Pan‐cancer analyses reveal genomics and clinical characteristics of the melatonergic regulators in cancer

Abstract

Melatonin, an endogenous hormone, plays protective roles in cancer. In addition to regulating circadian rhythms, sleep, and neuroendocrine activity, melatonin functions in various survival pathways. However, the mechanisms of melatonin regulation in cancer remain unknown. In the present study, we performed a comprehensive characterization of melatonin regulators in 9125 tumor samples across 33 cancer types using multi‐omic data from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia. In the genomic landscape, we identified the heterozygous amplification of AANAT and GPR50, and heterozygous deletion of PER3, CYP2C19, and MTNR1A as the dominant alteration events. Expression analysis revealed methylation‐mediated downregulation of melatonergic regulator expression. In addition, we found that melatonergic regulator expression could be used to predict patient survival in various cancers. In depth, microRNA (miRNA) analysis revealed an miRNA‐mRNA interaction network, and the deregulated miRNAs were involved in melatonin secretion and metabolism by targeting circadian clock genes. Pathway analysis showed that melatonergic regulators were associated with inhibition of apoptosis, the cell cycle, the DNA damage response, and activation of RAS/MAPK and RTK signaling pathways. Importantly, by mining the Genomics of Drug Sensitivity in Cancer database, we discovered a number of potential drugs that might target melatonergic regulators. In summary, this study revealed the genomic alteration and clinical characteristics of melatonergic regulators across 33 cancers, which might clarify the relationship between melatonin and tumorigenesis. Our findings also might provide a novel approach for the clinical treatment of cancers.

A Pan-Cancer Bioinformatic Analysis of RAD51 Regarding the Values for Diagnosis, Prognosis, and Therapeutic Prediction

“…Most of the similar studies exploring cancer biomarkers focused on one cancer type (36,37), while this study screened for 33 cancer types. Pan-cancer analysis has been reported previously (38)(39)(40) as a bioinformatic methodology to screen cancer types that are interesting to be further explored. One strength of the study is the use of TCGA and the analysis of different cancers, enabling us to have an overview of the biomarker value of RAD51 in cancers.…”

Correlation Between the RNA Methylation Genes and Immune Infiltration and Prognosis of Patients with Hepatocellular Carcinoma: A Pan-Cancer Analysis

“…Only genes with CNVs greater than 5% were considered significant variants. 11 , 12 The associations between matched mRNA levels and percentages of matched CNV samples were determined based on the Pearson product-moment correlation coefficient and t distribution. 13 P values were adjusted for the false discovery rate (FDR).…”

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

“…Raw data for 33 cancers with CNVs ( n =11495) were collected from the TCGA database using GISTIC v2.0 to extract the required fragment data, synthesize the signaling data, and combine the data with human reference genome data to obtain gene CNVs. To determine the percentage of amplification and deletion of CNVs for necroptosis genes in pan-cancer, percentage statistics were generated based on CNV isoforms with GISTIC-processed CNV data, and correlations with raw CNV data as well as mRNA-RSEM data were calculated—considering only genes with CNVs above 5% as significant variants ( 11 , 13 ). Associations between paired mRNA levels and the percentage of paired CNV samples were calculated based on Pearson’s product-moment correlation coefficient and t -distribution ( 14 ).…”