And Right-Sided Colon Cancer

Author Manuscript Published OnlineFirst on November 29, 2017; DOI: 10.1158/1541-7786.MCR-17-0483 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Multi-omics Approach Reveals Distinct Differences in Left- and Right-sided Colon Cancer Running title: Left- and Right-sided Colon Cancer Comparison Wangxiong Hu1*†, Yanmei Yang2*, Xiaofen Li1,3, Minran Huang1, Fei Xu1, Weiting Ge1†, Suzhan Zhang1,4†, Shu Zheng1,4† 1Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China

2Key Laboratory of Reproductive and Genetics, Ministry of Education, Women’s Hospital, Zhejiang University, Hangzhou, Zhejiang 310006, China 3Department of Abdominal Oncology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China 4Research Center for Air Pollution and Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310009, China

*These authors contributed equally to this work

†Correspondence: Wangxiong Hu Tel: +86-571-87784606 Mailing address: 88 Jiefang Rd, Hangzhou, China 310009 E-mail: [email protected]

Weiting Ge Tel: +86-571-87784606 Mailing address: 88 Jiefang Rd, Hangzhou, China 310009

E-mail: [email protected]

Suzhan Zhang Tel: +86-571-87784501 Mailing address: 88 Jiefang Rd, Hangzhou, China 310009

Downloaded from mcr.aacrjournals.org on September 23, 2021. © 2017 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 29, 2017; DOI: 10.1158/1541-7786.MCR-17-0483 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

E-mail: [email protected]

Shu Zheng Tel: +86-571-87784501 Fax: +86-571-87214404 Mailing address: 88 Jiefang Rd, Hangzhou, China 310009 E-mail: [email protected]

Conflicts of interest The authors declare that they have no competing financial interests.

Abstract Increasing evidence suggests that left-sided colon cancer (LCC) and right-sided colon cancer (RCC) are emerging as two different colorectal cancer (CRC) types with distinct clinical characteristics. However, the discrepancy in the underlying molecular event between these types of cancer has not been thoroughly elucidated to date and warrants comprehensive investigation. To this end, an integrated data set from The Cancer Genome Atlas (TCGA) was used to compare and contrast LCC and RCC, covering mutation, DNA methylation, gene expression, and miRNA. Briefly, the signaling pathway crosstalk is more prevalent in RCC than LCC, such as RCC-specific PI3K pathway, which often exhibits crosstalk with the RAS and P53 pathways. Meanwhile, methylation signatures revealed that RCC was hypermethylated relative to LCC. In addition, differentially expressed genes (n=253) and differentially expressed miRNAs (n=16) were determined between LCC and RCC. Especially for Prostate Cancer Susceptibility Candidate 1 (PRAC1), a gene that was closely associated with hypermethylation, was the top significantly down-regulated gene in RCC. Multi-omics comparison of LCC and RCC suggests that there are more aggressive markers in RCC and that tumor heterogeneity occurs within the location-based subtypes of colon cancer. These results clarify the debate regarding the conflicting prognosis between LCC and RCC, as proposed by different studies.

Implications: The underlying molecular features present in left- and right-sided colon cancer identified in this study are beneficial for adopting reasonable therapeutic approaches to prolong overall survival (OS) and progression-free survival (PFS) in CRC patients.

Key words: gene expression; hypermethylation; left-sided colon cancer; right-sided colon cancer; weighted correlation network analysis

Introduction Colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females (1). Typically, the cecum, ascending colon, hepatic flexure and transverse colon are classified as the right colon and the descending colon and sigmoid colon are classified as the left colon (2). It is known that the right and left colon have different embryologic origins (right colon arises from the embryonic midgut and left colon originates from the hindgut), microenvironments, and distinct blood supplies (3). Additionally, LCC and RCC showed distinct differences in epidemiology, biology, pathology, genetic mutations, and clinical outcomes (3-5). It has been shown that RCC patients were older, had increased tumor sizes and more advanced tumor stages, were more often female and had poorly differentiated tumors (5,6). In addition, increasing numbers of studies demonstrated a poor survival in right-sided colon cancer (RCC) compared to left-sided colon cancer (LCC) (7). Recently, Petrelli et al. (8) found that LCC was associated with a significantly reduced risk of death, which was independent of stage, race, adjuvant chemotherapy, year of study, number of participants, and quality of the included studies through interrogating 1,437,846 patients from publicly available data. However, the underlying molecular events associated with huge difference between LCC and RCC are poorly understood. In this study, we comprehensively characterize the somatic mutations, genome-wide transcriptional (mRNA and miRNA), and epigenetic (DNA methylation) profiles of the LCC and RCC combined with the correlative analyses of the expression, methylation, and clinical data from the Cancer Genome Atlas (TCGA).

