Genetic variants in chromatin-remodeling pathway associated with lung cancer risk in a Chinese population

Geng, Liguo; Zhu, Meng; Wang, Yuzhuo; Yang, Cheng; Liu, Jia; Shen, Wei; Li, Zhihua; Zhang, Jiahui; Wang, Cheng; Jin, Guangfu; Ma, Hongxia; Shen, Hongbing; Hu, Zhibin; Dai, Juncheng

doi:10.1016/j.gene.2016.05.013

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…NR3C1 , encoding a tissue-specific transcriptional activator, has been widely reported to be involved in chromatin remodeling (Geng et al, 2016) and cell proliferation in tissues in situ (Souza et al, 2014). As for its distinctive expression pattern in different tumor tissues, such gene has a relatively high expression pattern in various tumor subtypes, including lung cancer (Lajoie et al, 2014) and kidney cancer (Zaravinos et al, 2014), compared with other tumor subtypes.…”

Section: Discussionmentioning

confidence: 99%

Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models

et al. 2019

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Patient-derived tumor xenograft (PDX) mouse models are widely used for drug screening. The underlying assumption is that PDX tissue is very similar with the original patient tissue, and it has the same response to the drug treatment. To investigate whether the primary tumor site information is well preserved in PDX, we analyzed the gene expression profiles of PDX mouse models originated from different tissues, including breast, kidney, large intestine, lung, ovary, pancreas, skin, and soft tissues. The popular Monte Carlo feature selection method was employed to analyze the expression profile, yielding a feature list. From this list, incremental feature selection and support vector machine (SVM) were adopted to extract distinctively expressed genes in PDXs from different primary tumor sites and build an optimal SVM classifier. In addition, we also set up a group of quantitative rules to identify primary tumor sites. A total of 755 genes were extracted by the feature selection procedures, on which the SVM classifier can provide a high performance with MCC 0.986 on classifying primary tumor sites originated from different tissues. Furthermore, we obtained 16 classification rules, which gave a lower accuracy but clear classification procedures. Such results validated that the primary tumor site specificity was well preserved in PDX as the PDXs from different primary tumor sites were still very different and these PDX differences were similar with the differences observed in patients with tumor. For example, VIM and ABHD17C were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients.

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Section: Discussionmentioning

confidence: 99%

Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models

et al. 2019

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“…PBRM1 mutant cells were more sensitive to both the PARP inhibitor and, to a lesser extent, ABL1 inhibitor than the control cells (Fig 5 with plate photography in S5 Fig), whereas the PBRM1 mutant cells appeared less sensitive to the POLA1 inhibitor than the control cells (Fig 5). Interestingly, cells lacking ARID1A, which is another SWI/SNF subunit, are also selectively sensitive to PARP inhibitors [59, 60], which supports this relationship. We also note this ARID1A / PARP1 SSL interaction was not present in the BioGRID data used to generate our training set but was also predicted with a high probability by SLant.…”

Section: Resultsmentioning

confidence: 86%

Predicting synthetic lethal interactions using conserved patterns in protein interaction networks

Benstead-Hume¹,

Chen²,

Hopkins³

et al. 2019

PLoS Comput Biol

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In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.

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“…Pollutants in the atmosphere can invade the human respiratory system, causing chronic bronchitis, allergic diseases, acute respiratory infections, asthma, lung cancer and other respiratory diseases [4]. In particular, incidences of and mortality from lung cancer have increased rapidly over the past few decades [5].…”

Section: Introductionmentioning

confidence: 99%

Relationships between lung cancer incidences and air pollutants

Yue

Wang

et al. 2017

THC

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Genetic variants in chromatin-remodeling pathway associated with lung cancer risk in a Chinese population

Cited by 5 publications

References 31 publications

Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models

Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models

Predicting synthetic lethal interactions using conserved patterns in protein interaction networks

Relationships between lung cancer incidences and air pollutants

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