KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness.
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ABSTRACT:
SUBMITTER: Lin HY
PROVIDER: S-EPMC8084951 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Excessive alcohol intake is a well-known modifiable risk factor for many cancers. It is still unclear whether genetic variants or single nucleotide polymorphisms (SNPs) can modify alcohol intake's impact on prostate cancer (PCa) aggressiveness. The objective is to test the alcohol-SNP interactions of the 7501 SNPs in the four pathways (angiogenesis, mitochondria, miRNA, and androgen metabolism-related pathways) associated with PCa aggressiveness. We evaluated the impacts of three excessive alcohol intake behaviors in 3306 PCa patients with European ancestry from the PCa Consortium. We tested the alcohol-SNP interactions using logistic models with the discovery-validation study design. All three excessive alcohol intake behaviors were not significantly associated with PCa aggressiveness. However, the interactions of excessive alcohol intake and three SNPs (rs13107662 [CAMK2D, p = 6.2 × 10-6], rs9907521 [PRKCA,p = 7.1 × 10-5], and rs11925452 [ROBO1,p = 8.2 × 10-4]) were significantly associated with PCa aggressiveness. These alcohol-SNP interactions revealed contrasting effects of excessive alcohol intake on PCa aggressiveness according to the genotypes in the identified SNPs. We identified PCa patients with the rs13107662 (CAMK2D) AA genotype, the rs11925452 (ROBO1) AA genotype, and the rs9907521 (PRKCA) AG genotype were more vulnerable to excessive alcohol intake for developing aggressive PCa. Our findings support that the impact of excessive alcohol intake on PCa aggressiveness was varied by the selected genetic profiles.
| S-EPMC7867322 | biostudies-literature
Project description:Angiogenesis has been shown to be associated with prostate cancer development. The majority of prostate cancer studies focused on individual single nucleotide polymorphisms (SNPs) while SNP-SNP interactions are suggested having a great impact on unveiling the underlying mechanism of complex disease. Using 1,151 prostate cancer patients in the Cancer Genetic Markers of Susceptibility (CGEMS) dataset, 2,651 SNPs in the angiogenesis genes associated with prostate cancer aggressiveness were evaluated. SNP-SNP interactions were primarily assessed using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM) approach in the CGEMS group, and were then re-evaluated in the Moffitt group with 1,040 patients. For the identified gene pairs, cross-evaluation was applied to evaluate SNP interactions in both study groups. Five SNP-SNP interactions in three gene pairs (MMP16+ ROBO1, MMP16+ CSF1, and MMP16+ EGFR) were identified to be associated with aggressive prostate cancer in both groups. Three pairs of SNPs (rs1477908+ rs1387665, rs1467251+ rs7625555, and rs1824717+ rs7625555) were in MMP16 and ROBO1, one pair (rs2176771+ rs333970) in MMP16 and CSF1, and one pair (rs1401862+ rs6964705) in MMP16 and EGFR. The results suggest that MMP16 may play an important role in prostate cancer aggressiveness. By integrating our novel findings and available biomedical literature, a hypothetical gene interaction network was proposed. This network demonstrates that our identified SNP-SNP interactions are biologically relevant and shows that EGFR may be the hub for the interactions. The findings provide valuable information to identify genotype combinations at risk of developing aggressive prostate cancer and improve understanding on the genetic etiology of angiogenesis associated with prostate cancer aggressiveness.
| S-EPMC3618555 | biostudies-literature
Project description:Prostate cancer (PCa) is one of the most significant male health concerns worldwide. Single nucleotide polymorphisms (SNPs) are becoming increasingly strong candidate biomarkers for identifying susceptibility to PCa. We identified a number of SNPs reported in genome-wide association analyses (GWAS) as risk factors for aggressive PCa in various European populations, and then defined SNP-SNP interactions, using PLINK software, with nucleic acid samples from a New Zealand cohort. We used this approach to find a gene x environment marker for aggressive PCa, as although statistically gene x environment interactions can be adjusted for, it is highly impossible in practicality, and thus must be incorporated in the search for a reliable biomarker for PCa. We found two intronic SNPs statistically significantly interacting with each other as a risk for aggressive prostate cancer on being compared to healthy controls in a New Zealand population.
| S-EPMC5437948 | biostudies-literature
Project description:ObjectivesT o evaluate the value of multiparametric MRI (mpMRI) for the prediction of prostate cancer (PCA) aggressiveness.MethodsIn this single center cohort study, consecutive patients with histologically confirmed PCA were retrospectively enrolled. Four different ISUP grade groups (1, 2, 3, 4-5) were defined and fifty patients per group were included. Several clinical (age, PSA, PSAD, percentage of PCA infiltration) and mpMRI parameters (ADC value, signal increase on high b-value images, diameter, extraprostatic extension [EPE], cross-zonal growth) were evaluated and correlated within the four groups. Based on combined descriptors, MRI grading groups (mG1-mG3) were defined to predict PCA aggressiveness.ResultsIn total, 200 patients (mean age 68 years, median PSA value 8.1 ng/ml) were analyzed. Between the four groups, statistically significant differences could be shown for age, PSA, PSAD, and for MRI parameters cross-zonal growth, high b-value signal increase, EPE, and ADC (p < 0.01). All examined parameters revealed a significant correlation with the histopathologic biopsy ISUP grade groups (p < 0.01), except PCA diameter (p = 0.09). A mixed linear model demonstrated the strongest prediction of the respective ISUP grade group for the MRI grading system (p < 0.01) compared to single parameters.ConclusionsMpMRI yields relevant pre-biopsy information about PCA aggressiveness. A combination of quantitative and qualitative parameters (MRI grading groups) provided the best prediction of the biopsy ISUP grade group and may improve clinical pathway and treatment planning, adding useful information beyond PI-RADS assessment category. Due to the high prevalence of higher grade PCA in patients within mG3, an early re-biopsy seems indicated in cases of negative or post-biopsy low-grade PCA.Key points• MpMRI yields relevant pre-biopsy information about prostate cancer aggressiveness. • MRI grading in addition to PI-RADS classification seems to be helpful for a size independent early prediction of clinically significant PCA. • MRI grading groups may help urologists in clinical pathway and treatment planning, especially when to consider an early re-biopsy.
