Normal Control Samples Research Articles

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

BackgroundEndometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.MethodsNext-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.ResultsA total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.ConclusionsThis investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

Predicting gene signature in breast cancer patients with multiple machine learning models

AimsThe aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models.MethodsIn this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO).ResultsWe identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K–Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples.ConclusionsOur findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

ATM is a Prognostic Biomarker of Survival in Head and Neck Squamous Cell Carcinoma Patients

This review examines the role of ATM expression in head and neck squamous cell carcinoma (HNSCC). Analysis revealed significant overexpression of ATM in HNSCC cells compared to normal control samples, suggesting its involvement in cancer proliferation. ATM expression was notably upregulated across various clinical parameters, including different stages of cancer, racial groups, genders, and age groups, highlighting its role in cancer progression. Validation using the GEPIA2 tool confirmed strong ATM expression throughout all four stages of HNSCC, with the highest levels in stage II and the lowest in stage I. Promoter methylation analysis of ATM showed distinct patterns across different demographics and cancer stages, reinforcing its significance. The study also explored the relationship between ATM expression and patient outcomes using the KM plotter tool, finding that high ATM expression was associated with better overall survival (OS), while low ATM expression correlated with better disease-free survival (DFS). Genetic mutation analysis via cBioPortal identified minimal ATM mutations in HNSCC, including in-frame, splice, truncating, and missense mutations, suggesting their role in ATM dysregulation. The STRING tool was used to construct a protein-protein interaction (PPI) network, revealing that the ATM gene interacts with ten key genes (NBN, ATR, CHEK2, MDC1, MSH2, MSH6, MRE11, TP53, TP53BP1, BRCA1), indicating its involvement in various biological functions. Functional annotation of differentially expressed genes (DEGs) through the DAVID web server revealed their participation in critical biological processes, including double-strand break repair, cellular response to DNA damage, and DNA damage checkpoints. KEGG pathway analysis further linked DEGs to cellular senescence, platinum drug resistance, homologous recombination, p53 signaling, and the cell cycle, underscoring ATM’s multifaceted role in HNSCC.

Glutaminase 1 plays critical roles in myelodysplastic syndrome and acute myeloid leukemia cells.

Myelodysplastic syndrome (MDS) features bone marrow failure and a heightened risk of evolving into acute myeloid leukemia (AML), increasing with age and reducing overall survival. Given the unfavorable outcomes of MDS, alternative treatments are necessary. Glutamine, the most abundant amino acid in the blood, is metabolized first by the enzyme glutaminase (GLS). To investigate whether GLS is involved in the progression of MDS. The efficacy of GLS inhibitors (CB839 or IPN60090) and BCL2 inhibitor venetoclax was also examined. We employed GLS inhibitors (CB839, IPN60090) and the BCL2 inhibitor venetoclax, prepared as detailed. MDS and AML cell lines were cultured under standard and modified (hypoxic, glutamine-free) conditions. Viability, proliferation, and caspase activity were assessed with commercial kits. RT-PCR quantified gene expression post-shRNA transfection. Mitochondrial potential, ATP levels, proteasome activity, and metabolic functions were evaluated using specific assays. Statistical analyses (t-tests, ANOVA) validated the findings. The glutamine-free medium inhibited the growth of MDS cells. GLS1 expression was higher in AML cells than in normal control samples (GSE15061), whereas GLS2 expression was not. Treatment of MDS and AML cells for 72 h was inhibited in a dose-dependent manner by GLS inhibitors. Co-treatment with the B-cell lymphoma 2 (BCL2) inhibitor venetoclax and GLS inhibitors increased potency. Cells transfected with GLS1 short hairpin RNA showed suppressed proliferation under hypoxic conditions and increased sensitivity to venetoclax. Targeting glutaminolysis and BCL2 inhibition enhances the therapeutic efficacy and has been proposed as a novel strategy for treating high-risk MDS and AML.

Circulating small extracellular vesicle RNA profiling for the detection of T1a stage colorectal cancer and precancerous advanced adenoma.

It takes more than 20 years for normal colorectal mucosa to develop into metastatic carcinoma. The long time window provides a golden opportunity for early detection to terminate the malignant progression. Here, we aim to enable liquid biopsy of T1a stage colorectal cancer (CRC) and precancerous advanced adenoma (AA) by profiling circulating small extracellular vesicle (sEV)-derived RNAs. We exhibited a full RNA landscape for the circulating sEVs isolated from 60 participants. A total of 58,333 annotated RNAs were detected from plasma sEVs, among which 1,615 and 888 sEV-RNAs were found differentially expressed in plasma from T1a stage CRC and AA compared to normal controls (NC). Then we further categorized these sEV-RNAs into six modules by a weighted gene coexpression network analysis and constructed a 60-gene t-SNE model consisting of the top 10 RNAs of each module that could well distinguish T1a stage CRC/AA from NC samples. Some sEV-RNAs were also identified as indicators of specific endoscopic and morphological features of different colorectal lesions. The top-ranked biomarkers were further verified by RT-qPCR, proving that these candidate sEV-RNAs successfully identified T1a stage CRC/AA from NC in another cohort of 124 participants. Finally, we adopted different algorithms to improve the performance of RT-qPCR-based models and successfully constructed an optimized classifier with 79.3% specificity and 99.0% sensitivity. In conclusion, circulating sEVs of T1a stage CRC and AA patients have distinct RNA profiles, which successfully enable the detection of both T1a stage CRC and AA via liquid biopsy.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

TRF-33-P4R8YP9LON4VDP inhibits gastric cancer progression via modulating STAT3 signaling pathway in an AGO2-dependent manner.

It has been demonstrated that tRNA-derived small RNAs (tsRNAs) perform essential functions in the pathophysiology of cancer. In this study, we focused on the possible mechanisms of tRF-33-P4R8YP9LON4VDP (tRF-33) underlying the development of gastric malignancy. In total, 454 tissue samples with different gastric mucosal lesions were collected. The tRF-33 expression level in different cohorts was determined, and its value for diagnostic efficiency and prognosis evaluation were assessed. Cell proliferation assays, Transwell assay, flow cytometry, and xenotransplantation model were used to evaluate its effect on gastric cancer cells. The molecular mechanism was verified by fluorescence in situ hybridization, dual luciferase assay, Western blot, and RNA binding protein immunoprecipitation. The results showed that the expression of tRF-33 exhibited a gradual modification from normal control samples to gastritis tissues, early and latent stage of gastric cancer tissues. Consequently, tRF-33 holds significant potential as a predictive and diagnostic biomarker for gastric malignancy. Over-expression of tRF-33 inhibited gastric cancer cell progression and metastatic viability, and induced cell apoptosis. Tumorigenicity in nude mice showed the suppressive characteristics of tRF-33. Mechanistic investigation revealed that tRF-33 exerted silencing on STAT3 mRNA via binding to AGO2. In conclusion, tRF-33 exhibited values in diagnosing gastric cancer and evaluating its prognosis, and suppressed tumor cell viability by inhibiting STAT3 signaling pathway. The schematic mechanisms underlying tRF-33 regulating gastric cancer occurrence. tRF-33 binds to AGO2 proteins and then negatively regulates STAT3 expression through targeting its 3'UTR. The downregulated expression of STAT3 results in the decrease of STAT3 and p-STAT3 and further blocks the transcription of the downstream genes and finally inhibits the gastric cancer occurrence. MMP-9, matrix metalloproteinase-9; Bcl-2, B-cell lymphoma-2; STAT3, signal transducer and activator of transcription 3; UTR, untranslated region.

ZNF692 promotes the migration and response to immunotherapy of clear cell renal cell carcinoma cells by targeting metabolic pathway

Clear cell renal cell carcinoma (ccRCC), with high mortality and poor prognosis, is the most common type of renal malignancy. It is necessary to identify new biomarkers that can serve as indicators for the detection of ccRCC at its early stages. In this study, we analyzed the role of classical zinc finger protein 692 (ZNF692) in ccRCC using datasets from The Cancer Genome Atlas (TCGA) and Single Cell Portal and immunohistochemical (IHC) staining of a tissue-microarray, and analyzed the function of ZNF692 in ccRCC cells. The analyses indicated that ZNF692 was upregulated in ccRCC samples compared with normal or paracancerous control samples (P < 0.001) and that the expression of this gene was linked to poor overall survival (HR = 2.1, P < 0.0001). The knockdown of ZNF692 inhibited the proliferation and migration of ccRCC cells by target GTPase-activating protein (SH3 domain)-binding protein 2 (G3BP2), and transmembrane 9 superfamily member 2 (TM9SF2)). T, B, proximal, and collecting tubule cells are the dominant cell types in normal kidney tissue where ZNF692 is expressed. In addition, immune checkpoint blockade (ICB) therapy dramatically changed the expression patterns of ZNF692. Collectively, these data indicate that ZNF692 may serve as prognosis, and as a potential indicator of the response to ICB therapy, a possibility needs to be verified by a case‒control study.

Comprehensive analysis of lncRNA-miRNA-mRNA ceRNA network in ischemic stroke

ObjectiveCompetitive endogenous RNA (ceRNA) networks have uncovered a novel mode of RNA interaction, and are implicated in various biological processes and the pathogenesis of IS. This study aimed to explore the potential mechanisms underlying the ceRNA network in IS. MethodsFour public datasets containing lncRNA and mRNA (GSE22255 and GSE16561) and miRNA (GSE55937 and GSE43618) expression profiles from the GEO database were systematically analyzed to explore the role of RNAs in ischemic stroke (IS). Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between IS and normal control samples were identified. LncRNA-miRNA and miRNA-mRNA interactions were predicted, and the competing endogenous RNA (ceRNA) regulatory network was constructed using the Cytoscape software. The correlation between the RNAs in the ceRNA network and the clinical features of the samples was evaluated. Finally, principal component analysis was performed on the RNAs that constitute the ceRNA regulatory network, and their differential expression and principal component relationships among different types of samples were observed. ResultsA total of 224 DEmRNAs, 7 DEmiRNAs, and four DElncRNAs related to IS in four datasets were identified. Then, through target gene prediction, a lncRNA–miRNA–mRNA ceRNA network that contained 3 DElncRNAs, 2 DEmiRNAs, and 24 DEmRNAs was constructed. Correlations of the clinical characteristics showed that PART1 and SERPINH1 were related to clinical diseases, WNK1 was related to lifestyle, and seven RNAs were related to age. PCA results indicate that three principal components of PC1, PC2, and PC3 can clearly distinguish between control and IS samples. ConclusionOverall, we constructed a ceRNA network in IS, which could offer insights into the molecular mechanism and potential prognostic biomarkers for further research.

Identification and bioinformatics analysis of lncRNAs in serum of patients with ankylosing spondylitis

ObjectivesThe aim of this study was to explore the long non-coding RNA (lncRNA) expression profiles in serum of patients with ankylosing spondylitis (AS). The role of these lncRNAs in this complex autoimmune situation needs to be evaluated.MethodsWe used high-throughput whole-transcriptome sequencing to generate sequencing data from three patients with AS and three normal controls (NC). Then, we performed bioinformatics analyses to identify the functional and biological processes associated with differentially expressed lncRNAs (DElncRNAs). We confirmed the validity of our RNA-seq data by assessing the expression of eight lncRNAs via quantitative reverse transcription polymerase chain reaction (qRT-PCR) in 20 AS and 20 NC samples. We measured the correlation between the expression levels of lncRNAs and patient clinical index values using the Spearman correlation test.ResultsWe identified 72 significantly upregulated and 73 significantly downregulated lncRNAs in AS patients compared to NC. qRT-PCR was performed to validate the expression of selected DElncRNAs; the results demonstrated that the expression levels of MALAT1:24, NBR2:9, lnc-DLK1-35:13, lnc-LARP1-1:1, lnc-AIPL1-1:7, and lnc-SLC12A7-1:16 were consistent with the sequencing analysis results. Enrichment analysis showed that DElncRNAs mainly participated in the immune and inflammatory responses pathways, such as regulation of protein ubiquitination, major histocompatibility complex class I-mediated antigen processing and presentation, MAPkinase activation, and interleukin-17 signaling pathways. In addition, a competing endogenous RNA network was constructed to determine the interaction among the lncRNAs, microRNAs, and mRNAs based on the confirmed lncRNAs (MALAT1:24 and NBR2:9). We further found the expression of MALAT1:24 and NBR2:9 to be positively correlated with disease severity.ConclusionTaken together, our study presents a comprehensive overview of lncRNAs in the serum of AS patients, thereby contributing novel perspectives on the underlying pathogenic mechanisms of this condition. In addition, our study predicted MALAT1 has the potential to be deeply involved in the pathogenesis of AS.

