And Right-Sided Colon Cancer Wangxiong Hu1, Yanmei Yang2, Xiaofen Li1,3, Minran Huang1, Fei Xu1, Weiting Ge1, Suzhan Zhang1,4, and Shu Zheng1,4

Published OnlineFirst November 29, 2017; DOI: 10.1158/1541-7786.MCR-17-0483

Genomics Molecular Cancer Research Multi-omics Approach Reveals Distinct Differences in Left- and Right-Sided Colon Cancer Wangxiong Hu1, Yanmei Yang2, Xiaofen Li1,3, Minran Huang1, Fei Xu1, Weiting Ge1, Suzhan Zhang1,4, and Shu Zheng1,4

Abstract

Increasing evidence suggests that left-sided colon cancer determined between LCC and RCC. Especially for Prostate (LCC) and right-sided colon cancer (RCC) are emerging as two Cancer Susceptibility Candidate 1 (PRAC1), a gene that was different colorectal cancer types with distinct clinical character- closely associated with hypermethylation, was the top signif- istics. However, the discrepancy in the underlying molecular icantly downregulated gene in RCC. Multi-omics comparison event between these types of cancer has not been thoroughly of LCC and RCC suggests that there are more aggressive markers elucidated to date and warrants comprehensive investigation. in RCC and that tumor heterogeneity occurs within the loca- To this end, an integrated dataset from The Cancer Genome tion-based subtypes of colon cancer. These results clarify the Atlas was used to compare and contrast LCC and RCC, covering debate regarding the conflicting prognosis between LCC and mutation, DNA methylation, gene expression, and miRNA. RCC, as proposed by different studies. Briefly, the signaling pathway cross-talk is more prevalent in RCC than LCC, such as RCC-specific PI3K pathway, which often Implications: The underlying molecular features present in LCC exhibits cross-talk with the RAS and P53 pathways. Meanwhile, and RCC identified in this study are beneficial for adopting methylation signatures revealed that RCC was hypermethylated reasonable therapeutic approaches to prolong overall survival relative to LCC. In addition, differentially expressed genes and progression-free survival in colorectal cancer patients. Mol (n ¼ 253) and differentially expressed miRNAs (n ¼ 16) were Cancer Res; 1–10. 2017 AACR.

Introduction originates from the hindgut), microenvironments, and distinct blood supplies (3). In addition, left-sided colon cancer (LCC) Colorectal cancer is the third most commonly diagnosed and right-sided colon cancer (RCC) showed distinct differences cancer in males and the second in females (1). Typically, the in epidemiology, biology, pathology, genetic mutations, and cecum, ascending colon, hepatic flexure, and transverse colon clinical outcomes (3–5). are classified as the right colon, and the descending colon and It has been shown that RCC patients were older, had increased sigmoid colon are classified as the left colon (2). It is known tumor sizes and more advanced tumor stages, were more often that the right and left colons have different embryologic origins female, and had poorly differentiated tumors (5, 6). In addition, (right colon arises from the embryonic midgut, and left colon increasing numbers of studies demonstrated a poor survival in RCC compared with LCC (7). Recently, Petrelli and colleagues (8) fi 1Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China found that LCC was associated with a signi cantly reduced risk of National Ministry of Education), The Second Affiliated Hospital, Zhejiang death, which was independent of stage, race, adjuvant chemo- University School of Medicine, Hangzhou, Zhejiang, China. 2Key Laboratory of therapy, year of study, number of participants, and quality of the Reproductive and Genetics, Ministry of Education, Women's Hospital, Zhejiang included studies through interrogating 1,437,846 patients from University, Hangzhou, Zhejiang, China. 3Department of Abdominal Oncology, 4 publicly available data. However, the underlying molecular West China Hospital, Sichuan University, Chengdu, Sichuan, China. Research events associated with huge difference between LCC and RCC Center for Air Pollution and Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China. are poorly understood. In this study, we comprehensively char- acterize the somatic mutations, genome-wide transcriptional Note: Supplementary data for this article are available at Molecular Cancer (mRNA and miRNA), and epigenetic (DNA methylation) profiles Research Online (http://mcr.aacrjournals.org/). of the LCC and RCC combined with the correlative analyses of the W. Hu and Y. Yang contributed equally to this article. expression, methylation, and clinical data from the Cancer Corresponding Authors: W. Hu, Cancer Institute (Key Laboratory of Cancer Genome Atlas (TCGA). Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Rd, Hangzhou, Zhejiang 310009, China. Phone: 86-151-67166683; E-mail: Materials and Methods [email protected]; W. Ge, [email protected]; S. Zhang, [email protected]; and Mutation data retrieval and processing S. Zheng, [email protected] The colon adenocarcinoma (COAD) somatic mutation data doi: 10.1158/1541-7786.MCR-17-0483 and clinical information were downloaded from the TCGA data 2017 American Association for Cancer Research. portal (March 2, 2015). Silent mutation and RNA mutation

