Identification of Paxillin as a Prognostic Factor for Glioblastoma via Integrated Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Huang Z
PROVIDER: S-EPMC9246607 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeOvarian cancer is the most lethal gynecologic malignancy. Resistance to platinum-based chemotherapy affects the overall survival of patients. This study used an integrated bioinformatics to find the poorly understood molecular mechanisms underlying platinum resistance in ovarian cancer.MethodsBased on the RNA-seq data of tissues in The Cancer Genome Atlas (TCGA) and RNA-seq data of cells from the Cancer Cell Encyclopedia (CCLE), we integrated differentially expressed genes (DEGs) in ovarian cancer tissue and cells. After screening for DEGs related to platinum resistance, we conducted survival analysis and built protein interaction networks to identify genes that may affect prognosis and interact with each other. Least absolute shrinkage and selection operator (Lasso) regression analysis was used to construct a predictive model. Immunohistochemistry and Western blot were used to validate the results. Finally, gene set enrichment analysis (GSEA) was performed on the expression of genes individually.ResultsWe found that ATPase Na+/K+ transporting subunit alpha 2 (ATP1A2), calsequestrin 2 (CASQ2) and ryanodine receptor 2 (RYR2) interacted with each other and could predict resistance to platinum-based therapy, correlating negatively with prognosis. Moreover, we constructed a predictive model based on nine genes, including ATP1A2 and CASQ2. Immunohistochemistry and Western blot validated the upregulation of these genes in ovarian cancer tissue samples and cell lines. The immunohistochemistry results also confirmed the prognostic value of ATP1A2, CASQ2 and RYR2. GSEA predicted that ATP1A2, CASQ2 and RYR2 may act on the KRAS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells.ConclusionATP1A2, CASQ2 and RYR2 were highly expressed in platinum-resistant ovarian cancer. ATP1A2 and CASQ2 were related to the prognosis of platinum-resistant ovarian cancer patients. These genes might act on KARS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells.
| S-EPMC8607279 | biostudies-literature
Project description:Glioblastoma (GBM) is one of the most common and fatal malignancies worldwide, while its prognostic biomarkers are still being explored. This study aims to identify potential genes with clinical and prognostic significance by integrating bioinformatics analysis and investigating their function in HNSCC. Based on the Single-cell RNA sequencing (scRNA-seq) results of H3K27M-glioma cells, computational bioinformatics methods were employed for selecting prognostic biomarker for GBM. The protein NPC2 (NPC Intracellular Cholesterol Transporter 2), which has been shown to be related to lipoprotein metabolism and innate immune system, was identified to be upregulated in GBM. NPC2 showed a relatively higher expression in GBM samples, and a negative correlation with tumor purity and tumor infiltrating immune cells. Additionally, NPC2 was knocked down in U87-MG and U251 cells line, and cell proliferation and migration capability were evaluated with CCK-8, scratch and transwell assay, respectively. Cytological experiments has shown that NPC2 overexpression inhibited GBM cells proliferation and migration, indicating its important role in GBM progression. This is the first investigation into the prognostic value of NPC2 interact with GBM. The potential molecular factor NPC2 have been identified as a prognostic biomarker for GBM.
| S-EPMC7990766 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most prevalent tumor in lung carcinoma cases and threatens human life seriously worldwide. Here we attempt to identify a prognostic biomarker and potential therapeutic target for LUAD patients.MethodsDifferentially expressed genes (DEGs) shared by GSE18842, GSE75037, GSE101929 and GSE19188 profiles were determined and used for protein-protein interaction analysis, enrichment analysis and clinical correlation analysis to search for the core gene, whose expression was further validated in multiple databases and LUAD cells (A549 and PC-9) by quantitative real-time PCR (qRT-PCR) and western blot analyses. Its prognostic value was estimated using the Kaplan-Meier method, meta-analysis and Cox regression analysis based on the Cancer Genome Atlas (TCGA) dataset and co-expression analysis was conducted using the Oncomine database. Gene Set Enrichment Analysis (GSEA) was performed to illuminate the potential functions of the core gene.ResultsA total of 115 shared DEGs were found, of which 24 DEGs were identified as candidate hub genes with potential functions associated with cell cycle and FOXM1 transcription factor network. Among these candidates, HMMR was identified as the core gene, which was highly expressed in LUAD as verified by multiple datasets and cell samples. Besides, high HMMR expression was found to independently predict poor survival in patients with LUAD. Co-expression analysis showed that HMMR was closely related to FOXM1 and was mainly involved in cell cycle as suggested by GSEA.ConclusionHMMR might be served as an independent prognostic biomarker for LUAD patients, which needs further validation in subsequent studies.
| S-EPMC8710063 | biostudies-literature
Project description:BackgroundPhosphatidylinositol binding clathrin assembly protein interacting mitotic regulator (PIMREG) expression is upregulated in a variety of cancers. However, its potential role in breast cancer (BC) remains uncertain.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to gather relevant information. The expression of PIMREG and its clinical implication in BC were assessed by using Wilcoxon rank-sum test. The prognostic value of PIMREG in BC was evaluated through the Cox regression model and nomogram, and visualized by Kaplan-Meier survival curves. Genes/proteins that interact with PIMREG in BC were also identified through GeneMANIA and MaxLink. Gene set enrichment analysis (GSEA) was then performed. The correlations of the immune cell infiltration and immune checkpoints with the expression of PIMREG in BC were explored via TIMER, TISIDB, and GEPIA. Potential drugs that interact with PIMREG in BC were explored via Q-omic. The siRNA transfection, CCK-8, and transwell migration assay were conducted to explore the function of PIMREG in cell proliferation and migration.ResultsPIMREG expression was significantly higher in infiltrating ductal carcinoma, estrogen receptor negative BC, and progestin receptor negative BC. High expression of PIMREG was associated with poor overall survival, disease-specific survival, and progression-free interval. A nomogram based on PIMREG was developed with a satisfactory prognostic value. PIMREG also had a high diagnostic ability, with an area under the curve of 0.940. Its correlations with several immunomodulators were also observed. Immune checkpoint CTLA-4 was significantly positively associated with PIMREG. HDAC2 was found as a potentially critical link between PIMREG and BRCA1/2. In addition, PIMREG knockdown could inhibit cell proliferation and migration in BC.ConclusionsThe high expression of PIMREG is associated with poor prognosis and immune checkpoints in BC. HDAC2 may be a critical link between PIMREG and BRCA1/2, potentially a therapeutic target.
