Project description:DNA methylation markers in thyroid tumor We used Illumina HumanMethylation EPIC bead array containing over the 850,000 CpG sites in 34 thyroid normal and tumor samples to identify thyroid subtype-specfic DNA methylation markers.
Project description:Lung adenocarcinoma (LUAD) involves complex dysregulated cellular processes, including programmed cell death (PCD), influenced by N6-methyladenosine (m6A) RNA modification. This study integrates bulk RNA and single-cell sequencing data to identify 43 prognostically valuable m6A-related PCD genes, forming the basis of a 13-gene risk model (m6A-related PCD signature [mPCDS]) developed using machine-learning algorithms, including CoxBoost and SuperPC. The mPCDS demonstrated significant predictive performance across multiple validation datasets. In addition to its prognostic accuracy, mPCDS revealed distinct genomic profiles, pathway activations, associations with the tumour microenvironment and potential for predicting drug sensitivity. Experimental validation identified RCN1 as a potential oncogene driving LUAD progression and a promising therapeutic target. The mPCDS offers a new approach for LUAD risk stratification and personalised treatment strategies.
Project description:BackgroundEndometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the precise pathogenesis of EM remains poorly understood, leading to diagnostic delays. Consequently, identifying biomarkers associated with PCD is critical for advancing the diagnosis and treatment of EM.MethodsThis study used datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) following preprocessing. By cross-referencing these DEGs with genes associated with PCD, differentially expressed PCD-related genes (DPGs) were identified. Enrichment analyses for KEGG and GO pathways were conducted on these DPGs. Additionally, Mendelian randomization and machine learning techniques were applied to identify biomarkers strongly associated with EM.ResultsThe study identified three pivotal biomarkers: TNFSF12, AP3M1, and PDK2, and established a diagnostic model for EM based on these genes. The results revealed a marked upregulation of TNFSF12 and PDK2 in EM samples, coupled with a significant downregulation of AP3M1. Single-cell analysis further underscored the potential of TNFSF12, AP3M1, and PDK2 as biomarkers for EM. Additionally, molecular docking studies demonstrated that these genes exhibit significant binding affinities with drugs currently utilized in clinical practice.ConclusionThis study systematically elucidated the molecular characteristics of PCD in EM and identified TNFSF12, AP3M1, and PDK2 as key biomarkers. These findings provide new directions for the early diagnosis and personalized treatment of EM.
Project description:BackgroundTriple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.MethodsWe identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).ResultsA total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in 'cell cycle' and 'oocyte meiosis' related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.ConclusionsThe multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients' prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.
Project description:BackgroundLiver ischemia-reperfusion injury (LIRI) is a critical condition after liver transplantation. Understanding the role of immunogenic cell death (ICD) may provide insights into its diagnosis and potential therapeutic targets.MethodsDifferentially expressed genes (DEGs) between LIRI and normal samples were identified, and pathway enrichment analyses were performed, followed by immune infiltration assessment through the CIBERSORT method. The consensus clustering analysis was conducted to separate LIRI clusters and single-sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the distinct immune states between clusters. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify hub genes associated with ICD. To establish diagnostic models, four machine learning techniques, including Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Generalized Linear Models (GLM), were applied to filter gene sets. The receiver operating characteristic (ROC) curves were utilized to assess the performance of the models.ResultsPathway enrichment results revealed significant involvement of cytokines and chemokines among DEGs of LIRI. Immune infiltration analysis indicated higher levels of specific immune functions in Cluster 2 compared to Cluster 1. WGCNA identified significant modules linked to LIRI with strong correlations between module membership and gene significance. The RF and SVM machine learning algorithms were finally chosen to construct the models. Both demonstrated high predictive accuracy for diagnosing LIRI not only in training cohort GSE151648 but also in validation cohorts GSE23649 and GSE15480.ConclusionsThe study highlights the pivotal roles of ICD-related genes in LIRI, providing diagnosis models with potential clinical applications for early detection and intervention strategies against LIRI.