Materials and Methods Mutation data retrieval and processing The Colon Adenocarcinoma (COAD) somatic mutation data and clinical information were downloaded from the TCGA data portal (02/03/2015). Silent mutation and RNA mutation were discarded. Next, clinical information (i.e., neoplasm location) of each patient was added right after the mutational genes via the unique patient ID. Frequent 4

mutational gene sets (FMGSs) with point mutations and small insertions/deletions and

hidden association rules (ARs) were mined as in our previous work (9). Samples with ambiguous location annotation were excluded.

HM450k data retrieval and process COAD level three DNA methylation data (HumanMethylation450) were downloaded from the TCGA data portal (10/03/2015). The methylation level of each probe was measured as the beta value ranging from 0~1, which is calculated as the ratio of the methylated signal to the sum of the methylated and unmethylated signal. Probes with an “NA” value in more than 10% of the LCC or RCC samples were discarded. Next, bumphunter Bioconductor package was used to seek the differentially methylated region (DMR) in the remaining probes (10). GRCh37 genome annotation was retrieved from Ensembl (http://grch37.ensembl.org/index.html) since the TCGA level three DNA methylation data (HumanMethylation450) using GRCh37 for probe annotation. The circular diagram was plotted by the RCircos package (11).

Gene expression data processing and normalization All level 3 mRNA expression data sets (RNASeqV2) were obtained from the TCGA (October 2015). Gene expression data analysis was performed similar to our previous work (12). Briefly, differentially expressed mRNA analysis between LCC and RCC was performed by the limma package for R/Bioconductor. Genes with an expression level < 1 (RSEM-normalized counts) in more than 50% of the samples were removed. Significantly differentially expressed mRNAs were selected according to the false discovery rate (FDR) adjusted P-value <0.05 and fold change > 2 condition. Validation mRNA expression dataset was downloaded from the Gene Expression Omnibus (GEO) database under the accession numbers: GSE14333. The dataset was explored similarly to what was mentioned previously. Volcano plots were created with the ggplot2 package (http://ggplot2.org/).

Integrated analysis of miRNAs and mRNAs

Differentially expressed miRNAs between LCC and RCC were explored similar to DEGs. Putative target genes of differentially expressed miRNAs were predicted using miRanda (13) and TargetScan (14). Intersection of putative target genes and DEGs were retained for further analysis. We calculated correlation between miRNA and its mRNA targets and retained negatively correlated pairs (pearson correlation coefficients < 0 and P < 0.01). MiRNA-mRNA interaction network was constructed

by using Cytoscape (v3.2.1).

Network construction

Matched mRNA-miRNA samples were retained for co-expression analysis. The co-expression network was constructed by weighted correlation network analysis (WGCNA) package for R (15) and explored as in our previous work (12). The sub-networks constituted by the 50 top hub genes in the specific module were visualized by VisANT (16). Next, the top 50 hub genes were subjected to Gene ontology (GO) interrogation. The P-value was determined by the hypergeometric test with the whole annotation as reference set and then adjusted for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) correction method. GO enrichment analysis was conducted by BiNGO implemented in Cytoscape (17,18). All statistical analysis and graphical representations were performed in the R programming language (×64, version 3.0.2) unless otherwise specified.

Immunohistochemistry (IHC) staining

15 LCCs and 32 RCCs formalin-fixed paraffin-embedded (FFPE) samples were collected from the Second Affiliated Hospital, Zhejiang University School of Medicine between 2010 and 2016. IHC staining was performed similar to our previous work (19) except for antibody was replaced with anti-PRAC (1:500 dilution, Invitrogen, PA1-46237).

Results

Sparse somatic mutation pattern in RCC

Here we used Apriori algorithm to find the frequent mutational gene sets (FMGSs) with 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, respectively. The largest FMGS size identified in LCC and RCC was three. Deeper analysis of the FMGSs showed that 8-fold more one-itemsets (k = 1) of FMGSs were found in RCC than LCC (Supplementary table 1). Though the top driver mutation genes were basically the same, the pattern of the mutation frequencies differed greatly. In LCC, APC (support: 0.835) and TP53 (support: 0.647) were absolutely the dominant driver mutation genes. However, in RCC, a decreasing gene mutation pattern was observed. Despite that finding, APC and TP53 remained the top mutation genes. The sparse mutation pattern in RCC results in more co-occurred driver mutation genes. Except for TP53 and APC, other drivers (e.g., RNF43, FAT4, PIK3CA) also frequently co-occurred with other cancer-related genes in RCC. For example, in RCC, RNF43 often co-occurred with, for instance, SYNE1, OBSCN, BRAF, and LAMA5 (Supplementary table 1). It is well-known that microsatellite instability (MSI) patients have an elevated frequency of single-nucleotide variants (SNVs) because of inactivation of the DNA mismatch repair (MMR) system (20). MSI is observed in 15% of sporadic colorectal tumors diagnosed in the United States (21,22). Therefore, we further divided the LCC and RCC into MSI-high (MSI-H) and microsatellite stable (MSS) subgroups according to the clinical annotation by TCGA (23) and the findings by Hause et al. (21). Intriguingly, we found that the proportion of MSI-H in RCC was 20% (29 samples) and only ~5% in LCC (4 samples). In this context, we speculated that the sparse somatic mutation pattern in RCC may be biased by the MSI-H samples since the MSI-H samples had an elevated mutational load. As such, we independently calculated the FMGSs in MSS and MSI-H subgroups of RCC. However, similar results held in the MSS of RCC, with 127 unique k-1 FMGSs and 68 one-itemset (k = 1) FMGSs, which is two-fold and three-fold more than in LCC, respectively. Regarding the RCC-MSI-H, a much higher mutational burden was observed, with 7

1026 mutated genes being observed per tumor. Additionally, as many as 200 one-itemset (k = 1) FMGSs with support > 0.3 were found in RCC-MSI-H. Especially for RNF43, the frequent and validated mutation target in MSI-H cancers (21,24), was the most significantly mutated gene in RCC-MSI-H (support: 0.76). Nevertheless, an exclusivity of the MSI-H status and canonical CRC driver mutation genes, such as APC (support: 0.24 vs. 0.73 in MSS), TP53 (support: 0.24 vs 0.53 in MSS), and KRAS (support: 0.28 vs. 0.45 in MSS), were observed. This finding may account for a better MSI-H patient survival than MSS patients in CRC. Collectively, our data revealed that LCC and RCC had distinct driver patterns that should be considered in future targeted cancer therapy.

Hyper-methylation in RCC Changes in the DNA methylation status are thought to be involved in CRC initiation and development. Therefore, we attempted to identify the differentially methylated regions (DMRs) to decipher the epigenetic difference between LCC and RCC. Notably, 552 DMRs (bumps size => 2, cutoff = 0.2, permutation test P < 0.01, Supplementary table 2) were determined using bumphunter. Among them, 499 DMRs (92%) were located within CpG island and 27 DMRs (5%) were in the shores. Notably, a hypermethylation profile (540 DMRs) was observed in RCC (Fig 1). Of the 12 hypermethylated DMRs (34 probes) in LCC, three DMRs (13 probes) were associated with HOX family members HOXB5, HOXB7, and CDX2. GO enrichment analysis showed that the DMR associated genes were mainly enriched in organ morphogenesis (Benjamini & Hochberg FDR correction 3.1798E-10, 42 genes), cell differentiation (6.8791E-9, 71 genes), and embryonic morphogenesis (2.0480E-9, 29 genes).

Activation of oncogenes and silence of tumor-suppressor genes in RCC Using mRNAseq data compiled in TCGA, LCC vs. RCC revealed 253 differentially expressed genes (DEGs, fold change > 2 & adjusted P value < 0.05) between them. In LCC, 105 genes were up-regulated, and 148 genes were down-regulated, compared

with the RCC (Fig 2A). Functional annotation of these DEGs uncovered that oncogenes (e.g., ZIC5) were over-expressed in RCC, while tumor-suppressor genes (e.g., HOXB13) were repressed. To further confirm the DEGs identified using TCGA data, the GSE14333 dataset with 122 LCCs and 126 RCCs was used for identifying DEGs. In total, 330 DEGs were identified (Fig 2B), with 36 of them overlapping with the TCGA result (Fig 2C). The Prostate Cancer Susceptibility Candidate (PRAC) was the most significantly down-regulated gene in RCC (Fig 2D and E). To validate this result, 15 LCCs and 32 RCCs FFPE samples were used for IHC staining. Results showed that both the IHC staining score and positive rate in LCC were significantly higher than in RCCs (Supplementary Fig 1). The function of PRAC in COAD remains elusive, however, another highly down-regulated gene in RCC, HOXB13, ~2kb downstream of PRAC, has been shown to be a CRC suppressor (25,26). And HOXC6, which also belongs to homeobox transcription factor (TF) family, was the most significantly up-regulated gene in RCC (Fig 2F and G). Further correlation analysis of the DNA methylation and expression showed that 13 down-regulated genes (CLSTN2, ECEL1, ELAVL2, GDF10, LMX1A, PRAC, PRKAA2, RSPO4, SNX32, ST6GAL2, TAC1, VENTX, and WASF3) in RCC were possibly repressed by hyper-methylation of DMR. For example, one DMR, situated in the CpG island shore, was strongly associated with PRAC repression in RCC. The hypermethylated DMR in RCC encompassed 8 probes (cg00960395, cg04574034, cg09866983, cg12374721, cg14230397, cg20094830, cg20945566, cg27170782) that cover 388 bps in chromosome 17 (Fig 3). DNA methylation has been shown to play a role in the regulation of PRAC expression in prostate cancer (27).

miRNA expression In addition to mRNA, we also want to know if expression discrepancies exist between LCC and RCC in non-coding RNAs. Interestingly, 15 cancer-related miRNAs were differentially expressed (adjusted P-value < 0.05 and fold change > 1.5) between LCC and RCC. Eight miRNAs, namely, miR196b, miR466, miR296, miR552, miR592, miR1247, miR1275, and miR3131, were down-regulated in RCC, while seven 9

miRNAs were up-regulated, including miR10b, miR31, miR146a, miR155, miR615, miR625, and miR1293 (Fig 4A). The abundantly expressed mir-10b, an miRNA closely associated with cancer metastasis, had significantly higher expression level in RCC (Fig 4B). Further integrated analysis of miRNA and DEGs showed that 16 DEGs were putatively regulated by seven miRNAs (Fig 4C). Two miRNAs, miR625 and miR146a, were highly expressed in RCC, and their predicted targets were all downregulated in RCC. As for the other low expression of miRNAs in RCC, their predicted targets were upregulated in RCC. For example, miR-466 was downregulated and its predicted target, ZIC5, a transcriptional repressor that elevated expression contribute to cancer progression, was upregulated in RCC.

RCC tend to be more aggressive through orchestrating invasive gene modules Considering the huge expression difference between LCC and RCC, we speculate that the regulatory network also differs greatly. To this end, we used WGCNA to explore the core gene regulatory modules, which may shed light on the intrinsic expression variation between LCC and RCC. Interestingly, six and nine modules consisting of at least 100 genes were found in LCC and RCC, respectively. Five modules relate with mRNA metabolic process, DNA ligation, regulation of ARF GTPase activity, translational elongation, vasculature development, cell adhesion, complement activation, T cell activation, and lymphocyte activation were shared by LCC and RCC (Fig 5, linked by colored band). Of note, because certain modules such as brown- and blue modules in LCC were rather large. Therefore the top 50 hub genes were extracted in each module. We found that no miRNAs ranked in the top hubs in the module. This observation raises the possibility that miRNA, unlike TF, has a restricted role or at least does not act as a key orchestrator in COAD. The module peculiar to LCC was linked to cellular nitrogen compound metabolic process (FDR corrected P-value 3.3739E-2, 13 genes). The rest of the four RCC-relevant modules were linked with cell cycle phase (magenta module, 5.4696E-28, 27 genes), mitosis (magenta module, 2.9294E-29, 24 genes), generation of precursor metabolites and energy 10

(brown module, 1.0886E-4, nine genes), microtubule-based process (purple module, 3.1047E-2, five genes), response to virus (7.5314E-8, nine genes) and innate immune response (salmon module, 4.4101E-2, four genes). As such, enhanced activation of cell division, energy metabolism, and immune system process in RCC underscore the speculation of its more aggressive state.

