Breast cancer prediction using different machine learning methods applying multi factors.

OBJECTIVE: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS: The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT: Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION: Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

Association of a Genetic Variant in the Cyclin-Dependent Kinase Inhibitor 2B with Risk of Pancreatic Cancer.

Background: Pancreatic cancer (PC) is among the most aggressive tumors with a poor prognosis, indicating the need for the identification of a novel prognostic biomarker for risk stratifications. Recent genome-wide association studies have demonstrated common genetic variants in a region on chromosome 9p21 associated with an increased risk of different malignancies. Methods: In the present study, we explore the possible relationship between genetic variant, rs10811661, and gene expression of CDKN2B in 75 pancreatic cancer patients, and 188 healthy individuals. DNAs were extracted and genotyping and gene expression were performed by TaqMan real-time PCR and RT-PCR, respectively. Logistic regression was used to assess the association between risk and genotypes, while the significant prognostic variables in the univariate analysis were included in multivariate analyses. Results: The patients with PDAC had a higher frequency of a TT genotype for rs10811661 than the control group. Also, PDAC patients with dominant genetic model, (TT + TC), was associated with increased risk of developing PDAC (OR= 14.71, 95% CI [1.96-110.35], p= 0.009). Moreover, patients with CC genotype had a higher expression of CDKN2B, in comparison with TT genotype. Conclusion: Our findings demonstrated that CDKN2A/B was associated with the risk of developing PDAC, supporting further investigations in the larger and multicenter setting to validate the potential value of this gene as an emerging marker for PDAC.