Integrative genomic and epigenomic analyses identified IRAK1 as a novel target for chronic inflammation-driven prostate tumorigenesis

Saheed Oluwasina Oseni1,*, Olayinka Adebayo2, Adeyinka Adebayo3, Alexander Kwakye4, Mirjana Pavlovic5, Waseem Asghar5, James Hartmann1, Gregg B. Fields6, and James Kumi-Diaka1

Affiliations: 1 Department of Biological Sciences, Florida Atlantic University, Florida, USA 2 Morehouse School of Medicine, Atlanta, Georgia, USA 3 Georgia Institute of Technology, Atlanta, Georgia, USA 4 College of Medicine, Florida Atlantic University, Florida, USA 5 Department of Computer and Electrical Engineering, Florida Atlantic University, Florida, USA 6 Department of Chemistry & Biochemistry and I-HEALTH, Florida Atlantic University, Florida, USA

Corresponding Author: [email protected] (S.O.O)

List of Supplementary Materials Supplementary Method S1: Preparing genomic and clinical data for biological network analysis

Supplementary Method S2: WGCNA R packages for biological network analysis Supplementary Table S1: Enrichr enrichment analysis of the 10 inflammatory in the magenta module based on a significant p-value.

Supplementary Table S2: Functional impact ranking rubrics for IRAK1 gene mutations in PRAD samples using algorithms from different tools.

Supplementary Figure S1: Batch effect correction plots for the PRAD dataset.

Supplementary Figure S2: Hierarchical sample cluster heatmap of PRAD eigengene network.

Supplementary Figure S3: Hierarchical cluster heatmap of PRAD eigengene network.

Supplementary Figure S4: Kaplan-Meier DFS survival analysis for high-expressing vs low-expressing PRAD samples.

Supplementary Figure S5: Receiver Operating Characteristic (ROC) analysis for IRAK in predicting cancer relapse.

Supplementary Figure S6: Heatmap of the expression profile of IRAK family genes in indolent PRAD patients (n = 472) showing the overexpression of IRAK1 compared to other IRAKs.

Supplementary Figure S7: Heatmap of the expression profile of IRAK family genes in neuroendocrine PCa patients (n = 30) showing the overexpression of IRAK1 compared to other IRAKs.

Supplementary Figure S8: Heatmap of the expression profile of IRAK family genes in castration-resistant PCa patients (n = 444) showing the overexpression of IRAK1 compared to other IRAKs.

Supplementary Figure S9: Lollipop plots showing the identified structural mutations of IRAK1, IRAK2, IRAK3, and IRAK4 in PCa patients from the 20 cohort studies.

Supplementary Figure S10: -protein interaction and enrichment network analyses in PRAD.

Supplementary Figure S11: Genes Word-Cloud Generator showing all the 588 genes identified in the magenta module.

Supplementary Figure S12: Diseases Word-Cloud Generator showing all the diseases associated with aberrant IRAK1 signaling.

Supplementary Figure S13: Boxplots to compare Eigengene values between progressed vs non-progressed patients in the magenta and brown modules.

Supplementary Figure S14: A flow chart summarizing the post hoc multivariate analyses undertaken for screening and identification of significant inflammatory genes in the magenta module.

Supplementary Figure S15: A principal component analysis (PCA) dimensionality reduction of IRAK1 expression in PRAD and non-PRAD normal samples.

Supplementary Figure S16. GO Elite ontologies of the 9 biologically significant modules.

Supplementary Figure S17: A detailed genomic sketch of IRAK1 showing information on the 13 transcripts/isoforms identified as well as exons, gene coding sequence regions (CDS), and untranslated regions (UTR).

Supplementary Figure S18 (A-M): Correlation analysis plots using Pearson coefficient to identify the correlation between various IRAK1 CpG methylation values and the expression levels of IRAK1 isoforms.

Supplementary Method S1: Preparing clinical and genomic datasets for biological network analysis. The non-numeric clinical data were converted to numeric and binary units as follows: Tumor Type: “0” -> “PRAD Acinar-type” or “1” -> “Other PRAD Subtype”. PFS_Status: “0” -> “Remission (Non-progressed)” and “1” -> “Progressed/Recurred”. OS_Status/Vital Status: “0” -> “Dead” or “1” -> “Living”. DS_Status: “0” -> “Disease-free”; and “1” -> “Recurred”. Race: “1” -> “White American”; “2” -> “African American”; or “3” -> “Asian American”. Prior Radiation Therapy Status: “0”-> “No” or “1” -> “Yes”. Biochemical Recurrence: “0”-> “No” or “1”-> “Yes”. Gleason Score: “0” -> “G0” or “T0”; “1” -> “G6” or “T1/T2a/T2b/T2c”; “2” -> “G7 or “T3a/T3b”; and “3” -> “G8 or “T4”. Tumor (T) Staging Pathology: “0” -> “T0”; “1” -> “T1”; “2” -> “T2a”; “3” -> “T2b”; “4” -> “T2c”; “5” -> “T3a”; “6” -> “T3b” or “7” -> “T4”. Nearby Lymph Node (N) Staging: “0” -> “N0” or “1” -> “N1”. Distant Metastasis (M) Staging: “0” -> “M0”; “1” -> “M1”; “2” -> “M1a”; “3” -> “M1b”; or “4” -> “M1c”. DFS_Status: “0” -> “Alive or Dead with tumor-free” or “1” -> “Dead or Alive with tumor”. Cancer Status: “0” -> “Tumor-free” or “1” -> “With tumor”. Biochemical Recurrence: “0” -> No or “1” -> “Yes”. PFS_Months: “1” -> “Above cut-off: If > 25.8 (median) months” or “0 -> “Below cut-off, if < 25.8 (median) months”. DFS_Months: “1” -> “Above cut-off, If > 30.35 (median) months” or “0” -> “Below cut-off, if < 30.4 (median) months”. OS_Months: “0” -> “Below cut-off, if < 30.4 (median) months”; or “1” -> “Above cut-off: If > 30.4 (median) months”. DS_Months: “1” -> “Above cut-off: If > 30.4 (median) months”; or “0” -> “Below median cut-off, if < 30.4 (median) months”. Acronyms: OS: Overall survival; PFS: Progression-free survival; DFS: Disease-free survival; DS: Disease status

