scholarly journals Prospective validation in epithelial tumors of a gene expression predictor of liver metastasis derived from uveal melanoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Petros Tsantoulis ◽  
Mauro Delorenzi ◽  
Ivan Bièche ◽  
Sophie Vacher ◽  
Pascale Mariani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Uveal Melanoma ◽  
Meta Analysis ◽  
Prognostic Score ◽  
Penalized Regression ◽  
Breast Cancer Patients ◽  
Public Data

AbstractPredicting the risk of liver metastasis can have important prognostic and therapeutic implications, given the availability of liver-directed therapy. Uveal melanoma has a striking predisposition for liver metastasis despite the absence of anatomical proximity. Understanding its biology may uncover factors promoting liver metastasis in other malignancies. We quantified gene expression by RNAseq in 76 uveal melanomas and combined with public data in a meta-analysis of 196 patients. The meta-analysis of uveal melanoma gene expression identified 63 genes which remained prognostic after adjustment for chromosome 3 status. Two genes, PTP4A3 and JPH1, were selected by L1-penalized regression and combined in a prognostic score. The score predicted liver-specific relapse in a public pan-cancer dataset and in two public colorectal cancer datasets. The score varied between colorectal consensus molecular subtypes (CMS), as did the risk of liver relapse, which was lowest in CMS1. Additional prospective validation was done by real-time PCR in 463 breast cancer patients. The score was significantly correlated with liver relapse in hormone receptor positive tumors. In conclusion, the expression of PTP4A3 and JPH1 correlates with risk of liver metastasis in colorectal cancer and breast cancer. The underlying biological mechanism is an interesting area for further research.

scholarly journals High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoli Hu ◽  
Yang Liu ◽  
Zhitong Bing ◽  
Qian Ye ◽  
Chengcheng Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Systematic Review ◽  
Cancer Patients ◽  
Poor Prognosis ◽  
Data Extraction ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Breast Cancer Patients ◽  
Node Metastases

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases (P < 0.05), and high MSN expression was associated with poor outcome of breast cancer patients (HR = 1.99; 95% CI 1.73–2.24). In summary, there is available evidence to support that high MSN expression has valuable importance for the poor prognosis in breast cancer patients.Systematic Review Registrationhttps://inplasy.com/inplasy-2020-8-0039/.

scholarly journals Gene Expression Profiling of Evening Fatigue in Women Undergoing Chemotherapy for Breast Cancer

2016 ◽  
Vol 18 (4) ◽  
pp. 370-385 ◽  
Author(s):  
Kord M. Kober ◽  
Laura Dunn ◽  
Judy Mastick ◽  
Bruce Cooper ◽  
Dale Langford ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sickness Behavior ◽  
Cell Types ◽  
Breast Cancer Patients ◽  
Oncology Patients ◽  
Public Data ◽  
Comorbidity Scores

Moderate-to-severe fatigue occurs in up to 94% of oncology patients undergoing active treatment. Current interventions for fatigue are not efficacious. A major impediment to the development of effective treatments is a lack of understanding of the fundamental mechanisms underlying fatigue. In the current study, differences in phenotypic characteristics and gene expression profiles were evaluated in a sample of breast cancer patients undergoing chemotherapy (CTX) who reported low ( n = 19) and high ( n = 25) levels of evening fatigue. Compared to the low group, patients in the high evening fatigue group reported lower functional status scores, higher comorbidity scores, and fewer prior cancer treatments. One gene was identified as upregulated and 11 as downregulated in the high evening fatigue group. Gene set analysis found 24 downregulated and 94 simultaneously up- and downregulated pathways between the two fatigue groups. Transcript origin analysis found that differential expression (DE) originated primarily from monocytes and dendritic cell types. Query of public data sources found 18 gene expression experiments with similar DE profiles. Our analyses revealed that inflammation, neurotransmitter regulation, and energy metabolism are likely mechanisms associated with evening fatigue severity; that CTX may contribute to fatigue seen in oncology patients; and that the patterns of gene expression may be shared with other models of fatigue (e.g., physical exercise and pathogen-induced sickness behavior). These results suggest that the mechanisms that underlie fatigue in oncology patients are multifactorial.

scholarly journals A meta-analysis of Watson for Oncology in clinical application

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Jie ◽  
Zeng Zhiying ◽  
Li Li
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Meta Analysis ◽  
Small Cell ◽  
Breast Cancer Patients ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
Colorectal Cancer Patients

AbstractUsing the method of meta-analysis to systematically evaluate the consistency of treatment schemes between Watson for Oncology (WFO) and Multidisciplinary Team (MDT), and to provide references for the practical application of artificial intelligence clinical decision-support system in cancer treatment. We systematically searched articles about the clinical applications of Watson for Oncology in the databases and conducted meta-analysis using RevMan 5.3 software. A total of 9 studies were identified, including 2463 patients. When the MDT is consistent with WFO at the ‘Recommended’ or the ‘For consideration’ level, the overall concordance rate is 81.52%. Among them, breast cancer was the highest and gastric cancer was the lowest. The concordance rate in stage I–III cancer is higher than that in stage IV, but the result of lung cancer is opposite (P < 0.05).Similar results were obtained when MDT was only consistent with WFO at the "recommended" level. Moreover, the consistency of estrogen and progesterone receptor negative breast cancer patients, colorectal cancer patients under 70 years old or ECOG 0, and small cell lung cancer patients is higher than that of estrogen and progesterone positive breast cancer patients, colorectal cancer patients over 70 years old or ECOG 1–2, and non-small cell lung cancer patients, with statistical significance (P < 0.05). Treatment recommendations made by WFO and MDT were highly concordant for cancer cases examined, but this system still needs further improvement. Owing to relatively small sample size of the included studies, more well-designed, and large sample size studies are still needed.

scholarly journals Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xu Yang ◽  
Geng-Xi Cai ◽  
Bo-Wei Han ◽  
Zhi-Wei Guo ◽  
Ying-Song Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Cell Receptor ◽  
Chemotherapy Response ◽  
Breast Cancer Patients ◽  
Plasma Dna ◽  
Transcription Start Sites ◽  
Response To Chemotherapy

AbstractGene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

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