The diagnostic value and associated molecular mechanism study for fibroblast-related mitochondrial genes on keloid.

PURPOSE: This study aims to reveal the mechanism of fibroblast-related mitochondrial genes on keloid formation and explore promising signature genes for keloid diagnosis. METHOD: The distribution of fibroblasts between the keloid sample and control sample based on three keloid datasets, followed by the differentially expressed genes (DEGs) investigation and associated enrichment analysis. Then, hub genes were explored based on DEGs, mitochondrial genes from an online database, as well as fibroblast-related genes that were revealed by WCGNA. Subsequently, signature genes were screened through machine learning, and their diagnostic value was validated by nomogram. Moreover, the targeted drugs and related transcriptional regulation of these genes were analyzed. Finally, the verification analysis was performed on signature genes using qPCR analysis. RESULT: A total of totally 329 DEGs were revealed based on three datasets, followed by enrichment analysis. WGCNA revealed a total of 258 fibroblast-related genes, which were primarily assembled in functions like muscle tissue development. By using machine learning, we screened four signature genes (ACSF2, ALDH1B1, OCIAD2, and SIRT4) based on eight hub genes (fibroblast-related mitochondrial genes). Nomogram and validation analyses confirmed the well-diagnostic performance of these four genes in keloid. Immune infiltration and drug correlation analyses showed that SIRT4 was significantly associated with immune cell type 2 T helper cells and molecular drug cyclosporin. All these findings provided new perspectives for the clinical diagnosis and therapy of keloid. CONCLUSION: The fibroblast-related mitochondrial genes including SIRT4, OCIAD2, ALDH1B1, and ACSF2 were novel signature genes for keloid diagnosis, offering novel targets and strategies for diagnosis and therapy of keloid.

Identification and validation of ferroptosis related markers in erythrocyte differentiation of umbilical cord blood-derived CD34+ cell by bioinformatic analysis.

Ferroptosis has been observed to play an important role during erythrocyte differentiation (ED). However, the biological gene markers and ferroptosis mechanisms in ED remain unknown. We downloaded the datasets of ED in human umbilical cord blood-derived CD34+ cells from the Gene Expression Omnibus database. Using median differentiation time, the sample was categorized into long and short groups. The differentially expressed ferroptosis-related genes (DE-FRGs) were screened using differential expression analysis. The enrichment analyses and a protein-protein interaction (PPI) network were conducted. To predict the ED stage, a logistic regression model was constructed using the least absolute shrinkage and selection operator (LASSO). Overall, 22 DE-FRGs were identified. Ferroptosis-related pathways were enriched using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Gene Set Enrichment Analysis and Gene Set Variation Analysis revealed the primary involvement of DE-FRGs in JAK-STAT, MAPK, PI3K-AKT-mTORC1, WNT, and NOTCH signaling pathways. Ten-hub DE-FRGs were obtained using PPI analysis. Furthermore, we constructed mRNA-microRNA (miRNA) and mRNA-transcription factor networks. Immune cell infiltration levels differed significantly during ED. LASSO regression analysis established a signature using six DE-FRGs (ATF3, CDH2, CHAC1, DDR2, DPP4, and GDF15) related to the ED stage. Bioinformatic analyses identified ferroptosis-associated genes during ED, which were further validated. Overall, we identified ferroptosis-related genes to predict their correlations in ED. Exploring the underlying mechanisms of ferroptosis may help us better understand pathophysiological changes in ED and provide new evidence for clinical transformation.

Untargeted metabolomics study of mature human milk from women with and without gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder during pregnancy that alters the metabolites in human milk. Integrated Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS) were employed for comprehensive identification and comparison of metabolites in mature human milk (MHM) from women with and without GDM. A total of 268 differentially expressed metabolites (DEMs) were identified. Among these, linoleic acid, arachidonic acid, 9R-HODE and L-glutamic acid were significantly elevated and 12,13-DHOME was significantly decreased in MHM of women with GDM. These metabolites are significantly enriched in linoleic acid metabolism, fatty acid biosynthesis, galactose metabolism and ABC transporters pathways. Disorders in these metabolic pathways are associated with insulin resistance and poor glucose metabolism indicating these conditions may persist postpartum.

Identification of hub genes and key modules in laryngeal squamous cell carcinoma.

Background: Laryngeal squamous cell carcinoma (LSCC) is the prominent cancer in head and neck, which greatly affects life quality of patients. The pathogenesis of LSCC is not clear. Presently, the LSCC treatments include chemotherapy, surgery and radiotherapy; however, these methods have poor efficacy in patients with recurrent and persistent cancer. Therefore, the study identified the hub genes accompanied with LSCC, which may be a potential therapeutic target in the future. Methods: We extracted whole transcriptome high-throughput sequencing (HTS) LSCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and calculate differentially expressed genes (DEGs) between LSCC and normal samples using statistical software RStudio. Through weighted gene co-expression network analysis (WGCNA), enrichment examination of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) functions, and examination of protein-protein interaction (PPI) network, we obtained network hub genes and validated the hub genes prognostic value and expression levels of protein. Results: Through analysis of differential gene expression, from the GEO and TCGA databases 2,139 and 2,774 DEGs were obtained, respectively, 13 and 15 modules were screened from TCGA-LSCC and GSE127165 datasets by WGCNA, respectively. The most significant positive and negative correlation modules in the WGCNA and DEG lists were overlapped, and overall 36 co-expressed overlapping genes were retrieved. Through enrichment analysis of GO and KEGG, it was found that the gene functions were highly concentrated in cell junction assembly, basement membrane, extracellular matrix (ECM) structural constituent etc., and the pathways were mainly concentrated in ECM receptor interaction, focal adhesion, small cell lung cancer, and toxoplasmosis. Through analysis of PPI network analysis, 10 network hub genes (SNAI2, ITGA6, LAMB3, LAMC2, CAV1, COL7A1, GJA1, EHF, OAT, and GPT) were obtained. Finally, survival analysis and protein expression validation of these genes confirmed that low OAT expression and high CAV1 expression remarkably influenced the survival of patient's prognosis with LSCC. Conclusions: We recognized the hub genes and key modules nearly associated to LSCC and these genes were validated by survival analysis and the database of Human Protein Atlas (HPA), which is of high importance for unveiling the pathogenesis of LSCC and probing for new precise biological marker and potential therapeutic targets.

A systematic review and meta-analysis combined with bioinformatic analysis on the predictive value of E-cadherin in patients with renal cell carcinoma.

OBJECTIVES: Renal cell carcinoma (RCC) is the most common cancer of the kidney. This study aims to evaluate the potential predictive value of E-cadherin, a marker of the epithelial mesenchymal transit (EMT) process that has been associated with tumor metastasis. METHODS: We searched PubMed, Embase, and Cochrane Library to identify prospective studies. Hazard ratios (HRs), odds ratios (ORs), and 95% confidence intervals (CIs) were summarized to validate the relationship between E-cadherin and survival and clinical characteristics. The quality of the included studies was assessed using the NOS table. Then, we analyzed genetic data and clinical characteristics from The Cancer Genome Atlas Program (TCGA) database using R language with the dplyr package for validation. RESULTS: Including 21 articles. The analysis revealed a strong link between high E-cadherin expression and favorable prognosis (for OS, HR = 0.35, 95% CI: 0.19-0.62; for PFS, HR = 0.19, 95% CI: 0.03-0.53; for DSS, HR = 0.25, 95% CI: 0.08-0.76; for RFS, HR = 0.71, 95% CI: 0.44-1.16; for DFS, HR = 0.28, 95% CI: 0.13-0.61; for T stage, OR = 0.21, 95% CI: 0.11-0.41; for N stage, OR = 0.07, 95%CI: 0.02-0.25; for M stage, OR = 0.12, 95% CI: 0.02-0.60; for clinical stage, OR = 0.29, 95% CI: 0.18-0.47; for nuclear grade, OR = 0.23, 95% CI: 0.13-0.41; for tumor size, OR = 0.49, 95% CI: 0.26-0.92). The findings were supported by bioinformatic analysis which used TCGA RCC patient's cohort (P < 0.01). CONCLUSION: Based on the current data, E-cadherin may predict a better prognosis in RCC patients.

Pan-cancer analysis and single-cell analysis reveals FAM110B as a potential target for survival and immunotherapy.

Background: FAM110B belongs to the family that has a 110 sequence similarity (FAM110) and is located in the centrosome and mitotic spindle. FAM110B has been linked to tumor cell growth in earlier research. Uncertainty exists regarding FAM110B's function within the tumor microenvironment is unclear as well as pan-cancer. Methods: In order to assess the variation in FAM110B expression within normal and pan-cancer tissues, we combined the TCGA and GTEx databases. The cBioPortal database and the GSCALite platform were used to examine the variation in genome and methylation alteration of FAM110B. Cox regression, Kaplan-Meier, and SangerBox were employed to examine the clinical features and prognosis of FAM110B and pan-cancer. The purpose of the correlational research was to investigate the associations within immunerelated genes, tumor mutation burden, microsatellite instability, immune-related genes, and immunological checkpoints and FAM110B expression. ESTIMATE, EPIC, QUANTISEQ, and MCPCOUNTER methods were used to calculate the interaction among FAM110B expression as well as the tumor immune microenvironment. The immunoinfiltration and function of FAM110B were analyzed by single-cell databases (TISCH and CancerSEA). Finally, we evaluated the sensitivity of FAM110B to small-molecule medications through GDSC and CTRP databases. Results: The transcription and protein expression of FAM110B varies significantly throughout cancer types, and this has predictive value for the prognosis of some tumors; including brain lower grade glioma (LGG), stomach adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), etc. In the tumor microenvironment, the expression level of FAM110B was associated with immune cell infiltration, immune checkpoint immune regulatory genes, tumor mutational burden, and microsatellite fragility to a certain extent. Conclusion: This work investigates the possibility of utility of FAM110B as a marker to forecast pan-cancer immunotherapy response, providing a theoretical basis for cancer therapy.

Identifying key genes against rutin on human colorectal cancer cells via ROS pathway by integrated bioinformatic analysis and experimental validation.

Colorectal cancer (CRC) poses a significant global health challenge, characterized by substantial prevalence variations across regions. This study delves into the therapeutic potential of rutin, a polyphenol abundant in fruits, for treating CRC. The primary objectives encompass identifying molecular targets and pathways influenced by rutin through an integrated approach combining bioinformatic analysis and experimental validation. Employing Gene Set Enrichment Analysis (GSEA), the study focused on identifying potential differentially expressed genes (DEGs) associated with CRC, specifically those involved in regulating reactive oxygen species, metabolic reprogramming, cell cycle regulation, and apoptosis. Utilizing diverse databases such as GEO2R, CTD, and Gene Cards, the investigation revealed a set of 16 targets. A pharmacological network analysis was subsequently conducted using STITCH and Cytoscape, pinpointing six highly upregulated genes within the rutin network, including TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Gene Ontology (GO) analysis predicted functional categories, shedding light on rutin's potential impact on antioxidant properties. KEGG pathway analysis enriched crucial pathways like metabolic and ROS signaling pathways, HIF1a, and mTOR signaling. Diagnostic assessments were performed using UALCAN and GEPIA databases, evaluating mRNA expression levels and overall survival for the identified targets. Molecular docking studies confirmed robust binding associations between rutin and biomolecules such as TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Experimental validation included inhibiting colorectal cell HT-29 growth and promoting cell growth with NAC through MTT assay. Flow cytometric analysis also observed rutin-induced G1 phase arrest and cell death in HT-29 cells. RT-PCR demonstrated reduced expression levels of target biomolecules in HT-29 cells treated with rutin. This comprehensive study underscores rutin's potential as a promising therapeutic avenue for CRC, combining computational insights with robust experimental evidence to provide a holistic understanding of its efficacy.

Neutrophil extracellular trap related risk score exhibits crucial prognostic value in skin cutaneous melanoma, associating with distinct immune characteristics.

BACKGROUND: Neutrophil extracellular traps (NETs) are related to the prognosis of cancer patients. Nevertheless, the potential prognostic values of NETs in skin cutaneous melanoma (SKCM) remains largely unknown. MATERIALS AND METHODS: The NET-related gene signature was constructed by LASSO Cox regression analysis using the TCGA-SKCM cohort. The overall survival (OS) and immune status in SKCM patients between the high- and low-NET score (high-score, low-score) groups were explored. The scRNA-seq dataset GSE115978 was used to understand the role of NET score in SKCM at single cell resolution. RESULTS: A five NET genes-based signature (TLR2, CLEC6A, PDE4B, SLC22A4 and CYP4F3) was constructed as the NET-related prognostic model for SKCM. The OS of SKCM patients with low-score was better than that in patients with high-score. Additionally, NET score was negatively associated with infiltration of some immune cells (e.g. type I Macrophages, CD8-T cells, CD4-T cells). Moreover, patients with high-score had low stromal, immune and ESTIMATE scores. Furthermore, drug sensitivity analysis results showed that Lapatinib, Trametinib and Erlotinib may have better therapeutic advantages in patients with high-score. CONCLUSION: We established a NET-related five gene signature in SKCM and found that the NET-related signature may exhibit a good predictive ability for SKCM prognosis. The NET score may not only predict the survival outcome and drug sensitivity in SKCM, but also reflect the immune conditions of SKCM patients.

Network Pharmacology to Reveal the Mechanism of Fufang Banmao Capsule for Treating Unresectable Primary Liver Cancer and Clinical Data Validation.

INTRODUCTION: Fufang Banmao capsules (FFBM), a traditional Chinese medicine, has been used to treat primary liver cancer (PLC) for several years. However, the bioactive ingredients, and mechanism of FFBM for treating PLC remains unclear. Our objective is to utilize network pharmacology to investigate these aspects and subsequently validate their effectiveness through clinical data. MATERIALS AND METHODS: The FFBM ingredients were obtained from the HERB database and screened for bioactive ingredients using the SwissTargetPrediction database. The PharmMapper and GEO database were used to acquire targets and differentially expressed genes (DEGs) for FFBM and PLC, respectively. Common targets were identified using Venn diagrams, followed by enrichment and protein-protein interaction (PPI) analysis. Furthermore, the Cytoscape software was utilized to identify Hub genes and construct the ingredienttarget- pathway network. Subsequently, patients diagnosed with unresectable PLC who underwent transcatheter arterial chemoembolization (TACE) at our hospital between January 2008 and December 2019 were retrospectively collected. Finally, Cox analysis was conducted to reveal the role of FFBM in the treatment of unresectable PLC. RESULTS: FFBM had 232 targets, and PLC had 1582 DEGs. HSP90AA1 and SRC were identified as crucial targets. Alpha-santalol, glycyrrhizin, and morroniside were identified as the top three bioactive ingredients. Enrichment analysis revealed a significant connection between FFBM utilization for treating PLC and multiple pathways, such as chemical carcinogenesis, PI3K-AKT, Rap1, FoxO, MAPK, and VEGF pathway. Clinic data revealed that consuming FFBM significantly improved the prognosis of unresectable PLC with a hazard ratio of 0.69. CONCLUSION: Our study identified the bioactive ingredients of FFBM and its potential mechanisms for treating PLC. Additionally, we validated the effectiveness through clinical data.

Identification of lysosome-related hub genes as potential biomarkers and immune infiltrations of moyamoya disease by multiple bioinformatics methods and machine-learning strategies.

Background: Moyamoya disease (MMD), characterized by chronic cerebrovascular pathology, poses a rare yet significant clinical challenge, associated with elevated rates of mortality and disability. Despite intensive research endeavors, the exact biomarkers driving its pathogenesis remain enigmatic. Methods: The expression patterns of GSE189993 and GSE141022 were retrieved from the Gene Expression Omnibus (GEO) repository to procure differentially expressed genes (DEGs) between samples afflicted with MMD and those under control conditions. The Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine with Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed for identifying candidate diagnostic genes associated with MMD. Subsequently, these candidate genes underwent validation in an independent cohort (GSE157628). The CMAP database was ultimately employed to forecast drugs pertinent to MMD for clinical translation. Results: A collective of 240 DEGs were discerned. Functional enrichment scrutiny unveiled the enrichment of the cholesterol metabolism pathway, salmonella infection pathway, and allograft rejection pathway within the MMD cohort. EPDR1, DENND3, and NCSTN emerged as discerned diagnostic biomarkers for MMD. The CMAP database was ultimately employed to scrutinize the ten most auspicious pharmaceutical compounds for managing MMD. Finally, after validation through in vitro experiments, EPDR1, DENND3, and NCSTN were identified as the key genes. Conclusion: EPDR1, DENND3, and NCSTN have emerged as potential novel biomarkers for MMD. The involvement of T lymphocytes, neutrophilic granulocytes, dendritic cells, natural killer cells, and plasma cells could be pivotal in the pathogenesis and advancement of MMD.

In Silico Analysis of the ROP29 Protein as a Vaccine Candidate Against Toxoplasma gondii.

The progression of Toxoplasma gondii (T. gondii) invasion is aided by rhoptry proteins (ROPs), which are also crucial for the parasite's survival in host cells. In this study, in silico analysis was performed to examine the various aspects of the ROP29 protein, such as physicochemical properties, potential T- and B-cell epitopes, and other significant features. The research revealed that there were 55 possible sites for posttranslational modification in the ROP29 protein. The secondary structure of the ROP29 protein consists of a random coil, an alpha-helix, and an extended strand, which account for 49.69%, 36.81%, and 13.50%, respectively. Moreover, a number of putative T- and B-cell epitopes for ROP29 were found. The Ramachandran plot showed that 88.91% (crude model) and 97.54% (refine model) of the amino acid residues were located in the favored regions. Also, the testing of this protein's antigenicity and allergenicity showed that it was nonallergenic and immunogenic. Our results suggested that employing in silico tools to apply structural and functional predictions to the ROP29 protein can lower the likelihood that laboratory investigations will fail. This research served as a crucial foundation for further research. More research is required in the future in suitable animal model employing ROP29 alone or in combination with other antigens.

Screening of novel disease genes of sepsis-induced myocardial Disfunction by RNA sequencing and bioinformatics analysis.

BACKGROUND: There is still a lack of effective treatment for sepsis-induced myocardial dysfunction (SIMD), while the pathogenesis of SIMD still remains largely unexplained. METHODS: RNA sequencing results (GSE267388 and GSE79962) were used for cross-species integrative analysis. Bioinformatic analyses were used to delve into function, tissue- and cell- specificity, and interactions of genes. External datasets and qRT-PCR experiments were used for validation. L1000 FWD was used to predict targeted drugs, and 3D structure files were used for molecular docking. RESULTS: Based on bioinformatic analyses, ten differentially expressed genes were selected as genes of interest, seven of which were verified to be significantly differential expression. Bucladesine was considered as a potential targeted drug for SIMD, which banded to seven target proteins primarily by forming hydrogen bonds. CONCLUSION: It was considered that Cebpd, Timp1, Pnp, Osmr, Tgm2, Cp, and Asb2 were novel disease genes, while bucladesine was a potential therapeutic drug, of SIMD.

Determining expression changes of ANO7 and SLC38A4 membrane transporters in colorectal cancer.

Membrane transporters are proteins responsible for facilitating the movement of molecules within biological membranes. They play a vital role in maintaining cellular homeostasis by regulating the transport of nutrients, ions, and other molecules into and out of cells. Our aim is to identify biomarkers in colorectal cancer using membrane transporter proteins. We utilized COAD TCGA data for this purpose. Subsequently, we conducted differential gene analysis and feature selection using membrane transporter proteins. Furthermore, we identified two potential genes, including ANO7 and SLC38A4. To validate the expression profiles of ANO7 and SLC38A4, key genes in this context, RT-qPCR was employed on colorectal cancer samples and adjacent normal tissues. Additionally, utilizing GEPIA2, Kaplan-Meier survival analysis, and cBioPortal, we assessed the status of these genes in various cancers, examining their methylation and mutation patterns. In conclusion, we suggest that ANO7 and SLC38A4 serve as prognostic biomarkers in colorectal cancer.