Supplementary Method S2: WGCNA R packages for biological network analysis. The WGCNA package was installed from the Comprehensive R Archive Network (CRAN), the standard repository for R add-on packages. To run the WGCNA package, the installation tools/algorithms for the following packages were obtained from Bioconductor and installed in R: “GO.db”, “dynamicTreeCut”, “doParallel”, “parallel”, “stats”, “foreach”, “AnnotationDbi”, “impute”, “utils”, “splines”, “matrixStats”, “Hmisc”, “fastcluster”, “survival”, “grDevices”, and “preprocessCore”. Other supportive packages used while running the WGCNA pipeline include “NMF”, “igraph”, “ggplot2”, “RColorBrewer”, “Cairo”, "doParallel", "biomaRt", "plotly", "stringr", “boot”, and "callr".

Supplementary Table S1: Enrichr gene enrichment analysis of the 10 inflammatory genes in the magenta module based on a significant p-value. The first column is showing their enriched (biological processes), the second column is the p-value (< 0.05) while the last column indicates the genes of the 10 inflammatory genes involved in the biological processes. Of the 10 genes, IRAK1 and HMGB2 have the most biologically significant functions (p < 0.05).

Gene Ontologies P-values Genes

Positive regulation of megakaryocyte differentiation 0.0038941 HMGB2 (GO:0045654) Regulation of nuclease activity (GO:0032069) 0.0038941 HMGB2 V(D)J recombination (GO:0033151) 0.00454175 HMGB2 DNA topological change (GO:0006265) 0.0058359 HMGB2 Positive regulation of nuclease activity (GO:0032075) 0.0064824 HMGB2 DNA ligation involved in DNA repair (GO:0051103) 0.00777422 HMGB2 Apoptotic DNA fragmentation (GO:0006309) 0.00970904 HMGB2 Regulation of stem cell proliferation (GO:0072091) 0.01035321 HMGB2 DNA ligation (GO:0006266) 0.01164039 HMGB2 Regulation of cell development (GO:0060284) 0.01292602 HMGB2 Positive regulation of erythrocyte differentiation 0.01421011 HMGB2 (GO:0045648) DNA catabolic process, endonucleolytic (GO:0000737) 0.01421011 HMGB2 Apoptotic nuclear changes (GO:0030262) 0.01485157 HMGB2 Regulation of nervous system development (GO:0051960) 0.01613335 HMGB2 Positive regulation of myeloid cell differentiation 0.01869229 HMGB2 (GO:0045639) Regulation of erythrocyte differentiation (GO:0045646) 0.02060746 HMGB2 Positive regulation of DNA binding (GO:0043388) 0.02188232 HMGB2 Regulation of megakaryocyte differentiation (GO:0045652) 0.03139508 HMGB2 Regulation of DNA binding (GO:0051101) 0.03328735 HMGB2 Regulation of neurogenesis (GO:0050767) 0.03643356 HMGB2 Cell chemotaxis (GO:0060326) 0.03831674 HMGB2 Nucleosome assembly (GO:0006334) 0.03831674 HMGB2 Defense response to Gram-positive bacterium 0.04144784 HMGB2 (GO:0050830) Chromatin assembly (GO:0031497) 0.04144784 HMGB2 Positive regulation of endothelial cell proliferation 0.04269764 HMGB2 (GO:0001938) Defense response to Gram-negative bacterium 0.04519273 HMGB2 (GO:0050829) DNA conformation change (GO:0071103) 5.93e-05 HMGB2 HMGB3 DNA geometric change (GO:0032392) 1.07e-04 HMGB2 HMGB3 DNA metabolic process (GO:0006259) 0.01709624 HMGB2 HMGB3 Chromatin remodeling (GO:0006338) 0.00249491 HMGB2, HMGB3 DNA recombination (GO:0006310) 0.03517621 HMGB3 Negative regulation of interleukin-17 production 0.0058359 IL36RN (GO:0032700) Regulation of interferon-gamma secretion (GO:1902713) 0.0058359 IL36RN Negative regulation of response to cytokine stimulus 0.0064824 IL36RN (GO:0060761) Defense response to fungus (GO:0050832) 0.01099699 IL36RN Negative regulation of interferon-gamma production 0.01292602 IL36RN (GO:0032689) Regulation of interleukin-17 production (GO:0032660) 0.01356826 IL36RN Negative regulation of cytokine secretion (GO:0050710) 0.02569772 IL36RN Negative regulation of cytokine-mediated signaling 3.00e-04 IL36RN, TRAIP pathway (GO:0001960) Nucleic acid-templated transcription (GO:0097659) 0.04830315 ILF2 Toll-like 9 signaling pathway (GO:0034162) 0.00841955 IRAK1 Toll-like receptor 4 signaling pathway (GO:0034142) 0.00970904 IRAK1 Lipopolysaccharide-mediated signaling pathway 0.01035321 IRAK1 (GO:0031663) Activation of NF-kappaB-inducing activity 0.01035321 IRAK1 (GO:0007250) Regulation of response to cytokine stimulus 0.01292602 IRAK1 (GO:0060759) Cytoplasmic pattern recognition receptor signaling 0.01549266 IRAK1 pathway (GO:0002753) Nucleotide-binding domain, leucine-rich repeat-containing 0.01613335 IRAK1 receptor signaling pathway (GO:0035872) Nucleotide-binding oligomerization domain-containing 0.01805313 IRAK1 signaling pathway (GO:0070423) Myd88-dependent toll-like receptor signaling pathway 0.01869229 IRAK1 (GO:0002755) Positive regulation of NIK/NF-kappaB signaling 0.02251918 IRAK1 (GO:1901224) Pattern recognition receptor signaling pathway 0.03076356 IRAK1 (GO:0002221) Positive regulation of type I interferon production 0.03957031 IRAK1 (GO:0032481) Stress-activated MAPK cascade (GO:0051403) 0.03957031 IRAK1 Cellular response to type I interferon (GO:0071357) 0.04144784 IRAK1 Type I interferon signaling pathway (GO:0060337) 0.04144784 IRAK1 JNK cascade (GO:0007254) 0.04207293 IRAK1 Response to interleukin-1 (GO:0070555) 0.04830315 IRAK1 Cellular response to lipopolysaccharide (GO:0071222) 0.00154582 IRAK1, HMGB2 Response to a molecule of bacterial origin (GO:0002237) 0.00178962 IRAK1, HMGB2 Response to lipopolysaccharide (GO:0032496) 0.00440096 IRAK1, HMGB2 Positive regulation of gene expression (GO:0010628) 0.01221852 IRAK1, HMGB2, ILF2 Positive regulation of transcription, DNA-templated 0.03284618 IRAK1, HMGB2, ILF2 (GO:0045893) Regulation of cytokine-mediated signaling pathway 0.0015124 IRAK1, IL36RN (GO:0001959) Cellular response to cytokine stimulus (GO:0071345) 0.03426669 IRAK1, IL36RN Regulation of I-kappaB kinase/NF-kappaB signaling 0.00749946 IRAK1, TRIM59 (GO:0043122) Response to lipid (GO:0033993) 0.00360765 IRAK1, HMGB2 IRAK1, HMGB2, Regulation of transcription, DNA-templated (GO:0006355) 0.01616214 HMGB3, ILF2 Positive regulation of nucleic acid-templated transcription 0.00372615 IRAK1, HMGB2, ILF2 (GO:1903508) Cytoplasmic sequestering of protein (GO:0051220) 0.00970904 TONSL Cytoplasmic sequestering of 0.01035321 TONSL (GO:0042994) Negative regulation of transcription factor imports into the 0.01485157 TONSL nucleus (GO:0042992) Replication fork processing (GO:0031297) 0.01933106 TONSL DNA-dependent DNA replication maintenance of fidelity 0.02251918 TONSL (GO:0045005) Recombinational repair (GO:0000725) 0.04519273 TONSL Double-strand break repair via homologous recombination 0.04830315 TONSL (GO:0000724) Activation of MAPKKK activity (GO:0000185) 0.00454175 TRAF7 Regulation of apoptotic signaling pathway (GO:2001233) 0.01613335 TRAF7 Positive regulation of apoptotic signaling pathway 0.04830315 TRAF7 (GO:2001235) Positive regulation of MAP kinase activity (GO:0043406) 0.00569748 TRAF7, IRAK1 Activation of protein kinase activity (GO:0032147) 0.00968586 TRAF7, IRAK1 Positive regulation of MAPK cascade (GO:0043410) 0.01461125 TRAF7, IRAK1 Positive regulation of intracellular signal transduction 0.03749848 TRAF7, IRAK1 (GO:1902533) Apoptotic process (GO:0006915) 0.00952691 TRAF7, TRAIP Negative regulation of tumor necrosis factor-mediated 0.01099699 TRAIP signaling pathway (GO:0010804) Regulation of tumor necrosis factor-mediated signaling 0.03580507 TRAIP pathway (GO:0010803) Negative regulation of I-kappaB kinase/NF-kappaB 0.03013166 TRIM59 signaling (GO:0043124)

Supplementary Table S2: Functional impact ranking rubrics for IRAK1 gene mutations in PRAD samples using algorithms from different tools.

Analysis Tools Functional Impact Score Mutation Assessor Neutral Low Medium High Tolerated Deleterious SIFT Tolerated Deleterious low_confidence low_confidence Possibly Probably Polyphen Benign damaging damaging COSMIC Non-recurrent Recurrent Integrated Method Mild Moderate Severe

Supplementary Figure S1: Batch effect correction plots for the PRAD dataset. The boxplot (Left Panel), PCA (Middle Panel), and hierarchical clustering (Right Panel) plots showed uniform and less dispersion or variance between samples after preprocessing analysis using MBatch, ComBat, and TAMPOR to remove outliers. The hierarchical clustering, principal component analysis (PCA), and boxplot outputs from the batch effect viewer following the preprocessing indicate homogeneity between samples and a lower dispersion and variance (severity) between the samples based on a DSC value of less than 0.5 (p < 0.0005) (middle panel).

Supplementary Figure S2: Hierarchical sample cluster heatmap of PRAD eigengene network. The heatmap displays the hierarchical clustering of Z-scaled module eigengenes. Clusters are solely based on the relationship of eigengene to each sample.

Supplementary Figure S3: Hierarchical gene expression cluster heatmap of PRAD eigengene network. The heatmap displays the hierarchical clustering of Z-scaled module eigengenes. Clusters are solely based on the distinct heterogeneous expression profile of each patient.

Supplementary Figure S4: Kaplan-Meier DFS survival analysis for high-expressing vs low-expressing PRAD samples. Kaplan-Meier survival analysis was performed for the PRAD dataset (n=472). PRAD patients were stratified into two groups of IRAK1 high- expressing patients (n=246) vs IRAK1 low-expressing patients (n=246). Log-rank p- value and Hazard ratio were calculated for disease-free survival status (DFS) using the median expression value for all samples as the cut-off.

Supplementary Figure S5: Receiver Operating Characteristic (ROC) analysis for IRAK in predicting cancer relapse. This ROC analyzes the capability of IRAK1, a representative of inflammation-associated genes in the magenta module to predict survival risk. IRAK1 was able to predict 60% of PRAD relapse with an AUC of 0.6 for relapse vs non-relapse patients. The sensitivity is represented on the x-axis while 1- specificity is represented on the y-axis. The black dotted lines represent the line of significance (p = 0.05). The ROC curves represent the gold standard of diagnostic accuracy (any gene with AUC >0.6 is assumed to be of good clinical significance and potential diagnostic biomarker) on the upper and left axes in the unit square.

