ArticleTechnology in cancer research & treatment
Prediction of Poor Prognosis of HCC by Early Warning Model for Co-Expression of miRNA and mRNA Based on Bioinformatics Analysis.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Consensus artificial intelligence-driven prognostic signature for predicting the prognosis of hepatocellular carcinoma: a multi-center and large-scale study.NPJ precision oncology · 2025Article
- Elucidating hepatocellular carcinoma progression: a novel prognostic miRNA-mRNA network and signature analysis.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveHepatocellular Carcinoma (HCC) has the highest mortality rate worldwide with the intractability of its extremely complicated pathogenesis and unclear mechanism. The limited survival highlights the need for the further detection of prognosis for HCC. MicroRNAs (miRNAs) and messenger RNAs (mRNAs) have been identified as regulatory factors and target genes in human cancers, while some studies also found post-transcriptional modification plays a crucial role in the occurrence and development of HCC. The present study aimed to elucidate the prognostic significance of miRNA and mRNA models in HCC.
methodsData were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The miRNA and mRNA expressions were tested by the Wilcoxon and used funrich software to predict mRNA that might be related to miRNA. Then we determined the intersection with overlapped mRNA and miRNA Venn diagram, and screened out hub gene by using Degree algorithm in Cytoscape software. The COX models, with TCGA data as the training set and ICGC data as the test set, were constructed. All patients were divided into high-risk and low-risk groups. Data on overall survival of different groups were collected and analyzed by Kaplan-Meier method, and independent risk factors affecting prognosis were assessed by Cox analysis.
resultsThe miRNA and mRNA polygenic risk model showed a good true positive rate. Kaplan-Meier curve and Cox analysis suggested that the high-risk group was associated with poor prognosis, and the risk score could be used as an independent risk factor for HCC.
conclusionTumor risk models constructed in this study could effectively predict the prognosis of patients, which is expected to provide a reference for the prognostic stratification and treatment strategy development of HCC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.