A circRNA signature predicts postoperative recurrence in stage II/III colon cancer
2019; Springer Nature; Volume: 11; Issue: 10 Linguagem: Inglês
10.15252/emmm.201810168
ISSN1757-4684
AutoresHuai‐Qiang Ju, Qi Zhao, Feng Wang, Ping Lan, Zixian Wang, Zhixiang Zuo, Qi‐Nian Wu, Xin‐Juan Fan, Hai‐Yu Mo, Li Chen, Ting Li, Chao Ren, Xiang‐Bo Wan, Gong Chen, Yu‐Hong Li, Wei‐Hua Jia, Rui‐Hua Xu,
Tópico(s)MicroRNA in disease regulation
ResumoArticle2 September 2019Open Access Source DataTransparent process A circRNA signature predicts postoperative recurrence in stage II/III colon cancer Huai-Qiang Ju Huai-Qiang Ju orcid.org/0000-0003-1713-5465 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Qi Zhao Qi Zhao orcid.org/0000-0002-8683-6145 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Feng Wang Feng Wang State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Ping Lan Ping Lan The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Zixian Wang Zixian Wang State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Zhi-Xiang Zuo Zhi-Xiang Zuo State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Qi-Nian Wu Qi-Nian Wu State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Xin-Juan Fan Xin-Juan Fan The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Hai-Yu Mo Hai-Yu Mo State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Li Chen Li Chen State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Ting Li Ting Li State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Chao Ren Chao Ren State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Xiang-Bo Wan Xiang-Bo Wan The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Gong Chen Gong Chen State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Yu-Hong Li Yu-Hong Li State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Wei-Hua Jia Wei-Hua Jia State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Rui-Hua Xu Corresponding Author Rui-Hua Xu [email protected] orcid.org/0000-0001-9771-8534 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Huai-Qiang Ju Huai-Qiang Ju orcid.org/0000-0003-1713-5465 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Qi Zhao Qi Zhao orcid.org/0000-0002-8683-6145 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Feng Wang Feng Wang State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Ping Lan Ping Lan The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Zixian Wang Zixian Wang State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Zhi-Xiang Zuo Zhi-Xiang Zuo State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Qi-Nian Wu Qi-Nian Wu State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Xin-Juan Fan Xin-Juan Fan The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Hai-Yu Mo Hai-Yu Mo State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Li Chen Li Chen State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Ting Li Ting Li State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Chao Ren Chao Ren State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Xiang-Bo Wan Xiang-Bo Wan The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Search for more papers by this author Gong Chen Gong Chen State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Yu-Hong Li Yu-Hong Li State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Wei-Hua Jia Wei-Hua Jia State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Rui-Hua Xu Corresponding Author Rui-Hua Xu [email protected] orcid.org/0000-0001-9771-8534 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China Search for more papers by this author Author Information Huai-Qiang Ju1,‡, Qi Zhao1,‡, Feng Wang1,2,‡, Ping Lan3,‡, Zixian Wang1,2,‡, Zhi-Xiang Zuo1,4, Qi-Nian Wu1,5, Xin-Juan Fan3, Hai-Yu Mo1, Li Chen4, Ting Li1, Chao Ren1, Xiang-Bo Wan3, Gong Chen1,6, Yu-Hong Li1,2, Wei-Hua Jia1 and Rui-Hua Xu *,1,2 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China 2Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China 3The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China 4State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China 5Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China 6Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China ‡These authors contributed equally to this work *Corresponding author. Tel: +86 20 8734 3333; E-mail: [email protected] EMBO Mol Med (2019)11:e10168https://doi.org/10.15252/emmm.201810168 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Accurate risk stratification for patients with stage II/III colon cancer is pivotal for postoperative treatment decisions. Here, we aimed to identify and validate a circRNA-based signature that could improve postoperative prognostic stratification for these patients. In current retrospective analysis, we included 667 patients with R0 resected stage II/III colon cancer. Using RNA-seq analysis of 20 paired frozen tissues collected postoperation, we profiled differential circRNA expression between patients with and without recurrence, followed by quantitative validation. With clinical information, we generated a four-circRNA-based cirScore to classify patients into high-risk and low-risk groups in the training cohort. The patients with high cirScores in the training cohort had a shorter disease-free survival (DFS) and overall survival (OS) than patients with low cirScores. The prognostic capacity of the classifier was validated in the internal and external cohorts. Loss-of-function assays indicated that the selected circRNAs played functional roles in colon cancer progression. Overall, our four-circRNA-based classifier is a reliable prognostic tool for postoperative disease recurrence in patients with stage II/III colon cancer. Synopsis Novel molecular biomarkers allowing for better prognostic stratification of patients with stage II/III colon cancer are urgently needed. In this study, a circRNA-based signature (cirScore) was identified and validated to improve postoperative risk-stratification for these patients. Dysregulated circRNAs showed strong classification capacities in distinguishing between recurrent and nonrecurrent colon cancer patients. The proposed four-cirRNA-based cirScore can effectively classify patients with stage II/III colon cancer into groups with low and high risks of disease recurrence. The loss-of-function assay indicated that the representative circRNAs plays functional roles in the sophisticated regulation of colon cancer progression. Nomograms incorporating the cirScore with existing risk factors achieved excellent accuracy for predicting disease-free and overall survival for patients with stage II/III colon cancer. Introduction Approximately 60% of patients with colon cancer present with stage II/III disease (Rabeneck et al, 2015). Surgical resection is the only possible cure for these patients (Rabeneck et al, 2015). However, there are still 20–30% of patients who suffer from postoperative recurrence, which results in dismal survival (O'Connell et al, 2008; Andre et al, 2009). Traditionally, adjuvant chemotherapy has been the standard of care for patients with high-risk stage II, defined by clinicopathological features such as T4 lesion and the retrieval of < 12 lymph nodes, and all stage III colon cancer, defined as N1/N2M0 disease irrespective of T stage. However, clinicopathological risk factors and microsatellite instability status do not adequately distinguish between patients who have a high or low risk of disease recurrence, thereby not indicating which patients are likely to benefit from postoperative chemotherapy (Gray et al, 2007; Morris et al, 2007). In view of this clinical challenge, there is an unmet need for novel recurrence-specific molecular biomarkers that allow for better prognostic stratification and more appropriate therapies for patients with stage II/III colon cancer. Circular RNA (circRNA), a rediscovered, abundant RNA species, is a type of non-coding covalent closed RNAs formed from both exonic and intronic sequences (Morris & Mattick, 2014; Chen & Yang, 2015). circRNAs are characterized by several properties, such as being evolutionarily conserved, having tissue-specific expression, more stable than linear miRNA (Jeck et al, 2013; Memczak et al, 2013; Taborda et al, 2017). They can regulate gene expression, acting as real sponges for miRNAs, and also regulate alternative splicing or act as transcriptional factors and inclusive encoding for proteins (Taborda et al, 2017). However, to the best of our knowledge, the ability of circRNA-based signatures as novel prognostic biomarkers for colon cancer has not yet been comprehensively investigated. In this study, we conducted a multicenter, retrospective study to assess the ability of circRNA expression profiles to predict disease recurrence in patients with stage II/III colon cancer. We aimed to identify and validate a circRNA-based signature that could improve postoperative prognostic stratification for these patients. Results Clinicopathological features of patients As shown in Fig 1, the frozen tissue samples of 667 colon cancer patients with stage II/III disease were collected from Sun Yat-sen University Cancer Center (487 samples) for discovery (n = 20), selection (n = 96), training (n = 249), and internal validation (n = 122), and the Six Affiliated Hospital of Sun Yat-sen University for external validation (n = 180). The detailed clinicopathological characteristics of the training, and the internal and external validation datasets are shown in Table 1. All patients had undergone surgical resection with histologically negative resection margins. The median follow-up periods were 67 months (IQR, 50–78), 66 months (IQR, 48–79), and 57 months (IQR, 48–64), respectively, in the training and internal and external validation sets. The corresponding 5-year disease-free survival (DFS, defined as the time from the date of surgery to the date of confirmed tumor relapse or death from any cause, as the outcome) rates were 72.6% (95% CI, 68.1–77.3), 69.5% (95% CI, 61.6–78.4), and 75.2% (95% CI, 69.3–81.6), and 5-year overall survival (OS, defined as the time from the date of surgery to the date of death or the last known follow-up) rates were 81.1% (95% CI, 77.1–85.3), 78.1% (95% CI, 70.8–86.1), and 82.4% (95% CI, 77.1–88.0). Figure 1. Study flowchartSYUCC = Sun Yat-sen University Cancer Center. SAHSY = the Six Affiliated Hospital of Sun Yat-sen University. Training and internal validation sets were randomly selected at a 2:1 ratio from the samples from SYUCC. Download figure Download PowerPoint Table 1. Clinical characteristic of patients with stage II/III colon cancer involved in this study Training set (n = 249) Internal validation set (n = 122) External validation set (n = 180) Age ≥65 year 168 (67.5) 84 (68.9) 146 (81.1) < 65 year 81 (32.5) 38 (31.1) 34 (18.9) Sex Male 106 (42.6) 54 (44.3) 64 (35.6) Female 143 (57.4) 68 (55.7) 116 (64.4) Primary tumor location Left-sided 180 (72.3) 91 (74.6) 43 (23.9) Right-sided 69 (27.7) 31 (25.4) 137 (76.1) Perineural invasion Yes 177 (71.1) 85 (69.7) 138 (76.7) No 72 (28.9) 37 (30.3) 42 (23.3) Lymphatic or vascular invasion Yes 204 (81.9) 94 (77) 161 (89.4) No 45 (18.1) 28 (23) 19 (10.6) Tumor differentiation Well or moderately differentiated 176 (70.7) 89 (73) 136 (75.6) Poorly differentiated or undifferentiated 73 (29.3) 33 (27) 44 (24.4) Mismatch repair status Mismatch repair-deficient 27 (10.8) 6 (4.9) 19 (10.6) Mismatch repair-proficient 76 (30.5) 37 (30.3) 64 (35.5) Unexamined 146 (58.6) 79 (64.8) 97 (53.9) T stage T1 2 (0.8) 2 (1.6) 1 (0.6) T2 7 (2.8) 3 (2.5) 8 (4.4) T3 118 (47.4) 51 (41.8) 147 (81.7) T4 122 (49) 66 (54.1) 24 (13.3) N stage N0 128 (51.4) 57 (46.7) 77 (42.8) N1 73 (29.3) 41 (33.6) 77 (42.8) N2 48 (19.3) 24 (19.7) 26 (14.4) The total evaluated lymph node count < 12 79 (31.7) 41 (33.6) 5 (2.8) ≥ 12 170 (68.3) 81 (66.4) 175 (97.2) Clinical risk groupa Non-high-risk stage II 27 (10.8) 6 (4.9) 27 (15) High-risk stage II 91 (36.5) 49 (40.2) 39 (21.7) Non-high-risk stage III 43 (17.3) 15 (12.3) 81 (45) High-risk stage III 88 (35.3) 52 (42.6) 33 (18.3) Data are n (%). a Stage II disease was considered high-risk if positive for the biomarkers for poorly differentiated or undifferentiated histology (exclusive of mismatch repair-deficient cases), perineural invasion, lymphatic or vascular invasion, or T4 stage II. Stage III disease was considered high-risk if it was staged T4, N2, or both. Selection and validation of candidate circRNAs Based on the RNA-seq data and bioinformatics analysis, differential expression analysis identified 437 circRNAs (326 upregulated and 111 downregulated, marked as “TNcircles” afterward) between the tumor and adjacent normal tissues by using a soft threshold. The analysis also identified 103 differentially expressed circRNAs (48 upregulated and 55 downregulated, marked as “RNcircles” afterward) between recurrent and non-recurrent tumor tissues. Both TNcircles and RNcircles showed strong classification properties in distinguishing each of the groups (Fig 2A and B). In addition, the differential expression results indicated that circRNAs experienced more prominent changes between the normal and tumor tissues than between the recurrent and non-recurrent tumor tissues (Fig 2A and B). Figure 2. Marker validation and selection from the circRNA-sequencing experiment Expression profiling of differentially expressed circRNAs between the tumor and normal groups. Rows represent circRNAs, and columns represent samples. Rows were ordered by fold change, and columns were ordered by their group. The sample of N8 was not included due to low sequencing library size. Expression profiling of differentially expressed circRNAs between the recurrence and non-recurrence groups. Both the row and column were unsupervised and clustered with the hierarchical clustering method. The 4 of 22 differentially expressed circRNAs were confirmed by qRT–PCR, which were retained after marker selection procedure. **P < 0.01, Student's t-test, mean ± SD. Bar plot shows the resample model inclusion proportion (RMIP) of qualified circRNAs calculated in the training dataset. The red line presents the threshold used to obtain the final markers. Time-dependent AUC analysis of individual circle RNA and cirScore for predicting recurrence in the training dataset. P-values are shown for the indicated comparison of AUC between each marker and cirScore. Student's t-test, AUC = area under the