Risk factor prediction and immune correlation analysis of cuproptosis-related gene in osteoarthritis.

Osteoarthritis (OA) is a widespread inflammatory joint disease with significant global disability burden. Cuproptosis, a newly identified mode of cell death, has emerged as a crucial factor in various pathological conditions, including OA. In this context, our study aims to investigate the intrinsic relationship between cuproptosis-related genes (CRGs) and OA, and assess their potential as biomarkers for OA diagnosis and treatment. Datasets from the GEO databases were analysed the differential expression of CRGs, leading to the identification of 10 key CRGs (CDKN2A, DLD, FDX1, GLS, LIAS, LIPT1, MTF1, PDHA1, DLAT and PDHB). A logistic regression analysis and calibration curves were used to show excellent diagnostic accuracy. Consensus clustering revealed two CRG patterns, with Cluster 1 indicating a closer association with OA progression. RT-PCR confirmed a significant increase in the expression levels of these nine key genes in IL-1ß-induced C28/i2 cells, and the expression of CDKN2A and FDX1 were also elevated in conditioned monocytes, while the expression of GLS and MTF1 were significantly decreased. In vitro experiments demonstrated that the expression levels of these 7/10 CRGs were significantly increased in chondrocytes induced by IL-1ß, and upon stimulation with cuproptosis inducers, chondrocyte apoptosis was exacerbated, accompanied by an increase in the expression of cuproptosis-related proteins. These further substantiated our research findings and indicated that the nine selected cuproptosis genes have high potential for application in the diagnosis of OA.

TNF-α and RPLP0 drive the apoptosis of endothelial cells and increase susceptibility to high-altitude pulmonary edema.

High-altitude pulmonary edema (HAPE) is a fatal threat for sojourners who ascend rapidly without sufficient acclimatization. Acclimatized sojourners and adapted natives are both insensitive to HAPE but have different physiological traits and molecular bases. In this study, based on GSE52209, the gene expression profiles of HAPE patients were compared with those of acclimatized sojourners and adapted natives, with the common and divergent differentially expressed genes (DEGs) and their hub genes identified, respectively. Bioinformatic methodologies for functional enrichment analysis, immune infiltration, diagnostic model construction, competing endogenous RNA (ceRNA) analysis and drug prediction were performed to detect potential biological functions and molecular mechanisms. Next, an array of in vivo experiments in a HAPE rat model and in vitro experiments in HUVECs were conducted to verify the results of the bioinformatic analysis. The enriched pathways of DEGs and immune landscapes for HAPE were significantly different between sojourners and natives, and the common DEGs were enriched mainly in the pathways of development and immunity. Nomograms revealed that the upregulation of TNF-α and downregulation of RPLP0 exhibited high diagnostic efficiency for HAPE in both sojourners and natives, which was further validated in the HAPE rat model. The addition of TNF-α and RPLP0 knockdown activated apoptosis signaling in endothelial cells (ECs) and enhanced endothelial permeability. In conclusion, TNF-α and RPLP0 are shared biomarkers and molecular bases for HAPE susceptibility during the acclimatization/adaptation/maladaptation processes in sojourners and natives, inspiring new ideas for predicting and treating HAPE.

Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren's syndrome based on comprehensive bioinformatics and immunohistochemical analyses.

