An integrative multi-omics network-based approach identifies key regulators for breast cancer

Chen, Yixiao; Yu, Rong; Jiang, Feng; Chen, Jiabin; Duan, Yuanyuan; Dong, Shanshan; Zhu, Dong-Li; Chen, Hao; Yang, Tie‐Lin; Dai, Zhijun; Guo, Yan

doi:10.1016/j.csbj.2020.10.001

Cited by 16 publications

(10 citation statements)

References 69 publications

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“…RNASEH2A (RNase H2 subunit A) was a protein encoding gene, and its RNASEH2A related diseases include Aicardi-Goutieres syndrome and Aicardi-Goutieres syndrome, associated with cancer progression and cell cycle ( Marsili et al, 2021 ). In our study, it was related to the overall survival rate of cervical cancer, which was consistent with previous studies ( Huang et al, 2021 ), and this gene plays a similar role in lung cancer ( Zhang et al, 2021 ) and breast cancer ( Chen et al, 2020 ). CETN2 belonged to the calcium binding protein family and is the structural component of centrosome.…”

Section: Discussionsupporting

confidence: 93%

DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer

Wang

Sun

2022

Front. Genet.

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Background: The mechanism of DNA damage repair plays an important role in many solid tumors represented by cervical cancer.Purpose: The purpose of this study was to explore the effect of DNA damage repair-related genes on immune function of patients with cervical cancer, and to establish and evaluate a prognosis model based on DNA damage repair-related genes.Methods: In the study, we analyzed the genes related to DNA damage and repair, and obtained two subtypes (F1 and F2). We selected two groups of samples for different selection, and studied which pathways were enriched expression. For different subtypes, the immune score was explored to explain immune infiltration. We got the key genes through screening, and established the prognosis model through the key genes. These 11 key genes were correlated with the expression of common Clusters of Differentiation (CD) genes in order to explore the effects of these genes on immunity.Results: Through the Least absolute shrinkage and selection operator (LASSO) method, we screened 11 genes from 232 candidate genes as the key genes for the prognosis score. Through the Kaplan-Meier method, four genes (HAP1, MCM5, RNASEH2A, CETN2) with significant prognostic significance were screened into the final model, forming a Nomogram with C-index of 0.716 (0.649–1.0).Conclusion: In cervical cancer, DNA damage repair related genes and immune cell infection characteristics have certain association, and DNA damage repair related genes and immune cell infection characteristics can effectively predict the prognosis.

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Section: Discussionsupporting

confidence: 93%

DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer

Wang

Sun

2022

Front. Genet.

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“…In fact, it is important to emphasized that BC, as each biological phenomenon, is characterized by interdependent layers of biological features. While a single omics approach can catch only a slice of the complex pathological system, multi-omics integration offers an unprecedented opportunity that is the possibility of capturing a deeper and more complete description of the pathological phenomena under study, with translation into clinically relevant information [ 11 , 14 , 170 ]. In conclusion, in comparison to traditional analysis focused on single biological layer, integrative and holistic multi-omics approaches, despite being complicated by the high dimensionality and heterogeneity of the data and the lack of universal analysis protocols, represent new opportunities for studying complex diseases in a more comprehensive way [ 171 – 173 ].…”

Section: Discussionmentioning

confidence: 99%

Breast cancer in the era of integrating “Omics” approaches

et al. 2022

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Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

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“…Radiomic features alone show great promise in the stratifying response to NAC, and models incorporating a combination of radiomics and molecular feature are superior (45,46). It is conceivable that radiomic features could be a component of future multi-omic panels including genomic and metabolomic markers to aid in the management of breast cancer (47,48). We added ER status to moderately improve the overall accuracy of the model to predict response to NAC and produce a greater accuracy in classifying patients into poor and excellent response groups than conventional molecular subtype alone.…”

Section: Radiomic Model With Estrogen Receptor Statusmentioning

confidence: 99%

A Radiomic Model To Classify Response To Neoadjuvant Chemotherapy in Breast Cancer

McAnena¹,

Moloney²,

Browne³

et al. 2021

Preprint

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Background Medical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer. Methods Data on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced (DCE) breast MRI were included. Response to NAC was assessed using the Miller-Payne system on the excised tumour. Tumour segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator (LASSO) regression. A support vector machine (SVM) learning model was used to classify response to NAC. Results 74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity <90%, n=44) and an excellent response (>90% reduction in cellularity, n=30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor (ER) status improved the accuracy of the model with an AUC of 0.811. Conclusion This study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

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An integrative multi-omics network-based approach identifies key regulators for breast cancer

Abstract: Graphical abstract

Cited by 16 publications

References 69 publications

DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer

DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer

Breast cancer in the era of integrating “Omics” approaches

A Radiomic Model To Classify Response To Neoadjuvant Chemotherapy in Breast Cancer

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