Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer.
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ABSTRACT:
SUBMITTER: Qi A
PROVIDER: S-EPMC8262234 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThe aim of the study was to propose a signature based on genes associated with antigen processing and presentation (APscore) to predict prognosis and response to immune checkpoint inhibitors (ICIs) in advanced gastric cancer (aGC).BackgroundHow antigen presentation-related genes affected the immunotherapy response and whether they could predict the clinical outcomes of the immune checkpoint inhibitor (ICI) in aGC remain largely unknown.MethodsIn this study, an aGC cohort (Kim cohort, RNAseq, N=45) treated by ICIs, and 467 aGC patients from seven cohorts were conducted to investigate the value of the APscore predicting the prognosis and response to ICIs. Subsequently, the associations of the APscore with the tumor microenvironment (TME), molecular characteristics, clinical features, and somatic mutation variants in aGC were assessed. The area under the receiver operating characteristic curve (AUROC) of the APscore was analyzed to estimate response to ICIs. Cox regression or Log-rank test was used to estimate the prognosis of aGC patients.ResultsThe APscore constructed by principal component analysis algorithms was an effective predictive biomarker of the response to ICIs in the Kim cohort and 467 aGC patients (Kim: AUC =0.85, 95% CI: 0.69-1.00; 467 aGC: AUC =0.69, 95% CI: 0.63-0.74). The APscore also was a prognostic biomarker in 467 aGC patients (HR=1.73, 95% CI: 1.21-2.46). Inhibitory immunity, decreased TMB and low stromal scores were observed in the high APscore group, while activation of immunity, increased TMB, and high stromal scores were observed in the low APscore group. Next, we evaluated the value of several central genes in predicting the prognosis and response to ICIs in aGC patients, and verified them using immunogenic, transcriptomic, genomic, and multi-omics methods. Lastly, a predictive model built successfully discriminated patients with vs. without immunotherapy response and predicted the survival of aGC patients.ConclusionsThe APscore was a new biomarker for identifying high-risk aGC patients and patients with responses to ICIs. Exploration of the APscore and hub genes in multi-omics GC data may guide treatment decisions.
| S-EPMC9615473 | biostudies-literature
Project description:To identify prognostic signature that could predict the survival of patients with breast cancer (BC).Breast cancer samples and normal breast tissues in the TCGA-BRCA and GSE7390 were included. Differentially expressed genes (DEGs) were identified using the "limma" method. Overall survival (OS) associated with DEGs were obtained using univariate and multivariable Cox proportional-hazards regression analysis, and the corresponding prognostic signature and nomogram were constructed. Calibration analysis and decision curve analysis (DCA) were performed.In all, 742 DEGs were identified, 19 of which were independently correlated with the OS of BC patients. The OS of patients in the 19-gene signature low-risk group was significantly better than that in high-risk group (hazard ratio [HR] 0.3506, 95% confidence interval [CI] 0.2488-0.4939), and the 19-gene based signature was demonstrated to be an independent prognostic factor in patient with BC in the TCGA-BRCA cohort (HR 1.501, 95% CI 1.374-1.640) and validation cohort GSE7392 ((HR 0.3557, 95% CI 0.2155-0.5871, P < .0001)). The primary and internally validated C-indexes for the 19-gene signature-based nomogram were 0.817 and 8.013, respectively. The results of calibration analysis and DCA analysis confirmed the robustness and the clinical usability of the nomogram.We constructed a prognostic signature and nomogram for patient with BC, which showed good application prospect.