Supplementary Figure S6: Heatmap of the expression profile of IRAK family genes in indolent PRAD patients (n = 472) showing the overexpression of IRAK1 compared to other IRAKs.

Supplementary Figure S7: Heatmap of the expression profile of IRAK family genes in neuroendocrine PCa patients (n = 30) showing the overexpression of IRAK1 compared to other IRAKs.

Supplementary Figure S8: Heatmap of the expression profile of IRAK family genes in castration-resistant PCa patients (n = 444) showing the overexpression of IRAK1 compared to other IRAKs.

Supplementary Figure S9: Lollipop plots showing the identified structural mutations of IRAK1, IRAK2, IRAK3, and IRAK4 in PCa patients from the 20 cohort studies. We observed missense (green arrow), frameshift mutations (black arrow), and fusion mutations. The missense and frameshift mutations were mostly found in the death domains and protein kinase/pseudokinase domains of IRAKs. No missense mutation was found in all the CRPC or NEPC patients analyzed using tools such as MutSigCV.

A B

Supplementary Figure S10: Protein-protein interaction and enrichment network analyses in PRAD. A. Evidence-based protein-protein interaction (PPI) network analysis between IRAK1 and selected inflammation and PCa-progression . B. mode of action protein-protein interaction (PPI) network analyses between IRAK1 and selected inflammation and PCa-progression proteins. Pathway interactions were determined using evidence and mode of action associated functional annotations/gene ontology which predicts the degree of interaction between proteins as determined using the String_db (v11) software. Our PPI analysis as shown on the right panel predicted a posttranslational modification of IRAK1 by AKT1, IRAK2, and IRAK4 as well as uncharacterized effects on PI3K and AKT genes. IRAK1 could also directly bind to TLR4, TLR8, TLR9, MYD88, IRAK2, IRAK3, and IRAK4. Predicted interactions between IRAK1 and these genes include gene neighborhood, gene fusions, gene co-occurrence, gene co-expression, and protein homology.

TSG101 UBE2V2 PA2G4P4 PSMD14 TUBA1C LRRC20 UBE2N LPCAT1 DLX1 ACTL8 CDK2AP1 HSF2BP GPR137C ATG3 NMU SOX11 EIF2S2 ORC5L CCNE1 EPCAM C18orf45 CDK5R1 IL1F5 LETM1 STK3 ATP5B C8orf33 CENPW C5orf30 CBX8 GPRIN1 C2orf15 UBE2R2 TTC35 SQLE PDHX HIST1H3B VDAC1 C9orf85 CLCN2 HMGB2 MRPS30 DIRAS2 SCLT1 HIST1H3J TIPIN C2orf47 MTCH2 ZNF467 MAPK9 SSRP1 DPH2 GATAD2A CYCS CCDC43 CTNND2 CCDC41 FBXO45 ARHGDIG MCM3 ULBP1 C4orf43 MNS1 CDCA4 C1orf43 BMP6 CAPZA1 C9orf140 DCAF4 LOC338651 PSMC2 ORC1L RAN OTUD6B KIAA0020 ARMC10 TTYH3 CASP8 NARS2 PRR16 ORC6L CSE1L MGAT4B TARS2 POP1 C17orf53 STAU2 PWP1 C12orf48 NHSL1 CCT8 HJURP PABPC1 HEATR2 MTHFD1L QSOX2 C4orf46 CDK1 TMEM106C ZNF544 C14orf143 RFC4 NVL DLGAP5 NUSAP1 PTDSS1 KIF4B MSL3L2 TMEM97 SLC25A33 STIL FANCD2 PHF14 FEN1 ERCC6L MKI67 CKAP2L HN1L SRXN1 SRPK1 CDT1 DSN1 RFC3 ELOVL6 MTERF SLC25A17 TTC9C TIPRL MCM10 RAD1 ARL6IP1 SET CCT4 KIF4A C16orf75 TRMT12 CASP3 TMEM132A HNRNPK TRAIP AURKA RAD54B FAM72A TPX2 KIAA0101 POLE2 VARS ADK WDHD1 SPC25 NCAPH PCNA WDR76 NSUN2 TMEM5 CDC25A CCNF NUP107 VRK1 ASPM MTL5 PIGW RAD51AP1 CIT WHSC1 C9orf100 ZNF367 FAM54A ILF2 NUF2 NEK2 RRM1 STMN1 MSL3 SLC25A5 EME1 RRM2 KNTC1 NRD1 YY1 NUDT19 RG9MTD1 METTL6 KIF18B H2AFY DEPDC1 MELK CBX2 C15orf42 BUB1 DBF4 SHCBP1 MKRN1 WASF1 SPC24 CHAF1B C16orf88 MRPS22 DLX2 PSMD10 DAP3 CNIH2 NEK11 CCDC18 RCL1 APOBEC3B AURKB REPIN1 CPSF3 CENPK DENR E2F8 CCDC51 C2orf48 C17orf75 FANCC NONO CENPF ESPL1 KIF2C DHFR PABPC3 E2F7 CDC45 H2AFZ PLK4 DSCR6 CDKN2C DTL SGEF PBK DCAF13 NUP62CL HMGB3 FBXO43 SHMT2 TOP2A MCM4 DDN SMC4 FBXO5 CEP55 MRPL44 MDH2 COL10A1 C16orf87 TPM3 TCP1 PRC1 CBX1 TIMELESS MTERFD1 PPIL5 KIF20A UNG MCM2 MYO19 SPAG5 WDR67 MEMO1 CENPM EZH2 TOMM70A FAM120AOS FAM185A TRAF7 TFB2M MLF1IP KIF14 GINS4 MMP11 GMPS TACC3 OIP5 BUB1B ARHGAP11A TRIM59 EFNA2 MSL1 NDC80 C8orf51 MAD2L1 CDCA2 KIFC1 GINS1 TRIB3 HMMR VAV2 SUB1 GRPEL2 C18orf56 SGOL2 WDR62 ELK1 PSMA1 MRPL42 ACAT1 RG9MTD2 NCAPG ZWILCH CASC5 POC1A CDCA3 MCM5 RAB6B KIF23 CCT2 RAD54L UHRF1 GGCT ATPBD4 SIP1 RACGAP1 MRPL13 C8orf55 HIST3H2BB CSPP1 KPNA2 MPP6 CCNB2 SKA1 UM PS TUBA1B RCC1 CDCA5 FH RPA3 CCNB1 FANCI RNASEH2A ZNF695 GTSE1 FAM72B RACGAP1P CCDC34 TDP1 CDKN3 SKA3 KIF11 HPRT1 WDR12 EARS2 DPF1 NUP37 RAD51 ZNF138 SGOL1 AGK DMBX1 MCCC1 BRCA1 TTK KIF18A RUVBL1 ISX MAP4K4 C11orf82 CENPA ACP1 SRCIN1 PRKAG1 SDCBP GINS3 CS GSG2 MTFR1 NEIL3 YARS2 MINA RALA NFKBIL2 HSPD1 EXO1 DIAPH3 CKS2 CDCA8 TTL C8orf38 YEATS4 SETDB1 C6orf62 RBM45 C16orf59 UBE2T PUS7 ASF1B FANCA UTP18 NCAPG2 IMMT XRCC2 DEPDC1B IPO4 PTBP1 C20orf11 CKS1B C16orf63 BFAR EPR1 TK1 BIRC5 LMNB2 HSPA9 UBE2C AIMP2 ABCF2 DSCC1 EI24 CDC6 TYMS C14orf104 BTF3L4 MRPL50 SHOX2 ISCA1P1 RARS MND1 BLM GARS RFC5 CENPE RCC2 UGT2B15 MRPL3 TRIP13 CCNE2 CHEK2 CNOT10 CBX3 CENPH PPA2 DNAJC9 KIF24 NUPL2 FAM64A MTBP AFP FOXM1 HELLS GBAS CDC20 EIF2AK1 NCL AIFM1 GLE1 SLC19A2 CAMKV LOC100128191 RAD23B CDC25C CCDC99 IRAK1 C1orf135 CHCHD3 COPS5 DNAJA3 FANCG FGFR1OP CCNA2 C12orf11 SNW1 PTCD3 TCF19 CCT6A MYBL2 STX3 KIF15 ACTL6A GNL3 C21orf45 LMNB1 PAICS CLSPN DONSON TEX19 ANLN LHX2 PSMD6 AKR1D1 CDK2 ZFAND1 PKMYT1 RDM1 IQGAP3 FAM72D ZWINT GRSF1 FAM111B ATAD2 ZNF3 TROAP STIP1 PTTG1 CENPI ESCO2 RABIF MRPS35 KCTD5 FANCF C1orf112 POLQ DCLRE1B C17orf42 INTS8 TMPO KCMF1 C1orf131 B4GALT3 C15orf23 C7orf70 CCT5 MRPL15 FAM84B SLBP ZFP62 NUDCD1 TIMM8A GPR19 CELSR3 UTP6 C3orf67 GPR160 ZNF823 TTC13 RPP40 CDC7 ZNRF2 DNMT3A OSGEPL1 TSPAN13 PWP2 CST2 CEP70 POLA2 SBK1 PTGES3 C7orf68 PTTG3P TUBB MRPL30 DUS4L PCCB CSPG5 ZNF738 PNPT1 SLC7A11 SLC5A6 NCBP2 ETFA PSMD12 LSM14B MRPS14 C4orf14 FNDC8 RIOK1 OSGIN2 HIST1H1B CCDC160 NOP14 C9orf6 PSRC1 FOXRED2 NT5C3 C6orf115 GINS2 RBM28 DNA2 PGD LASS2 HIST1H2BN HYLS1 CIAO1 ACBD7 SLC35B2 HSP90AB1 NIPSNAP1 PPP1R8 LDHA RFX6 LOC399815 MORF4L2 TTLL6 SHISA3 Supplementary Figure S11: Genes Word-Cloud Generator showing all the 588 genes identified in the magenta module. bone osteosarcoma lung squamous cell carcinoma melanoma breast adenocarcinoma Infection [P. aeruginosa] immunologic deficiency syndrom allergic asthma bacterial infection acute monocytic leukemia lung adenocarcinoma rheumatoid arthritis autoimmune encephalomyelitis colon adenocarcinoma Infection [S. enterica] Infection [S. enterica] acne vulgaris renal cell carcinoma asthma osteoarthritis crohn's disease psoriasis bladder cancer

type 2 diabetes mellitus hyperlipidemia ulcerative colitis papillary thyroid cancer Infection [M. abscessus] bone osteosarcoma frontotemporal lobar degeneratcolitis renal cell carcinoma

influenza A pneumonia

colonatopic adenocarcinoma dermatitisfibrosarcoma chronic obstructive pulmonary Infection [P. gingivalis]

Supplementary Figure S12: Diseases Word-Cloud Generator showing all the diseases associated with aberrant IRAK1 signaling. This is based on results of studies text-mined from scientific literature (PubMed and Google Scholar) and data-mined using the activity plot (p < 0.05) in Ingenuity Pathway Analysis (IPA) software by Qiagen.

A B

Progressed progressed Progressed progressed - - Non Non

Supplementary Figure S13: Boxplots to compare Eigengene values between progressed vs non-progressed patients in the magenta and brown modules. The degree of significance between progressed and non-progressed PRAD patients was determined using Kruskal-Wallis (K-W) test or one-way ANOVA. A. Boxplot showing the difference between the median eigengene values (of genes in the magenta module) between progressed PRAD patients and the non-progressed patients. This indicates a significant upregulation (p = 5.8e-06) and connectedness of genes of the magenta (M9) module in progressed patients compared to the non-progressed PRAD patients. B. Boxplot showing the difference between the median eigengene values (of genes in the brown module) between progressed and non-progressed PRAD patients. This indicates a significant downregulation (p = 0.015) of genes of the brown (M3) module in progressed patients compared to the non-progressed PRAD patients. IRAK1 is one of the 10 inflammatory hub genes identified in the magenta module, highly expressed in PRAD patients as well as progressed patients. Whereas IRAK3 is one of the inflammatory genes in the brown module. IRAK3 was observed to be differentially downregulated in PRAD samples/patients as well as progressed compared to non- progressed patients.