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were discarded. Next, clinical information (i.e., neoplasm loca- The P value was determined by the hypergeometric test with tion) of each patient was added right after the mutational genes the whole annotation as reference set and then adjusted for via the unique patient ID. Frequent mutational gene sets multiple testing using the Benjamini–Hochberg FDR correc- (FMGS) with point mutations and small insertions/deletions tion method. GO enrichment analysis was conducted by BiN- and hidden association rules (AR) were mined as in our GO implemented in Cytoscape (17, 18). All statistical analyses previous work (9). Samples with ambiguous location annota- and graphical representations were performed in the R pro- tion were excluded. gramming language ( 64, version 3.0.2) unless otherwise specified. HM450k data retrieval and process COAD level three DNA methylation data (HumanMethyla- Immunohistochemistry staining tion450) were downloaded from the TCGA data portal (March FifteenLCCand32RCCformalin-fixed paraf fin-embedded 10, 2015). The methylation level of each probe was measured (FFPE) samples were collected from the Second Affiliated as the beta value ranging from 0 to 1, which is calculated as the Hospital, Zhejiang University School of Medicine between ratio of the methylated signal to the sum of the methylated and 2010 and 2016. Immunohistochemistry (IHC) staining was unmethylated signal. Probes with an "NA" value in more than performed similar to our previous work (19) except for anti- 10% of the LCC or RCC samples were discarded. Next, bum- body was replaced with anti-PRAC (1:500 dilution, Invitrogen, phunter Bioconductor package was used to seek the differen- PA1-46237). tially methylated region (DMR) in the remaining probes (10). GRCh37 genome annotation was retrieved from Ensembl Results (http://grch37.ensembl.org/index.html) since the TCGA level three DNA methylation data (HumanMethylation450) using Sparse somatic mutation pattern in RCC fi GRCh37 for probe annotation. The circular diagram was plot- Here, we used the Apriori algorithm to nd the FMGSs with ted by the RCircos package (11). point mutations and small insertions/deletions from 84 LCCs and 145 RCCs as part of the TCGA Pan-Cancer effort. Totally, 65 and 326 unique k-1 FMGSs were identified in LCC and RCC, Gene expression data processing and normalization respectively. The largest FMGS size identified in LCC and RCC All level three mRNA expression datasets (RNASeqV2) were was three. Deeper analysis of the FMGSs showed that 8-fold obtained from the TCGA (October 2015). Gene expression more one-itemsets (k ¼ 1) of FMGSs were found in RCC than data analysis was performed similar to our previous work LCC (Supplementary Table S1). Though the top driver muta- (12). Briefly, differentially expressed mRNA analysis between tion genes were basically the same, the pattern of the mutation LCC and RCC was performed by the limma package for frequencies differed greatly. In LCC, APC (support: 0.835) and R/Bioconductor. Genes with an expression level < 1(RSEM- TP53 (support: 0.647) were absolutely the dominant driver normalized counts) in more than 50% of the samples were mutation genes. However, in RCC, a decreasing gene mutation removed. Significantly differentially expressed mRNAs were pattern was observed. Despite that finding, APC and TP53 selected according to the FDR-adjusted P value < 0.05 and remained the top mutation genes. The sparse mutation pattern fold change > 2 condition. Validation mRNA expression in RCC results in more co-occurred driver mutation genes. dataset was downloaded from the Gene Expression Omnibus Except for TP53 and APC, other drivers (e.g., RNF43, FAT4, database under the accession numbers: GSE14333. The data- PIK3CA) also frequently co-occurred with other cancer-related set was explored similarly to what was mentioned previously. genes in RCC. For example, in RCC, RNF43 often co-occurred Volcano plots were created with the ggplot2 package (http:// with, for instance, SYNE1, OBSCN, BRAF,andLAMA5 (Sup- ggplot2.org/). plementary Table S1). It is well-known that microsatellite instability (MSI) patients Integrated analysis of miRNAs and mRNAs have an elevated frequency of single-nucleotide variants Differentially expressed miRNAs between LCC and RCC were because of inactivation of the DNA mismatch repair (MMR) explored similar to differentially expressed genes (DEG). Putative system (20). MSI is observed in 15% of sporadic colorectal target genes of differentially expressed miRNAs were predicted tumors diagnosed in the United States (21, 22). Therefore, we using miRanda (13) and TargetScan (14). Intersection of putative further divided the LCC and RCC into MSI-high (MSI-H) and target genes and DEGs was retained for further analysis. We microsatellite stable (MSS) subgroups according to the clinical calculated correlation between miRNA and its mRNA targets and annotation by TCGA (23) and the findings by Hause and fi retained negatively correlated pairs (Pearson correlation coef - colleagues (21). Intriguingly, we found that the proportion of < P < – cients 0 and 0.01). MiRNA mRNA interaction network was MSI-H in RCC was 20% (29 samples) and only approximately constructed by using Cytoscape (v3.2.1). 5% in LCC (4 samples). In this context, we speculated that the sparse somatic mutation pattern in RCC may be biased by the Network construction MSI-H samples since the MSI-H samples had an elevated Matched mRNA–miRNA samples were retained for coexpres- mutational load. As such, we independently calculated the sion analysis. The coexpression network was constructed by FMGSs in MSS and MSI-H subgroups of RCC. However, similar weighted correlation network analysis (WGCNA) package for R results held in the MSS of RCC, with 127 unique k-1 FMGSs and (15) and explored as in our previous work (12). The subnet- 68 one-itemset (k ¼ 1) FMGSs, which is 2-fold and 3-fold more works constituted by the 50 top hub genes in the specific than in LCC, respectively. module were visualized by VisANT (16). Next, the top 50 hub Regarding the RCC-MSI-H, a much higher mutational burden genes were subjected to Gene Ontology (GO) interrogation. was observed, with 1,026 mutated genes being observed per

