Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes.
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ABSTRACT:
SUBMITTER: Mei J
PROVIDER: S-EPMC7075995 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAccurate classification of breast cancer using gene expression profiles has contributed to a better understanding of the biological mechanisms behind the disease and has paved the way for better prognostication and treatment prediction.ResultsWe found that miRNA profiles largely recapitulate intrinsic subtypes. In the case of HER2-enriched tumors a small set of miRNAs including the HER2-encoded mir-4728 identifies the group with very high specificity. We also identified differential expression of the miR-99a/let-7c/miR-125b miRNA cluster as a marker for separation of the Luminal A and B subtypes. High expression of this miRNA cluster is linked to better overall survival among patients with Luminal A tumors. Correlation between the miRNA cluster and their precursor LINC00478 is highly significant suggesting that its expression could help improve the accuracy of present day's signatures.ConclusionsWe show here that miRNA expression can be translated into mRNA profiles and that the inclusion of miRNA information facilitates the molecular diagnosis of specific subtypes, in particular the clinically relevant sub-classification of luminal tumors.
| S-EPMC6580620 | biostudies-literature
Project description:Purpose: Patients with regionally advanced melanoma were treated with neoadjuvant ipilimumab in a previously reported study (PLOS One 2014). Gene expression profiles of tumors of treated patients were investigated for their association with immunotherapeutic benefit. Methods: Patients were treated with ipilimumab (10 mg/kg intravenously every 3 weeks × 2 doses) before and after surgery. Tumor specimens were obtained at baseline and at definitive surgery (weeks 6-8). Gene expression profiling was performed on the tumor biopsies of 27 patients. The primary endpoint was mRNA expression profiling using U133A 2.0 Affymetrix gene chips. Significance analysis of microarrays was performed to test the association of each gene with outcome. Pathway analysis was performed using Ingenuity Pathway Analysis software. The Benjamini and Hochberg method was used to adjust for multiple testing in the pathway analysis. Results: Pathway analysis identified biologically relevant pathways enriched with genes that are significantly associated with clinical outcome at baseline in relation to relapse-free survival (RFS) and disease non-progression (as assessed preoperatively at week 6) as well as early on-treatment (RFS and overall survival). The molecules and pathways that achieved differential expression of highest statistical significance were notably immune related. Association of the gene signature with clinical outcome overlapped between baseline and on-treatment specimens and across clinical endpoints tested. Conclusion: Gene expression profiling identified a signature reflecting an immune active and proinflammatory tumor microenvironment that derived clinical benefit from neoadjuvant ipilimumab at baseline and early on-treatment. These findings warrant further investigation in relation to ipilimumab and other immunotherapeutics.
| S-EPMC5353928 | biostudies-other
Project description:BackgroundIdentification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers.Methodology/principal findingsIn a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes.Conclusions/significanceThe methodology employed here may be used to facilitate disease-specific biomarker discovery.
| S-EPMC2816221 | biostudies-literature
Project description:Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.
| S-EPMC5769863 | biostudies-literature
Project description:Background: Recent studies have identified several molecular subtypes of lung adenocarcinoma (LUAD) that have different prognoses to help predict the efficacy of immunotherapy. However, the prognostic prediction is less than satisfactory. Alterations in intracellular copper levels may affect the tumor immune microenvironment and are linked to cancer progression. Previous studies have identified some genes related to cuproptosis. The characteristics of the cuproptosis molecular subtypes have not been thoroughly studied in LUAD. Methods: The transcriptomic data and clinical information of 632 LUAD patients were used to investigate the LUAD molecular subtypes that are associated with the cuproptosis-related genes (CRGs), the tumor immune microenvironment, and stemness. The cuproptosis score was constructed using univariate Cox regression and the minor absolute shrinkage and selection operator (LASSO) to quantify the prognostic characteristics. Results: Three different molecular subtypes related to cuproptosis, with different prognoses, were identified in LUAD. Cluster A had the highest cuproptosis score and the worst prognosis. Patients in the high cuproptosis score group had a higher somatic mutation frequency and stemness scores. Patients in the low cuproptosis score group had more immune infiltration and better prognosis. Conclusion: Molecular subtypes of LUAD based on CRGs reflect the differences in LUAD patients. The cuproptosis score can be used as a promising biomarker, which is of great significance to distinguish the relationship between cuproptosis and the immune microenvironment. The cuproptosis signature based on the cuproptosis score and clinical characteristics of individual patients will be useful for guiding immunotherapy in LUAD.
