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.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC. METHODS: The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC. RESULTS: Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis. CONCLUSION: We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Screening, identification and targeted intervention of necroptotic biomarkers of asthma.

BACKGROUND: As a pivotal pathway of programmed cell death, necroptosis significantly contributes to the pathogenesis of respiratory disorders. However, its role in asthma is not yet fully elucidated. Therefore, this study aimed to identify markers associated with necroptosis, evaluate their functions in asthma, and explore potential therapeutic agents targeting necroptosis for the management of asthma. METHODS: Firstly, machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were utilized to identify necroptosis-related differentially expressed genes (NRDEGs) in asthma patients compared to healthy controls. Concurrently, the expression of NRDEGs was validated using external datasets, Western blot, and quantitative real-time polymerase chain reaction (qPCR). Secondly, the clinical relevance of NRDEGs was assessed through Receiver Operating Characteristic (ROC) curve analysis and correlation with clinical indicators. Thirdly, the relationship between NRDEGs and pulmonary immune cell infiltration, as well as the signaling interactions between different cells types, were analyzed through immune infiltration and single-cell analysis. Fourthly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), were conducted to elucidate the functional roles of NRDEGs. Finally, compounds targeting NRDEGs were screened, and their binding affinities were evaluated using molecular docking studies. RESULTS: In asthma, necroptosis is activated, leading to the identification of four NRDEGs: NLRP3, PYCARD, ALOX15, and VDAC3. Among these, NLRP3, PYCARD, and ALOX15 are upregulated, whereas VDAC3 is downregulated in asthma. Comprehensive clinical evaluations indicated that NRDEGs hold diagnostic value for asthma. Specifically, NLRP3 was inversely correlated with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), while VDAC3 showed an inverse correlation with sputum neutrophils. Conversely, ALOX15 expression was positively correlated with fractional exhaled nitric oxide (FeNO) levels, as well as sputum eosinophils, blood eosinophils, and blood IgE levels. Subsequent immune infiltration analysis revealed associations between NRDEGs and activated dendritic cells, mast cells, and eosinophils. Single-cell RNA sequencing (scRNA-seq) further confirmed the communication signals between myeloid dendritic cells, fibroblasts, neutrophils, and helper T cells, predominantly related to fibrosis and immune-inflammatory responses. Pathway enrichment analysis demonstrated that NRDEGs are involved in ribosomal function, oxidative phosphorylation, and fatty acid metabolism. Finally, resveratrol and triptonide were identified as potential therapeutic agents targeting the proteins encoded by NRDEGs for asthma treatment. CONCLUSIONS: The necroptosis pathway is activated in asthma, with NRDEGs-namely PYCARD, NLRP3, ALOX15, and VDAC3-correlated with declines in lung function and airway inflammation. These genes serve as reliable predictors of asthma risk and are involved in the regulation of the immune-inflammatory microenvironment. Resveratrol and triptolide have been identified as promising therapeutic candidates due to their potential to target the proteins encoded by these genes.

Prognostic marker CXCL5 in glioblastoma polyformis and its mechanism of immune invasion.

Glioblastoma multiforme (GBM) is the most aggressive brain cancer with a poor prognosis. Therefore, the correlative molecular markers and molecular mechanisms should be explored to assess the occurrence and treatment of glioma.WB and qPCR assays were used to detect the expression of CXCL5 in human GBM tissues. The relationship between CXCL5 expression and clinicopathological features was evaluated using logistic regression analysis, Wilcoxon symbolic rank test, and Kruskal-Wallis test. Univariate, multivariate Cox regression and Kaplan-Meier methods were used to assess CXCL5 and other prognostic factors of GBM. Gene set enrichment analysis (GSEA) was used to identify pathways associated with CXCL5. The correlation between CXCL5 and tumor immunoinfiltration was investigated using single sample gene set enrichment analysis (ssGSEA) of TCGA data. Cell experiments and mouse subcutaneous transplanted tumor models were used to evaluate the role of CXCL5 in GBM. WB, qPCR, immunofluorescence, and immunohistochemical assays showed that CXCL5 expression was increased in human GBM tissues. Furthermore, high CXCL5 expression was closely related to poor disease-specific survival and overall survival of GBM patients. The ssGSEA suggested that CXCL5 is closely related to the cell cycle and immune response through PPAR signaling pathway. GSEA also showed that CXCL5 expression was positively correlated with macrophage cell infiltration level and negatively correlated with cytotoxic cell infiltration level. CXCL5 may be associated with the prognosis and immunoinfiltration of GBM.

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

BACKGROUND: Lupus 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: This 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.

SCUBE1 Promotes Gestational Diabetes Mellitus: A Bioinformatics and Experimental Investigation.

Gestational diabetes mellitus (GDM) is one of the most common metabolic diseases in pregnant women, posing significant risks to the life and health of both mothers and fetuses. With improving living standards, the incidence of GDM is increasing rapidly. Therefore, understanding the underlying mechanism of GDM is of paramount importance. We downloaded two datasets from the Gene Expression Omnibus (GEO) database, containing sequencing data specifically related to "gestational diabetes" and "placenta". By merging these two datasets, a mRNA expression dataset was obtained and subjected to bioinformatics analyses. To screen out corresponding genes, differential analysis and weighted correlation network analysis (WGCNA) were carried out. Lasso, support vector machine and random forest analyses were subsequently performed for identifying key genes from the differentially expressed genes (DEGs) jointly screened out through differential analysis and WGCNA. Afterwards, immunoinfiltration and correlation analysis were performed to screen immune cells that play a role in disease progression and explore the correlation between the screened key genes and immune cells, after which Western Blot, quantitative real-time polymerase chain reaction, Immunohistochemistry, methyl thiazolyl tetrazolium, flow cytometry, scratch and Transwell assays were, respectively, performed for verification. For further verification, we found that the expression levels of MAP6D1 and SCUBE1 in embryonic tissues of GDM patients was higher compared to those of healthy pregnant women, which was consistent with the results of bioinformatics analysis. Consequently, SCUBE1 was selected for follow-up experiment. In order to explore the role of SCUBE1 in the development of GDM, we treated the trophoblastic cells HTR-8/SVneo with high glucose, and on this basis downregulated the expression of SCUBE1. Through further analysis, we observed that SCUBE1 had a role in reducing cell activity, migration and invasion, and promoting cell apoptosis. In summary, SCUBE1 promotes the development of GDM by increasing cell apoptosis and reducing cell activity, migration, and invasion.

Exploring the molecular mechanisms of ferroptosis-related genes in periodontitis: a multi-dataset analysis.

PURPOSE: This study aims to elucidate the biological functions of ferroptosis-related genes in periodontitis, along with their correlation to tumor microenvironment (TME) features such as immune infiltration. It aims to provide potential diagnostic markers of ferroptosis for clinical management of periodontitis. METHODS: Utilizing the periodontitis-related microarray dataset GSE16134 from the Gene Expression Omnibus (GEO) and a set of 528 ferroptosis-related genes identified in prior studies, this research unveils differentially expressed ferroptosis-related genes in periodontitis. Subsequently, a protein-protein interaction network was constructed. Subtyping of periodontitis was explored, followed by validation through immune cell infiltration and gene set enrichment analyses. Two algorithms, randomForest and SVM(Support Vector Machine), were employed to reveal potential ferroptosis diagnostic markers for periodontitis. The diagnostic efficacy, immune correlation, and potential transcriptional regulatory networks of these markers were further assessed. Finally, potential targeted drugs for differentially expressed ferroptosis markers in periodontitis were predicted. RESULTS: A total of 36 ferroptosis-related genes (30 upregulated, 6 downregulated) were identified from 829 differentially expressed genes between 9 periodontitis samples and the control group. Subsequent machine learning algorithm screening highlighted 4 key genes: SLC1A5(Solute Carrier Family 1 Member 5), SLC2A14(Solute Carrier Family 1 Member 14), LURAP1L(Leucine Rich Adaptor Protein 1 Like), and HERPUD1(Homocysteine Inducible ER Protein With Ubiquitin Like Domain 1). Exploration of these 4 key genes, supported by time-correlated ROC analysis, demonstrated reliability, while immune infiltration results indicated a strong correlation between key genes and immune factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was conducted for the four key genes, revealing enrichment in GO/KEGG pathways that have a significant impact on periodontitis. Finally, the study predicted potential transcriptional regulatory networks and targeted drugs associated with these key genes in periodontitis. CONCLUSIONS: The ferroptosis-related genes identified in this study, including SLC1A5, SLC2A14, LURAP1L, and HERPUD1, may serve as novel diagnostic and therapeutic targets for periodontitis. They are likely involved in the occurrence and development of periodontitis through mechanisms such as immune infiltration, cellular metabolism, and inflammatory chemotaxis, potentially linking the ferroptosis pathway to the progression of periodontitis. Targeted drugs such as flurofamide, L-733060, memantine, tetrabenazine, and WAY-213613 hold promise for potential therapeutic interventions in periodontitis associated with these ferroptosis-related genes.

Exploring the diagnostic value, prognostic value, and biological functions of NPC gene family members in hepatocellular carcinoma based on a multi-omics analysis.

Liver cancer is a cunning malignancy with a high incidence and mortality rate among cancers worldwide. The NPC gene family members (NPCs: NPC1, NPC2, and NPC1L1) are closely linked to the development of multiple cancers, but their role in liver cancer remains unclear. As a result, we must investigate their functions in liver hepatocellular carcinoma (LIHC). NPCs were significantly differentially expressed between normal and LIHC tissues, with a high mutation frequency in LIHC. The ROC curve analysis revealed that NPC1/NPC2 had high diagnostic and prognostic values in LIHC. NPC1 expression was also found to be negatively correlated with its methylation level. The differentially expressed genes between high and low NPC1 expression groups in LIHC were mainly related to channel activity, transporter complexes, and plasma membrane adhesion molecules. Additionally, NPC1 expression was significantly associated with multiple immune cells and immunization checkpoints. It was hypothesized that a TUG1/SNHG4-miR-148a-3p-NPC1 regulatory axis is associated with hepatocarcinogenesis. Finally, the protein expression of NPC1 in LIHC tissues and paraneoplastic tissues was detected, and NPC1-knockdown HepG2 cells (NPC1KO) inhibited the proliferation, migration, and invasion. This study helped to identify new prognostic markers and potential immunotherapeutic targets for LIHC and revealed the molecular mechanisms underlying NPC1 regulation in LIHC. The NPCs play a key role in the prognosis and diagnosis of LIHC and may be an important indicator for LIHC prognosis and diagnosis; NPC1 might be a potential therapeutic target in LIHC.

Exploration of the biomarkers of comorbidity of psoriasis with inflammatory bowel disease and their association with immune infiltration.

BACKGROUND: There was evidence that significant bidirectional associations between psoriasis and inflammatory bowel diseases (IBDs), which influences management strategy of the patients, so the investigation on the mechanisms by which these two diseases co-occur is important. METHODS: The Gene Expression Omnibus (GEO) database was used to download gene expression profiles of psoriasis and IBD. The differentially expressed genes (DEGs) between disease and health control groups for each data set were calculated, and Venn diagram was used to obtain for intersection. We performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the intersection, followed by developing a protein-protein interaction network and module construction, and identified hub genes by cytoHubba. Thereafter, least absolute shrinkage and selection operator algorithms was used to identify the co-biomarkers of psoriasis and IBD from the top 50 hub genes. The biomarkers were used to construct a screening model, the discriminatory capacity of which was verified by receiver operating characteristic (ROC) curves. CIBERSORT algorithm was utilized to estimate the compositional patterns of immune cell infiltration in biomarkers of psoriasis and IBD. Spearman rank correlation analysis was used to further evaluate the correlation between the identified biomarkers and immune cells. RESULTS: A total of 271 shared DEGs were screened. The GO and KEGG enrichment analysis indicated that the shared DEGs were mainly enriched in response to lipopolysaccharide, secretory granule lumen, cytokine activity, and interleukin (IL)-17 signaling pathway. Fifty genes such as IL1B, IL6, were identified as hub genes, based on which, FOS, IFI44, MMP9, MNDA, PTGS2, S100A9, and STAT1 were identified as biomarkers of psoriasis. CCL20, CD274, CTGF, CXCL1, CXCL10, CXCL2, CXCL9, FCGR3B, FOS, GBP1, GZMB, IFI27, IFI6, IL1RN, ISG15, ISG20, LCN2, LILRB2, MMP12, MMP7, S100A8, TLR8, and TNFSF13B were identified as biomarkers of IBD. FOS was the common biomarker of psoriasis and IBD. Screening models were validated in the validation data set (Psoriasis: area under the curve (AUC) = 1.000, IBD: AUC = 0.870). Immunocyte infiltration analysis showed the macrophages cells, mast cells resting, and T cells CD4 memory activated have the common characteristics in psoriasis and IBD. CONCLUSIONS: FOS may play a key role in the occurrence and development of psoriasis complicated with IBD and macrophages cells may be an entrance for treating this comorbidity.

GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data.

BACKGROUND: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes. METHODS: Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded. RESULTS: By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD. CONCLUSIONS: Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD.

Construction of a NETosis-related gene signature for predicting the prognostic status of sepsis patients.

Background: Sepsis is a common traumatic complication of response disorder of the body to infection. Some studies have found that NETosis may be associated with the progression of sepsis. Methods: Data of the sepsis samples were acquired from Gene Expression Omnibus (GEO) database. Gene set enrichment score was calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks analysis, and stepwise multivariable regression analysis were performed to identify NETosis-associated genes for sepsis prognosis. To assess the infiltration of immune cells, the ESTIMATE and CIBERPSORT algorithms were used. Functional enrichment analysis was conducted in the clusterProfiler package. Results: Different programmed death pathways were abnormally activated in sepsis patients as compared to normal samples. We screened five important NETosis associated genes, namely, CEACAM8, PGLYRP1, MAPK14, S100A12, and LCN2. These genes were significantly positively correlated with entotic cell death and ferroptosis and negatively correlated with autophagy. A clinical prognostic model based on riskscore was established using the five genes. The ROC curves of the model at 7 days, 14 days and 21 days all had high AUC values, indicating a strong stability of the model. Patients with high riskscore had lower survival rate than those with low riskscore. After the development of a nomogram, calibration curve and decision curve evaluation also showed a strong prediction performance and reliability of the model. As for clinicopathological features, older patients and female patients had a relatively high riskscore. The riskscore was significantly positively correlated with cell cycle-related pathways and significantly negatively correlated with inflammatory pathways. Conclusion: We screened five NETosis-associated genes that affected sepsis prognosis, and then established a riskscore model that can accurately evaluate the prognosis and survival for sepsis patients. Our research may be helpful for the diagnosis and clinical treatment of sepsis.

Identification of TTC21A as a Potential Prognostic Marker in Head and Neck Squamous Cell Carcinoma: In Silico Analysis.

BACKGROUND/AIM: Tetratricopeptide repeat domain 21A (TTC21A) plays a crucial role in ciliary function and has been associated with various pathogenic processes, including carcinogenesis. However, its role in head and neck squamous cell carcinoma (HNSCC) has not been elucidated. MATERIALS AND METHODS: Based on the sequencing and microarray data of HNSCC from publicly available databases, the expression of TTC21A was compared between different subgroups based on clinical and molecular parameters. The survival analysis and regression analysis were conducted using the Kaplan-Meier method and the Cox method, respectively. Functional analysis was performed by the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) tools. Immune infiltration analysis was performed based on the expression of TTC21A. RESULTS: TTC21A decreased in tumor tissues and was associated with N stage, histologic grade, HPV infection, and TP53 mutation in HNSCC. TTC21A was an independent indicator of overall survival for patients with HNSCC. A high level of TTC21A expression indicated a favorable prognosis. The TTC21A expression level was involved with immune-related signaling regulation, immune-related gene expression, and immune cell infiltration. TTC21A expression was potent in predicting immunotherapeutic benefits. CONCLUSION: TTC21A, as a potential predictor of favorable outcomes and immunotherapy response for HNSCC, is related to immune-related signaling regulation, immune-related gene expression, and immune cell infiltration.

Identification and immunoinfiltration analysis of key genes in ulcerative colitis using WGCNA.

