Anoikis classification of lung squamous cell carcinoma reveals correlation with clinical prognosis and immune characteristics

TL;DR

This study explores how anoikis-related genes predict lung cancer outcomes and immune features, offering potential for personalized treatment.

Contribution

A novel anoikis-related gene model is developed to predict prognosis and immune characteristics in lung squamous cell carcinoma.

Findings

A predictive model using FADD, SNAI1, and BAG4 genes was constructed for LUSC prognosis.

SNAI1 was identified as an independent prognostic gene and its knockout reduced cancer cell proliferation.

Drug sensitivity analysis revealed distinct drug responses in high- and low-risk groups based on the model.

Abstract

Anoikis is a new mode of cell death that has been shown to correlate significantly with tumors. However, the clinical prognostic significance of anoikis in lung squamous cell carcinoma (LUSC) remains poorly studied.

The differentially expressed ARGs and candidate genes were selected by the differential analysis to construct a predictive model. Independent prognostic gene was determined by Cox and LASSO analysis and we used the HCC95 and NCI H520 cell line to verify the gene function. We used the data from TCGA, GEO, GeneCards, and Harmonizome databases to analyze the immune microenvironment, functional enrichment, and drug sensitivity analysis.

We identified 717 differentially expressed and selected 3 ARGs (FADD, SNAI1, and BAG4) to construct a predictive model. We found that SNAI1 is an independent prognostic gene and confirmed that knocking out the SNAI1 inhibited the HCC95/NCI H520 cell proliferation. We used single-sample gene-set enrichment analysis (ssGSEA) to evaluate the immune infiltration based on the 3 ARG expression levels. We constructed a risk score and provided a visual representation of the prophetic implications of the ARGs-based signature through a nomogram. We found 15 susceptible drugs in the high-risk group and 15 sensitive drugs in the low-risk group by the drug sensitivity analysis.

We used ARGs to construct a prognosis model for LUSC that can accurately predict the prognosis of LUSC patients. ARGs, especially SNAI1, play an essential role in developing LUSC. These findings could provide individualized treatment plans and new research ideas for LUSC patients.

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