Determining the Prognostic Significance of Alternative Splicing Events in Soft Tissue Sarcoma Using Data From The Cancer Genome Atlas

2019 ◽  
Author(s):  
Xia Yang ◽  
Wen-ting Huang ◽  
Rong-quan He ◽  
Jie Ma ◽  
Peng Lin ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Alternative Splicing Events ◽  
Using Data ◽  
Genome Atlas

scholarly journals Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 227 ◽  
Author(s):  
Qi Wan ◽  
Xuan Sang ◽  
Lin Jin ◽  
Zhichong Wang
Keyword(s):  
Alternative Splicing ◽  
Uveal Melanoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Support Vector ◽  
Cancer Genome Atlas ◽  
Alternative Splicing Events ◽  
Genome Atlas

Growing evidence has revealed that abnormal alternative splicing (AS) events are closely related to carcinogenic processes. However, the comprehensive study on the prognostic value of splicing events involved in uveal melanoma (UM) is still lacking. Therefore, splicing data of 80 UM patients were obtained from the Cancer Genome Atlas (TCGA) SpliceSeq and RNA sequence data of UM and patient clinical features were downloaded from the Cancer Genome Atlas (TCGA) database to identify survival related splicing events in UM. As a result, a total of 37996 AS events of 17911 genes in UM were detected, among which 5299 AS events of 3529 genes were significantly associated with UM patients’ survival. Functional enrichment analysis revealed that this survival related splicing genes are corelated with mRNA catabolic process and ribosome pathway. Based on survival related splicing events, seven types of prognostic markers and the final overall prognostic signature could independently predict the overall survival of UM patients. Finally, an 11 spliced gene was identified in the final signature. On the basis of these 11 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. The results showed that the 11 genes could determine short- and long-term survival with a predicted accuracy of 97.5%. Besides, the splicing factors and alternative splicing events correlation network was constructed to serve as therapeutic targets for UM treatment. Thus, our study depicts a comprehensive landscape of alternative splicing events in the prognosis of UM. The correlation network and associated pathways would provide additional potential targets for therapy and prognosis.

Identification of Aneuploid Circulating Tumor Cells in Soft-Tissue Sarcoma Patients: A Pilot Study

Oncology ◽  
2020 ◽  
Vol 98 (12) ◽  
pp. 893-896
Author(s):  
Andrea Napolitano ◽  
Alessandro Minelli ◽  
Daniele Santini ◽  
Giuseppe Tonini ◽  
Bruno Vincenzi
Keyword(s):  
Overall Survival ◽  
Pilot Study ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Tumor Cells ◽  
In Silico ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
First Time ◽  
Genome Atlas

<b><i>Background:</i></b> Circulating tumor cells (CTCs) have been identified and shown to have prognostic and predictive roles in several types of carcinoma. More recently, aneuploid CTCs have become subject of a growing interest, as aneuploidy is considered a hallmark of cancer often associated with poor prognosis. Here, we aimed to identify for the first time aneuploid CTCs in soft-tissue sarcoma (STS) patients and show supportive in silico evidence on the prognostic role of aneuploidy in mesenchymal cancers. <b><i>Methods:</i></b> In our pilot study, we collected blood from 4 metastatic STS patients and 4 age- and sex-matched healthy controls. After sample processing, cells were cyto-centrifuged onto glass slides and FISH was performed using 5 probes. The in silico analysis was performed using data from The Cancer Genome Atlas cohort of STS patients, using the validated Aneuploidy Score. We divided the patients in two populations (aneuploidy-high, Ane-Hi, and aneuploidy-low, Ane-Lo) using the median value of the Aneuploidy Score as a cutoff. Kaplan-Meier curves associated with log-rank test were used to compare progression-free and overall survival between groups. GraphPad Prism 8.0 (La Jolla, CA, USA) was used for statistical analyses. <b><i>Results:</i></b> Aneuploid CTCs were identified in all 4 STS patients and in none of the controls, with a median value of 4 (range 3–6) per 7 mL of blood. Ane-Hi patients showed a significantly worse progression-free and overall survival compared to Ane-Lo patients. The same trend was maintained when analyzing the data based on the different histologies. <b><i>Conclusions:</i></b> We identified for the first time aneuploid CTCs in STS patients using fluorescence in situ hybridization in a surface marker-independent way. We also showed that the Aneuploidy Score has a prognostic value both in terms of progression-free survival and overall survival in STS patients using The Cancer Genome Atlas data, regardless of the histology.

scholarly journals Identification of ST7 Alteration Profile, Frequency of Alteration and Correlation with ST7-Related Genes using TCGA Data

2019 ◽  
Vol 16 (3) ◽  
pp. 217-230
Author(s):  
Nurdina CHARONG ◽  
Moltira PROMKAN
Keyword(s):  
Disease Free Survival ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Using Data ◽  
Liver Hepatocellular Carcinoma ◽  
Disease Free ◽  
Genome Atlas

ST7 (Suppression of Tumorigenicity 7) was reported as a protein playing a role in maintaining cellular structure. This study aims to investigate the ST7 alteration profiles and frequency of alteration in different cancers using data from The Cancer Genome Atlas (TCGA). The correlation between alterations of ST7 and angiogenesis-related genes, SERPINE1, MMP13, and VEGFA, was determined and the relation between ST7 and genes involved in suppression of ST7 transcription, PRMT5 and SMARCA4, were also analyzed. Data of 6 cancer groups from The Cancer Genome Atlas (TCGA) including ovarian serous cystadenocarcinoma (OSC), liver hepatocellular carcinoma (LHC), bladder urothelial adenocarcinoma (BUA), stomach adenocarcinoma (SC), prostate adenocarcinoma (PRAD) and glioblastoma multiforme (GBM) were downloaded for this study. The results indicated that 3 alteration patterns including amplification, missense mutation, and deletion were observed in 6 cancer studies. Gene pair between ST7 and SERPINE1 indicated the co-occurrent alteration in BUC, OSC and SC (p < 0.05). However, no association between alterations of these 2 genes and survival events in our study was observed. Shorter overall survival rate and disease-free survival were found in BUC patients with ST7, PRMT5, and  SMARCA4 alterations. These findings suggest that using TCGA data can target the potential genes involved in carcinogenesis. Combining ST7 with PRMT5 and SMARCA4 could be used as indicators for analyzing the patient survival in BUC patients and may serve as the potential therapeutic target for cancer in the future.

Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽  
2021 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

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