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tumor. In addition, as many as 200 one-itemset (k ¼ 1) FMGSs patterns that should be considered in future targeted cancer with support > 0.3 were found in RCC-MSI-H. Especially for therapy. RNF43, the frequent and validated mutation target in MSI-H cancers (21, 24), was the most signiﬁcantly mutated gene in Hypermethylation in RCC RCC-MSI-H (support: 0.76). Nevertheless, an exclusivity of the Changes in the DNA methylation status are thought to be MSI-H status and canonical colorectal cancer driver mutation involved in colorectal cancer initiation and development. There- genes, such as APC (support: 0.24 vs. 0.73 in MSS), TP53 (support: fore, we attempted to identify the DMRs to decipher the epigenetic 0.24 vs. 0.53 in MSS), and KRAS (support: 0.28 vs. 0.45 in MSS), difference between LCC and RCC. Notably, 552 DMRs (bumps were observed. This ﬁnding may account for a better MSI-H size 2, cutoff ¼ 0.2, permutation test P < 0.01, Supplementary patient survival than MSS patients in colorectal cancer. Collec- Table S2) were determined using bumphunter. Among them, 499 tively, our data revealed that LCC and RCC had distinct driver DMRs (92%) were located within CpG island, and 27 DMRs (5%)

22 1 21

LINC00632

KAL1

TAL1 KLHL22

RUNX3 SCARF2 MMEL1 19 PODN

CECR2 RNF220 COL6A2 DLGAP3 CBS PRKAA2 DNAJC6 LPHN2 SHE 2 LINC01141 AP000282.2 IL12RB2 NTNG1

CD1D RASSF2 LRRC71 ANKRD34A SIX3 HSP O 18 RSPO4XT A12B TMEM178A KCNK12 C093702.1 MEIS1 LINGO3 A JSRP1 MEIS1−AS3 EMX1 AMH AC007392.3CYP26B1 XI3 NOTOO F TCF4 CC RFX8 17 AQP4−AS1 L POU3F3

CC C104655.3 R RP11−13J10.1A ST6GAL2 RP11−1113L8.1 CAND2 FBLN2 RP11−214O1.1 MYL3 MYH10 PTH1R 3 EFNB3 16 ATP1B2 SLC38A3BOC STX1B ADCY5 ESYT3 HS3ST4 FOXL2 RP11−420N3.2 C3orf72 CDIP1 CLSTN2 HS3ST6 LMF1 RP11−372E1.6MED12L IDUA PRTG ZFYVE28 15 PYGO1 CRMP1 GLDN JAKMIP1 WHAMMP2 RP11−586D19.1 RP11−578F21.6 NKX3−2 GOLGA8M KIAA1239 FIP1L1 Methylation ZDHHC22 EPHA5 PAPLN RP11−807H7.1 SIX1 ADAMTS3 PAX9 0.0 0.2 0.4 0.6 0.8 1.0 TRIM2 107502500 107503000 107503500 107504000 14 RP11−964E11.2 CTD−2231H16.1 NOVA1−AS1 genomic location Chr 2 UBE2QL1 SLC22A17 ADCY2 C5orf49 POU4F1 CTNND2 CTD−2353F22.1 RNF219−AS1 NPR3 PCDH20X1 ENO GHR FREM2 RNF180 CDX2 MAP1B 13 WASF3 PDE8B HIST1H2BKEDIL3 5 COL2A1 HIST1H4I NELL2 HCG17 PRICKLE1 HLA−L ST8SIA1 HLA−DPB2 TNXB C12orf39 PTPRO HMGCLL1KCNK17GRM4 RERG COL19A1 PIANP AC093627.12 B3GA C11orf96 RP11−797H7.5 WT1−AS FAM20C T2 12 WT1 RP5−942I16.1 RP5−1024C24.1 DFNA5 ELMO1 MPPED2 CTD−2135J3.3 CALCB ZNF273 RP11−1134I14.2 ZNF804B DKK3 RP11−213G6.2RP11−175E9.1 DYNC1I1 6 INS−IGF2 IGF2 SGCE GRID1 EGR2 GFRA2 SO ZNF365 XKR6

GDF10 DM RGS20 A3 PURG CXCL12 X7 ZEB1 AT PA

ZEB1−AS1 CDLG PTF1A R G

AVL2

T3 NTNG2 RP11−123K19.1 PAP SVEP1 LINC00092 TRPM3 11 EL

10 8

Figure 1. Identiﬁcation of the DMRs between LCC and RCC. Notably, 540 hypermethylated regions were found in RCC, and only 12 hypermethylated regions were found in LCC. The innermost circle corresponds to the RCC methylation signature (depicted by red scatter plots), and the outer circle corresponds to the LCC methylation signature (depicted by blue scatter plots). The scatter location in each track is corresponding to the methylation level (0–1). Selected DMR-related genes are present inside the hg19 human chromosome ideogram. An example of RCC hypermethylated regions relative to LCC were shown in the center of the circle.

