Prediction of Risk and Clinical Outcome of cuproptosis in Lung Squamous carcinoma

Background: Lung squamous cell carcinoma (LUSC) is a common histopathologic type of lung cancer, and chemotherapy is still the main means for advanced LUSC. Cuproptosis is a newly discovered form of cell death different from known programmed apoptosis, which regulates the proliferation and progression of tumor cells. However, the molecular mechanism and prognosis of cuproptosis-related genes (CRGs) in LUSC have not yet been reported. Methods: RNA sequencing profiles and related clinical data of LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and combined into a new LUSC cohort. The data were processed using multiple R packets, and CRGs associated with the prognosis of pulmonary squamous carcinoma were screened based on differences in gene expression. Through the interaction network analysis of tumor mutation load, copy number variation and CRGs, the patients were divided into two subtypes according to the expression of CRGs, and three different gene clusters according to the difference in gene expression. The key differential genes were screened to construct prognostic markers, and the correlation between LUSC immune cell infiltration and immunity was further analyzed. A more accurate nomogram map was constructed by risk score and clinical factors. In addition, drug sensitivity analysis was performed on CRGs of LUSC. Results: Patients were divided into two cuproptosis-related subtypes and subsequently regrouped into three gene clusters, showing different immune infiltrations. The results of risk score showed that compared with the low-score risk group, the high-score risk group had higher tumor microenvironment score, lower frequency of tumor mutational burden, poor prognosis. At the same time, high-score risk group had higher sensitivity to chemotherapy of cisplatin, doxorubicin, etoposide, paclitaxel, vinorelbine and other drugs. Conclusions: We developed a new scoring system to predict the prognosis and immune status of patients with LUSC. This feature shows a satisfactory predictive effect and has the potential to guide patients' integrative treatment.