| S-EPMC9597639 | biostudies-literature
Project description:Background: Accurate classification of breast cancer using gene expression profiles has contributed to a better understanding of the biological mechanisms behind the disease and has paved the way for better prognostication and treatment prediction. Results: We found that miRNA profiles largely recapitulate intrinsic subtypes. In the case of HER2-enriched tumors a small set of miRNAs including the HER2-encoded mir-4728 identifies the group with very high specificity. We also identified differential expression of the miR-99a/let-7c/miR-125b miRNA cluster as a marker for separation of the Luminal A and B subtypes. High expression of this miRNA cluster is linked to better overall survival among patients with Luminal A tumors. Correlation between the miRNA cluster and their precursor LINC00478 is highly significant suggesting that its expression could help improve the accuracy of present day’s signatures. Conclusions: We show here that miRNA expression can be translated into mRNA profiles and that the inclusion of miRNA information facilitates the molecular diagnosis of specific subtypes, in particular the clinically relevant sub-classification of luminal tumors.
2019-06-20 | GSE131599 | GEO
Project description:The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis-related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high-risk group exhibited worse 5-year OS, and the result was consistent in the external cohort. Additionally, high-risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high-risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score-based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.
| S-EPMC8995442 | biostudies-literature
Project description:Lung cancer is the most prevalent cancer in humans and has the lowest survival outcomes due to its high metastatic potential. The aim of the present study was to screen for metastasis‑related genes (MRGs) by investigating the differential expression genes (DEGs) identified by the mRNA expression profiles in SPC‑A‑1sci (highly metastatic) and SPC-A-1 (parental) cells. DEGs were screened using Genespring software. Gene Ontology and pathway enrichment analyses of these DEGs were performed. Interaction networks between the proteins encoded by the DEGs were identified using the database BioGRID and were visualized by Cytoscape. Modular analysis of the protein‑protein interaction network was performed in CFinder. Among these DEGs, the expression levels of 18 genes were examined in SPC‑A‑1sci and SPC‑A‑1 cell lines with reverse transcription‑quantitative polymerase chain reaction, and 10 of the 18 genes were assessed by western blotting to validate the results of the microarray. Furthermore, the role of metallothionein 1X (MT1X) in non‑small cell lung cancer was explored in functional assays and 72 pairs of clinical samples in vitro. Finally, 4,838 DEGs were screened, including 798 upregulated and 4,040 downregulated genes. The significantly enriched functions included gene expression, cytosol and poly‑(A) RNA binding, and the most enriched pathway was biosynthesis of antibiotics. Furthermore, MT1X was revealed to promote the migration and invasion ability in SPC‑A‑1sci and PC‑9 lung cancer cell lines. Therefore, MT1X was identified as a candidate MRG through systematic analysis in the present microarray, which was demonstrated to offer potential reference value in screening MRGs.
| S-EPMC5355670 | biostudies-literature
Project description:To uncover potential molecular subtypes of non-keratinizing nasopharyngeal carcinoma, gene expression profiles of primary NK-NPCs and NPs were conducted and analyzed using bioinformatics methods.
2016-10-05 | GSE40290 | GEO
Project description:Molecular classification of tumors based on their gene expression profiles promises to significantly refine diagnosis and management of cancer patients. The establishment of organ-specific gene expression patterns represents a crucial first step in the clinical application of the molecular approach. Here, we report on the gene expression profiles of 154 primary adenocarcinomas of the lung, colon, and ovary. Using high-density oligonucleotide arrays with 7129 gene probe sets, comprehensive gene expression profiles of 57 lung, 51 colon, and 46 ovary adenocarcinomas were generated and subjected to principle component analysis and to a cross-validated prediction analysis using nearest neighbor classification. These statistical analyses resulted in the classification of 152 of 154 of the adenocarcinomas in an organ-specific manner and identified genes expressed in a putative tissue-specific manner for each tumor type. Furthermore, two tumors were identified, one in the colon group and another in the ovarian group, that did not conform to their respective organ-specific cohorts. Investigation of these outlier tumors by immunohistochemical profiling revealed the ovarian tumor was consistent with a metastatic adenocarcinoma of colonic origin and the colonic tumor was a pleomorphic mesenchymal tumor, probably a leiomyosarcoma, rather than an epithelial tumor. Our results demonstrate the ability of gene expression profiles to classify tumors and suggest that determination of organ-specific gene expression profiles will play a significant role in a wide variety of clinical settings, including molecular diagnosis and classification.
| S-EPMC1850519 | biostudies-literature