Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer.

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan-Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co-infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD-1 and CTLA-4. We also identified Pan-Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-ß signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed 'GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population-based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan-immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti-PD-1) in advance GC (AUC = 0.833). Intriguingly, PAD-II (PIAMhighPIDGlow) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD-I, PIAMhighPIDGhigh, 0%; PAD-III, PIAMlowPIDGhigh, 0%; PAD-IV, PIAMlowPIDGlow, 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti-PD-L1) (P = 0.018). In all, we provided 'GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy.

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.

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.

In silico prediction of polyketide biosynthetic gene clusters in the genomes of Hypericum-borne endophytic fungi.

BACKGROUND: The search for new bioactive natural compounds with anticancer activity is still of great importance. Even though their potential for diagnostics and treatment of cancer has already been proved, the availability is still limited. Hypericin, a naphthodianthrone isolated essentially from plant source Hypericum perforatum L. along with other related anthraquinones and bisanthraquinones belongs to this group of compounds. Although it has been proven that hypericin is synthesized by the polyketide pathway in plants, none of the candidate genes coding for key enzymes has been experimentally validated yet. Despite the rare occurrence of anthraquinones in plants, their presence in microorganisms, including endophytic fungi, is quite common. Unlike plants, several biosynthetic genes grouped into clusters (BGCs) in fungal endophytes have already been characterized. RESULTS: The aim of this work was to predict, identify and characterize the anthraquinone BGCs in de novo assembled and functionally annotated genomes of selected endophytic fungal isolates (Fusarium oxysporum, Plectosphaerella cucumerina, Scedosporium apiospermum, Diaporthe eres, Canariomyces subthermophilus) obtained from different tissues of Hypericum spp. The number of predicted type I polyketide synthase (PKS) BGCs in the studied genomes varied. The non-reducing type I PKS lacking thioesterase domain and adjacent discrete gene encoding protein with product release function were identified only in the genomes of C. subthermophilus and D. eres. A candidate bisanthraquinone BGC was predicted in C. subthermophilus genome and comprised genes coding the enzymes that catalyze formation of the basic anthraquinone skeleton (PKS, metallo-beta-lactamase, decarboxylase, anthrone oxygenase), putative dimerization enzyme (cytochrome P450 monooxygenase), other tailoring enzymes (oxidoreductase, dehydrogenase/reductase), and non-catalytic proteins (fungal transcription factor, transporter protein). CONCLUSIONS: The results provide an insight into genetic background of anthraquinone biosynthesis in Hypericum-borne endophytes. The predicted bisanthraquinone gene cluster represents a basis for functional validation of the candidate biosynthetic genes in a simple eukaryotic system as a prospective biotechnological alternative for production of hypericin and related bioactive anthraquinones.

LncRNA and mRNA expression characteristic and bioinformatic analysis in myocardium of diabetic cardiomyopathy mice.

BACKGROUND: Diabetic cardiomyopathy (DCM) is becoming a very well-known clinical entity and leads to increased heart failure in diabetic patients. Long non-coding RNAs (LncRNAs) play an important role in the pathogenesis of DCM. In the present study, the expression profiles of lncRNAs and mRNAs were illuminated in myocardium from DCM mice, with purpose of exploring probable pathological processes of DCM involved by differentially expressed genes in order to provide a new direction for the future researches of DCM. RESULTS: The results showed that a total of 93 differentially expressed lncRNA transcripts and 881 mRNA transcripts were aberrantly expressed in db/db mice compared with the controls. The top 6 differentially expressed lncRNAs like up-regulated Hmga1b, Gm8909, Gm50252 and down-regulated Msantd4, 4933413J09Rik, Gm41414 have not yet been reported in DCM. The lncRNAs-mRNAs co-expression network analysis showed that LncRNA 2610507I01Rik, 2310015A16Rik, Gm10503, A930015D03Rik and Gm48483 were the most relevant to differentially expressed mRNAs. CONCLUSION: Our results showed that db/db DCM mice exist differentially expressed lncRNAs and mRNAs in hearts. These differentially expressed lncRNAs may be involved in the pathological process of cardiomyocyte apoptosis and fibrosis in DCM.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease.

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However, the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

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.

Multi-omic analysis revealed the immunological patterns and diagnostic value of exhausted T cell-derived PTTG1 in patients with psoriasis.

BACKGROUND: Psoriasis, characterized by chronic inflammation, is a persistent skin condition that is notoriously challenging to manage and prone to relapse. Despite significant advancements in its treatment, many adverse reactions still occur. Therefore, exploring the mechanisms behind the occurrence and development of psoriasis is extremely important. METHODS: The weighted correlation network analysis (WGCNA) algorithm was used to identify phenotype-related genes in patients with psoriasis. We recruited clinical samples of patients with psoriasis, and used single-cell RNA sequencing (scRNA-seq) to visualize divergent genes and metabolisms of varied cells for the psoriasis. Various machine-learning methods were used to identify core genes, and molecular docking was used to analyze the stability of leptomycin B targeting pituitary tumor transforming 1 (PTTG1). Immunofluorescence (IHC) analysis, multiplex immunofluorescence (mIF) analysis, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to validate the results. RESULTS: Our results identified 1391 genes associated with the phenotype in patients with psoriasis and highlighted the significant alterations in T-cell functionality observed in the disease by WGCNA. There were nine distinct cellular clusters in psoriasis analyzed with the aid of scRNA-seq data. Each subtype of cell exhibited distinct genetic profiles, functional roles, signaling mechanisms, and metabolic characteristics. Machine-learning methods further demonstrated the potential diagnostic value of T cell-derived PTTG1 and its relationship with T-cell exhaustion in psoriasis. Lastly, the leptomycin B was scrutinized and verified had high stability targeting PTTG1. CONCLUSIONS: This study elucidates the biological basis of psoriasis. At the same time, it was discovered that PTTG1 derived from exhausted T cells serves as a diagnostic biomarker for psoriasis. Leptomycin B could be a potential drug for targeted treatment of psoriasis on PTTG1.

Inhibition of RAC attenuates Adriamycin-induced podocyte injury.

Minimal Change Disease (MCD), which is associated with podocyte injury, is the leading cause of nephrotic syndrome in children. A considerable number of patients experience relapses and require prolonged use of prednisone and immunosuppressants. Multi-drug resistance and frequent relapses can lead to disease progression to focal and segmental glomerulosclerosis (FSGS). To identify potential targets for therapy of podocyte injury, we examined microarray data of mRNAs in glomerular samples from both MCD patients and healthy donors, obtained from the GEO database. Differentially expressed genes (DEGs) were used to construct the protein-protein interactions (PPI) network through the application of the search tool for the retrieval of interacting genes (STRING) tool. The most connected genes in the network were ranked using cytoHubba. 16 hub genes were selected and validated by qRT-PCR. RAC2 was identified as a potential therapeutic target for further investigation. By downregulating RAC2, Adriamycin (ADR)-induced human podocytes (HPCs) injury was attenuated. EHT-1864, a small molecule inhibitor that targets the RAC (RAC1, RAC2, RAC3) family, proved to be more effective than RAC2 silencing in reducing HPCs injury. In conclusion, our research suggests that EHT-1864 may be a promising new molecular drug candidate for patients with MCD and FSGS.

Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.).

BACKGROUND: Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear. RESULTS: We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut. CONCLUSION: Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.

Genome-wide analysis of the switchgrass YABBY family and functional characterization of PvYABBY14 in response to ABA and GA stress in Arabidopsis.

BACKGROUND: The small YABBY plant-specific transcription factor has a prominent role in regulating plant growth progress and responding to abiotic stress. RESULTS: Here, a total of 16 PvYABBYs from switchgrass (Panicum virgatum L.) were identified and classified into four distinct subgroups. Proteins within the same subgroup exhibited similar conserved motifs and gene structures. Synteny analyses indicated that segmental duplication contributed to the expansion of the YABBY gene family in switchgrass and that complex duplication events occurred in rice, maize, soybean, and sorghum. Promoter regions of PvYABBY genes contained numerous cis-elements related to stress responsiveness and plant hormones. Expression profile analysis indicated higher expression levels of many PvYABBY genes during inflorescence development and seed maturation, with lower expression levels during root growth. Real-time quantitative PCR analysis demonstrated the sensitivity of multiple YABBY genes to PEG, NaCl, ABA, and GA treatments. The overexpression of PvYABBY14 in Arabidopsis resulted in increased root length after treatment with GA and ABA compared to wild-type plants. CONCLUSIONS: Taken together, our study provides the first genome-wide overview of the YABBY transcription factor family, laying the groundwork for understanding the molecular basis and regulatory mechanisms of PvYABBY14 in response to ABA and GA responses in switchgrass.

Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment.

BACKGROUND: Gliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data. METHODS: In this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted. RESULTS: CD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram's predictions were supported by calibration plots, further validating its accuracy. CONCLUSION: In conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma.

Identification of differentially expressed ER stress-related genes and their association with pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a complex and progressive illness that has a multifaceted origin, significant fatality rates, and profound effects on health. The pathogenesis of PAH is poorly defined due to the insufficient understanding of the combined impact of endoplasmic reticulum (ER) stress and immune infiltration, both of which play vital roles in PAH development. This study aims to identify potential ER stress-related biomarkers in PAH and investigate their involvement in immune infiltration. METHODS: The GEO database was used to download gene expression profiles. Genes associated with ER stress were obtained from the MSigDB database. Weighted gene co-expression network analysis (WGCNA), GO, KEGG, and protein-protein interaction (PPI) were utilized to conduct screening of hub genes and explore potential molecular mechanisms. Furthermore, the investigation also delved into the presence of immune cells in PAH tissues and the correlation between hub genes and the immune system. Finally, we validated the diagnostic value and expression levels of the hub genes in PAH using subject-workup characterization curves and real-time quantitative PCR. RESULTS: In the PAH and control groups, a total of 31 genes related to ER stress were found to be differentially expressed. The enrichment analysis revealed that these genes were primarily enriched in reacting to stress in the endoplasmic reticulum, dealing with unfolded proteins, transporting proteins, and processing proteins within the endoplasmic reticulum. EIF2S1, NPLOC4, SEC61B, SYVN1, and DERL1 were identified as the top 5 hub genes in the PPI network. Immune infiltration analysis revealed that these hub genes were closely related to immune cells. The receiver operating characteristic (ROC) curves revealed that the hub genes exhibited excellent diagnostic efficacy for PAH. The levels of SEC61B, NPLOC4, and EIF2S1 expression were in agreement with the findings of bioinformatics analysis in the PAH group. CONCLUSIONS: Potential biomarkers that could be utilized are SEC61B, NPLOC4, and EIF2S1, as identified in this study. The infiltration of immune cells was crucial to the development and advancement of PAH. This study provided new potential therapeutic targets for PAH.

Plasma-derived exosomal miRNA profiles associated with type 1 diabetes.

AIMS: Recently, exosomal miRNAs have been shown to play important roles in multiple diseases, including type 1 diabetes (T1D). To assess the biomarker potential of exosomal miRNAs for T1D, we measured the expression profiles of plasma-derived exosomal miRNAs in T1D and explored their potential functions by bioinformatic analysis. MATERIALS AND METHODS: In the discovery phase, exosome samples were isolated from plasma by size exclusion chromatography from 10 T1D patients and 10 sex- (p = 0.36), age- (p = 0.97), and body mass index-matched (p = 0.47) healthy control subjects. Exosomal miRNA expression profiles were measured using the Illumina NovaSeq 6000 platform. With verification by quantitative real-time PCR (qRT-PCR), we used multiple bioinformatics approaches to explore the potential biological functions of the identified differentially expressed miRNAs. The diagnostic signature of exosomal miRNAs was evaluated by least absolute shrinkage and selection operator (LASSO) regression and evaluated based on the area under the receiver operating characteristic curve (AUC). RESULTS: In total, 43 differentially expressed miRNAs, among which 34 were upregulated and 9 were downregulated, were identified in T1D. After correcting for multiple testing using false discovery rate, 11 identified exosomal miRNAs still showed statistical significance. Among the 5 selected miRNAs, 3 miRNAs (miR-103a-3p, miR-144-5p and miR-454-3p) were successfully validated by qRT-PCR. The biological analysis-enriched terms included protein autophosphorylation and the Hedgehog signalling pathway. The highest AUC of exosomal miRNA was 0.889 under the LASSO model. The expression levels of 5 selected exosomal miRNAs were correlated with multiple clinical characteristics such as fasting C-peptide and postprandial C-peptide. CONCLUSIONS: Our results indicated that plasma-derived exosomal miRNAs could serve as promising diagnostic biomarkers of T1D.

Retinol-binding protein type 1 expression predicts poor prognosis in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent malignancy worldwide, with high incidence and poor survival rates. RBP1 is highly expressed in several kinds of cancer and plays a potential prognostic factor. However, the relationship between RBP1 and HNSCC were analyzed based on The Cancer Genome Atlas (TCGA) database. MATERIALS AND METHODS: RBP1 expression and clinical information were obtained from the Cancer Genome Atlas (TCGA) database. Tumor tissue and adjacent normal tissue of 6 HNSCC patients were collected to analyze the RBP1 mRNA expression level by quantitative PCR. Cox regression analysis was used to evaluate the prognostic values of RBP1 and clinical data in HNSCC. A nomogram was also established to predict the impact of RBP1 on prognosis based on Cox multivariate results. The methylation level of RBP1 in HNSC and its prognosis were analyzed in UALACN and MethSurv. Finally, the potential biological functions of RBP1 were investigated using gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA). RESULTS: The mRNA expression levels of RBP1 were highly expressed in HNSCC tissue. The Cox analyses demonstrate that highly-expressed RBP1 is an independent prognosis marker(P < 0.05). ROC curve analysis showed that performances of RBP1 (area under the ROC curve: 0.887, sensitivity: 84.1%, specificity: 79.9%). The methylation was increased in HNSCC patients compared with normal subjects(P < 0.05) and was associated with better prognosis at sites cg06208339, cg12298268, cg12497564, cg15288618, cg20532370, cg23448348. Additionally, RBP1 expression is mildly associated with immune cell infiltration and immunological checkpoints. CONCLUSION: RBP1 is overexpressed and associated with poor patient prognosis in head and neck squamous cell carcinoma.

Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification.

BACKGROUND: Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy. MATERIALS AND METHODS: Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers. RESULTS: The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples. CONCLUSIONS: These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.

Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics.

PURPOSE: Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets. METHODS: The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene-miRNA network and drug-DEG interaction network. RESULTS: In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT-PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group. CONCLUSION: The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.

Comprehensive analysis of the prognostic value of glutathione S-transferases Mu family members in breast cancer.

Breast cancer (BC) remains a significant public health concern globally, with a high number of reported cases and a substantial number of deaths every year. Accumulating reactive oxygen species (ROS) and oxidative stress are related to BC and the Glutathione S-transferases Mu (GSTM) family is one of the most important enzymatic detoxifiers associated with many cancers. In this study, UALCAN, Kaplan-Meier plotter, bc-GenExMiner, cBioPortal, STRING, Enrichr, and TIMER databases were employed to carry out a comprehensive bioinformatic analysis and provide new insight into the prognostic value of GSTMs in BC. GSTM2-5 genes in mRNA and protein levels were found to be expressed at lower levels in breast tumors compared to normal tissues, and reduction in mRNA levels is linked to shorter overall survival (OS) and relapse-free survival (RFS). The lower mRNA levels of GSTMs were strongly associated with the worse Scarff-Bloom-Richardson (SBR) grades (p < 0.0001). The mRNA levels of all five GSTMs were substantially higher in estrogen receptor (ER)-positive and progesterone receptor (PR)-positive compared to ER-negative and PR-negative BC patients. As well, when nodal status was compared, GSTM1, GSTM3, and GSTM5 were significantly higher in nodal-positive BC patients (p < .01). Furthermore, GSTM4 had the most gene alteration (4%) among other family members, and GSTM5 showed the strongest correlation with CD4+ T cells (Cor= .234, p = 2.22e-13). In conclusion, our results suggest that GSTM family members may be helpful as biomarkers for prognosis and as therapeutic targets in BC.

