Survey of Tumors Reveals Second Gene "Signature"

Abstract In a previous study we reported that prediabetic rats have a unique gene signature that was apparent even in neonates. Several of the changes we observed, including enhanced expression of pro-inflammatory genes and dysregulated UPR and metabolism genes were first observed in the liver followed by the pancreas. In the present study we investigated further early changes in hepatic innate immunity and metabolism in two models of type 1 diabetes (T1D), the BBdp rat and NOD mouse. There was a striking increase in lipid deposits in liver, particularly in neonatal BBdp rats, with a less striking but significant increase in neonatal NOD mice in association with dysregulated expression of lipid metabolism genes. This was associated with a decreased number of extramedullary hematopoietic clusters as well as CD68+ macrophages in the liver of both models. In addition, PPARɣ and phosphorylated AMPKα protein were decreased in neonatal BBdp rats. BBdp rats displayed decreased expression of antimicrobial genes in neonates and decreased M2 genes at 30 days. This suggests hepatic steatosis could be a common early feature in development of T1D that impacts metabolic homeostasis and tolerogenic phenotype in the prediabetic liver.

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Normal tissue content impact on the GBM molecular classification

Briefings in Bioinformatics ◽

10.1093/bib/bbaa129 ◽

2020 ◽

Author(s):

Rodrigo Madurga ◽

Noemí García-Romero ◽

Beatriz Jiménez ◽

Ana Collazo ◽

Francisco Pérez-Rodríguez ◽

...

Keyword(s):

Normal Tissue ◽

In Silico Analysis ◽

Molecular Classification ◽

Gene Signature ◽

Normal Brain ◽

Normal Cells ◽

Final Model ◽

Tumor Subtypes ◽

Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

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Single Cell RNA Sequencing of Peripheral Blood Mononuclear Cells in Idiopathic Pulmonary Fibrosis Reveals the Cellular Origin of the Outcome Predictive 52-Gene Signature

10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a6211 ◽

2020 ◽

Author(s):

A. Unterman ◽

N. Neumark ◽

A. Zhao ◽

J.C. Schupp ◽

C. Ryu ◽

...

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Peripheral Blood Mononuclear Cells ◽

Peripheral Blood ◽

Mononuclear Cells ◽

Gene Signature ◽

Peripheral Blood Mononuclear ◽

Cellular Origin ◽

Single Cell Rna Sequencing ◽

Blood Mononuclear Cells

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Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

Scientific Reports ◽

10.1038/s41598-021-83628-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Feng Jiang ◽

Chuyan Wu ◽

Ming Wang ◽

Ke Wei ◽

Jimei Wang

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Cox Regression ◽

Single Gene ◽

Prediction Method ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Models ◽

Normal Tissues ◽

Predictive Risk

AbstractOne of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

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Ovarian cycle stage critically affects 21-gene recurrence scores in Mmtv-Pymt mouse mammary tumours

BMC Cancer ◽

10.1186/s12885-021-08496-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sarah M. Bernhardt ◽

Pallave Dasari ◽

Danielle J. Glynn ◽

Lucy Woolford ◽

Lachlan M. Moldenhauer ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Menstrual Cycle ◽

Recurrence Score ◽

Gene Signature ◽

Ovarian Cycle ◽

Oncotype Dx ◽

Mammary Tumours ◽

Er Positive ◽

The Impact

Abstract Background The Oncotype DX 21-gene Recurrence Score is predictive of adjuvant chemotherapy benefit for women with early-stage, estrogen receptor (ER)-positive, HER2-negative breast cancer. In premenopausal women, fluctuations in estrogen and progesterone during the menstrual cycle impact gene expression in hormone-responsive cancers. However, the extent to which menstrual cycling affects the Oncotype DX 21-gene signature remains unclear. Here, we investigate the impact of ovarian cycle stage on the 21-gene signature using a naturally cycling mouse model of breast cancer. Methods ER-positive mammary tumours were dissected from naturally cycling Mmtv-Pymt mice at either the estrus or diestrus phase of the ovarian cycle. The Oncotype DX 21-gene signature was assessed through quantitative real time-PCR, and a 21-gene experimental recurrence score analogous to the Oncotype DX Recurrence Score was calculated. Results Tumours collected at diestrus exhibited significant differences in expression of 6 Oncotype DX signature genes (Ki67, Ccnb1, Esr1, Erbb2, Grb7, Bag1; p ≤ 0.05) and a significant increase in 21-gene recurrence score (21.8 ± 2.4; mean ± SEM) compared to tumours dissected at estrus (15.5 ± 1.9; p = 0.03). Clustering analysis revealed a subgroup of tumours collected at diestrus characterised by increased expression of proliferation- (p < 0.001) and invasion-group (p = 0.01) genes, and increased 21-gene recurrence score (p = 0.01). No correlation between ER, PR, HER2, and KI67 protein abundance measured by Western blot and abundance of mRNA for the corresponding gene was observed, suggesting that gene expression is more susceptible to hormone-induced fluctuation compared to protein expression. Conclusions Ovarian cycle stage at the time of tissue collection critically affects the 21-gene signature in Mmtv-Pymt murine mammary tumours. Further studies are required to determine whether Oncotype DX Recurrence Scores in women are similarly affected by menstrual cycle stage.

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