Background: Colon cancer (CC) is a common malignant cancer. Recently, circFNDC3B was found to exert biological function in multiple cancers. However, it was unclear whether the potential protein encoded by circFNDC3B is involved in carcinogenesis of CC. Methods: We used Sanger sequence and RNase R digestion assay to confirm the existence of circFNDC3B, and quantitative real-time PCR was used to evaluate the circRNA’s expression. Then fluorescence in situ hybridization (FISH) was performed to study location of circFNDC3B. The identification of protein encoded by circFNDC3B was performed using LC-MS/MS. The function of circFNDC3B-218aa on proliferation, invasion and migration were assessed by CCK8 assays, colony formation assays, transwell assays, wound-healing assays and animal experiments. RNA-sequencing and western blot were used to identify the gene regulated by circFNDC3B-218aa. Finally, glucose metabolism-related assays were performed to further investigate function of circFNDC3B-218aa. Results: CircFNDC3B was localized mostly in the cytoplasm, and was decreased in CC cell lines and tissues. The patients with low circFNDC3B expression had a shorter OS (P = 0.0014) than patients with high expression. Moreover, circFNDC3B inhibited the proliferation, invasion and migration of CC cells. Next, we identified that circFNDC3B could encode a novel protein circFNDC3B-218aa. Furthermore, circFNDC3B-218aa, not circFNDC3B, inhibited the proliferation, invasion and migration of CC. Additionally, the in vivo experiments implied that up-regulated circFNDC3B-218aa exhibited an inhibitory effect on CC progression. By RNA-sequencing, western blot and glucose metabolism-related assays, we found that circFNDC3B-218aa inhibited the expression of Snail, and subsequently promoted the tumor-suppressive effect of FBP1 in CC. Conclusions: The novel circFNDC3B-218aa may serve as a tumor suppressive factor and potential biomarker which may supply the potential therapeutic target for CC.

Current tumor-node-metastasis (TNM) stage is unable to accurately predict the overall survival (OS) in breast cancer (BC) patients. This study aimed to construct a microRNA (miRNA)-based model to improve survival prediction of BC. We confirmed 99 differentially expressed miRNAs (DEMs) in 1044 BC samples compared to 102 adjacent normal breast tissues from The Cancer Genome Atlas (TCGA) database. Prognostic DEMs were used to establish a miRNA-based nomogram via Cox regression model. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses (KEGG) were executed to analyze target genes of miRNAs. A six-miRNA signature was screened to effectively distinguish high-risk patients in the primary and validation cohort (all P<0.001). Furthermore, we established a novel prognostic model incorporating the six-miRNA signature and clinical risk factors to predict 5-year OS of BC. Time-dependent receiver operating characteristic analysis suggested that the predictive accuracy of the six-miRNA-based nomogram was distinctly higher than that of TNM stage (0.758 vs 0.650, P<0.001). GO and KEGG pathway analyses showed that the 39 target genes mainly enrichment in protein binding, cytoplasm and MAPK signaling pathway. Our six-miRNA-based model is a reliable prognostic tool for survival prediction and provides information for individualized treatment decisions in BC patients.

Introduction: Colon carcinoma is a common malignant tumor worldwide. Accurately predicting prognosis of colon adenocarcinoma (CA) patients may facilitate clinical individual decision-making. Many studies have reported that microRNAs (miRNAs) were associated with prognosis for patients with colon carcinoma. This study aimed to identify the prognosis-related miRNAs for predicting the overall survival (OS) of CA patients. Methods: Firstly, we analyzed the CA datasets from the Cancer Genome Atlas (TCGA), and looked for the prognosis-related miRNAs. Then, we developed a novel prediction model based on these miRNAs and the clinical characteristics. Time-dependent receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the discrimination and accuracy of the signature and model. Finally, cell function assays and bioinformatics analyses were performed to evaluate the role of these selected miRNAs in modulating biological process in CA. Results: Six prognosis-related miRNAs were included in the miRNA-based signature, and it could effectively distinguish low-risk patients and high-risk patients. Furthermore, we established a prognostic model incorporating the six-miRNA-based signature and clinical characteristics. Areas under curves (AUCs) indicated that the six-miRNA-based model has a better predictive ability than TNM stage (AUC: 0.805 vs. 0.694). The calibration plots suggested close agreement between model predictions and actual observations. GO analysis showed that the target genes of these miRNAs are mainly involved in enrichment in protein binding and regulation of transcript and cytosol. KEGG pathway enrichment analysis indicated that these genes were mainly enriched in PI3K-Akt signaling pathway. Finally, we found that the five miRNAs except miR-152 were upregulated in tumor tissues and CA cells. The functional experiments revealed that miR-1245a, miR-3682, miR-33b, and miR-5683 promoted the migratory abilities and proliferation of CA cell, whereas miR-152 showed opposite effects. However, miR-4444-2 did not influence the migratory ability and proliferation of CA cell. Conclusions: In conclusion, we developed a novel six-miRNA-based model to predict 5-year survival probabilities for CA patients. This model has the potential to facilitate individualized treatment decisions.

Background: The eighth edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system for survival prediction and risk stratification in breast cancer (BC) patients after neoadjuvant chemotherapy (NCT) is of limited efficacy. This study aimed to establish a novel prognostic nomogram for predicting disease-free survival (DFS) in BC patients after NCT. Patients and methods: A total of 567 BC patients treated with NCT, from two independent centers, were included in this study. Cox proportional-hazards regression (CPHR) analysis was conducted to identify the independent prognostic factors for DFS, in order to develop a model. Subsequently, the discrimination and calibration ability of the prognostic model were assessed in terms of its concordance index (C-index), risk group stratification, and calibration curve. The performance of the nomogram was compared with that of the eighth edition of the AJCC TNM staging system via C-index. Results: Based on the CPHR model, eight prognostic predictors were screened and entered into the nomogram. The prognostic model showed better performance (p<0.01) in terms of DFS prediction (C-index: 0.738; 95% CI: 0.698–0.779) than the eighth edition of the AJCC TNM staging system (C-index: 0.644; 95% CI: 0.604–0.684). Stratification into three risk groups highlighted significant differences between the survival curves in the training cohort and those in the validation cohort. The calibration curves for likelihood of 3- and 5-year DFS indicated optimal agreement between nomogram predictions and actual observations. Conclusion: We constructed and externally validated a novel nomogram scoring system for individualized DFS estimation in BC patients treated with NCT. This user-friendly predictive tool may help oncologists to make optimal clinical decisions.