Integrated Bioinformatic Analysis of the Expression and Prognosis of Caveolae-Related Genes in Human Breast Cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Tian Y
PROVIDER: S-EPMC8427033 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In the microenvironment of breast cancer, immune cell infiltration is associated with an improved prognosis. To identify immune-related prognostic markers and therapeutic targets, we determined the lymphocyte-specific kinase (LCK) metagene scores of samples from breast cancer patients in The Cancer Genome Atlas. The LCK metagene score correlated highly with other immune-related scores, as well as with the clinical stage, prognosis and tumor suppressor gene mutation status (BRCA2, TP53, PTEN) of patients in the four breast cancer subtypes. A weighted gene co-expression network analysis was performed to detect representative genes from LCK metagene-related gene modules. In two of these modules, the levels of the co-expressed genes correlated highly with LCK metagene levels, so we conducted an enrichment analysis to discover their functions. We also identified differentially expressed genes in samples with high and low LCK metagene scores. By examining the overlapping results from these analyses, we obtained 115 genes, and found that 22 of them were independent predictors of overall survival in breast cancer patients. These genes were validated for their prognostic and diagnostic value with external data sets and paired tumor and non-tumor tissues. The genes identified herein could serve as diagnostic/prognostic markers and immune-related therapeutic targets in breast cancer.
| S-EPMC6874454 | biostudies-literature
Project description:Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.
| S-EPMC6723760 | biostudies-literature
Project description:Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis. Methods: CRC's data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients' overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan-Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes' expression was examined in the GSE41258 dataset. Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan-Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.
| S-EPMC9421078 | biostudies-literature
Project description:BackgroundBreast cancer (BC) is the primary cause of cancer mortality. Herein, we aimed to establish and verify a prognostic model consisting of endoplasmic reticulum stress and apoptosis related genes (ERAGs) to predict patient survival.MethodsThe Cancer Genome Atlas (TCGA) database was used to download gene expression and clinical data to identify the differentially expressed genes (DEGs). Using univariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis, the prognostic ERAGs were screened. The predictive performance was evaluated using Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Furthermore, a nomogram model incorporating clinical parameters and risk scores was constructed and subsequently evaluated using ROC and KM analysis. The correlation analysis, mutation analysis, functional enrichment analysis, and immune infiltration analysis were employed to investigate the specific mechanism of ERAGs. We also used Quantitative Real-Time PCR (RT-qPCR) to verify the differential expression of DE-ERAGs between the breast cancer cell line and mammary epithelial cell line.ResultsWe constructed a prognostic signature comprising 16 ERAGs. ROC, KM analysis and the nomogram model demonstrated high effectiveness in accurately predicting the overall survival (OS) of BRCA patients. The results of these analysis could provide reference for further mechanism exploration.ConclusionWe developed and assessed a novel molecular predictive model for breast cancer that focuses on endoplasmic reticulum stress and apoptosis in this study. It is a valuable complement to the existing prognostic prediction models for breast cancer.
| S-EPMC10966707 | biostudies-literature
Project description:Telomeres at the ends of eukaryotic chromosomes play a critical role in tumorgenesis. Using microfluidic PCR and next-generation bisulfite sequencing technology, we investigated the promoter methylation of 29 telomere related genes in paired tumor and normal tissues from 184 breast cancer patients. The expression of significantly differentially methylated genes was quantified using qPCR method.We observed that the average methylation level of the 29 telomere related genes was significant higher in tumor than that in normal tissues (P = 4.30E-21). A total of 4 genes (RAD50, RTEL, TERC and TRF1) showed significant hyper-methylation in breast tumor tissues. RAD51D showed significant methylation difference among the four breast cancer subtypes. The methylation of TERC showed significant association with ER status of breast cancer. The expression profiles of the 4 hyper-methylated genes showed significantly reduced expression in tumor tissues. The integration analysis of methylation and expression of these 4 genes showed a good performance in breast cancer prediction (AUC = 0.947).Our results revealed the methylation pattern of telomere related genes in breast cancer and suggested a novel 4-gene panel might be a valuable biomarker for breast cancer diagnosis.
