scholarly journals The role of ferroptosis‐related genes for overall survival prediction in breast cancer

2021 ◽  
Author(s):  
Li‐Yan Jin ◽  
Yan‐lin Gu ◽  
Qi Zhu ◽  
Xiao‐hua Li ◽  
Guo‐Qin Jiang
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Survival Prediction ◽  
Overall Survival Prediction

scholarly journals Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Li Tong ◽  
Jonathan Mitchel ◽  
Kevin Chatlin ◽  
May D. Wang
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Overall Survival ◽  
Mirna Expression ◽  
Survival Prediction ◽  
Omics Data ◽  
Breast Cancer Patients ◽  
Omics Integration ◽  
Personalized Diagnosis ◽  
Overall Survival Prediction

Abstract Background Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient’s condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). Methods Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction. Results For breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641 ± 0.031 with ConcatAE, and 0.63 ± 0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583 ± 0.058) or miRNA expression (0.616 ± 0.057). Conclusions In conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.

Is it time to consider the Androgen receptor as a therapeutic target in breast cancer?

2021 ◽  
Vol 21 ◽  
Author(s):  
Melika Kooshki Forooshani ◽  
Rosa Scarpitta ◽  
Giuseppe Nicolò Fanelli ◽  
Mario Miccoli ◽  
Antonio Giuseppe Naccarato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Estrogen Receptor ◽  
Androgen Receptor ◽  
Prognostic Impact ◽  
Nuclear Receptor Superfamily ◽  
Cancer Pathogenesis ◽  
Novel Biomarkers ◽  
Tumorigenic Mechanism

: Breast cancer (BC) is a heterogeneous disease and the most prevalent malignant tumor in women worldwide. The majority of BC cases are positive for estrogen receptor (ER) and progesterone receptor (PgR), both known to be involved in cancer pathogenesis, progression, and invasion. In line with this, hormonal deprivation therapy appears to be a useful tool and an effective treatment for these BC subtypes. Unfortunately, prognosis among patients with hormone-negative tumors or therapy-refractory and metastatic patients remains poor. Novel biomarkers are urgently needed in order to predict the course of the disease, make better therapy decisions and improve the overall survival of patients. In this respect, the androgen receptor (AR), a member of the hormonal nuclear receptor superfamily and ER and PgR, emerges as an interesting feature widely expressed in human BCs. Despite the advances, the precise tumorigenic mechanism of AR and the role of its endogenous ligands are yet not well-understood. In this review, we aim to elaborate on the prognostic impact of AR expression and current AR-targeting approaches based on previous studies investigating AR's role in different BC subtypes.

scholarly journals A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

2021 ◽  
Author(s):  
Rupal Agravat ◽  
Mehul Raval
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Automatic Segmentation ◽  
High Volume ◽  
Physical Symptoms ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Neural Network Models ◽  
Brain Tumor Segmentation ◽  
Overall Survival Prediction

<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

The Regulatory Role of Both MBNL1 and MBNL1-AS1 in Several Common Cancers

2021 ◽  
Vol 27 ◽  
Author(s):  
Qi Zhang ◽  
Yinxin Wu ◽  
Jinlan Chen ◽  
Yuxuan Cai ◽  
Bei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
Gastric Cancer ◽  
Lung Cancer ◽  
Overall Survival ◽  
Survival Rate ◽  
Rna Splicing ◽  
Metabolic Regulation ◽  
Overall Survival Rate

Background: MBNL1, a protein encoded by q25 gene on chromosome 3, belongs to the tissue-specific RNA metabolic regulation family, which controls RNA splicing.[1]MBNL1 formed in the process of development drive large transcriptomic changes in cell differentiation,[2] it serves as a kind of tumor differentiation inhibitory factor.MBNL1 has a close relationship with cancer, comprehensive analysis, [3]found that breast cancer, leukemia, stomach cancer, esophageal adenocarcinoma, glial cell carcinoma and another common tumor in the cut, and cut in Huntington's disease. But MBNL1 plays a promoting role in cervical cancer, is contradictory in colorectal cancer, It promotes colorectal cancer cell proliferation, On the other hand, it inhibits its metastasis, so it is an important physiological marker in many cancers. When we integrated the role of MBNL1 protein in various tumors, we found that its antisense RNA, MBNL1-AS1, had a good inhibitory effect in several colorectal cancer, non-small cell lung cancer, and gastric cancer. Objective: To elucidate the expression of MBNL1 and MBNL1-AS1 in various tumors, and to search for their physiological markers. Methods: It was searched by the PUMUB system and summarized its expression in various cancers. Results: MBNL1 was down-regulated, leukemia, breast cancer, glioblastoma, gastric cancer, overall survival rate, recurrence, metastasis increased. While the metastasis of colon cancer decreased, proliferation was promoted, and the effect of both was promoted for cervical cancer.MBNL1-AS1 was down-regulated, and the overall survival rate, recurrence, and metastasis of lung cancer, colorectal cancer, and bladder cancer increased. Conclusion: MBNL1 may be an important regulator of cancer, and MBNL1-AS1 is a better tumor suppressor.

PO-1187 Overall survival prediction in NSCLC: a radiomic approach with a multi-VOI analysis

2021 ◽  
Vol 161 ◽  
pp. S985
Author(s):  
S. Silipigni ◽  
M. Miele ◽  
S. Gentile ◽  
E. Molfese ◽  
P. Soda ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Prediction ◽  
Overall Survival Prediction
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