Objective: Ulcerative colitis (UC) is a chronic non-specific inflammatory bowel disease characterized by an unclear pathogenesis. This study aims to screen out key genes related to UC pathogenesis. Methods: Bioinformatics analysis was conducted for screening key genes linked to UC pathogenesis, and the expression of the screened key genes was verified by establishing a UC mouse model. Results: Through bioinformatics analysis, five key genes were obtained. Subsequent infiltration analysis revealed seven significantly different immune cell types between the UC and general samples. Additionally, animal experiment results illustrated markedly decreased body weight, visible colonic shortening and damage, along with a significant increase in the DAI score of the DSS-induced mice in the UC group in comparison with the NC group. In addition, H&E staining results demonstrated histological changes including marked inflammatory cell infiltration, loss of crypts, and epithelial destruction in the colon mucosa epithelium. qRT-PCR analysis indicated a down-regulation of ABCG2 and an up-regulation of IL1RN, REG4, SERPINB5 and TRIM29 in the UC mouse model. Notably, this observed trend showed a significant dependence on the concentration of DSS, with the mouse model of UC induced by 7% DSS demonstrating a more severe disease state compared to that induced by 5% DSS. Conclusion: ABCG2, IL1RN, REG4, SERPINB5 and TRIM29 were screened out as key genes related to UC by bioinformatics analysis. The expression of ABCG2 was down-regulated, and that of IL1RN, REG4, SERPINB5 and TRIM29 were up-regulated in UC mice as revealed by animal experiments.

The prognostic genes model of breast cancer drug resistance based on single-cell sequencing analysis and transcriptome analysis.

Breast cancer (BC) represents a multifaceted malignancy, with escalating incidence and mortality rates annually. Chemotherapy stands as an indispensable approach for treating breast cancer, yet drug resistance poses a formidable challenge. Through transcriptome data analysis, we have identified two sets of genes exhibiting differential expression in this context. Furthermore, we have confirmed the overlap between these genes and those associated with exosomes, which were subsequently validated in cell lines. The investigation screened the identified genes to determine prognostic markers for BC and utilized them to formulate a prognostic model. The disparities in prognosis and immunity between the high- and low-risk groups were validated using the test dataset. We have discerned different BC subtypes based on the expression levels of prognostic genes in BC samples. Variations in prognosis, immunity, and drug sensitivity among distinct subtypes were examined. Leveraging data from single-cell sequencing and prognostic gene expression, the AUCell algorithm was employed to score individual cell clusters and analyze the pathways implicated in high-scoring groups. Prognostic genes (CCT4, CXCL13, MTDH, PSMD2, and RAB27A) were subsewoquently validated using RT-qPCR. Consequently, we have established a model for predicting prognosis in breast cancer that hinges on drug resistance and ERGs. Furthermore, we have evaluated the prognostic value of this model. The genes identified as prognostic markers can now serve as a reference for precise treatment of this condition.

Multi-omics and Single Cell Sequencing Analyses Reveal Associations of Mitophagy-Related Genes Predicting Clinical Prognosis and Immune Infiltration Characteristics in Osteosarcoma.

Despite recent advances in clinical treatments, identifying high-risk osteosarcoma (OS) patients remains an unresolved clinical challenge. Mitophagy, a specialized form of cellular autophagy, selectively reduces the number of mitochondria or repairs their abnormal functions in response to external stress, thereby ensuring mitochondrial quality and maintaining mitochondrial function. Mitophagy plays a crucial role in cancer development, including processes such as mitochondrial repair, homeostasis maintenance, and tumor metabolism. However, its impact on OS has not yet been reported. In this study, we collected 58 mitophagy-related genes (MPRGs) from the TARGET and GEO databases and bioinformatically screened for those associated with OS prognosis. By LASSO-multivariable Cox regression algorithm, we subsequently developed a novel scoring system, the MPRG score, and validated its significance in predicting OS prognosis. Immune landscape analysis showed patients in the low MPRG group had a higher immune infiltration level than those in the high MPRG group. Drug sensitivity differences highlighted the potential need for alternative therapeutic strategies based on MPRG scoring system. The distribution characteristics of the MPRG signature in different cell subtypes of OS were explored by single-cell sequencing analyses. In vitro experiments further confirmed the abnormal expression of screened targets in OS. Our findings highlight the role of mitophagy in OS and its potential as a therapeutic target.

