Integrative prognostic subtype discovery in high‐grade serous ovarian cancer

Xie, Hongyu; Xu, Huan; Hou, Yan; Cai, Yuqing; Rong, Zhiwei; Song, Wei; Wang, Wenjie; Li, Kang

doi:10.1002/jcb.29049

Cited by 8 publications

(7 citation statements)

References 40 publications

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“…25 mRNA profiling using the TCGA data set found that upregulated DSG2 expression correlated with worse highgrade serous ovarian cancer (HGSC) prognosis among platinum-sensitive patients. 26 Chen et al correlated DSG2 expression and survival of ovarian cancer patients and concluded that DSG2 may be involved in the progression of specific types of ovarian cancer. 27 Here we specifically investigated DSG2, including shed serum DSG2, as a potential ovarian cancer biomarker.…”

Section: Introductionmentioning

confidence: 99%

Desmoglein-2 as a prognostic and biomarker in ovarian cancer

Kim

Beidler

Wang

et al. 2020

Cancer Biology & Therapy

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Greater than 80% of all cancer cases are carcinomas, formed by the malignant transformation of epithelial cells. One of the key features of epithelial tumors is the presence of intercellular junctions, which link cells to one another and act as barriers to the penetration of molecules. This study assessed the expression of desmoglein-2, an epithelial junction protein, as a prognostic and diagnostic biomarker for ovarian cancer. Ovarian cancer sections were stained for DSG2 and signal intensity was correlated to cancer type and grade. DSG2 immunohistochemistry signals and mRNA levels were analyzed in chemo-resistant and chemo-sensitive cases. Ovarian cancer patient serum levels of shed DSG2 were correlated to diseasefree and overall survival. Primary ovarian cancer cells were used to study DSG2 levels as they changed in response to cisplatin treatment. DSG2 expression was found to be positively correlated with cancer grade. Ovarian cancer patients with high serum levels of shed DSG2 fared significantly worse in both progression-free survival (median survival of 16 months vs. 26 months, p = .0023) and general survival (median survival of 37 months vs. undefined, p < .0001). A subgroup of primary chemotherapy-resistant cases had stronger DSG2 IHC/Western signals and higher DSG2 mRNA levels. Furthermore, our in vitro studies indicate that non-cytotoxic doses of cisplatin can enhance DSG2 expression, which, in turn, can contribute to chemo-resistance. We suggest that DSG2 can be used in stratifying patients, deciding on where to use aggressive treatment strategies, predicting chemoresistance, and as a companion diagnostic for treatments targeting DSG2.

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Section: Introductionmentioning

confidence: 99%

Desmoglein-2 as a prognostic and biomarker in ovarian cancer

Kim

Beidler

Wang

et al. 2020

Cancer Biology & Therapy

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“…(B) The Kaplan-Meier Plotters demonstrated the different overall survival of TPX2 highly expressed LUAD patients (red) and TPX2 lowly expressed LUAD patients (black) in TCGA, GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets systems based on the transcriptional profiling of cancers are developed. Self-organizing maps (SOMs) clustering, 52,53 integrative iCluster clustering, 5,54,55 networkbased consensus molecular classification (CMS), 56,57 and nonnegative matrix factorization (NMF) clustering 26,28,31,34 are all proved to be robust cancer classification systems. However, for the same cohort of tumor patients, distinct clustering methods may result different classification.…”

Section: Discussionmentioning

confidence: 99%

Nonnegative matrix factorization‐based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub‐consensuses of lung adenocarcinoma

Wang

et al. 2021

Cancer Medicine

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“…In addition to beforementioned works, there are a huge examples of iCluster use with TCGA. For instance, in Xie et al (2019) an integrative analysis was carried out with iCluster through RNAseq and proteomics data to analyse the OV subtype. The results showed two clusters with different survival rates; the method identified 18 mRNAs and 38 proteins as distinct molecules among subtypes.…”

Section: Machine Learning As a Source Of New Knowledgementioning

confidence: 99%

Machine learning analysis of TCGA cancer data

Liñares-Blanco

Fernández-Lozano

2021

PeerJ Computer Science

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In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.

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Integrative prognostic subtype discovery in high‐grade serous ovarian cancer

Cited by 8 publications

References 40 publications

Desmoglein-2 as a prognostic and biomarker in ovarian cancer

Desmoglein-2 as a prognostic and biomarker in ovarian cancer

Nonnegative matrix factorization‐based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub‐consensuses of lung adenocarcinoma

Machine learning analysis of TCGA cancer data

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