| S-EPMC6426514 | biostudies-literature
Project description:To identify the novel, noninvasive biomarkers to assess the outcome and prognosis of breast cancer (BC), patients with high sensitivity and specificity are greatly desired. Herein, the miRNA expression profile and matched clinical features of BC patients were extracted from The Cancer Genome Atlas (TCGA) database. The preliminary candidates were screened out by the univariate Cox regression test. Then, with the help of LASSO Cox regression analysis, the hsa-let-7b, hsa-mir-101-2, hsa-mir-135a-2, hsa-mir-22, hsa-mir-30a, hsa-mir-31, hsa-mir-3130-1, hsa-mir-320b-1, hsa-mir-3678, hsa-mir-4662a, hsa-mir-4772, hsa-mir-493, hsa-mir-556, hsa-mir-652, hsa-mir-6733, hsa-mir-874, and hsa-mir-9-3 were selected to construct the overall survival (OS) predicting signature, while the hsa-mir-130a, hsa-mir-204, hsa-mir-217, hsa-mir-223, hsa-mir-24-2, hsa-mir-29b-1, hsa-mir-363, hsa-mir-5001, hsa-mir-514a-1, hsa-mir-624, hsa-mir-639, hsa-mir-659, and hsa-mir-6892 were adopted to establish the recurrence-free survival (RFS) predicting signature. Referring to the median risk scores generated by the OS and RFS formulas, respectively, subgroup patients with high risk were strongly related to a poor OS and RFS revealed by Kaplan-Meier (K-M) plots. Meanwhile, receiver operating curve (ROC) analysis validated the accuracy and stability of these two signatures. When stratified by clinical features, such as tumor stage, age, and molecular subtypes, we found that the miRNA-based OS and RFS classifiers were still significant in predicting OS/RFS and showed the best predictive values than any other features. Besides, functional prediction analyses showed that these targeted genes of the enrolled miRNAs were enriched in cancer-associated pathways, such as MAPK/RTK, Ras, and PI3K-Akt signaling pathways. In summary, our observations demonstrate that the novel miRNA-based OS and RFS signatures are independent prognostic indicators for BC patients and worthy to be validated by further prospective studies.
| S-EPMC6954483 | biostudies-literature
Project description:Methamphetamine (Meth) is abused by over 35 million people worldwide. Chronic Meth abuse may be particularly devastating in individuals who engage in unprotected sex with multiple partners because it is associated with a 2-fold higher risk for obtaining HIV and associated secondary infections. We report the first specific evidence that Meth at pharmacological concentrations exerts a direct immunosuppressive effect on dendritic cells and macrophages. As a weak base, Meth collapses the pH gradient across acidic organelles, including lysosomes and associated autophagic organelles. This in turn inhibits receptor-mediated phagocytosis of antibody-coated particles, MHC class II antigen processing by the endosomal-lysosomal pathway, and antigen presentation to splenic T cells by dendritic cells. More importantly Meth facilitates intracellular replication and inhibits intracellular killing of Candida albicans and Cryptococcus neoformans, two major AIDS-related pathogens. Meth exerts previously unreported direct immunosuppressive effects that contribute to increased risk of infection and exacerbate AIDS pathology.
| S-EPMC2242831 | biostudies-literature
Project description:Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC). Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database. Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset. Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.
| S-EPMC8693898 | biostudies-literature
Project description:BackgroundThe progression of breast cancer (BC) is highly dependent on the tumor microenvironment. Inflammation, stromal cells, and the immune landscape have been identified as significant drivers of BC in multiple preclinical studies. Therefore, this study aimed to clarify the predictive relevance of stromal and immune cell-associated genes in patients suffering from BC.MethodsWe employed the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm to calculate the stromal and immunological scores, which were then used to evaluate differentially expressed genes (DEGs) in BC samples using The Cancer Genome Atlas (TCGA) database. Univariate analyses were conducted to identify the DEGs linked to survival in BC patients. Next, the prognostic DEGs (with a log-rank P<0.05) were used to create a risk signature, and the least absolute shrinkage and selection operator (LASSO) regression method was used to analyze and optimize the risk signature. The following formula was used to compute the prognostic risk score values: Risk score = Gene 1 * β1 + Gene 2 * β2 +… Gene n * βn. The median prognostic risk score values were used to divide BC patients into the low-risk (LR) and high-risk (HR) groups. The patient samples of the validation cohort were then assessed using this formula. We used principal component analysis (PCA) to determine the expression patterns of the different patient groups. Gene Set Enrichment Analysis (GSEA) was used to determine whether there were significant variations between the groups in the evaluated gene sets.ResultsThe present study revealed that DEGs linked with survival were closely associated with immunological responses. A prognostic signature was constructed that consisted of 12 genes (ASCL1, BHLHE22, C1S, CLEC9A, CST7, EEF1A2, FOLR2, KLRB1, MEOX1, PEX5L, PLA2G2D, and PPP1R16B). According to their survival, BC patients were separated into LR and HR groups using the identified 12-gene signature. The immunological status and immune cell infiltration were observed differently in the LR and HR groups.ConclusionsOur results provide novel insights into several microenvironment-linked genes that influence survival outcomes in patients with BC, which suggests that these genes could be candidate therapeutic targets.
| S-EPMC8904996 | biostudies-literature
Project description:PurposeUveal melanoma (UM) is the most common primary malignant tumor with poor prognosis. The role of metabolism-related genes in the prognosis of UM remains unrevealed. This study aimed to establish and validate a prognostic prediction model for UM based on metabolism-related genes.MethodsGene expression profiles and clinicopathological information were downloaded from The Cancer Genome Atlas, and the Gene Expression Omnibus database. Univariable Cox regression, least absolute shrinkage and selection operator Cox regression, and stepwise regression were performed to establish the model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and calibration and discrimination analyses were used to evaluate the prognostic model.ResultsThree metabolism-related genes, carbonic anhydrase 12, acyl-CoA synthetase long-chain family member 3, and synaptojanin 2, and three clinicopathological parameters (i.e., age, gender, and metastasis staging) were identified to establish the model. The risk score was found to be an independent prognostic factor for UM survival. High-risk patients demonstrated significantly poorer prognosis than low-risk patients. ROC analysis suggested the promising prognostic efficiency of the model. The calibration curve manifested satisfactory agreement between the predicted and observed risk. A nomogram and online survival calculator were developed to predict the survival probability.ConclusionsThe novel metabolism-based prognostic model could accurately predict the prognosis of UM patients, which facilitates the prediction of the survival probability by both ophthalmologists and patients with the online dynamic nomogram.Translational relevanceThe dynamic nomogram links gene expression profiles to clinical prognosis of UM and is useful to evaluate the survival probability.
| S-EPMC9100464 | biostudies-literature
Project description:BackgroundAlthough increased early detection, diagnosis and treatment have improved the outcome of breast cancer patients, prognosis estimation still poses challenges due to the disease heterogeneity. Accumulating data indicated an evident correlation between tumor immune microenvironment and clinical outcomes.ObjectiveTo construct an immune-related signature that can estimate disease prognosis and patient survival in breast cancer.MethodsGene expression profiles and clinical data of breast cancer patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, which were further divided into a training set (n = 499), a testing set (n = 234) and a Meta-validation set (n = 519). In the training set, immune-related genes were recognized using combination of gene expression data and ESTIMATE algorithm-derived immune scores. An immune-related prognostic signature was generated with LASSO Cox regression analysis. The prognostic value of the signature was validated in the testing set and the Meta-validation set.ResultsA total of 991 immune-related genes were identified. Twelve genes with non-zero coefficients in LASSO analysis were used to construct an immune-related prognostic signature. The 12-gene signature significantly stratified patients into high and low immune risk groups in terms of overall survival independent of clinical and pathologic factors. The signature also significantly stratified overall survival in clinical defined groups, including stage I/II disease. Several biological processes, such as immune response, were enriched among genes in the immune-related signature. The percentage of M2 macrophage infiltration was significantly different between low and high immune risk groups. Time-dependent ROC curves indicated good performance of our signature in predicting the 1-, 3- and 5-year overall survival for patients from the full TCGA cohort. Furthermore, the composite signature derived by integrating immune-related signature with clinical factors, provided a more accurate estimation of survival relative to molecular signature alone.ConclusionWe developed a 12-gene prognostic signature, providing novel insights into the identification of breast cancer with a high risk of death and assessment of the possibility of immunotherapy incorporation in personalized breast cancer management.
| S-EPMC6997532 | biostudies-literature
Project description:Neoantigens are tumor-specific antigens that are mostly particular for each patient. Since the immune system is able to mount a specific immune response against these neoantigens, they are a promising tool for the development of therapeutic personalized cancer vaccines. Neoantigens must be presented to T cells by antigen presenting cells (APC) in the context of MHC-I or MHC-II molecules. Therefore, the strategy of vaccine delivery may have a major impact on the magnitude and quality of T cell responses. Neoantigen-based vaccines are frequently administered as a pool of individual synthetic peptides that induce mainly CD4+ T cell responses. MHC-I-mediated presentation and the elicitation of CD8+ T cell responses may be improved using DNA or RNA sequences that code for a unique long polypeptide that concatenates the different neoantigens spaced by linker sequences. When administered this way, the selection of the spacer between neoantigens is of special interest, as it might influence the processing and presentation of the right peptides by APCs. Here, we evaluate the impact of such linker regions on the MHC-I-dependent antigen presentation using an in vitro assay that assesses the MHC-I presentation of SIINFEKL, a H-2 Kb-restricted OVA peptide. Our results show that spacers used to generate epitope concatenates have a large impact on the efficiency of neoantigen processing and presentation by MHC-I molecules; in contrast, the peptide position and the flanking regions have a minimal impact. Moreover, linkers based on alanine residues promote a more efficient peptide presentation than the commonly used GGGS linker.
| S-EPMC10264286 | biostudies-literature
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)
2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress