Project description:Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Gene expression analysis was performed on five serous borderline tumors with BRAF mutation and five serous borderline tumors without BRAF mutation randomly. RNA was extracted from microdissected tumor cells. Expression profiling was carried out with Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays

Project description:Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed.

Project description:Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear. Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N 6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA). Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs. Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Dataset Information

Advanced stage, high-grade primary tumor ovarian cancer: a multi-omics dissection and biomarker prediction process.

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Advanced stage, high-grade primary tumor ovarian cancer: a multi-omics dissection and biomarker prediction process.

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