Background
Small cell lung cancer (SCLC) is an aggressive lung malignancy with high relapse rates and poor survival outcomes. Ferroptosis is a recently identified type of cell death caused by excessive intracellular iron accumulation and lipid peroxidation, which may mediate tumor-infiltrating immune cells to influence anti-cancer immunity. But prognostic value of ferroptosis-related genes and its relationship with the treatment response of immunotherapies in SCLC have not been elucidated.
Conclusions
The ferroptosis-related prognostic risk-scoring model proposed in this study can potentially predict the prognosis of SCLC patients. TXNIP may serve as a potential biomarker to predict the prognosis and efficacy of chemotherapy combined with immunotherapy in SCLC patients.
Methods
The RNA-sequencing and clinical data of SCLC patients were downloaded from the cBioPortal database. A ferroptosis-related prognostic risk-scoring model was constructed based on univariable and multivariable Cox-regression analysis. Kaplan-Meier (K-M) survival curves and receiver operating characteristics (ROC) curves were constructed to assess the sensitivity and specificity of the risk-scoring model. And the correlations between ferroptosis-related prognostic genes and immune microenvironment were explored. The IC50 values of anti-cancer drugs were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database and the correlation analysis with the key gene thioredoxin-interacting protein (TXNIP) was performed. In addition, immunohistochemistry (IHC) staining was employed to detect the expression of TXNIP in 20 SCLC patients who received first-line chemo-immunotherapy. Immunotherapeutic response according to iRECIST (Response Evaluation Criteria in Solid Tumours for immunotherapy trials) were recorded.
Results
We constructed a risk-score successfully dividing patients in the low- and high-risk groups (with better and worse prognosis, respectively). The area under the curve (AUC) of this risk-scoring model was 0.812, showing it had good utility in predicting the prognosis of SCLC. Moreover, ferroptosis-related genes were associated with the degree of immune infiltration of SCLC. Most importantly, we found that the TXNIP expression was highly correlated with the degree of immune invasion and the efficacy of chemotherapy in combination with immunotherapy in SCLC patients. Conclusions: The ferroptosis-related prognostic risk-scoring model proposed in this study can potentially predict the prognosis of SCLC patients. TXNIP may serve as a potential biomarker to predict the prognosis and efficacy of chemotherapy combined with immunotherapy in SCLC patients.