uterine carcinosarcoma
Recently Published Documents


TOTAL DOCUMENTS

408
(FIVE YEARS 117)

H-INDEX

32
(FIVE YEARS 3)

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Shuxia Han ◽  
Qing Liu ◽  
ZhiJuan Yang ◽  
JingWen Ma ◽  
Dan Liu ◽  
...  

Purpose. Iron metabolism and ferroptosis play crucial roles in the pathogenesis of cancer. In this study, we aim to study the role of ferroptosis-related genes (FRGs) in uterine carcinosarcoma (UCS) and identify potential target for UCS. Methods. Prognostic differentially expressed FRGs were identified of in the TCGA cohort. Integrated analysis, cox regression, and the least absolute shrinkage and selection operator (LASSO) methods of FRGs were performed to construct a multigene signature prognostic model. Moreover, a dataset from Gene Expression Omnibus (GEO) served as an external validation. HSF1 was knockdown in MES-SA and FU-MMT-1 cells, and cell viability, lipid ROS, and intracellular iron level were detected when combined with doxorubicin or gemcitabine. Result. Five FRGs were selected to construct a prognostic model of UCS. The group with high-risk signature score exhibited obviously lower overall survival (OS) than the group with low risk signature score in both TCGA and validated GEO cohorts. Multivariate Cox regression analysis further indicated that the risk score was an independent factor for the prognosis of UCS patients. The high-risk group of UCS has a higher sensitivity in the treatment of doxorubicin and gemcitabine. Knocking down of HSF1 in MES-SA and FU-MMT-1 cells was more sensitive to doxorubicin and gemcitabine via increasing ferroptosis. Conclusions. The five FRGs risk signature prognostic model having a superior and drug sensitivity predictive performance for OS in UCS, and HSF1 is a potential marker sensitive to doxorubicin and gemcitabine in UCS patients.


2021 ◽  
pp. 100904
Author(s):  
A. ElNaggar ◽  
N. Zhang ◽  
C.B. Scalise ◽  
C. Sirard ◽  
M.H. Kagey ◽  
...  

2021 ◽  
Vol 14 (12) ◽  
pp. e247643
Author(s):  
Pedro Carvalho Almeida ◽  
Luís Amaral Ferreira ◽  
Paulo Donato

2021 ◽  
pp. 100912
Author(s):  
Miller P. Singleton ◽  
Sirisha Thambuluru ◽  
Teresa Samulski ◽  
Sarah E. Paraghamian ◽  
Leslie H. Clark

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jesse Lopes da Silva ◽  
Lucas Zanetti de Albuquerque ◽  
Fabiana Resende Rodrigues ◽  
Guilherme Gomes de Mesquita ◽  
Cláudia Bessa Pereira Chaves ◽  
...  

Abstract Objective To examine the prevalence and prognostic role of tumor microenvironment (TME) markers in uterine carcinosarcoma (UCS) through immunohistochemical characterization. Methods The internal database of our institution was queried out for women with UCS who underwent surgery and thereafter postoperative chemotherapy with carboplatin and paclitaxel between January 2012 and December 2017. Tissue microarrays containing surgical samples of UCS from 57 women were assessed by immunohistochemistry for CD3, CD4, CD8, FOXP3, PD-1, PD-L1, and PD-L2. Results The mean age was 65.3 years (range, 49 to 79 years). For the epithelial component (E), CD3_E and CD4_E were highly expressed in 38 (66.7%) and in 40 (70.1%) patients, respectively, and were significantly associated with more advanced stages (p = 0.038 and p = 0.025, respectively). CD8_E was highly expressed in 42 (73.7%) patients, FOXP3_E 16 (28.1%), PD-1_E 35 (61.4%), PD-L1_E 27 (47.4%) and PD-L2_E 39 (68.4%). For the sarcomatous component (S), the prevalence of high expression was: CD3_S 6 (10.5%), CD4_S 20 (35.1%), CD8_S 44 (77.2%), FOXP3_S 8 (14%), PD-1_S 14 (24.6%), PD-L1_S 14 (24.6%) and PD-L2_S 8 (14%). By multivariate analysis, the CD8/FOXP3_S ratio (p = 0.026), CD4_E (p = 0.010), PD-L1_E (p = 0.013) and PD-L1_S (p = 0.008) markers significantly influenced progression-free survival. CD4/FOXP3_S ratio (p = 0.043), PD-1_E (p = 0.011), PD-L1_E (p = 0.036) and PD-L1_S (p = 0.028) had a significant association with overall survival. Conclusion Some differences in UCS clinical outcomes may be due to the subtype of TILs and PD-1/PD-L1 axis immune checkpoint signaling.


2021 ◽  
Vol 8 ◽  
Author(s):  
Maksim Sorokin ◽  
Elizaveta Rabushko ◽  
Victor Efimov ◽  
Elena Poddubskaya ◽  
Marina Sekacheva ◽  
...  

Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.


Author(s):  
Allison R Hickman ◽  
Yuqing Hang ◽  
Rini Pauly ◽  
Frank A Feltus

Abstract Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene co-expression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential co-regulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared to previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.


Sign in / Sign up

Export Citation Format

Share Document