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Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis.

ABSTRACT: Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n?=?920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/-) and recurrence (+/-). We identified a sub-network containing two candidate genes (LST1 and IGHM) and six novel genes (IGHA1, IGHD, IGHG1, IGHG3, IGLC2, and IGLJ3) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR?=?0.87, p?=?0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR?=?0.77, p?=?0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.

SUBMITTER: Hsu HM

PROVIDER: S-EPMC6418134 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis.

Hsu Huan-Ming HM Chu Chi-Ming CM Chang Yu-Jia YJ Yu Jyh-Cherng JC Chen Chien-Ting CT Jian Chen-En CE Lee Chia-Yi CY Chiang Yueh-Tao YT Chang Chi-Wen CW Chang Yu-Tien YT

Scientific reports 20190314 1

Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups a ...[more]

PMID: 30872752

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Project description:IntroductionTriple-negative breast cancer (TNBC) is a heterogeneous disease with no definitive prognostic markers. As a major component of tight junctions, claudins (CLDNs) presumably play an important role in carcinogenesis and progression of breast cancer. This study was aimed at determining the relationship between the expression of CLDNs and the clinical outcomes of TNBCs.Materials and methodsThe surgical specimens of primary breast tumors from a consecutive cohort of 173 TNBC patients were retrospectively collected. The membranous expression of CLDN1, CLDN2, CLDN4, and CLDN7 was measured by immunohistochemistry. Then, the associations between CLDN expression, clinicopathological features, and clinical outcomes were assessed.ResultsPositive CLDN1, CLDN2, CLDN4, and CLDN7 membrane expression was detected in 44.5%, 54.9%, 76.9%, and 73.4% of the cohort specimens, respectively. A lack of CLDN1 expression was related to only lymph node metastasis (P?=?0.014). The rate of CLDN4-positive tumors was significantly increased in tumors of a higher grade (P?=?0.003). Importantly, negative CLDN1 expression was associated with worse relapse-free survival (RFS) in both lymph node positive (LN+) and negative (LN-) cases (both P<0.001). Similarly it was also associated with shorter overall survival (OS)(P?=?0.003 in LN+ cases; P?=?0.018 in LN- cases). In the LN+ subgroup, CLDN2-negative cases had a significantly higher risk of recurrence (P?=?0.008). Multivariate analysis revealed that negative CLDN1 expression was an independent prognostic factor for high risk of both recurrence and death (HR 5.529, 95% CI 2.664-11.475, P<0.001; HR 3.459, 95% CI 1.555-7.696, P?=?0.002). However, neither CLDN4 nor CLDN7 expression was associated with survival.ConclusionIn TNBC, the CLDN1-negative phenotype predicts a high risk of recurrence and death. The absence of CLDN1 expression is strongly suggested to be an independent adverse prognostic factor in this heterogeneous subtype of breast cancer.

Dataset Information

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis.

Publications

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis.

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