A gene signature predictive for outcome in advanced ovarian cancer identifies a novel survival factor: MAGP2
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE18521 | GEO | 2009/10/17
SECONDARY ACCESSION(S): PRJNA120193
REPOSITORIES: GEO
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Project description:BackgroundGastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC.Materials and methodsIn our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups.ResultsWe identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients.ConclusionThe signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.
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Project description:(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d'Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.
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Project description:This study aimed to investigate the predictive value of liver metastases (LM) in patients with various advanced cancers received immune-checkpoint inhibitors (ICIs). First, clinical and survival data from a published cohort of 1,661 patients who received ICIs therapy were downloaded and analyzed. Second, a retrospective review of 182 patients with advanced non-small-cell lung cancer (NSCLC) who received PD-1/PD-L1 monotherapy was identified. Third, a meta-analysis of published trials was performed to explore the impact of LM on the efficacy of anti-PD-1/PD-L1 based therapy in advanced lung cancers. Pan-cancer analysis revealed that patients with LM had significantly shorter overall survival (OS) than those without LM (10 vs. 20 months; P < 0.0001). Subgroup analysis showed that the presence of LM was associated with markedly shorter OS than those without LM in ICI monotherapy group (P < 0.0001), but it did not reach the statistical significance in ICI-based combination therapy (P = 0.0815). In NSCLC, the presence of LM was associated with significantly inferior treatment outcomes in both pan-cancer and real-world cohort. Interestingly, ICI-based monotherapy and combination therapy could simultaneously prolong progression-free survival (PFS) and OS than chemotherapy in patients without LM. However, ICI-based monotherapy could not prolong PFS than chemotherapy in patients with LM while ICI-based combination therapy could dramatically prolong both PFS and OS. Together, these findings suggested that the presence of LM was the negative predictive factor in cancer patients received ICIs monotherapy, especially in NSCLC. ICI-based combination therapy might overcome the intrinsic resistance of LM to ICIs while the optimal combinatorial strategies remain under further investigation.
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Project description:ObjectiveTo evaluate impact of germline BRCA mutational status on prognosis in patients with advanced ovarian cancer.MethodsA total of 128 patients diagnosed with FIGO stage III-IV high-grade serous ovarian cancer (HGSOC) between 2008 and 2017 and underwent BRCA1/2 gene testing at the time of or within two years from cancer diagnosis were included in this study. We compared patients' clinicopathological characteristics and survival outcomes after primary treatment according to germline BRCA mutational status. Treatment-related factors that might affect patients' survival outcome were also investigated.ResultsGermline BRCA1/2 mutations were observed in 51 women (39.8%). There were no differences in age and serum CA-125 levels at the time of HGSOC diagnosis, use of neoadjuvant chemotherapy (NAC), extent of debulking surgery, and overall survival (OS) between the BRCA mutation and wild-type BRCA groups. In contrast, the BRCA mutation group displayed longer progression-free survival (PFS) (median, 22.9 vs. 16.9 months, P = 0.001). Multivariate analyses identified germline BRCA1/2 mutation as an independent favorable prognostic factor for PFS (adjusted HR, 0.502; 95% CI, 0.318-0.795; P = 0.003). In the wild-type BRCA group, patients who received NAC as the primary treatment had shorter PFS compared to those who received primary debulking surgery (PDS) (median, 14.2 vs. 16.9 months, P = 0.003). However, in the BRCA mutation group, PFS did not differ between the NAC and PDS groups (P = 0.082).ConclusionsIn advanced-stage HGSOC, patients with germline BRCA1/2 mutations have better prognosis with longer PFS than those lacking BRCA mutations. Prognosis after NAC was different according to the BRCA mutational status.
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Project description:OBJECTIVES:Molecular prognostic biomarkers for gastric cancer (GC) are still limited. We aimed to identify potential messenger RNAs (mRNAs) associated with GC prognosis and further establish an mRNA signature to predict the survival of GC based on the publicly accessible databases. METHODS:Discovery of potential mRNAs associated with GC survival was undertaken for 441 patients with GC based on the Cancer Genome Atlas (TCGA), with information on clinical characteristics and vital status. Gene ontology functional enrichment analysis and pathway enrichment analysis were conducted to interrogate the possible biological functions. We narrowed down the list of mRNAs for validation study based on a significance level of 1.00 × 10, also integrating the information from the methylation analysis and constructing the protein-protein interaction network for elucidating biological processes. A total of 54 mRNAs were further studied in the validation stage, using the Gene Expression Omnibus (GEO) database (GSE84437, n = 433). The validated mRNAs were used to construct a risk score model predicting the prognosis of GC. RESULTS:A total of 13 mRNAs were significantly associated with survival of GC, after the validation stage, including DCLK1, FLRT2, MCC, PRICKLE1, RIMS1, SLC25A15, SLCO2A1, CDO1, GHR, CD109, SELP, UPK1B, and CD36. Except CD36, DCLK1, and SLCO2A1, other mRNAs are newly reported to be associated with GC survival. The 13 mRNA-based risk score had good performance on distinguishing GC prognosis, with a higher score indicating worse survival in both TCGA and GEO datasets. CONCLUSIONS:We established a 13-mRNA signature to potentially predict the prognosis of patients with GC, which might be useful in clinical practice for informing patient stratification.
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