Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes

1 Jan 2022

Abstract excerpt

Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Cells and Metastasis
Share to Cancer, Hypoxia, and Metabolism
Share to Ferroptosis and cancer prognosis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35765647
DOI: 10.1016/j.csbj.2022.06.011

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypesPMID 35765647Read ↗

Correlation of Hepatocellular Carcinoma Stemness Indices and Clinical Characteristics and PrognosisSemantic proximity Read ↗

Integrative Analysis of Genes Associated With Cancer Stem Cell Characteristics via Stemness Indices Combined With Multi-Omics Data in Liver CancerSemantic proximity Read ↗

Identification of a 9-gene signature related to stemness for prognosis in hepatocellular carcinoma basing on weighted gene coexpression network analysisSemantic proximity Read ↗

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinomaSemantic proximity Read ↗

Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular CarcinomaSemantic proximity Read ↗

A Comprehensive Prognostic and Immunological Analysis of a New Three-Gene Signature in Hepatocellular CarcinomaSemantic proximity Read ↗

A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinomaSemantic proximity Read ↗

Identification of Cancer Cell Stemness-Associated Long Noncoding RNAs for Predicting Prognosis of Patients with Hepatocellular CarcinomaSemantic proximity Read ↗

Identification of a Cancer Stemness Driven Prognostic Model and Therapeutic Drug Targets by Machine Learning in Hepatocellular CarcinomaSemantic proximity Read ↗

Deep dissection of stemness-related hierarchies in hepatocellular carcinomaSemantic proximity Read ↗

mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell BiomarkersSemantic proximity Read ↗

Stemness Landscape and Its Clinical Implications in Hepatocellular CarcinomaSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.