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Breast Cancer Subtypes Based on Hypoxia-Related Gene Sets Identify Potential Therapeutic Agents

ABSTRACT: Purpose: The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of breast cancer (BC). We aimed to establish a hypoxia-related gene signature to identify a new BC subtype through the clustering analysis and explore potential compounds targeting the BC subtypes. Methods: Gene expression data and clinical features of BC and adjacent non-tumor tissues were downloaded from the Cancer Genome Atlas-Breast cancer (TCGA-BRCA) database. We comprehensively revealed the activity changes of Gene Ontology (GO) biological processes (BP) gene sets in BC by gene set variation analysis (GSVA) and identified three hypoxia-related BC subtypes. We then matched the differentially expressed gene profile of each subtype with the gene profile in CMap database to identify the potential agents targeting the BC subtypes. Results: 562 of Gene Ontology biological processes gene sets significantly correlated with hypoxia score in breast cancer. 969 BC patients were clustered into three subtypes based on the enrichment score of hypoxia-associated gene sets. Subtype 1 patients displayed better survival than subtype 2 and 3. KEGG pathway enrichment analysis of each subtype was performed based on the unique differential expression genes profile. In subtype 1, the upregulated genes were associated with lipid and amino acid metabolism regulation; in subtype 2, the upregulated genes were associated with metabolic energy regulation, while in subtype 3, the upregulated genes were associated with apoptosis and protein process. Using the CMap database, 55, 111 and 63 compounds were identified, targeting subtype 1, 2, and 3, respectively. Conclusion: In this study, novel hypoxia-related subtypes were developed for patients with BC. In addition, biological processes associated with differential expression genes profile and potential therapeutic target compounds were identified in each subtype. The new classification might provide a better understanding of the role of hypoxia in breast cancer and more individualized treatment for patients.

SUBMITTER: Xiong Z

PROVIDER: S-EPMC9277110 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND & AIMS:Little is known about the factors that affect outcomes of patients with biliary atresia and there are no medical therapies that increase biliary drainage. METHODS:Liver biopsies and clinical data were obtained from infants with cholestasis and from children without liver disease (controls); messenger RNA (mRNA) was isolated, randomly assigned to discovery (n = 121) and validation sets (n = 50), and analyzed by RNA sequencing. Using the Superpc R package followed by Cox regression analysis, we sought to identify gene expression profiles that correlated with survival without liver transplantation at 24 months of age. We also searched for combinations of gene expression patterns, clinical factors, and laboratory results obtained at diagnosis and at 1 and 3 months after surgery that associated with transplant-free survival for 24 months of age. We induced biliary atresia in BALB/c mice by intraperitoneal administration of Rhesus rotavirus type A. Mice were given injections of the antioxidants N-acetyl-cysteine (NAC) or manganese (III) tetrakis-(4-benzoic acid)porphyrin. Blood and liver tissues were collected and analyzed by histology and immunohistochemistry. RESULTS:We identified a gene expression pattern of 14 mRNAs associated with shorter vs longer survival times in the discovery and validation sets (P < .001). This gene expression signature, combined with level of bilirubin 3 months after hepatoportoenterostomy, identified children who survived for 24 months with an area under the curve value of 0.948 in the discovery set and 0.813 in the validation set (P < .001). Computer models correlated a cirrhosis-associated transcriptome with decreased times of transplant-free survival; this transcriptome included activation of genes that regulate the extracellular matrix and numbers of activated stellate cells and portal fibroblasts. Many mRNAs expressed at high levels in liver tissues from patients with 2-year transplant-free survival had enriched scores for glutathione metabolism. Among mice with biliary atresia given injections of antioxidants, only NAC reduced histologic features of liver damage and serum levels of aminotransferase, gamma-glutamyl transferase, and bilirubin. NAC also reduced bile duct obstruction and liver fibrosis and increased survival times. CONCLUSIONS:In studies of liver tissues from infants with cholestasis, we identified a 14-gene expression pattern that associated with transplant-free survival for 2 years. mRNAs encoding proteins that regulate fibrosis genes were increased in liver tissues from infants who did not survive for 2 years, whereas mRNAs that encoded proteins that regulate glutathione metabolism were increased in infants who survived for 2 years. NAC reduced liver injury and fibrosis in mice with biliary atresia, and increased survival times. Agents such as NAC that promote glutathione metabolism might be developed for treatment of biliary atresia.

