Dataset Information

Identification of the prognostic value of Th1/Th2 ratio and a novel prognostic signature in basal-like breast cancer.

ABSTRACT:

Background

Breast cancer is a heterogeneous group of diseases. The polarization of CD4+ T helper (Th) lymphocytes (mainly Th1 and Th2) may differ in breast cancers with different outcomes, but this has not been fully validated.

Methods

This study is a bioinformatic analysis, in which differentially expressed genes (DEGs) were identified in patients with low and high Th1/Th2 ratios. And then, DEG functions, hub genes and independent predictors were determined.

Results

Low Th1/Th2 ratio was associated with poor outcome in Luminal A and basal-like breast cancer (p < 0.05). GSEA and KEGG analysis of DEGs obtained from comparing low and high Th1/Th2 ratios illuminated downregulation of immune-related gene sets and pathways affecting Th1/Th2 balance toward Th2 polarization (p < 0.05). Survival and Cox analyses of all the DEGs confirmed CCL1 and MYH6 were independent protective factors and IFNK and SOAT2 were independent risk factors for basal-like breast cancer (95%CI: 1.06-2.5, p = 0.026). Then a four-gene signature was constructed and achieved a promising prognostic value (C-index = 0.82; AUC = 0.826).

Conclusions

Low Th1/Th2 ratio predicts poor outcome in Luminal A and Basal-like breast cancer, and downregulation of immune-related gene sets and pathways contribute to Th1/Th2 balance toward Th2 polarization. CCL1, MYH6, IFNK, and SOAT2 have an independent prognostic value of survival outcome and might be novel markers in basal-like breast cancer.

SUBMITTER: Xiao Y

PROVIDER: S-EPMC9875389 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of the prognostic value of Th1/Th2 ratio and a novel prognostic signature in basal-like breast cancer.

Xiao Yu Y Huang Yi Y Jiang Jianping J Chen Yan Y Wei Changyuan C

Hereditas 20230125 1

<h4>Background</h4>Breast cancer is a heterogeneous group of diseases. The polarization of CD4+ T helper (Th) lymphocytes (mainly Th1 and Th2) may differ in breast cancers with different outcomes, but this has not been fully validated.<h4>Methods</h4>This study is a bioinformatic analysis, in which differentially expressed genes (DEGs) were identified in patients with low and high Th1/Th2 ratios. And then, DEG functions, hub genes and independent predictors were determined.<h4>Results</h4>Low Th ...[more]

PMID: 36694223

Similar Datasets

Project description:BackgroundRobust and precise molecular prognostic predictors for luminal breast cancer are required. This study aimed to identify key methylation sites in luminal breast cancer, as well as precise molecular tools for predicting prognosis.MethodsWe compared methylation levels of normal and luminal breast cancer samples from The Cancer Genome Atlas dataset. The relationships among differentially methylated sites, corresponding mRNA expression levels and prognosis were further analysed. Differentially expressed genes in normal and cancerous samples were analysed, followed by the identification of prognostic signature genes. Samples were divided into low- and high-risk groups based on the signature genes. Prognoses of low- and high-risk groups were compared. The Gene Expression Omnibus dataset were used to validate signature genes for prognosis prediction. Prognosis of low- and high-risk groups in Luminal A and Luminal B samples from the TCGA and the Metabric cohort dataset were analyzed. We also analysed the correlation between clinical features of low- and high- risk groups as well as their differences in gene expression.ResultsFourteen methylation sites were considered to be related to luminal breast cancer prognosis because their methylation levels, mRNA expression and prognoses were closely related to each other. The methylation level of SOSTDC1 was used to divide samples into hypo- and hyper-methylation groups. We also identified an mRNA signature, comprising eight transcripts, ESCO2, PACSIN1, CDCA2, PIGR, PTN, RGMA, KLK4 and CENPA, which was used to divide samples into low- and high-risk groups. The low-risk group showed significantly better prognosis than the high-risk group. A correlation analysis revealed that the risk score was an independent prognostic factor. Low- and high- risk groups significantly correlated with the survival ratio in Luminal A samples, but not in Luminal B samples on the basis of the TCGA and the Metabric cohort dataset. Further functional annotation demonstrated that the differentially expressed genes were mainly involved in cell cycle and cancer progression.ConclusionsWe identified several key methylation sites and an mRNA signature for predicting luminal breast cancer prognosis. The signature exhibited effective and precise prediction of prognosis and may serve as a prognostic and diagnostic marker for luminal breast cancer.

Project description:BackgroundBreast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.MethodsGene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.ResultsWe constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).ConclusionOur study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Project description:IntroductionCervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection.MaterialsDifferentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six-gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co-expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six-gene signature.ResultsWe discovered 178 overlapping DEGs in three microarray datasets and established a six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six-gene signature was closely associated with the immune response and tumour immune microenvironment. The six-gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer.ConclusionOur study established a novel six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six-gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six-gene signature have the potential to become novel drug targets.

Project description:BackgroundTriple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge.ResultsHere, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients' clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas.ConclusionThe application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.

Dataset Information

Identification of the prognostic value of Th1/Th2 ratio and a novel prognostic signature in basal-like breast cancer.

Background

Methods

Results

Conclusions

Publications

Identification of the prognostic value of Th1/Th2 ratio and a novel prognostic signature in basal-like breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets