Construction of an immune-related gene prognostic model for obese endometrial cancer patients based on bioinformatics analysis
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ABSTRACT:
SUBMITTER: Tong Y
PROVIDER: S-EPMC11336703 | biostudies-literature | 2024 Jul
REPOSITORIES: biostudies-literature
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Project description:ObjectFocus on immune-related gene pairs (IRGPs) and develop a prognostic model to predict the prognosis of patients with lung adenocarcinoma (LUAD).MethodsFirst, the LUAD patient dataset was downloaded from The Cancer Genome Atlas database, and paired analysis of immune-related genes was subsequently conducted. Then, LASSO regression was used to screen prognostic IRGPs for building a risk prediction model. Meanwhile, the Gene Expression Omnibus database was used for external validation of the model. Next, the clinical predictive power of IRGPs features was assessed by uni-multivariate Cox regression analysis, the infiltration of key immune cells in high and low IRGPs risk groups was analyzed with CIBERSORT, quanTIseq, and Timer, and the key pathways enriched for IRGPs were assessed using the Kyoto Encyclopedia of Genes and Genomes. Finally, the expression and related functions of key immune cells and genes were verified by immunofluorescence and cell experiments of tissue samples.ResultsIt was revealed that the risk score of 19 IRGPs could be used as accurate indicators to evaluate the prognosis of LUAD patients, and the risk score was mainly related to T cell infiltration based on CIBERSORT analysis. Two genes of IRGPs, IL6, and CCL2, were found to be closely associated with the expression of PD-1/PD-L1 and the function of T-cells. Depending on the results of tissue immunofluorescence, IL6, CCL2, and T cells were highly expressed in the LUAD tissues of patients. Furthermore, IL6 and CCL2 were positively correlated with the expression of T cells. Besides, qRT-PCR assay in four different LUAD cells proved that IL6 and CCL2 were positively correlated with the expression of PD-L1 (P < .001).ConclusionsBased on 19 IRGPs, an effective prognosis model was established to predict the prognosis of LUAD patients. In addition, IL6 and CCL2 are closely related to the function of T-cells.
| S-EPMC9834935 | biostudies-literature
Project description:N-7 methylguanine (m7G) is one of the most common RNA base modifications in post-transcriptional regulation, which participates in multiple processes such as transcription, mRNA splicing and translation during the mRNA life cycle. However, its expression and prognostic value in uterine corpus endometrial carcinoma (UCEC) have not been systematically studied. In this paper, the data such as gene expression profiles, clinical data of UCEC patients, somatic mutations and copy number variants (CNVs) are obtained from the cancer genome atlas (TCGA) and UCSC Xena. By analyzing the expression differences of m7G-related mRNA in UCEC and plotting the correlation network maps, a risk score model composed of four m7G-related mRNAs (NSUN2, NUDT3, LARP1 and NCBP3) is constructed using least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression in order to identify prognosis and immune response. The correlation of clinical prognosis is analyzed between the m7G-related mRNA and UCEC via Kaplan-Meier method, receiver operating characteristic (ROC) curve, principal component analysis (PCA), t-SNE, decision curve analysis (DCA) curve and nomogram etc. It is concluded that the high risk is significantly correlated with (P < 0.001) the poorer overall survival (OS) in patients with UCEC. It is one of the independent risk factors affecting the OS. Differentially expressed genes are identified by R software in the high and low risk groups. The functional analysis and pathway enrichment analysis have been performed. Single sample gene set enrichment analysis (ssGSEA), immune checkpoints, m6A-related genes, tumor mutation burden (TMB), stem cell correlation, tumor immune dysfunction and rejection (TIDE) scores and drug sensitivity are also used to study the risk model. In addition, we have obtained 3 genotypes based on consensus clustering, which are significantly related to (P < 0.001) the OS and progression-free survival (PFS). The deconvolution algorithm (CIBERSORT) is applied to calculate the proportion of 22 tumor infiltrating immune cells (TIC) in UCEC patients and the estimation algorithm (ESTIMATE) is applied to work out the number of immune and matrix components. In summary, m7G-related mRNA may become a potential biomarker for UCEC prognosis, which may promote UCEC occurrence and development by regulating cell cycles and immune cell infiltration. It is expected to become a potential therapeutic target of UECE.
| S-EPMC9637130 | biostudies-literature
Project description:Endometrial cancer (EC) is one of the most common female reproductive system tumors, with close to 200,000 new cases each year. It accounts for approximately 7% of the total number of female cancers, but until now the cause of EC has remained unclear. Ferroptosis is regulated cell death that distinguishes apoptosis and caused by oxidative damage. The process has unique biological effects on metabolism and redox biology. In this study, we analyzed the relationship between EC and ferroptosis. According to the different expression levels of related genes, we first divided 544 EC samples into four clusters and found that most of the infiltrating immune cells were significantly different among the four groups. A differential gene expression analysis between Fe.cluster groups was performed, and the samples were again divided into three Fe.gene.cluster groups. The molecular characteristics and clinical characteristics of the groups were significantly different. Finally, 13 characteristic genes were selected as ferroptosis gene signatures, and the Fe.score was obtained by calculation. The Fe.score is closely related to the clinical and molecular characteristics of EC, and a low Fe.score has a significant survival advantage. The GDSC predicts that the IC50 of multiple chemotherapeutic drugs is also significantly different between the two groups. In conclusion, our research has explored the relationship between EC and ferroptosis in detail, provides comprehensive insights for ferroptosis-mediated EC mechanism research, and emphasizes the clinical application potential of Fe.score-based immunotherapy strategies.
| S-EPMC8505971 | biostudies-literature
Project description:Background Glioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued. Purpose Here, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM. Methods Glioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB). Results Six IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB. Conclusion Herein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research.
| S-EPMC9366078 | biostudies-literature
Project description:BackgroundOral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.MethodsThis work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.ResultsThe present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.ConclusionIn this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.
| S-EPMC9789624 | biostudies-literature
Project description:Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients' survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.
| S-EPMC9554662 | biostudies-literature
Project description:To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC). Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and Immunology Database and Analysis Portal (ImmPort) were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452. In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic (ROC) curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group. The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.
| S-EPMC8252190 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the most common malignant tumors of the digestive system. Chinese cases of GC account for about 40% of the global rate, with approximately 1.66 million people succumbing to the disease each year. Despite the progress made in the treatment of GC, most patients are diagnosed at an advanced stage due to the lack of obvious clinical symptoms in the early stages of GC, and their prognosis is still very poor. The m7G modification is one of the most common forms of base modification in post-transcriptional regulation, and it is widely distributed in the 5' cap region of tRNA, rRNA, and eukaryotic mRNA.MethodsRNA sequencing data of GC were downloaded from The Cancer Genome Atlas. The differentially expressed m7G-related genes in normal and tumour tissues were determined, and the expression and prognostic value of m7G-related genes were systematically analysed. We then built models using the selected m7G-related genes with the help of machine learning methods.The model was then validated for prognostic value by combining the receiver operating characteristic curve (ROC) and forest plots. The model was then validated on an external dataset. Finally, quantitative real-time PCR (qPCR) was performed to detect gene expression levels in clinical gastric cancer and paraneoplastic tissue.ResultsThe model is able to determine the prognosis of GC samples quantitatively and accurately. The ROC analysis of model has an AUC of 0.761 and 0.714 for the 3-year overall survival (OS) in the training and validation sets, respectively. We determined a correlation between risk scores and immune cell infiltration and concluded that immune cell infiltration affects the prognosis of GC patients. NUDT10, METTL1, NUDT4, GEMIN5, EIF4E1B, and DCPS were identified as prognostic hub genes and potential therapeutic agents were identified based on these genes.ConclusionThe m7G-related gene-based prognostic model showed good prognostic discrimination. Understanding how m7G modification affect the infiltration of the tumor microenvironment (TME) cells will enable us to better understand the TME's anti-tumor immune response, and hopefully guide more effective immunotherapy methods.
| S-EPMC9281447 | biostudies-literature
Project description:Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer. Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential. Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups. Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer.
| S-EPMC9800979 | biostudies-literature
Project description:BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan-Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related "lncRNA-mRNA" co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.
| S-EPMC8419360 | biostudies-literature