It was well established that inflammation has a complex association with carcinogenesis, tumor development, and the tumor immune microenvironment. However, little was known about how lung squamous carcinoma (LUSC) is associated with inflammation-related genes (IRGs). For a study, researchers identified IRGs linked to overall survival (OS), developed an IRGs signature for risk assessment, and looked at how IRGs affected the immune infiltration pattern in LUSC patients.
The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, which were designated as the training and validation cohorts, were used to download the RNA-sequencing and clinicopathological information of LUSC patients. To create an IRG signature, Cox regression, least absolute shrinkage, and selection operator analyses were used. The immune infiltration study was carried out using the CIBERSORT, microenvironment cell populations-counter, and tumor immune dysfunction and rejection (TIDE) algorithms.
According to the training set, a two-IRG signature made up of KLF6 and SGMS2 was discovered that might divide patients into two separate OS risk categories. Patients in the low-risk group had a stronger infiltration of anti-tumor immune cells, whereas patients in the high-risk group had a lower TIDE score and higher expression levels of immune checkpoint molecules. Further research revealed the IRG signature to be a stand-alone OS prognostic factor. To predict customized OS, a predictive nomogram using the IRG signature, age, and cancer stage was created. The concordance index values for the nomogram were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in the validation set. When compared to the conventional tumor stage alone, the nomogram demonstrated a greater prediction accuracy, according to time-dependent receiver operator characteristic curves.
The IRG signature was a predictor for LUSC patients and may be used as a possible gauge of immunotherapy effectiveness. The nomogram built using the IRG signature performed rather well in terms of survival prediction.
Reference: onlinelibrary.wiley.com/doi/10.1002/cam4.5190