Gene Total: 588

Functional Gene Enrichment Annotations Analysis Gene Ontology

IRAK1 TRIM59 IL36RN

TRAIP HMGB3 TRAF7

ILF2 HMGB2 PPIL5

NFKBIL2

WGCNA kME Value Inflammation Score Differential Oncoscore Score Expression Prognostic Diagnostic Significance Significance

IRAK1

Supplementary Figure S14: A flow chart summarizing the post hoc multivariate analyses undertaken for screening and identification of significant inflammatory genes in the magenta module.

Supplementary Figure S15: A principal component analysis (PCA) dimensionality reduction of IRAK1 expression in PRAD and non-PRAD normal samples. The 3D PCA plot demonstrates a capability to separate expression of IRAK1 in PRAD samples (n=494; green dots) from matched normal prostate samples from PRAD patients (n = 52; orange dots) and unmatched normal prostate samples (downloaded from GTEx database, n=245; blue dots) from PRAD patients and non-PRAD patients using PCA analysis tool of GEPIA2 (http://gepia2.cancer-pku.cn/#dimension).

Supplementary Figure S16: GO Elite ontologies of the 9 biologically significant modules. The x-axis represents the calculated Z-scores for the gene ontologies. The y axis has the biological processes (green bars), the molecular functions (blue bars), and the cellular components for each module (brown bars). The red vertical lines shown across the bars represent the direction of the standard deviations.

Supplementary Figure S17: A detailed genomic sketch of IRAK1 showing information on the 13 transcripts/isoforms identified as well as exons, gene coding sequence regions (CDS), and untranslated regions (UTR). cg08401365 cg20494209 cg23604959 cg02742918 cg18998000 cg06334238

A R = - 0.068 , p = 0.12 R = - 0.21 , p = 6.7e−07 R = - 0.18 , p = 2e−05 R = - 0.19 , p = 1.4e−05 R = 0.0046 , p = 0.92 R = 0.032 , p = 0.47 3

2

1

0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −5 −4 −6 −4 −2 0 cg23121114 cg01353347 cg09520212 cg27167979 cg03050491 cg19572242

R = 0.026 , p = 0.55 R = - 0.037 , p = 0.39 R = - 0.096 , p = 0.029 R = - 0.11 , p = 0.0085 R = - 0.021 , p = 0.63 R = - 0.063 , p = 0.15 3

2

1 ENST00000467236.1 Log2 (TPM+1)

0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation

R = 0.1 , p = 0.019 R = - 0.21 , p = 1.3e−06 3

2

1

0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg20494209 cg23604959 cg02742918 cg18998000 cg06334238 2.0 B R = - 0.084 , p = 0.055 R = - 0.16 , p = 0.00022 R = - 0.1 , p = 0.019 R = - 0.12 , p = 0.0045 R = 0.018 , p = 0.67 R = 0.0063 , p = 0.88

1.5

1.0

0.5

0.0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −5 −4 −6 −4 −2 0 cg23121114 cg01353347 cg27167979 cg09520212 cg03050491 cg19572242 2.0 R = 0.016 , p = 0.71 R = - 0.059 , p = 0.18 R = - 0.01 , p = 0.81 R = - 0.11 , p = 0.009 R = 0.039 , p = 0.37 R = - 0.059 , p = 0.17

1.5

1.0

0.5 ENST00000455690.5 Log2 (TPM+1)

0.0

−6 −5 −5 −4 −3 −5.5 −5.0 −4.5 −4.0 −3.5 −4 −3 −2 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation 2.0 R = 0.075 , p = 0.086 R = - 0.14 , p = 0.00085

1.5

1.0

0.5

0.0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg27616996 cg19572242 cg03050491 cg27167979 cg09520212

C R = - 0.087 , p = 0.046 R = 0.12 , p = 0.0062 R = - 0.096 , p = 0.028 R = - 0.014 , p = 0.74 R = - 0.028 , p = 0.51 R = - 0.097 , p = 0.026 2.0

1.5

1.0

0.5

0.0

0 1 2 3 −5 −4 −3 −5.0 −4.5 −4.0 −3.5 −3.0−6.0 −5.5 −5.0 −4.5 −4.0 −5.5 −5.0 −4.5 −4.0 −3.5 −4 −3 −2 cg01353347 cg23121114 cg18998000 cg06334238 cg02742918 cg23604959

R = - 0.016 , p = 0.72 R = 0.017 , p = 0.7 R = - 0.052 , p = 0.24 R = - 0.027 , p = 0.54 R = - 0.15 , p = 6e−04 R = - 0.073 , p = 0.095 2.0

1.5

1.0

0.5 ENST00000429936.6 Log2 (TPM+1)

0.0

−5 −4 −3 −6 −5 −6 −5 −4 −6 −4 −2 0 −2.5 0.0 2.5 5.0 −2 0 2 4 cg20494209 Aggregation

R = - 0.11 , p = 0.0083 R = - 0.15 , p = 0.00079 2.0

1.5

1.0

0.5

0.0

0 1 2 3 4 −3.6 −3.2 −2.8 −2.4

cg08401365 cg27167979 cg09520212 cg01353347 cg23121114 cg18998000

D R = - 0.13 , p = 0.0023 R = - 0.001 , p = 0.98 R = - 0.16 , p = 0.00015 R = - 0.088 , p = 0.044 R = 0.015 , p = 0.73 R = - 0.085 , p = 0.051

6

4

2

0 1 2 3−5.5 −5.0 −4.5 −4.0 −3.5 −4 −3 −2 −5 −4 −3 −6 −5 −6 −5 −4 cg06334238 cg02742918 cg23604959 cg20494209 cg27616996 cg19572242

R = 0.011 , p = 0.8 R = - 0.17 , p = 8.2e−05 R = - 0.13 , p = 0.003 R = - 0.12 , p = 0.0042 R = 0.0061 , p = 0.89 R = - 0.081 , p = 0.063

6

4 ENST00000369980.7 Log2 (TPM+1) 2

−6 −4 −2 0 −2.5 0.0 2.5 5.0 −2 0 2 4 0 1 2 3 4 −5 −4 −3 −5.0 −4.5 −4.0 −3.5 −3.0 cg03050491 Aggregation

R = 0.015 , p = 0.72 R = - 0.19 , p = 8e−06

6

4

2

−6.0 −5.5 −5.0 −4.5 −4.0 −3.6 −3.2 −2.8 −2.4

cg08401365 cg27167979 cg09520212 cg01353347 cg23604959 cg20494209

E 6 R = - 0.1 , p = 0.017 R = - 0.045 , p = 0.3 R = - 0.12 , p = 0.0077 R = - 0.09 , p = 0.039 R = - 0.3 , p = 2.5e−12 R = - 0.23 , p = 6.6e−08

4

2

0

0 1 2 3−5.5 −5.0 −4.5 −4.0 −3.5 −4 −3 −2 −5 −4 −3 −2 0 2 4 0 1 2 3 4 cg06334238 cg02742918 cg18998000 cg23121114 cg03050491 cg27616996

6 R = - 0.12 , p = 0.0077 R = - 0.3 , p = 3e−12 R = - 0.023 , p = 0.59 R = - 0.017 , p = 0.69 R = - 0.014 , p = 0.75 R = 0.15 , p = 0.00078

4

2 ENST00000369974.6 Log2 (TPM+1)

0

−6 −4 −2 0 −2.5 0.0 2.5 5.0 −6 −5 −4 −6 −5 −6.0 −5.5 −5.0 −4.5 −4.0 −5 −4 −3 cg19572242 Aggregation

6 R = - 0.083 , p = 0.057 R = - 0.33 , p = 8.4e−15

4

2

0

−5.0 −4.5 −4.0 −3.5 −3.0 −3.6 −3.2 −2.8 −2.4

cg08401365 cg20494209 cg23604959 cg02742918 cg06334238 cg18998000

F 1.2 R = - 0.079 , p = 0.071 R = - 0.13 , p = 0.0022 R = - 0.05 , p = 0.25 R = - 0.052 , p = 0.23 R = - 0.012 , p = 0.78 R = - 0.019 , p = 0.66

0.8

0.4

0.0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −4 −2 0 −6 −5 −4 cg23121114 cg01353347 cg09520212 cg27167979 cg03050491 cg27616996

1.2 R = - 0.029 , p = 0.5 R = - 0.032 , p = 0.47 R = - 0.16 , p = 0.00033 R = - 0.13 , p = 0.0037 R = - 0.054 , p = 0.21 R = 0.037 , p = 0.39

0.8

0.4 ENST00000463031.1 Log2 (TPM+1)

0.0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −6.0 −5.5 −5.0 −4.5 −4.0 −5 −4 −3 cg19572242 Aggregation

1.2 R = - 0.061 , p = 0.16 R = - 0.13 , p = 0.0023

0.8

0.4

0.0

−5.0 −4.5 −4.0 −3.5 −3.0 −3.6 −3.2 −2.8 −2.4

cg08401365 cg27167979 cg09520212 cg01353347 cg23121114 cg18998000 G R = - 0.031 , p = 0.48 R = - 0.019 , p = 0.67 R = - 0.039 , p = 0.37 R = 0.034 , p = 0.44 R = - 0.039 , p = 0.37 R = - 0.06 , p = 0.17 0.75

0.50

0.25

0.00

0 1 2 3−5.5 −5.0 −4.5 −4.0 −3.5 −4 −3 −2 −5 −4 −3 −6 −5 −6 −5 −4 cg06334238 cg02742918 cg23604959 cg20494209 cg03050491 cg19572242

R = 0.081 , p = 0.062 R = - 0.064 , p = 0.14 R = 0.019 , p = 0.67 R = 0.016 , p = 0.71 R = 0.012 , p = 0.78 R = 0.00054 , p = 0.99

0.75

0.50

0.25 ENST00000443220.1 Log2 (TPM+1)

0.00

−6 −4 −2 0 −2.5 0.0 2.5 5.0 −2 0 2 4 0 1 2 3 4 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation

R = 0.069 , p = 0.11 R = - 0.0043 , p = 0.92

0.75

0.50

0.25

0.00

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg23604959 cg20494209 cg02742918 cg06334238 cg18998000 H R = - 0.087 , p = 0.047 R = 0.018 , p = 0.68 R = - 0.012 , p = 0.78 R = - 0.062 , p = 0.15 R = 0.022 , p = 0.61 R = - 0.0017 , p = 0.97

1.0

0.5

0.0

0 1 2 3 −2 0 2 4 0 1 2 3 4 −2.5 0.0 2.5 5.0 −6 −4 −2 0 −6 −5 −4 cg23121114 cg01353347 cg09520212 cg27167979 cg19572242 cg03050491

R = 0.0083 , p = 0.85 R = 0.052 , p = 0.23 R = - 0.07 , p = 0.11 R = - 0.044 , p = 0.32 R = - 0.053 , p = 0.23 R = - 0.0033 , p = 0.94

1.0

0.5 ENST00000444254.1 Log2 (TPM+1)

0.0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −5.0 −4.5 −4.0 −3.5 −3.0−6.0 −5.5 −5.0 −4.5 −4.0 cg27616996 Aggregation

R = 0.042 , p = 0.34 R = - 0.037 , p = 0.39

1.0

0.5

0.0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg20494209 cg23604959 cg02742918 cg06334238 cg18998000