IgA nephropathy (IgAN) and Sjogren's syndrome (SS) are two autoimmune diseases with undetermined etiology and related to abnormal activation of lymphocytes. This study aims to explore the crucial genes, pathways and immune cells between IgAN and SS. Gene expression profiles of IgAN and SS were obtained from the Gene Expression Omnibus and Nephroseq data. Differentially expressed gene (DEG) and weighted gene co-expression network analyses (WGCNA) were done to identify common genes. Enrichment analysis and protein-protein interaction network were used to explore potential molecular pathways and crosstalk genes between IgAN and SS. The results were further verified by external validation and immunohistochemistry (IHC) analysis. Additionally, immune cell analysis and transcription factor prediction were also conducted. The DEG analysis revealed 28 commonly up-regulated genes, while WGCNA identified 98 interactively positive-correlated module genes between IgAN and SS. The enrichment analysis suggested that these genes were mainly involved in the biological processes of response to virus and antigen processing and presentation. The external validation and IHC analysis identified 5 hub genes (PSMB8, PSMB9, IFI44, ISG15, and CD53). In the immune cell analysis, the effector memory CD8 T and T follicular helper cells were significantly activated, and the corresponding proportions showed positively correlations with the expressions of the 5 hub genes in the two autoimmune diseases. Together, our data identified the crosstalk genes, molecular pathways, and immune cells underlying the IgAN and SS, which provides valuable insights into the intricate mechanisms of these diseases and offers potential intervention targets.

Identification of MAP1LC3A as a promising mitophagy-related gene in polycystic ovary syndrome.

Increasing evidence suggests that mitophagy is crucially involved in the progression of polycystic ovary syndrome (PCOS). Exploration of PCOS-specific biomarkers related to mitophagy is expected to provide critical insights into disease pathogenesis. In this study, we employed bioinformatic analyses and machine learning algorithms to determine novel biomarkers for PCOS that may be tied with mitophagy. A grand total of 12 differential expressed mitophagy-related genes (DE-MRGs) associated with PCOS were identified. TOMM5 and MAP1LC3A among the 12 DE-MRGs were recognized as potential marker genes by LASSO, RF and SVM-RFE algorithms. The area under the ROC curve (AUROC) of MAP1LC3A were all greater than 0.8 both in the training set and validation sets. The CIBERSORT analysis indicated a potential association between alterations in the immune microenvironment of PCOS individuals and MAP1LC3A expression. In addition, we found that MAP1LC3A was positively related to the testosterone levels of PCOS patients. Overall, MAP1LC3A was identified as optimal PCOS-specific biomarkers related to mitophagy. Our findings created a diagnostic strength and offered a perspective for investigating the mitophagy process in PCOS.

The "Dry-Lab" Side of Food Authentication: Benchmark of Bioinformatic Pipelines for the Analysis of Metabarcoding Data.

Next Generation Sequencing Technologies (NGS), particularly metabarcoding, are valuable tools for authenticating foodstuffs and detecting eventual fraudulent practices such as species substitution. This technique, mostly used for the analysis of prokaryotes in several environments (including food), is in fact increasingly applied to identify eukaryotes (e.g., fish, mammals, avian, etc.) in multispecies food products. Besides the "wet-lab" procedures (e.g., DNA extraction, PCR, amplicon purification, etc.), the metabarcoding workflow includes a final "dry-lab" phase in which sequencing data are analyzed using a bioinformatic pipeline (BP). BPs play a crucial role in the accuracy, reliability, and interpretability of the metabarcoding results. Choosing the most suitable BP for the analysis of metabarcoding data could be challenging because it might require greater informatics skills than those needed in standard molecular analysis. To date, studies comparing BPs for metabarcoding data analysis in foodstuff authentication are scarce. In this study, we compared the data obtained from two previous studies in which fish burgers and insect-based products were authenticated using a customizable, ASV-based, and command-line interface BP (BP1) by analyzing the same data with a customizable but OTU-based and graphical user interface BP (BP2). The final sample compositions were compared statistically. No significant difference in sample compositions was highlighted by applying BP1 and BP2. However, BP1 was considered as more user-friendly than BP2 with respect to data analysis streamlining, cost of analysis, and computational time consumption. This study can provide useful information for researchers approaching the bioinformatic analysis of metabarcoding data for the first time. In the field of food authentication, an effective and efficient use of BPs could be especially useful in the context of official controls performed by the Competent Authorities and companies' self-control in order to detect species substitution and counterfeit frauds.

Exploring the role of pyroptosis and immune infiltration in sepsis based on bioinformatic analysis.