A CLRN3-Based CD8+ T-Related Gene Signature Predicts Prognosis and Immunotherapy Response in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8+ T cells significantly influences the prognosis and progression of tumor patients. METHODS: This study establishes a CRC immune risk model based on CD8+ T cell-related genes. CD8+ T cell-related genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA), and the enriched gene sets were annotated via Gene Ontology (GO) and Reactome pathway analysis. Employing machine learning methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Random Forest (RF), we identified nine genes associated with CD8+ T-cell infiltration. The infiltration levels of immune cells in CRC tissues were assessed using the ssGSEA algorithm. RESULTS: These genes provide a foundation for constructing a prognostic model. The TCGA-CRC sample model's prediction scores were categorized, and the prediction models were validated through Cox regression analysis and Kaplan-Meier curve analysis. Notably, although CRC tissues with higher risk scores exhibited elevated levels of CD8+ T-cell infiltration, they also demonstrated heightened expression of immune checkpoint genes. Furthermore, comparison of microsatellite instability (MSI) and gene mutations across the immune subgroups revealed notable gene variations, particularly with APC, TP53, and TNNT1 showing higher mutation frequencies. Finally, the predictive model's efficacy was corroborated through the use of Tumor Immune Dysfunction and Exclusion (TIDE), Immune Profiling Score (IPS), and immune escape-related molecular markers. The predictive model was validated through an external cohort of CRC and the Bladder Cancer Immunotherapy Cohort. CLRN3 expression levels in tumor and adjacent normal tissues were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Subsequent in vitro and in vivo experiments demonstrated that CLRN3 knockdown significantly attenuated the malignant biological behavior of CRC cells, while overexpression had the opposite effect. CONCLUSIONS: This study presents a novel prognostic model for CRC, providing a framework for enhancing the survival rates of CRC patients by targeting CD8+ T-cell infiltration.

An Apoptosis-Related Specific Risk Model for Breast Cancer: From Genomic Analysis to Precision Medicine.

BACKGROUND: Breast cancer (BC) ranks as the most prevalent malignancy affecting women globally, with apoptosis playing a pivotal role in its pathological progression. Despite the crucial role of apoptosis in BC development, there is limited research exploring the relationship between BC prognosis and apoptosis-related genes (ARGs). Therefore, this study aimed to establish a BC-specific risk model centered on apoptosis-related factors, presenting a novel approach for predicting prognosis and immune responses in BC patients. METHODS: Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes. RESULTS: A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib. CONCLUSIONS: This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.

Fully human anti-B7-H3 recombinant antibodies inhibited tumor growth by increasing T cell infiltration.

Mortality due to malignant tumors is one of the major factors affecting the life expectancy of the global population. Therapeutic antibodies are a cutting-edge treatment method for restricting tumor growth. B7-H3 is highly expressed in tumor tissues, but rarely in normal tissues. B7-H3 is closely associated with poor prognosis in patients with tumors. B7-H3 is an important target for antitumor therapy. In this study, the fully human anti-B7H3 single-chain antibodies (scFvs) were isolated and screened from the fully human phage immune library with B7H3 as the target. The antibodies screened from a fully human phage library had low immunogenicity and high affinity, which was more beneficial for clinical application. Leveraging B7-H3 scFvs as a foundation, we constructed two distinct recombinant antibody formats, scFv-Fc and IgG1, characterized by elevated affinity and a prolonged half-life. The results demonstrated that the recombinant antibodies had high specificity and affinity for the B7-H3 antigen and inhibited tumor cell growth by enhancing the ADCC. After treatment with anti-B7H3 recombinant antibody, the number of infiltrating T cells in the tumor increased and the secretion of IFN- γ by infiltrating T cells increased in vivo. Additionally, the use of pleural fluid samples obtained from tumor-afflicted patients revealed the ability of anti-B7-H3 recombinant antibodies to reverse CD8+ T cell exhaustion. In summary, we screened the fully human anti-B7H3 recombinant antibodies with specificity and high affinity that increase immune cell infiltration and IFN-γ secretion, thereby inhibiting tumor cell growth to a certain extent. This finding provides a theoretical basis for the development of therapeutic tumor antibodies and could help promote further development of antibody-based drugs.