Project description:Breast cancer (BC) is a lethal malignancy with a poor prognosis. Necroptosis is critical in the progression of cancer. However, the expression of genes involved in necroptosis in BC and their association with prognosis remain unclear. We investigated the predictive potential of necroptosis-related genes in BC samples from the TCGA dataset. We used LASSO regression to build a risk model consisting of twelve necroptosis-related genes in BC. Using the necroptosis-related risk model, we were able to successfully classify BC patients into high- and low-risk groups with significant prognostic differences (p = 4.872 × 10 -7). Additionally, we developed a matched nomogram predicting 5, 7, and 10-year overall survival in BC patients based on this necroptosis-related risk model. Our next step was to perform multiple GSEA analyses to explore the biological pathways through which these necroptosis-related risk genes influence cancer progression. For these twelve risk model genes, we analyzed CNV, SNV, OS, methylation, immune cell infiltration, and drug sensitivity in pan-cancer. In addition, immunohistochemical data from the THPA database were used to validate the protein expression of these risk model genes in BC. Taken together, we believe that necroptosis-related genes are considered potential therapeutic targets in BC and should be further investigated.
Project description:Programmed cell death (PCD) is thought to have multiple roles in tumors. Here, the roles of PCD-related genes were comprehensively analyzed to evaluate their values in hepatocellular carcinoma (HCC) diagnosis and prognosis. Gene expression and single-cell data of HCC patients, and PCD-related genes were collected from public databases. The diagnostic and prognostic roles of differentially expressed PCD-related genes in HCC were explored by univariate and multivariate Cox regression analyses. Single-cell data were further analyzed for the immune cells and expression of feature genes. Finally, we evaluated the expression of genes by quantitative real-time polymerase chain reaction and Western blot, and the proportion of immune cells was detected by flow cytometry in HCC samples. We obtained 52 differentially expressed PCD-related genes in HCC, based on which the consensus clustering analysis cluster 2 was found to have a worse prognosis than cluster 1. Then 10 feature genes were identified using LASSO analysis, and programmed cell death index (PCDI) was calculated to divided HCC patients into high-PCDI and low-PCDI groups. Worse prognosis was observed in high-PCDI group. Cox regression analysis showed that PCDI is an independent prognostic risk factor for HCC patients. Additionally, SERPINE1 and G6PD of feature genes significantly affect patient survival. Macrophages and Tregs were significantly positively correlated with PCDI. G6PD mainly expressed in macrophages, SERPINE1 mainly expressed in fibroblast. The experimental results confirmed the high expression of SERPINE1 and G6PD in HCC compared with the control, and the infiltration level of macrophages and Treg in HCC was also obviously elevated. PCDI may be a new predictor for the diagnosis of patients with HCC. The association of SERPINE1 and G6PD with the immune environment will provide new clues for HCC therapy.
