Evidence map›Paper›PMID 31007607›Full record

ArticleCancer cell international2019

Systems biology based meth-miRNA-mRNA regulatory network identifies metabolic imbalance and hyperactive cell cycle signaling involved in hepatocellular carcinoma onset and progression.

Kashif Rafiq Zahid, Mingyang Su, Abdur Rehman Raza Khan, Shiming Han, Gou Deming, Umar Raza

Open access · goldAbstract read
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Article in Cancer cell international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 43% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Kashif Rafiq Zahid *1Shenzhen Key Laboratory of Microbial Genetic Engineering, College of Life Sciences and Oceanography, Carson International Cancer Center, Shenzhen University, Shenzhen, 518060 Guangdong China.
Mingyang Su *1Shenzhen Key Laboratory of Microbial Genetic Engineering, College of Life Sciences and Oceanography, Carson International Cancer Center, Shenzhen University, Shenzhen, 518060 Guangdong China.
Abdur Rehman Raza Khan2Military College of Signals, National University of Science and Technology (NUST), Khadim Hussain Rd, Rawalpindi, Pakistan.
Shiming Han3School of Biological Sciences and Technology, Liupanshui Normal University, Liupanshui, 553004 China.
Gou Deming1Shenzhen Key Laboratory of Microbial Genetic Engineering, College of Life Sciences and Oceanography, Carson International Cancer Center, Shenzhen University, Shenzhen, 518060 Guangdong China.
Umar RazaDepartment of Biological Sciences, National University of Medical Sciences (NUMS), Abid Majeed Road, Rawalpindi, Pakistan.ORCID 0000-0002-0081-6996
Shenzhen University · CNLiupanshui Normal University · CNNational University of Medical Sciences · PKNational University of Sciences and Technology · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is one of the leading cause of cancer associated deaths worldwide. Independent studies have proposed altered DNA methylation pattern and aberrant microRNA (miRNA) levels leading to abnormal expression of different genes as important regulators of disease onset and progression in HCC. Here, using systems biology approaches, we aimed to integrate methylation, miRNA profiling and gene expression data into a regulatory methylation-miRNA-mRNA (meth-miRNA-mRNA) network to better understand the onset and progression of the disease.

methodsPatients' gene methylation, miRNA expression and gene expression data were retrieved from the NCBI GEO and TCGA databases. Differentially methylated genes, and differentially expressed miRNAs and genes were identified by comparing respective patients' data using two tailed Student's t-test. Functional annotation and pathway enrichment, miRNA-mRNA inverse pairing and gene set enrichment analyses (GSEA) were performed using DAVID, miRDIP v4.1 and GSEA tools respectively. meth-miRNA-mRNA network was constructed using Cytoscape v3.5.1. Kaplan-Meier survival analyses were performed using R script and significance was calculated by Log-rank (Mantel-Cox) test.

resultsWe identified differentially expressed mRNAs, miRNAs, and differentially methylated genes in HCC as compared to normal adjacent tissues by analyzing gene expression, miRNA expression, and methylation profiling data of HCC patients and integrated top miRNAs along with their mRNA targets and their methylation profile into a regulatory meth-miRNA-mRNA network using systems biology approach. Pathway enrichment analyses of identified genes revealed suppressed metabolic pathways and hyperactive cell cycle signaling as key features of HCC onset and progression which we validated in 10 different HCC patients' datasets. Next, we confirmed the inverse correlation between gene methylation and its expression, and between miRNA and its targets' expression in various datasets. Furthermore, we validated the clinical significance of identified methylation, miRNA and mRNA signatures by checking their association with clinical features and survival of HCC patients.

conclusionsOverall, we suggest that simultaneous (1) reversal of hyper-methylation and/or oncogenic miRNA driven suppression of genes involved in metabolic pathways, and (2) induction of hyper-methylation and/or tumor suppressor miRNA driven suppression of genes involved in cell cycle signaling have potential of inhibiting disease aggressiveness, and predicting good survival in HCC.

Indexed as

Cell cycle signalingHepatocellular carcinomaMetabolic pathwaysmiRNA–mRNA regulatory networkSystems biologyTumor progression

Identifiers

PMID31007607
PMCPMC6454777
OpenAlexW2944466384

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.