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A random forest classifier predicts recurrence risk in patients with ovarian cancer.

ABSTRACT: Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non?recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein?protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with 'positive regulation of steroid hormone biosynthetic processes'. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA?4; fibroblast growth factor 9; aromatase; 3??hydroxysteroid dehydrogenase/?5?4?isomerase type 2; corticosteroid 11??dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left?right determination factor 1; homeobox protein ARX; estrogen receptor ?; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist?related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non?recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

SUBMITTER: Cheng L

PROVIDER: S-EPMC6102638 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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A random forest classifier predicts recurrence risk in patients with ovarian cancer.

Cheng Li L Li Lin L Wang Liling L Li Xiaofang X Xing Hui H Zhou Jinting J

Molecular medicine reports 20180719 3

Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non‑recurrent OC samples) and GSE49997 (204 OC samples), were ...[more]

PMID: 30066910

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Project description:Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from patients with non‑recurrent and recurrent OC were revealed via a homogeneity test and quality control analysis. A protein‑protein interaction (PPI) network was subsequently established for the DEGs using data from Biological General Repository for Interaction Datasets, Human Protein Reference Database and Database of Interacting Proteins. Degrees of interaction and betweenness centrality (BC) scores were calculated for each node in the PPI network. The top 100 genes ranked by BC scores were selected to identify feature genes via recursive feature elimination using the GSE17260 dataset. Following this, a SVM classifier was constructed and further validated using the GSE44104 and GSE51088 datasets and independent gene expression data obtained from the Cancer Genome Atlas (TCGA). A total of 639 DEGs were identified from the three gene expression datasets, and a PPI network including 249 nodes and 354 edges was constructed. A SVM classifier consisting of 39 feature genes (including cullin 3, mouse double minute 2 homolog, aurora kinase A, WW domain containing oxidoreducatase, large tumor suppressor kinase 2, sirtuin 6, staphylococcal nuclease and tudor domain containing 1, leucine rich repeats and immunoglobulin like domains 1 and aurora kinase 1 interacting protein 1) was subsequently constructed. The prediction accuracies of the SVM classifier for GSE17260, GSE44104 and GSE51088 datasets as well as data downloaded from TCGA were revealed to be 92.7, 93.3, 96.6 and 90.4%, respectively. Furthermore, the results of the present study revealed that patients with predicted non‑recurrent OC survived significantly longer compared with the patients with predicted recurrent OC (P=6.598x10‑6). A SVM classifier consisting of 39 feature genes was established for predicting the recurrence and prognosis of OC. Therefore, the results of the present study suggested that the 39 feature genes may serve important roles in the development of OC and may represent therapeutic biomarkers of OC.

Project description:BackgroundMicrosatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size.ResultsWe developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases).ConclusionThese results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.

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A random forest classifier predicts recurrence risk in patients with ovarian cancer.

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A random forest classifier predicts recurrence risk in patients with ovarian cancer.

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