Prediction of novel miRNA biomarker candidates for diagnostic and prognostic analysis of STAD and LIHC: An integrated in silico approach

Computational epigenetic landscape analysis reveals association of CACNA1G-AS1, F11-AS1, NNT-AS1, and MSC-AS1 lncRNAs in prostate cancer progression through aberrant methylation

Aberrant expression of long non-coding RNAs (lncRNAs), caused by alterations in DNA methylation, is a driving factor in several cancers. Interplay between lncRNAs’ aberrant methylation and expression in prostate cancer (PC) progression still remains largely elusive. Therefore, this study characterized the genome-wide epigenetic landscape and expression profiles of lncRNAs and their clinical impact by integrating multi-omics data implementing bioinformatics approaches. We identified 62 differentially methylated CpG-sites (DMCs) and 199 differentially expressed lncRNAs (DElncRNAs), where 32 DElncRNAs contain 32 corresponding DMCs within promoter regions. Significant negative correlation was observed between 8 DElncRNAs-DMCs pairs. 3 (cg23614229, cg23957912, and cg11052780) DMCs and 4 (CACNA1G-AS1, F11-AS1, NNT-AS1, and MSC-AS1) DElncRNAs were identified as high-risk factors for poor prognosis of PC patients. Overexpression of hypo-methylated CACNA1G-AS1, F11-AS1, and NNT-AS1 and down-regulation of hyper-methylated MSC-AS1 significantly lower the survival of PC patients and could be a potential prognostic and therapeutic biomarker. These DElncRNAs were found to be associated with several molecular functions whose deregulation can lead to cancer. Involvement of these epigenetically deregulated DElncRNAs in cancer-related biological processes was also noticed. These findings provide new insights into the understanding of lncRNA regulation by aberrant DNA methylation which will help to clarify the epigenetic mechanisms underlying PC.

Bioinformatics Analysis Using ATAC-seq and RNA-seq for the Identification of 15 Gene Signatures Associated With the Prediction of Prognosis in Hepatocellular Carcinoma

Background

Gene expression (RNA-seq) and overall survival (OS) in TCGA were combined using chromosome accessibility (ATAC-seq) to search for key molecules affecting liver cancer prognosis.

Methods

We used the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) to analyse chromatin accessibility in the promoter regions of whole genes in liver hepatocellular carcinoma (LIHC) and then screened differentially expressed genes (DEGs) at the mRNA level by transcriptome sequencing technology (RNA-seq). We obtained genes significantly associated with overall survival (OS) by a one-way Cox analysis. The three were screened by taking intersection and further using a Kaplan–Meier (KM) for validation. A prognostic model was constructed using the obtained genes by LASSO regression analysis.The expression of these genes in hepatocellular carcinomas was then analysed. The protein expression of these genes was verified using the Human Protein Atlas(HPA) online datasets and immunohistochemistry.

Results

ATAC-seq, RNA-seq and survival analysis, combined with a LASSO prediction model, identified signatures of 15 genes (PRDX6, GCLM, HTATIP2, SEMA3F, UCK2, NOL10, KIF18A, RAP2A, BOD1, GDI2, ZIC2, GTF3C6 SLC1A5, ERI3 and SAC3D1), all of which were highly expressed in hepatocellular carcinoma. The LASSO prognostic model showed that this risk score had high predictive accuracy for the survival prognosis at 1, 3 and 5 years. A KM curve analysis showed that high expression of all 15 gene signatures was significantly associated with a poor prognosis in LIHC patients. HPA analysis of protein expression showed that PRDX6, GCLM, HTATIP2, NOL10, KIF18A, RAP2A and GDI2 were highly expressed in the hepatocellular carcinoma tissues compared with normal control tissues.

Conclusions

PRDX6, GCLM, HTATIP2, SEMA3F, UCK2, NOL10, KIF18A, RAP2A, BOD1, GDI2, ZIC2, GTF3C6, SLC1A5, ERI3 and SAC3D1 may affect the prognosis of LIHC.

MicroRNAs expression analysis shows key affirmation of Synaptopodin-2 as a novel prognostic and therapeutic biomarker for colorectal and cervical cancers

MicroRNAs play a crucial role in tumorigenesis, tumor progression, and metastasis, and thus they contribute in development of different malignancies including cervical cancer (CC) and colorectal cancer (CRC). Through integrated strategies of computational biology, this study aims to identify prognostic biomarkers responsible for CRC and CC prognosis, and potential therapeutic agents to halt the progression of these cancers. Expression analysis of miRNA datasets of CRC and CC identified 17 differentially expressed miRNAs (DEMs). SYNPO2, NEGR1, FGF7, LIFR, RUNX1T1, CFL2, BNC2, EPHB2, PMAIP1, and CDC25A differentially expressed genes (DEGs) regulated by these DEMs were classified as candidate genes responsible for CRC and CC. Down-regulation of Synaptopodin-2 (SYNPO2) is involved in emergence and progression of these cancers by activating ER, PI3K/AKT, and EMT pathways as well as by suppressing DNA damage response, and cell cycle pathways. Higher methylation rate in promoter region of SYNPO2 could be a possible reason for lowering the expression of SYNPO2 in tumor stages. Hence, the lower expression of SYNPO2 is associated with poor prognosis of CRC and CC and could function as prognostic biomarker and therapeutic target. Fourteen transcription factors were recognized which can activate/inhibit the transcription of SYNPO2 and may be a potential target to regulate expression of SYNPO2 in CRC and CC. Retinoic acid and Estradiol were identified as putative therapeutic drugs for CRC and CC patients. This study will thus help in understanding the underlying molecular events in CRC and CC that may improve the detection of malignant lesions in primary screening and will broaden the clinical applications.