PURPOSE: Sepsis is a disease that is typically treated in intensive care units with high mortality and morbidity. Pyroptosis is a newly identified type of programmed cell death and is characterized by inflammatory cytokine secretion. However, the role of pyroptosis in sepsis remains unclear. METHODS: GSE28750 and GSE134347 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis genes (DEPGs) were identified between sepsis and healthy controls. Machine learning was used to further narrow the gene range. Receiver operating curves (ROC) were generated to estimate the diagnostic efficacy. Immune infiltration levels were estimated via single-sample gene set enrichment analysis (ssGSEA). A network database was used to predict the upstream transcription factors and miRNAs of DEPGs. Finally, the expression of the genes was validated by qRT-PCR between sepsis patients and healthy controls. RESULTS: We found that the pyroptosis pathway was enriched and activated in sepsis. 8 DEPGs were identified. A heatmap showed that the genes, NLRC4, NAIP, IL-18, AIM2 and ELANE, were abundant in the sepsis samples, and the genes, NLRP1, CHMP7 and TP53, were abundant in the healthy control samples. The ssGSEA results showed that the abundances of activated dendritic cells, MDSC, macrophage, plasmacytoid dendritic cells, regulatory T-cells, and Th17-cells were significantly higher, while the activated B-cell, activated CD8 T-cell, CD56 dim tural killer cell, immature B-cell, monocyte, and T follicular helper cell abundances were lower in sepsis samples compared to healthy controls. The qRT-PCR results showed that the expression levels of NAIP, IL-18, TP53, CHMP7, NLRC4, ELANE and NLRP1 were consistant with the bioinformatic analyses, while the expression level of AIM2 has no significant difference. CONCLUSION: Our study identified seven potential pyroptosis-related genes, NAIP, IL-18, TP53, CHMP7, NLRC4, ELANE and NLRP1. This study revealed that pyroptosis may promote sepsis development by activating the immune response.

iTRAQ proteomics analysis of placental tissue with gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus is an endocrine and metabolic disorder that appears for the first time during pregnancy and causes varying degrees of short- and/or long-term effects on the mother and child. The etiology of the disease is currently unknown and isobaric tags for relative and absolute quantitation proteomics approach, the present study attempted to identify potential proteins in placental tissues that may be involved in the pathogenesis of GDM and adverse foetal pregnancy outcomes. METHODS: Pregnant women with GDM hospitalised were selected as the experimental group, and pregnant women with normal glucose metabolism as the control group. The iTRAQ protein quantification technology was used to screen the differentially expressed proteins between the GDM group and the normal control group, and the differentially expressed proteins were analysed by GO, KEGG, PPI, etc., and the key proteins were subsequently verified by western blot. RESULTS: Based on the proteomics of iTRAQ, we experimented with three different samples of placental tissues from GDM and normal pregnant women, and the total number of identified proteins were 5906, 5959, and 6017, respectively, which were similar in the three different samples, indicating that the results were reliable. Through the Wayne diagram, we found that the total number of proteins coexisting in the three groups was 4475, and 91 differential proteins that could meet the quantification criteria were strictly screened, of which 32 proteins were up-regulated and 59 proteins were down-regulated. By GO enrichment analysis, these differential proteins are widely distributed in extracellular membrane-bounded organelle, mainly in extracellular exosome, followed by intracellular vesicle, extracellular organelle. It not only undertakes protein binding, protein complex binding, macromolecular complex binding, but also involves molecular biological functions such as neutrophil degranulation, multicellular organismal process, developmental process, cellular component organization, secretion, regulated exocytosis. Through the analysis of the KEGG signaling pathway, it is found that these differential proteins are mainly involved in HIF-1 signaling pathway, Glycolysis/Gluconeogenesis, Central carbon metabolism in cancer, AMPK signaling pathway, Proteoglycans in cancer, Protein processing in endoplasmic reticulum, Thyroid cancer, Alcoholism, Glucagon signaling pathway. DISCUSSION: This preliminary study helps us to understand the changes in the placental proteome of GDM patients, and provides new insights into the pathophysiology of GDM.