Pathway alteration difference between LCC and RCC Integrated comparison of mutations and mRNA expression alteration in 85 LCCs and 116 RCC-MSS will enable better understanding of the intrinsic difference in the pathway dysregulation. Alteration frequency between LCC and RCC-MSS was examined in five well-known pathways (WNT, RAS, PI3K, TGF-beta, and P53) that are closely associated with CRC. Basically, the mutation rate in RCC-MSS is much more frequent than LCC in TGF-beta, PI3K, and RAS signaling pathways, but almost the same in WNT and TP53 pathways (Fig 6). The WNT signaling pathway was altered in 89% LCC and 85% RCC-MSS, respectively, which mainly contributed by inactivation of APC (78% and 73%), followed by the mutation of FAM123B, FBXW7, SOX9, CTNNB1, AXIN2, LRP1B. Down-regulation of LRP1B in colon cancer has recently been shown to be associated with promoting growth and migration of cancer cells (28). In the TGF-beta pathway, higher mutation rates (LCC vs. RCC-MSS, SMAD2- 0% vs. 3%, SMAD3- 1% vs. 4%, SMAD4- 12% vs. 16%) in RCC-MSS were observed in SMAD family members. As for the PI3K pathway, PIK3CA was mutated in 11% LCC samples and 26% RCC-MSS samples. Resembling the case in PI3K, a much higher mutation rate of KRAS (30% vs. 45%) and BRAF (7% vs. 11%) was found in RCC-MSS (Fig 6). Activation of the PI3K and RAS pathways may be more prone to progression for RCC-MSS. To further explore the co-occurrence of the dysregulation of different pathways in LCC and RCC-MSS, we re-examined the FMGSs that were identified as mentioned earlier. The results showed that five FMGSs (k > 1, support > 0.1) were in common between LCC and RCC-MSS. Inactivation of WNT and P53, but activation of the RAS pathway occurred simultaneously in more than 15% of the COAD samples (both 11

LCC and RCC-MSS). However, we observed nine more examples of RCC-MSS-specific pathway crosstalk that have > 10% support (Fig 6, right bottom panel). In addition to the interaction with the WNT pathway, the PI3K pathway often crosstalks with the RAS and P53 pathways in RCC-MSS. Additionally, in RCC-MSS, the TGF-beta pathway also often interacted with the RAS and WNT pathways via SMAD4 and KRAS or APC, respectively. Of note, we also observed single pathway members that were inactivated simultaneously, represented by APC and SOX9 (support = 0.121) or FBXW7 (support = 0.103) in the WNT pathway. Collectively, we found numerous molecular signatures that were associated with tumor aggressiveness in RCC-MSS, which suggests a poor prognosis for RCC-MSS patients.

Discussion The large molecular difference between LCC and RCC identified in this study consolidates the different embryonic origins of LCC and RCC. And the difference is independent of sex (X2 test, P = 1). Actually, DNA methylation alteration is an early event that occurs in cancer. Koestler et al. (29) determined that 168 probes enriched in homeobox genes that were differentially methylated between the right- and left-colon adenomas using the Illumina HumanMethylation450 BeadChip. Consistent with their observation of PRAC hypermethylation in right-colon adenomas and hypermethylation of CDX2 in left-colon adenomas, the difference also held true in the carcinoma. As a transcription factor (TF), HOXC6 promotes prostate cancer metastasis via inducing aberrant metastatic microRNA expression (30). It is tempting to believe that this result held in RCC due to the elevated expression of HOXC6. Previous studies demonstrate that the over-expression mir-10b can promote tumor invasion (31,32) and confers chemoresistance in CRC cells to 5-Fluorouracil (33). In this context, 5-Fluorouracil may not be suitable for the treatment of RCC due to a much higher expression level of miR-10b in RCC than LCC. In addition, high miR-10b expression in primary CRC tissue can independently predict distant metastasis (34), which suggests that primary RCC is more vulnerable to metastasis. Other onco-miRNAs 12

