Hsa_circ_0066351 acts as a prognostic and immunotherapeutic biomarker in colorectal cancer

Circular RNA (circRNA), a novel class of non-coding RNA, has been reported in various diseases, especially in tumors. However, the key signatures of circRNA-competitive endogenous RNA (ceRNA) network are largely unclear in colorectal cancer (CRC). We first characterized circRNAs profile by using circRNA-seq analysis from real-word dataset. The expression level of hsa_circ_0066351 in CRC tissues and cell lines was detected by quantitative real-time PCR. Then, cell proliferation assay was used to confirm the proliferation function of hsa_circ_0066351. Next, Cytoscape was used to construct circRNA–miRNA–mRNA networks. Last but not least, the landscape of hsa_circ_0066351–miRNA–mRNA in CRC had been investigated in the bulk tissue RNA-Seq level and single-cell Seq level. We proved that hsa_circ_0066351 was significantly downregulated in CRC cell lines and tissues (P < 0.001), and was negatively associated with distant metastasis (P < 0.01). Significantly, the expression of hsa_circ_0066351 was associated with better survival in patients with CRC. Function assays showed that hsa_circ_0066351 could inhibit CRC cells proliferation. In addition, a ceRNA network, including hsa_circ_0066351, two miRNAs, and ten mRNAs, was constructed. Our analyses showed that these ten mRNAs were consistently downregulated in pan-cancer and enriched in tumor suppressive function. A risk score model constructed by these ten downstream genes also indicated that they were related to the prognosis and immune response in CRC. In conclusion, we demonstrated that a novel circRNA (hsa_circ_0066351) inhibited CRC prol Total RNA from each sample was quantified using the NanoDrop ND-1000. The sample preparation and microarray hybridization were performed based on the Arraystar’s standard protocols. Briefly, total RNAs were digested with Rnase R (Epicentre, Inc.) to remove linear RNAs and enrich circular RNAs. Then, the enriched circular RNAs were amplified and transcribed into fluorescent cRNA utilizing a random priming method (Arraystar Super RNA Labeling Kit; Arraystar). The labeled cRNAs were hybridized onto the Arraystar Human circRNA Array V2 (8x15K, Arraystar). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed using the R software limma package. Differentially expressed circRNAs with statistical significance between two groups were identified through Volcano Plot filtering. Differentially expressed circRNAs between two samples were identified through Fold Change filtering. Hierarchical Clustering was performed to show the distinguishable circRNAs expression pattern among samples.

环状RNA（Circular RNA, circRNA）作为一类新型非编码RNA，已被报道与多种疾病密切相关，尤其在肿瘤性疾病中。然而，结直肠癌（Colorectal cancer, CRC）中环状RNA竞争性内源RNA（circRNA-competitive endogenous RNA, ceRNA）网络的关键特征仍未完全阐明。本研究首先通过对真实世界数据集进行circRNA测序（circRNA-seq）分析，刻画了结直肠癌组织中的circRNA表达谱。采用定量实时PCR（quantitative real-time PCR）检测了hsa_circ_0066351在结直肠癌组织及细胞系中的表达水平。随后通过细胞增殖实验验证了hsa_circ_0066351的细胞增殖调控功能。接下来使用Cytoscape软件构建了circRNA–miRNA–mRNA调控网络。最后，本研究在批量组织RNA测序（bulk tissue RNA-Seq）及单细胞测序（single-cell Seq）层面，探究了结直肠癌中hsa_circ_0066351–miRNA–mRNA调控轴的全貌。本研究证实，hsa_circ_0066351在结直肠癌组织与细胞系中显著下调（P < 0.001），且与远处转移呈负相关（P < 0.01）。值得注意的是，hsa_circ_0066351的表达水平与结直肠癌患者更佳的生存预后相关。功能实验结果显示，hsa_circ_0066351可抑制结直肠癌细胞的增殖能力。此外，本研究构建了包含hsa_circ_0066351、2种miRNA及10种mRNA的ceRNA调控网络。分析结果显示，这10个靶mRNA在泛癌队列中均呈显著下调趋势，且富集于肿瘤抑制相关的生物学功能。基于这10个下游基因构建的风险评分模型同样表明，其与结直肠癌患者的预后及免疫应答密切相关。综上，本研究证实新型环状RNA（hsa_circ_0066351）可抑制结直肠癌细胞增殖。所有样本的总RNA均采用NanoDrop ND-1000进行定量。样本制备及芯片杂交均遵循Arraystar的标准实验流程。简要而言，总RNA经Rnase R（Epicentre, Inc.）消化以去除线性RNA并富集环状RNA。随后，富集得到的环状RNA经随机引物法进行扩增并转录为荧光cRNA（使用Arraystar Super RNA Labeling Kit；Arraystar）。将标记好的cRNA杂交至Arraystar Human circRNA Array V2 (8x15K, Arraystar)芯片上。玻片洗涤完成后，采用Agilent Scanner G2505C扫描仪对芯片进行扫描。采用Agilent Feature Extraction软件（版本11.0.1.1）对获取的芯片图像进行分析。采用R语言limma软件包进行分位数归一化及后续的数据处理。通过火山图筛选鉴定两组间具有统计学显著性的差异表达circRNA；通过倍数变化筛选鉴定两个样本间的差异表达circRNA。采用分层聚类分析展示不同样本间circRNA的差异化表达模式。