Dataset Information

A PARP1-related prognostic signature constructing and PARP-1 inhibitors screening for glioma.

ABSTRACT: The current standard treatments of glioma include surgical resection, supplemented with radiotherapy and chemotherapy, but the prognosis is poor. PARP-1 (Poly ADP-ribose polymerase 1) is a hot spot for cancer-targeted therapy and was reported to be significantly elevated in glioma. In this study, we analyzed the role of PARP-1 in DNA damage repair, constructed a PARP1-related DNA-repair prognostic signature (DPS), and screened targeted drugs for glioma. RNA-seq data of 639 glioma samples were downloaded from the GEO (Gene Expression Omnibus) database and divided into PARP1_H and PARP1_L according to the front and rear thirds of the expression level of PARP-1. First, we systematically analyzed the influence of PARP-1 on DNA damage repair, prognosis, and chemoradiotherapy sensitization of glioma. All glioma patients and patients with radiotherapy or chemotherapy had a better prognosis in PARP1_L than in PARP1_H. Next, differentially expressed DNA-repair related genes (DEGs) were identified between PARP1_H and PARP1_L by LASSO (Least Absolute Shrinkage and Selection Operator) Cox analysis and applied for constructing DPS. Based on the four-gene DPS, we then developed a new nomogram to assess overall survival in glioma patients. Additionally, PARP-1 was proved an effective target for glioma therapy. So, a series of computer-aided techniques, including Discovery Studio 4.5, Schrodinger, and PyMol, were applied for the virtual screening of favorable PARP-1 inhibitors. In conclusion, this study investigated the effect of PARP-1 on glioma prognosis and the sensitization effect of radiotherapy and chemotherapy, established a novel nomogram to evaluate the overall survival of glioma patients, and further explored targeted therapy for glioma.

SUBMITTER: Li H

PROVIDER: S-EPMC9450093 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A PARP1-related prognostic signature constructing and PARP-1 inhibitors screening for glioma.

Li Hui H Wang Zhenhua Z Hou Yuanyuan Y Xi Jianxin J He Zhenqiang Z Lu Han H Du Zhishan Z Zhong Sheng S Yang Qunying Q

Frontiers in cell and developmental biology 20220824

The current standard treatments of glioma include surgical resection, supplemented with radiotherapy and chemotherapy, but the prognosis is poor. PARP-1 (Poly ADP-ribose polymerase 1) is a hot spot for cancer-targeted therapy and was reported to be significantly elevated in glioma. In this study, we analyzed the role of PARP-1 in DNA damage repair, constructed a PARP1-related DNA-repair prognostic signature (DPS), and screened targeted drugs for glioma. RNA-seq data of 639 glioma samples were do ...[more]

PMID: 36092717

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Project description:Introduction: The effect of the conventional treatment methods of glioblastoma (GBM) is poor and the prognosis of patients is poor. The expression of MCL-1 in GBM is significantly increased, which shows a high application value in targeted therapy. In this study, we predicted the prognosis of glioblastoma patients, and therefore constructed MCL-1 related prognostic signature (MPS) and the development of MCL-1 small molecule inhibitors. Methods: In this study, RNA-seq and clinical data of 168 GBM samples were obtained from the TCGA website, and immunological analysis, differential gene expression analysis and functional enrichment analysis were performed. Subsequently, MCL-1-associated prognostic signature (MPS) was constructed and validated by LASSO Cox analysis, and a nomogram was constructed to predict the prognosis of patients. Finally, the 17931 small molecules downloaded from the ZINC15 database were screened by LibDock, ADME, TOPKAT and CDOCKER modules and molecular dynamics simulation in Discovery Studio2019 software, and two safer and more effective small molecule inhibitors were finally selected. Results: Immunological analysis showed immunosuppression in the MCL1_H group, and treatment with immune checkpoint inhibitors had a positive effect. Differential expression gene analysis identified 449 differentially expressed genes. Build and validate MPS using LASSO Cox analysis. Use the TSHR HIST3H2A, ARGE OSMR, ARHGEF25 build risk score, proved that low risk group of patients prognosis is better. Univariate and multivariate analysis proved that risk could be used as an independent predictor of patient prognosis. Construct a nomogram to predict the survival probability of patients at 1,2,3 years. Using a series of computer-aided techniques, two more reasonable lead compounds ZINC000013374322 and ZINC000001090002 were virtually selected. These compounds have potential inhibitory effects on MCL-1 and provide a basis for the design and further development of MCL-1 specific small molecule inhibitors. Discussion: This study analyzed the effect of MCL-1 on the prognosis of glioblastoma patients from the perspective of immunology, constructed a new prognostic model to evaluate the survival rate of patients, and further screened 2 MCL-1 small molecule inhibitors, which provides new ideas for the treatment and prognosis of glioblastoma.

Project description:BackgroundCurrently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC.MethodsImmune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples.ResultsThe IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher.ConclusionIn summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

Dataset Information

A PARP1-related prognostic signature constructing and PARP-1 inhibitors screening for glioma.

Publications

A PARP1-related prognostic signature constructing and PARP-1 inhibitors screening for glioma.

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