scholarly journals Random forest-based modelling to detect biomarkers for prostate cancer progression

2019 ◽  
Author(s):  
Reka Toth ◽  
Heiko Schiffmann ◽  
Claudia Hube-Magg ◽  
Franziska Büscheck ◽  
Doris Höflmayer ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Random Forest ◽  
Candidate Genes ◽  
Cancer Progression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Gleason Grade ◽  
Tissue Micro Array

AbstractThe clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy and robust prognostic markers for treatment decisions. We present a random forest-based classification model to predict aggressive behaviour of PCa. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n=70) were used as input. The model was validated with data from two large independent PCa cohorts from the “International Cancer Genome Consortium” (ICGC) and “The Cancer Genome Atlas” (TCGA). Ranking of cancer progression-related DNA methylation changes allowed selection of candidate genes for additional validation by immunohistochemistry. We identified loss of ZIC2 protein expression, mediated by alterations in DNA methylation, as a promising novel prognostic biomarker for PCa in >12,000 tissue micro-array tumors. The prognostic value of ZIC2 proved to be independent from established clinico-pathological variables including Gleason grade, tumor stage, nodal stage and PSA. In summary, we have developed a PCa classification model, which either directly orviaexpression analyses of the identified top ranked candidate genes might help in decision making related to the treatment of prostate cancer patients.

scholarly journals Random forest-based modelling to detect biomarkers for prostate cancer progression

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Reka Toth ◽  
Heiko Schiffmann ◽  
Claudia Hube-Magg ◽  
Franziska Büscheck ◽  
Doris Höflmayer ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Random Forest ◽  
Protein Expression ◽  
Candidate Genes ◽  
Cancer Progression ◽  
Poor Prognosis ◽  
Classification Model ◽  
Discovery Cohort ◽  
Test Set

Abstract Background The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy. Overtreatment of indolent PCa cases, which likely do not progress to aggressive stages, may be associated with severe side effects and considerable costs. These could be avoided by utilizing robust prognostic markers to guide treatment decisions. Results We present a random forest-based classification model to predict aggressive behaviour of prostate cancer. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n = 70) were used as input. DNA was extracted from formalin-fixed tumour tissue, and genome-wide DNA methylation differences between both groups were assessed using Illumina HumanMethylation450 arrays. For the random forest-based modelling, the discovery cohort was randomly split into a training (80%) and a test set (20%). Our methylation-based classifier demonstrated excellent performance in discriminating prognosis subgroups in the test set (Kaplan-Meier survival analyses with log-rank p value < 0.0001). The area under the receiver operating characteristic curve (AUC) for the sensitivity analysis was 95%. Using the ICGC cohort of early- and late-onset prostate cancer (n = 222) and the TCGA PRAD cohort (n = 477) for external validation, AUCs for sensitivity analyses were 77.1% and 68.7%, respectively. Cancer progression-related DNA hypomethylation was frequently located in ‘partially methylated domains’ (PMDs)—large-scale genomic areas with progressive loss of DNA methylation linked to mitotic cell division. We selected several candidate genes with differential methylation in gene promoter regions for additional validation at the protein expression level by immunohistochemistry in > 12,000 tissue micro-arrayed PCa cases. Loss of ZIC2 protein expression was associated with poor prognosis and correlated with significantly shorter time to biochemical recurrence. The prognostic value of ZIC2 proved to be independent from established clinicopathological variables including Gleason grade, tumour stage, nodal stage and prostate-specific-antigen. Conclusions Our results highlight the prognostic relevance of methylation loss in PMD regions, as well as of several candidate genes not previously associated with PCa progression. Our robust and externally validated PCa classification model either directly or via protein expression analyses of the identified top-ranked candidate genes will support the clinical management of prostate cancer.

scholarly journals Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel towards Prostate Cancer Relapse

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 257
Author(s):  
Yan Gu ◽  
Mathilda Jing Chow ◽  
Anil Kapoor ◽  
Xiaozeng Lin ◽  
Wenjuan Mei ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Differential Expression ◽  
Cancer Progression ◽  
Lncap Cell ◽  
Endocrine Resistance ◽  
Gene Panel ◽  
The Cancer Genome Atlas ◽  
Cancer Center ◽  
Poor Overall Survival ◽  
Cell Renal Cell Carcinoma

Contactin 1 (CNTN1) is a new oncogenic protein of prostate cancer (PC); its impact on PC remains incompletely understood. We observed CNTN1 upregulation in LNCaP cell-derived castration-resistant PCs (CRPC) and CNTN1-mediated enhancement of LNCaP cell proliferation. CNTN1 overexpression in LNCaP cells resulted in enrichment of the CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_3 gene set that facilitates endocrine resistance in breast cancer. The leading-edge (LE) genes (n = 10) of this enrichment consist of four genes with limited knowledge on PC and six genes novel to PC. These LE genes display differential expression during PC initiation, metastatic progression, and CRPC development, and they predict PC relapse following curative therapies at hazard ratio (HR) 2.72, 95% confidence interval (CI) 1.96–3.77, and p = 1.77 × 10−9 in The Cancer Genome Atlas (TCGA) PanCancer cohort (n = 492) and HR 2.72, 95% CI 1.84–4.01, and p = 4.99 × 10−7 in Memorial Sloan Kettering Cancer Center (MSKCC) cohort (n = 140). The LE gene panel classifies high-, moderate-, and low-risk of PC relapse in both cohorts. Additionally, the gene panel robustly predicts poor overall survival in clear cell renal cell carcinoma (ccRCC, p = 1.13 × 10−11), consistent with ccRCC and PC both being urogenital cancers. Collectively, we report multiple CNTN1-related genes relevant to PC and their biomarker values in predicting PC relapse.

scholarly journals Epigenetic Variation Analysis Leads to Biomarker Discovery in Gastric Adenocarcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yan Zhang ◽  
Dianjing Guo
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Gastric Adenocarcinoma ◽  
Histone Modifications ◽  
Biomarker Discovery ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Epigenetic Variation

As one of the most common malignant tumors worldwide, gastric adenocarcinoma (GC) and its prognosis are still poorly understood. Various genetic and epigenetic factors have been indicated in GC carcinogenesis. However, a comprehensive and in-depth investigation of epigenetic alteration in gastric cancer is still missing. In this study, we systematically investigated some key epigenetic features in GC, including DNA methylation and five core histone modifications. Data from The Cancer Genome Atlas Program and other studies (Gene Expression Omnibus) were collected, analyzed, and validated with multivariate statistical analysis methods. The landscape of epi-modifications in gastric cancer was described. Chromatin state transition analysis showed a histone marker shift in gastric cancer genome by employing a Hidden-Markov-Model based approach, indicated that histone marks tend to label different sets of genes in GC compared to control. An additive effect of these epigenetic marks was observed by integrated analysis with gene expression data, suggesting epigenetic modifications may cooperatively regulate gene expression. However, the effect of DNA methylation was found more significant without the presence of the five histone modifications in our study. By constructing a PPI network, key genes to distinguish GC from normal samples were identified, and distinct patterns of oncogenic pathways in GC were revealed. Some of these genes can also serve as potential biomarkers to classify various GC molecular subtypes. Our results provide important insights into the epigenetic regulation in gastric cancer and other cancers in general. This study describes the aberrant epigenetic variation pattern in GC and provides potential direction for epigenetic biomarker discovery.

scholarly journals FXR silencing in human colon cancer by DNA methylation and KRAS signaling

2014 ◽  
Vol 306 (1) ◽  
pp. G48-G58 ◽  
Author(s):  
Ann M. Bailey ◽  
Le Zhan ◽  
Dipen Maru ◽  
Imad Shureiqi ◽  
Curtis R. Pickering ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Colon Cancer ◽  
Epithelial To Mesenchymal Transition ◽  
Human Colon ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Human Colon Cancer ◽  
Mesenchymal Transition ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Farnesoid X receptor (FXR) is a bile acid nuclear receptor described through mouse knockout studies as a tumor suppressor for the development of colon adenocarcinomas. This study investigates the regulation of FXR in the development of human colon cancer. We used immunohistochemistry of FXR in normal tissue ( n = 238), polyps ( n = 32), and adenocarcinomas, staged I–IV ( n = 43, 39, 68, and 9), of the colon; RT-quantitative PCR, reverse-phase protein array, and Western blot analysis in 15 colon cancer cell lines; NR1H4 promoter methylation and mRNA expression in colon cancer samples from The Cancer Genome Atlas; DNA methyltransferase inhibition; methyl-DNA immunoprecipitation (MeDIP); bisulfite sequencing; and V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) knockdown assessment to investigate FXR regulation in colon cancer development. Immunohistochemistry and quantitative RT-PCR revealed that expression and function of FXR was reduced in precancerous lesions and silenced in a majority of stage I-IV tumors. FXR expression negatively correlated with phosphatidylinositol-4, 5-bisphosphate 3 kinase signaling and the epithelial-to-mesenchymal transition. The NR1H4 promoter is methylated in ∼12% colon cancer The Cancer Genome Atlas samples, and methylation patterns segregate with tumor subtypes. Inhibition of DNA methylation and KRAS silencing both increased FXR expression. FXR expression is decreased early in human colon cancer progression, and both DNA methylation and KRAS signaling may be contributing factors to FXR silencing. FXR potentially suppresses epithelial-to-mesenchymal transition and other oncogenic signaling cascades, and restoration of FXR activity, by blocking silencing mechanisms or increasing residual FXR activity, represents promising therapeutic options for the treatment of colon cancer.

scholarly journals Prediction of Biochemical Recurrence-Free Survival of Prostate Cancer Patients Leveraging Multiple Gene Expression Profiles in Tumor Microenvironment

2021 ◽  
Vol 11 ◽  
Author(s):  
Rui Zhou ◽  
Yuanfa Feng ◽  
Jianheng Ye ◽  
Zhaodong Han ◽  
Yuxiang Liang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Tumor Microenvironment ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Biochemical Recurrence ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cell Contact ◽  
Recurrence Free Survival ◽  
Free Survival

Tumor-adjacent normal (TAN) tissues, which constitute tumor microenvironment and are different from healthy tissues, provide critical information at molecular levels that can be used to differentiate aggressive tumors from indolent tumors. In this study, we analyzed 52 TAN samples from the Cancer Genome Atlas (TCGA) prostate cancer patients and developed a 10-gene prognostic model that can accurately predict biochemical recurrence-free survival based on the profiles of these genes in TAN tissues. The predictive ability was validated using TAN samples from an independent cohort. These 10 prognostic genes in tumor microenvironment are different from the prognostic genes detected in tumor tissues, indicating distinct progression-related mechanisms in two tissue types. Bioinformatics analysis showed that the prognostic genes in tumor microenvironment were significantly enriched by p53 signaling pathway, which may represent the crosstalk tunnels between tumor and its microenvironment and pathways involving cell-to-cell contact and paracrine/endocrine signaling. The insight acquired by this study has advanced our knowledge of the potential role of tumor microenvironment in prostate cancer progression.

scholarly journals Candidate genes for predicting the survival of patients with gastric cancer: a study based on The Cancer Genome Atlas (TCGA) database

2020 ◽  
Vol 9 (4) ◽  
pp. 2599-2608
Author(s):  
Xiqiao Liu ◽  
Liying Gao ◽  
Dongqiong Ni ◽  
Chengao Ma ◽  
Yuping Lu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Candidate Genes ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiyi Pu ◽  
Chao Li ◽  
Haining Yuan ◽  
Xiaoju Wang
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
In Silico ◽  
Prostate Cancer Screening ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Translation Rate ◽  
Normal Prostate ◽  
Cancer Data ◽  
Significant Difference

Abstract Background Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns. Results We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1). Conclusions Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value.

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