Jalees Ur Rahman

Abnormali�es in DNA Methyla�on: A Biomarker for Kidney Cancer Jalees Ur Rahman, Raja Hashim, and Mohammad Hanif Faculty of Computer Sciences and Engineering Ghulam Ishaq Khan Ins�tute of Engineering Sciences and Technology Corresponding email:- 1 Introduc�on 2 Role of Methyla�on Epigene�c modifica�ons via methyla�on play a significant role in deoxyribonucleic acid (DNA) replica�on and transcrip�on by switching genes expression ON or OFF. Any abnormality in methyla�on can lead to complex diseases like cancer, diabetes, schizophrenia and various common neurologic diseases [1]. DNA methyla�on is one of the most common epigene�c modifica�ons in developing cancer by controlling the process of transcrip�on. The hypermethyla�on (elevated ac�vity of methyltransferase) of tumor suppressor genes [4] and the hypomethyla�on (elevated ac�vity of demethylase) of oncogenes [3]; both lead to cancer. 3 Hypermethyla�on Leads To Leads To Hypomethyla�on 1. Hypermethyla�on of tumor suppressor genes = Tumor cells [1] 2. Hypomethyla�on of oncogenes = Tumor cells [4] 3. Hypermethyla�on of oncogenes = Normal cells [3] 4. Hypomethyla�on of tumor suppressor genes = Normal cells [5] Objec�ve Hypermethyla�on of tumor genes and Hypomethyla�on of tumor suppressor genes. Demethylase Methyltransferase Leads To Hypomethyla�on Hypermethyla�on Leads To Methyla�on flow between normal and tumor cells Balance required 4 Dataset 5 Data Processing Project Name: Kidney Renal Cell Carcinoma Poject ID: TCGA-KIRC [6] Disease Type: Adenomas and Adenocarcinomas Data Catagory: DNA Methyla�on Pla�orm: Illumina Human Methyla�on 450K BeadChip Access: Open source Cases: 319 (214 alive + 105 dead pa�ents) Samples: 485 (324 tumors + 161 normal cells) Data Format: TXT Data Size: 63.8 GBs Dataset categoriza�on Data Separa�on: Separated all DNA methyla�on data (450K BeadChip pla�orm) available on GDC portal of KIRC project Processing of Each Sample: Considered each case’s DNA methyla�on data file separately and then processed Data Normaliza�on: Removed data redundancy and normalized the data Beta Value Check: Unmethylated: 0.25 >= β Hemimethylated: 0.25 < β < 0.75 Fully Methylated: β >= 0.75 Averaged Top Genes: Averaged all genes between all samples and then selected top involved genes Data Separa�on Data Normaliza�on Clean Data Beta Value Check 3 4 5 6 7 8 DIP2C RPTOR INPP5A HDAC4 PRDM16 TBCD - - SNHG14 ATP11A TNXB MCF2L ADARB2 AGAP1 - Gene SHANK2 PRKCZ Hemi-Methylated Fully Methylated Frequent Genes Frequent Genes Frequent Genes Venn Diagram Flow chart of data preprocessing Index 41 42 Gene EIF2B5 NXN CUX1 LMF1 ARHGEF10 ANKRD11 PRKAR1B TRAPPC9 - NFATC1 EBF3 PRRC2A SLC12A7 SMOC2 PCGF3 - NCOR2 KCNQ1 CFAP46 MGMT COL11A2 DLGAP2 - EHMT2 CAMTA1 SUN1 LRP5 SLC45A4 GALNT9 RASA3 SDK1 ERICH1-AS1 RPS6KA2 C7orf50 - GAK GABBR1 MSLN TSNARE1 MYT1L - AGO2 RIMBP2 PPT2-EGFL8 CASZ1 GOLGA3 BANP 40 FOXK1 60 Y_RNA Genes: CASZ1 TSSC1 TFDP1 PRKAG2 TNRC18 SYNGAP1 DNAAF5 l cel Norma genes Index 21 22 Unmethylated Averaged Top Genes Results Gene PTPRN2 MAD1L1 Normal Sample Each Sample Noisy Data Technologies Used Index 1 2 Processing of Each Sample Separately Tumor Samples Tumor ce ll genes 6 450K BeadChip Methyla�on Data Genes: NOTCH4 TENM4 PDE10A GPR133 OBSCN UVSSA Top 100 methylated genes comparison between normal and tumor cells 7 Next Steps Different comparisons between methylated and unmethylated genes of normal and tumor cells have been performed. Pa�ents are divided into different labels shown in the stacked bar. Next plan is to compare methylated and unmethylated genes of these pa�ents under each label and see the correla�on and devia�on between each label. Another plan is to add other features to these labels from available clinical drug data and pa�ents history data like drug given, therapy type, ethnicity and race of pa�ents etc. and train a neural network model. Final goal is to predict kidney cancer from the trained neural network model at early stages using DNA methyla�on data of pa�ent. Alive | Dead (Pa�ents) Comparison line between normal and tumor cells top 60 methylated genes Labels of pa�ents: status vs pathologic stage 8 References 1. Ba�agli, C., Uzzo, R.G., Dulaimi, E., de Caceres, I.I., 2003. Promoter hypermethyla�on of tumor suppressor genes in urine from kidney cancer pa�ents. Cancer Research, 63(24), pp-. Di�harot, K., Dakeng, S., Suebsakwong, P., Suksamrarn, 2019. Cucurbitacin B Induces Hypermethyla�on of Oncogenes in Breast Cancer Cells. Planta medica, 85(05), pp-. Ashktorab, H. and Brim, H., 2014. DNA methyla�on and colorectal cancer. Current colorectal cancer reports, 10(4), pp-. Garzon, R., Liu, S., 2009. MicroRNA-29b induces global DNA hypomethyla�on and tumor suppressor gene reexpression in acute myeloid leukemia by targe�ng directly DNMT3A and 3B and indirectly DNMT1. Blood, 113(25), pp-. Cancer Genome Atlas Research Network, 2013. Comprehensive molecular characteriza�on of clear cell renal cell carcinoma. Nature,-), p.43. For more informa�on