ArticleCancer medicine2023
Risk stratification and prognosis prediction based on inflammation-related gene signature in lung squamous carcinoma.
Article in Cancer 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, 7 citations in OpenAlex.
- Article
- Risk stratification and prognosis prediction based on inflammation-related gene signature in lung squamous carcinoma.Cancer medicine · 2023Article
- A novel gene signature related to focal adhesions for distinguishing and predicting the prognosis of lung squamous cell carcinoma.Frontiers in medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients.
methodsThe RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis.
resultsA two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone.
conclusionThe IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.
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.