A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.
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ABSTRACT: Background
This study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).Methods
Cluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.Results
We ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.Conclusion
Our model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.
SUBMITTER: Gao L
PROVIDER: S-EPMC9775865 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.
Gao Lulu L Wang Xiangdong X Wang Xuehai X Wang Fengxu F Tang Juan J Ji Jinfeng J
Frontiers in oncology 20221208
<h4>Background</h4>This study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).<h4>Methods</h4>Cluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug ...[more]
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Project description:Necroptosis is a recently identified caspase-independent form of cell death which plays a significant role in the onset and progression of cancer. MicroRNAs (miRNAs) are vital for the development of uterine corpus endometrial carcinoma (UCEC) because they are an important regulatory component in necroptosis. This study developed a new necroptosis-related miRNAs profile to predict the prognosis of patients with UCEC. The TCGA-UCEC cohort's RNA sequencing data, consisting of 534 tumor samples and 33 normal samples, was downloaded. Ten differentially expressed miRNAs related to necroptosis were identified. A prediction model for necroptosis-related miRNAs was then created through COX regression and nomograms analysis. Clinical and pathological parameters were integrated to construct a nomogram and evaluate the model. Prognosis-related miRNAs were further used to predict target genes, and functional analysis was conducted to explore the potential mechanisms of these target genes. Subsequently, immune infiltration analysis was performed using transcriptome data to identify immune genes associated with prognosis, and the expression levels of target gene was validated using UCEC tissues. We identified 7 up-regulated miRNAs (hsa-miR-577, hsa-miR-7-5p, hsa-miR-210-3p, hsa-miR-210-5p, hsa-miR-200a-5p, hsa-miR-141-3p, hsa-miR-425-5p) and 3 down-regulated miRNAs (hsa-miR-7-2-3p, hsa-miR-383-5p, hsa-miR-29a-3p). The risk signature was based on univariate and multivariate COX analyses, constructed using 2 independent prognostic factors and miRNAs (hsa-miR-425-5p, hsa-miR-7-5p) associated with necroptosis. Nomograms demonstrated the prognostic value of risk level, age, FIGO stage, and histological type. Kaplan-Meier analysis revealed significant differences in overall survival (OS) outcomes associated with the expression of hsa-miR-425-5p (P < 0.001) and hsa-miR-7-5p (P = 0.015). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) investigations indicated that these miRNAs play crucial roles in tumor development, metastasis, and prognosis. Immune infiltration analysis showed decreased infiltration of CD8+ T cells, CD8+ T cells, NK cells, and M1 macrophages in normal tissues. Subsequently, a necroptosis-related immune gene significantly associated with prognosis (THRB) was identified, western blot and immunohistochemical staining confirmed the differential expression of THRB in normal endometrial tissues and tumor. Our findings demonstrate a close association between necroptosis and UCEC. The two necroptosis-related miRNAs used in this study may serve as valuable prognostic markers for UCEC patients, and are associated with immune cell infiltration. This suggests that necroptosis may be involved in the development of UCEC through its interaction with immune responses.
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Project description:Background: As the fourth most common malignant tumors in women, uterine corpus endometrial carcinoma (UCEC) requires novel and reliable biomarkers for prognosis prediction to improve the overall survival. Oxidative phosphorylation (OXPHOS) is found to be strongly correlated with the progression of tumor. Here, we aimed to construct an OXPHOS-related and immune microenvironment prognostic signature to stratify UCEC patients for optimization of treatment strategies. Method: Prognosis-associated OXPHOS-related differentially expressed genes were identified by multivariable Cox regression from TCGA-UCEC cohort. Based on the candidate genes, an OXPHOS-related prognostic signature was constructed by the train set data and verified by the entire set. When integrated with relevant clinical characteristics, a nomogram was also created for clinical application. Through comparison of tumor microenvironment between different risk groups, the underlying mechanism of the model and the inner correlation between immune microenvironment and energy metabolism were further investigated. Results: An OXPHOS-related signature containing ATP5IF1, COX6B1, FOXP3, and NDUFB11 was constructed and had better predictive ability compared with other recently published signatures in UCEC. Patients with lower risk score showed higher immune cell infiltration, higher ESTIMATE score (p = 2.808E-18), lower tumor purity (p = 2.808E-18), higher immunophenoscores (IPSs) (p < 0.05), lower expression of mismatch repair (MMR) proteins (p < 0.05), higher microsatellite instability (MSI), lower expression of markers of N6-methyladenosine (m6A) mRNA methylation regulators, higher tumor mutation burden (TMB) (p = 1.278E-9), and more sensitivity to immune checkpoint blockade (ICB) (p < 0.001) and chemotherapy drugs, thus, possessing improved prognosis. Conclusion: An OXPHOS-related and immune microenvironment prognostic signature classifying EC patients into different risk subsets was constructed in our study, which could be used to predict the prognosis of patients and help to select a specific subset of patients who might benefit from immunotherapy and chemotherapy, thus, improving the overall survival rate of UCEC. These findings may contribute to the discovery of novel and robust biomarkers or target therapy in UCEC and give new insights into the molecular mechanism of tumorigenesis and progression of UCEC.
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Project description:Uterine corpus endometrial carcinoma (UCEC) is one of the most common malignancies of the female genital tract. A recently discovered protein-coding gene, PPP1R14B, can inhibit protein phosphatase 1 (PP1) as well as different PP1 holoenzymes, which are important proteins regulating cell growth, the cell cycle, and apoptosis. However, the association between PPP1R14B expression and UCEC remains undefined. The expression profiles of PPP1R14B in multiple cancers were analysed based on TCGA and GTE databases. Then, PPP1R14B expression in UCEC was investigated by gene differential analysis and single gene correlation analysis. In addition, we performed gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis, and Kaplan-Meier survival analysis to predict the potential function of PPP1R14B and its role in the prognosis of UCEC patients. Then, a tool for predicting the prognosis of UCEC, namely, a nomogram model, was constructed. PPP1R14B expression was higher in UCEC tumour tissues than in normal tissues. The results revealed that PPP1R14B expression was indeed closely associated with tumour development. The results of Kaplan-Meier plotter data indicated that patients with high PPP1R14b expression had poorer overall survival, disease-specific survival, and progression-free interval than those with low expression. A nomogram based on the results of multifactor Cox regression was generated. PPP1R14B is a key player in UCEC progression, is associated with a range of adverse outcomes, and can serve as a prognostic marker in the clinic.
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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.
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