ArticleBiochemistry and biophysics reports2021
Identification of
Article in Biochemistry and biophysics reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 6 citations in OpenAlex.
- PROFET predicts continuous gene expression dynamics from scRNA-seq data to elucidate heterogeneity of cancer treatment responses.Cell systems · 2026Article
- Insight into the Association between Slitrk Protein and Neurodevelopmental and Neuropsychiatric Conditions.Biomolecules · 2024Review
- SLITRK6 promotes the progression of lung adenocarcinoma by regulating PI3K/AKT/mTOR signaling and Warburg effect.Apoptosis : an international journal on programmed cell death · 2023Article
- An update on antibody-drug conjugates in urothelial carcinoma: state of the art strategies and what comes next.Cancer chemotherapy and pharmacology · 2022Review
- Systematic review of recent advancements in antibody-drug and bicycle toxin conjugates for the treatment of urothelial cancer.Therapeutic advances in urologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignancy of the adult liver and morbidity are increasing in recent years, however, there is still no effective strategy to prevent and diagnose HCC. Therefore, it is urgent to research the effective biomarker to predict clinical outcomes of HCC tumorigenesis. In the current study, differentially expressed genes in HCC and normal tissues were investigated using the Gene Expression Omnibus (GEO) dataset GSE144269 and The Cancer Genome Atlas (TCGA). Gene differential expression analysis and weighted correlation network analysis (WGCNA) methods were used to identify nine and 16 key gene modules from the GEO dataset and TCGA dataset, respectively, in which the green module in the GEO dataset and magenta module in TCGA were significantly correlated with HCC occurrence. Third, the enrichment score of gene function annotation results showed that these two key modules focus on the positive regulation of inflammatory response and cell differentiation, etc. Besides, PPI network analysis, mutation analysis, and survival analysis found that SLITRK6 had high connectivity, and its mutation significantly impacted overall survival. In addition,
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.