Project description:BackgroundDermatomyositis (DM) is an autoimmune disease mainly diagnosed by its symptoms and a physical examination, with only some subtypes of DM showing clear molecular changes. To date, few biomarkers have been identified to assess DM progression. Autophagy-related genes have been significantly correlated with inflammation, several types of autoimmune diseases, and the immune response, but few studies have explored the role of autophagy-related genes in DM. Therefore, this study aimed to investigate the roles of autophagy-related genes in DM.MethodsWe collected three datasets of dermatomyositis-related transcriptome from the Gene Expression Omnibus (GEO) database: GSE1551, GSE46239, and GSE143323 and analyzed the differentially expressed genes (DEGs). We also conducted functional enrichment analyses with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. To explore whether the autophagy-related genes were differentially expressed in DM compared with normal samples, we performed an intersection between the DEGs and autophagy-related genes obtained from the Human Autophagy Database (HADb, http://www.autophagy.lu/). Finally, we used selected autophagy-related genes as biomarkers for diagnosing and analyzing the correlation with immune cell infiltration.ResultsOur results showed that 143 genes were upregulated, and 14 were downregulated in the DM samples compared with healthy samples. The functional enrichment analysis revealed that these DEGs played a significant role in the type I interferon signaling pathway, cytokine activity, chemokine activity, double-stranded RNA binding, and blood microparticles. The intersection results identified CCL2, CDKN1A, FOS, MYC, and TNFSF10 as the primary autophagy-related genes in DM. All showed significantly increased expressions in DM samples compared with healthy samples. We were also curious to investigate immune cell infiltration in DM. Our results showed that the selected autophagy-related genes significantly influenced the infiltration of multiple immune cells, such as B cells, macrophages, and natural killer cells. Finally, we assessed the diagnostic sensitivity of CCL2, CDKN1A, FOS, MYC, and TNFSF10 for DM. The results showed the area under the curve (AUC) values of the ROC were 0.855, 0.889, 0.744, 0.826, and 0.816, respectively. The combined genes' diagnostic AUC value was 0.951.ConclusionsCCL2, CDKN1A, FOS, MYC, and TNFSF10 are potential diagnostic biomarkers for DM.
Project description:We examined the role of differentially expressed autophagy-related genes (DEARGs) in clear cell Renal Cell Carcinoma (ccRCC) using high-throughput RNA-seq data from The Cancer Genome Atlas (TCGA). Cox regression analyses showed that 5 DEARGs (PRKCQ, BID, BAG1, BIRC5, and ATG16L2) correlated with overall survival (OS) and 4 DEARGs (EIF4EBP1, BAG1, ATG9B, and BIRC5) correlated with disease-free survival (DFS) in ccRCC patients. Multivariate Cox regression analysis using the OS and DFS prognostic risk models showed that expression of the nine DEARGs accurately and independently predicted the risk of disease recurrence or progression in ccRCC patients (area under curve or AUC values > 0.70; all p < 0.05). Moreover, the DEARGs accurately distinguished healthy individuals from ccRCC patients based on receiver operated characteristic (ROC) analyses (area under curve or AUC values > 0.60), suggesting their potential as diagnostic biomarkers for ccRCC. The expression of DEARGs also correlated with the drug sensitivity of ccRCC cell lines. The ccRCC cell lines were significantly sensitive to Sepantronium bromide, a drug that targets BIRC5. This makes BIRC5 a potential therapeutic target for ccRCC. Our study thus demonstrates that DEARGs are potential diagnostic and prognostic biomarkers and therapeutic targets in ccRCC.
Project description:Failure of the normal process of cell death pathways contributes to the defection of immune systems and the occurrence of cancers. The key genes, the multimolecular mechanisms, and the immune functions of these genes in pan-cancers remain unclear. Using online databases of The Cancer Genome Atlas, GEPIA2, TISIDB, HPA, Kaplan-Meier Plotter, PrognoScan, cBioPortal, GSCALite, TIMER, and Sangerbox, we identified the key genes from the six primary cell death-related pathways and performed a comprehensive analysis to investigate the multimolecular characteristics and immunological functions of the hub genes in 33 human cancers. We identified five hub genes in the six primary cell death-related pathways (JUN, NFKB1, CASP3, PARP1, and TP53). We found that CASP3, PARP1, and TP53 were overexpressed in 28, 23, and 27 cancers. The expression of the five genes was associated with the development and prognosis of many cancers. Particularly, JUN, NFKB1, CASP3, and TP53 have prognostic values in Brain Lower Grade Glioma (LGG), while PARP1 and CASP3 could predict the survival outcomes in Adrenocortical carcinoma (ACC). In addition, an extensive association between five genes' expression, DNA methylation, and tumor-immune system interactions was noticed. The five cell death-related hub genes could function as potential biomarkers for various cancers, particularly LGG and ACC. The immunological function analysis of the five genes also proposes new targets for developing immunosuppressants and improving the immunotherapy efficacy of cancers. However, further extensive clinical and experimental research are required to validate their clinical values.