Project description:BackgroundSingle sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.MethodsThirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.ResultsSCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.ConclusionOur results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

Project description:Background: Lung adenocarcinoma (LUAD) is a life-threatening malignant tumor, contributing for the largest cancer burden worldwide. Tumor microenvironment (TME) is composed of various immune cells, stromal cells and tumor cells, which is highly associated with the cancer prognosis and the response to immunotherapy, in which macrophages in TME have been revealing a potential target for cancer treatment. In this study, we sought to further explore the role of macrophages in LUAD progression and establish a risk model related to macrophages for LUAD. Methods: We explored immune-related pathways that might be affected by counting positively associated genes in macrophages. Molecular typing was also constructed by mining macrophage-associated genes with prognostic value through COX regression and other analyses. RiskScore prognostic models were constructed using lasso regression and stepwise multifactorial regression analysis. The differences on clinical characteristics among three subtypes (C1, C2, and C3) and RiskScore subtypes were analyzed in TCGA dataset. Immunological algorithms such as TIMER, ssGSEA, MCP-Counter, ESTIMATE, and TIDE were used to calculate the level of difference in immune infiltration between the different subtypes. The TCGA mutation dataset processed by mutect2 was used to demonstrate the frequency of mutations between different molecular subtypes. Finally, nomograms, calibration curves, and decision curves were created to assess the predictive accuracy and reliability of the model. Results: The C1 subtype demonstrated the best prognostic outcome, accompanied by higher levels of immune infiltration and lower mutation frequency, while the majority of patients in the C1 subtype were women under 65 years of age. Myeloid-derived suppressor cell (MDSC) scores were higher in the C3 subtype, suggesting a more severe immune escape, which may have contributed to the tumor evading the immune system resulting in a poorer prognosis for patients. In addition, our RiskScore prognostic model had good predictive accuracy and reliability. Conclusion: This paper provides a study of macrophage-related pathways, immunosuppression, and their mechanisms of action in lung cancer, along with targets for future treatment to guide the optimal treatment of lung cancer.

Project description:BACKGROUND: Transcript profiling of closely related species provides a means for identifying genes potentially important in species diversification. However, the predictive value of transcript profiling for inferring downstream-physiological processes has been unclear. In the present study we use shotgun proteomics to validate inferences from microarray studies regarding physiological differences in three Pachycladon species. We compare transcript and protein profiling and evaluate their predictive value for inferring glucosinolate chemotypes characteristic of these species. RESULTS: Evidence from heterologous microarrays and shotgun proteomics revealed differential expression of genes involved in glucosinolate hydrolysis (myrosinase-associated proteins) and biosynthesis (methylthioalkylmalate isomerase and dehydrogenase), the interconversion of carbon dioxide and bicarbonate (carbonic anhydrases), water use efficiency (ascorbate peroxidase, 2 cys peroxiredoxin, 20 kDa chloroplastic chaperonin, mitochondrial succinyl CoA ligase) and others (glutathione-S-transferase, serine racemase, vegetative storage proteins, genes related to translation and photosynthesis). Differences in glucosinolate hydrolysis products were directly confirmed. Overall, prediction of protein abundances from transcript profiles was stronger than prediction of transcript abundance from protein profiles. Protein profiles also proved to be more accurate predictors of glucosinolate profiles than transcript profiles. The similarity of species profiles for both transcripts and proteins reflected previously inferred phylogenetic relationships while glucosinolate chemotypes did not. CONCLUSIONS: We have used transcript and protein profiling to predict physiological processes that evolved differently during diversification of three Pachycladon species. This approach has also identified candidate genes potentially important in adaptation, which are now the focus of ongoing study. Our results indicate that protein profiling provides a valuable tool for validating transcript profiles in studies of adaptive divergence.

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Breast Cancer Subtypes Based on Hypoxia-Related Gene Sets Identify Potential Therapeutic Agents

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