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At least 1,063 records · Page 59Linked to original sources
PubMed2005

Proteome analysis of maternal serum samples for trisomy 21 pregnancies using ProteinChip arrays and bioinformatics.

A surface-enhanced laser desorption/ionization time of flight (SELDI-TOF)-based ProteinChip System was used as a tool for rapid discovery and identification of protein patterns in serum that discriminate between trisomy 21 and unaffected pregnancies. We analyzed 24 serum samples from women carrying a trisomy 21 pregnancy and 32 with an unaffected pregnancy, ranging from 10.0 to 14.0 weeks of gestation. The resulting protein profiles were submitted to a clustering algorithm, a rule extraction, a rating, and a rule base construction step. For the generated combined rule base, the specificity and sensitivity for the prediction of a trisomy 21 pregnancy reach 97% and 91%, respectively.

Computational Biology↗
PubMed2024

Identification of cuproptosis-realated key genes and pathways in Parkinson's disease via bioinformatics analysis.

INTRODUCTION: Parkinson's disease (PD) is the second most common worldwide age-related neurodegenerative disorder without effective treatments. Cuproptosis is a newly proposed conception of cell death extensively studied in oncological diseases. Currently, whether cuproptosis contributes to PD remains largely unclear. METHODS: The dataset GSE22491 was studied as the training dataset, and GSE100054 was the validation dataset. According to the expression levels of cuproptosis-related genes (CRGs) and differentially expressed genes (DEGs) between PD patients and normal samples, we obtained the differentially expressed CRGs. The protein-protein interaction (PPI) network was achieved through the Search Tool for the Retrieval of Interacting Genes. Meanwhile, the disease-associated module genes were screened from the weighted gene co-expression network analysis (WGCNA). Afterward, the intersection genes of WGCNA and PPI were obtained and enriched using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the key genes were identified from the datasets. The receiver operating characteristic curves were plotted and a PPI network was constructed, and the PD-related miRNAs and key genes-related miRNAs were intersected and enriched. Finally, the 2 hub genes were verified via qRT-PCR in the cell model of the PD and the control group. RESULTS: 525 DEGs in the dataset GSE22491 were identified, including 128 upregulated genes and 397 downregulated genes. Based on the PPI network, 41 genes were obtained. Additionally, the dataset was integrated into 34 modules by WGCNA. 36 intersection genes found from WGCNA and PPI were significantly abundant in 7 pathways. The expression levels of the genes were validated, and 2 key genes were obtained, namely peptidase inhibitor 3 (PI3) and neuroserpin family I member 1 (SERPINI1). PD-related miRNAs and key genes-related miRNAs were intersected into 29 miRNAs including hsa-miR-30c-2-3p. At last, the qRT-PCR results of 2 hub genes showed that the expressions of mRNA were up-regulated in PD. CONCLUSION: Taken together, this study demonstrates the coordination of cuproptosis in PD. The key genes and miRNAs offer novel perspectives in the pathogenesis and molecular targeting treatment for PD.

Humans↗
PubMed2024

Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes.

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

Humans↗
PubMed2024

Bioinformatics analysis to identify the relationship between human papillomavirus-associated cervical cancer, toll-like receptors and exomes: A genetic epidemiology study.

INTRODUCTION: Genetic variants may influence Toll-like receptor (TLR) signaling in the immune response to human papillomavirus (HPV) infection and lead to cervical cancer. In this study, we investigated the pattern of TLR expression in the transcriptome of HPV-positive and HPV-negative cervical cancer samples and looked for variants potentially related to TLR gene alterations in exomes from different populations. MATERIALS AND METHODS: A cervical tissue sample from 28 women, which was obtained from the Gene Expression Omnibus database, was used to examine TLR gene expression. Subsequently, the transcripts related to the TLRs that showed significant gene expression were queried in the Genome Aggregation Database to search for variants in more than 5,728 exomes from different ethnicities. RESULTS: Cancer and HPV were found to be associated (p<0.0001). TLR1(p = 0.001), TLR3(p = 0.004), TLR4(221060_s_at)(p = 0.001), TLR7(p = 0.001;p = 0.047), TLR8(p = 0.002) and TLR10(p = 0.008) were negatively regulated, while TLR4(1552798_at)(p<0.0001) and TLR6(p = 0.019) were positively regulated in HPV-positive patients (p<0.05). The clinical significance of the variants was statistically significant for TLR1, TLR3, TLR6 and TLR8 in association with ethnicity. Genetic variants in different TLRs have been found in various ethnic populations. Variants of the TLR gene were of the following types: TLR1(5_prime_UTR), TLR4(start_lost), TLR8(synonymous;missense) and TLR10(3_prime_UTR). The "missense" variant was found to have a risk of its clinical significance being pathogenic in South Asian populations (OR = 56,820[95%CI:40,206,80,299]). CONCLUSION: The results of this study suggest that the variants found in the transcriptomes of different populations may lead to impairment of the functional aspect of TLRs that show significant gene expression in cervical cancer samples caused by HPV.