Abstract LB432: Identification of novel drug targets using the ADAPT biotargeting system on FFPE tissue from patients with glioblastoma multiforme

Abstract Glioblastoma Multiforme (GBM) is one of the most aggressive and lethal brain tumor types with a five-year survival rate of 6.9%. In 2023, more than 14,490 Americans were expected to receive a diagnosis. Although great progress has been achieved in protein target identification and treatment over the last few years, current treatment options are limited, and tumor recurrence occurs at a high rate due to treatment resistance. Aptamers, short single-stranded oligonucleotide ligands with high-affinity binding to molecular targets, are well suited tools for exploring novel targets in highly heterogeneous tumors with complex etiologies like GBM, where clinically relevant biomarkers are limited. In this study, aptamer-based cancer-specific drug target identification was performed by Adaptive Dynamic Artificial Polyligand Targeting (ADAPT) Biotargeting System. In short, a GBM-specific library was enriched through a Systematic Evolution of Ligands by EXponential enrichment (SELEX) process using micro dissected Formalin-Fixed Paraffin-Embedded (FFPE) tissues from GBM patients, along with whole body normal tissue for counterselection. Microdissection of tissue allows the partitioning of different types of cells to focus on specific cells of interest. The enriched libraries have been evaluated on clinical GBM and normal brain control samples through aptamer mediated tissue staining, resulting in preferential binding of enriched libraries compared to controls in the vast majority of GBM patients with little binding to normal brain samples. The final library was subsequently applied in an in-house developed library-based affinity pull-down assay using lysates from FFPE tissue of the same group of GBM patients and normal brain samples, followed by unbiased Mass Spectrometry for protein target identification. Combining the results from this proteomics analysis and data from the internal Whole Transcriptome Sequencing (WTS) database, we were able to identify several targets that are highly expressed in GBM and other cancer lineages. Antibody staining provided further validation of the expression levels and the cellular localization. The targets identified in this study include not only potential novel therapeutic targets, but also well-studied biomarkers and targets with available drugs already in clinical trials, demonstrating the platform has great potential for identifying high affinity binding and selectivity compared to traditional proteomics and other methods in drug target discovery. Citation Format: Xixi Wei, Myunggi An, Radhika Santhanam, Barbara Richardson, Lucas Vu, Justin Saul, Anna Walton, Michael Gee, Daniel Martin, Amanda Hubbard, Alexis Rodin, Matthew Rosenow, Tassilo Hornung. Identification of novel drug targets using the ADAPT biotargeting system on FFPE tissue from patients with glioblastoma multiforme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB432.

Analysis of m6A-regulated genes and subtype classification in lupus nephritis

BackgroundLupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE). N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, the roles of m6A regulators in LN are not fully demonstrated.MethodsWe downloaded the kidney tissue transcriptome dataset of LN patients and normal controls from the GEO database and extracted the expression levels of m6A regulators. We constructed and compared Random Forest (RF) and Support Vector Machine (SVM) models, and subsequently selected featured genes to develop nomogram models. The m6A subtypes were identified based on significantly differentially expressed m6A regulators, and the m6A gene subtypes were identified based on m6A-associated differential genes, and the two m6A modification patterns were comprehensively evaluated.ResultsWe obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples. We extracted the expression levels of 20 m6A regulators. By RF analysis we identified 7 characteristic m6A regulators and constructed nomogramh models with these 7 genes. We identified two m6A subtypes based on these seven important m6A regulators, and the immune cell infiltration levels of the two subtype clusters were significantly different. We identified two more m6A gene subtypes based on m6A-associated DEGs. We calculated the m6A scores using the principal component analysis (PCA) algorithm and found that the m6A scores of m6A cluster A and gene cluster A were lower than those of m6A cluster B and gene cluster B. In addition, we found that the levels of inflammatory factors were also significantly different between m6A clusters and gene clusters.ConclusionThis study confirms that m6A regulators are involved in the LN process through different modes of action and provide new diagnostic and therapeutic targets for LN.