| S-EPMC10358341 | biostudies-literature
Project description:BackgroundColon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.MethodsThe gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.ResultsFive colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients' age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.ConclusionsThe 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.
| S-EPMC7807455 | biostudies-literature
Project description:BackgroundLung cancer is the most common cause of cancer-related death among all human cancers and the five-year survival rates are only 23%. The precise molecular mechanisms of non-small cell lung cancer (NSCLC) are still unknown. The aim of this study was to identify and validate the key genes with prognostic value in lung tumorigenesis.MethodsFour GEO datasets were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software and Molecular Complex Detection (MCODE) were utilized to PPI network to pick out meaningful DEGs. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database. The expressions and prognostic values of hub genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier plotter. Finally, quantitative PCR and the Oncomine database were used to verify the differences in the expression of hub genes in lung cancer cells and tissues.ResultsA total of 121 DEGs (49 upregulated and 72 downregulated) were identified from four datasets. The PPI network was established with 121 nodes and 588 protein pairs. Finally, AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 were selected by Cytohubba, and they all correlated with worse overall survival (OS) in NSCLC.ConclusionThe results showed that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC.Key pointsOur results indicated that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. Our methods showed a new way to explore the key genes in cancer development.
| S-EPMC7113067 | biostudies-literature
Project description:BackgroundGastric cancer is one of the most common malignancies worldwide. Although the diagnosis and treatment of this disease have substantially improved in recent years, the five-year survival rate of gastric cancer is still low due to local recurrence and distant metastasis. An in-depth study of the molecular pathogenesis of gastric cancer and related prognostic markers will help improve the quality of life and prognosis of patients with this disease. The purpose of this study was to identify and verify key SNPs in genes with prognostic value for gastric cancer.MethodsSNP-related data from gastric cancer patients were obtained from The Cancer Genome Atlas (TCGA) database, and the functions and pathways of the mutated genes were analyzed using DAVID software. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized by Cytoscape software, and molecular complex detection (MCODE) was used to screen the PPI network to extract important mutated genes. Ten hub genes were identified using cytoHubba, and the expression levels and the prognostic value of the central genes were determined by UALCAN and Kaplan-Meier Plotter. Finally, quantitative PCR and Western blotting were used to verify the expression of the hub genes in gastric cancer cells.ResultsFrom the database, 945 genes with mutations in more than 25 samples were identified. The PPI network had 360 nodes and 1616 edges. Finally, cytoHubba identified six key genes (TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4), and their expression levels were closely related to the survival rate of gastric cancer patients.ConclusionOur results indicate that TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4 may be key genes for the development and prognosis of gastric cancer. Our research provides an important bioinformatics foundation and related theoretical foundation for further exploring the molecular pathogenesis of gastric cancer and evaluating the prognosis of patients.
| S-EPMC8112818 | biostudies-literature
Project description:Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.
| S-EPMC9260928 | biostudies-literature
Project description:Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase II? (TOP2A) predicted poor overall survival in HCC patients (all log-rank P < 0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.
| S-EPMC6893264 | biostudies-literature
Project description:BackgroundEndometrial carcinoma (EC) is one of the most common malignant tumors in gynecology. The potential functions and mechanisms of long noncoding RNAs (lncRNAs) in the occurrence and progression of EC remains unclear. It's meaningful to explore lncRNAs signature for providing prognostic value of EC.MethodsThe differentially expressed lncRNAs and their prognostic values in EC were investigated based on The Cancer Genome Atlas (TCGA) database; the transcriptional factors (TFs), the competing endogenous RNA (ceRNA) mechanism, functional regulatory network and immune infiltration of RP11-89K21.1 and RP11-357H14.17 were further explored by various bioinformatics tools and databases.ResultsWe firstly identified high expression of RP11-89K21.1 and RP11-357H14.17 were closely associated with shorten overall survival (OS) and poor prognosis in patients with EC. We also elucidated the networks of transcription factor and co-expression genes associated with RP11-89K21.1 and RP11-357H14.17. Furthermore, the ceRNA network mechanism was successfully constructed through 2 lncRNAs (RP11-89K21.1 and RP11-357H14.17), 11 miRNAs and 183 mRNAs. Functional enrichment analysis revealed that the targeting genes of RP11-89K21.1 and RP11-357H14.17 were strongly associated with microRNAs in cancer, vessel development, growth regulation, growth factor and cell differentiation, and involved in pathways including pathways in cancer, microRNAs in cancer and apoptotic signaling pathway.ConclusionsWe demonstrated for the first time that RP11-89K21.1 and RP11-357H14.17 may play crucial roles in the occurrence, development and malignant biological behavior of EC, and can be regarded as potential prognostic biomarkers for EC.
| S-EPMC7313119 | biostudies-literature