Evidence has been provided that long noncoding RNAs (LncRNAs) play major roles in affecting essential physiological processes, and many of which seem to have functional roles in tumorigenesis and progression. However, the intrinsic molecular mechanism of LncRNAs acting on papillary thyroid carcinoma is not well understood. In the present study, we found that PROX1-AS1 levels were obviously increased in thyroid cancer cells compared with the normal thyroid epithelial cells. Knockdown of PROX1-AS1 gene expression by siRNA could inhibit cell proliferation. Subsequently, we also observed that silencing PROX1-AS1 might inhibit invasion and migration of thyroid cancer cell lines via modulating the expression of epithelial–mesenchymal transition related proteins. In conclusion, our study indicated that LncRNA PROX1-AS1 could promote papillary thyroid carcinoma development and might serve as a potential targeting marker for papillary thyroid carcinoma.

Given the relatively poor understanding of the expression and functional effects of the N6-methyladenosine (m6A) RNA methylation on colorectal cancer (CRC), we attempted to measure its prognostic value and clinical significance. We comprehensively screened 37 m6A-related prognostic long non-coding RNAs (lncRNAs) with significant differences in expression based on 21 acknowledged regulators of m6A modification and data on 473 colorectal cancer tissues and 41 para-cancer tissues obtained from the TCGA database. Accordingly, we classified 473 CRC patients into two clusters by consensus clustering on the basis of significantly different survival outcomes. We also found a potential correlation between m6A-related prognostic lncRNAs and BRAF-KRAS expression, as well as immune cell infiltration. Then, we established a prognostic model by selecting 16 m6A-related prognostic lncRNAs via LASSO Cox analysis and grouped the CRC patients into low- and high-risk groups to calculate risk scores. Then, we performed stratified sampling to validate and confirm our model by categorising the 473 samples into a training group (N = 208) and a testing group (N = 205) in a 1:1 ratio. The survival curve showed a distinct clinical outcome in the low- and high-risk subgroups. We reconfirmed the reliability and independence of the prognostic model through various measures: risk curve, heat map and univariate and multivariate Cox analyses. To ensure that the outcomes were applicable to clinical settings, we performed stratified analyses on different clinical features, such as age, lymph node status and clinical stage. CRC patients with downregulated m6A-related gene expression, lower immune score, distant metastasis, lymph node metastasis or more advanced clinical staging had higher risk scores, indicating less-desirable outcomes. Moreover, we explored the immunology of colorectal cancer cells. The risk score showed positive correlations with eosinophils, M2 macrophages and neutrophils. In summary, our effort revealed the significance of m6A RNA methylation regulators in colorectal cancer, and the prognostic model we constructed may be used as an essential reference for predicting the outcome of CRC patients.

Background: Breast cancer (BC) is increasingly becoming the primary reason for death in women, which sounded the alarm. Thus, finding a novel management target for BC is imminent. Materials and Methods: The data on gene expression and clinicopathological characteristics were downloaded from The Cancer Genome Atlas (TCGA). The expression of GNPNAT1 in 40 paired breast cancer and adjacent tissues was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Univariate and Multivariate logistic regression methodology was applied to analyze the prognostic factors for lymph node metastasis (LNM). Based on the status of breast cancer-relative receptors, patients were distributed into six groups, and then the Kaplan-Meier survival analysis with a Log rank test was applied to investigate the involvement among the expression of GNPNAT1 and overall survival (OS). Results: We found higher expression of GNPNAT1 was connected with poor survival in breast cancer by COX regulation analysis. GO, KEGG, and GSEA analysis prompted that GNPNAT1 was connected with the defense mechanism of cells, cell proliferation, and division. Immunization infiltration analysis showed that high GNPNAT1 was negatively connected with 16 immunization infiltration cell types and positively connected with four immunization infiltration cell types. Conclusion: As a whole, our results indicated that GNPNAT1 might be a probable biomarker for diagnosis and prognosis in breast cancer.