| S-EPMC8921105 | biostudies-literature
Project description:Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.
| S-EPMC8657292 | biostudies-literature
Project description:Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.
| S-EPMC8792745 | biostudies-literature
Project description:Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation. Magnetic resonance spectroscopy (MRS)-based metabolomics of histologically-benign tissues from cancerous prostates can predict disease aggressiveness, offering clinically-translatable prognostic information. This retrospective study of 185 patients (2002-2009) included prostate tissues from prostatectomies (n?=?365), benign prostatic hyperplasia (BPH) (n?=?15), and biopsy cores from cancer-negative patients (n?=?14). Tissues were measured with high resolution magic angle spinning (HRMAS) MRS, followed by quantitative histology using the Prognostic Grade Group (PGG) system. Metabolic profiles, measured solely from 338 of 365 histologically-benign tissues from cancerous prostates and divided into training-testing cohorts, could identify tumor grade and stage, and predict recurrence. Specifically, metabolic profiles: (1) show elevated myo-inositol, an endogenous tumor suppressor and potential mechanistic therapy target, in patients with highly-aggressive cancer, (2) identify a patient sub-group with less aggressive prostate cancer to avoid overtreatment if analysed at biopsy; and (3) subdivide the clinicopathologically indivisible PGG2 group into two distinct Kaplan-Meier recurrence groups, thereby identifying patients more at-risk for recurrence. Such findings, achievable by biopsy or prostatectomy tissue measurement, could inform treatment strategies. Metabolomics information can help transform a morphology-based diagnostic system by invoking cancer biology to improve evaluation of histologically-benign tissues in cancer environments.
| S-EPMC5980000 | biostudies-literature
Project description:PurposeThe Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.Material and methodsA total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.ResultsThe 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.ConclusionsMRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.
| S-EPMC11019191 | biostudies-literature
Project description:IntroductionFinancial toxicity (FT) is a growing concern among cancer survivors that adversely affects the quality of life and survival. Individuals diagnosed with aggressive cancers are often at a greater risk of experiencing FT. The objectives of this study were to estimate FT among prostate cancer (PCa) survivors after 10-15 years of diagnosis, assess the relationship between PCa aggressiveness at diagnosis and FT, and examine whether current cancer treatment status mediates the relationship between PCa aggressiveness and FT.MethodsPCa patients enrolled in the North Carolina-Louisiana Prostate Cancer Project (PCaP) were recontacted for long-term follow-up. The prevalence of FT in the PCaP cohort was estimated. FT was estimated using the COmprehensive Score for Financial Toxicity, a validated measure of FT. The direct effect of PCa aggressiveness and an indirect effect through current cancer treatment on FT was examined using causal mediation analysis.ResultsMore than one-third of PCa patients reported experiencing FT. PCa aggressiveness was significantly independently associated with high FT; high aggressive PCa at diagnosis had more than twice the risk of experiencing FT than those with low or intermediate aggressive PCa (adjusted odds ratio [aOR] = 2.13, 95% CI = 1.14-3.96). The proportion of the effect of PCa aggressiveness on FT, mediated by treatment status, was 10%, however, the adjusted odds ratio did not indicate significant evidence of mediation by treatment status (aOR = 1.05, 95% CI = 0.95-1.20).ConclusionsAggressive PCa was associated with high FT. Future studies should collect more information about the characteristics of men with high FT and identify additional risk factors of FT.
| S-EPMC10087487 | biostudies-literature
Project description:Genome-wide association studies have identified genetic markers in kallikrein-related peptidase 3 (KLK3) associated with prostate cancer. However, some of these markers are also associated with prostate-specific antigen (PSA) levels, so it is unclear whether the polymorphisms are causal or if the association with risk is solely due to detection bias through PSA screening. PSA is a biologically active serine protease, cleaving insulin-like growth factor-binding protein. We examined the association of single-nucleotide polymorphisms (SNPs) in KLK3 with prostate cancer risk, disease-specific survival and pre-diagnostic PSA levels in a case-control study nested within the Physicians' Health Study, which began in 1982, with over 27 years of follow-up. We genotyped SNPs spanning the entire KLK3 locus to capture common variation at high resolution. Six polymorphisms were significantly associated with prostate cancer incidence (P < 0.05); the odds ratios per minor allele ranged from 0.88 to 0.73. For four of these, the odds ratios were lower when restricting to cases diagnosed in the pre-PSA screening era (before 1989). The four alleles significantly associated with lower PSA levels were also associated with lower prostate cancer risk. KLK3 variants were not significantly associated with stage at diagnosis, risk of lethal cancer or survival. Our results suggest that detection bias due to the association of KLK3 variants with PSA levels cannot completely explain the association with prostate cancer risk. Understanding the mechanism by which genetic variation in KLK3 affects prostate cancer risk has important implications for study of the biological role of PSA in prostate tumorigenesis.
| S-EPMC3106437 | biostudies-literature