ArticleBMC genomics2023
A gene mutation-based risk model for prognostic prediction in liver metastases.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 4 citations in OpenAlex.
- Research Progress on the Mechanisms of Traditional Chinese Medicine in Preventing and Treating HCC Invasion and Metastasis Based on Lipid Metabolic Reprogramming.Current topics in medicinal chemistry · 2026Review
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 1 institution in 1 country.
Funding
Abstract
backgroundLiver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver metastases.
methodWe identified the gene mutations associated with survival and constructed the risk model in the training cohort including 800 patients with liver metastases from Memorial Sloan-Kettering Cancer Center (MSKCC) dataset. Other 794 patients with liver metastases were collected from 4 cohorts for validation. Furthermore, the analyses of tumor microenvironment (TME) and somatic mutations were performed on 51 patients with breast cancer liver metastases (BCLM) who had both somatic mutation data and RNA-sequencing data.
resultsA gene mutation-based risk model involved 10 genes was constructed to divide patients with liver metastases into the high- and low-risk groups. Patients in the low-risk group had a longer survival time compared to those in the high-risk group, which was observed in both training and validation cohorts. The analyses of TME in BCLM showed that the low-risk group exhibited more immune infiltration than the high-risk group. Furthermore, the mutation signatures of the high-risk group were completely different from those of the low-risk group in patients with BCLM.
conclusionsThe gene mutation-based risk model constructed in our study exhibited the reliable ability of predicting the prognosis in liver metastases. The difference of TME and somatic mutations among BCLM patients with different risk score can guide the further research and treatment decisions for liver metastases.
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.