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ACB TCGA LCC vs. RCC GSE14333 LCC vs. RCC The number of up gene is 135 The number of up gene is 97 TCGA The number of down gene is 118 The number of down gene is 233 GSE14333 PRAC PRAC 30 30

217 36 201 HOXB13 Change Change DOWN 20 DOWN P 20 GNG4 NOT NOT 10 P HOXC6 UP UP 10 ELAVL2

DRD5 −Log APCDD1 ELAVL2 B3GNT7 INSL5 10 10 HOXC6 −Log PRAC LY6G6D WIF1 ZIC5 XPNPEP2 LYZ ERP27 PNPLA3 SLC19A3 PCP4 HOXC6 GNG4 PRKAA2 PNMA2 ZNF880 DUSP4 0 0 SEMG1 TCN1 WASF3 FOXD1 −50 5 −4 −20 2 RAB27B CLDN8 TRNP1 ZNF813 MUC12 ZIC2 CLDN18 ST6GAL2 Log2 fold change Log2 fold change GPR126 PYY HOXB13 NR1H4 DGE F HOXC6 TCGA PRAC Expression GSE14333 PRAC Expression TCGA HOXC6 Expression GSE14333 Expression

12.5 10.0

10.0 9 7.5 10

7.5

(value + 1) 5.0 (Value + 1) 2 Value

Value 6 2 Log

Log 5.0 5

2.5

2.5 3

0 0.0 0.0 Left Right Left Right Left Right Left Right

Figure 2. Identiﬁcation of the DEGs between LCC and RCC. A, Volcano plot revealed that 135 genes were overexpressed in RCC, and 118 genes were underexpressed in RCC using TCGA data. B, Volcano plot uncovered 97 genes were overexpressed in RCC, and 233 genes were underexpressed in RCC using publicly available GSE14333 data. C, Venn diagram revealed that 36 DEGs were overlapped between TCGA and GSE14333. For D to G, boxplot distribution of PRAC and HOXC6 expression level by using TCGA (D and F) and GSE14333 (E and G) data.

were in the shores. Notably, a hypermethylation profile Candidate (PRAC) was the most significantly downregulated gene (540 DMRs) was observed in RCC (Fig. 1). Of the 12 hypermethy- in RCC (Fig. 2D and E). To validate this result, 15 LCC and 32 RCC lated DMRs (34 probes) in LCC, 3 DMRs (13 probes) were FFPE samples were used for IHC staining. Results showed that associated with HOX family members HOXB5, HOXB7, and both the IHC staining score and positive rate in LCC were CDX2. GO enrichment analysis showed that the DMR-associated significantly higher than in RCCs (Supplementary Fig. S1). The genes were mainly enriched in organ morphogenesis (Benjamini– function of PRAC in COAD remains elusive; however, another Hochberg FDR correction 3.1798E–10, 42 genes), cell differenti- highly downregulated gene in RCC, HOXB13, approximately 2 kb ation (6.8791E–9, 71 genes), and embryonic morphogenesis downstream of PRAC, has been shown to be a colorectal cancer (2.0480E–9, 29 genes). suppressor (25, 26). And HOXC6, which also belongs to homeobox transcription factor (TF) family, was the most significantly Activation of oncogenes and silence of tumor-suppressor upregulated gene in RCC (Fig. 2F and G). genes in RCC Further correlation analysis of the DNA methylation and Using mRNAseq data compiled in TCGA, LCC versus RCC expression showed that 13 downregulated genes (CLSTN2, revealed 253 DEGs (fold change > 2 and adjusted P value ECEL1, ELAVL2, GDF10, LMX1A, PRAC, PRKAA2, RSPO4, SNX32, < 0.05) between them. In LCC, 105 genes were upregulated, and ST6GAL2, TAC1, VENTX, and WASF3) in RCC were possibly 148 genes were downregulated, compared with the RCC (Fig. 2A). repressed by hypermethylation of DMR. For example, one Functional annotation of these DEGs uncovered that oncogenes DMR, situated in the CpG island shore, was strongly associated (e.g., ZIC5) were overexpressed in RCC, whereas tumor-suppres- with PRAC repression in RCC. The hypermethylated DMR in RCC sor genes (e.g., HOXB13) were repressed. To further confirm the encompassed 8 probes (cg00960395, cg04574034, cg09866983, DEGs identified using TCGA data, the GSE14333 dataset with 122 cg12374721, cg14230397, cg20094830, cg20945566, and LCCs and 126 RCCs was used for identifying DEGs. In total, 330 cg27170782) that cover 388 bps in chromosome 17 (Fig. 3). DEGs were identified (Fig. 2B), with 36 of them overlapping with DNA methylation has been shown to play a role in the regulation the TCGA result (Fig. 2C). The Prostate Cancer Susceptibility of PRAC expression in prostate cancer (27).

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12.5 12.5 12.5 12.5

10.0 10.0 10.0 10.0

7.5 7.5 7.5 7.5 Left 5.0 5.0 5.0 Right 5.0

2.5 2.5 2.5 2.5 PRAC Expression PRAC PRAC Expression PRAC PRAC Expression PRAC PRAC Expression PRAC

0.0 0.0 0.0 0.0 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 DNA Methylation (cg12374721) DNA Methylation (cg04574034) DNA Methylation (cg09866983) DNA Methylation (cg20094830)

12.5 12.5 12.5 12.5

10.0 10.0 10.0 10.0

7.5 7.5 7.5 7.5

5.0 5.0 5.0 5.0 C Expression

2.5 2.5 2.5 2.5 PRAC Expression PRAC PRAC Expression PRAC PRA PRAC Expression PRAC 0.0 0.0 0.0 0.0 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 DNA Methylation (cg00960395) DNA Methylation (cg14230397) DNA Methylation (cg20945566) DNA Methylation (cg27170782)

Figure 3. Downregulation of PRAC correlates with high DNA methylation in RCC. Black dots and red dots correspond to LCC and RCC, respectively. miRNA expression linked to cellular nitrogen compound metabolic process (FDR- In addition to mRNA, we also want to know if expression corrected P value 3.3739E–2, 13 genes). The rest of the four RCC- discrepancies exist between LCC and RCC in noncoding RNAs. relevant modules were linked with cell-cycle phase (magenta Interestingly, 15 cancer-related miRNAs were differentially module, 5.4696E–28, 27 genes), mitosis (magenta module, expressed (adjusted P value < 0.05 and fold change > 1.5) 2.9294E–29, 24 genes), generation of precursor metabolites and between LCC and RCC. Eight miRNAs, namely, miR-196b, energy (brown module, 1.0886E–4, nine genes), microtubule- miR-466, miR-296, miR-552, miR-592, miR-1247, miR-1275, based process (purple module, 3.1047E–2, five genes), response and miR-3131, were downregulated in RCC, whereas seven to virus (7.5314E–8, nine genes), and innate immune response miRNAs were upregulated, including miR-10b, miR-31, miR- (salmon module, 4.4101E–2, four genes). As such, enhanced 146a, miR-155, miR-615, miR-625, and miR-1293 (Fig. 4A). activation of cell division, energy metabolism, and immune The abundantly expressed mir-10b, an miRNA closely associ- system process in RCC underscores the speculation of its more ated with cancer metastasis, had significantly higher expression aggressive state. level in RCC (Fig. 4B). Further integrated analysis of miRNA and DEGs showed that 16 Pathway alteration difference between LCC and RCC DEGs were putatively regulated by seven miRNAs (Fig. 4C). Two Integrated comparison of mutations and mRNA expression miRNAs, miR-625 and miR-146a, were highly expressed in RCC, alteration in 85 LCCs and 116 RCC-MSS will enable better and their predicted targets were all downregulated in RCC. As for understanding of the intrinsic difference in the pathway dysre- the other low expression of miRNAs in RCC, their predicted gulation. Alteration frequency between LCC and RCC-MSS was targets were upregulated in RCC. For example, miR-466 was examined in five well-known pathways (WNT, RAS, PI3K, TGF- downregulated, and its predicted target, ZIC5, a transcriptional beta, and P53) that are closely associated with colorectal cancer. repressor that elevated expression contributed to cancer progres- Basically, the mutation rate in RCC-MSS is much more frequent sion, was upregulated in RCC. than LCC in TGF-beta, PI3K, and RAS signaling pathways, but almost the same in WNT and TP53 pathways (Fig. 6). The WNT RCCs tend to be more aggressive through orchestrating invasive signaling pathway was altered in 89% LCC and 85% RCC-MSS, gene modules respectively, which mainly contributed by inactivation of APC Considering the huge expression difference between LCC and (78% and 73%), followed by the mutation of FAM123B, RCC, we speculate that the regulatory network also differs greatly. FBXW7, SOX9, CTNNB1, AXIN2,andLRP1B.Downregulation To this end, we used WGCNA to explore the core gene regulatory of LRP1B in colon cancer has recently been shown to be modules, which may shed light on the intrinsic expression var- associated with promoting growth and migration of cancer iation between LCC and RCC. Interestingly, six and nine modules cells (28). In the TGF-beta pathway, higher mutation rates consisting of at least 100 genes were found in LCC and RCC, (LCC vs. RCC-MSS, SMAD2,0%vs.3%;SMAD3,1%vs.4%; respectively. Five modules related with mRNA metabolic process, SMAD4, 12% vs. 16%) in RCC-MSS were observed in SMAD DNA ligation, regulation of ARF GTPase activity, translational family members. As for the PI3K pathway, PIK3CA was mutated elongation, vasculature development, cell adhesion, complement in 11% LCC samples and 26% RCC-MSS samples. Resembling activation, T-cell activation, and lymphocyte activation were thecaseinPI3K,amuchhighermutationrateofKRAS (30% vs. shared by LCC and RCC (Fig. 5, linked by colored band). Of 45%) and BRAF (7% vs. 11%) was found in RCC-MSS (Fig. 6). note, because certain modules such as brown- and blue modules Activation of the PI3K and RAS pathways may be more prone to in LCC were rather large. Therefore, the top 50 hub genes were progression for RCC-MSS. extracted in each module. We found that no miRNAs ranked in the To further explore the co-occurrence of the dysregulation of top hubs in the module. This observation raises the possibility different pathways in LCC and RCC-MSS, we re-examined the that miRNA, unlike TF, has a restricted role or at least does not act FMGSs that were identified as mentioned earlier. The results as a key orchestrator in COAD. The module peculiar to LCC was showed that five FMGSs (k > 1, support > 0.1) were in common

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AB Cutoff for log2FC is 0.585 The number of up miRNA is 7 hsa−miR−10b The number of down miRNA is 8 12 hsa-miR-615

9 P

10 6 Change hsa-miR-592 hsa-miR-155 DOWN

hsa-miR-10b NOT Reads per million −Log hsa-miR-196b hsa-miR-625 3 hsa-miR-466 UP hsa-miR-296hsa-miR-552 hsa-miR-146a hsa-miR-1275 hsa-miR-1293 hsa-miR-3131 hsa-miR-31 hsa-miR-1247

0 0 50,000 100,000 150,000 200,000

−10123 LCC RCC

Log2 Fold change

hsa-miR-592 hsa-miR-466 KRT23 PLA2G3 PAX9

AMT

CRTAM DAPK1 ZIC5 hsa-miR-625 SCN5A hsa-miR-552 hsa-miR-1275

hsa-miR-196b hsa-miR-146a FOXD1

ACSL6

CLSTN2 MIA2 GPR126

HOXC8 WASF3

Figure 4. Identiﬁcation of differentially expressed miRNAs between LCC and RCC. A, Volcano plot revealed that 11 miRNAs were overexpressed in RCC, and 5 miRNAs were underexpressed in RCC using TCGA data. B, Comparison of the miR-10b expression level between LCC and RCC by boxplot. C, Integrated analysis of miRNA and DEGs revealed that some DEGs were putatively regulated by miRNAs. Yellow elliptical nodes represent miRNAs, and magenta rectangle nodes represent miRNA targets.

between LCC and RCC-MSS. Inactivation of WNT and P53, but Discussion activation of the RAS pathway, occurred simultaneously in more The large molecular difference between LCC and RCC iden- than 15% of the COAD samples (both LCC and RCC-MSS). tiﬁed in this study consolidates the different embryonic origins However, we observed nine more examples of RCC-MSS–speciﬁc of LCC and RCC. And the difference is independent of sex pathway cross-talk that have >10% support (Fig. 6, right bottom (X2 test, P ¼ 1). Actually, DNA methylation alteration is an plot). In addition to the interaction with the WNT pathway, the early event that occurs in cancer. Koestler and colleagues (29) PI3K pathway often crosstalks with the RAS and P53 pathways in determined that 168 probes enriched in homeobox genes that RCC-MSS. In addition, in RCC-MSS, the TGF-beta pathway also were differentially methylated between the right- and left-colon often interacted with the RAS and WNT pathways via SMAD4 and adenomas using the Illumina HumanMethylation450 Bead- KRAS or APC, respectively. Of note, we also observed single Chip. Consistent with their observation of PRAC hypermethy- pathway members that were inactivated simultaneously, repre- lation in right-colon adenomas and hypermethylation of sented by APC and SOX9 (support ¼ 0.121) or FBXW7 (support ¼ CDX2 in left-colon adenomas, the difference also held true in 0.103) in the WNT pathway. Collectively, we found numerous the carcinoma. molecular signatures that were associated with tumor aggres- As a TF, HOXC6 promotes prostate cancer metastasis siveness in RCC-MSS, which suggests a poor prognosis for via inducing aberrant metastatic miRNA expression (30). It RCC-MSS patients.

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mRNA Metabolic process

Cellular nitrogen compound Metabolic process Mitosis DNA Ligation Cell-cycle phase

ecursor erg GeneraGenerationtion of pprecursorr MMetabolitesetabolites and enenergy

Regulation of ARF GTPase activity Vasculature development Cell adhesion Translational elongation

Microtubule-based process T-Cell activating LCC RCC

Vasculature development Lymphocyte activating Cell adhesion Complement activation

T-cell activation Lymphocyte activation

Response to virus Innate immune response

Complement activation

0.8 0.4 0.0 0.0 0.4 0.8 Height Height Dynamic tree cut Dynamic tree cut

Figure 5. Comparison of the regulatory network between LCC and RCC using WGCNA. Hierarchical cluster analysis dendrogram used to detect coexpression clusters from the dataset with 230 LCCs and 220 RCCs along with corresponding color assignments. Genes that were not coexpressed were assigned to the gray group. Each vertical line corresponds to a gene, and branches are expression modules of the highly interconnected groups of genes. In total, six and nine modules ranging from 117 to 4,118 genes in size were identified, respectively. Functional annotation of each module is based on the GO enrichment analysis of the top 50 hub genes. Modules with similar functional annotation in LCC and RCC were linked by colored bands. In addition, two modules were selected for visualization of the subnetwork connections among the most connected genes. The plots show network connections whose topological overlap is above the threshold of 0.10. is tempting to believe that this result held in RCC due to the which are TFs. In animals, miRNAs target transcripts via elevated expression of HOXC6. Previous studies demonstrate imperfect base-pairing to multiple sites in the 30 untranslated that the overexpression mir-10b can promote tumor invasion regions by the seed sequence (50 endofmiRNAsthatcom- (31, 32) and confers chemoresistance in colorectal cancer cells prises nucleotides 2–7). For this reason, over 90% transcripts to 5-fluorouracil (33). In this context, 5-fluorouracil may not be were putatively targeted by miRNAs, and one transcript was suitable for the treatment of RCC due to a much higher often under combinatorial control of multiple miRNAs (41, expression level of miR-10b in RCC than LCC. In addition, 42). In this context, animal miRNAs generally act as fine- high miR-10b expression in primary colorectal cancer tissue can tuners that exert wide subtle regulatory effects across the independently predict distant metastasis (34), which suggests transcriptome and tend not to be hub genes in the regulatory that primary RCC is more vulnerable to metastasis. Other onco- network. miRNAs (miR-31, miR-155, miR-625) that were overexpressed PRAC, a small protein with a molecular weight of only 6 kDa, in RCC were all closely associated with colorectal carcinoma has been shown to be specifically expressed in the human pros- cell proliferation, migration, and invasion (35–37). In contrast, tate, rectum, and colon (27, 43). In prostate cancer, the expression two tumor-suppressor miRNAs in colorectal cancer, miR-296 level of PRAC is significantly downregulated compared with and miR-592, were underexpressed in RCC. MiR-296 inhibits benign prostatic hyperplasia. Here, we demonstrated that its the metastasis and epithelial–mesenchymal transition by tar- expression level is significantly downregulated in RCC, which is geting S100A4 and miR-592, which inhibits cell proliferation in line with the findings by Bauer and colleagues (44). Although by suppressing of CCND3 expression (38, 39). the concrete function of PRAC remains elusive in COAD, a It must be noted that a more aggressive status was observed possible cotranscribed gene HOXB13 has been shown to be in RCC through determining the modules consisting of tightly involved in suppressing colorectal cancer progression (25, 45). coregulated genes. The large brown module unique to RCC In RCC, HOXB13 was significantly downregulated, as well. Thus, harbors 1,353 genes that were mainly involved in electron PRAC may serve as a tumor suppressor through interacting with transport chain. In addition, three other RCC-specific modules HOXB13 in COAD. were enriched in cell-cycle regulating, mitosis/nuclear division, In addition, RCC-MSS had more mutated BRAF (11% vs. 7%) microtubule-based process, and innate immune response, and KRAS (45% vs. 30%), and similar results were observed in which were all linked to tumor aggressiveness. In parallel, as our Zhejiang University Cancer Institute–collected samples described earlier, miRNAs tend not to be hub genes with an through sequencing 338 pairs of matched fresh frozen colo- extremely high connectivity. This finding can be ascribed to rectal cancer tissue and adjacent normal tissue samples (unpub- the intrinsic target recognition discrepancy between plant and lished data). BRAF mutation has been shown to be associated animal miRNAs (40). In plants, almost all miRNA targets have with a worse prognosis (46) and a poorer response to cetux- a single miRNA-responsive element and are regulated by just imab therapy (3, 47). In addition, colon cancer patients with one miRNA or one miRNA family through nearly perfectly wild-type KRAS treated with cetuximab have significantly complementary miRNA-target pairs. Thus, the scope of miRNA improved overall survival (OS; ref. 47). Thus, a worse prognosis targets appears to be only a handful of key targets, most of in RCC can be partly ascribed to a higher mutation rate of KRAS

www.aacrjournals.org Mol Cancer Res; 2018 OF7

Hu et al. RCC-MSS RCC-MSS RCC-MSS RCC-MSS β PI3K Signaling RAS Signaling LCC LCC LCC 20% 32% 40% 61% WNT Signaling 89% 85% TGF Signaling 20% 36% LCC

WNT TGFβ Activin DKK1-4 1% 4% RNF43 LRP5 FZD10 TGFBR1 TGFBR2 ACVR2A ACVR1B ERBB2 ERBB3 IGF1R 5% 6% 1% <1% 1% <1% 2% 4% 1% 3% 1% 6% 2% 2% 5% 3% 2% 3% SOS1/2 IRS2 4% 4% LRP1B SMAD2 SMAD3 5% 19% FAM123B AXIN2 APC 1% 4% NRAS KRAS 0% 3% 1% 4% 5% 17% 1% 3% 78% 73% PTEN 4% 5% 30% 45% 6% 6% PIK3CA BRAF CTNNB1 DVL2 SMAD4 PIK3R1 11% 26% 7% 11% 4% 9% 0% <1% 12% 16% 5% 3%