curve. Data information: Exact P-values are specified in Appendix Table S5. Source data are available online for this figure. Source Data for Figure 2 [emmm201810168-sup-0003-SDataFig2.xlsx] Download figure Download PowerPoint Next, we investigated whether circRNAs could be used as prognostic biomarkers in patients with stage II/III colon cancer. First, 38 significantly upregulated circRNAs were selected from TNcircles for further validation according to the aforementioned retaining criteria. In addition, we prioritized 62 circRNAs from RNcircles using the same selection criteria to obtain a total of 100 circRNAs for validation assays. Considering a potential false discovery that might be introduced by the inadequate sensitivity of the RNA-seq and sample size, we enrolled 48 recurrent and 48 non-recurrent samples for further validation using qRT–PCR assay. Among these candidates, 22 circRNAs (10 from TNcircles and 12 from RNcircles) were further selected based on the extremely significant difference (P < 0.01; Figs 2C and EV1). We quantified these 22 circRNAs with qRT–PCR in the training cohort (n = 249) and further reduced the number of candidates using the (least absolute shrinkage and selection operator) LASSO-bagging procedure as described in Materials and Methods (Fig 2D). Finally, we obtained four circRNAs that were strongly predictive of DFS, i.e., hsa_circ_0122319, hsa_circ_0087391, hsa_circ_0079480, and hsa_circ_0008039 (Fig 2D). Notably, multivariate Cox regression analysis showed that these four circRNAs are mutually independent (Appendix Table S1). We also observed that the predicting performance of the four-circRNA-based risk score (cirScore) mostly outperforms than single circRNA with the time-dependent AUC analysis (Fig 2E). The circularity and stability of the four selected circRNAs were verified by Sanger sequencing and RNase R treatment. After examined by RT–PCR with divergent primers, the sequenced PCR product was corresponding from the bioinformatics analysis with the exact back-splice junction (Fig EV2A). We next validated the circularity of these candidates by RNase R treatment, and the mouse GAPDH mRNA was used as spike-in for normalization. The results indicated that these circRNAs were more resistance to digestion with RNase R exonuclease compared with linear host genes, which further confirmed that these circRNAs harbors a circular RNA structure (Fig EV2B). Taken together, these results indicated that the circRNA may be served as novel prognostic biomarkers for colon cancer. Click here to expand this figure. Figure EV1. Expression status of candidate circRNAs as colon cancer recurrence-specific biomarkers qRT–PCR validation of the nine selected upregulated circRNAs in an independent validation cohort of 48 pairs of colon cancer tissues. qRT–PCR validation of nine selected upregulated circRNAs in an independent validation cohort of 48 colon cancer tissues with or without recurrence. Data information: The horizontal bar represents mean expression levels; N = adjacent normal tissues; T = tumor tissues; R = tumor tissues from the recurrence group. **P < 0.01, Student's t-test, mean ± SD. Exact P-values are specified in Appendix Table S5. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Characterization of selected circRNAs The genomic locus of the selected circRNAs, and the circRNA back-splice junction sites were detected by RT–PCR followed by Sanger sequencing. qRT–PCR analysis for the expression of four selected circRNAs and the corresponding host genes after treatment with RNase R in HCT116 cells. The data were normalized to mouse GAPDH mRNA by adding a mouse RNA spike to each fraction. **P < 0.01, Student's t-test, mean ± SD (n = 3). Exact P-values are specified in Appendix Table S5. Download figure Download PowerPoint Construction and validation of the four-circRNA-based prognostic model Then, a risk score was calculated for each patient using a formula derived from the expression levels of the four circRNAs weighted by their regression coefficient: Using the cirScore, we divided patients into high- and low-risk groups with its median value (−0.323) among the training cohort. Survival analysis showed that patients in the high-risk group had a poorer DFS than those in the low-risk group (hazard ratio [HR], 4.38; 95% confidence interval [CI], 2.52–7.64, P < 0.0001; Fig 3A). Moreover, we observed a similar impact of the cirScore on OS (high vs. low risk, HR, 5.13, 95% CI, 2.56–10.16, P < 0.001; Fig 3B). Figure 3. Kaplan–Meier curves of DFS and OS based on the cirScore in patients with stage II/III colon cancer A–F. Kaplan–Meier curves of DFS (A) and OS (B) in 249 patients in the training set. Kaplan–Meier curves of DFS (C) and OS (D) in 122 patients in the internal validation dataset. Kaplan–Meier curves