Preconditioning exercise reduces brain damage of ischemic stroke in rats via PI3K-AKT pathway by bioinformatic analysis.

Ischemic stroke is one of the most vital causes of high neurological morbidity and mortality in the world. Preconditioning exercise is considered as the primary prevention of stroke to resistance to subsequent injury. We tried to research the underlying biological mechanisms of this exercise. Forty-two SD rats were randomly divided into three groups: middle cerebral artery occlusion (MCAO) group, exercise group with MCAO (EX + MCAO) group, and sham group, with 14 rats in each group. The EX + MCAO group underwent exercise preconditioning for 3 weeks before occlusion, and the other two groups were fed and exercised normally. After 3 weeks, MCAO model was made by thread plug method in the EX + MCAO group and MCAO group. After successful modeling, the Longa scale was used to evaluate the neurological impairment of rats at day 0, day 1, and day 2. The rats in each group were killed on the third day after modeling. TTC staining measured the infarct volume of each group. The morphology and apoptosis of cortical cells were observed by HE and Tunel staining. Three rats in each group underwent high-throughput sequencing. Bioinformatic analysis was used to find the deferentially expressed genes (DEGs) and predict the transcription factor binding sites (TFBS) of the next-generation sequencing results. Gene enrichment (GSEA) was used to analyze potential functional genes and their corresponding signaling pathways. The Longa scale showed EX + MCAO group had the neurological function better than the modeling group (P < 0.001). TTC staining showed that the infarct size of EX + MCAO group was less than MCAO group (P < 0.05). HE and Tunel staining showed that the cells in the EX + MCAO group and the sham group had normal morphology and fewer apoptotic cells than MCAO group. A new gene named 7994 was discovered and TFBS of this gene was predicted, which could interact with key genes such as Foxd3, Foxa2, NR4A2, SP1, CEBPA, and SOX10. GSEA showed that EX + MCAO group could promote and regulate angiogenesis and apoptosis through PI3K-AKT pathway. Preconditioning exercise could improve nerve function and reduce infarct size in rats. The underlying mechanism is to regulate the PI3K-AKT pathway through several key genes, promote cerebral angiogenesis, and reduce apoptosis.

Insight into the role of mitochondrion-related gene anchor signature in mitochondrial dysfunction of neutrophilic asthma.

OBJECTIVE: At present, targeting molecular-pharmacological therapy is still difficult in neutrophilic asthma. The investigation aims to identify and validate mitochondrion-related gene signatures for diagnosis and specific targeting therapeutics in neutrophilic asthma. METHODS: Bronchial biopsy samples of neutrophilic asthma and healthy people were identified from the GSE143303 dataset and then matched with human mitochondrial gene data to obtain mitochondria-related differential genes (MitoDEGs). Signature mitochondria-related diagnostic markers were jointly screened by support vector machine (SVM) analysis, least absolute shrinkage, and selection operator (LASSO) regression. The expression of marker MitoDEGs was evaluated by validation datasets GSE147878 and GSE43696. The diagnostic value was evaluated by receiver operating characteristic (ROC) curve analysis. Meanwhile, the infiltrating immune cells were analyzed by the CIBERSORT. Finally, oxidative stress level and mitochondrial functional morphology for asthmatic mice and BEAS-2B cells were evaluated. The expression of signature MitoDEGs was verified by qPCR. RESULTS: 67 MitoDEGs were identified. Five signature MitoDEGs (SOD2, MTHFD2, PPTC7, NME6, and SLC25A18) were further screened out. The area under the curve (AUC) of signature MitoDEGs presented a good diagnostic performance (more than 0.9). There were significant differences in the expression of signature MitoDEGs between neutrophilic asthma and non-neutrophilic asthma. In addition, the basic features of mitochondrial dysfunction were demonstrated by in vitro and in vivo experiments. The expression of signature MitoDEGs in the neutrophilic asthma mice presented a significant difference from the control group. CONCLUSIONS: These MitoDEGs signatures in neutrophilic asthma may hold potential as anchor diagnostic and therapeutic targets in neutrophilic asthma.