Whole-exome sequencing reveals genetic variants that may play a role in neurocytomas.

Neurocytomas (NCs) are rare intracranial tumors that can often be surgically resected. However, disease course is unpredictable in many patients and medical therapies are lacking. We have used whole exome sequencing to explore the molecular etiology for neurocytoma and assist in target identification to develop novel therapeutic interventions. We used whole exome sequencing (WES) to compare the molecular landscape of 21 primary & recurrent NCs to five normal cerebellar control samples. WES data was analyzed using the Qiagen Clinical Insight program, variants of interest (VOI) were interrogated using ConSurf, ScoreCons, & Ingenuity Pathway Analysis Software to predict their potential functional effects, and Copy number variations (CNVs) in the genes of interest were analyzed by Genewiz (Azenta Life Sciences). Of 40 VOI involving thirty-six genes, 7 were pathogenic, 17 likely-pathogenic, and 16 of uncertain-significance. Of seven pathogenic NC associated variants, Glucosylceramidase beta 1 [GBA1 c.703T > C (p.S235P)] was mutated in 5/21 (24%), Coagulation factor VIII [F8 c.3637dupA (p.I1213fs*28)] in 4/21 (19%), Phenylalanine hydroxylase [PAH c.975C > A (p.Y325*)] in 3/21 (14%), and Fanconi anemia complementation group C [FANCC c.1162G > T (p.G388*)], Chromodomain helicase DNA binding protein 7 [CHD7 c.2839C > T (p.R947*)], Myosin VIIA [MYO7A c.940G > T (p.E314*)] and Dynein axonemal heavy chain 11 [DNAH11 c.3544C > T (p.R1182*)] in 2/21 (9.5%) NCs respectively. CNVs were noted in 85% of these latter 7 genes. Interestingly, a Carboxy-terminal domain RNA polymerase II polypeptide A small phosphatase 2 [CTDSP2 c.472G > A (p.E158K)] of uncertain significance was also found in > 70% of NC cases. The variants of interest we identified in the NCs regulate a variety of neurological processes including cilia motility, cell metabolism, immune responses, and DNA damage repair and provide novel insights into the molecular pathogenesis of these extremely rare tumors.

LncRNA TTTY14 participates in the progression of repeated implantation failure by regulating the miR-6088/SEMA5A axis.

To identify potential biomarkers and the molecular mechanisms associated with repeated implantation failure (RIF), three microarray datasets, GSE71331 (lncRNA + mRNA), GSE111974 (lncRNA + mRNA), and GSE71332 (miRNA), were retrieved from the Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs (DEMs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between normal control samples (C group) and RIF samples (RIF group) were identified, and then a module partition analysis was performed based on weighted correlation network analysis (WGCNA). Following enrichment analysis of the genes, the lncRNA-miRNA-mRNA interactions (ceRNA) were examined. The mRNAs in the ceRNA network were evaluated using the GSE58144 dataset. Finally, the key RNAs were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Fifty-three DEmiRNAs, 327 DEMs, and 13 DElncRNAs were identified between the C and RIF groups. According to WGCNA, the magenta module was positively correlated with RIF disease status. The lncRNA-mRNA interaction analysis based on genes in the magenta module revealed the intersecting lncRNAs, including peptidylprolyl isomerase E-like pseudogene (PPIEL) and the testis-specific transcript, y-Linked 14 (TTTY14); these lncRNAs are mainly involved in functions, such as plasma membrane organization. The ceRNA network analysis revealed several interactions, such as TTTY14-miR-6088-semaphorin 5A (SEMA5A). Finally, SEMA5A and the zinc finger protein 555 (ZNF555) were identified to be significantly upregulated in the RIF group compared with those in the C group in the GSE58144 dataset. The RT-qPCR results aligned with the above results. Overall, TTTY14, ZNF555, SEMA5A, PPIEL, and miR-6088 could serve as novel biomarkers of RIF.