of DFS (E) and OS (F) in 180 patients in the external independent validation dataset. Hazard ratios (HRs) were calculated with a univariate Cox regression analysis, and P-values were calculated with the log-rank test. Source data are available online for this figure. Source Data for Figure 3 [emmm201810168-sup-0004-SDataFig3.xlsx] Download figure Download PowerPoint To validate the prognostic prediction performance of the cirScore, patients in the internal and external validation cohorts were classified into high- and low-risk groups using the same cutoff obtained from the training cohort. In the internal validation cohort, patients with a high cirScore had a shorter DFS (HR, 2.89, 95% CI, 1.37–6.09, P < 0.001; Fig 3C) and a shorter OS (HR, 4.22, 95% CI, 1.61–11.03, P < 0.001; Fig 3D). Likewise, a high cirScore was associated with worse DFS (HR, 3.63, 95% CI, 1.81–7.29, P < 0.01; Fig 3E) and OS (HR, 4.25, 95% CI, 1.9–9.54, P < 0.0001; Fig 3F) in the external validation cohort. After adjustment for baseline clinicopathologic factors, the cirScore remained a powerful and significant predictor of DFS and OS in the training set (HR = 4.64 [95% CI, 2.64–8.17], P < 0.0001 and HR = 5.45 [95% CI, 2.70–11.00], P < 0.0001, respectively). We also noted similar results in the internal validation set (HR = 2.96 [95% CI, 1.37–6.42], P = 0.0058 for DFS and HR = 3.82 [95% CI, 1.44–10.15], P = 0.007 for OS) and in the external validation set (HR = 2.50 [95% CI, 1.16–5.36], P = 0.008 for DFS and HR = 4.15 [95% CI, 1.79–9.64], P = 0.0009 for OS). Loss-of-function assay of selected circRNAs regulating cell metastasis We thus determined to evaluate the biological roles of the selected circRNA in colon cancer. Among the four circRNA markers, three circRNA (hsa_circ_0122319, hsa_circ_0079480, and hsa_circ_0087391) were significantly overexpressed in the recurrent samples and in the colon cancer cells (Figs 2C and EV3A). The circularity of these circRNAs was further verified by RT–PCR with divergent or convergent primers (Figs 4A and EV3B). To assess whether these circRNAs promoted colon cancer progression, SW620 and HCT116 cells with high metastatic potential were used to conduct loss-of-function assay by lentivirus-mediated stable gene silencing. The knockdown efficiency and specificity were verified by qRT–PCR, immunoblotting, and RNA-seq analysis. The results demonstrated that knockdown of these circRNAs had no effects on the mRNA or protein expression of the host genes (Figs 4B and EV3C and D), and had a high similarity of gene expression profile between two independent shRNA group in SW620 and HCT116 cells (Fig EV3E), suggesting that the following regulatory effects directly result from targeting the circRNAs rather than off-targets. Remarkably, knockdown of these circRNAs using two independent shRNAs significantly suppressed cell migration capacity in the detected cells (Fig 4C and D). Click here to expand this figure. Figure EV3. Characterization of selected circRNAs qRT–PCR analysis of four circRNA expression in 11 colon cancer cell lines and 2 colon epithelial cells (CCD112 and CCD841). Data are presented as mean ± SD (n = 3). RT–PCR products with divergent and convergent primers showing circularization of has_circ_0122319. cDNA, complementary DNA; gDNA, genomic DNA. qRT–PCR evaluated the knockdown efficiency of has_circ_0122319 and has_circ_0097391 and the host gene (PLOD2 and AGTPBP1) expression in SW620 and HCT116 cells transfected with two unique shRNAs (#1, #2). **P < 0.01, Student's t-test, mean ± SD (n = 3). Exact P-values are specified in Appendix Table S5. qRT–PCR evaluated the host gene (PLOD2, AGTPBP1, and ISPD) mRNA expression, and immunoblotting evaluated the PLOD2 protein expression in indicated cells. β-Actin antibody was used as a loading control. Data are presented as mean ± SD (n = 3). Correlation analysis of transcriptome between two independent shRNA groups in SW620 and HCT116 cells. In each scatter plot, the log10-transformed Transcripts Per Million (TPM) of reads of each gene were utilized for calculating the spearman's coefficient. R represents Spearman's correlation coefficients, and P-values were calculated by Spearman's correlation test. Download figure Download PowerPoint Figure 4. Loss-of-function assay of candidate circRNAs regulating cell invasion A. RT–PCR products with divergent and convergent primers showing circularization of has_circ_0079480 and has_circ_0087391. cDNA, complementary DNA; gDNA, genomic DNA. B. qRT–PCR evaluated the knockdown efficiency of has_circ_0079480 and has_circ_0087319 in SW620 and HCT116 cells transfected with two unique shRNAs (#1, #2). **P < 0.01, Student's t-test, mean ± SD (n = 3). C. Representative images of the migration phenotype
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