Humans↗
PubMed2024

Interactions between microbiota and uterine corpus endometrial cancer: A bioinformatic investigation of potential immunotherapy.

Microorganisms in the gut and other niches may contribute to carcinogenesis while also altering cancer immune surveillance and therapeutic response. However, determining the impact of genetic variations and interplay with intestinal microbes' environment is difficult and unanswered. Here, we examined the frequency of thirteen mutant genes that caused aberrant gut in thirty different types of cancer using The Cancer Genomic Atlas (TCGA) database. Substantially, our findings show that all these mutated genes are quite frequent in uterine corpus endometrial cancer (UCEC). Further, these mutant genes are implicated in the infiltration of different subset of immune cells within the Tumor Microenvironment (TME) of UCEC patients. The top-ranking mutant genes that promote immune cell invasion into the TME of UCEC patients were PGLYRP2, OLFM4, and TLR5. In this regard, we used the same deconvolution of the TCGA database to analyze the microbiome that have a strong association with immune cells invasion with TME of UCEC patients. Several bacteria and viruses have been linked to the invasion of immune cells, such as B cell memory and T cell regulatory (Tregs), into the TME of UCEC patients. As a result, our findings pave the way for future research into generating novel immunizations against bacteria or viruses as immunotherapy for UCEC patients.

Humans↗
PubMed2024

Elucidating the roles of SOD3 correlated genes and reactive oxygen species in rare human diseases using a bioinformatic-ontology approach.

Superoxide Dismutase 3 (SOD3) scavenges extracellular superoxide giving a hydrogen peroxide metabolite. Both Reactive Oxygen Species diffuse through aquaporins causing oxidative stress and biomolecular damage. SOD3 is differentially expressed in cancer and this research utilises Gene Expression Omnibus data series GSE2109 with 2,158 cancer samples. Genome-wide expression correlation analysis was conducted with SOD3 as the seed gene. Categorical SOD3 Pearson Correlation gene lists incrementing in correlation strength by 0.01 from &#x3c1;&#x2265;|0.34| to &#x3c1;&#x2265;|0.41| were extracted from the data. Positively and negatively SOD3 correlated genes were separated for each list and checked for significance against disease overlapping genes in the ClinVar and Orphanet databases via Enrichr. Disease causal genes were added to the relevant gene list and checked against Gene Ontology, Phenotype Ontology, and Elsevier Pathways via Enrichr before the significant ontologies containing causal and non-overlapping genes were reviewed with a literature search for possible disease and oxidative stress associations. 12 significant individually discriminated disorders were identified: Autosomal Dominant Cutis Laxa (p = 6.05x10-7), Renal Tubular Dysgenesis of Genetic Origin (p = 6.05x10-7), Lethal Arteriopathy Syndrome due to Fibulin-4 Deficiency (p = 6.54x10-9), EMILIN-1-related Connective Tissue Disease (p = 6.54x10-9), Holt-Oram Syndrome (p = 7.72x10-10), Multisystemic Smooth Muscle Dysfunction Syndrome (p = 9.95x10-15), Distal Hereditary Motor Neuropathy type 2 (p = 4.48x10-7), Congenital Glaucoma (p = 5.24x210-9), Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome (p = 3.77x10-16), Classical-like Ehlers-Danlos Syndrome type 1 (p = 3.77x10-16), Retinoblastoma (p = 1.9x10-8), and Lynch Syndrome (p = 5.04x10-9). 35 novel (21 unique) genes across 12 disorders were identified: ADNP, AOC3, CDC42EP2, CHTOP, CNN1, DES, FOXF1, FXR1, HLTF, KCNMB1, MTF2, MYH11, PLN, PNPLA2, REST, SGCA, SORBS1, SYNPO2, TAGLN, WAPL, and ZMYM4. These genes are proffered as potential biomarkers or therapeutic targets for the corresponding rare diseases discussed.

Humans↗