(miR31, miR155, miR625) that were over-expressed in RCC were all closely associated with colorectal carcinoma cell proliferation, migration, and invasion (35-37). In contrast, two tumor suppressor miRNAs in CRC, miR-296 and miR592 were underexpressed in RCC. MiR-296 inhibits the metastasis and epithelial-mesenchymal transition by targeting S100A4 and miR-592, which inhibits cell proliferation by suppressing of CCND3 expression (38,39). It must be noted that a more aggressive status was observed in RCC through determining the modules consisting of tightly co-regulated genes. The large brown module unique to RCC harbors 1353 genes that were mainly involved in electron transport chain. Additionally, three other RCC-specific modules were enriched in cell cycle regulating, mitosis/nuclear division, microtubule-based process, and innate immune response, which were all linked to tumor aggressiveness. In parallel, as described earlier, miRNAs tend not to be hub genes with an extremely high connectivity. This finding can be ascribed to the intrinsic target recognition discrepance between plant and animal miRNAs (40). In plants, almost all miRNA targets have a single miRNA responsive element and are regulated by just one miRNA or one miRNA family through nearly perfectly complementary miRNA-target pairs. Thus, the scope of miRNA targets appears to be only a handful of key targets, most of which are TFs. In animals, miRNAs target transcripts via imperfect base-pairing to multiple sites in the 3′ untranslated regions (UTRs) by the seed sequence (5′ end of miRNAs that comprises nucleotides 2–7). For this reason, over 90% transcripts were putatively targeted by miRNAs, and one transcript was often under combinatorial control of multiple miRNAs (41,42). In this context, animal miRNAs generally act as fine-tuners that exert wide subtle regulatory effects across the transcriptome and tend not to be hub genes in the regulatory network. PRAC, a small protein with a molecular weight of only 6 kDa, has been shown to be specifically expressed in the human prostate, rectum and colon (27,43). In prostate cancer, the expression level of PRAC is significantly down-regulated compared to benign prostatic hyperplasia. Here, we demonstrated that its expression level is significantly down-regulated in RCC, which is in line with the findings by Bauer et al. 13

(44). Although the concrete function of PRAC remains elusive in COAD, a possible co-transcribed gene HOXB13, has been shown to be involved in suppressing colorectal cancer progression (25,45). In RCC, HOXB13 was significantly down-regulated, as well. Thus, PRAC may serve as a tumor suppressor through interacting with HOXB13 in COAD. In addition, RCC-MSS had more mutated BRAF (11% vs. 7%) and KRAS (45% vs. 30%), and similar results were observed in our Zhejiang University Cancer Institute collected samples through sequencing 338 pairs of matched fresh frozen CRC tissue and adjacent normal tissue samples (unpublished data). BRAF mutation has been shown to be associated with a worse prognosis (46) and a poorer response to cetuximab therapy (3,47). In addition, colon cancer patients with wild-type KRAS treated with cetuximab have significantly improved OS (47). Thus, a worse prognosis in RCC can be partly ascribed to a higher mutation rate of KRAS and BRAF (Fig 6). More recently, Ptashkin RN et al. (48) find that chromosome 20q amplification is associated with a better OS in LCC. Additionally, since cetuximab is mainly used in advanced stages of CRC patients, FMGS mining is further conducted in four pathological stages according to the American Joint Committee on Cancer (AJCC) standards. Notably, we found KRAS harbored great stage heterogeneity between LCC (stage I: 25%, II: 53%, III: 9%, and IV: 20%) and RCC (53%, 46%, 31%, 62%). Nevertheless, we should bear in mind that current targeted drugs used in treating colon cancer 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 PFS in RCC can benefit from combination therapy since frequent pathway crosstalk occurred in RCC. Recently, Le et al. (49) demonstrated that PD-1 blockade works well in mismatch-repair-deficient CRC, and prolonged PFS was associated with high somatic mutational loads. In this manner, RCC patients that are deficient in mismatch repair (MMR), ~20% of RCC, can benefit from PD1 therapy (23,50). In short, the underlying molecular feature present in LCC and RCC identified in this study is beneficial for medical decision making and prolonged OS and PFS. Our results help 14

to elucidate the molecular basis of treating LCC and RCC as two different diseases in future cancer treatment.

Financial support This work was supported by the National High Technology Research and Development Program of China (863 Program) (2012AA02A204) to W Ge. This work was also partially supported by China Postdoctoral Science Foundation (2016M590532) and Postdoctoral Foundation of Zhejiang Province to W Hu (BSH1502129).

Acknowledgments The authors thank Dr. Lizhen Zhu for her help in IHC staining.

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Figure legends Figure 1 Identification of the DMRs between LCC and RCC. Notably, 540 hyper-methylated regions were found in RCC and only 12 hyper-methylated 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 hyper-methylated regions relative to LCC were shown in the center of the circle.

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

Figure 3 Down-regulation of PRAC correlates with high DNA methylation in RCC. Black dots and red dots correspond to LCC and RCC, respectively.

Figure 4 Identification of differentially expressed miRNAs between LCC and RCC. A. Volcano plot revealed 11 miRNAs were over-expressed in RCC and 5 miRNAs were under-expressed in RCC using TCGA data. B. Comparison of the miR10b 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, magenta rectangle nodes represent miRNA targets.

Figure 5 Comparison of the regulatory network between LCC and RCC using 20

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 co-expressed 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 4118 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.

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 = 85) or RCC (n = 116) samples. Mutation frequency of TP53, APC, and KRAS in MSI samples (n = 29) of RCC are shown separately in the right panel. Red denotes the predicted activated genes and blue represents the inactivated genes. Right bottom panel demonstrates the pathway crosstalk difference between LCC and RCC-MSS.

Figure 1 Downloaded from mcr.aacrjournals.org on September 23, 2021. © 2017 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 29, 2017; DOI: 10.1158/1541-7786.MCR-17-0483 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A B C

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 Change HOXB13 Change DOWN 20 DOWN 20 NOT NOT GNG4 UP HOXC6 UP

ELAVL2 DRD5 APCDD1 ELAVL2 B3GNT7 INSL5 10 10 HOXC6 −Log10p−value

−Log10p−value PRAC LY6G6D WIF1 ZIC5 XPNPEP2 LYZ ERP27 PNPLA3 SLC19A3 PCP4 HOXC6 GNG4 PRKAA2 PNMA2 ZNF880 DUSP4 0 0 SEMG1 TCN1 WASF3 FOXD1 RAB27B CLDN8 TRNP1 ZNF813 −5 0 5 −4 −2 0 2 Log2 fold change Log2 fold change MUC12 ZIC2 CLDN18 ST6GAL2 GPR126 PYY HOXB13 NR1H4 D E F G

TCGA PRAC expression GSE14333 PRAC expression TCGA HOXC6 expression GSE14333 HOXC6 expression

12.5 10.0

10.0 9 7.5 10

7.5

5.0 value

value 6 log2(value + 1) + log2(value log2(Value + 1) + log2(Value 5.0 5

2.5

2.5 3

0 0.0 0.0 Left Right Left Right Left Right Left Right

Figure 2 Downloaded from mcr.aacrjournals.org on September 23, 2021. © 2017 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 29, 2017; DOI: 10.1158/1541-7786.MCR-17-0483 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

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 5.0 Right

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

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 (cg00960395) DNA Methylation (cg14230397) DNA Methylation (cg20945566) DNA Methylation (cg27170782)

Figure 3

A B 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 value Change 6 hsa-miR-592 hsa-miR-155 DOWN

hsa-miR-10b NOT Million Reads Per hsa-miR-196b hsa-miR-625 hsa-miR-466 UP

−log10 p− 3 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 50000 100000 150000 200000 −1 0 1 2 3 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

mRNA metabolic process

cellular nitrogen compound metabolic process mitosis DNA ligation cell cycle phase

generation of precursor metabolites and energy

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 as.dist(dissTOM)

as.dist(dissTOM) T cell activation hclust (*, "average") hclust (*, "average") 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 Dynamic Tree Cut Dynamic Tree Figure 5

PI3K signalling RAS signalling LCC LCC LCC WNT signalling 89% 85% TGF-β signalling 20% 36% LCC 20% 32% 40% 61%

WNT TGF-β 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% 0% 3% 1% 4% NRAS KRAS 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

12% 13% 6% 12% RCC-MSS TCF7L2 Nucleus 9% 6% P53 signalling 70% 64% LCC RCC-MSI-H ATM SOX9 CTNNB1 Proliferation, stem/ 6% 12% TP53 24% MYC 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 0.379 10~20% APC, TP53 0.529 Activation > 20% APC, TP53, KRAS 0.165 APC, TP53, KRAS 0.198 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

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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