ArticleJournal of clinical laboratory analysis2022
A novel model based on liquid-liquid phase separation-Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma.
Article in Journal of clinical laboratory analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 10 citations in OpenAlex.
- Machine learning-driven investigation on liquid-liquid phase separation-related prognostic signature in diffuse large B-cell lymphoma.British journal of haematology · 2026Article
- Condensatopathies as a mechanistic framework for disease and integrated theranostic intervention.Theranostics · 2026Review
- Pan-Cancer Analysis of Mutations Affecting Protein Liquid-Liquid Phase Separation Revealing Clinical Implications.Biology · 2025Article
- Leveraging diverse liquid-liquid phase separation patterns to predict the prognosis and immunotherapy of pediatric acute myeloid leukemia.BMC cancer · 2025Article
- Liquid-liquid phase separation-related genes associated with prognosis, tumor microenvironment characteristics, and tumor cell features in bladder cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Liquid-liquid phase separation: an emerging perspective on the tumorigenesis, progression, and treatment of tumors.Frontiers in immunology · 2025Review
- Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.Frontiers in immunology · 2024Article
- A Liquid-Liquid Phase Separation-Related Index Associate with Biochemical Recurrence and Tumor Immune Environment of Prostate Cancer Patients.International journal of molecular sciences · 2023Article
- Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.Scientific reports · 2022Article
- Comprehensive analysis of liquid-liquid phase separation-related genes in prediction of breast cancer prognosis.Frontiers in genetics · 2022Article
- A novel model based on liquid-liquid phase separation-Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma.Journal of clinical laboratory analysis · 2022Article
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
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of the study was to construct and validate a robust prognostic model based on liquid-liquid phase separation (LLPS)-related genes in lung squamous cell carcinoma (LUSC).
methodsThe Cancer Genome Atlas dataset was used as the discovery set to identify the LLPS-related differentially expressed genes (DEGs) between LUSC and normal tissue. These DEGs were screened by the LASSO Cox regression analysis to identify the genes with nonzero coefficient, which were next included in the multivariate Cox regression analysis to construct the prediction model. The dataset GSE41271 was adopted as the validation set to verify the efficacy of the model. Enrichment analysis and the CIBERSORT were performed to illustrate potential immune mechanisms underlying the prediction model.
resultsA total of 48 LLPS-related genes were aberrantly expressed in LUSC. Among them, 7 genes were selected by the LASSO Cox regression analysis to construct the prediction model. Risk index (RI) was calculated according to the model for each patient. The prognosis was significantly different between the patients with high and low RI in the discovery set and the validation set (p < 0.001 and p = 0.028, respectively). The multivariate survival analysis confirmed RI as an independent prognostic factor in LUSC (in the discovery set: p < 0.001, HR = 2.643, 95% CI = 1.986-3.518; in the validation set: p = 0.042, HR = 2.144, 95% CI = 1.026-4.480). A series of pathways involving immune cells were found to be related to RI. The distribution pattern of immune cells and chemokines varied according to the value of RI.
conclusionThe prediction model based on LLPS-related genes was constructed and validated as a robust prognostic tool for LUSC using multiple datasets. LLPS might have an impact on LUSC through immune pathways.
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.