptionally inhibited SCIN expression. In summary, our study reveals that RSRC2 acts as a tumor suppressor in TNBC development and progression through negatively regulating SCIN-mediated cell function, thus providing a potential target for TNBC treatment.