Dataset Information

HLA-J, a Non-Pseudogene as a New Prognostic Marker for Therapy Response and Survival in Breast Cancer.

ABSTRACT: The human leukocyte antigen (HLA) genes are cell-surface proteins, essential for immune cell interaction. HLA-G is known for their high immunosuppressive effect and its potential as predictive marker in breast cancer. However, nothing is known about the HLA-J and its immunosuppressive, prognostic and predictive features, as it is assumed to be a "pseudogene" by in silico sequence interpretation. HLA-J, ESR1, ERBB2, KRT5 and KRT20 mRNA expression were analysed in 29 fresh frozen breast cancer biopsies and their corresponding resectates obtained from patients treated with neoadjuvant chemotherapy (NACT). mRNA was analysed with gene specific TaqMan-based Primer/Probe sets and normalized to Calmodulin 2. All breast cancer samples did express HLA-J and frequently increased HLA-J mRNA levels after NACT. HLA-J mRNA was significantly associated with overexpression of the ESR1 mRNA status (Spearman ? 0,5679; p?=?0.0090) and KRT5 mRNA (Spearman ? 0,6121; p?=?0.0041) in breast cancer core biopsies and dominated in luminal B subtype. Kaplan Meier analysis revealed that an increase of HLA-J mRNA expression after NACT had worse progression free survival (p?=?0,0096), indicating a counterreaction of tumor tissues presumably to prevent elimination by enhanced immune infiltration induced by NACT. This counterreaction is associated with worse prognosis. To our knowledge this is the first study identifying HLA-J as a new predictive marker in breast cancer being involved in immune evasion mechanisms.

SUBMITTER: Wurfel FM

PROVIDER: S-EPMC7647720 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

HLA-J, a Non-Pseudogene as a New Prognostic Marker for Therapy Response and Survival in Breast Cancer.

Würfel Franziska M FM Wirtz Ralph M RM Winterhalter Christoph C Taffurelli Mario M Santini Donatella D Mandrioli Anna A Veltrup Elke E Rübner Matthias M Fasching Peter A PA Würfel Wolfgang W Zamagni Claudio C

Geburtshilfe und Frauenheilkunde 20201106 11

The human leukocyte antigen (HLA) genes are cell-surface proteins, essential for immune cell interaction. HLA-G is known for their high immunosuppressive effect and its potential as predictive marker in breast cancer. However, nothing is known about the HLA-J and its immunosuppressive, prognostic and predictive features, as it is assumed to be a "pseudogene" by in silico sequence interpretation. HLA-J, ESR1, ERBB2, KRT5 and KRT20 mRNA expression were analysed in 29 fresh frozen breast cancer bio ...[more]

PMID: 33173240

Similar Datasets

Project description:BackgroundGiven the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival.ResultsIn our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood.ConclusionsWe recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.

Dataset Information

HLA-J, a Non-Pseudogene as a New Prognostic Marker for Therapy Response and Survival in Breast Cancer.

Publications

HLA-J, a Non-Pseudogene as a New Prognostic Marker for Therapy Response and Survival in Breast Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets