In recent years, the diagnosis and treatment of colorectal cancer (CRC) have been continuously improved, but the mortality rate continues to be high, especially in advanced patients. CRC patients usually have no obvious symptoms in the early stage and are already in the advanced stage when they are diagnosed. The 5-year survival rate is only 10%. The blood markers currently used to screen for CRC, such as carcinoembryonic antigen and carbohydrate antigen 19-9, have low sensitivity and specificity, whereas other methods are invasive or too expensive. As a result, recent research has shifted to the development of minimally invasive or noninvasive biomarkers in the form of body fluid biopsies. Non-coding RNA molecules are composed of microRNAs, long non-coding RNAs, small nucleolar RNAs, and circular RNAs, which have important roles in the occurrence and development of diseases and can be utilized for the early diagnosis and prognosis of tumors. In this review, we focus on the latest findings of mRNA-ncRNA as biomarkers for the diagnosis and prognosis of CRC, from fluid to tissue level.

Circular RNA (circRNA), a type of RNA that is widely expressed in mammalian cells, is considered to be essential in tumorigenesis. CircRNA can regulate target gene expression by interacting with the corresponding microRNA (miRNA). Our preliminary results showed that the expression levels of 1,817 circRNAs were significantly different in colon cancer tissue compared with paracancerous tissue, of which 1,236 were upregulated and 581 were downregulated. By using RT-PCR, we confirmed that the expression of hsa_circ_0007843, hsa_circ_0010575, hsa_circ_0007331, and hsa_circ_0001615 was significantly higher in colon cancer tissue than in normal colonic tissue; however, the expression levels of hsa_circ_0014879 and hsa_circRNA_401801 were not significantly different between normal and neoplastic colonic tissue. Among the circRNAs that were confirmed to be upregulated in colon cancer tissue, hsa_circ_0007843 was also found to be highly expressed in colon cancer SW480 cells. Overexpression of hsa_circ_0007843 promoted the invasion and migration of SW480 cells, whereas its downregulation suppressed their invasion and migration. Overexpression of hsa_circ_0007843 promoted tumor growth, whereas its downregulation inhibited tumor growth. We found that hsa_circ_0007843 interacted with miR-518c-5p and suppressed its expression, and miR-518c-5p interacted with matrix metallopeptidase 2 (MMP2) and promoted its expression and translation. Taken together, this study demonstrated that hsa_circ_0007843 acted as an miRNA sponge to regulate MMP2 expression by removing the inhibitory effect of miR-518c-5p on MMP2 gene translation, which further affected the invasive capability of SW480 cells.

Recent evidence has demonstrated that circular RNAs (circRNAs) played crucial roles in fine‐tuning the levels of gene expression by sequestering the corresponding microRNA (miRNAs). Their interaction with disease‐associated miRNAs indicates that circRNAs are important for the development of disease, and miR‐145 has been previously shown to have antitumor effect in prostate cancer. In the current study, we successfully established the miR‐145‐overexpressed prostate cancer LNCaP cells (LNCaP‐miR‐145) using lentiviral vectors. LNCaP cells expressing the empty vector (LNCaP‐NC) were used as the negative control. The circRNA expression in LNCaP‐miR‐145 cells was detected by microarray analysis, and the miRNA targets of circRNAs were predicted using the bioinformatics software TargetScan and miRanda. Quantitative reverse transcription polymerase chain reaction was used to detect the expression levels of circRNAs in the prostate cancer tissue, nonmalignant tissue, LNCaP‐miR‐145 cells, and LNCaP‐NC cells. The interaction of miRNA and circRNA was further confirmed by the dual‐luciferase reporter assay. A total of 267 and 149 circRNAs were significantly up‐ and downregulated in LNCaP‐miR‐145 cells, respectively. hsa_circRNA_101981, hsa_circRNA_101996 and hsa_circRNA_09142 were the 3 circRNAs that interacted with hsa‐miR‐145‐5p; hsa_circRNA_008068 and hsa_circRNA_406557 were the 2 circRNAs that interacted with hsa‐miR‐145‐3p. Most of the circRNAs corresponded to the protein‐coding exons. The expression levels of hsa_circRNA_101981, hsa_circRNA_00806, and hsa_circRNA_406557 were upregulated in the LNCaP‐miR‐145 cells, but downregulated in the prostate cancer tissue. In contrast, the expression levels of hsa_circRNA_101996 and hsa_circRNA_091420 were downregulated in the LNCaP‐miR‐145 cells, but upregulated in the prostate cancer tissue. Moreover, miR‐145‐5P might regulate the expression of hsa_circRNA_101981, hsa_circRNA_101996, and hsa_circRNA_09142, while miR‐145‐3P might regulate the expression of hsa_circRNA_008068 and hsa_circRNA_406557. Overexpression of miR‐145 promoted the expression of hsa_circRNA_101981, hsa_circRNA_008068, and hsa_circRNA_406557 but suppressed the expressions of hsa_circRNA_101996 and hsa_circRNA_091420 in LNCaP cells. The results from the current study should give us a clue to clarify the tumor suppressive effect of miR‐145.

Background: Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges.Methods: The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS). In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort.Results: Organic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D-glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy-3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (AUC=0.750), and pyroglutamic acid (AUC=0.929). Conclusions: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa.