MicroRNA-148a-3p suppresses cell proliferation and migration of esophageal carcinoma by targeting CEP55

Non-coding RNAs' function in cancer development, diagnosis and therapy

While previous research on cancer biology has focused on genes that code for proteins, in recent years it has been discovered that non-coding RNAs (ncRNAs)play key regulatory roles in cell biological functions. NcRNAs account for more than 95% of human transcripts and are an important entry point for the study of the mechanism of cancer development. An increasing number of studies have demonstrated that ncRNAs can act as tumor suppressor genes or oncogenes to regulate tumor development at the epigenetic level, transcriptional level, as well as post-transcriptional level. Because of the importance of ncRNAs in cancer, most clinical trials have focused on ncRNAs to explore whether ncRNAs can be used as new biomarkers or therapies. In this review, we focus on recent studies of ncRNAs including microRNAs (miRNAs), long ncRNAs (lncRNAs), circle RNAs (circRNAs), PIWI interacting RNAs (piRNAs), and tRNA in different types of cancer and explore the application of these ncRNAs in the development of cancer and the identification of relevant therapeutic targets and tumor biomarkers. Graphical abstract drawn by Fidraw.

CEP55: an immune-related predictive and prognostic molecular biomarker for multiple cancers

BACKGROUND

Centrosomal protein 55 (CEP55) plays a significant role in specific cancers. However, comprehensive research on CEP55 is lacking in pan-cancer.

METHODS

In-house and multi-center samples (n = 15,823) were used to analyze CEP55 in 33 cancers. The variance of CEP55 expression levels among tumor and control groups was evaluated by the Wilcoxon rank-sum test and standardized mean difference (SMD). The clinical value of CEP55 in cancers was assessed using receiver operating characteristic (ROC) curves, Cox regression analysis, and Kaplan-Meier curves. The correlations between CEP55 expression and the immune microenvironment were explored using Spearman's correlation coefficient.

RESULTS

The data of clustered regularly interspaced short palindromic repeats confirmed that CEP55 was essential for the survival of cancer cells in multiple cancer types. Elevated CEP55 mRNA expression was observed in 20 cancers, including glioblastoma multiforme (p < 0.05). CEP55 mRNA expression made it feasible to distinguish 21 cancer types between cancer specimens and their control samples (AUC = 0.97), indicating the potential of CEP55 for predicting cancer status. Overexpression of CEP55 was correlated with the prognosis of cancer individuals for 18 cancer types, exhibiting its prognostic value. CEP55 expression was relevant to tumor mutation burden, microsatellite instability, neoantigen counts, and the immune microenvironment in various cancers (p < 0.05). The expression level and clinical relevance of CEP55 in cancers were verified in lung squamous cell carcinoma using in-house and multi-center samples (SMD = 4.07; AUC > 0.95; p < 0.05).

CONCLUSION

CEP55 may be an immune-related predictive and prognostic marker for multiple cancers, including lung squamous cell carcinoma.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.