| S-EPMC5421942 | biostudies-literature
Project description:BACKGROUND Gliomas are the most common primary tumors of the brain and spinal cord. The tumor microenvironment (TME) is the cellular environment in which tumors exist. This study aimed to identify the role of the TME and the effects of genes involved in the TME of malignant glioma. MATERIAL AND METHODS The ESTIMATE algorithms in the R package were used to calculate the immune and stromal scores of samples in the TCGA and GSE4290 datasets. The associations of stromal and immune scores with clinicopathological characteristics and overall survival of malignant glioma patients were assessed by analysis of variance and Kaplan-Meier analysis. Differentially expressed genes (DEGs) were obtained through the median immune and stromal score using the R package "limma". Functional enrichment analysis and the PPI network MCODE were used to analyze DEGs. RESULTS Increased immune and stromal scores were closely related with advanced glioma grade and poor prognosis (all P<0.01). In total, 558 DEGs were found and most were related to tumor prognosis. Functional enrichment analysis showed that DEGs were associated with cell-matrix regulation and immune response. Four hub modules related to tumor angiogenesis, collagen formation, and immune response were found and analyzed. Previously overlooked microenvironment-related genes such as LAMB1, FN1, ACTN1, TRIM, SERPINH1, CYBA, LAIR1, and LILRB2 showed prognostic values in malignant glioma patients. CONCLUSIONS The glioma stromal/immune scores are closely related to glioma grade, histology, and survival time. Some glioma microenvironment-related genes including LAMB1, FN1, ACTN1, TRIM6, SERPINH1, CYBA, LAIR1, and LILRB2 show prognostic values in malignant gliomas and serve as potential biomarkers.
| S-EPMC7780890 | biostudies-literature
Project description:Breast cancer (BRCA) is a highly heterogeneous malignancy with an urgent need to build a proper model to predict its prognosis. Cuproptosis is a recently discovered form of cell death, mediated by protein fatty acylation and tightly associated with mitochondrial metabolism. The role of cuproptosis-related genes (CRGs) in BRCA remains to be explored. We aimed to investigate the applications of CRGs in BRCA prognosis in different clinical contexts, including chemotherapy and immunotherapy, via bioinformatics analysis of the messenger RNA profiles and clinical data obtained from public databases. Molecular subtyping of CRGs was performed through consistent clustering analysis. Differentially expressed genes between different CRG clusters were identified. The differentially expressed genes were then used to build a risk assessment model using least absolute shrinkage and selection operator regression to predict patient survival with BRCA. The model was then validated with the data from the Molecular Taxonomy of Breast Cancer International Consortium, GSE96058, and GSE20685. Differences in somatic mutations, copy number variations, hallmark pathways, drug responses, and prognosis of immunotherapy and chemotherapy were analyzed by comparing the high-risk and low-risk groups. Patients with high-risk scores showed worse overall survival than those with low-risk scores. The results indicated significant differences between the 2 groups immune-related biological pathways and the variable immune status. It also suggests the differential sensitivity to chemotherapy between the 2 groups. The CRGs model showed the promise to predict the prognosis of BRCA patients and shed light on their treatment.
| S-EPMC10402946 | biostudies-literature
Project description:BackgroundThe role of copper in cancer treatment is multifaceted, with copper homeostasis-related genes associated with both breast cancer prognosis and chemotherapy resistance. Interestingly, both elimination and overload of copper have been reported to have therapeutic potential in cancer treatment. Despite these findings, the exact relationship between copper homeostasis and cancer development remains unclear, and further investigation is needed to clarify this complexity.MethodsThe pan-cancer gene expression and immune infiltration analysis were performed using the Cancer Genome Atlas Program (TCGA) dataset. The R software packages were employed to analyze the expression and mutation status of breast cancer samples. After constructing a prognosis model to separate breast cancer samples by LASSO-Cox regression, we examined the immune statement, survival status, drug sensitivity and metabolic characteristics of the high- and low-copper related genes scoring groups. We also studied the expression of the constructed genes using the human protein atlas database and analyzed their related pathways. Finally, copper staining was performed with the clinical sample to investigate the distribution of copper in breast cancer tissue and paracancerous tissue.ResultsPan-cancer analysis showed that copper-related genes are associated with breast cancer, and the immune infiltration profile of breast cancer samples is significantly different from that of other cancers. The essential copper-related genes of LASSO-Cox regression were ATP7B (ATPase Copper Transporting Beta) and DLAT (Dihydrolipoamide S-Acetyltransferase), whose associated genes were enriched in the cell cycle pathway. The low-copper related genes scoring group presented higher levels of immune activation, better probabilities of survival, enrichment in pathways related to pyruvate metabolism and apoptosis, and higher sensitivity to chemotherapy drugs. Immunohistochemistry staining showed high protein expression of ATP7B and DLAT in breast cancer samples. The copper staining showed copper distribution in breast cancer tissue.ConclusionThis study displayed the potential impacts of copper-related genes on the overall survival, immune infiltration, drug sensitivity and metabolic profile of breast cancer, which could predict patients' survival and tumor statement. These findings may serve to support future research efforts aiming at improving the management of breast cancer.
| S-EPMC10172490 | biostudies-literature
Project description:The claudin multigene family is associated with various aberrant physiological and cellular signaling pathways. However, the association of claudins with survival prognosis, signaling pathways, and diagnostic efficacy in colon cancer remains poorly understood. Methods: Through the effective utilization of various bioinformatics methods, including differential gene expression analysis, gene set enrichment analysis protein-protein interaction (PPI) network analysis, survival analysis, single sample gene set enrichment analysis (ssGSEA), mutational variance analysis, and identifying receiver operating characteristic curve of claudins in The Cancer Genome Atlas colon adenocarcinoma (COAD). Results: We found that: CLDN2, CLDN1, CLDN14, CLDN16, CLDN18, CLDN9, CLDN12, and CLDN6 are elevated in COAD. In contrast, the CLDN8, CLDN23, CLDN5, CLDN11, CLDN7, and CLDN15 are downregulated in COAD. By analyzing the public datasets GSE15781 and GSE50760 from NCBI-GEO (https://www.ncbi.nlm.nih.gov/geo/), we have confirmed that CLDN1, CLDN2, and CLDN14 are significantly upregulated and CLDN8 and CLDN23 are significantly downregulated in normal colon, colon adenocarcinoma tumor, and liver metastasis of colon adenocarcinoma tissues from human samples. Various claudins are mutated and found to be associated with diagnostic efficacy in COAD. Conclusion: The claudin gene family is associated with prognosis, immune regulation, signaling pathway regulations, and diagnosis of COAD. These findings may provide new molecular insight into claudins in the treatment of colon cancer.
| S-EPMC10550083 | biostudies-literature
Project description:BackgroundWhile many studies have assessed the predictive value of secreted phosphoprotein (SPP) genes in cancer, the findings have been inconsistent. To resolve these inconsistencies, we systematically analyzed the available data to determine whether SPP1 and SPP2 are prognostic markers in the context of human cancer.MethodsThe expression of SPP1 and SPP2 was assessed by Oncomine analysis. The PrognoScan database was used to assess the prognostic value of SPP1 and SPP2, with cBioPortal used to assess copy number variations. The STRING database was used to generate a Protein - Protein Interaction (PPI) network for SPP genes.ResultsSPP1 was more likely to be over-expressed in breast, bladder, colorectal, head, neck, liver, lung, and esophageal cancers. SPP2 was expressed at lower levels in colorectal cancer, leukemia, liver cancer and pancreatic cancer. In addition, SPP1 and SPP2 mutations mainly occurred in cutaneous melanoma and endometrial cancer.ConclusionsOur results suggest that SPP1 and SPP2 may be effective therapeutic or diagnostic targets in certain cancers. Further research is required to confirm these results and verify the value of SPP1 and SPP2 as clinical markers of cancer prognosis.
| S-EPMC6918603 | biostudies-literature