ArticleAnnals of translational medicine2023
Construction of a novel mRNAsi-related risk model for predicting prognosis and immunotherapy response in osteosarcoma.
Article in Annals of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients.Biochemical genetics · 2025Article
- Application of hydrogels in cancer immunotherapy: a bibliometric analysis.Frontiers in immunology · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Targeting cancer stem cells (CSC) may represent a future therapeutic direction for osteosarcoma (OS), which mainly relies on the identification of CSC markers. This study aimed to classify OS based on messenger ribonucleic acid (mRNA) stemness indices (mRNAsi) and construct a mRNAsi-related risk model to predict the prognosis of OS. Methods: The one-class logistic regression (OCLR) algorithm was applied to the RNA- sequencing (seq) data of human embryonic stem cells (hESC) and induced pluripotent stem cell (iPSC) lines to calculate mRNAsi. Weighted gene co-expression network analysis (WGCNA) was performed on data obtained from the TARGET database to screen the mRNAsi-related genes. Univariate Cox regression analysis was implemented to screen mRNAsi-related genes with prognostic significance for consensus clustering of OS. The least absolute shrinkage and selection operator (LASSO) and COX regression analysis were conducted to construct a risk model based on mRNAsi-related genes. Results: Six gene modules were identified in the TARGET database. The yellow module showed the strongest negative correlation with mRNAsi and the strongest significant positive correlation with the immune score and stromal score. OS was divided into three molecular subtypes with significant survival differences based on 73 mRNAsi-related genes with prognostic value for OS. The survival rate was ranked as C3 < C1 < C2 from low to high. The levels of immune components in C2 was significantly higher than those in C1 and C3. HSD11B2, GBP1, RNF130, APBB1IP, and NPC2 in the yellow module were used as variables for building the mRNAsi-related risk model. The survival rate of the high-risk group (as defined by this model) was significantly higher than that of the low-risk group, and it had significant survival prediction ability in 28 types of cancer. In addition, the mRNAsi-related risk model was superior to the Tumor Immune Dysfunction and Exclusion (TIDE) model in predicting the prognosis and immunotherapy response in all three immunotherapy cohorts. Conclusions: This study classified OS and constructed a mRNAsi-related risk model based on mRNAsi-related genes, which provides a potential tool for more accurate risk stratification of OS and prediction of immunotherapy response.
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.