Predicting mitophagy-related genes and unveiling liver endothelial cell heterogeneity in hepatic ischemia-reperfusion injury.

Hepatic Ischemia-Reperfusion Injury (HIRI) is a major complication in liver transplants and surgeries, significantly affecting postoperative outcomes. The role of mitophagy, essential for removing dysfunctional mitochondria and maintaining cellular balance, remains unclear in HIRI. To unravel the role of mitophagy-related genes (MRGs) in HIRI, we assembled a comprehensive dataset comprising 44 HIRI samples alongside 44 normal control samples from the Gene Expression Omnibus (GEO) database for this analysis. Using Random Forests and Support Vector Machines - Recursive Feature Elimination (SVM-RFE), we pinpointed eight pivotal genes and developed a logistic regression model based on these findings. Further, we employed consensus cluster analysis for classifying HIRI patients according to their MRG expression profiles and conducted weighted gene co-expression network analysis (WGCNA) to identify clusters of genes that exhibit high correlation within different modules. Additionally, we conducted single-cell RNA sequencing data analysis to explore insights into the behavior of MRGs within the HIRI. We identified eight key genes (FUNDC1, VDAC1, MFN2, PINK1, CSNK2A2, ULK1, UBC, MAP1LC3B) with distinct expressions between HIRI and controls, confirmed by PCR validation. Our diagnostic model, based on these genes, accurately predicted HIRI outcomes. Analysis revealed a strong positive correlation of these genes with monocytic lineage and a negative correlation with B and T cells. HIRI patients were divided into three subclusters based on MRG profiles, with WGCNA uncovering highly correlated gene modules. Single-cell analysis identified two types of endothelial cells with different MRG scores, indicating their varied roles in HIRI. Our study highlights the critical role of MRGs in HIRI and the heterogeneity of endothelial cells. We identified the macrophage migration inhibitory factor (MIF) and cGAS-STING (GAS) pathways as regulators of mitophagy's impact on HIRI. These findings advance our understanding of mitophagy in HIRI and set the stage for future research and therapeutic developments.

Anti-BIRC5 autoantibody serves as a valuable biomarker for diagnosing AFP-negative hepatocellular carcinoma.

Autoantibodies targeting tumor-associated antigens (TAAbs) have emerged as promising biomarkers for early cancer detection. This research aimed to assess the diagnostic capacity of anti-BIRC5 autoantibody in detecting AFP-negative hepatocellular carcinoma (ANHCC). This research was carried out in three stages (discovery phase, validation phase, and evaluation phase) and included a total of 744 participants. Firstly, the anti-BIRC5 autoantibody was discovered using protein microarray, exhibiting a higher positive rate in ANHCC samples (ANHCCs) compared to normal control samples (NCs). Secondly, the anti-BIRC5 autoantibody was validated through enzyme-linked immunosorbent assay (ELISA) in 85 ANHCCs and 85 NCs from two clinical centers (Zhengzhou and Nanchang). Lastly, the diagnostic usefulness of the anti-BIRC5 autoantibody for hepatocellular carcinoma (HCC) was evaluated by ELISA in a cohort consisting of an additional 149 AFP-positive hepatocellular carcinoma samples (APHCCs), 95 ANHCCs and 244 NCs. The association of elevated autoantibody to high expression of BIRC5 in HCC was further explored by the database from prognosis, immune infiltration, DNA methylation, and gene mutation level. In the validation phase, the area under the ROC curve (AUC) of anti-BIRC5 autoantibody to distinguish ANHCCs from NCs in Zhengzhou and Nanchang centers was 0.733 and 0.745, respectively. In the evaluation phase, the AUCs of anti-BIRC5 autoantibody for identifying ANHCCs and HCCs from NCs were 0.738 and 0.726, respectively. Furthermore, when combined with AFP, the AUC for identifying HCCs from NCs increased to 0.914 with a sensitivity of 77.5% and specificity of 91.8%. High expression of BIRC5 gene is not only correlated with poor prognosis of HCCs, but also significantly associated with infiltration of immune cells, DNA methylation, and gene mutation. The findings suggest that the anti-BIRC5 autoantibody could serve as a potential biomarker for ANHCC, in addition to its supplementary role alongside AFP in the diagnosis of HCC. Next, we can carry out specific verification and explore the function of anti-BIRC5 autoantibody in the occurrence and development of HCC.

Single Nucleotide Variation in the Promoter Region of the APOA1 Gene as a Candidate Biomarker for Dyslipidemia

Dyslipidemia is a lipid profile abnormality that can escalate the risk of cardiovascular disease. The rate of cardiovascular events in Indonesia is very high. One of the causes of dyslipidemia is due to polymorphisms in genes associated with lipid metabolism. The APOA-1 gene encodes the APOA-1 protein which functions to regulate HDL protein synthesis. The objective of this study is to determine APOA1 gene polymorphisms in patients with dyslipidemia. Samples of healthy controls and dyslipidemia patients were used in this investigation. The lipid profiles of the patients and normal controls are determined at the onset of the study. Following DNA extraction, the APOA-1 gene was amplified and sequenced using the serum sample. MEGA X and BLAST were used to analyze the sequencing results. The study's findings demonstrated that the APOA-1 gene length in samples of dyslipidemic patients and normal controls was 433 bp. While the normal control samples have the same sequence as the database, the dyslipidemic patient samples have an APOA-1 gene polymorphism in the promoter region. APOA-1 gene polymorphism results in disturbances in lipid profiles, particularly HDL which is at risk of developing dyslipidemia. The APOA-1 gene has the potential to be developed as a biomarker for diagnosing dyslipidemia involving a larger number of samples.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BackgroundKidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.MethodsTranscriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.ResultsBy intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.ConclusionIn summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Analysis of immune infiltration and cuproptosis-related genes in acute myocardial infarction

Abstract Background Acute myocardial infarction (AMI) is a major global health concern. The identification of key genes associated with AMI and cuproptosis is crucial for developing effective diagnostic and prognostic tools. Purpose To identify differentially expressed genes (DEGs) associated with AMI, and investigate their correlation with cuproptosis and immune infiltration to improve diagnosis and prognosis. Methods Six datasets (GSE48060, GSE83500, GSE97320, GSE974, GSE19339, and GSE66360) containing normal control and AMI samples were integrated from the Gene Expression Omnibus (GEO) database. DEGs between AMI and control samples were identified and cross-referenced with ferroptosis-associated genes. GO and KEGG analyses were performed to explore the functions and pathways associated with the identified genes. Immune infiltration analysis was also conducted to investigate the correlation between the genes and immune infiltration. An AMI prediction model was constructed using statistical analysis. Results Fifteen DEGs were identified, including nine upregulated and six downregulated genes. Among these genes, 11 were found to be associated with cuproptosis, including SLC31A1, PDHB, PDHA1, LIPT1, LIAS, FDX1, DLS1, DLD, DLAT, DBT, and ATP7B. These genes were mainly enriched in the TCA cycle, pyruvate metabolism, glycolysis/gluconeogenesis, and carbon metabolism pathways. Immune infiltration analysis revealed significant differences in macrophages, Th1 cells, Th2 cells, tumor-infiltrating lymphocytes, cytolytic activity, and MHC class 1 between normal and AMI samples. A correlation between these genes and immune infiltration was also observed. The AMI prognostic model constructed had an AUC of 0.61047. Conclusion Cuproptosis-related genes (SLC31A1, PDHB, PDHA1, LIPT1, LIAS, FDX1, DLS1, DLD, DLAT, DBT, and ATP7B) may play significant roles in AMI and its immune infiltration. The identified genes and pathways could potentially serve as biomarkers for improving the diagnosis and prognosis of AMI.Flow chart of research designCuproptosis-related AMI genes