NDC80 Kinetochore Complex Serves as a Potential Prognostic Predictor and Correlates with Immune Infiltrates in Epithelial Ovarian Cancer Patients.

Purpose: This study focuses on evaluating the prognostic value of the NDC80 kinetochore complex in ovarian cancer (OC) using the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database and reveals the relationship between the NDC80 complex and immune infiltrates in OC. Methods: We collected data on NDC80 complex expression levels in both OC tissues and non-OC ovarian tissues from the University of California Santa Cruz Xena and GEO databases. The clinicopathological characteristics correlated with overall survival were analyzed using Cox regression and the Kaplan-Meier method. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis and CIBERSORT were performed using data from TCGA database. Immunohistochemical staining was used to verify the higher expression level of NUF2 protein in OC in vitro. Meanwhile, we utilized the Tumor Immune Estimation Resource to analyze the correlation between the NDC80 complex and immunocyte infiltration. Results: The NDC80 complex expression level was prominently higher in OC tissues than in non-OC ovarian tissues and correlated with advanced histologic grade characteristics. Gene expression profiling interactive analysis and the Kaplan-Meier survival curve uncovered a close relationship between high expression of the NDC80 complex and poor overall survival in OC patients. The univariate Cox regression hazard model produced age, pathologic stage, tumor status, primary therapy outcome, SPC24 expression level, and Karnofsky performance score as prognostic factors for OC patients. NDC80 complex expression levels were highly associated with immune cell infiltration, showing NK CD56 bright cells and NK cells with a negative correlation and T helper 2 cells with a positive correlation (P<0.05). Conclusion: These findings provide evidence that an increased expression level of the NDC80 complex is closely associated with the progression of OC and could also serve as a novel target of immunotherapy in OC.

Constructing a screening model to obtain the functional herbs for the treatment of active ulcerative colitis based on herb-compound-target network and immuno-infiltration analysis.

The therapeutic effect of most traditional Chinese medicines (TCM) on ulcerative colitis is unclear, The objective of this study was to develop a core herbal screening model aimed at facilitating the transition from active ulcerative colitis (UC) to inactive. We obtained the gene expression dataset GSE75214 for UC from the GEO database and analysed the differentially expressed genes (DEGs) between active and inactive groups. Gene modules associated with the active group were screened using WGCNA, and immune-related genes (IRGs) were obtained from the ImmPort database. The TCMSP database was utilized to acquire the herb-molecule-target network and identify the herb-related targets (HRT). We performed intersection operations on HRTs, DEGs, IRGs, and module genes to identify candidate genes and conducted enrichment analyses. Subsequently, three machine learning algorithms (SVM-REF analysis, Random Forest analysis, and LASSO regression analysis) were employed to refine the hubgene from the candidate genes. Based on the hub genes identified in this study, we conducted compound and herb matching and further screened herbs related to abdominal pain and blood in stool using the Symmap database.Besides, the stability between molecules and targets were assessed using molecular docking and molecular dynamic simulation methods. An intersection operation was performed on HRT, DEGs, IRGs, and module genes, leading to the identification of 23 candidate genes. Utilizing three algorithms (RandomForest, SVM-REF, and LASSO) for analyzing the candidate genes and identifying the intersection, we identified five core targets (CXCL2, DUOX2, LYZ, MMP9, and AGT) and 243 associated herbs. Hedysarum Multijugum Maxim. (Huangqi), Sophorae Flavescentis Radix (Kushen), Cotyledon Fimbriata Turcz. (Wasong), and Granati Pericarpium (Shiliupi) were found to be capable of relieving abdominal pain and hematochezia during active UC. Molecular docking demonstrated that the compounds of the four aforementioned herbs showed positive docking activity with their core targets. The results of molecular dynamic simulations indicated that well-docked active molecules had a more stable structure when bound to their target complexes. The study has shed light on the potential of TCMs in treating active UC from an immunomodulatory perspective, consequently, 5 core targets and 4 key herbs has been identified. These findings can provide a theoretical basis for subsequent management and treatment of active UC with TCM, as well as offer original ideas for further research and development of innovative drugs for alleviating UC.