FBXW7 ARID1A Proliferation, cell survival

TCF7L2 12% 13% 6% 12% RCC-MSS Nucleus 9% 6% P53 Signaling 70% 64% LCC RCC-MSI-H ATM SOX9 CTNNB1 6% 12% TP53 24% MYC Proliferation, stem/ 8% 13% 4% 9% progenitor phenotype APC 24% TP53 Proliferation KRAS 27% 66% 53% % Cell survival

LCC RCC-MSS Gene mutation frequency Activated Inactivated APC, KRAS 0.271 APC, KRAS 0.388 LCC RCC-MSS < 1% TP53, KRAS 0.200 TP53, KRAS 0.233 Downregulated Upregulated 1–5% PIK3CA, APC 0.129 APC, PIK3CA 0.207 5–10% APC, TP53 0.529 APC, TP53 0.379 10–20% APC, TP53, KRAS 0.198 Activation > 20% APC, TP53, KRAS 0.165 Inhibition APC, SOX9 0.121 SMAD4, KRAS 0.103 PIK3CA, KRAS 0.181 PIK3CA, TP53 0.138 APC, FBXW7 0.103 SMAD4, APC 0.112 PIK3CA, APC, KRAS 0.147 PIK3CA, TP53, APC 0.112 PIK3CA, TP53, KRAS 0.103

Figure 6. Comparison of the genetic change frequency leading to signaling pathway alteration between LCC and RCC. Alteration frequencies are deﬁned as the percentage of LCC (n ¼ 85) or RCC (n ¼ 116) samples. Mutation frequency of TP53, APC,andKRAS in MSI samples (n ¼ 29) of RCC are shown separately in the right plot. Red denotes the predicted activated genes, and blue represents the inactivated genes. Right bottom plot demonstrates the pathway cross-talk difference between LCC and RCC-MSS.

and BRAF (Fig.6).Morerecently,Ptashkinandcolleagues(48) Disclosure of Potential Conflicts of Interest find that chromosome 20q amplification is associated with a No potential conflicts of interest were disclosed. better OS in LCC. In addition, because cetuximab is mainly used in advanced stages of colorectal cancer patients, FMGS Authors' Contributions mining is further conducted in four pathologic stages according Conception and design: W. Hu, W. Ge, S. Zhang, S. Zheng to the American Joint Committee on Cancer standards. Nota- Analysis and interpretation of data (e.g., statistical analysis, biostatistics, bly, we found KRAS harbored great stage heterogeneity between computational analysis): W. Hu, Y. Yang, X. Li, M. Huang LCC (stage I: 25%, II: 53%, III: 9%, and IV: 20%) and RCC Writing, review, and/or revision of the manuscript: W. Hu, X. Li, F. Xu (53%, 46%, 31%, and 62%). Nevertheless, we should bear in Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M. Huang, W. Ge, S. Zhang, S. Zheng mind that current targeted drugsusedintreatingcoloncancer Study supervision: W. Ge, S. Zhang, S. Zheng patients are rather limited and that the prognosis may be reversed if the targeted drug is based on mutated KRAS or BRAF. Meanwhile, OS and progression-free survival (PFS) in Acknowledgments RCC can benefit from combination therapy since frequent This work was supported by the National High Technology Research and Development Program of China (863 Program, 2012AA02A204) to W. Ge. pathway cross-talk occurred in RCC. This work was also partially supported by China Postdoctoral Science Recently, Le and colleagues (49) demonstrated that PD-1 Foundation (2016M590532) and Postdoctoral Foundation of Zhejiang blockade works well in MMR-deficient colorectal cancer, and Province to W. Hu (BSH1502129). prolonged PFS was associated with high somatic mutational The authors thank Dr. Lizhen Zhu for her help in IHC staining. loads. In this manner, RCC patients that are deficient in MMR, approximately 20% of RCC, can benefit from PD1 therapy (23, The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked 50). In short, the underlying molecular feature present in LCC and advertisement fi fi in accordance with 18 U.S.C. Section 1734 solely to indicate RCC identi ed in this study is bene cial for medical decision- this fact. making and prolonged OS and PFS. Our results help to elucidate the molecular basis of treating LCC and RCC as two different Received August 31, 2017; revised October 26, 2017; accepted November 27, diseases in future cancer treatment. 2017; published OnlineFirst November 29, 2017.

OF8 Mol Cancer Res; 2018 Molecular Cancer Research

Left- and Right-Sided Colon Cancer Comparison

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OF10 Mol Cancer Res; 2018 Molecular Cancer Research

Multi-omics Approach Reveals Distinct Differences in Left- and Right-Sided Colon Cancer

Wangxiong Hu, Yanmei Yang, Xiaofen Li, et al.

Mol Cancer Res Published OnlineFirst November 29, 2017.

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