I 6 R = 0.068 , p = 0.12 R = - 0.069 , p = 0.11 R = - 0.12 , p = 0.0067 R = - 0.058 , p = 0.18 R = - 0.084 , p = 0.054 R = 0.093 , p = 0.033

4

2

0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −4 −2 0 −6 −5 −4 cg23121114 cg01353347 cg09520212 cg27167979 cg03050491 cg19572242

6 R = 0.0031 , p = 0.94 R = 0.047 , p = 0.28 R = 0.14 , p = 0.0012 R = - 0.082 , p = 0.061 R = - 0.057 , p = 0.19 R = - 0.048 , p = 0.27

4

2

ENST00000444230.5 Log2 (TPM+1) 0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation

6 R = 0.06 , p = 0.17 R = - 0.065 , p = 0.13

4

2

0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg27616996 cg19572242 cg03050491 cg27167979 cg09520212

J R = 0.0012 , p = 0.98 R = - 0.076 , p = 0.083 R = 0.0068 , p = 0.88 R = 0.0041 , p = 0.92 R = 0.047 , p = 0.28 R = - 0.078 , p = 0.073 4

2

0

0 1 2 3 −5 −4 −3 −5.0 −4.5 −4.0 −3.5 −3.0−6.0 −5.5 −5.0 −4.5 −4.0 −5.5 −5.0 −4.5 −4.0 −3.5 −4 −3 −2 cg01353347 cg23121114 cg18998000 cg06334238 cg02742918 cg23604959

R = 0.011 , p = 0.8 R = - 0.096 , p = 0.027 R = - 0.14 , p = 0.0013 R = - 0.044 , p = 0.31 R = 0.082 , p = 0.058 R = 0.13 , p = 0.0029

4

2

0 ENST00000477274.1 Log2 (TPM+1)

−5 −4 −3 −6 −5 −6 −5 −4 −6 −4 −2 0 −2.5 0.0 2.5 5.0 −2 0 2 4 cg20494209 Aggregation

R = 0.078 , p = 0.073 R = 0.049 , p = 0.26

4

2

0

0 1 2 3 4 −3.6 −3.2 −2.8 −2.4

cg08401365 cg20494209 cg23604959 cg02742918 cg06334238 cg18998000 K 6 R = 0.068 , p = 0.12 R = - 0.069 , p = 0.11 R = - 0.12 , p = 0.0067 R = - 0.058 , p = 0.18 R = - 0.084 , p = 0.054 R = 0.093 , p = 0.033

4

2

0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −4 −2 0 −6 −5 −4 cg23121114 cg01353347 cg09520212 cg27167979 cg03050491 cg19572242

6 R = 0.0031 , p = 0.94 R = 0.047 , p = 0.28 R = 0.14 , p = 0.0012 R = - 0.082 , p = 0.061 R = - 0.057 , p = 0.19 R = - 0.048 , p = 0.27

4

2

ENST00000444230.5 Log2 (TPM+1) 0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation

6 R = 0.06 , p = 0.17 R = - 0.065 , p = 0.13

4

2

0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg20494209 cg23604959 cg02742918 cg18998000 cg06334238

L R = - 0.017 , p = 0.7 R = 0.038 , p = 0.39 R = 0.042 , p = 0.33 R = 0.0087 , p = 0.84 R = - 0.032 , p = 0.47 R = 0.045 , p = 0.3 3

2

1

0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −5 −4 −6 −4 −2 0 cg23121114 cg01353347 cg09520212 cg27167979 cg03050491 cg19572242

R = - 0.014 , p = 0.75 R = - 0.018 , p = 0.67 R = - 0.064 , p = 0.14 R = 0.078 , p = 0.075 R = 0.031 , p = 0.48 R = - 0.033 , p = 0.45 3

2

1 ENST00000393687.6 Log2 (TPM+1)

0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation

R = - 0.12 , p = 0.0057 R = 0.015 , p = 0.74 3

2

1

0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

cg08401365 cg20494209 cg23604959 cg02742918 cg06334238 cg18998000 M 6 R = 0.068 , p = 0.12 R = - 0.069 , p = 0.11 R = - 0.12 , p = 0.0067 R = - 0.058 , p = 0.18 R = - 0.084 , p = 0.054 R = 0.093 , p = 0.033

4

2

0

0 1 2 3 0 1 2 3 4 −2 0 2 4 −2.5 0.0 2.5 5.0 −6 −4 −2 0 −6 −5 −4 cg23121114 cg01353347 cg09520212 cg27167979 cg03050491 cg19572242

6 R = 0.0031 , p = 0.94 R = 0.047 , p = 0.28 R = 0.14 , p = 0.0012 R = - 0.082 , p = 0.061 R = - 0.057 , p = 0.19 R = - 0.048 , p = 0.27

4

2

ENST00000444230.5 Log2 (TPM+1) 0

−6 −5 −5 −4 −3 −4 −3 −2 −5.5 −5.0 −4.5 −4.0 −3.5 −6.0 −5.5 −5.0 −4.5 −4.0 −5.0 −4.5 −4.0 −3.5 −3.0 cg27616996 Aggregation

6 R = 0.06 , p = 0.17 R = - 0.065 , p = 0.13

4

2

0

−5 −4 −3 −3.6 −3.2 −2.8 −2.4

Supplementary Figure S18 (A-M): Correlation analysis plots using Pearson coefficient to identify the correlation between various IRAK1 CpG methylation values and the expression levels of IRAK1 isoforms. Only 6 (ENST00000369974.6 (r = -0.338; p = 8.4e-15), ENST00000369980.7 (r = -0.19; p = 8e-06), ENST00000429936.6 (r = -0.15; p = 0.00079), ENST00000455690.5 (r = -0.14; p = 0.00085), ENST00000463031.1 (r = - 0.13; p = 0.0023), ENST00000467236.1 (r = -0.21; p = 1.36e-6)) of the 13 isoforms identified show negative correlation (p < 0.05) with the methylation values of IRAK1.